In the past seven years as an indie maker, I’ve built multiple apps and spoken with dozens of people experimenting with “vibe coding.” For the uninitiated, vibe coding is the practice of building apps quickly with heavy reliance on AI coding assistants and scaffolding tools. It’s fast, it feels magical, and it gets you results—until it doesn’t.
After interviewing more than 50 vibe coders, I’ve noticed repeating patterns. The majority run into the same mistakes, and many end up frustrated or rebuilding their projects from scratch. The good news? With a few mindset shifts and some best practices, you can avoid the traps and build solid projects—without losing the speed that makes vibe coding exciting.
The Two Kinds of Vibe Coders
Most people I talk to fall into one of these two groups:
- People who know a little code (80%) — they can tinker, understand logic, maybe work with databases, but they’re not fully confident digging deep.
- People with no coding background at all (20%) — they rely entirely on AI or “no-code-like” tooling to carry them through.
Knowing which camp you’re in helps you understand where your blind spots might be.
Principles Before You Start
Before you fire up AI to scaffold your entire SaaS, remember these essentials:
- Use reputable starter kits. Don’t reinvent the wheel—leverage templates with authentication, design systems, and structure built in.
- Own your database models. Even if you let AI scaffold, you should know what tables exist, how they relate, and where constraints are.
- Research existing tools. For example, background jobs can often be automated with platforms like n8n or Zapier instead of reinventing them in code.
- Security isn’t optional. Plan permissions, authentication, and access control as you go—not as an afterthought.
Common Vibe Coding Pitfalls (And Fixes)
Here are the recurring issues I’ve seen vibe coders face, why they happen, and how to avoid them:
| Issue | Why It Happens | Fix |
|---|---|---|
| Hard to fix AI-generated errors | People treat AI as a black box. | Read what AI wrote. Ask why it made certain decisions. Visualize logic flows with diagrams (e.g. Mermaid, Excalidraw). |
| Adding new features feels impossible | Weak understanding of database models. | Define your schema explicitly. For example, adding a referral program? Create a separate Transactions model rather than letting AI improvise. |
| Security holes (e.g. with Supabase) | RLS or backend checks not configured. | Always enforce row-level security. Never trust the frontend to filter data. Test your endpoints as if you were an attacker. |
| Clunky design | Entire pages generated inconsistently. | Start with a component library (Shadcn, MUI, Tailwind UI). Reuse components for consistency. |
| High AI token costs | Generating too much code at once. | Build incrementally. Break down features into small, testable chunks. Use editors like Cursor or Windsurf that optimize context usage. |
| Rewrites when stuck | Building alone without feedback. | Join a community or seek mentorship. A second pair of eyes early saves rewrites later. |
Case Study: Prototypes vs Long-Term Reality — Performance & Schema Pain
Let’s look at a real scenario that highlights one of the biggest vibe coding pitfalls.
What happened:
A solo founder quickly prototyped a SaaS using AI-generated scaffolding with Supabase as the backend. It worked beautifully at first—authentication, a few basic tables, and a dashboard. Within weeks, they launched to early users.
But as new features were added, cracks appeared:
- Queries slowed to a crawl because none of the tables had indexes.
- Relationships between tables were messy—some were circular, some used JSON blobs instead of normalized data.
- When they needed to add analytics, the schema couldn’t handle aggregate queries efficiently.
In their words: “I thought I was saving time. Instead, I spent months rewriting my schema so new features wouldn’t break everything.”
Why it happened:
- The database was scaffolded by AI without oversight.
- The founder didn’t visualize or document the schema early on.
- Prototype-first thinking (“ship now, fix later”) created technical debt that became unavoidable.
How they fixed it:
- They mapped out an entity-relationship diagram (ERD) for their main models.
- They normalized tables, added indexes, and documented model purposes.
- Future features (like payments) were planned in advance, so the schema was designed to accommodate them.
Lesson:
Even if you’re moving fast, you need to own your database models. Spend one extra day designing your schema—it will save you weeks (or months) of pain later.
What You Can Do Today
If you’re serious about vibe coding without getting burned:
- Audit your schema. Draw an ER diagram of your tables and relationships. Ask yourself: does this design support the next few features I plan to build?
- Pick a good starter kit. Choose one with built-in auth, secure defaults, and a design system.
- Adopt a component library. Don’t reinvent UIs—reuse, adapt, and keep things consistent.
- Treat security as part of the build. Every new feature should trigger the question: who should be able to access this, and how do I enforce it?
- Build in small loops. Ship one feature, test it, secure it, then move on. Don’t try to scaffold everything at once.
- Find your community. Don’t go it alone—share progress, ask for reviews, get unstuck faster.
Final Thoughts
Vibe coding is powerful. It lowers barriers, sparks creativity, and helps people bring ideas to life faster than ever. But speed without fundamentals is a trap. If you ignore schemas, security, and structure, you’ll eventually pay the price—whether in rewrites, performance headaches, or exposed data.
The good news: with just a little intentionality, you can avoid those pitfalls. Own your models. Plan for security. Reuse components. Build incrementally. And don’t hesitate to lean on community or mentorship when you hit a wall.
That’s how you turn vibe coding from a fun experiment into a sustainable way to build real, secure, and scalable products.
Further Reading: Day in the life of a developer working at a startup
