How to Make Vibe Coding Production-Ready (Without Losing Your Mind)
How to ship production apps with AI without the security holes, technical debt spirals, and skill atrophy that trap most vibe coders.
What I learned fixing security holes, cleaning up technical debt, and fighting off skill atrophy while shipping real apps with AI.
Vibe coding creates real problems: security holes, technical debt that compounds overnight, and skill atrophy that sneaks up on you. After shipping several production apps with AI, I hit every one of them. But they're not reasons to stop — they're patterns with specific fixes. This guide covers the 3 core problems, what I did to solve each one, the role shift that made it all click, and a free checklist you can use before your next push to production.
Hi, I'm Jenny 👋 I help non-technical people ship real products with AI. AI builder behind VibeCoding.Builders and other products with hundreds of paying customers. See all my launches →
If you're new to Build to Launch, welcome! Here's what you might enjoy:
- Smoke Testing Your Vibe-Coded App — the checklist to run before you ship
- The Essential Software Engineering Practices Every AI Builder Needs — the fundamentals worth keeping
- 12 Claude Code Project Ideas — where to apply all of this
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Do you vibe code? Have you run into any headaches along the way?
If you scroll through X, Reddit, or even mainstream media, you'll eventually stumble into the vibe coding hate train. Developers call it "productivity theater." Security experts highlight glaring vulnerabilities. Senior engineers groan that it creates "pseudo-developers" who can't maintain their own code.
But when I looked closer, most of these problems boil down to three core issues: security, technical debt, and skill atrophy. After building dozens of projects and actually shipping production apps with AI, I hit almost every trap the critics warned about.
The thing is — fixing these issues taught me something I wasn't expecting. The critics aren't entirely right, but the enthusiasts aren't either. The real story is somewhere else entirely — and fixing these three things didn't slow me down. It made me better at managing what AI creates, which turns out to be the whole skill.
What you'll go through:
- Problem 1: The Security Blind Spot in Vibe-Coded Apps — the 3 ways AI exposed my production apps, with the exact fixes that caught them
- Problem 2: The Technical Debt Spiral in AI-Generated Code — why AI-generated code compounds into chaos overnight, and the instruction file system that prevents it
- Problem 3: The Skill Atrophy Trap When Vibe Coding — what it feels like to not understand your own code, and the 3 habits that keep you grounded
- The Moment Everything Clicked — the role shift that reframes all three problems at once
- Where This Is All Heading — and Your Next Move — the 5-step sequence for your next project
Problem 1: The Security Blind Spot in Vibe-Coded Apps
How it happened to me
1. Plaintext passwords.
When I was building Image Finder at the end of 2024, everything looked smooth. Authentication worked, users could log in. Then I peeked into the database. Passwords were stored as literal "password". No hashing. No encryption. Just sitting there in plain text.
I only caught it because I already knew passwords should never be stored as-is.
2. Direct use of environment variables.
In a toy project, AI dropped my database connection string straight into the script, no .env, no safeguards.
3. API key leakage.
On my personal site, AI generated code that called API keys directly from the client side. Anyone could open dev tools and see my private keys.
Each of these would've been catastrophic if they had slipped into production unnoticed.
What I did to fix it
- Rule files. Every time I catch a bad practice, I write it into a "rules file" that Cursor reads before generating code.
- AI code reviews. If I'm unsure, I literally ask AI to roast its own code: "Find all security problems and fix them."
- Security-first prompting. I now prompt AI explicitly:
Implement this following programmer guidelines, with server-side validation, secret management, and protection against injection attacks.
- Platforms with guardrails. I mostly stick with Supabase for auth, Stripe for payments, Cloudflare for security. Popular platforms have built-in best practices AI follows more consistently.
- Know your limits. I still never trust AI with cryptography. For that, I only use established libraries.
The point is: vibe coding doesn't mean you just hand everything off. Frameworks catch the obvious stuff, but business logic security still needs human reasoning.
The bigger lesson
AI optimizes for working code, not secure code. That's the fundamental issue.
Problem 2: The Technical Debt Spiral in AI-Generated Code
Ever feel like AI has no memory for what it just built? Like it happily forgets what it did five minutes ago, and then recreates the exact same thing, but different? That's technical debt in its AI form.
How it Happened to Me
1. Feature inconsistency
This hit me head-on while upgrading Quick Viral Notes. I needed edit boxes in a few different places across the app. I just asked AI to add editors in multiple places. And it worked… until the next day.
When I tried to update the editor functionality, I was juggling three separate editors: each location had its own editor component, completely different styling and logic, slightly different bugs.
So updating one didn't fix the others. A classic violation of DRY (Don't Repeat Yourself).
2. Performance issues
I saw this most painfully in Substack Explorer. It felt perfect with smooth UI and functional data fetching. But as soon as I supplied large sets of data, it slowed to a crawl.
Turns out: every single request was being made separately, no caching, no query optimization, a giant pile of N+1 problems.
The code worked. But the system design was broken.
What I did to fix it
My first move was obvious: force a refactor. I asked AI to "create a reusable editor component" and consolidated everything into one.
For the next apps, I got more systematic:
- Frameworks. Sticking to React/Next.js patterns helps AI follow consistent habits.
- Instruction files. I now keep a living doc in every project that Cursor reads. It spells out naming conventions, database schema rules, fetching best practices, and a note to "never create duplicates without asking first."
The bigger lesson
This is where vibe coding shows its true weakness: it optimizes for "does this feature work right now?", not "will this system hold up over time?"
When critics say vibe coding produces "demo apps that collapse under real load," they're not wrong. I've lived that.
Problem 3: The Skill Atrophy Trap When Vibe Coding
How it happened to me
Have you ever had that sinking feeling when someone asks you to explain something you supposedly built?
That was me, early in my vibe coding days. I'd gotten drunk on the speed. My whole attitude was: "Just build everything. Claude, make it work." And it did work. Beautiful, functional apps showing up on my screen faster than I'd ever managed before.
Until the day someone asked me about the implementation details. I opened the codebase, stared at the AI-generated logic, and realized: I had absolutely no idea how it worked.
I felt like a manager who'd been away from the shop floor so long that they couldn't actually operate their own machine anymore.
What I did to mitigate it
I started forcing myself to slow down and build some habits:
- Know your problem before coding. I stopped saying "just make it work" and instead clarified exactly what I wanted and why.
- Maintain core programming principles. Components, state, data flow, just enough fundamentals to guide AI and spot when it was doing something silly.
- Document everything. Every time I debugged something painful, I had AI summarize our dead ends, our solutions, and the lessons.
The scariest part
The more I leaned on AI, the more subtle costs I started noticing:
- Decision paralysis. When AI gives you five "valid" solutions, which one do you choose if you don't deeply understand the trade-offs?
- Context switching. You're constantly bouncing between your own thinking and AI's suggestions.
- False confidence. AI writes with such authority that you believe the solution is solid. You ship it, only to discover scaling issues or nasty edge cases later.
I talked to a developer who'd been vibe coding for eight months. He said it felt like "imposter syndrome on steroids." He could ship features like crazy, but when it came to architectural decisions, he felt unqualified. The essential software engineering practices worth keeping are fewer than you'd think — but they're the exact ones that prevent this.
Part 4: The Moment Everything Clicked
After building across different apps, a pattern emerged:
- Security fixes didn't come from AI being smarter. They came from me reviewing its work systematically.
- Technical debt fixes didn't come from better prompts. They came from enforcing consistency with docs and guardrails.
- Skill atrophy fixes didn't come from AI teaching fundamentals. They came from me drawing lines, what I must understand vs. what I can safely delegate.
That's when it clicked:
The problems might be coming from the AI, but they can be solved by better management.
Management of tools. Management of expectations. Management of my own intellectual diligence.
The Role Shift
With AI, your role levels up: developer → AI manager.
And like any manager, you don't eliminate problems, you manage trade-offs.
Where Vibe Coding Actually Shines
1. Compression of effort.
Before AI, I spent three weeks of evenings digging into housing market data. Now? I can do the same (actually deeper) study in under a week. AI doesn't remove the thinking. It compresses the overhead.
2. Amplifier of expertise.
Imagine domain experts lean in: lawyers building legal workflow tools, doctors designing apps around patient care, real estate analysts scaling their insights into products. That's not toy projects. That's production with leverage.
Part 5: Where This Is All Heading
The vibe coding debate misses the real question: What kind of developer do you want to be when AI can already write most of the code?
What's emerging isn't just "developer + AI." It's a new role: Technical Product Builder.
Someone who:
- Thinks architecturally enough to guide AI toward sane design
- Stays security-aware enough to catch what AI misses
- Builds debugging intuition for when things break weirdly
- Brings domain knowledge to decide what matters and why
- Manages AI like a capable teammate
But also someone who doesn't need to reinvent every wheel or memorize every API.
The future doesn't belong to people who can code without AI. And it doesn't belong to people who trust AI blindly. It belongs to those who can manage AI effectively.
Your Next Move
On your next project, try this sequence:
- Clarify before you code. Write down the concept and requirements yourself, then use AI as your "junior dev" to implement.
- Prototype fast. Let AI scaffold features, but review them with a critical eye.
- Run a security sweep. Use a simple checklist: inputs validated, secrets safe, endpoints protected.
- Document decisions. Keep a lightweight log of what you built, why, and how data flows.
- Stress test one thing. Pick a part of your project (auth, DB queries, scaling) and push it harder than AI assumes.
If you want the more systematic version of this — the rule files, instruction files, and the full guardrail setup in one place — the vibe coding production-ready playbook has it.
Which of the three problems — security, technical debt, or skill atrophy — has hit you the hardest?
— Jenny
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