The speedy adoption of vibe coding has laid the muse for a brand new market: vibe code cleanup. As a result of it’s an rising market, there’s rather a lot to unpack right here. How will we method the cleanup course of? Can we use AI to assist, or does that defeat the aim? And crucially, how will we measure the success of a cleanup undertaking?
Why chopping the code shouldn’t be the reply
Ask any skilled developer, they usually’ll agree that readable, maintainable, dependable, and environment friendly code is the alternative of code sprawl. AI-generated code is a significant contributor to this uncontrolled sprawl as a result of code duplication is probably the most widespread challenge present in vibe-coded apps. AI coding brokers preserve including code with out consolidation, particularly when correct restrictions and evaluations are absent. You may usually see the identical logic with very slight variations throughout totally different parts.
It appears logical to prune as a lot as attainable to attain the specified code high quality. It’s possible you’ll encounter engineers who provide to refactor vibe-coded codebases by decreasing them from “one thing like 100,000 strains right down to 35,000, identical performance.” On high of that, their engagement mannequin is closely tied to code discount alone: the extra code they take away, the extra they cost.
However a smaller repository can nonetheless comprise conflicting enterprise logic, hardcoded credentials, unsafe dependencies, lacking checks, and no rollback path. Code discount is a sexy however deceptive metric.
What it’s best to measure as a substitute
You may present a consumer that their repository shrank by 40 %. It looks like progress as a result of it’s measurable and directional: fewer strains, fewer issues. This logic assumes the harmful code was the code you deleted, and that the code you saved is protected. Neither assumption holds up when you take a look at what really goes mistaken in vibe-coded purposes.
Not too long ago, whereas serving to a consumer launch vibe-coded options safely, we examined their app’s pricing funnel and noticed a wonderfully rendered listing of paid plans. Beneath that polished exterior, nevertheless, two separate parts have been fetching that information independently. This created an enormous vulnerability: updating a plan in a single place didn’t propagate to the opposite, destroying the one supply of fact and leaving the applying with conflicting info. Shrinking the codebase by deleting unused CSS or redundant boilerplate doesn’t inherently repair this sort of architectural rot.
Or take routing. On the identical undertaking, 13 routes, some nested, all rendered the proper pages in the proper sequence. The person circulate labored precisely as designed. However there have been zero route guards. Any customer might navigate on to the billing web page with out creating an account. This isn’t sloppy code a linter would flag; it’s a lacking safety idea. An AI agent that generates a functioning route construction doesn’t routinely generate the authorization logic that ought to govern entry. You possibly can consolidate each route handler, lower the file in half, and this hole would stay broad open.
Then there are varieties. We reviewed an software the place each type seemed polished, had validation guidelines, and dealt with submissions appropriately. However the markup had no semantic type parts. The inputs have been styled containers with JavaScript occasion handlers. For a sighted person with a mouse, every little thing labored. For somebody counting on a display screen reader, the applying couldn’t talk which enter had focus, which type it belonged to, or the way to submit it. This can be a WCAG 2.2 failure that’s utterly invisible to code discount metrics.
Can AI repair AI?
With 83% of builders utilizing AI instruments, how is the best way they use them totally different from how non-developers produce code?
For one, there’s a nice divide between the vibe coding instruments builders use versus these favored by non-coders. Cursor and Codex aren’t the identical as Replit or Lovable. Can’t founders preserve prompting to troubleshoot and repair points? They will, however finally, the method turns into too tedious, and the danger of a whole rewrite relatively than a cleanup solely will increase.
Can we use AI coding brokers to scrub up the generated code? Sure, however largely to verify compliance with undertaking tips as a primary step, search for overengineering and pointless complexity, and scan for attainable safety leaks because the final step.
When a vibe-coded function is really fastened
After we deal with AI code cleanup as a easy refactoring process targeted on discount, we ignore the applying’s structural integrity. Extra importantly, charging for deleted strains of code contradicts the basic actuality of how production-ready software program is constructed. It creates a perpetual treadmill: a non-technical founder pays to trim the primary construct, however the second they want a brand new function, the AI generates extra bloated code, they usually need to pay for one more spherical of deletion.
Even when the function set is frozen, you can not merely delete code, stroll away, and count on the applying to outlive. Software program hardly ever lasts six months with out upkeep. Third-party APIs replace, new working programs are launched, and customers undertake gadgets with completely totally different display screen resolutions. Escaping this cycle requires an precise software program improvement course of constructed on self-discipline, clear specs, structured launch cycles, and deep high quality assurance.
If deleted strains of code are the mistaken metric, we want a brand new definition of what it means for a vibe-coded function to be “fastened.” Fastened ought to imply vital conduct is totally understood, rigorously examined, secured in opposition to exploitation, and maintainable by the subsequent engineering crew that inherits it.
Enterprise worth have to be the final word measure of success, not a vendor’s bill tied to a smaller file dimension. If an AI instrument is great at scaffolding and creating prototypes, we must always use it for precisely that. However with regards to manufacturing readiness, don’t simply pay to shrink your AI slop. Pay to grasp it, safe it, and implement the engineering self-discipline required to maintain it alive.
SD Occasions Q&A
What metrics ought to I exploit to judge vibe code cleanup high quality?
Strains-of-code discount is an unreliable proxy for code high quality after vibe coding. Higher metrics embrace elimination of duplicate data-fetching logic, presence of route guards and authorization checks, WCAG accessibility compliance, take a look at protection on vital enterprise flows, and absence of hardcoded credentials or unsafe dependencies. Enterprise worth delivered and maintainability by the subsequent engineering crew are the final word benchmarks.
What are the commonest safety points in vibe-coded purposes?
Widespread safety points embrace lacking route guards that enable unauthenticated entry to protected pages, hardcoded credentials embedded in supply code, unsafe third-party dependencies, and no rollback path for failed deployments. These gaps are architectural in nature and won’t be resolved by merely decreasing the variety of strains of code.
Can AI instruments repair AI-generated code routinely?
AI coding brokers can help with cleanup duties similar to checking compliance with undertaking tips, figuring out overengineering, and scanning for safety leaks. Nevertheless, they can not reliably resolve architectural issues like conflicting enterprise logic, lacking authorization layers, or accessibility failures with out human architectural evaluate.
What’s the distinction between vibe coding instruments utilized by builders vs. non-developers?
Developer-oriented instruments similar to Cursor and OpenAI Codex provide tighter integration with current codebases, model management, and engineering workflows. Instruments in style with non-technical founders, similar to Replit and Lovable, prioritize speedy scaffolding and UI era. The ensuing code from the latter class sometimes requires extra structural remediation earlier than it’s production-ready.
When is vibe-coded code really production-ready?
Vibe-coded code is production-ready when vital conduct is totally understood, lined by rigorous checks, secured in opposition to identified exploits, and documented properly sufficient for an incoming engineering crew to take care of it. It additionally requires a repeatable software program improvement course of with clear specs, structured launch cycles, and high quality assurance gates — not only a diminished line rely.
