Vibe Coding Problems: What Goes Wrong and How to Fix It

Let's get one thing out of the way: vibe coding your MVP was probably the right call.

You had an idea, no team, and a market that wasn't going to wait. You pointed an AI tool at the problem, described what you wanted, and shipped something real users could touch. That's not a mistake — that's exactly what the first phase of a startup is supposed to look like. Speed to signal beats everything else, and nothing on earth produces speed to signal like an unsupervised coding agent.

The problems start later, and they start quietly. A bug fix breaks two features you didn't touch. A new engineer opens the repo, reads for a day, and asks you — carefully — who wrote this. A feature that should take a week takes a month, then gets shelved. Somewhere in there you search "vibe coding problems" and land on articles that tell you to rewrite everything, which is advice worth exactly what the person giving it will be charged for it: nothing.

Here's the honest version instead. Vibe-coded codebases fail in four specific, recurring, diagnosable ways — and every one of them is a solvable engineering problem, not a death sentence. We know because fixing them is part of what we do for a living. Let's take them one at a time.

The thing to hold onto throughout: vibe code doesn't fail when you write it. It fails when you change it. Every problem below is invisible on day one and expensive on day one hundred, because every one of them is a tax on change — and a startup is nothing but change.

Problem 1: Untestable code — and no tests to catch the fall

The obvious symptom is that there are no tests. Zero. The agent was never asked for them, demos never required them, and the "no tests, no CI" startup codebase is now the default artifact of the vibe coding era.

But the deeper problem is worse: the code is untestable. Business logic is welded to the UI layer. Functions reach into the network, the database, and global state from anywhere, because each one was generated to make one feature work in one session, with no memory of the others. You can't test a unit when nothing in the codebase is a unit.

This is why the standard advice — "just add tests" — stalls. You can't unit-test your way out, because the units don't exist yet, and you can't refactor to create them, because without tests, every refactor is a gamble.

The fix is an old and well-understood move from legacy-code engineering (and make no mistake: a six-month-old vibe-coded repo is legacy code). You don't start with unit tests. You start with characterization tests — coarse, end-to-end tests that pin down what the system actually does right now, bugs and quirks included, at the boundaries where behavior is observable: HTTP responses, rendered pages, database writes. Wire those into CI so they run on every change. Now the bleeding stops: you can start restructuring the inside, because the outside is under guard. Testability becomes something you restore, seam by seam — and the unit tests come after, once there are units.

Problem 2: No type safety — every change is a leap of faith

Vibe-coded projects skew heavily toward untyped or barely-typed code — plain JavaScript, Python without annotations, any sprinkled like seasoning wherever a type error interrupted the vibe. Each generation step took the shortest path to "it runs."

Here's why that hurts more in an AI-built codebase than a human-built one. In a typed codebase, the compiler tells you what a change breaks before you run anything. In an untyped one, the only way to discover a broken contract is to hit it at runtime — in the best case on your machine, in the worst case in front of a paying customer. Human teams partially compensate with tribal memory of what connects to what. In a vibe-coded repo, nobody has that memory, because nobody wrote the code.

The fix is incremental, and the tooling for it is genuinely good: TypeScript over JavaScript, type annotations plus a checker in Python — adopted module by module, strictest at the boundaries first. Start where the money is: the data models, the API contracts, the payment paths. Every typed boundary converts a class of silent runtime failures into loud compile-time ones. And there's a compounding bonus most founders don't expect: types are machine-checkable context. The same AI tools that wrote the untyped mess perform dramatically better inside a typed codebase, because the types tell them what's true. You're not just fixing the past — you're raising the quality of everything generated from here on.

Problem 3: Hidden coupling — touch one thing, break another

This is the problem founders describe to us most vividly, because it's the one that feels like haunting: you change the settings page and checkout breaks.

It happens because each vibe-coded feature was generated in isolation, and the model solved each request with whatever was in reach — copy-pasting logic between features, reusing a database query because it existed, reaching across layers because nothing stopped it. No architecture was ever decided, so the architecture is "whatever the model felt like that day," accumulated over hundreds of sessions. The result is a codebase where the dependency graph exists but nobody — human or machine — knows what it is.

The fix starts with making the invisible visible. Dependency analysis tooling will draw you the real graph of imports and calls, and it is always ugly and always survivable. From there the work is triage, not heroics: find the worst tangles — usually a handful of god-modules that everything touches — and untangle those, critical paths first. Define module boundaries, move each behavior behind one owner, and enforce the boundaries mechanically (import rules in CI), because a boundary that exists only in a diagram will be violated by the next generated change. You do not need a microservices migration or a grand redesign. You need the ten worst edges of the graph cut, in order of how much they're costing you.

Problem 4: Zero documentation — the intent lives nowhere

A vibe-coded repo has no README worth the name, no architecture notes, no record of why anything is the way it is. The reason is structural: the intent was in the prompts, and the prompts are gone — scattered across chat sessions that were never saved. The code is the only artifact that survived, and code records what, never why.

This is the problem founders rank last and engineers rank first. It's why onboarding fails: a strong hire can read code, but they can't read minds, and in a vibe-coded repo even the code contradicts itself. Every engineer you add burns their first weeks reverse-engineering decisions nobody remembers making. It's also why your AI tools plateau — an agent working without written intent will invent its own definition of done, satisfy it, and report success.

The fix is not "write a wiki," which produces prose that's stale in a month. Write down the things that gate change, in the repo, where review can enforce them: what the system is for, where the module boundaries are (you just drew them, in Problem 3), what invariants must hold, and — as short architecture decision records — why each big call went the way it did, including a one-line entry for every decision you're reconstructing from the code. From that point forward, intent-first is the rule: no change, human or agent, without a written statement of done. Documentation stops being homework and becomes the thing that makes the next change checkable.

The uncomfortable fifth problem: you can't see the first four from inside

Notice what the four fixes have in common. None of them is "rewrite it." None of them is exotic. Characterization tests, incremental typing, boundary enforcement, decision records — this is standard engineering, applied in an unglamorous order: triage, stabilize, restore, prevent. First map what actually works and what only pretends to. Then pin down current behavior so nothing regresses silently. Then fix critical paths, worst tangles first. Then leave behind the discipline — specs, review gates, CI, evals — so the next feature ships fast without re-creating the mess.

The honest footnote: triage occasionally returns the other verdict. Some codebases — usually the ones that are small enough to re-describe completely — genuinely are cheaper to rebuild under discipline than to rescue, and anyone who won't say so before starting is selling you billable hours. The point of triage is that it's a ranked, written diagnosis before you commit to either path, instead of a vibe about your vibe code.

But here's the catch-22 of doing this yourself: the skill set that rescues a codebase — legacy-code testing patterns, dependency surgery, type-migration strategy — is exactly the skill set whose absence produced the problem. This has become common enough that it's now a service category; ours is called Vibe-Code Rescue, and the method above is its playbook.

And there's a twist in that playbook worth knowing about even if you never hire anyone: the fastest way through the mechanical bulk of a rescue is agents — backfilling characterization tests, tightening types, chasing dead code across hundreds of files. Yes, AI agents cleaning up after AI agents. The difference the second time around is the orchestration: systems like Paperclip and Claude Code running under written specs, behind review gates and CI, with evals watching quality — so this pass adds discipline instead of more entropy. The tools were never the problem. The missing engineering around them was. Everybody can vibe code — we know how to ship proper software with AI agents.

Your codebase got you here. That counts for something — most ideas die with zero users, and yours didn't. Now it needs to get you to the next stage: fundable, hireable, shippable. If you recognize your repo in the four problems above and want a straight answer on rescue versus rewrite, describe the symptoms to us — we'll tell you honestly what we'd do first, even if the answer is "not us."

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Dmitry, CEO, Exit Code