Converge the drift
Three refactors and three half-applied patterns become one clean structure, so a change stops having surprise blast radius across the app.
Vibe coding rescue · Claude Code
Your Claude Code app breaks every time you prompt it.
That is the AI hitting its ceiling, not you failing — and it is measurable. Researchers tracking iterative AI code generation found a 37.6% increase in critical vulnerabilities after just five rounds of asking a model to improve its own code. Once a Claude Code app outgrows the tool’s context window, each prompt fixes one thing and quietly breaks two others. The fix is an engineer who can hold the whole codebase in view.
App down right now? WhatsApp us or email [email protected] — we reply within one business day.
There is a predictable wall in every vibe-coding tool. For the first few dozen screens it feels like magic; then the codebase gets big enough that the AI stops seeing all of it at once. From there, every fix is a gamble: the change you asked for lands, and something you shipped last week silently regresses. You end up prompting more and trusting less.
This is a structure problem, not a prompting problem, and it does not fix itself with a better prompt. We read the whole codebase with senior eyes, converge the layers of half-replaced logic into one coherent structure, add tests around the parts that matter, and hand you back an app that is safe to keep prompting on, by us, or by you in {Tool}.
How we fix it.
The review that never happened
Hundreds of accepted AI suggestions get the full-codebase review the tab key skipped, individually fine, collectively drifting, now made coherent.
Tests around the money paths
We add tests where it counts first, auth, payments, data writes, so future prompting is safe rather than roulette.
Specific to Claude Code
What usually needs attention in Claude Code apps.
Secrets in git and the client
Agentic edits paste keys where they were convenient, a frontend file, a committed `.env`, the shell history. We scan the whole repo and git history, move every secret server-side, and rotate what was exposed.
Backend built, not secured
The agent scaffolds auth and a database that work in the demo but leave row-level rules open and permission checks in the browser. We enforce access on the server, table by table.
Multi-file edits that drift
A single big turn touches thirty files and context is lost between sessions, so a later refactor breaks the build or undoes an earlier fix. We review the whole codebase and make it coherent again.
How a rescue works.
Fixed price, milestone payments, and a written audit before you commit to anything.
Audit, 48 hours
Send repo or tool access. You get a plain-language report of what is solid, what is dangerous, what is unfinished, and a fixed price for the rest.
Fix and harden
Security first: auth, data access, secrets, rate limits, payments. The invisible work that keeps your launch out of the news for the wrong reason.
Finish and launch
The stalled features get built, the deploy pipeline gets set up, and the app goes live on your domain with monitoring, alerts, and daily backups.
Stay if you want
Every rescue includes 30 days of fixes. Many founders keep us on afterwards for the roadmap, through Resident™, our monthly retainer.
Questions
Why does my Claude Code app break every time I prompt a change?
The codebase has outgrown the tool’s context window. The AI can no longer hold all of it in view, so a fix in one place regresses something elsewhere it can no longer see. It is a size and structure limit, not a mistake you made.
Can you make my Claude Code app stable to keep building on?
Yes, that is the point. We restructure the codebase into something coherent and documented, add tests around the critical paths, and hand it back with conventions that make future prompting in Claude Code far less likely to break things.
Will you rewrite it from scratch?
No. Full rewrites are usually ego, not engineering. Claude Code produces plenty of usable code; we keep what works, restructure what will not scale, and rebuild only the unsafe or broken parts. You already paid for the 80%, we make the 20% that ships it reliable.
Is it true that asking the AI to fix its own code makes things worse?
Measurably, yes. A study of iterative AI code generation (Shukla, Joshi and Syed, arXiv:2506.11022) recorded a 37.6% increase in critical vulnerabilities after five rounds of prompting a model to improve its own output. Separately, Veracode’s 2025 GenAI Code Security Report found models across 80 tasks chose the insecure implementation 45% of the time, and got no better as they got newer. Each pass optimises for the error in front of it, with no memory of the threat model you never described — which is exactly why the prompt loop feels like it is converging when it is not.
Send us your Claude Code project.
Book a 30-minute call with a founder. Within 48 hours of access you will know what is solid, what is dangerous, and what it costs to launch.
Prefer email? WhatsApp us or email [email protected] — we reply within one business day.
