I built an entire app with Cursor in a weekend. Full auth, database, API routes, even a nice-looking frontend. It felt like magic.
Then I audited the code.
And honestly? It scared me a little.
The auth system had no rate limiting. SQL queries were wide open to injection. The API endpoints had zero input validation. The frontend was sending raw user data to the server without sanitization. And the "secure" session handling? It was storing tokens in localStorage — a classic XSS attack vector that any junior dev would catch in a code review.
Here's the thing nobody talks about when they celebrate vibe coding: the AI doesn't know what "production-ready" means. It knows syntax. It knows patterns. It can generate code that looks right and even works in development. But it has no concept of hostile users, edge cases at scale, or what happens when your app processes thousands of concurrent requests.
What Even Is Vibe Coding?
If you've been living under a rock (no judgment — it's been a wild couple of years), vibe coding is a term coined by AI researcher Andrej Karpathy in early 2025. The idea is simple: you describe what you want in natural language, and an AI coding assistant — Cursor, GitHub Copilot, Windsurf, Claude Code — generates the code for you.
It's not just autocomplete on steroids. We're talking about full application scaffolding, complete features, even deployment configurations. Tools like Cursor let you have a conversation with your codebase. You can say "add a payment processing flow with Stripe" and watch it scaffold out controllers, routes, models, and frontend components.
And it works. Sort of.
The Gap Nobody's Talking About
The problem isn't that vibe coding produces bad code. It's that it produces good enough code — the kind that passes the eye test, works in local development, and gives you a false sense of security.
I've been doing this for 13+ years. I've seen plenty of production incidents in my career. And what I can tell you is that the scariest bugs aren't the ones that crash your app. They're the ones that silently work — leaking data, creating security holes, accumulating technical debt — until something breaks catastrophically at 3 AM on a Saturday.
Here's what I typically find when I audit vibe-coded projects:
Security Vulnerabilities
- No input validation on API endpoints
- SQL injection vectors in dynamic queries
- Missing CSRF protection
- Improper authentication token handling
- Hardcoded secrets in configuration files
Performance Issues
- N+1 query patterns that work fine with 10 users but collapse at 1,000
- Missing database indexes on frequently queried columns
- Unoptimized image handling and asset loading
- No caching strategy whatsoever
- Memory leaks in event listeners and subscriptions
Architecture Problems
- God classes that do everything
- No separation of concerns
- Tightly coupled components that make testing impossible
- Missing error handling and logging
- No graceful degradation when external services fail
My Actual Process: From Vibe to Production
When I work on a project — whether it's for a US-based client or my own products like Wishloom.app — I use vibe coding as a starting point, not an end point. Here's what my actual workflow looks like:
Phase 1: Rapid Prototyping (The "Vibe" Part)
I use Cursor, GitHub Copilot, or Claude Code to get a working prototype fast. This is where AI genuinely shines — turning ideas into functional code in hours instead of days.
Phase 2: Architecture Review
I step back and look at the big picture. Does the structure make sense? Are concerns properly separated? Is this thing going to be maintainable in 6 months? I reorganize, refactor, and establish clear boundaries between components.
Phase 3: Security Audit
I go through every endpoint, every database query, every user input path. I add rate limiting, input validation, CSRF protection, proper auth flows. This is the stuff that AI consistently misses because it doesn't think adversarially.
Phase 4: Performance Hardening
I check for N+1 queries, add proper indexing, implement caching layers, optimize asset delivery. I test with realistic load scenarios, not just "does it return 200 OK?"
Phase 5: Production Validation
I deploy to staging, run integration tests, verify error handling, check logging, and confirm that monitoring is in place. Only then does it go live.
A Real-World Example
When I built Wishloom.app — a children's audio storytelling platform — I used exactly this approach. Cursor helped me scaffold the Laravel/Livewire application fast. But the production version? That required weeks of hardening.
I caught authentication vulnerabilities that could have exposed children's data. I optimized N+1 query patterns that would have made the app unusable as the story library grew. I enforced input validation across every endpoint. And I packaged it as a proper Android app with Flutter, managed the Google Play submission lifecycle, and deployed it on managed infrastructure with automated backups.
The AI got me from 0 to 60 in a weekend. I spent the next few weeks getting from 60 to 100. That last 40%? That's where engineering experience matters.
Why This Matters For Your Business
If you're a founder, a startup, or a business looking to build software quickly — vibe coding is incredible. But please, please don't ship the raw output to production. At minimum, you need someone who:
- Understands security at a deeper level than "use HTTPS"
- Knows what happens when your app scales beyond 100 users
- Can read code critically, not just optimistically
- Has been burned by production incidents before (so you don't have to be)
That's the Human-in-the-Loop approach. It's not about being anti-AI. It's about being pro-reliability.
The Bottom Line
Vibe coding is the future. I believe that genuinely. But "the future" doesn't mean "skip the engineering." It means engineering faster — with AI as your copilot and human expertise as your safety net.
The best software in 2026 won't be built by AI alone. And it won't be built by humans alone. It'll be built by experienced engineers who know how to leverage AI's speed while applying the judgment, experience, and adversarial thinking that only comes from years of building things that actually run in production.
I'm Nahid Hossain — a Senior Software Engineer with 13+ years of experience who specializes in turning AI-generated prototypes into production-grade software. If you've got a vibe-coded project that needs hardening, or you want to build something right from the start, I'd love to chat.