This book is not for the faint-hearted,
the easily offended, or the delusional.

You were warned.

Table of Contents

Table of Contents      3

Intrusive Thoughts      4

S.H.I.T.      7

Lost and Misguided      7

Communism in Disguise      11

P.I.S.S.      16

Deadly Kisses      16

Entropy Incarnate      19

D.I.C.K.      24

Dependencies Be Wildin’      24

Injectile Dysfunction      28

B.O.O.B.S.      32

Hanging Abstractions      32

Dangerous Implants      36

A.S.S.      39

Jank Alert!      39

Firm Foundation      41

C.U.N.T.      44

Cunny Lures      44

Untamed Urges      45

P.O.R.N.      47

Predictable Clichés      47

Accepting The Kink      49

S.L.U.T.      52

Undefined Pronouns      52

Plug It Up      54

F.U.C.K.      57

Shallow Relationship      57

Consent Is King      59

The Last Taboo      61

Glossary Of Naughty Words      63

About the Author      65

References      66

      Intrusive Thoughts

For a couple of years now, a very inspiring thought has been brewing in my mind about new ways to instill programming best practices, ideas, and common pitfalls into developers’ minds without making them feel like they’re learning something. At least not in a structured, formal way. As a self-taught developer with undiagnosed ADHD, I know all too well the hellish torture chamber of boredom that is a classroom, a poorly structured presentation, or a dense, theory-filled book without a single ounce of engaging discourse or a funny off-hand comment.

One way I thought of combating this was to write funny songs about programming and create YouTube videos, but I quickly realized that if I overdid it, people might think it’s all a joke. Programming is definitely not a joke. It’s comprehensive and hard. I was naturally worried that this approach might have the reverse effect and steer people away from programming for good. Especially not now, when AI doomsayers are flooding the discourse with fear and uncertainty. Instead, I started a YouTube channel in my name where I taught people about rarely discussed topics in programming: hiring, interviewing tactics, workplace dynamics, not just lines of code, but the people skills needed to truly succeed in this job. I found it profoundly fulfilling. But I still felt I wasn’t addressing something even more central to the job I was leading people toward actual, pragmatic programming knowledge. While I recommended books and courses, I always felt someone would get bored by them. There had to be something better, something that doesn’t feel like formal learning but teaches the same skills. More often than not, this knowledge is locked behind jargon and dry explanations. I wanted something different that would make people feel accepted, keep them excited about programming, and feel entertaining and new.

One day, on that same YouTube channel, I received a totally incomprehensible comment on one of my videos—pure gibberish. It was filled with well-known programming acronyms like DRY, KISS, SOLID, and ACID for no apparent reason. I guess the user wanted everyone to accept his authority by throwing in every acronym he knew. Immediately after reading this acronym monstrosity, I burst out laughing! Not because I’d read something stupid (I read plenty of stupid things daily), but because of one simple intrusive thought: imagine how funny it would be if the words these acronyms formed were really naughty words, like Not Safe For Work naughty words! Picture going to your colleague all professional and saying, “Yeah, Bob, please try to stick to the ASS principle, will ya?” Or, “Hey, Bob, I think we have a SLUT issue again.” Wouldn’t that make everyone happy instantly? Even Bob.

And suddenly, there it was, the idea I’d been searching for, staring me right in the face. From that moment, I knew it was my calling. Let’s face it: the programming landscape is practically paved with these often-forgotten acronyms, each hailed as the holy grail of efficiency, only to become another entry in a developer’s mental glossary of vaguely familiar terms. We’ve reached a point where half the conversation sounds like an elaborate game of letter bingo, and you’re left wondering if the actual code is less complex than remembering what each capitalized concoction stands for. It’s already a rich source of inside jokes among seasoned programmers, this relentless abbreviation obsession, a quiet acknowledgment that sometimes we’re just making things sound more sophisticated than they are. If we’re already chuckling at the absurdity, why not lean into the humor and make those cryptic pronouncements a little more… memorable?

Thankfully, despite my relatively short tenure in programming compared to other developers I’ve spoken with, for better or worse, I’ve experienced quite a lot. Humble, I know! I’ve worked on over a dozen production systems at both large companies and a handful of startups, then doubled that in projects that were either not market-ready or never saw the light of day. Plus, because of my YouTube channel and large network of contacts, I’ve been called to help or “just take a look” at countless personal projects. Some things I really wish I hadn’t seen. But beyond that, I’d been collecting a wealth of insights, principles, and hard-earned lessons long before I ever thought of sharing them publicly. From my earliest days of coding, I made a habit of jotting down mentors’ advice, clever hacks, unspoken rules, and the kind of wisdom you only pick up deep in the trenches, so to speak. Over time, those scattered notes became a personal reference, mix of practical guidance and behind-the-scenes truths rarely spoken aloud. Like most programmers, I must confess I’m also sitting on a pile of rough, half-written blog posts full of thoughts and reflections that never quite found a home. This book brings all that together: a collection of unfiltered moments, lessons that stuck, and ideas I’ve been meaning to share for a long time.

Now, before I continue, I know exactly what you’re thinking: “How will this ever be taken seriously enough for people to want to read it?” I understand that concern, but I don’t think we, on a global scale, appreciate the true power of naughty or bad words enough. Or we at least downplay their important place in society. I’m not promoting vulgarity for its own sake here. For me, they work on an almost transcendent level of communication. I’d argue they’re a kind of connective tissue with the power to bridge gaps between seemingly unconnected groups of people. Even whole cultures!

To give a bit of background, though I’m not a linguist, I have a bachelor’s degree in archaeology. What I always found amusing is how we mystify and often misrepresent ancient cultures when we fail to realize one simple truth: they were human, too. It’s not revolutionary or shocking to say we have evidence of naughty words in ancient Egyptian hieroglyphs or even earlier in Sumerian cuneiform script. I vividly recall a professor mentioning in a lecture that one of the earliest recorded insults comes from Akkadian, a language used alongside Sumerian in ancient Mesopotamia. The word kabû, meaning “filth” or roughly “crap,” was used to scorn shoddy goods like rope sold in a market. This is backed by later Akkadian texts, where kabû appears in contexts of disdain or worthlessness. Mesopotamian insults are a bit of a fun fact in archaeological circles. This shows that a word for something abysmally bad was carefully etched into clay tablets, meaning it was universally understood! Not just its surface meaning, but the deeper content it carried. I’m pretty sure the Akkadians knew the rope wasn’t literally made of crap. And if you think about it, it’s kind of like the acronym words we use almost daily.

Another fascinating cultural phenomenon worth mentioning to back up my theory is how some cultures almost completely accept outsiders who use naughty words but shun their own for doing the same. For example, in my home country of Serbia, if you’re a foreigner who doesn’t speak Serbian (don’t worry, most of the world doesn’t), a simple hack to be accepted in any social situation is to end your sentences with jebiga (pronounced YEH-bee-gah). This essentially means—“Fuck it.” It’s your expressway to the hearts and souls of your Serbian friends with minimal effort. Frequently, just saying this to a group of native Serbians will get you at least a free drink, sometimes even more! The informal rule of thumb is: the more curse words you know, the more you’ll be accepted. I know this holds true for most Balkan countries, but I’m pretty sure it’s an almost universal bonding technique observed worldwide.

In exactly that manner, I hope the new meanings given to naughty words in this book will bridge the gap between developers worldwide. No matter how different we are or how many heated debates we have over nearly meaningless things in this profession, programming is still a thread that keeps us connected, whether we like it or not. I think this approach will foster a better understanding of what we as programmers do daily, perhaps even encourage us to reconsider deeply entrenched beliefs that often alienate rather than connect, and provide another way to overcome obstacles while having fun.

So, without further ado, I give you this book, which contains an assorted selection of acronyms in the form of naughty words you were taught not to say out loud. I hope they shine a light on some of the most important concepts you, as a programmer, should definitely know or at least be aware of. I sincerely hope you’ll share these naughty words with other programmers and always keep them close to your heart. Because not everything has to be super serious 100% of the time.

S.H.I.T.

“Bob, dude, you really need to SHIT. Stop farting around and do something useful.”

It wouldn’t be fair to mention a Mesopotamian word for literal crap out of left field and not make it our first talking point, right? So, let’s define what we’re talking about. SHIT here is an acronym for Stop Hunting In Tests.

“Hunting,” especially in tests, usually means writing tests to uncover obscure or purely theoretical bugs that haven’t manifested. Often in the name of “completeness” or, let’s be honest, pure developer anxiety dressed up as engineering discipline. This isn’t a rare affliction; it’s everywhere. It’s especially common among junior devs eager to impress their seniors with how many edge cases they’ve considered. Almost as if finding a bug no user would realistically hit is a rite of passage. The worst part? Much of this obsessive testing gets glorified under the banner of TDD, which, if we’re real, often stands for Test-Driven Delusion. You’re not building software anymore, now you’re playing whack-a-mole with imaginary moles! Writing 15 tests to implement a function that returns a constant isn’t craftsmanship; it’s cargo cult theater. You’re not test-driving anything; you’re looping around a parking lot, convincing yourself you’ve been somewhere.

This chapter aims to stop you from pretending this is noble work. You’re not a genius setting elegant traps and shouting, “Aha! Got you, sneaky undefined state!” when your fifth describe block triggers. You’re stinking up the codebase literally and figuratively! You need to SHIT! Sure, maybe you “caught” something, but did you ask the most important question: does this state even matter? What’s the tradeoff? What’s the risk? Is it worth the noise?

Spoiler alert: it usually isn’t. Let me explain…

Lost and Misguided

No, this isn’t the title of a trashy romance novel. It's my unfiltered take on developers who test everything. Poor souls, trapped in a web of their own making, chasing bugs like hunters with nets or, worse, buying into that tired “safety net” metaphor. You’ve heard it: tests as a magical catch-all for every coding sin. Spoiler alert: they’re not. This section will show why that mindset is a trap, using a story to unpack what tests should be.

Picture yourself at a sturdy bridge, wide enough to cross with confidence. But the guardrails? They’re comically low, barely reaching your shins. You shrug. There is obviously plenty of room to walk safely, and you start crossing. Halfway across, a freak gust of wind knocks you off your feet, sending you tumbling over the pathetic guardrails. As you plummet toward certain doom, you brace for the end… but wait! A tightly knit net catches you. Limbs intact, wallet miraculously caught! Oh, damn it! Your keys slip through the net’s holes. Oh well, at least you’re alive.

As you lie there, heart pounding, you notice a man on the far side of the bridge scribbling notes. He pulls a remote from his pocket, points it at you, and the net starts moving, dragging you to safety via an elaborate mechanism. He greets you with a grin: “Hi, I’m the engineer who built this bridge. Sorry about your keys, I gotta tighten that net. My tests showed people might fall, so I built this net and a wind machine to simulate those gusts.”

“Wind machine?” you ask, dumbfounded.

He points to a jet-engine-sized contraption aimed at the bridge. “Yup, built that too!” he beams. “This is my test infrastructure. Gotta test for those gusts. Like my automatic net-recovery system?”

Before you can respond, he’s back to scribbling, muttering about widening the net for lighter people who might get blown farther. You’ve had enough. You smack him! Not out of malice, but to snap him out of his lunacy. Why? Because you, the one who fell, see the real issue: it’s not the net, the recovery system, or your lost keys. It’s the goddamn guardrails. If they were higher, like on any sane bridge, you wouldn’t need a net at all. This engineer’s obsession with catching falls blinded him to preventing them.

This is the trap of overzealous testing. The engineer’s “safety net” obsession mirrors developers who write tests for every edge case, mistaking test coverage for good design. Tests aren’t a substitute for understanding. His wind machine and net are like mocking a complex API to test bizarre edge cases. Can be impressive, but it’s mostly useless if the core system (low guardrails) is flawed. Overengineered tests, like that net’s recovery system, are technical debt. They are complex, fragile, and distracting from the real issue.

Instead of adding another test for every possibility, ask why these problems exist at all. The answer is almost always a leaky domain model or a brittle interface. And this may actually be a very good contender for a caption on the Boromir meme: “One does not simply add more tests to a bad design”. Because, you’re supposed to fix the design itself!

This misguided focus leads to optimizing for the wrong things. The engineer’s pride in his “automatic recovery system” and sophisticated wind machine is a perfect example of wasted effort on a secondary problem, creating a solution that’s complex and prone to failure. Every test, like every part of that net contraption, incurs maintenance liability. A test that doesn’t validate core, valuable behavior becomes technical debt, creating a dangerous cycle of false confidence and flakiness. An overabundance of “just-in-case” tests, like an overly engineered net, leads to a brittle, implementation-coupled system that generates false positives and developer burnout. You, sitting in the net, felt the absurdity. The engineer was obsessed with a litany of what-ifs when the real-world issue was simple: the guardrails were too low, goddamn it! The goal was never to build a better net; it was to build a bridge that didn’t need one in the first place.

Now, some will say this bridge is an unrealistic comparison to a software product. A bridge is built once and rarely re-engineered unless something’s disastrously wrong, unlike software that changes frequently, looking completely different in just weeks of its lifecycle, as we like to call it.

But that’s precisely why I used this analogy. The bridge engineer’s mindset is a trap many software developers fall into. Fixing the guardrails is refactoring. Something bridge engineers rarely do, but we do every day. Like any refactoring, it means clarifying murky business rules and admitting the initial design was flawed. That’s hard. It’s intimidating. It might mean telling a project manager the system design needs rethinking, delaying client-facing features. Everyone can see the guardrails are low! To be fair, maybe they’re not the most critical part, like the support pillars or anchors. Because without those, you couldn’t cross the bridge, right? So, what’s easier? Adding a test, obviously, then adding safety measures, more tests, more measures, and more infrastructure overhead, and so on…rinse and repeat!

It feels productive. Creating a test or safety measure is a contained task you can complete in an afternoon. You find a bug, write a regression test to “prove” you fixed it, and move on. The engineer’s pride in his “automatic recovery system” and wind machine is the perfect analogy for a developer showing off a complex, heavily mocked test setup for a bizarre edge case. It looks impressive, generates a green checkmark, but it’s pure technical theater! A monument to a problem that shouldn’t exist. This is how a codebase rots from the inside, accumulating a thousand “safety net” tests while the core structure—the bridge—grows ever more unstable.

Let’s ground this in a common scenario: a shopping cart feature. A bug report comes in: “When a user has a ‘10% off total order’ coupon and a ‘buy one, get one free’ item, the final price is wrong.”

The initial, reactive, symptom-focused approach is to dive in, find the specific logical flaw, fix it, and add a highly specific integration test to cover that case:

The test passes, and the ticket is closed. A week later, another bug appears: the same issue, but with a “$5 off” coupon. So, you adapt the code and add another specific test:

If you’ve been programming for a few years, you’ve seen tests like these. This is how test suites grow in a bad way, becoming a patchwork of fixes for individual symptoms, each a monument to a past failure. Does this remind you of anything? Perhaps adding nets for lighter people blown off the bridge? There’s no difference. The list grows on and on.

A more effective approach starts not with code but with a question: “Why is this happening?” This leads to the realization that the system lacks a clear, defined order for applying discounts. Aha! The root cause isn’t a single miscalculation; it’s a flawed pricing model design. The real solution is to refactor the logic into a clear, predictable process. Perhaps a pipeline where discounts are applied in a specific, documented sequence. By building this robust unit, you create a system that handles any discount combination because the design is sound. The few tests you write for this model are simple and powerful, confirming the corrected design’s integrity. The first path, however, leads to high maintenance and low confidence. The team spends more time updating tests than improving the feature. When the business changes how BOGO deals work, dozens of brittle, specific tests break, even if the core logic is sound. This is when flakiness and burnout set in. The test suite becomes The Boy Who Cried Wolf. Developers say, “Oh, ignore those test failures; they’re always breaking,” and the safety apparatus becomes worse than useless. It now generates noise and distrust.

This is the ultimate danger: a test suite that provides a false sense of security. A team seeing “5,000/5,000 tests passing” feels confident. But if those tests merely confirm that a broken implementation behaves consistently broken, they’re worthless, a defense against ghosts! Ask this critical question for every test: If I can’t explain the real-world scenario or user/business concern this test addresses, what am I protecting? If there’s no clear answer, you’re not testing; you’re adding noise and complexity while a fundamental design flaw waits to cause the next outage.

To be clear, testing edge cases isn’t wrong. Robust software is defined by how well it handles boundaries. The distinction lies between testing known, relevant boundaries and fabricating improbable “just-in-case” scenarios. Testing for leap years, maximum file sizes, or special characters in input fields is valuable. These are known edge cases. Writing a test for a nonsensical scenario that violates business logic is not. Stakeholders will thank you for ensuring the payment system handles dollar-to-euro conversions correctly; they won’t thank you for spending a day testing what happens if a user’s name is 10,000 characters when the database limit is 255.

There’s a revelatory QA joke: “QA walks into a bar and orders a beer. Orders 0 beers. Orders 99999999999 beers. Orders a lizard. Orders -1 beer. Orders a ueicbksjdhd. Another customer asks where the bathroom is—the bar bursts into flames, killing everyone.”

When the bar bursts into flames, it’s obvious where the issue lies. It’s not that users try weird inputs. That’s what users do daily. The problem is the bar exploding over a simple request. If this happened, what would the headlines say? “Bar owner correctly handled crazy requests” or “Bar explodes over a simple question”?

The goal is to be pragmatic. I’m not saying ignore validating external inputs, as that would be absurd and non-negotiable. Data from outside your system, everything from user forms, APIs, file uploads, all of that must be treated as untrustworthy. Tests for empty, malformed, or malicious inputs are critical for security and stability. But don’t ignore the system as a whole. Test areas ripe for bugs: numerical precision in financial calculations, race conditions in concurrent operations, or failure modes in I/O (like full disks or lost connections). Testing these known weak points is a hallmark of professional engineering. If production logs show an API call occasionally timing out or a library leaking memory under load, write tests to simulate these conditions to ensure graceful handling.

The guideline is simple: if an edge case doesn’t exist in production data or defined domain rules, it likely doesn’t need a test yet. Don’t hunt for it. Don’t think about it. Leave it alone. Focus on solidifying the core design and testing boundaries grounded in reality. Is it possible a customer will ask for the toilet? Yes? Write a test. See what happens. Let the system’s actual use, not your imagination, guide your testing strategy. Those obscure, weird things users might do aren’t daily occurrences. Like thinking the Bermuda Triangle or a black hole was a daily threat when we were nine. We were lost then, misguided by information without context. The goal isn’t to test every imaginary threat. It’s to deliver value. You can stay lost on the bridge, weaving bigger nets for phantom gusts conjured by your own wind machine. Or you can find your way, step back from testing for a moment, and fix the damn guardrails.

Communism in Disguise

When talking about tests, we can’t skip ghosts. No, not the spooky kind, the ideological kind. I’m making a controversial claim: TDD is like communism! Bet you didn’t see that coming. This comes from someone who lived through the wreckage communism left behind in former Yugoslavia. A place where utopian promises turned into gray reality, inefficiency, and quiet suffering from corruption. I don’t make this comparison lightly or for shock value. I know what it looks like when rigid ideology ignores how people behave. Like communism, TDD is a utopian idea. Born from noble intentions, but collapses into a bureaucratic nightmare when it meets the messiness, pressure, and corner-cutting of the real world. Its most passionate defenders are often those who’ve never truly lived under it, metaphorically, viewing it through rose-colored glasses in private pet projects.

This stark reality, combined with TDD’s near-religious presence in our industry, makes it impossible to ignore in any serious discussion of testing. It might be the most overhyped abbreviation in programming. Let’s break down what TDD promises and what happens when you try this communist-flavored paradigm in the real world.

To be fair, Test-Driven Development wasn’t a con. It wasn’t a consultant’s scheme to pad invoices or win “street cred.” It was a response to pain. To try and control fragile code, endless regressions, and features exploding in production. TDD emerged as a promise: a repeatable discipline bringing structure and safety. The core practice was simple: write a failing test, write just enough code to make it pass, refactor, repeat. Kent Beck, its loudest early voice, broke it down in his 2002 book Test-Driven Development: By Example¹: “Red. Green. Refactor.” Three words. An entire religion built on them. Like any ideology with a massive following, TDD didn’t just offer technique. It offered transformation.

The first promise was correctness. Write the test first, and you only write code for explicit requirements. No unnecessary logic, no clever flourishes, no surprises. Beck claimed, without irony, that this gives confidence that your code works because you tested it first. The implication is bold: done right, TDD doesn’t just catch bugs; it prevents them, eliminating gaps where mistakes hide. TDD wasn’t just about correctness, it was about communication too. Beck wrote, “Tests become documentation that stays correct over time.” Tests tell you if you’ve broken something. Refactoring, per Martin Fowler² and others, was only safe with a good test suite. With TDD, you always had one—because tests came first. This was its great gift: freedom through constraint. A safety net so tight, it gave confidence to leap.

But TDD wasn’t done. Beyond correctness, documentation, and safety, it promised design. The “right” design, as they claimed. Steve Freeman and Nat Pryce, in Growing Object-Oriented Software, Guided by Tests³, argued TDD could be a compass. Not just for verifying correctness but for uncovering the architecture your application needs. You don’t design upfront; you let the system emerge. Each failing test lights a candle on the path. You follow, one green checkmark at a time.

It’s an alluring vision, a worker’s paradise of code. Everyone contributes. Everything’s clean. The system works for the people. No hacks. No regressions. No fear. Just a disciplined orchestra of programmers moving in sync through the Red-Green-Refactor rhythm. So, what goes wrong when this ideology hits the real world?

In Soviet communism, the five-year plan was the ultimate symbol of faith in central planning. Bureaucrats in Moscow decided how much grain a Ukrainian farm should produce, how many shoes a Leningrad factory should churn out, and how much steel the country needed five years in advance. Workers were expected to meet these quotas regardless of weather, supply chains, or logic. Unsurprisingly, plans routinely failed. Local conditions weren’t just ignored…they were irrelevant. What mattered was obedience to the Plan. TDD begins in a similar spirit. Red. Green. Refactor. Write a failing test. Make it pass. Clean up. That’s the TDD five-year plan, dictated not from a Central Committee but from conference keynote stages. You’re not allowed to just write code, poke at a problem, sketch a spike, or experiment. No, comrade! The test must come first! The test is the plan! And the plan is perfect!

Now you can see that this isn’t design; it’s performance art, prioritizing appearances over truth. A meta-analysis by Rafique and Mišić⁴, spanning 27 studies, found TDD slightly improves code quality but has little to no effect on productivity. In industrial settings where deadlines are real and clients don’t care about test coverage, TDD often hurts productivity more than in academic experiments. It’s like a shiny tractor that only works in perfect weather while the harvest rots.

Communism’s seductive idea was universal equality. Farmers, physicists, janitors, engineers all played roles in the state’s grand machinery. But in flattening distinctions, it denied the wildly different complexity, creativity, and impact between roles. TDD promises equality. Not of people, but of code. Every class, method, and microscopic function must follow the same ceremony. Want a simple getter? Write a test first. Green it. Refactor it. Even if it’s a trivial one-liner that can’t fail. Even if the test adds no information. It’s like handing a master carpenter and a nervous apprentice the same hammer and insisting they use the same three-swing technique, whether driving a nail into pine or anchoring a beam into concrete. We pretend all tasks are the same, so the process should be too. But it isn’t. We waste hours testing things that don’t matter while critical parts wait. This isn’t engineering; it’s glorified box-checking.

Under central planning, a factory’s purpose shifts. It’s not judged by useful goods but by meeting the plan. Quotas are sacred; reality is secondary. TDD introduces its own quota: the test suite. Your mission isn’t to write good software, it’s to keep the pipeline green, reach 100% test coverage, ensure every commit passes. Never mind what the tests are or if they’re meaningful. Thus begins the age of mocking everything. You no longer test your code. You test a simulation. You mock the database, service calls, user inputs. You mock so much you lose sight of what’s real! When mocks or the reality they simulate change, you rewrite them all. You’ve built a perfect model of a tractor factory. Every conveyor belt in the simulation works, every test confirms the imaginary parts function. But…you haven’t built a tractor yet.

This frustration echoes across the internet. Chris Fox⁵, with over 20 years of experience, argues TDD’s constant test revisions waste time, calling it “fundamentally wrong” and ethically questionable when it extends timelines by 20–50%. Cedric Beust⁶, creator of TestNG, notes TDD’s focus on microdesign neglects the bigger picture, making it “hard to apply in complex systems.” Imagine a factory meeting quotas but taking so long the market moves on. The code might be “better” subjectively, but if it’s late, over-budget, and the team’s burned out from maintaining a sprawling test suite, was that the point?

Ideologies, when institutionalized, grow hostile to dissent. In a totalitarian state, deviation is heresy. In TDD’s kingdom, the refrain is: “You’re not doing it right.” Push back, and you’re not met with reflection but correction. If it’s not working, you’re the problem! You didn’t follow the ritual properly, didn’t believe hard enough. This shuts down discussion and silences creativity. Developers who want to explore, play with an idea, or understand the system before codifying it are labeled undisciplined, lazy, not “real professionals.” Sometimes, you don’t know the right design until you build something and see it fail. Sometimes, you need to try first, then solidify with tests. But TDD demands perfect foresight. When you don’t have it, you’re told directly or indirectly to blame yourself. This is TDD’s worst subcultural trait. If you’re not strong-willed, confident, or are early in your career, your brain becomes a servant to the process, not the engine of discovery it’s meant to be. You can’t allow this as a developer. You’re meant to break stuff, take it apart, rebuild it, let it explode! Not always set boundaries before starting. Writing code to test code you haven’t written is… I don’t know what it is. Read that sentence again, and if it makes sense, please tell the rest of us.

I know I’ll get hate for this, but I’m not here to make half-assed excuses for something I don’t believe in. I listen less to what people say and more to how they behave. Looking at the hardcore TDD crowd, I see a pattern. Every ideology has its elite. In communism, it was party officials living far removed from the governed, praising struggle from estates in the woods. TDD has its own: overconfident conference speakers, Medium testing evangelists, architects of pet projects in billion-dollar companies that fund fantasy software indefinitely. You know the type…authors of books with clean, academic examples about banking applications and precious Calculator classes demonstrating “enterprise-level design.” They sell polished versions of what everyone’s already using, packaged in a sophisticated box. They live in a world of purity, not wrestling with six-year-old monoliths lacking test suites or debugging flaky CI pipelines at 2 a.m. They don’t deal with real-world constraints. When developers object, the reply is always: “TDD wasn’t done right.” Like communism’s failure blamed on “true communism never being tried,” it’s never the ideology’s fault. It’s yours.

As someone whose home country still grapples with that kind of thinking, I say: fuck that… and fuck communism. Just as communist economies buckled under bureaucratic weight, software projects collapse under overgrown test suites. What began as a safety net becomes a straitjacket. A simple change, a function tweak, a new parameter, demand updating dozens of brittle, coupled tests. The pipeline, once a symbol of progress, becomes a gauntlet of red failures. TDD’s “Great Lie” is “Fearless Refactoring.” In reality, it’s “Fear-Filled Refactoring.” You’re not free to change code; you’re paralyzed by test failures waiting to bury you. This is where projects die! And not with a fantastic bang, but a whimper. The cost of change becomes so high the business throws in the towel. The TDD-enforced architecture, too rigid to evolve, collapses under its own weight, a monument to ideological purity over practical utility. When the system becomes unbearable, people create a black market. In TDD’s world, this is the // TODO: write test comment, the meaningless test asserting true, developers disabling pre-commit hooks to get something done. You get the worst of both worlds: process slowdown and none of the promised safety. It’s a false sense of security that is far worse than no tests. It’s the software equivalent of a Yugoslav factory reporting record production while shelves stay empty. Everyone knows it’s a sham, but no one says it aloud.

And this really isn’t a call for anarchy. It’s a call for intelligence. I’m not saying “don’t write tests”, that’s insane. I’m saying your brain is your most important tool. Use it. Don’t subordinate it to a three-letter acronym because someone says so. Embrace “Test-Informed Development.” Think, then code, then test. Understand the problem. Explore a solution. Once you have a handle, write tests to validate critical paths and protect against regressions. Test value, not implementation. Who cares if your private method was called three times? Does the public function return the right result? Does it write the correct value to the database? Test behaviors, not mechanics. Stop obsessing over gears and check if the machine runs.

A few high-value integration or end-to-end tests are worth more than a thousand trivial unit tests mocking everything. Start with tests giving the most confidence for the least effort. Quality over quantity, comrades. Your title is “Software Engineer,” not “Test-First Dogmatist.” Your job is to deliver robust, maintainable, valuable software. TDD is one tool, in one context, it might help. But it’s not one ring to rule them all! There are many cases where it doesn’t work, and that’s fine. It’s not the beginning and end of our craft. Stop treating it like a sacred relic and treat it like a hammer. A good hammer is always useful when needed, but you do not need a hammer for every job. A good codebase often needs surprisingly few tests to stay healthy, and you can’t test your way out of a poorly designed one, no matter how hard you try. Write maintainable, understandable, change-resilient code! That’s the higher-leverage activity. Software exists to solve problems, not to pass tests.

Be a pragmatist. Be an engineer. Question everything, especially utopias.

P.I.S.S.

“Please PISS before you present anything to your team.”

Continuing the trend of bodily functions, let’s talk about PISS. Many of you might think this sounds like that overused abbreviation you’ve heard a million times—KISS, or Keep It Simple, Stupid. But let’s be honest: most developers treat KISS like it’s tattooed on a goth chick’s thighs. Half worship it as gospel; the other half use it as an excuse to avoid thinking deeply. The result? Code that’s “simple” only because nobody bothered to understand the problem. Or worse, code that’s outright wrong for the task’s complexity. Enter PISS: “Prove It’s Simple, Stupid.” It’s a subtle reframe of the principle that shifts its impact. Before you rush to make it “simple,” you must prove it is simple! To your team, to the compiler, and most importantly, to yourself. The hard truth? Some problems aren’t simple. Some are impossible. Slapping together a half-assed, happy-path-only solution under KISS will likely land you in trouble. Underengineering isn’t a term you hear often in this industry, but when it happens, it’s because someone underestimated the task or got overly ambitious with KISS.

This chapter is about knowing when simplicity is smart and when it’s just cowardice wearing skinny jeans and quoting Uncle Bob. Before you start typing, always take a moment to PISS. You’ll thank yourself later! Especially when you’re debugging the mess at 2 a.m., tears in your eyes, wondering what “simple” was supposed to mean.

Deadly Kisses

Let’s get one thing straight: KISS sounds like a hug from your grandma, all warm and cozy, but in the wrong hands, it’s a smooch from the Grim Reaper! Keep It Simple, Stupid is a phrase developers cling to like a life raft in a storm, but half the time, they’re drowning in stupidity. Simplicity isn’t bad, it’s the holy grail! But KISS gets twisted into a hall pass for laziness, a mantra for dodging the hard work of understanding what you’re building. When that happens, you’re not keeping it simple. You’re keeping it stupid.

I’ve seen this play out too many times. I’ve even done it myself, especially early on. You know the situation: you’re in a sprint planning meeting, and some dev, let’s call him Chad, stands up and says: “Let’s simplify this feature. No need to overthink it.” Chad’s heard about the feature for the first time and likely hasn’t checked the code, but everyone nods like he’s quoting Socrates. What Chad really means is, “I don’t want to deal with edge cases, error handling, or anything that makes my brain hurt.” So, post-meeting, Chad slaps together a function that works if the user follows the spec exactly, the database is feeling generous, the stars align, and Mercury isn’t in retrograde. The result? A “simple” piece of code as resilient as a paper towel in a hurricane! No validation, no retries, just a happy-path solution that crumbles when a user types a decimal instead of an integer. Chad’s not a hero; he’s a ticking time bomb. And let’s face it: we’ve all been Chad at some point.

The problem with KISS is that some treat it as profound wisdom, others as a get-out-of-jail-free card. The gospel crowd thinks long code is a sin. The other crowd thinks KISS means “do the bare minimum.” Both are wrong. Simplicity isn’t about writing less code or avoiding thought. It’s about clarity, and clarity takes work. Instead, we get codebases that are “simple” only because they ignore reality: no abstraction because “it’s just one use case,” no planning for tomorrow because “we’ll refactor later,” no deeper thought because you’re confident you’ll fix any issues later. Newsflash: you won’t. You’ll be too busy putting out fires you likely started.

“Simple” is a slippery word. What’s simple to you might be a cryptic rune to your teammate. I once debugged a “simple” shipping cost function. The original dev was proud of its 20 lines. At least it’s clean, right? But it was a mess of weirdly named API calls, hardcoded constants posing as variables, and magic numbers thrown in for fun. “I just wanted to keep it simple,” they said. At first glance, it looked clean, but I only understood the code, not the problem it solved. When we added a new shipping zone, it exploded, gobbling server resources and crashing the system. My teammate and I both messed up for different reasons. He wrote a “simple” solution that passed code review’s “smell test” but underestimated the problem. I accepted his explanation without pushing for deeper understanding. My “simple” wasn’t his “simple,” and without a shared understanding of the problem, KISS bred confusion. Your teammates aren’t mind readers, and your code isn’t a haiku. If “simple” means only you get it, you’ve failed.

This lesson scales. Different teams and organizations, with their unique histories and technical cultures, define “simple” differently. For some, it means fast; for others, bulletproof. Same word, opposite outcomes. Within a company, one team might call a one-line functional method “simple,” while another, focused on maintainability, finds it obtuse and prefers a longer, explicit solution. These clashing definitions cause friction, miscommunication, and disagreement. And it’s all happening while everyone thinks they’re doing the “simple” thing. This is why reframing the principle as “Prove It’s Simple, Stupid” is crucial: it forces you past personal definitions toward a shared, verified understanding. You must fully understand both the code and the problem for it to be simple. If one undermines the other—congrats! You’ve created technical debt!

The more experience I gain, the more KISS feels driven by fear. Fear of complexity, like it’s a monster under the bed. Abstraction? “Too fancy.” Reusable modules? “Overengineering.” A 40-line function? “Unmaintainable.” So devs fall back on hardcoded logic, repeated conditionals, and call it a day. All in the name of simplicity! I saw a codebase where every endpoint had its own validation logic because a shared validator was “too complex.” Twenty copy-pasted if-statements later, it was a maintenance nightmare. Each PR repeated the same 20-line changes, or the API wouldn’t work. That’s not simple, that’s technical debt dressed as minimalism. KISS makes you think you’re pragmatic, but you’re kicking the can down the road. When the business says, “We need a special case for our VIP customer,” your “simple” code laughs in your face. You realize you’ve been building special cases all along, just pretending it was for simplicity.

KISS screws you another way: it tricks you into premature optimization. It doesn’t show up in a trench coat with villain music. It gaslights you, whispering you’re smart, pragmatic, “lean.” You hear “simple” and think, “Let’s make this tight, fast, minimal.” It feels virtuous, like a programming monk shaving off redundant bytes. But you’re making a devil’s bargain. You skip input validation, error handling “just for now,” hardcode values “because it’s only used once,” skip sanitization “because it’s internal.” One cut corner at a time. The road to technical hell is paved with good intentions and “TODO: make this proper” comments. It’s like building a brakeless bicycle because you’re only riding downhill today. Then the project manager adds a requirement, and your code does a backflip with no safety net. What you thought was lean is brittle; what seemed elegant is duct tape posing as design. You backpedal, patch, rework, and write long-winded commit messages explaining why your “simple” code now needs six layers of logic and an edge-case handler that looks like a conspiracy theorist’s notebook. KISS doesn’t just say keep it simple. It seduces you into thinking you can predict the future or that the future doesn’t matter. That your function won’t grow, no one will use it differently, your assumptions will hold. That’s the trap. Premature simplification isn’t clever, it’s arrogant. You’re assuming the problem is small before measuring it, shipping an illusion billed as engineering.

I worked on a healthcare app integrating with hospital systems for easier access to information. In one integration package, we had a proprietary scheduling app. The first version “kept it simple”: time slots as strings, user roles assumed via email domains and a lookup table, no validation logic, because doctors know what they’re doing. The code looked something like this:

At first glance, it’s a normal function returning a new appointments object, right? But here’s what happens under the hood:

Our client insisted this was fine. They needed it fast; lives were at stake! They wanted simple, it looked simple, and it was approved quickly. “We’ll add stuff later,” we said. It worked for two weeks. That is while onboarded users used it. Then other healthcare professionals, onboarded by users, joined. Errors piled up, If I remember specifically, many were regarding cancellations and time zones. Also for some reason, “doctor@example.com” was an admin across two hospitals. Every assumption broke. Instead of fixing the foundation, we duct-taped logic into role-based views, fallback controllers, and helper scripts. Nobody wanted to rewrite it because it was “too simple to fail.” Six months in, onboarding a junior dev took a week just to untangle why Friday schedules worked in Berlin but not Zurich. We KISSed the app to death. Nobody backtracked to unpack if we “got it.” KISS makes you feel like you’re saving time, but you’re signing a contract with the devil, trading your soul (and weekends) for technical debt. Premature simplicity is premature stupidity! You are just pretending the problem is smaller than it is, leaving a codebase as fragile as glass.

I reviewed a pull request with a method called simplePolarizer(). Inside? Ninety lines of logic across three nested loops, two mysterious boolean flags, and zero comments. Why call it “simple” when there’s no complex polarizer in the codebase? It was a helper function, but when I asked, “What’s this do?” the dev said, “It’s simple if you read it.” The PR comments were brutal! Our Tech Lead was not happy. The biggest lie in software is “It’s obvious.” If it’s obvious, write it down. If you can’t, it’s not obvious! It’s tribal knowledge. You know it. You just want to seem smarter than everyone. Say it out loud, so next time you pull that “it’s obvious” shit, I can call you out for being a smart-ass with proof.

Proving it’s simple isn’t just for you. It’s a breadcrumb trail for future devs (including you, six months later with a migraine). KISS devotees skip comments, READMEs, or clear variable names because “the code is simple enough.” We’ve all inherited “simple” scripts or codebases that took ages to understand. No comments, no docs, just a 50-to-100-line function called process(). It’s a black box only the original dev understood. When they left, the team was stuck reverse-engineering their “simple” masterpiece like archaeologists piecing together context. KISS encourages this lone-wolf mentality, where code is “simple” to you but a riddle to everyone else.

This is how KISS becomes deadly. When abused, it’s a shortcut for “don’t think.” It’s not about simplicity. It’s about cowardice, dodging the hard work of understanding the problem, planning for change or ensuring your team isn’t left with bugs. Do you want simplicity? Earn it. That starts with PISS.

Entropy Incarnate

Modern developers are taught to worship elegance above all else. We idolize short code! I’m looking at you, Uncle Bob! Short isn’t inherently bad, but we’re trained to drool over one-liners. Elegance isn’t always clarity, and short doesn’t always mean simple. So why do we cling to it? I think it’s because developers fear looking dumb. Complexity makes us feel like impostors. We tie quality to lines of code because a clean abstraction risks judgment. So we hide behind “keep it simple” like a spell to ward off scrutiny. But true simplicity is brave. It’s staring chaos in the face and saying, “I get it.” It’s taking responsibility, not hiding behind a principle. Simplicity isn’t writing less for the sake of it. It’s writing less by understanding more.

How do you dodge the KISS trap and build something simple? Prove It’s Simple, Stupid. Before opening your IDE, take a few seconds to prove to yourself, your team, and the universe, that your solution is as simple as you think. Don’t start with code; wrestle with the problem mentally until you can explain it to a 10-year-old with a short attention span. You need a clear mental model of what you’re solving, how it fits, how it behaves, and what it interacts with. If you can’t break it into clear, human-readable steps, you don’t understand it. Sorry, but that “I understand machines better than humans” shtick is juvenile. We’re not in high school. Stop trying to impress. If you can’t explain it, your “simple” solution is a disaster waiting to happen. Nothing more.

The P.I.S.S. approach starts with a brain dump. Grab a notebook, whiteboard, computer notepad, or napkin. Write out what you’re doing. Not code! Plain ENGLISH! I know, it sounds like your CS professor’s droning, but it’s a tried-and-true secret weapon. It’s the dry run your brain needs before diving in. Say you’re building a feature to calculate order totals with discounts. Your algorithm in plain English might look like this:

      That’s it! No syntax, no libraries, just raw logic. You’ve proved you understand the problem. As you write, you’ll spot holes: What if the discount expires? What if the tax API fails? What if the cart’s empty? Proving it’s simple forces you to face these before you’re knee-deep in code, debugging why your “simple” function pukes errors. It’s your pre-flight checklist. Skip it, and you’re cool with crashing. Here’s a KISS-only calculateOrderTotal. Short, sweet, and “simple”:

It’s a house of cards. It works in most cases, but there’s no validation, hardcoded tax, and it assumes every discount code works. Now, apply PISS:

This code (excluding comments for clarity) is still “simple.” It reads clearly, follows the described algorithm flow, and faces chaos. It's validating inputs, handling discounts properly, adjusting tax by location. It’s longer, yes, but it’s adequate because we proved its simplicity first. No surprises, no entropy, just readable, trustworthy code.

The beauty of PISS is it keeps you honest. Some problems are complex, and that’s okay. Not everything can be a one-liner, no matter how much you squint. Take scheduling logic for a delivery app. You have time zones, driver availability, traffic patterns. Good luck “keeping it simple” without a plan. Forcing simplicity on a complex domain is like fitting a square peg in a round hole: you’ll break the peg or the hole, and either way, you’re screwed. PISS saves you from fake simplicity, the code that looks clean because it’s short but is a house of cards. Ask, “What exactly does this function do?” If your answer is a shrug or “It processes stuff,” you’ve failed. PISS makes you articulate purpose, inputs, outputs, and edge cases. If you can’t explain it to Bob, who’s been coding since before floppy disks, your solution isn’t simple. You’re pretending.

Code should be simple, yes? Cool, I agree. But simplicity requires prioritizing understanding the problem, don’t you think? I’d argue that “adequate” is a better word than “simple” for what KISS aims for, but then PIAS isn’t a naughty word. Prove It’s Adequate Stupid doesn't sound right. Here’s an even spicier take: abstraction is simplicity when done right. Wrapping repeated logic in a function, building reusable modules, naming things clearly, that’s not overengineering, it’s clarity. But you can’t slap abstractions together like a kid with Lego bricks, each with two lines of code and zero integrity. Prove they’re necessary! Like reporting to the Ministry of Code Spending on why you used extra mental effort. Can you explain why your DiscountCalculator class beats a 100-line procedural function? Can you defend it in a code review without sweating? If not, don’t build it. If you can? Well, you’ve earned simplicity through understanding. That’s the goal.

I was on a team that rewrote a “simple” document caching system because we avoided abstraction, thinking it’d be “too complex.” We implemented it line by line, like writing poetry blindfolded. Ten duplicated conditionals later, the code was screaming to be a module. It wasn’t copy-pasted; it just felt the same, like déjà vu with indentation. Months later, during refactoring triggered by a client complaint, we rediscovered the shared logic. The final version? A tidy 33-line function that should’ve existed from day one. That moment left us terrified of what else was rotting in the codebase. Bad abstraction is painful, but no abstraction when it’s deserved? That’s a codebase leaking sanity. Proving an abstraction’s necessity would’ve saved time and trust. Once trust goes, so does “quality.” You get paranoid, second-guessing every function. Every PR becomes a crime scene. It starts when you assume “simple” means flat and repetitive. That’s not simple! It’s Entropy Incarnate, a Lovecraftian monster pretending to be humble while eating your sprint velocity and whispering bugs into releases.

Proving simplicity is also about your team, not just you. Simplicity isn’t about feeling clever at 3 a.m. It’s about Sarah, the QA engineer, not cursing you when she finds a bug. It’s about Bob understanding your code without a Rosetta Stone. PISS forces you to think about shared understanding. Can you walk your team through your logic without them glazing over? Can you justify skipping an edge case? If not, your “simple” solution is lone-wolf bullshit that’ll haunt standups when it breaks. Proving simplicity aligns you with the team before you commit code that makes everyone want to strangle you. You might still mess up. Even proven simplicity can be messy. But at least you’ll know it’s messy. You’ve exposed the complexity upfront, so you can design for it instead of pretending it’s not there. If the problem’s a beast, plan for extensibility, add error handling, or push back on requirements: “Hey, boss, this isn’t a one-day task unless we’re okay skipping XYZ.” That’s not failure, that’s engineering. Don’t hide behind KISS like a coward, underengineering out of laziness. Face the problem like a goddamn adult.

This inherently prevents disasters by making you verify your solution before building it. It’s not about writing less. It's about writing the right code. KISS is like a lie so simple it sounds true until the polygraph beeps. You say, “It’s a small feature,” but you’re dodging the truth: you don’t fully get the problem. PISS is your lie detector, forcing you to prove simplicity to yourself and your team before committing. Why’s that better than KISS’s quick-and-dirty promises? It builds trust in your judgment and from your team that you’re not Chad serving technical debt. Every dev cursing their past self at 2 a.m. knows unproven simplicity is a betrayal, a shortcut that screws everyone. Proving simplicity is the antidote! Gritty honesty saying, “I’ve checked my work, and it’s not bullshit.”

Proving simplicity starts with you staring in the mirror, asking, “Do I understand this?” If you can’t write what you want to do in a human language or explain the logic to your cat without tripping over edge cases, you’re lying to yourself. That’s KISS’s trap! Literal self-delusion! PISS makes you sweat the details upfront, so you don’t gaslight yourself into thinking a 20-line function solves a 200-edge-case problem.

The real gold is proving simplicity to your team. Misused KISS breeds mistrust. When Chad’s “simple” code crashes, everyone rolls their eyes, and standups become interrogations. PISS flips that. By laying out your logic with flowcharts or plain English, you’re not just saying “trust me.” You’re showing receipts. You’re proving the solution works for Bob’s edge cases, Sarah’s QA tests, and the intern’s onboarding. PISS turns your described algorithm into a contract: you’ve vetted it, and they can rely on it. That’s worth more than any one-liner because it’s not just simple—it’s proven simple, keeping the team from whispering, “What the hell is this person doing?”

PISS isn’t paralysis by analysis. It’s conscious restraint before reckless execution. Don’t spend four hours sketching algorithms for a button click or write a ten-page doc to fetch a user profile. Don’t PISS on trivial bugs like you’re marking territory. The magic is knowing when it’s needed. Is it user-facing? Business-critical? Reusable or maintainable by others? If yes, take the moment. If not, maybe hold it in.

There’s a graveyard of “simple” ideas out there, each with a well-meaning dev whispering, “Let’s keep it simple.” Now they haunt production, breaking under load, confusing new hires, making devs wish they could time-travel to build something adequate, no matter how complex. Next time you’re tempted to “keep it simple,” don’t. Step away from the keyboard. Grab a coffee, napkin, or rubber duck, and prove it first. Prove it’s simple to the rubber duck. Your codebase won’t become a KISS-fueled dumpster fire, your team won’t hate you, and you won’t cry at 2 a.m. debugging Chad’s “simple” shitshow. Simplicity isn’t the starting point. It’s the fucking prize! Earn it! Without understanding, simplicity is a mirage, and you’re coding blindfolded, holding a live grenade.

D.I.C.K.

“That server codebase is a pile of DICKs! Be careful.”

Let’s shift from physiology to anatomy. I know what you’re thinking, but I’m sorry to disappoint. No genitalia talk here, nor tales of people whose personalities scream the word, though I’m tempted to dish on some proper dicks I’ve worked with over the years. No, I need to stay focused and share how I lost one of my first paid programming jobs. This is a warning many developers sense but only grasp when it’s too late: “Domain Interaction Could Kill.” It’s the silent assassin that ambushes devs who think their job ends at writing clean, isolated code. When domains collide, much like a poorly planned sitcom crossover, things get messy fast! One minute, your e-commerce app is humming; the next, the payment system is bickering with inventory like an old married couple, and your users are staring at a 500 error.

Two main issues make domains deadly. First, they act like drunk partygoers playing Twister, tangling in ways you don’t expect. Second, and sneakier, the lifecycle of objects and service instantiation can get tricky fast if you’re not careful. When both hit at once, you might question the reality of your work altogether. This chapter is your wake-up call. We’ll dissect why domain interactions go wrong, spot warning signs, including rogue instance chaos, and build systems that play nice without turning your codebase into a bowl of spaghetti. Buckle up, because it’s gonna be a spicy ride.

Dependencies Be Wildin’

I learned this the hard way at one of my first paid gigs, back when I thought “domain-driven design” meant locking each module in its own ivory tower, magically shielding me from headaches. Naive and stupid. Picture me, a cocky junior freelance web dev, landing a sweet gig on Upwork (back when it was still Freelancer), tasked with integrating a shiny new user authentication system into an existing platform. “Easy,” I thought, strutting into the codebase like I owned it. I built a slick auth system! Users, roles, tokens, the works. Tested it in isolation, green checks all around. Then I hooked it to the billing system, which was migrating to subscription-based payments. Suddenly, users couldn’t log in if their recurring payment lapsed, even for features that didn’t require payment. Why? The billing domain was throwing tantrums, demanding payment status checks before auth could do its job. Okay, no problem. Let’s hook billing to payments for status checks. Server resources spiked, weird errors popped up when users chose certain payment options. Alright, add a separate instance for tracking in-process orders. Now the app was devouring resources. Before I knew it, my perfect little auth kingdom was brought to its knees by domain interactions I hadn’t anticipated. Cue all-nighters, angry emails, and a humbling lesson: domains don’t live in a vacuum just because you split your code. Who’d have thought? Ignore their interactions from the start, and you’re screwed. Later, mentoring juniors, I realized domain-driven design doesn’t come naturally to most. It’s a skill you learn the hard way.

Developers often overdo it, treating domains as completely isolated, ignoring how they’ll dance together in production. Take an e-commerce app: the inventory domain decrements stock when an order is placed, but the payment domain only finalizes after a fraud check. If the fraud check fails, inventory’s already updated, leaving phantom stock deductions. Or worse, a race condition lets two users snag the last item because domains weren’t synchronized. These aren’t bugs in the domains themselves. They’re bugs in how they interact. In the sequence of events that must be respected for logic to flow. Disrespect that, and this silent assassin kills your app’s reliability faster than a NullPointerException.

The fix sounds easy: “Model your interactions explicitly.” Map how domains talk before writing a line of code. Use sequence diagrams or event storming to visualize data flow and dependencies. Use a modern dependency injection framework. For my auth-billing mess, a simple diagram would’ve shown billing’s status check needed to happen after auth validated the user, not before. Sounds obvious, but you’d be amazed how many teams skip these steps, arrogantly assuming “we’ll figure it out in code.” Even when I’m stating the obvious, years after it happened, it’s hard to realize this in the thick of it.

My auth-billing approach wasn’t just wrong due to tight coupling. It was a lifecycle mismatch. Billing expected a fully validated user object before acting, but auth hadn’t finished constructing the user’s state. Billing assumed auth had done its job, but the data was either missing or half-baked in a transitional state. With each domain managing its own lifecycle independently, they tripped over each other. In dev, everything ran linearly and worked fine. In production, concurrency, retries, and lazy loading made those assumptions collapse. I thought I had an instancing or ordering problem, but it was a lifecycle mismatch where billing woke up before auth had its pants on. In a simple system, this is bad enough; in complex ones with parallel steps, it’s a nightmare.

Here’s a mockup of my auth-billing mess, tightly coupled and flawed:

}

See it? That single const billing = new Billing(auth) wrecks the logic. Tight coupling makes domains a tangled mess! A classic D.I.C.K. setup where one domain overreaches. Billing assumes auth’s lifecycle and risks acting on incomplete user objects. No race condition. The await keeps order, but the design is rotten at its core.

The fix? Lean on proper Dependency Injection, like NestJS for TypeScript. If my AuthService was an Injectable, my BillingService could look like this:

With DI, both services share a single managed instance. No rogue instantiations, no half-baked user objects. The system is consistent, flexible, and maintainable.

But tight coupling isn’t the only trap. Sneakier is wild instancing. Imagine the order-processing domain checking user permissions by casually creating a new UserService instance. No big deal, right? Wrong. Each order process spins up its own UserService, each with its own database connection, cache, or session state. Suddenly, your server’s memory chokes on duplicate instances, each querying the same user data but with different states. One thinks the user’s an admin; another doesn’t because it missed a cache update. Chaos ensues! Orders fail, users get locked out, logs look like a horror movie. In TypeScript or Java without a framework, you’d make this mistake 8/10 times. At least I would. Understanding instancing is one thing; framing it in a domain-driven system is another. The worst part? You won’t catch it in development. Your local setup has one user, one call, and everything’s peachy. In production, race conditions, bloated memory, and logs screaming about undefined values or stale role data reveal the truth. Each domain fights for its own version of reality, and the system’s expected to make sense.

How do you keep domains from stepping on each other’s toes or spawning rogue clones? Centralize instance management. Use a dependency injection framework (like NestJS or Spring) to ensure domains share a single, controlled instance of shared services. Better yet, make domains communicate via events or APIs, not direct class instantiation. If the order domain needs auth data, it should query an AuthService endpoint or listen for a UserValidated event, not spin up its own UserService. This keeps things lean and prevents your codebase from becoming a rogue instance factory.

Another good habit: enter contracts. Not legal ones, but explicit agreements on how domains interact. Think of it as a prenup for your code: everyone knows the rules, and nobody gets screwed when things get messy. A contract defines what data one domain expects, what it promises in return, and how to handle failures. Without this, you’re hoping domains play nice, as reliable as trusting a cat to guard your sandwich. For the inventory-payment example, a contract might say:

Inventory reserves stock when an order is placed and sends a StockReserved event.

Payment processes the transaction and responds with a PaymentConfirmed or PaymentFailed event.

If payment fails, inventory rolls back the reservation.

This contract forces you to consider failure modes. It prevents rogue instantiations by defining a clear API or event boundary, not direct class access. In my auth-billing disaster, a contract would’ve clarified that billing only runs after auth succeeds, saving me from late-night debugging.

Keep contracts lean and focused. Don’t overengineer with endless edge cases (we’re not falling into that Lost and Misguided trap from Chapter 2). Start with the happy path, then add just enough to handle real failures, like network timeouts or invalid data. Test contracts explicitly, not just domains in isolation. A unit test for inventory is great, but a contract test verifying inventory and payment sync is what keeps your app alive in production. Tools like Pact or Spring Cloud Contract help, but a shared JSON schema and a few integration tests work too. The goal: make domain interactions boringly predictable, not Russian roulette. Boring is good. Boring is the goal. When someone says, “Hm, this is very interesting,” it’s one of the four horsemen of the programming apocalypse, alongside “It works on my machine,” “I’ll fix that later,” and “We don’t need to monitor that.”

Here’s a hard truth: domain interactions don’t fix themselves. Someone’s gotta own the handshake between domains, or you’re praying for luck. If you don’t know who that someone is, assume it’s you. Don’t dodge this, because your teammates might too. Especially across teams handling different domains, you’ll see folks assume “the other team will handle it” or “we’ll sort it in prod.” That’s how outages happen. Owning the handshake means defining clear boundaries and responsibilities for domain communication. Ask: Who decides when inventory updates? Who handles payment failures? Who ensures auth isn’t instantiated a hundred times? If you’re shrugging, you’re the problem. Assign ownership. Ideally, one team or module oversees interaction logic. An OrderOrchestrator service could coordinate inventory, payment, and auth, ensuring stock is reserved only when payment’s likely to succeed, rolling back if it fails, and using a single auth instance. This isn’t just code—it’s a mindset.

Consider alternatives to direct API calls. Use events, queues, or a service layer to decouple interactions. And document the hell out of it. A quick README or diagram can save your team from playing detective when things break. What was the fix for my auth-billing nightmare? A clear flow: auth first, then billing, with a fallback for failures and a single auth instance. That could’ve saved it. Instead, I learned the hard way that underestimating domain interactions leaves you with a codebase that’s, well, a total DICK. Don’t be one yourself. Map interactions, define contracts, control instances, and own the handshake. Your domains will thank you, your users won’t see error pages, and you might sleep without a PagerDuty alert. Build systems that work like a well-rehearsed band, not a bar fight. It’s not about perfect domains themselves. It’s about making them play nice without killing your app.

Injectile Dysfunction

Your tests glow green, your local dev purrs like a kitten, and you’re ready to conquer the world. Then production hits. Womp, womp! Your app collapses like a bad stand-up act, coughing up 500 errors faster than a slot machine spitting losing tickets. Orders fizzle, users get double-charged, some see “order confirmed” with no payment processed, and your logs scream cryptic nonsense that might as well be hieroglyphs. You half-wonder if the system’s hacked, but it’s worse: “injectile dysfunction”. DI, your supposed savior for modular, testable code, turns your masterpiece into a flaccid disaster that can’t perform under pressure. This isn’t just a bug. It feels more like a betrayal! DI containers like NestJS’s auto-wiring promise elegance but deliver chaos, hiding dependencies in a fog of “magic” that explodes in runtime errors. The framework’s sleight-of-hand masks logic leaks. Like a payment service snooping into order quantities, as if a cashier at McDonald’s checked the kitchen’s burger stock for every paid order. And the performance hit? DI containers resolve dependencies at runtime, adding sneaky delays that snowball under Black Friday traffic. Debugging? A nightmare. Errors lurk in auto-wired modules, invisible until production, leaving you digging through logs while your app wheezes and your confidence crumbles.

Why revisit the payment example? Plot twist: I wasn’t done screwing up! I was tasked with wiring payment transactions to order checkouts for that same e-commerce platform, now a high-stakes project for a startup chasing investor dollars. I leaned into NestJS’s DI like it was my superhero, injecting the payment service into the order controller with the swagger of a coding Casanova. It felt slick! Local dev ran like a dream, unit tests passed with flying colors, and I could almost hear the applause as I pushed to deploy. As the youngest dev on the team, I was nailing a critical piece of the system. Untouchable… or so I thought.

Launch day was a catastrophe. Requests timed out, users were double-charged, others saw confirmed orders with no payment processed. Logs were a horror show! Vague errors about missing dependencies and failed transactions, buried in the framework’s auto-wired maze. For a heart-stopping moment, we thought the app was under attack. My manager’s glare could’ve burned through the screen on the all-hands call. Everyone knew it was me. My DI setup let the order controller meddle in payment’s logic, while payment made assumptions about order state, creating a tangled mess that buckled under load. The container’s runtime resolution added just enough lag to choke, and debugging was like chasing ghosts through a haunted codebase. Errors hid in NestJS’s magic wiring, perfect in local dev but fatal in production. We rolled back, but the damage was done. The company brought in a grizzled dev, a veteran who’d seen a thousand codebases bleed. On his first day, he pulled me aside and said, “Kid, they hired me to clean up your mess and show you the door, but I’m gonna teach you something first. You might not keep this job, but you’ll keep the lesson.”

He showed me my sin: no boundaries. My order controller rummaged through payment’s domain like a nosy neighbor, checking quantities and stock when it should’ve just passed the baton. Payment made assumptions about order state, creating a feedback loop that spiraled into double billings and dropped requests. The fix? Strict interfaces to keep domains separate, like lines in the sand. He sketched a payment service interface that became my lifeline:

Simple, right? I thought so too, but this hard cutoff forced broader changes, reengineering processes from scratch. He slashed other services similarly, creating a connection layer using events and queues, never direct calls. This interface was a revelation, it proved simplicity. It forced payment to focus on transactions. Just order ID, amount, currency. Without peeking into order quantities or stock. It was like telling the cashier, “Take the money, don’t count the burgers; the order’s already made.” That boundary could’ve saved my app, my team from panic, and me from unemployment. But the lesson came too late. The startup couldn’t afford another flop, and my name was on the bug. I was let go, my first job figuratively ending in a walk of shame past virtual cubicles that truly felt like a gauntlet.

Luckily, it was spring of 2019, and the software market was in a gold rush! I landed another gig before the sting faded. But that failure branded a truth: DI isn’t a magic bullet; it’s a loaded gun. Lean on auto-wiring, and you’ll get runtime errors that hide until the worst moment, performance lags that choke under load, and debugging hell that makes you question your career. Let domains overstep, like payment meddling with order logic, and your codebase becomes a house of cards, collapsing under pressure. The real pain wasn’t just losing the job; it was what my mistake cost the team. Every bug became a trust issue. Developers tiptoed around the codebase, afraid to touch payment lest it break shipping. New hires were fed horror stories: “Don’t mess with the order controller; it’s got weird ties to payment.” What was meant to be modular became a minefield, and every sprint felt like defusing a bomb. DI’s promise of clean, testable code turned into a nightmare of hidden dependencies and tribal knowledge. The irony? DI is a precision tool, but overusing its magic could end up being closer to carving a sculpture with a chainsaw! Messy, destructive, and uncontrollable.

That disaster taught me DI is a responsibility, not a shortcut. Define clear interfaces, audit your wiring, and test boundaries like your livelihood depends on it. Because, it actually might! My domains were a fumbled first date, awkward and chaotic, when they should’ve been a seasoned team passing the ball with precision. Injectile dysfunction creeps in. A sloppy injection here, a lazy assumption there, always hiding in tests and staging until the spotlight hits. Black Friday surges, investor demos, or traffic spikes expose your weaknesses, and the code doesn’t just break! It betrays your assumptions.

Don’t be that dev who thinks DI will magically save the day. I was, and it cost me my first job. But it gave me a hard-won truth: good architecture isn’t born from convenience; it’s forged with intention. Keep domains tight, interfaces clear, and DI deliberate. Let payment handle payment, order handle order, and never let them cross the line. Because when the pressure’s on, you don’t want to be holding a limp codebase, scrambling to explain why it can’t perform. Trust me, I’ve been there. The only thing worse than the error logs is your team’s look when they realize you’re the reason it all fell apart. Grab that interface, draw those boundaries, and code like your job’s on the line. Nobody wants to debug a disaster on Black Friday or live with the nickname “the dev who broke the checkout.”

B.O.O.B.S.

“My God… look at those BOOBS! That surely carries some serious weight.”

You know the type! Impossibly large, impossible to ignore, and yet everyone pretends they’re not staring. That’s right! I’m talking about “Bloated Object-Oriented Bullshit Syndrome,” or BOOBS for short. It’s a structural affliction plaguing well-meaning developers who were just “trying to do it right” and ended up with a codebase resembling an academic paper on abstract zoology. It starts with a noble idea: “Let’s be clean, modular, and extensible.” Next thing you know, you’re neck-deep in IAnimal, AbstractAnimal, AnimalFactory, AnimalManager, and ActualAnimalImplV2. Every class ends in Helper, Service, or Controller, and 90% of them contain nothing but overridden methods passing data down a rabbit hole like a bureaucratic relay race. You need five interfaces and six folders just to find the one method that says print("Ping.").

This isn’t architecture anymore. It’s religion. You’re not writing software; you’re building temples to abstraction on top of other abstractions. BOOBS are especially seductive in enterprise environments, where verbosity is mistaken for professionalism, and “we’ll refactor later” is the company’s unofficial motto. But it’s not serving its purpose. That codebase is likely already a fossil. This chapter is about recognizing when OOP has jumped the shark! When inheritance isn’t reuse but a cry for help. It’s about keeping your architecture firm without it getting top-heavy, if you know what I mean wink, wink. Everyone loves BOOBS, but when they interfere with basic function and make your life miserable, it’s time for reduction surgery.

Hanging Abstractions

Let’s say you want to log a user’s name. That should be a piece of cake, right? A quick function, maybe a class with a logName("Alice") method, and you’re done. But no, you’ve been to the mountaintop of “Clean Code” seminars, baptized in the holy waters of SOLID principles, and armed with a UML diagram that could double as a subway map. You’re not just coding now, you’re architecting…or so you’ve been told. So, you spin up an IUserLogger interface, a UserLoggerFactory, an AbstractUserLogger, a UserLoggerService, and, because why the hell not, a UserLoggerManager to rule them all. By the time you’re finished, your “simple” logging logic is buried under six layers of indirection, and your codebase looks like the org chart of a failing corporation. Welcome to Hanging Abstractions, where good intentions build temples to complexity, and your code prays for a refactor that’ll never come.

Let’s use Java, the go-to OOP language where BOOBS often strikes first, to show this. Below is how a developer with BOOBS might “architect” a user name logger, followed by a sane approach. Brace yourself.

The BOOBS version takes 15 lines, four classes, and an interface to print “User: Alice”. The simple version? Two lines, one class, same result. No factories, no managers, no nonsense. Which would you rather debug at 2 a.m.?

This is the seduction of over-abstraction, the programmer’s equivalent of wearing a tuxedo to a dive bar. It’s not about solving problems, it’s more about looking like a Serious Engineer™. In enterprise environments, where buzzwords like “scalable” and “modular” are slung around like cheap shots at happy hour, developers feel pressure to create architectures screaming, “I read the Gang of Four book!” So, they pile on interfaces, factories, and managers, each a shiny “just in case” layer that hides the actual work. Remember the payment processing system? Its goal: charge a credit card, save the transaction. It turned into a labyrinth of IPaymentGateway, PaymentGatewayFactory, AbstractPaymentAdapter, PaymentService, and a PaymentOrchestrator (yes, orchestrator). The actual logic? A single method in StripePaymentAdapterImpl calling a third-party API. The rest was boilerplate, empty interfaces, and methods playing hot potato with data objects. Why? “We might support PayPal someday.” Spoiler: PayPal never happened, but the team spent weeks chasing null pointer exceptions in their precious factory.

The difference between useful abstraction and performative nonsense is intent. Useful abstraction simplifies complexity. Like a DatabaseConnection class hiding SQL connection details so you can call query(). Performative abstraction takes a simple problem and dresses it in so many unnecessary layers. At that point it’s not about making life easier anymore; it’s about signaling to your boss, team, or future self that you’re “doing it right.” But “right” doesn’t mean wrapping print("Ping") in a PingGeneratorFactory. It means solving the problem without building a cathedral to house a single light bulb.

Spot Hanging Abstractions by their telltale signs. First, class names start sounding like corporate job titles. If your codebase has more Managers, Controllers, and Helpers than a mid-sized accounting firm, you’re in deep. I once found a class called DataTransformationOrchestrationServiceHelper. Its job? Calling string methods like toUpperCase() on a field. I nearly puked. If your code reads like a Dilbert cartoon, take a hard look in the mirror.

Another red flag: interfaces for everything, even one-offs. Interfaces are great for multiple implementations, like database drivers, but if you’re writing an ICustomerValidator for a single CustomerValidator that’ll never have a sibling, you’re not extensible; you’re extra. We’ve all worked on projects where every class had a corresponding interface “just in case.” Result? A codebase twice as large, with zero polymorphism! Like buying a second car in case the first grows wings. Then there’s the empty method trap. Classes full of methods that just call another method, like a game of telephone with super.doTheThing(). If your inheritance tree is a stack of pass-throughs, you’re not reusing code, you’re reusing pain. Worst, when a new developer asks, “Why do we need a UserProfileFactoryFactory?”, and the room goes silent except for mumbling about “future-proofing,” your abstractions are hanging low. Good architecture clarifies; bad architecture confuses. If your team spends more time drawing UML diagrams than writing code, you might not be building software, you’re building bureaucracy.

The biggest justifier is the sacred cow of extensibility. “We need to make it extensible!” is the rallying cry of every overengineered codebase. It sounds noble. Who doesn’t want code that grows with the product? But extensibility is often an excuse for avoiding decisions. Instead of a simple EmailSender class, you create an IEmailProvider, EmailProviderFactory, and implementations for SMTP, SendGrid, and a hypothetical FutureEmailService. Why? “We overheard in a product meeting we might swap providers.” Your product sticks with SMTP forever, and you’ve spent three sprints building a framework for a non-existent problem.

This obsession stems from fear of being “wrong.” Developers, especially in enterprise settings, dread code that might need to change. So, they hedge bets with abstraction layers, hoping to be future-proof. But most code doesn’t need extensibility. That payment system? It’s sticking with Stripe until the company folds. That user logger? It won’t support carrier pigeons. Overengineering for extensibility is like buying a 12-bedroom mansion for potential quintuplets. It’s not planning; it’s developer paranoia.

The costs of Hanging Abstractions are brutal. Every layer adds cognitive overhead. Developers spend hours tracing method calls through factories and interfaces to find the one line doing work. Debugging becomes a treasure hunt, with stack traces spanning 20 classes for a null check. Onboarding takes weeks because new hires need a PhD to understand why CustomerServiceImpl delegates to CustomerManager to CustomerRepository to CustomerDao just to fetch a name. Maintenance is a nightmare! New features require updating five classes instead of one, and bug fixes mean navigating a maze of interfaces and overrides. When the inevitable refactor comes, because “we’ll refactor later” is tech’s biggest lie, you’re dismantling a cathedral brick by brick. I’ve seen teams spend months rewriting systems because their abstractions were so convoluted that adding a database field required updating 15 classes. Worst, Hanging Abstractions erode trust. When developers can’t understand the codebase, they stop believing in it. They hack around it, adding quick fixes in utility classes or, God forbid, copy-pasting code because it’s easier than navigating the AbstractDataProcessorFactory. The temple becomes a ruin, and the codebase a fossil, as warned. We’ll dissect this further in an upcoming chapter.

So, how do you escape? Embrace simplicity! Ask, “What’s the simplest way to solve this?” If a function works, use it. Don’t create a class unless you need state. Don’t create an interface unless you have multiple implementations. Don’t create a factory unless you’re building cars. Delay abstraction until needed, keeping YAGNI (You Ain’t Gonna Need It) as your mantra. Write the simple version first, then refactor when requirements demand it. Prioritize clarity over cleverness. You’re here to write working software. If your abstractions are hanging, it’s time for reduction surgery. Cut the fat, keep it firm, and let your code do the talking. Nobody likes BOOBS so big they get in the way of getting shit done.

Dangerous Implants

You’ve seen the horror of Hanging Abstractions. Those towering temples of overengineered code where developers sacrifice simplicity on the altar of “doing it right.” Your codebase groans under the weight of AbstractAnimalFactoryImplV2 and IUserProfileManagerService, and you suspect your quest for extensibility has led you into the swamp of Bloated Object-Oriented Bullshit Syndrome (BOOBS). Fear not, there’s a way out, and it doesn’t involve praying for a refactor that’ll never come. You just have to be aware of some Dangerous Implants, that's all. Here where we rip out brittle, overextended inheritance hierarchies and replace them with something that works: composition. When your code’s sagging under bad decisions, stop extending everything and start assembling things that don’t suck.

Why is inheritance a one-way ticket to FragileTown? Picture building a notification system, going full OOP warrior. You create an AbstractNotification class with a send() method, extended by EmailNotification, SMSNotification, and PushNotification. Each subclass overrides send() for its channel. Clean, right? Then requirements change. They always do. You need logging for every notification, so you add a log() method to AbstractNotification. Then retry logic, so you cram that in. Now, some notifications need batching, but only for SMS and push, not email. What now? Hack a flag into the base class (gross), create an AbstractBatchableNotification (grosser), or copy-paste code (grossest). Your elegant hierarchy is a brittle mess where changing one class risks breaking everything downstream. Inheritance’s dirty secret: it locks you into rigid hierarchies that crumble under real-world complexity. It’s like building a house from concrete blocks. Very solid until you need to move a wall, then you’re calling the wrecking ball, and the original wall’s never the same.

Inheritance is tight coupling pretending to be reusability. Extending a class means marrying it. All its baggage, bugs, and assumptions. If AbstractNotification assumes every notification needs a recipient field, but your new SlackNotification doesn’t, you’re screwed. Force-fit the field, hack with nulls, or break the hierarchy. Deep inheritance trees are worse. I worked on a project with a seven-level hierarchy where AbstractBaseEntity begat AbstractDomainEntity begat AbstractBusinessEntity begat…you get it. By ConcreteCustomerImpl, nobody knew what methods did or why they existed. Changing one required a week of regression testing because subclasses were chained like a dysfunctional family reunion. Inheritance here wasn’t reuse, it was an obvious cry for help.

Enter composition, the unsung hero of maintainable code. Instead of extending classes for behavior, assemble it from small, focused components. Think LEGO bricks: each does one thing well, and you snap them together. For a notification system, ditch AbstractNotification for a Notification class with a Sender (for sending), Logger (for logging), and Retrier (for retries). Each component is standalone, with a single job, and you mix and match as needed. Batching for SMS but not email? Plug in a BatchSender for SMS. New feature? Add a component without touching others. Composition is more like dating. You swap parts without a messy divorce. Don’t tell my wife I wrote this!

Let’s refactor a bloated payment system. You’ve got an AbstractPaymentProcessor with a processPayment() method, extended by CreditCardProcessor, PayPalProcessor, and CryptoProcessor. Each overrides processPayment() for a third-party API but inherits cruft like a validateCredentials() method only relevant for credit cards. Someone adds logTransaction() to the base class, but PayPal’s logging format differs, so you hack an override. Adding a new processor means wading through inherited baggage. Now, with composition:

Need a new processor? Create new components and plug them in. Crypto payments skip validation? Use a no-op validator. PayPal needs unique logging? Swap in a PayPalLogger. The PaymentProcessor stays lean, and components are testable in isolation. You’ve gone from a brittle concrete tower to a flexible LEGO castle. New requirements, like retry logic? Add a Retrier component without touching the rest. This is the “just enough” principle: build for now, make it easy to change later, avoid hypothetical futures.

Composition’s power is flexibility. Unlike inheritance, which locks you into a hierarchy upfront, composition defers decisions. Swap, add, or remove components without rewriting half the codebase. It’s more testable. Each component is a small, focused unit, no mocking entire inheritance trees. Composition PRs often raise flags for removing lines, not adding them. Modern languages make it easier: JavaScript’s functional composition chains map, filter, and reduce for complex behavior from simple parts. Python’s decorators or context managers wrap logic without inheritance. Java’s interfaces and dependency injection (think Spring) enable composition without abstract classes. Look at React, components as functions taking props and returning UI, snapped together like LEGO bricks. The result? Code that’s easier to reason about, test, and extend, unlike extending AbstractBaseComponent and praying the parent doesn’t break overrides.

Composition requires a certain shift in a developer’s mindset. Resist building temples to abstraction; build tools that solve problems. Say, “I don’t need a 12-bedroom mansion for a one-bedroom problem.” It requires discipline to keep components small, focused, with clear contracts between parts. Trust you can refactor later instead of overengineering upfront. Value clarity over cleverness. Developers avoid composition because it feels “too simple,” like they’re not “real engineers” without a five-layer inheritance tree. But clever code gets claps in code reviews; clear code ships products.

To switch, start small. Next time you’re tempted to write an AbstractSomething, ask, “Can I compose this?” Break the problem into reusable pieces. Functions, classes, modules. Snap them together. Refactoring a bloated hierarchy? Identify behaviors (sending, logging, validating) and pull them into components. Test each in isolation, then wire them together. Use language features, dependency injection, functional pipelines, or plain functions. Use everything to make composition idiomatic. When in doubt, YAGNI. Don’t build for unpredictable futures. Build for now, make change easy. The fight against Dangerous Implants is for sanity. Inheritance is a bad boob job! They are often flashy, rigid, prone to complications. Composition is the natural, flexible alternative that lets your code breathe. It’s not about elegance or UML diagrams. It’s about code that works, that you understand six months later, and that doesn’t make colleagues want to quit. Rip out brittle hierarchies, assemble something lean, and remember: clear, boring, working code beats clever, elegant, unreadable bullshit every time. Your codebase (and sanity!) will thank you.

A.S.S.

“Stick to that ASS and you’ll be fine!”

Some of us might not be into BOOBS, I get it, but we can agree that everyone can get behind a nice, juicy ASS. (literally) By that, I mean “Avoid Silly Solutions.” I don’t know why this isn’t a thing already, but it’s about damn time someone said it: stick to ASS, and your codebase will thank you. Too many developers live at extremes. Either they’re crafting ten-layered monstrosities with generics, factories, and more indirection than a corporate press release, or they’re duct-taping broken functions together and calling it “clever hacks.” Neither is impressive. One’s an unnecessary flex; the other’s a fire hazard.

We’ve all heard it: “It’s silly, but it works.” That phrase should trigger a mental warning emoji in any conversation. If it sounds silly when you explain it, that’s a damn good sign it’s time to rethink your approach. You’re not a street magician pulling party tricks; you’re an engineer writing production code. Avoiding silly solutions isn’t about being minimal. It’s about being sane. It’s resisting the temptation to hack things to death or build baroque monstrosities to look smart. You don’t need a recursive metaclass when a loop will do. Gluing two broken utilities isn’t “resourceful”, it's tech debt in clown makeup.

This chapter is your reminder that elegance isn’t about looking fancy. It was always about not acting silly, actually. Avoid ridiculous workarounds, call out hacky behavior, and aim for adequate solutions! Do not become the classic programming meme “haha, this is janky but YOLO.”

Jank Alert!

You’ve finished a fancy feature at 3 a.m. under a tight deadline. Your code looks like duct tape and baling wire, but it works! You push to production, lean back, and mutter, “It’s silly, but it works.” Cue the mental warning emoji! Because you’ve planted a landmine in your codebase. This should immediately trigger your Jank Alert! It’s where you spotlight those “quick hacks” and “clever” one-offs developers slap together in a crunch, only to watch them explode into maintenance nightmares. In the spirit of Avoiding Silly Solutions, this section is your wake-up call to stop treating code like a street magician’s trick and start treating it like an engineering project. Silly solutions aren’t just embarrassing, they’re dangerous, and your codebase deserves better than that.

A myth haunts developers, disguised as a programming joke since the first line of BASIC: “If it works, it’s fine” or “If it works, don’t touch it.” This mantra fuels every hacky workaround, chanted by sleep-deprived coders who think, “I’ll fix it later.” But working code isn’t good code. A MacGyver contraption spitting out the right output isn’t production-ready. It’s like a car held together with duct tape and prayers. That’s maybe enough to get you to the local grocery store, but it’ll fall apart on the highway. Fragile, unreadable, or unmaintainable code is a ticking time bomb. Not a solution, but a liability.

I once reviewed a friend’s pet project. He wasn’t a web dev and wanted a quick check. He needed to parse a date string from an API. Simple, right? Use a library like date-fns or Python’s datetime. Nope. He wrote a 50-line regex that looked like it was summoning Cthulhu. It “worked” for the API’s format, until a new timezone broke it, crashing the feature. This happened more than he expected. The fix? A one-line call to a proper date parser. His hack saved an hour but cost a week of debugging and me calling him a dumbass over beers.

Silly solutions NEVER stay contained! Remember that! They’re weeds in a garden. These weeds don’t just choke maintainability. They strangle morale and productivity. A codebase full of hacks turns every feature into a battle. Developers waste hours deciphering cryptic workarounds instead of writing code. Onboarding becomes a horror show as new hires grapple with why a global variable doubles as a database. You know the feeling.

Consider a startup’s e-commerce platform needing quick Black Friday discount calculations. A developer hardcoded percentages in a massive switch statement. It “worked” for the sale, but when marketing wanted dynamic discounts for user tiers and rewards, it ballooned into a 500-line monster of nested conditionals. Every change broke something, costing weeks to fix edge cases. A simple DiscountCalculator class with a configuration object could’ve avoided this. The hack saved a day but cost a month and thousands in revenue when the site crashed under peak traffic. Here’s the comparison:

The switch statement is brittle, slow with more cases, and crashes your sale. The DiscountCalculator? Three lines of flexible, fast code that scales.

Why do developers fall for this? Pressure, deadlines, impatient managers, or a “just ship it” culture. But there’s also a weird machismo in tech, where hacky workarounds feel like a badge of honor. “Look, I made it work with three lines of regex and a prayer!” It’s like a street magician pulling a rabbit from a hat. Impressive for a second, until you see the terrified rabbit and the hat’s secret holes. Engineers aren’t magicians. Unless your requirements demand a clever trick (rarely), your job is to build predictable, maintainable systems. A hack might survive a sprint, but not a production outage or security audit.

The antidote? Aim for adequate solutions! Not minimal, not maximal, just right. Adequate isn’t half-assed; it’s solving the problem with the simplest, clearest approach that doesn’t paint you into a corner. Need to parse a date? Use a library. Need to cache data? Set up a caching layer, even if it takes an hour. Need discounts? Build a configurable class, not a 500-line switch statement. Adequate solutions are like well-fitted jeans. Not too tight, not too baggy, and they don’t fall apart when you bend over. They’re probably boring, but as I’ve said—that’s the point. Boring code doesn’t crash servers or make your team cry.

Your job should always be to spot silly solutions early. If you’re explaining your code and say, “Yeah, it’s a bit janky, but…,” stop. That’s your brain waving a red flag. If your solution needs a PowerPoint to justify, it’s silly. If it relies on edge cases or undocumented API behavior, it’s definitely silly. If you think, “I’ll refactor later,” you’ve lost. Avoid silliness now, not during a 2 a.m. outage.

Use tools and processes to keep silliness at bay. Code reviews catch these janky hacks. Always call them out, not to shame, but to save future pain. Automated tests expose fragile solutions; if a “clever” hack breaks under a unit test, it’s not clever—it’s brittle. Revisit that discount disaster. A DiscountCalculator with a calculateDiscount(order, user) interface and a config like {"tier": "gold", "discount": 0.2} would’ve allowed dynamic discounts by updating the config, not rewriting a 500-line function. It’d be boring, testable, and wouldn’t crash on Black Friday. Or the cookie fiasco: a session management library like JWT or OAuth, set up in a day, would’ve been secure, scalable, and maintainable. No trusting client-side cookies. Just server-side session data and validated tokens. It’s not flashy, but it’s robust. Engineers build lasting systems; magicians build illusions that collapse.

Jank Alert! reminds you that silly solutions are a choice, not a necessity. You don’t need to hack to hit deadlines or build monstrosities to prove you’re a “real” developer. Write clear, predictable code that doesn’t make your future self or team want to punch a wall. Stick to ASS: aim for adequate, not too simple, not too complex, just right. Your codebase isn’t a stage for party tricks, it’s a foundation for something that lasts. So just ditch the clown makeup, call out the jank, and write code that makes your ASS proud.

Firm Foundation

Okay, you’ve dodged the clown makeup of janky hacks, but the whispers of overengineering tempt you with “elegance” and “extensibility.” On one side, developers cobble code with duct tape and prayers; on the other, architects build temples to abstraction that collapse under their weight. Both are traps, and neither yields a codebase to be proud of. What you actually need is a Firm Foundation. This is where we find the Goldilocks Zone of software engineering. Where solutions are neither too simple nor too complex, but just right. This is about embracing ASS by building pragmatic, sane code that doesn’t make you look like a try-hard or a slacker. It’s about a foundation that’s solid, clear, and ready for production chaos without needing a PhD to understand or a fire extinguisher to maintain.

The Goldilocks Zone isn’t a cute metaphor—it’s engineering’s holy grail! Too simple, and your code crumbles when a user does something unexpected. Too complex, and you’re debugging a factory that builds factories to instantiate a logger printing “Hello.” The sweet spot is where code does exactly what’s needed, with enough structure for change but not so much it feels like solving world hunger. In web dev, you might build a user profile service. One developer dumps everything into a JSON blob. Fast, but a nightmare to extend. Another spins up a microservices architecture with Kubernetes, message queues, and a UserProfileEventSourcingAggregateRoot. Both miss the mark. The Goldilocks solution? A UserProfileService class with getProfile() and updateProfile(), backed by a database and a clear API contract. It takes a day, is easy to test, and handles requirements without breaking a sweat. That’s the ASS we aim for! Firm, functional, no fuss.

How do you find this sweet spot? Resist overbuilding and corner-cutting. Overbuilding feels like being a “real” engineer. SOLID principles, design patterns, enterprise-grade scalability tempt you to spin up interfaces and factories for a script emailing a report. It’s like buying a tank for the grocery store! Impressive, but you’re not invading Poland. Corner-cutting is tempting for speed. Deadlines loom, managers push, and a regex to parse a CSV feels like a lifeline. But quick fixes are junk food: satisfying now, painful later when your codebase bloats and your team suffers. The Goldilocks Zone says no to both. Write robust code a new hire can understand without a decoder ring.

Start with the problem, not the solution. Before coding, solve it in plain English, like explaining to a non-technical friend at a bar. For a welcome email feature: “When a user signs up, send an email with their name and app link.” Design the simplest solution for that, no more, no less. A WelcomeEmailSender class with a send(user) method might suffice. No factories, abstract classes, or event-driven microservices. If requirements grow, say, adding SMS, refactor then. Don’t build a skyscraper when a single-story house works. Teams waste weeks on “flexible” systems assuming Google-scale needs. Spoiler: most don’t. Start small, solve the problem, keep your ASS grounded.

Embrace constraints. They’re guardrails against overengineering and hackery. Set boundaries: “I’ll use one class unless there’s a clear reason for more” or “No dependencies unless they save significant time.” I worked on a project filtering products by category. The team wanted a third-party query builder, but a constraint—stick to standard library functions—led to a simple filter(lambda x: x.category == target, products) in Python. It was clear, fast, and avoided a 50MB dependency. Constraints also prevent hacks. A “no global variables” rule stops quick fixes that haunt you later. Constraints are your codebase’s personal trainer, and they are keeping your ASS in shape.

Clarity is your North Star in the Goldilocks Zone. Clear code doesn’t need a 20-minute explanation or UML diagram. Your future self should read it without swearing. Use sensible names sendWelcomeEmail() beats executeNotificationProtocol() and keep logic straightforward. I reviewed a PR with a 200-line function to validate form inputs, full of nested conditionals and cryptic names like tmpVal. A clearer solution? A 20-line function with isValidEmail(email) and isValidPhone(phone). It did the same job, was easier to test, and didn’t make me want to yeet my laptop. Clear code is a gift to your team and sanity.

Why does “adequate and clear” beat “clever and janky”? Clever code impresses in reviews but haunts maintenance. A one-liner cramming three operations into a regex saves 10 minutes today, costs hours tomorrow. Janky code, in the form of “I’ll fix it later” hacks, is tech debt with regret. Adequate, clear code is a well-built house: not flashy, but it keeps the rain out and stands firm. For a shipping cost calculator, a “clever” function with 15 parameters and conditionals was a maze. A ShippingCalculator with a calculate(order, address) method and clear rate config took an hour more but saved weeks of debugging. Clear and adequate wins because it lasts.

Stick to ASS daily with tools, habits, and mindset. Use linters like ESLint or Pylint to flag complex code or suspicious patterns. Set them to catch functions over 50 lines or classes with too many methods. Formatters like Prettier or Black enforce consistent style, exposing messy logic. Habits matter too. Refactor as you go. Don’t wait for a “refactor sprint.” Break complex functions into smaller pieces before committing. Write docstrings first, a one-sentence description of what your function or class does. If you can’t explain it simply, you’re overcomplicating. For a CSV processing project, a docstring like “Parses a CSV file into a list of dictionaries” kept us focused. No regex hacks or abstractions. Pair programming or code reviews catch overengineering or shortcuts. The “rubber duck” test works too!

The mindset shift is hardest but vital. Embrace humility over heroics. You’re not here to look like a genius, you’re here to build working systems. Admit when you’re tempted to overbuild (“Do I need a factory?”) or cut corners (“Will this hack bite me?”). Value your team’s time over ego. A developer I worked with built a generic DataProcessor framework for a one-off migration. It sparked two weeks of confusion in the team. A simple script would’ve taken a day. Humility trusts you can refactor later, per YAGNI: build for now, but keep it flexible.

Sticking to ASS means committing to the Goldilocks Zone daily! Rejecting baroque monstrosities and reckless hacks. Choose tools, habits, and a mindset prioritizing clarity, adequacy, and sanity. Your codebase isn’t a canvas for showing off or a dumping ground for fixes. It’s a foundation for lasting systems. Keep your ASS firm, aim for just right, and write code that makes you proud without making your team cry. A solid foundation is the sexiest thing you can build.

C.U.N.T.

“Man, don't be a CUNT. Stop chasing shiny stuff and get back to real work.”

Ah, the allure of the new! The siren call of that hot framework everyone’s tweeting about, the latest tool promising to shave milliseconds off your build time, or the buzzword architecture that’ll supposedly make your app scale to Mars. But if you’re constantly ditching proven solutions for the flavor of the month, you’re deep in CUNT territory: “Chasing Unnecessary New Trends.” It’s the naughty habit that turns solid codebases into Frankenstein experiments, wastes weeks on migrations that solve problems you don’t have, and leaves you with a resume full of half-learned tech stacks.

CUNT strikes when hype overrides reason. You see a blog post screaming “Why We Switched to [Hot New Thing] and Never Looked Back,” and suddenly your perfectly fine React app needs a Next.js overhaul, or your Node backend must migrate to Deno because… reasons? It’s not innovation—it’s distraction porn, the kind that feels good in the moment but leaves you regretting it when the bugs hit. Like the rest of our naughty words, CUNT is a reminder that chasing trends without a damn good reason is just developer FOMO in disguise, and it’ll screw your productivity faster than a bad merge conflict.

This chapter is your intervention. We’ll expose why trend-chasing is a trap, how to spot when a new tool is worth your time (spoiler: rarely), and why sticking to what works isn’t boring but smart. Because in the end, the sexiest codebase isn’t the one built with the latest hype; it’s the one that ships features without drama. Let’s plug that CUNT before it plugs you.

Cunny Lures

You’re scrolling X, and there it is, another thread: “Why I Ditched Express for XYZ and Gained 10x Performance!” Your brain lights up like a slot machine! “My app could be faster,” you think, ignoring that your traffic is 10 users a day and your bottlenecks are in the database, not the router. Before you know it, you’re ripping out half your backend for a tool you read about 20 minutes ago. Welcome to Cunny Lures, where CUNT tempts you with seductive promises, only to leave you wrecked and regretting. It’s the developer equivalent of a one-night stand with a bad idea. Thrilling at first, but by morning, you’re dealing with the mess.

The cunny lures run on FOMO and shiny-object syndrome. A new framework drops, influencers hype it as “game-changing,” and suddenly everyone’s migrating like lemmings off a cliff. But here’s the dirty secret: most “revolutions” solve problems you don’t have. I once fell for it hard on a side project. Saw a post about Svelte being “lighter than React,” so I rewrote my simple blog from React to Svelte. It was fun… for a day. Then I spent a week debugging edge cases with state management that React handled out of the box. My blog had maybe 10 visitors a month! Did it need to be 0.5 KB lighter? Nope. But my CUNT flared up, and I wasted a weekend for zero gain. Sound familiar?

Why do these cunny lures wreck you? First, migration costs. Switching tools isn’t free. It's weeks of rewriting, testing, and fixing what wasn’t broken. Then come the learning curves. Every new tool has quirks. You think you’re boosting productivity, but you’re back to Stack Overflow hell, debugging obscure errors while deadlines laugh. Third, abandonment risk. Trends die. Remember Meteor? Parse? Hot in 2015, ghosts now. Chase too many, and your codebase becomes a graveyard of orphaned tech, impossible to maintain.

Spot a cunny lure by its signs. Buzzword salad: If a tool’s pitch is “revolutionary paradigm shift with zero-cost abstractions,” it’s probably overpromising. Echo chambers: Everyone on X is hyping it, but no one’s shipping real apps. Premature optimization: “It’s 20% faster!” Yeah, but your bottleneck’s elsewhere. If you’re adopting without a clear problem it solves, you’re in CUNT mode.

The real wreck? Opportunity cost. Time spent chasing trends is time not spent on features, bugs, or—gasp—life outside code! I saw a startup burn three months migrating from Mongo to Cassandra for “scalability.” Their user base? 500. They could’ve scaled on a Raspberry Pi. Meanwhile, competitors shipped features and stole market share. CUNT doesn’t just waste your time, it hands wins to the pragmatic folks who stick to what works.

So, escape the cunny lures by asking: Does this solve a real pain? Benchmark it. Prototype a small piece. If it’s not a clear win, walk away. Remember ASS from Chapter 5? Avoid Silly Solutions, even if they are currently the trendy ones! Your codebase isn’t a fashion show; it’s a tool. Keep it functional, not flashy, and your ass stays firm while others crash on the hype train.

Untamed Urges

You’ve dodged the cunny lures, but the whispers of “What if this new thing is better?” still tempt you. It’s easy to feel like a dinosaur for sticking with “old” tech while everyone chases the next shiny object. But here’s the secret: the sexiest codebase isn’t built on trends but on solid choices that solve real problems without drama. Welcome to Untamed Urges, where we flip CUNT on its head by embracing proven tools, resisting FOMO, and focusing on what delivers value. This isn’t about being a Luddite; it’s about being smart and picking tech that lasts, scales without headaches, and lets you ship instead of tinker.

Untamed urges start with a mindset: prioritize stability over novelty. Ask, “Does this tool solve my problem better than what I have?” Not “Is it trending?” I once resisted switching from Express to Fastify despite the hype. Express was battle-tested, with plugins for everything we needed. Fastify was faster in benchmarks, but our bottlenecks were database queries, not routing. Sticking with Express saved migration time and avoided learning quirks. Result? We shipped faster, and the app scaled fine. Trends are fun, but stability wins races.

How do you tame those urges? Benchmark ruthlessly. Don’t trust blog posts. Prototype. For a caching layer, I tested Redis versus Memcached on our workload. Redis won for our use case (pub/sub needs), but Memcached would’ve been fine too. The key? We chose based on data, not hype. Vet community and longevity. A tool with 10k GitHub stars and active maintainers beats a 1k-star newcomer. I learned this when we adopted a hot ORM that died six months later. Literally migration hell! Now, I check commit history and issue responses before committing.

Tie choices to business needs. In a fintech app, you will obviously stick with SQL over NoSQL hype because audits will require relational integrity. It won't be sexy, but it will avoid compliance nightmares. Only rely on solid choices that align with your domain. E-commerce? Prioritize proven payment integrations. Healthcare? Focus on compliant, secure tools.

Resist upgrade fatigue. Not every version bump is essential. We delayed a major framework upgrade until it solved a specific pain (performance in high-traffic endpoints). Skipping minor hype kept us stable. When adopting new tech, do it incrementally. Pilot in a non-critical feature. For a logging switch, we tested in one microservice first, worked great, and rolled out gradually.

These untamed urges echo the previous principles: like P.I.S.S., at least prove a tool’s value before adopting. They’re about sustainability. Picking tech that lets you build, not constantly rebuild.

In the end, taming those urges keeps your ASS firm amid hype storms. Chase value, not trends. Your codebase will be reliable, your team productive, and your sanity intact. That’s the real win!

P.O.R.N.

“Listen up, this is a PORN-friendly workplace. Don’t say you weren’t warned!”

Get ready to dive into the wild world of “Plan Often, Refactor Nevertheless,” or PORN for short. Yes, it’s as addictive as it sounds. This isn’t about chasing a perfect codebase that survives a client demo. PORN is about facing facts: no matter how many UML diagrams you sketch or Jira tickets you polish, your code’s gonna get messy. That’s not failure, it’s software development. PORN is for devs who plan like masterminds but know a rogue requirement or “quick fix” will turn their design into a hot mess.

Planning feels good. Your Miro board looks like NASA’s, your user stories are pristine, and you’re sure you’ve nailed it this time. Then reality hits: a new feature, a legacy API tantrum, or a “temporary” hack that lingers like a bad tattoo. Suddenly, you’re refactoring like it’s your job. PORN says embrace it. Technical debt isn’t a sin, it kinda comes with the gig. This chapter unpacks why overplanning is a trap, how to plan just enough to keep the team aligned, and why refactoring is your cleanup crew, not your enemy. You’ll learn to spot overplanning (hint: arguing about class names pre-coding is a red flag) and why being a PORN addict is the sanest way to survive the spicy chaos of coding. Let’s get to it!

Predictable Clichés

Let’s talk about the siren song of planning! That sweet delusion that one more hour on your Miro board or one more meeting debating REST versus GraphQL will deliver a codebase so flawless it’ll make angels weep and senior devs nod approvingly in code reviews. Welcome to Predictable Clichés, where every developer, from wide-eyed bootcamp grad to grizzled architect with a neckbeard longer than a CVS receipt, falls into the trap of thinking they can plan a perfect system before writing a line of code. It’s like choreographing a dance for drunk toddlers. Good luck, but reality will body-slam your blueprint faster than you can say “agile sprint.”

We’ve all been there. You start a project with visions of grandeur. Your Jira board is a work of art, with color-coded epics and user stories so detailed they read like Tolkien novels. Your diagram looks like a conspiracy theorist’s map of Area 51. You’ve spent three days arguing whether to call your main class UserControllerManager or UserController, because naming is hard. You’re convinced you’ve accounted for every edge case, user error, and database hiccup. You’re not just building software; you’re crafting a digital utopia. Then, two weeks into the sprint, the product manager strolls in with a “quick update”: the client wants a feature that breaks half your assumptions, the legacy system you swore you wouldn’t touch is mission-critical, and the deadline’s moved up. Your perfect blueprint? Yeeted into the shredder.

This is the first predictable cliché: the fantasy that you can outsmart chaos with planning. It’s not optimism. Software development isn’t a math problem with one right answer; it’s a bar fight with constantly moving goalposts. The more time you spend polishing your brass knuckles in advance, the less time you have to swing. Overplanning is what happens when developers confuse preparation with productivity. Tweaking your architecture diagram for the 17th time isn’t progress. At best, it’s procrastination with extra steps, like a chef sharpening knives all day but never cooking a damn thing.

Let me tell you about my first big planning screw-up as a self-taught dev at a startup. Green and eager to impress, I’d just discovered “enterprise-grade” design patterns. For my first big project, I spent weeks planning. Flowcharts, entity-relationship diagrams, a 30-page Google Doc outlining every endpoint, schema, and failure mode. I was the Leonardo da Vinci of overpreparation, sketching masterpieces no one asked for. By the time I started coding, I was so married to my plan that I refused to deviate, even when a major client pivoted from physical to digital downloads. My beautiful plan? Useless. My database schema? A relic. My ego? Bruised like a peach in a blender. I wasted so much time predicting the unpredictable that I nearly tanked the project. Lesson learned: no plan survives first contact with reality.

Another cliché is the “completeness” obsession. Some devs treat code like a chess game, anticipating ten moves ahead. They’ll debate singleton versus factory patterns like the free world depends on it or write 47 test cases for a date formatter in case someone uses a Mayan calendar. This is anxiety cosplaying as discipline. I worked with a guy—let’s call him Dave—who was so obsessed with “future-proofing” that he built a plugin system for a settings page with one toggle: dark mode. Three weeks for a feature that never shipped. When I asked why, he said, “You never know what they’ll want next.” Spoiler: they didn’t want anything else. Dave’s code was a monument to a future that never came, and we waded through his labyrinth of interfaces to fix a CSS bug.

These clichés get glorified as “best practices” or “clean architecture.” Planning isn’t bad, kinda like foreplay: necessary, sets the mood, but if you spend all night on it, everyone’s frustrated. Plan just enough. Focus on the core problem: what’s the minimum viable thing you’re building? What’s the one feature that makes the project worth it? Nail that, sketch a rough map, and code. You’re not writing the Constitution here, you’re building software that evolves. Your plan should be a napkin sketch, not a 500-page novel.

To avoid these clichés: First, embrace the 80/20 rule. Eighty percent of your project’s value comes from 20% of the features. Identify that 20%, the critical path, and plan it rigorously. The rest? Sketch lightly and move on. If you’re planning edge cases more than the happy path, you’re doing it wrong. Second, timebox planning. A day for a small project, a week for a big one. When the timer’s up, code, even if your diagram isn’t a Renaissance painting. Third, talk to people! Stakeholders, users, the folks who’ll use your software. They’ll clarify what matters faster than your brain can guess. I saved a project by asking a client over coffee, “What’s the one thing this app must do?” They didn’t care about half our planned features. We shipped in half the time. Finally, accept your plan is a hypothesis, not a prophecy. You’re not Nostradamus, you’re a coder. Test assumptions with code, not PowerPoint. Every plan’s wrong when it meets reality, so build something you can iterate on. Clear, modular, easy to change. Not because you’re a genius, but because you know a curveball’s coming.

The PORN mindset treats planning as a tool, not a religion. Plan to align, set direction, avoid stepping on toes. Don’t plan to avoid surprises, surprises are the job. Stop chasing “the perfect architecture” or “bulletproof plan.” My old mentor said, “A good plan today is better than a perfect plan tomorrow! Because tomorrow, the client’s gonna ask for a blockchain integration.” He wasn’t wrong. Plan enough to move forward, code to learn, leave room for chaos. Your blueprint’s gonna get screwed…and that’s okay, because refactoring’s in your back pocket, where the real magic happens.

Accepting The Kink

Let’s get real about software development’s dirty secret: your code’s gonna get kinky, and not in the fun, consensual way. We’re talking the kind of kink that makes you wince during a production outage caused by a “temporary” hack you swore you’d fix last sprint. Welcome to Accepting The Kink, the PORN lifestyle where you stop pretending your codebase will be a pristine masterpiece and embrace refactoring as the cleanup crew for the inevitable mess. Refactoring isn’t punishment for “bad code”. It’s what happens when you build software in a world of shifting requirements, looming deadlines, and interns sneaking if (true) blocks into the main branch. Grab a coffee, crank up the lo-fi beats, and let’s talk about refactoring without regret, making peace with chaos, and maybe enjoying the ride.

Refactoring isn't a failure. Too many devs treat it like a walk of shame, as if reworking code means you didn’t plan enough or aren’t a “real” engineer. Bullshit. Refactoring is as natural as breathing or, keeping with our theme, adjusting the ropes when the knot’s not right. Software isn’t a statue carved once for a museum; it’s a living thing that grows, mutates, and occasionally throws up on itself. Requirements change because clients don’t know what they want until they see it. Bugs emerge because production’s a crueler dungeon master than any test suite. That “quick fix” for the demo? It’s now load-bearing, like a bad tattoo from a drunken Vegas trip. Refactoring keeps the codebase alive, not admits defeat.

I learned this the hard way at a healthcare startup on a dashboard app demo. Tight deadline, client breathing down our necks, a backend held together with duct tape and vibes. I wrote a calculateMetrics function to aggregate data, simple, summing numbers. Then the client wanted averages, medians, a weird weighted score based on preferences. By shipping, calculateMetrics was a 300-line monstrosity with more nested conditionals than a tax code. I was proud. Just look at those features! A month later, a new metric request showed I couldn’t read my own code without a flowchart and a prayer. Refactoring wasn’t failure; it was survival. I broke it into smaller functions, added clear interfaces, and made it maintainable. The kink was still there, sort of winking at me still, but at least it was manageable, and I could sleep.

How do you refactor without losing your mind or making a bigger mess? Step one: accept the kink is inevitable. Perfection’s a lie told by people who don’t ship. Every project has technical debt. Compromises to hit deadlines or work around legacy systems older than your grandma. The trick is knowing which debt to pay off. That if statement with three conditions? If it works and isn’t touched often, let it be! That’s small potatoes. But a 500-line controller doing everything from database queries to HTML rendering? That’s a kink to untangle before it chokes the project.

Prioritize refactoring by impact. The “squeaky wheel” rule. Fix what’s touched most: core business logic, API endpoints, stuff breaking weekly. In that dashboard app, calculateMetrics was the squeakiest wheel in fact. Every feature request meant diving into its mess. Refactoring it saved weeks. Don’t polish stable, rarely changed code.

Keep refactoring focused to avoid scope creep. It’s tempting to pull one loose thread and unravel the codebase. You fix a function, notice the class is a mess, decide the module needs an overhaul, and suddenly you’re rewriting authentication because it “could be better.” Stop. Set boundaries. Write down what you’re fixing and why: “I’m splitting this 200-line method into smaller functions to make it testable.” Straying? Take a breath, step away, maybe touch some grass. Scope creep turns a one-hour fix into a week-long odyssey that breaks production and makes your team hate you.

Refactor incrementally. You don’t need one heroic pull request. Break work into safe chunks. Monster class? Split one method at a time. Spaghetti API? Extract one endpoint. Incremental refactoring is like cleaning an apartment, you need to take it one messy corner at a time. On a legacy codebase that looked like a caffeinated squirrel’s work, we chipped away slowly. Extracting utilities, simplifying logic, adding tests. Six months later, it was quirky but workable.

Tests are your friend, but don’t get dogmatic. Unit tests catch regressions, but writing one for every line before refactoring is TDD hell (see S.H.I.T., Chapter). Focus on critical paths, business logic, user-facing features. Refactoring a payment processor? Test it processes payments. Don’t test if the logger logs. In a zero-test codebase, refactor carefully, lean on manual testing initially, add tests as you go. Not ideal, but better than leaving the kink untouched waiting for a perfect test suite.

Refactoring can feel like wading through a swamp in flip-flops. You open a file, see a function written by a drunk AI, and wonder how you got here. Maybe it’s your code from six months ago, when hackyWorkaroundV3 seemed like a great name. Don’t beat yourself up. Code is a snapshot of what you knew under past constraints. Refactoring is your chance to improve, not atone. Laugh at the mess, learn, move on. I found a function I wrote with a comment: “// TODO: Fix this later, lol.” Past me was an idiot; present me is the hero who cleaned it up. That’s PORN! Accept the kink, make it work.

Refactoring is where you show off not “perfect code” swagger, but “I tamed this beast” swagger. Turning gnarly code into something clear and maintainable is like transforming a dive bar into a speakeasy. It’s still got character, but now inviting. Share the win: show your team how you broke a 300-line method into five clean functions, explain why you extracted logic into a utility. It makes you look badass and helps your team handle the next one easier.

Accepting The Kink means embracing refactoring as core to dev life, not a dirty secret. Software’s messy because the world’s messy, clients change minds, APIs break, deadlines don’t care. PORN says, “It’s gonna get kinky, and that’s fine.” Plan to start, refactor to keep going. Don’t aim for perfection—aim for progress. The only thing worse than a messy codebase is one you’re too scared to touch. Roll up your sleeves, dive into the chaos!

S.L.U.T.

“I’m not gonna lie, you kinda have a serious SLUT issue here.”

I don’t throw this word around lightly. But if you’re pushing complex state through JSON, mapping it across three layers, and relying on TypeScript to “make it all work,” you’re dealing with a full-blown “State Loss Under Types”.

Here’s the deal: types aren’t state. Revolutionary, I know. They don’t enforce truth either. Declaring something as User | null doesn’t stop it from sneaking in as undefined, an empty object, or a half-baked API response that’s technically valid but semantically garbage. If this is news to you, welcome to the dark side of static typing they don’t teach in tutorials. SLUTs sneak in quietly, under the radar, and then screw everything at runtime in the worst way. Ever seen a Date object turn into a string from careless serialization? Or a discriminated union’s “impossible” default case trigger because someone forgot to update the type mapping? That’s a classic SLUT move.

It gets worse when you over-design types early, locking your system down like a Victorian nanny before the app’s behavior settles. You force hacks, workarounds, and unnecessary casting that hide real state problems behind a smug sense of type safety. This chapter is your wake-up call: just because your code compiles doesn’t mean it works. And just because the type system says it’s valid doesn’t mean the state agrees. SLUTs are a real problem. Once you’ve had one, you see them everywhere. Let’s talk about spotting them, avoiding them, and stopping the delusion that types are the final word on truth.

Undefined Pronouns

You’ve got a shiny TypeScript codebase, interfaces for every nook and cranny, and a smug sense of superiority because your IDE’s squiggly red lines are gone. Your types are tight, your User | null is bulletproof, and your code compiles faster than you can say “type safety.” But at 3 a.m., production crashes because an API returned {} instead of null, and your perfect User type didn’t save you. Welcome to Undefined Pronouns, where the SLUT rears its ugly head, proving that a clean compile isn’t a working app. Types aren’t state, and they sure as hell aren’t truth! They’re a map, not the actual terrain they aim to represent. If you treat your type system like a magic shield against runtime chaos, you’re in for a rude awakening. This is your guide to spotting sneaky SLUTs, understanding why static types don’t guarantee correct state, and keeping your codebase from getting screwed by misplaced trust in a compiler.

Static types peddle an illusion of safety like a shady car salesman. You write interface User { id: number; name: string; }, slap it on an API response, and think you’re golden. The compiler’s happy, your tests pass (thanks to perfect mocks), and you’re ready to ship. But types are a design-time promise, not a runtime guarantee. That User interface doesn’t stop an API from sending { id: "123", name: null } or {}. Your code compiles, but at runtime, you’re calling .toUpperCase() on null, and your app throws a tantrum. I worked on a project where we defined a Product type with price: number. Solid, until the API sent prices as strings ("19.99") because a backend dev “forgot” to parse them. TypeScript coerced strings to numbers in some cases but blew up with NaN in others. The type system didn’t save us. It gave a false sense of security while the real state laughed in our faces.

This gap between type system and runtime reality is where SLUTs thrive. Types describe what data should look like, not what it is. External data, APIs, database queries, user uploads, is the wild west, ignoring your interfaces. A team I saw got burned with a CartItem type: quantity: number. The API was supposed to send numbers, but a bug sent "0" for empty carts. TypeScript’s structural typing said, “Close enough,” and let it through, only for the frontend to crash on quantity + 1. The fix? A runtime check: typeof quantity === "number". Types gave confidence; reality gave an outage. Your type system is a guide, not gospel. Like a weather app saying it’s sunny. Great, but check outside before ditching your umbrella.

SLUTs leave calling cards, common bugs from type/state mismatches. The undefined sneak attack is a classic. You define User | null, thinking you’ve covered all bases, but an API returns undefined from an uninitialized field or {} from a bad database join. Your code assumes user.name is a string, but it’s undefined, logging TypeError: Cannot read property 'name' of undefined. In a project, we used a Response type with data: string[]. The API usually sent an array, but sometimes null. TypeScript didn’t complain, null was assignable to string[] in our config, but our .map() calls exploded.

Another SLUT move is the semantic mismatch. Your types are structurally correct, but the data’s meaning is garbage. Imagine a Status type: type Status = "active" | "inactive" | "pending". Airtight…until the API sends "archived" because a new status was added without telling the frontend. Your discriminated union’s switch statement hits the default case, crashing or silently misbehaving. In a payment system, we had a TransactionStatus type: "success" | "failed" | "pending". A backend update added "refunded", but the frontend wasn’t updated. The app treated "refunded" as a generic error, confusing users and costing a week of support tickets. Here’s an example:

The union looks bulletproof, but "refunded" slips through and crashes at runtime. That’s a SLUT because types promise safety, state delivers pain.

The premature typing trap is another issue. Over-designing types before understanding the app’s behavior. You create a UserProfile interface with 20 properties, half optional or | null because you’re unsure what the backend sends. This leads to “as” assertions and “!” non-null checks. Literal hacks. A team defined a Customer type with preferences: Preferences, a nested interface with 10 fields. The backend didn’t support preferences yet, so it was always null, cluttering code with pointless checks. When preferences were added, the schema changed, forcing a type rewrite. Over-designed types don’t solve problems. They in fact create them, hiding state issues behind “safety.”

The core issue: “it compiles” doesn’t mean “it works.” Compiling is a checkpoint, not a finish line. Types catch design-time errors, but runtime is where reality lives. APIs change, databases return weird data, users upload 10MB JSON with emoji keys. Your type system can’t predict that, and leaning on it like a crutch invites SLUTs. In a Flow-typed form submission project, pristine types didn’t stop partial form data from breaking logic. We added runtime validation for missing fields, proving types weren’t enough. The compiler said “ship it”; reality said “fix it.”

To keep SLUTs at bay: First, validate at the boundary. For external data, APIs, databases, user inputs, never trust types alone. Use runtime validation libraries like Zod, Joi, or Yup to check shape and semantics. For a User with name: string, validate it’s a non-empty string with reasonable length. Second, keep types simple and flexible. Don’t lock down fields early. Use broad types like Record<string, unknown> in early development, refining as you learn the data’s shape. I started a project with a generic ApiResponse type, saving rewrites when the backend changed. Third, test with real data. Mocks are lies. Simple as that. Run integration tests with actual API responses or database queries. We caught a SLUT when a staging API sent null instead of an empty array, our types didn’t care, but our code did. Finally, embrace runtime checks. Sprinkle if statements or assertions at critical points. A simple if (!user) throw new Error("User missing") saves hours of debugging.

Undefined Pronouns reminds you that types are a tool, not truth. SLUTs sneak in when you trust your compiler over reality, letting type/state mismatches, careless serialization, or premature typing screw your codebase. Spot them with boundary validation, flexible types, real-data tests, and runtime checks. Your code might compile, but state runs the show. Don’t fall for the type system’s sweet talk. Build a codebase ready for reality’s messy pronouns.

Plug It Up

You’ve survived a SLUT ambush, your codebase limping after a runtime crash your pristine TypeScript types swore was impossible. Undefined Pronouns taught you that types are a flirty wink, not a binding contract! A clean compile is as trustworthy as a politician’s promise. Knowing your type system’s a tease isn’t enough. You need to stop SLUTs from sneaking in and screwing things up. Welcome to Plug It Up, where we’re not just patching holes with runtime Band-Aids but building systems that keep state and types in lockstep. This isn’t about piling on validation libraries or overengineering types, it’s about designing with state in mind, aligning your team, and keeping types lean to handle reality without breaking a sweat. Grab a plunger, and let’s stop those state leaks before your codebase turns into a hot mess.

Prioritize state over types. Types are like a menu at a shady diner, they describe what’s supposed to be on the plate, but there is a very good chance kitchen might serve something else. Obsessing over interface User { id: number; name: string; } sets you up for a fall when the API sends name: null or {}. Design with state first: examine the actual data. In a project, the frontend team built a Profile type with status: "active" | "inactive". Clean, until the backend sends status: "pending" for new users, choking our type-safe code. A state-first approach would’ve sampled real API responses,logging a hundred to see what’s coming. We’d have crafted status: string | null with a runtime check for valid values. Starting with state keeps you grounded, not chasing idealized types that collapse at runtime.

Collaboration plugs SLUTs. State mismatches thrive when frontend and backend teams treat each other like distant cousins. If the backend sends price: string while the frontend expects price: number, you’re begging for a crash. Fix it with a contract. An OpenAPI spec or JSON Schema defining what every endpoint sends. In an e-commerce app, we used an OpenAPI spec for the Item object: quantity: number, no exceptions. When the backend sent "0", our spec-generated tests caught it before production. No SLUT, just a quick fix and coffee for the team. Schema-first design is group therapy for your API, forcing agreement on state so types don’t lie.

Keep types simple to plug SLUTs. Over-engineered types are a corset. Structured but suffocating. If your Order type has 25 fields, 15 optional, and a nested ShippingDetails with more optional subfields, you’re hiding bugs behind | null and as assertions. I saw a team’s Order type balloon with optional fields for speculative features, leading to constant casting and crashes when fields were missing. Simplifying to a lean Order with required fields and a separate OptionalOrderDetails type cut complexity and caught issues earlier.

Design for partial data. Real-world systems are messy, APIs drop fields, databases return sparse records, users submit half-empty forms. Instead of rigid types, use flexible ones like Partial<Order> in TypeScript with helpers to normalize data. In a reporting tool, the API sent incomplete Report objects like { revenue: 1000 } instead of { revenue: 1000, expenses: 500 }. A rigid Report type crashed, so we used Partial<Report> and a normalizer: const normalizeReport = (data: Partial<Report>): Report => ({ revenue: 0, expenses: 0, ...data }). This handled missing data, keeping types and state in sync without complexity.

Monitoring and logging are your safety net. Even well-designed systems miss edge cases, so watch state in production. Log data that doesn’t match expected types, like an API response with an unexpected field. I added a console.warn to a Product parser for non-numeric price values, catching price: "19.99" before users saw broken totals. Tools like Sentry or Datadog automate this, but a simple log statement works. Pair with health checks validating critical endpoints against your schema to plug leaks before they flood.

The biggest plug is a mindset shift: don’t treat a clean compile as a gold star. A green IDE and passing tests tempt cockiness, but that’s when SLUTs strike. Ask, “Have I tested with real data?” Relying on mocks or assuming backend perfection is flirting with disaster. In a project, we mocked a PaymentResponse with status: "success" | "failed". In production, the API sent "processing", crashing the app. Logging unexpected statuses and adding a fallback UI fixed it, but integration tests with live API data were the real win. Stay skeptical. Types are a tool, not truth. Assume state’s messier than your compiler thinks to plug leaks early.

Plug It Up builds systems that don’t let state slip through. Think state-first, not type-first. Collaborate for clear API contracts. Keep types simple and flexible. Monitor production to catch leaks. Never trust a clean compile for safe state. Your codebase isn’t a runway for type theatrics. It’s a machine that should tend run smoothly. Plug those holes and build something that doesn’t let SLUTs ruin the party.

F.U.C.K.

“Congratulations—you’re FUCKed. You don’t understand anything, and it shows!”

Every developer hits a moment when they stare at code, squint, scroll, squint again… and realize they’re completely, utterly FUCKed! That’s “Failing to Understand Contextual Knowledge” the silent killer of productivity, sanity, and any chance of sounding competent in a code review.

FUCK happens when you dive into a system without grasping why it was built that way. You see a janky if-else block and think, “This is stupid, I’ll clean it up,” not realizing it’s the result of five business constraints, three legacy systems, and a legal compliance rule written in ancient Sanskrit. You didn’t ask, or worse, didn’t realize you needed to. Now, you try to “fix” something without understanding it, renaming variables, deleting “dead” code, or refactoring a module tied to an undocumented third-party integration. Shit breaks, and guess who’s on call this weekend? Congratulations—you’re FUCKed.

This chapter is about the power of context and how ignoring it turns brilliant devs into dangerous liabilities. No amount of clean code or good intentions compensates for ignorance of the bigger picture and the business logic the code serves. Code doesn’t exist in a vacuum; it has a purpose. If you’re working on it, you’d better understand that purpose. To stop getting FUCKed, get comfortable with the domain your code operates in. Ask why. Ask who. Ask about the horrible history behind that function name. What’s its purpose? Is it tied to industry specifics you don’t yet grasp? If you don’t, don’t be surprised when the codebase gets on top of you and doesn’t even buy you dinner first.

Shallow Relationship

You’re staring at a 200-line function with nested if-else blocks that look like a caffeinated squirrel wrote them. Your first thought? “This is garbage. I’ll refactor it into something clean.” You slice and dice, rename variables, delete “redundant” checks, and feel like a code-cleaning superhero. Then production crashes, and you learn that janky function handled a GDPR compliance rule, a legacy integration with a ‘90s mainframe, and a client-specific tax calculation nobody documented. Welcome to Shallow Relationship, where we unravel the invisible web of domain context that makes seemingly stupid code a critical cog. Ignoring this web is like fixing a clock without knowing how time works. To stop breaking shit, dig deeper into why your code is the way it is.

Every piece of code is tangled in an invisible web of industry rules, business logic, legal constraints, and historical decisions you can’t see by reading the source. That if statement checking if a date is before 2018? It’s not sloppy, it’s there because a regulation changed, and the client’s audited under old rules. That weird variable like cust_id_x? Not a typo, a holdover from a legacy system feeding your app. I worked on a financial app where a function called adjustBalanceWeird() raised my hackles until I learned it handled an IRS rule about fractional cents for specific accounts. Without that context, I’d have “cleaned” it and triggered an audit nightmare. Code is a product of its environment, and without understanding that environment, you’re swinging a wrecking ball blindfolded.

Why does ignoring domain context screw you? Code isn’t just instructions to a computer. It’s always a reflection of real-world constraints. In healthcare, a “simple” form field might have 10 validation checks for HIPAA compliance. In e-commerce, a discount function looks insane because it handles international tax law edge cases. I saw a team refactor a payment processor with a bizarre loop checking currency codes against a hardcoded list. They swapped it for a “cleaner” database query, only to learn the list was mandated by a banking regulation not updated in the database. Result? Failed transactions and an angry CFO. Without domain context, you’re not refactoring, you’re gambling with production.

Examples of this invisible web are everywhere. In a logistics app, a function padded shipment IDs to 12 digits. Pointless? Nope! Actually required for a third-party tracking system that choked on shorter IDs. Deleting it would’ve broken every shipment lookup. In a retail app, a calculateTax() method had 50 lines of conditionals. Easily mistaken for a mess, but each branch handled a state-specific sales tax rule, some from the ‘80s. Rewriting it without understanding those rules would’ve been a legal disaster. These “weird” patterns aren’t dumb. They’re purpose-built. Assuming they’re mistakes because they don’t fit clean-code ideals is a one-way ticket to FUCKsville.

The cost of a shallow relationship with domain context is steep, broken features, compliance violations, or lost customer trust. A developer I worked with deleted “dead” code parsing XML responses from an API, not knowing it was used by a monthly legacy client integration. The client noticed when reports stopped working, costing us a week of restoration and apologies. Another team renamed a database column from order_ref to orderId for “clarity,” unaware it was referenced by an external partner’s script. Orders failed for days, costing thousands. These weren’t bad devs, they just didn’t ask “why” the code was that way.

Asking “why” is your lifeline. Don’t just read code—interrogate it. Why does this function exist? What business problem does it solve? What constraints shaped it? If you see a check like if (user.region === "EU"), don’t assume it’s arbitrary. Dig into whether it’s tied to GDPR or a client rule. Check commit history, old tickets, or—gasp—talk to someone who’s been on the project longer. I avoided a FUCK-up by asking a senior dev about a cryptic restrictAccess() function. It enforced a licensing agreement limiting features for certain users. Without that context, I’d have ripped it out and risked a lawsuit.

Lean on documentation (if it exists) or create it. In a billing system project with no docs, I started a “Why Log” in the repo, noting the purpose of weird functions. It took 10 minutes a day but saved hours of guesswork. In regulated industries like finance or healthcare, Google “HIPAA for developers” or “PCI compliance 101” for context to spot why a function checks credit card numbers oddly. If all else fails, assume the code has a reason, even if it looks dumb. That assumption stops you from breaking critical systems.

The goal isn’t to become a domain expert overnight. It’s to respect the invisible web shaping your code. Before refactoring, ask: “What don’t I know?” Look for clues in structure, comments, or naming. A variable like legacyAccountFlag isn’t there for fun. A function named handleSpecialCase() begs you to find out what’s special. Asking “why” instead of “how” avoids turning a compliance-critical function into a bug report. A shallow relationship with context makes you a liability; a deeper one makes you a hero who doesn’t break production. Dig into the web, respect the weird, and keep your ASS out of FUCKed territory.

Consent Is King

You know code is tangled in an invisible web of domain context, but that doesn’t make you a domain expert. You’re staring at restrictLegacyAccessV2(), wondering why it exists, tempted to rewrite or ignore it and hope it doesn’t bite. Either way, you’re on the edge of getting FUCKed, The antidote? Consent Is King, a call to develop contextual empathy by learning industry specifics, business goals, and historical quirks shaping your codebase. This isn’t about memorizing compliance manuals or becoming besties with the product manager. It’s about approaching code with respect, curiosity, and a desire to be an insider, not an outsider breaking shit for lack of asking.

Contextual empathy starts with admitting you don’t know everything. You’re a detective in a world of business rules, legacy systems, and industry quirks. The fastest way to gain domain awareness is talking to subject matter experts (SMEs), product managers, senior devs, or customer support reps who know why calculateTax() has 20 conditionals or why the app logs every click. On a healthcare project, I was baffled by a patient ID validation regex that looked Klingon in origin. A coffee chat with a business analyst revealed it was a federal requirement for hospital system interoperability. That 10-minute talk saved me from a compliance-violating refactor. Find SMEs and ask: “What’s the deal with this code?” They’ll turn cryptic functions into critical features.

Reading documentation is another shortcut. Check wikis, READMEs, or old Jira tickets for clues about why code is the way it is. In a retail app, a discountOverride() function seemed redundant until a ticket revealed it was for a one-off client promotion turned permanent. That stopped me from breaking a major customer’s workflow. If docs are sparse, start your own. A “Context Notes” file in the repo, like “restrictAccess() enforces licensing for EU clients”, saves future FUCK-ups. It’s not a novel; it’s breadcrumbs for the next dev tempted to “clean” things.

Exploring business processes is like reading a game’s rulebook. Shadow a customer support rep or sit in on a sales demo to see how the app’s used. Shadowing a support team for a logistics app, I learned a “redundant” shipment weight check caught errors in a third-party scale integration. That insight preserved a “stupid” function. Ask for a business flow walkthrough. How does an order go from cart to delivery? What regulations apply? Even seasoned devs miss context without engaging the business side. A product owner chat can reveal why a feature’s overcomplicated, saving you from breaking client trust.

Understanding industry context empowers smarter coding. Knowing the “why” behind code lets you refactor confidently, not guess. A “useless” logAudit() function I kept intact was required for PCI compliance! Documenting its purpose saved an audit failure. Domain knowledge builds credibility. In a project, I read client documentation and explained how a function, with minor tweaks, fit their licensing model. They trusted us more because we understood their world, not just code. Contextual empathy also eases team tension. Mocking “bad” code without understanding its context alienates the dev who wrote it. Asking “Why’s this here?” instead of “Who wrote this crap?” fosters collaboration.

Practical techniques build empathy without a PhD. Spend a “context hour” weekly, read a ticket, talk to an SME, or skim an industry blog. Pair with a senior dev on a tricky feature to learn its business logic. Keep a glossary of domain terms like “GDPR” or “AML” and their meanings. Treat weird code like an archaeological artifact. Dig for its purpose; it might be garbage or a critical find. This mindset turns frustration into curiosity, curiosity into competence.

Consent Is King embraces the human side of coding. Learn the industry, respect quirks, ask questions like your codebase depends on it—because it does! Contextual empathy drives smarter decisions, earns trust, and avoids FUCK-ups from ignorance. Charm the domain and code like someone who knows the whole story.

The Last Taboo

We’ve reached the end. We’ve dragged programming’s dirty secrets out from under the rug, given them naughty names, and had a good, hard laugh. I’ve armed you with an arsenal of words to make stand-ups spicier and code reviews brutally honest.

But now, a confession.

All of it—S.H.I.T., B.O.O.B.S., P.O.R.N., and the rest was a trick! A sleight of hand. The naughty words were never the point. They’re the spoonful of sugar, or, better yet, a shot of homemade Serbian rakija! To make the medicine go down. The real point, the final and most controversial idea in this book, isn’t an acronym. It’s the last taboo in our profession, the one you’re never supposed to say out loud: It’s just a job.

I feel you flinch. I see the hustle culture disciples sharpening their pitchforks from their standing desks, screaming, “Programming isn’t a job! It’s a craft! A passion! A calling!” To them, I say: bullshit. Just because a few loud, annoying software engineering prophets base their entire existence on coding doesn’t mean the rest of us can’t see it as a job. Sure, we love programming. Realistically, it’s a job you can’t do without some interest. But it’s still a job. A wonderful, fulfilling, well-paid job, like many before it. You can feel the pull of coding, even make friends through it, but it shouldn’t define your whole personality.

The belief that programming must be the center of your universe is the most dangerous ideology of all. More destructive than a TDD cult or a bad case of Bloated Object-Oriented Bullshit Syndrome. It’s a gilded cage that traps us in 60-hour weeks, ties our self-worth to commit histories, and guilts us for wanting to see our families. This book, every naughty word, is a weapon against that cage. It’s an argument for sanity.

Think about it. Why Stop Hunting In Tests (S.H.I.T.)? Because life’s too short to chase imaginary bugs with no real-world value. You’ve got better things to do.

Why Avoid Silly Solutions (A.S.S.)? To spare yourself—and your team—the pain of maintaining fragile hacks or baroque monstrosities. A good-enough solution gets the job done, letting you clock out with a clear conscience.

Why Plan Often, Refactor Nevertheless (P.O.R.N.)? Because reality’s messy and unpredictable. We refuse to chain ourselves to a perfect plan from months ago, freeing ourselves from the anxiety of predicting the future. We adapt, fix, move on.

Every principle in this book is a tool to do good work. And then, crucially—to stop. It’s about building a sturdy bridge, not living on it. It’s about making the bar safe for customers, not sleeping on its floor in case someone needs the bathroom.

The cult of passionate programmers says love the grind, that burnout’s a badge of honor. That’s a lie designed to squeeze more work out of you for less. Especially by startup bros. The greatest senior developers I’ve known didn’t code 24/7. They solved complex problems in hours and went home. They had hobbies, families, non-programming stories to share. They had lives outside the IDE. Their code was great because their minds were clear, not because their identity was tied to a text editor.

To be a great programmer, you don’t need to sacrifice your life at the compiler’s altar. Be a pragmatic, intelligent, often cynical human who solves problems efficiently. Understand context (F.U.C.K.) to avoid wasting time on the wrong issues. Define clear boundaries (D.I.C.K.) so systems don’t collapse into chaos that keeps you up at night.

This isn’t a call for mediocrity. It's for sustainability. For professionalism. Your job is to deliver value, solve problems with code. Not to be a martyr.

My final advice: Keep these naughty words close. Use them to write clean, effective, sane code. Build things you’re proud of. Then, when the work’s done, turn off your computer. Close the laptop. Go outside. Read a book. Have a drink with a friend. Annoy your spouse or kids. Argue about something meaningless, far from technology. Touch grass. Live your life. In today’s world, LIFE is the last and most important naughty word of all.

Glossary Of Naughty Words

S.H.I.T. - Stop Hunting In Tests - Refrain from writing excessive tests for unlikely edge cases that clutter the codebase with minimal benefit. Instead, focus on resolving core design flaws to create robust, maintainable software. Overzealous testing, especially in rigid TDD practices, often leads to brittle, high-maintenance test suites that distract from building a solid system architecture.

P.I.S.S. - Prove It’s Simple, Stupid - Before writing code, ensure the solution is genuinely simple by clearly understanding and articulating the problem, its logic, and edge cases. This prevents the misuse of KISS (Keep It Simple, Stupid), which often leads to lazy, brittle code that ignores complexity. By creating your algorithms first in plain English or explaining the solution to your team, you build trust and avoid technical debt disguised as minimalism.

D.I.C.K. - Domain Interaction Could Kill - Prevent catastrophic failures by carefully managing how separate logic domains interact in a system. Unchecked domain interactions, like tight coupling or rogue instantiations, lead to chaotic bugs, performance issues, and unreliable apps. Use explicit contracts, dependency injection frameworks, and event-based communication to define clear boundaries and lifecycles, ensuring domains work harmoniously without creating a tangled, error-prone codebase.

B.O.O.B.S. - Bloated Object-Oriented Bullshit Syndrome - Avoid overcomplicating code with excessive object-oriented abstractions, such as unnecessary interfaces, factories, and deep inheritance hierarchies, which create bloated, hard-to-maintain codebases. Instead, favor composition over inheritance to build flexible, clear, and maintainable systems by assembling small, focused components that address immediate needs without speculative overengineering.

A.S.S. - Avoid Silly Solutions - Steer clear of both overengineered, complex code and fragile, hacky workarounds that seem clever but create technical debt. Aim for adequate solutions in the "Goldilocks Zone" that solve the problem without unnecessary abstraction or shortcuts, using constraints, clear naming, and tools like linters to ensure a robust codebase.

C.U.N.T. - Chasing Unnecessary New Trends - Resist the urge to adopt trendy tools or frameworks just because they’re hyped, as they often waste time and create maintenance issues without solving real problems. Focus on proven, stable solutions that align with your project’s needs, benchmarking new tech carefully to ensure it delivers measurable value before adoption.

P.O.R.N. - Plan Often, Refactor Nevertheless - Plan just enough to align your team and tackle core features, but accept that chaos and changing requirements will make code messy. Embrace refactoring as a natural part of development to manage technical debt incrementally, keeping the codebase clear and maintainable without chasing perfect plans.

S.L.U.T. - State Loss Under Types - Prevent runtime errors by recognizing that type systems don’t guarantee correct state, as external data like APIs or databases can mismatch type definitions. Use runtime validation, flexible types, real-data testing, and collaboration with clear API contracts to align state and types, avoiding crashes from over-reliance on static typing.

F.U.C.K. - Failing to Understand Contextual Knowledge - Avoid breaking critical functionality by understanding the business, industry, and historical context behind code before refactoring. Engage with subject matter experts, read documentation, and explore business processes to gain contextual empathy, ensuring changes respect the invisible web of constraints shaping the codebase.

About the Author

Filip Ristović is a self-taught Software Engineer, husband, and father. His formal programming roots are in Applied Statistics and Computational Archaeology, though most of his professional life has been spent in Web Technologies. A very early adopter of the internet, he set up his first email account on a Sega Dreamcast at the age of seven.

Most of his teenage life he spent trying to do things people told him not to do. Like dismantling computers and other electronics, figuring out how to hack games, changing school schedules and teaching people how to illegally pirate movies and games. After completing his dream of becoming real-life Indiana Jones, having acquired a Bachelors degree in Archaeology at University of Belgrade, he started writing his Masters thesis in Applied Statistics and Computational Archaeology only to drop out at the last minute because Web Development suddenly became more interesting to him.

      He spent his next few years chasing the best tech stack in the game only to learn it’s a myth worth debunking. After spending a couple of years working in the industry he became increasingly frustrated by programming’s fading allure among younger generations in his home country, Filip launched a YouTube channel after COVID pandemic under his name to inspire the next wave of coders with humor and hard-won lessons. In this book, he distills his experience into irreverent insights for programmers navigating the chaos of code. He also wrote this entire bio in the third person, which is probably the most serious thing about it.

References

(I know you looked here first TDD boys ‘n’ girls! It’s okay I got you covered…)

Beck, K. (2002). Test-driven development: By example. Addison-Wesley. (ISBN: 9780321146533)

Fowler, M. (2018). Refactoring: Improving the design of existing code (2nd ed.). Addison-Wesley. (ISBN: 9780134757599) https://martinfowler.com/books/refactoring.html

Freeman, S., & Pryce, N. (2009). Growing object-oriented software, guided by tests. Addison-Wesley. (ISBN: 9780321503626)

Rafique, Y., & Mišić, V. B. (2013). The effects of test-driven development on external quality and productivity: A meta-analysis. IEEE Transactions on Software Engineering, 39(6), 835–856. https://doi.org/10.1109/TSE.2012.85

Fox, C. (2019, November 3). Test-driven development is fundamentally wrong. HackerNoon. https://hackernoon.com/test-driven-development-is-fundamentally-wrong-hor3z4d

Beust, C. (2014, May 11). The pitfalls of Test-Driven Development. Otaku, Cédric's blog. https://www.beust.com/weblog/the-pitfalls-of-test-driven-development/