The Realization
I was working with Claude on some gnarly legacy code when it hit me: I’ve been vibe coding long before anyone called it that.
You probably know what vibe coding is, even if you’ve never heard the term. It’s often pinned on junior devs who use AI to write code they barely understand—but real vibe coding is something else entirely.
It’s what experienced developers do when we glance at a block of code and just know if it’ll work. We skim Stack Overflow, yank in half-understood libraries, and adapt stranger-solutions to weirdly specific problems. To new devs, it looks like wizardry. To us, it’s Tuesday.
And here’s the kicker: everyone treats AI-assisted coding like uncharted territory, as if we’re all stumbling into the future hand-in-hand. But we’ve been collaborating with “AI” for decades—it just used to look like Google, forums, and community wikis. The interface changed; the instinct didn’t.
That instinct is pattern recognition, forged in the fires of VB6 and DLL Hell. The ability to trust—but verify—unfamiliar code. To extract signal from noise. To feel when something’s off without needing a stack trace to prove it.
This isn’t nostalgia. It reframes the whole AI coding conversation. We’re not learning something new—we’re scaling something we’ve practiced for years. The developers thriving with AI aren’t prompt whisperers. They’re the ones who already learned how to work with distributed intelligence, back when it was crowdsourced instead of model-generated.
That has big implications: for how we train new devs, how we measure AI’s real value, and why some of us seem weirdly fluent in this new workflow. We’re not starting from zero—we’re continuing a 30-year habit.
The Archaeological Evidence
Vibe coding didn’t start with AI. It started when we first reached beyond our own heads for answers.
In the ’90s, that meant flipping through stained manuals and cursing the expensive, cryptic documentation bundled with libraries you barely understood. If you got stuck, you had three options: brute-force it, find someone smarter, or give up.
Then came Usenet. Now you could shout into the void of comp.lang.* and maybe—just maybe—get a useful reply. It was chaotic, full of flame wars and pedantry, but it taught us how to extract signal from noise. You learned to trust patterns, not just people. That was early vibe coding: picking the wheat from the firestorm.
The early web turned up the volume. Suddenly, every CS undergrad with a modem had a homepage full of weird little code samples. If you were persistent (and lucky), you’d stumble across some elegant solution buried in a forgotten university directory. You didn’t always understand it, but you could feel when it was right. That feeling? Vibe coding.
Then Google rewrote the game. Now the hive mind had a search box. PageRank was primitive algorithmic curation—our first brush with machine-assisted judgment. Searching well became a core dev skill. So did deciding what to trust.
Every search, every sketchy tutorial, every copy-pasted snippet was sharpening the skill we now use with LLMs. We were already learning to collaborate with intelligence beyond ourselves. We just didn’t call it AI.
Stack Overflow: The First Neural Network
When Stack Overflow launched in 2008, it wasn’t just a better Q&A site—it was the first real collective programming brain. With voting, comments, and acceptance markers, it turned scattered answers into a self-vetting system of distributed expertise. Suddenly, you could trust strangers’ code, because the community had already debugged it for you.
This is where vibe coding leveled up. You’d land on a vaguely-related question, skim the answers, and just know which one fit your context. Fewer upvotes but a newer answer? Maybe it reflects best practices. Top comment says “beware of thread safety”? You file that away. Accepted answer plus spicy debate in the comments? Probably a landmine.
Stack Overflow trained us to read not just code, but its social metadata. Reputation scores. Tone. Depth of explanation. You learned to smell real-world experience vs. theoretical fluff.
Most importantly, it made copying not just acceptable, but expected. The smartest devs weren’t writing every line from scratch—they were remixing battle-tested patterns with confidence and speed.
This was vibe coding with training wheels: trust the crowd, verify the fit, move faster. It wasn’t AI, but it sure acted like a neural network.
Open Source: Vibe Coding Goes Professional
If Stack Overflow taught us to vibe code with snippets, open source taught us to vibe code with entire architectures. And that raised the stakes. A bad copy-paste might cost you an hour. A bad dependency? Years.
The signals got louder—and messier. A GitHub repo with 50,000 stars might be rock-solid… or a half-baked experiment that got Hacker News’d into the stratosphere. You had to read codebases like crime scenes: commit histories, issue tracker tone, CI config, dependency bloat, API shape. Every detail told you something about whether this thing would hold up—or blow up later.
This wasn’t just “does it work?” It was “will it keep working?” “Will it scale?” “Do the maintainers have their act together?” You weren’t choosing code—you were betting on future behavior.
I called it architectural intuition—that gut sense from a glance at the folder structure, the naming, the tests (or lack thereof). Open source turned vibe coding into professional due diligence.
We became venture capitalists for code. Not just evaluating features, but assessing the human ecosystem behind them. The same instincts now help us judge AI output: can I trust this? Should I build on it? Will it haunt me?
Open source was vibe coding at scale—and it trained us for what came next.
What Vibe Coding Actually Is
Vibe coding isn’t guessing. And it’s not just copying code and hoping it works. It’s a high-speed pattern recognition skill built from years of exposure to what does and doesn’t work.
When an experienced dev glances at a block of code and knows it’s wrong—even without running it—that’s not magic. It’s memory. Thousands of feedback loops, mistakes made, bugs debugged, and libraries loved or loathed. It’s unconscious competence, trained through pain.
Junior devs often confuse the speed for the skill. They think vibe coding is about shortcuts. But the speed only works because of the depth underneath. Without the reps, it’s just guesswork in a trench coat.
Vibe coding isn’t speed-reading code. It’s recognizing shape. It’s scanning an unfamiliar repo and thinking, “This author has never used their own API.” Or seeing a function and instinctively knowing, “This will break under concurrency.” You don’t have to understand every line—you feel the pattern.
It’s like a chef tasting a sauce and knowing what’s missing, while someone else still needs the recipe.
Vibe coding is architectural taste. It’s risk assessment at the speed of instinct. And it’s the reason experienced developers are thriving in the AI age—because the tools changed, but the skill? The skill stayed exactly the same.
AI as Evolution, Not Revolution
The AI coding boom isn’t a revolution. It’s an acceleration.
Large language models didn’t invent vibe coding—they just became the newest source of external intelligence we collaborate with. Same instincts, new interface.
This is why experienced devs seem so fluent with AI tools. We’re not learning something new—we’re applying old skills to a faster feedback loop. We’ve been evaluating, remixing, and adapting third-party code for decades. Now that intelligence just happens to be synthetic.
Prompt engineering isn’t the skill. Pattern recognition is the skill. The same one we used to navigate Stack Overflow answers, sketchy GitHub projects, and forum threads from 2003. We already learned how to trust-but-verify.
Working with AI is just the next evolution of collaborative coding. The model generates, we assess. It explores solution space, we decide what’s viable. The division of labor hasn’t changed—we’ve just outsourced more of the typing.
We’re not starting over. We’re leveling up.
The Vibe Gap
Here’s where things get interesting—and a little dangerous.
New developers are entering the field in an AI-first world. They’re trying to vibe code without the pattern recognition that makes it safe. They see experienced devs using AI tools fluently and assume the secret is in the prompt. It’s not. It’s in the history behind the prompt—the instincts shaped by debugging, by failure, by weird edge cases that burned you once and never again.
The risk isn’t just that junior devs will use AI badly. It’s that they’ll skip the struggle that builds judgment. They’ll ship code that works, but not know why—and when it breaks, they’ll have no intuition for how to fix it.
Meanwhile, experienced devs are flying. AI lets us prototype ideas, test architectures, and explore trade-offs at insane speed. We’re not learning how to use AI. We’re using AI to express what we already know—faster.
That gap is widening.
The challenge isn’t deciding whether AI is good or bad. It’s figuring out how to make sure the next generation still builds the muscle memory of good engineering, even as the tools get smarter. We can’t let the shortcuts erase the long road that taught us to recognize quality in the first place.
But here’s the good news: we’ve done this before.
Every wave of external intelligence—books, forums, search engines, Stack Overflow, open source—raised the same questions. Each one made us faster. Each one demanded we adapt. And each one worked out, because we kept the skill of evaluating truth from noise.
AI is just the latest version of that same pattern. A new partner in the long tradition of coding with help.
We were vibe coding before it was cool. That’s why we’re ready for what comes next.
