Vibe Code Cleanup
Your AI built it. We make it production-ready.
The Vibe Coding Hangover
You shipped fast. Cursor, Bolt.new, Lovable, Copilot — whatever tool you used, it worked. The prototype came together in days instead of months. You had a working product before you had a business plan. Investors saw a demo. Users signed up. The thing was real.
And then the morning after hit.
You tried to add a payment flow and couldn’t figure out where to put it. You fixed a bug on Monday and it came back on Wednesday. You hired a developer and they spent their first week just trying to understand the codebase. You asked the AI to fix something and it rewrote a module that was working fine.
The app still works. From the outside, it looks like a product. But from the inside, it feels like a house of cards. Every change feels risky. Every feature takes longer than the last. You’re spending more time managing the code than building the product.
This is the vibe coding hangover. You’re not alone. Forty-one percent of all code on GitHub is now AI-generated, and most of it was never reviewed by someone who understood the system it was being added to. The code works, but it wasn’t engineered. There’s a difference, and that difference is what’s slowing you down.
What AI Gets Wrong
AI coding tools are impressive. They generate working code from natural language prompts. But working code and production-ready code are different things. Here’s what we see in nearly every AI-generated codebase.
Monolithic files. AI tools tend to put everything in one place. A single component file with 800 lines of UI, state management, API calls, and business logic all tangled together. It works, but you can’t test it, can’t reuse it, and can’t change one thing without risking everything else.
Duplicated logic. You asked the AI the same question twice in different files, and it gave you two different implementations of the same thing. Now you have three ways to format a date, two ways to validate an email, and four slightly different API client wrappers. When you fix a bug in one, the others stay broken.
Missing error handling. AI-generated code handles the happy path beautifully. User submits form, data saves, success message appears. But what happens when the API is down? When the user submits invalid data? When the database connection drops? Nothing. The app crashes, silently fails, or shows a white screen. No fallbacks, no retry logic, no user-facing error messages.
Hallucinated patterns. The AI trained on millions of code examples and sometimes generates code that references APIs, methods, or patterns that don’t exist — or that existed in a previous version of a library. The code compiles. It might even run. But it’s calling functions with wrong signatures, using deprecated patterns, or relying on behavior that’s not guaranteed.
Hardcoded secrets. API keys in frontend code. Database connection strings in component files. Third-party credentials committed to the repository. AI tools don’t understand security boundaries. They put the secret where it makes the code work, not where it’s safe.
No tests. This is the big one. AI tools generate code, but they rarely generate meaningful tests. And the tests they do generate often test implementation details rather than behavior. The result: a codebase where nothing is verified, nobody knows what’s actually working, and every change is a leap of faith.
Why This Isn’t Regular Tech Debt
Technical debt accumulates when experienced developers make intentional tradeoffs. They know they’re cutting corners. They understand the risks. They plan to pay it down later. The debt has context.
AI-generated debt is different. There’s no intentional tradeoff. The AI doesn’t know it’s creating debt. It doesn’t understand the system. It generates what looks right based on pattern matching against training data. There’s no institutional knowledge of why a decision was made, because no decision was made — a model just predicted the next token.
This creates a unique problem: false confidence. The code looks professional. Variable names are good. Functions are reasonably sized. Comments exist. From a quick scan, it looks like senior engineering work. But the architecture is incoherent. The patterns are inconsistent. The abstractions don’t abstract the right things. The code is fluent but not thoughtful.
Regular tech debt cleanup assumes someone on the team understands the system. AI-generated debt cleanup assumes no one does — because the “developer” was a language model that has no memory between sessions.
How We Clean It Up
We don’t start by writing code. We start by understanding what’s there.
Audit first. We run a code audit to map the codebase. What’s working. What’s fragile. Where the security issues are. Where the patterns are inconsistent. This gives us a prioritized list of problems and a plan of attack. If you’ve already had an audit, we work from that.
Extract and restructure. We break monolithic files into proper modules. Business logic moves out of components. Shared utilities get consolidated. API clients get unified. The goal is a codebase where each file has one job and does it clearly.
Add error handling. Every API call gets proper error handling. Every user input gets validated. Every external dependency gets a fallback. We add the defensive code that AI tools skip because it doesn’t affect the demo.
Write tests. We add tests for critical paths first — authentication, payments, data mutations, integrations. Not vanity coverage metrics. Tests that catch real bugs. Tests that give you confidence to change code without breaking things.
Fix security. Secrets move to environment variables. Authentication gets verified server-side. Input gets sanitized. Dependencies get audited and updated. We close the holes that AI tools opened without knowing they were opening them.
Consolidate patterns. Three date formatters become one. Four API wrappers become one. Two authentication flows become one. We establish consistent patterns and refactor the codebase to follow them. New developers can learn the patterns once and apply them everywhere.
What We Fix by Tool
Every AI tool has its own failure modes. We’ve cleaned up enough of each to know the patterns.
Cursor codebases tend to have context fragmentation — each file was generated with a different understanding of the system, so patterns are inconsistent across the codebase. The fix-loop problem is real: Cursor fixes one thing and breaks another because it doesn’t see the whole picture.
Bolt.new codebases are browser-first prototypes that need production infrastructure. No deployment pipeline, no environment separation, client-heavy architecture that belongs on the server.
Lovable codebases generate clean-looking React apps with Supabase backends, but the data model is usually wrong, row-level security is misconfigured, and the component structure doesn’t scale.
Copilot codebases accumulate inconsistency over time. Each autocomplete suggestion is locally reasonable but globally contradictory. Security vulnerabilities creep in because Copilot optimizes for function, not safety.
Claude Code codebases produce cleaner architecture than most, but suffer from assumption debt — the AI makes reasonable assumptions about your business logic that happen to be wrong, and those wrong assumptions compound.
Devin codebases tend to over-engineer simple requirements and under-engineer complex ones. The autonomous agent pattern means large chunks of code were written without human review, so dead code and unnecessary abstractions pile up.
When This Makes Sense
Your AI-built MVP is hitting real users. It works in the demo. But production traffic, real payment flows, and actual user data demand code that was engineered, not generated. You need the transition from prototype to product.
You’re preparing to hire engineers. Nobody wants to join a team where the codebase is an incomprehensible tangle of AI-generated patches. Clean the code before you hire, and your new developers will be productive in days instead of weeks.
You’re scaling beyond the prototype. The architecture that works for 100 users won’t work for 10,000. The database queries that are fast with test data are slow with real data. You need to restructure before you scale.
You’re raising a round. Investors will look at the code — or hire someone who will. AI-generated codebases that haven’t been cleaned up are a red flag. Get the code audit and the cleanup done before you enter diligence.
You want to keep using AI tools effectively. This isn’t about abandoning AI. It’s about giving AI tools a clean, well-structured codebase to work with. Cursor is dramatically more effective when the codebase it operates on has clear patterns, good tests, and coherent architecture. We set up that foundation.
Ready to Make It Production-Ready?
The AI got you here. That matters. You validated an idea, attracted users, and built something real — in a fraction of the time it would have taken with traditional development. Don’t let anyone tell you the approach was wrong.
But the approach has a shelf life. AI-generated code that was good enough for launch isn’t good enough for growth. The longer you build on a shaky foundation, the more expensive the cleanup becomes.
Get in touch to talk about your codebase. We’ll assess what you have, tell you what needs to change, and give you a realistic timeline for getting production-ready.