How QuickLaunch made $120k+ fixing vibe-coded apps in 2025
I made $120k+ in 2025 fixing apps that founders built with Cursor, Lovable, and v0. Here's what was actually broken.
P.S.: We are not an agency, its my portfolio and offerings
I didn't plan to build a business around cleaning up AI-generated code. It kind of found me. In January 2025, I launched QuickLaunch as an idea-to-MVP platform. Got 100+ users in the first week. Cool. But something weird started happening in the DMs. Founders kept asking if I could look at their existing apps. Not build new ones. Fix the ones they'd already built with vibe-coding tools. At first I said no. That wasn't the business. But they kept asking. And the problems they described sounded familiar because I'd reviewed 500+ pull requests mentoring bootcamp developers. I knew what messy code looked like. So I took on a few projects. And holy shit.
The God Component Problem
Almost every React app I opened had the same issue: everything was stuffed into a handful of massive components. I'm talking 800-line files where the login logic, API calls, state management, and UI were all tangled together. One founder's dashboard component had 47 useState hooks. Forty-seven. Every change risked breaking something unrelated because there was no separation of concerns. The AI just kept adding "one more function" to existing files until they became unmaintainable monsters.
Memory Leaks That Crashed Production
A trading dashboard client came to me because their app crashed after 2-3 hours of use. Users were complaining. They thought it was a backend issue. It wasn't. The app had WebSocket subscriptions that never got cleaned up. Every time a user navigated between pages, new subscriptions were created but old ones kept running. After a few hours, the browser was holding 2GB of memory. Classic useEffect cleanup problem that the AI just... didn't handle. I've seen this pattern dozens of times now. Timers that keep running after component unmount. Event listeners that stack up. Closures holding references to dead components.
Security Holes You Could Drive a Truck Through
This one scared me the most. Veracode tested 100+ LLMs in 2025 and found 45% of AI-generated code fails security tests. But seeing it in the wild is different than reading a report. One app had Supabase service keys exposed in the frontend bundle. Another stored passwords in plain text because the founder asked for "a login form" and the AI gave them exactly that, minus any actual security. I found apps with no input validation, no rate limiting, auth flows that could be bypassed by editing a URL parameter. One app let any user access any other user's data because there was no row-level security. Just vibes. Escape's research team found 2000+ vulnerabilities across 5,600 vibe-coded apps. 400+ exposed secrets. That's not a bug, that's a pattern.
The Duplication Problem
AI doesn't refactor. It just... adds more code.
I'd find the same API call written five different ways across an app. The same validation logic copy-pasted with slight variations. Helper functions that did almost the same thing scattered across random files.
One codebase had three different date formatting utilities. None of them worked correctly for all timezones.
How I Actually Fixed These
I started offering what I called Rescue Sprints. 7 days, fixed price, I go in and rebuild the foundation while keeping the UI that's working.
Here's the typical process:
Week 1: Audit the codebase. Find the god components, identify memory leaks, run security scans, map out the duplication.
Then I fix it in order of what's going to hurt them first:
Security holes (auth, exposed keys, injection vulnerabilities)
Memory leaks and performance issues
God component refactoring into proper architecture
Deduplication and cleanup
I don't rewrite everything. That's a trap. I keep what works and fix what's broken.
The Numbers
In 2025, this turned into $120k+ in revenue. Mostly word of mouth. Founders talk to other founders, and it turns out a lot of them hit the same wall.
Y Combinator's Winter 2025 batch was 25% AI-generated codebases. Those founders needed to scale. Many of them couldn't without serious technical work.
70% of investors now demand technical validation before writing checks for vibe-coded MVPs. That's created real urgency for founders to fix their code before fundraising.
Why I Work US Hours
Most of my clients are US-based startups. I'm in India but I work full US timezone overlap. Real-time Slack. No 12-hour delays waiting for responses.
This matters because when you're debugging production issues, you need someone who can jump on a call now, not tomorrow.
I've done this working with US companies like Alteryx, CardCapture, and through Toptal for years. The timezone thing isn't a hack. It's how I've always worked.
What I Learned
Vibe-coding tools are incredible for getting from zero to prototype. Seriously. Cursor, Lovable, v0, Bolt - they've changed what's possible for non-technical founders.
But there's a gap between "it works in the demo" and "it works in production with real users trying to break it."
45% of AI code fails security tests. That's not a bug in the tools. That's just what they are. They're optimized for making things work, not making things safe or maintainable.
If you're building with vibe-coding tools, here's my honest advice: ship fast, validate the idea, but budget for a technical cleanup before you scale. The problems compound. A $1,000 fix today becomes a $30,000 crisis in six months.
Anyone else building a business around fixing what AI builds?
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