## My List ### HirschDaniel 1. [Coding Unbreakable Encryption in C | One-Time Pad ](https://www.youtube.com/watch?v=NLk7nWt7On0) 2. [Coding Ray Tracing in C ](https://www.youtube.com/watch?v=2BLRLuczykM) ### Codeolences 1. [The Tragic Story of a Genius Schizophrenic Programmer ](https://www.youtube.com/watch?v=YMUhbIAA9-8) 29/07/25 00:15 Terrence Andrew Davis (December 15, 1969 – August 11, 2018) Arizona State University (BS, MS) He knew assembly. TempleOS included the design of its original programming language, editor, compiler and kernel, and it ultimately had over 120,000 lines of code. And over 12 years, he built his own operating system, writing over 100,000 lines of code, because he believed that God told him to. With 100,000-120,000 lines of code you can have a kernel, video processing, grep, games. 640x480 16bit, single audio channel. ### Tsoding Daily 1. [OOP in Pure C](https://www.youtube.com/watch?v=6Riy9hVIFDE) ### 0Mean1Sigma 1. [Mini Project: How to program a GPU? | CUDA C/C++](https://www.youtube.com/watch?v=GetaI7KhbzM) ### Computer Graphics at TU Wien [Rendering (186.101, 2021S)](https://www.youtube.com/watch?v=5sY_hoh_IDc&list=PLmIqTlJ6KsE2yXzeq02hqCDpOdtj6n6A9) ### The Coding Sloth 1. [20 Programming Projects That Will Make You A God At Coding](https://www.youtube.com/watch?v=jTJvyKZDFsY) [Build your own ](https://github.com/codecrafters-io/build-your-own-x?tab=readme-ov-file#build-your-own-neural-network) Implementing a Git clone for version control Algorithm visualizer HTTP server Read-Time multi-user google doc QR code generator ## Nic Barker 1. [A New Programming Fundamentals Course](https://www.youtube.com/watch?v=EV13CNrq4ZA) [Generated with](https://www.youtube-transcript.io) The speaker admits to being a *poor student* in both high school and university, attributing failures to a *poor work ethic* and a *sense of giftedness* that led to irresponsibility. - **Shift in Perspective**: After working professionally and teaching others, a *profound realization* emerged: **blaming oneself is less productive than understanding the learning process**. - **Self-Analysis**: The speaker began *observing their own learning style*, treating it as a *puzzle to solve*, leading to insights about how they absorb information. #### The Learning Process as a Recursive Search - **Deep Dive Technique**: When encountering unfamiliar words or concepts (e.g., on Wikipedia), the speaker employs a *depth-first search* approach: - Clicks on unfamiliar terms, opening linked articles. - Continues recursively if new unfamiliar terms appear. - **Implication**: This *recursive exploration* mirrors *programming algorithms* and reflects a *natural learning style*—digging deep into interconnected ideas. | **Learning Approach** | **Description** | **Analogy** | |------------------------|-----------------|--------------| | Depth-First Search | Exploring linked concepts recursively | Programming algorithm | | Surface Learning | Skimming or superficial understanding | Less effective for deep comprehension | - **Key Insight**: *Maximum learning* occurs when the material is *just beyond* current understanding, aligning with the **zone of proximal development**. #### The Zone of Proximal Development (ZPD) - **Origin**: Coined by **Lev Vygotsky**, ZPD describes the *sweet spot* where *learning is most effective*—just outside current capabilities but within reach with minimal effort. - **Application**: The speaker realized that *effective learning* involves *staying within this zone*, which can be visualized as *the edges of a web of ideas*. #### Learning as Dependency Resolution - **Concept**: *Complex ideas* are *built upon simpler ones*. To understand advanced concepts, one must *master foundational ideas first*. - **Visualization**: - Ideas represented as **nodes** in a *web*. - **Arrows** indicate *building blocks* from simple to complex. | **Knowledge Web** | **Description** | |-------------------|-----------------| | Nodes | Individual ideas or concepts | | Edges | Dependencies or foundational links | - **Implication**: Learning is *most effective* when *new concepts* are *just beyond* the current web, requiring *dependency resolution*. --- #### Benefits for Learners - **For Beginners**: - *Start from scratch*. - *Build confidence* with foundational knowledge. - **For Experienced Programmers**: - *Gain low-level understanding*. - *Deepen comprehension* of *how software operates*. #### Content Highlights - **Topics Covered**: - Machine code. - Programming language internals. - Software architecture. - Hardware-software interaction. --- ### Final Thoughts: Embracing a Thoughtful Educational Philosophy - The speaker emphasizes that **effective learning** hinges on *understanding human cognition*. - **Key Takeaways**: - Learning is *most effective* when *material is just beyond current understanding*. - *Designing educational content* that *stays within the zone of proximal development* enhances comprehension. - *Starting from first principles* can *unify* diverse learners and *eliminate gaps*. - *Patience and persistence* are essential, especially when *creating foundational content*. ## Summary Table: Key Takeaways | **Concept** | **Explanation** | **Application** | |--------------|-----------------|-----------------| | Depth-First Search in Learning | Recursive exploration of linked ideas | Deep understanding, not ideal for quick exams | | Zone of Proximal Development | Learning just beyond current ability | Design content to stay within this zone | | Dependency Web of Ideas | Building knowledge on foundational concepts | Structure curriculum to extend web gradually | | First Principles Approach | Teaching from the ground up | Create comprehensive, foundational series | | Adaptive Content | Recognizing varied backgrounds | Focus on flexible, layered explanations | --- ## DIBEOS 1. 3 Ways Mathematics Alters Your Brain A 2016 study compared the brains of 15 advanced mathematicians and 15 non-mathematicians while they judged math and non-math statements inside an MRI scanner. Both groups used language-related brain areas for general knowledge, but mathematicians showed strong activation in a distinct math-specific parietal-frontal network when processing mathematical statements. Non-mathematicians instead relied on general semantic networks, even for math. This demonstrates that mathematicians’ brains route math through a dedicated neural system that processes meaning, not just symbols. Further research confirmed that difficulty level didn’t affect this activation—it was content-specific. Everyone has this math network (it also activates for simple arithmetic in non-mathematicians), but mathematicians use it more strongly. A 2020 study from the University of Sydney extended this by showing that people with stronger math training performed better on logical reasoning tasks, took more time to think carefully, and avoided common reasoning errors. Key Takeaways: Mathematicians use a specialized brain network for math, distinct from language and general reasoning areas. Non-mathematicians process math like general language, without activating the math-specific network. More mathematical training improves logical reasoning and decision-making in everyday life. Math reshapes the brain, fostering deeper, more deliberate, and logical thinking—a "mental superpower" that extends beyond numbers.