The rapid adoption of vibe coding has laid the foundation for a new market: vibe code cleanup. Because it is an emerging market, there’s a lot to unpack here. How do we approach the cleanup process? Can we use AI to help, or does that defeat the purpose? And crucially, how do we measure the success of a cleanup project?
Why cutting the code is not the answer
Ask any professional developer, and they’ll agree that readable, maintainable, reliable, and efficient code is the opposite of code sprawl. AI-generated code is a major contributor to this uncontrolled sprawl because code duplication is the most widespread issue found in vibe-coded apps. AI coding agents keep adding code without consolidation, especially when proper restrictions and reviews are absent. You can often see the same logic with very slight variations across different components.
It seems logical to prune as much as possible to achieve the desired code quality. You may encounter engineers who offer to refactor vibe-coded codebases by reducing them from “something like 100,000 lines down to 35,000, same functionality.” On top of that, their engagement model is heavily tied to code reduction alone: the more code they remove, the more they charge.
But a smaller repository can still contain conflicting business logic, hardcoded credentials, unsafe dependencies, missing tests, and no rollback path. Code reduction is an attractive but misleading metric.
What you should measure instead
You can show a client that their repository shrank by 40 percent. It feels like progress because it is measurable and directional: fewer lines, fewer problems. This logic assumes the dangerous code was the code you deleted, and that the code you kept is safe. Neither assumption holds up once you look at what actually goes wrong in vibe-coded applications.
Recently, while helping a client release vibe-coded features safely, we examined their app’s pricing funnel and saw a perfectly rendered list of paid plans. Underneath that polished exterior, however, two separate components were fetching that data independently. This created a massive vulnerability: updating a plan in one place failed to propagate to the other, destroying the single source of truth and leaving the application with conflicting information. Shrinking the codebase by deleting unused CSS or redundant boilerplate does not inherently fix this kind of architectural rot.
Or take routing. On the same project, 13 routes, some nested, all rendered the right pages in the right sequence. The user flow worked exactly as designed. But there were zero route guards. Any visitor could navigate directly to the billing page without creating an account. This isn’t sloppy code a linter would flag; it’s a missing security concept. An AI agent that generates a functioning route structure does not automatically generate the authorization logic that should govern access. You could consolidate every route handler, cut the file in half, and this gap would remain wide open.
Then there are forms. We reviewed an application where every form looked polished, had validation rules, and handled submissions correctly. But the markup had no semantic form elements. The inputs were styled containers with JavaScript event handlers. For a sighted user with a mouse, everything worked. For someone relying on a screen reader, the application could not communicate which input had focus, which form it belonged to, or how to submit it. This is a WCAG 2.2 failure that is completely invisible to code reduction metrics.
Can AI fix AI?
With 83% of developers using AI tools, how is the way they use them different from how non-developers produce code?
For one, there is a great divide between the vibe coding tools developers use versus those favored by non-coders. Cursor and Codex are not the same as Replit or Lovable. Can’t founders keep prompting to troubleshoot and fix issues? They can, but eventually, the process becomes too tedious, and the risk of a complete rewrite rather than a cleanup only increases.
Do we use AI coding agents to clean up the generated code? Yes, but mostly to check compliance with project guidelines as a first step, look for overengineering and unnecessary complexity, and scan for possible security leaks as the last step.
When a vibe-coded feature is truly fixed
When we treat AI code cleanup as a simple refactoring task focused on reduction, we ignore the application’s structural integrity. More importantly, charging for deleted lines of code contradicts the fundamental reality of how production-ready software is built. It creates a perpetual treadmill: a non-technical founder pays to trim the first build, but the moment they need a new feature, the AI generates more bloated code, and they have to pay for another round of deletion.
Even if the feature set is frozen, you cannot simply delete code, walk away, and expect the application to survive. Software rarely lasts six months without maintenance. Third-party APIs update, new operating systems are released, and consumers adopt devices with entirely different screen resolutions. Escaping this cycle requires an actual software development process built on discipline, clear specifications, structured release cycles, and deep quality assurance.
If deleted lines of code are the wrong metric, we need a new definition of what it means for a vibe-coded feature to be “fixed.” Fixed should mean critical behavior is fully understood, rigorously tested, secured against exploitation, and maintainable by the next engineering team that inherits it.
Business value must be the ultimate measure of success, not a vendor’s invoice tied to a smaller file size. If an AI tool is excellent at scaffolding and creating prototypes, we should use it for exactly that. But when it comes to production readiness, do not just pay to shrink your AI slop. Pay to understand it, secure it, and implement the engineering discipline required to keep it alive.
SD Times Q&A
What metrics should I use to evaluate vibe code cleanup quality?
Lines-of-code reduction is an unreliable proxy for code quality after vibe coding. Better metrics include elimination of duplicate data-fetching logic, presence of route guards and authorization checks, WCAG accessibility compliance, test coverage on critical business flows, and absence of hardcoded credentials or unsafe dependencies. Business value delivered and maintainability by the next engineering team are the ultimate benchmarks.
What are the most common security issues in vibe-coded applications?
Common security issues include missing route guards that allow unauthenticated access to protected pages, hardcoded credentials embedded in source code, unsafe third-party dependencies, and no rollback path for failed deployments. These gaps are architectural in nature and will not be resolved by simply reducing the number of lines of code.
Can AI tools fix AI-generated code automatically?
AI coding agents can assist with cleanup tasks such as checking compliance with project guidelines, identifying overengineering, and scanning for security leaks. However, they cannot reliably resolve architectural problems like conflicting business logic, missing authorization layers, or accessibility failures without human architectural review.
What is the difference between vibe coding tools used by developers vs. non-developers?
Developer-oriented tools such as Cursor and OpenAI Codex offer tighter integration with existing codebases, version control, and engineering workflows. Tools popular with non-technical founders, such as Replit and Lovable, prioritize rapid scaffolding and UI generation. The resulting code from the latter category typically requires more structural remediation before it is production-ready.
When is vibe-coded code actually production-ready?
Vibe-coded code is production-ready when critical behavior is fully understood, covered by rigorous tests, secured against known exploits, and documented well enough for an incoming engineering team to maintain it. It also requires a repeatable software development process with clear specifications, structured release cycles, and quality assurance gates — not just a reduced line count.
