The fast 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 lots 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 mission?
Why reducing the code shouldn’t be the reply
Ask any skilled developer, and so they’ll agree that readable, maintainable, dependable, and environment friendly code is the other of code sprawl. AI-generated code is a serious contributor to this uncontrolled sprawl as a result of code duplication is probably the most widespread situation present in vibe-coded apps. AI coding brokers maintain including code with out consolidation, particularly when correct restrictions and opinions are absent. You’ll be able to usually see the identical logic with very slight variations throughout totally different elements.
It appears logical to prune as a lot as attainable to realize the specified code high quality. You might encounter engineers who provide to refactor vibe-coded codebases by decreasing them from “one thing like 100,000 traces right down to 35,000, similar 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 include conflicting enterprise logic, hardcoded credentials, unsafe dependencies, lacking assessments, and no rollback path. Code discount is a pretty however deceptive metric.
What you need to measure as a substitute
You’ll be able to present a shopper that their repository shrank by 40 %. It looks like progress as a result of it’s measurable and directional: fewer traces, fewer issues. This logic assumes the damaging code was the code you deleted, and that the code you stored is protected. Neither assumption holds up when you have a look at what really goes mistaken in vibe-coded purposes.
Not too long ago, whereas serving to a shopper launch vibe-coded options safely, we examined their app’s pricing funnel and noticed a superbly rendered record of paid plans. Beneath that polished exterior, nevertheless, two separate elements have been fetching that information independently. This created a large vulnerability: updating a plan in a single place did not 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 type of architectural rot.
Or take routing. On the identical mission, 13 routes, some nested, all rendered the precise pages in the precise sequence. The person circulation 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 might consolidate each route handler, minimize the file in half, and this hole would stay large open.
Then there are varieties. We reviewed an utility the place each type regarded polished, had validation guidelines, and dealt with submissions accurately. 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, all the pieces labored. For somebody counting on a display screen reader, the applying couldn’t talk which enter had focus, which type it belonged to, or how you can 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 usually are not the identical as Replit or Lovable. Can’t founders maintain prompting to troubleshoot and repair points? They’ll, however ultimately, the method turns into too tedious, and the danger of an entire rewrite relatively than a cleanup solely will increase.
Will we use AI coding brokers to scrub up the generated code? Sure, however largely to examine compliance with mission pointers as a primary step, search for overengineering and pointless complexity, and scan for attainable safety leaks because the final step.
When a vibe-coded characteristic is really fastened
After we deal with AI code cleanup as a easy refactoring job centered on discount, we ignore the applying’s structural integrity. Extra importantly, charging for deleted traces of code contradicts the elemental 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 characteristic, the AI generates extra bloated code, and so they need to pay for an additional spherical of deletion.
Even when the characteristic set is frozen, you can not merely delete code, stroll away, and anticipate the applying to outlive. Software program not often lasts six months with out upkeep. Third-party APIs replace, new working techniques are launched, and customers undertake gadgets with totally totally different display screen resolutions. Escaping this cycle requires an precise software program growth course of constructed on self-discipline, clear specs, structured launch cycles, and deep high quality assurance.
If deleted traces of code are the mistaken metric, we’d like a brand new definition of what it means for a vibe-coded characteristic to be “fastened.” Mounted ought to imply essential conduct is absolutely understood, rigorously examined, secured towards exploitation, and maintainable by the following engineering staff 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 device is superb at scaffolding and creating prototypes, we must always use it for precisely that. However relating to manufacturing readiness, don’t simply pay to shrink your AI slop. Pay to know it, safe it, and implement the engineering self-discipline required to maintain it alive.
SD Occasions Q&A
What metrics ought to I take advantage of to guage vibe code cleanup high quality?
Traces-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, check protection on essential enterprise flows, and absence of hardcoded credentials or unsafe dependencies. Enterprise worth delivered and maintainability by the following engineering staff are the final word benchmarks.
What are the most typical safety points in vibe-coded purposes?
Frequent 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 traces of code.
Can AI instruments repair AI-generated code routinely?
AI coding brokers can help with cleanup duties equivalent to checking compliance with mission pointers, 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 overview.
What’s the distinction between vibe coding instruments utilized by builders vs. non-developers?
Developer-oriented instruments equivalent to Cursor and OpenAI Codex provide tighter integration with present codebases, model management, and engineering workflows. Instruments common with non-technical founders, equivalent to Replit and Lovable, prioritize fast scaffolding and UI technology. The ensuing code from the latter class usually 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 essential conduct is absolutely understood, coated by rigorous assessments, secured towards recognized exploits, and documented effectively sufficient for an incoming engineering staff to take care of it. It additionally requires a repeatable software program growth course of with clear specs, structured launch cycles, and high quality assurance gates — not only a lowered line depend.
