DEV Community

Cover image for Vibe coding is gone? the logs don’t lie.
<devtips/>
<devtips/>

Posted on

Vibe coding is gone? the logs don’t lie.

#vibecoding #coding #webdev #programming

Why vibe coding felt like god mode and why production shut it down fast

For a short, dangerous window of time, vibe coding felt like cheating. You’d drop a prompt into an AI editor, watch entire features appear, and think: wait… is this what senior devs have been doing this whole time? Dashboards before coffee. Auth “handled.” Tests politely postponed. The app ran, the demo landed, and confidence showed up way earlier than understanding.

And honestly? It ruled.

Then production showed up. Quietly. No dramatic announcement. Just logs.

The shift didn’t happen overnight. It crept in. Fewer “look what I built in a weekend” posts. More screenshots of stack traces, rollback graphs, and messages that start with “has anyone seen this before?” You’ve probably felt it too that moment when users are clicking around and you realize you don’t actually know why the system works. Or worse, what happens when it doesn’t.

That’s where vibe coding started to crack. Not because the tools got worse. Not because the hype died. But because production doesn’t care about vibes. It cares about edge cases, retries, permissions, and the one user who will click the same button twelve times because nothing told them not to.

TL;DR: vibe coding still works great for prototypes. It falls apart when reliability, security, and maintenance enter the room. The next era isn’t “prompt and ship.” It’s AI-assisted engineering with guardrails, context, and accountability.

What vibe coding really was (and why it felt like god mode)

Vibe coding didn’t come from nowhere. It came from momentum.

For the first time in a long while, writing software felt instant. You described intent instead of mechanics. “Build a CRUD app with auth” used to mean an afternoon of setup, docs, and mild regret. Suddenly it meant a single prompt and a blinking cursor. Of course people leaned into it. Anyone pretending otherwise either didn’t try it or forgot how slow things used to be.

The mistake was confusing speed with understanding.

Vibe coding worked because most early projects lived on the happy path. One user. One flow. One set of assumptions that never got challenged. In that world, AI feels magical. It fills in gaps you didn’t know existed. It picks defaults that seem reasonable. And because the output is fluent, your brain gives it way more credit than it deserves.

I’ve shipped prototypes this way. They worked. They even impressed people. But the moment someone asked “can we tweak this?” or “what happens if two users do this at once?” the illusion cracked. You realize you’re not holding a system you’re holding a screenshot of one.

It reminded me of discovering Stack Overflow years ago. Copy, paste, run, success. The difference now is scale. The code isn’t a snippet anymore. It’s the whole app. And when you don’t build the mental model, you also don’t notice when something subtle is wrong.

Vibe coding felt like god mode because consequences were disabled. Production turns them back on.

The hangover: what breaks first when real users arrive

The first thing to break isn’t performance. It’s trust.

Vibe-coded apps usually survive the happy path. Click the button once, get the result, ship the demo. Production isn’t a demo environment. It’s a stress test run by bored humans, flaky networks, and automation scripts that don’t care about your feelings. That’s when the boring stuff shows up swinging: auth edge cases, retries, race conditions, and error states no one ever prompted for.

I’ve opened repos where the UI looked great and the feature list was impressive, but the internals read like a fever dream. Hundreds of lines of generated code, no tests, no comments, and variable names that feel like they were picked by autocomplete roulette. Everything “worked” until someone refreshed at the wrong time or clicked twice because nothing told them not to.

That’s the real hangover: maintenance. When you don’t understand the code, every bug feels like archaeology. You’re not debugging you’re deciphering intent. You’re scared to touch anything because you don’t know which invisible string will snap.

This is where oncall becomes a personality test. You stop asking “how do we add features?” and start asking “what is this even doing?” Logs pile up. Metrics go sideways. Someone eventually says the quiet part out loud: “maybe we should rewrite this,” which is dev-speak for “we don’t trust what we shipped.”

Production doesn’t kill vibe coding instantly. It lets it run long enough to collect receipts.

Why it breaks: incentives, missing context, false confidence

Vibe coding didn’t collapse because the tools are bad. It collapsed because the incentives are warped.

Most teams don’t actually reward maintainability. They reward visible progress. Demos. Features. Commit graphs that look busy. Vibe coding fits that system perfectly. You can move fast, show something shiny, and defer the hard thinking to “later,” which is how technical debt quietly becomes a lifestyle.

Then there’s the context gap. Models don’t know your system. They infer. They guess. They autocomplete based on patterns that look right in isolation. That’s fine when you’re generating boilerplate. It’s dangerous when you’re making decisions about auth, data lifecycles, concurrency, or failure modes. Production code needs reasons, not vibes.

The worst part is the confidence mismatch. AI-generated code reads clean. It’s structured. It looks intentional. Your brain mistakes fluency for correctness, the same way a well-written paragraph can convince you someone knows what they’re talking about even when they don’t. If you’re not careful, review turns into rubber-stamping.

I’ve spent more time reviewing AI output than it would’ve taken to write the code myself tracing paths, validating assumptions, checking for the one missing condition that will only show up under load. At that point, the speed advantage is gone, but the risk remains.

Vibe coding doesn’t fail randomly. It fails predictably wherever understanding was replaced with momentum.

The pro strat: how to use AI without shipping haunted code

The trick isn’t to stop using AI. It’s to stop treating it like autopilot.

The fastest engineers I know use AI the same way they use a junior pairing partner: great at typing, surprisingly confident, and absolutely in need of supervision. If you wouldn’t merge a PR from a new hire without understanding it, you shouldn’t do it just because a model produced it.

The first guardrail is simple: never merge what you can’t explain. If you can’t walk someone through the logic without re-prompting, you don’t own that code yet. The second is scope. Keep diffs small. AI loves to refactor half the file because it “felt right.” Resist that. Small changes are readable. Large ones hide mistakes.

Tests aren’t optional here. They’re the fastest way to turn vibes into facts. Even basic tests force assumptions into the open and catch hallucinations before users do. Linting, types, and formatters help too not because they’re exciting, but because they add friction where your brain would otherwise coast.

The moment I started asking the model to explain its changes and write tests alongside them, the failure modes became obvious. Hallucinations don’t disappear, but they get louder and easier to spot.

Guardrails don’t slow you down. They stop you from waking up to a haunted codebase you’re afraid to touch.

What’s next: who actually wins in the AI dev era

The people losing right now aren’t “vibe coders.” They’re anyone who thought engineering was optional.

The market is already correcting. Demos still matter, but reliability matters more. The teams shipping calmly, without heroics or panic rewrites, are the ones who understand their systems. They read code more than they generate it. They debug instead of re-prompting. They know where the sharp edges are because they’ve hit them before.

The winning skill set isn’t mysterious. It’s the stuff that was always valuable but briefly unfashionable: systems thinking, failure analysis, and the ability to explain why something works. Writing specs. Setting constraints. Knowing when not to automate. AI doesn’t replace those skills it amplifies the gap between people who have them and people who don’t.

Here’s the uncomfortable part: agents will keep getting better. Editors will get smarter. More of the “easy” work will disappear. That doesn’t make engineers obsolete. It makes judgment the job. Someone still has to decide what gets built, how it fails, and who gets paged when it does.

Vibe coding isn’t gone. It’s been demoted. It’s a prototyping tool now, not a production strategy. The logs don’t lie but they do reward the people who can read them.

Press enter or click to view image in full size

Conclusion

Here’s the part that took me a while to admit: vibe coding didn’t fail because it was dumb. It failed because we asked it to do a job it was never meant to do.

Vibe coding is great at getting you from zero to something. It’s fun. It’s motivating. It makes ideas feel cheap and possible again. That’s a good thing. The mistake was pretending that “it runs” meant “it’s ready.” Production has always punished that mindset AI just let more people reach that wall faster.

The logs don’t lie. They don’t care how confident the demo felt or how clean the generated code looks. They only tell you what actually happened. And once you’ve stared at enough of them, the lesson sticks: speed without understanding is just borrowed time.

What’s replacing vibe coding isn’t slower work. It’s calmer work. Engineers who use AI deliberately. Who put guardrails around it. Who know when to trust it and when to stop and think. The ones who can move fast and sleep at night because they understand what they shipped.

So no, vibe coding isn’t dead. It’s just back where it belongs early, playful, and disposable. Production belongs to people who can read their own code, explain their systems, and take responsibility when things break.

And honestly? That was always the job.

Helpful resources

Top comments (0)