After seeing dozens of vibe-coded projects up close, it’s clear the gap between prototype and production-ready product is wider than most think, and eventually impossible to ignore. Let’s explore why that gap happens, how experienced developers are invaluable when closing it, and I’ll illustrate with a real-world example from the world of Evil Martian open source.

As you probably know, LLMs have changed everything, and a new trend has emerged: vibe coding. You describe what you want, AI writes the code. No need to learn syntax or understand frameworks. Just explain your idea and watch it come to life.

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Have a vibe-coded project? Get experienced developers involved from the get-go.

And it works. People are building working apps in hours. Designers are creating functional prototypes. Founders are testing ideas before they hire a single developer. The barrier to entry has never been lower.

But here’s what I’ve noticed: there’s a gap between “it works” and “it’s ready for users”. A proof of concept is not a product. And the code that AI generates (while functional) often creates problems you won’t see until later.

This isn’t an attack on vibe coding. I think it’s genuinely useful. But if you plan to turn your vibe-coded project into something sustainable, you’ll eventually need a developer to clean it up.

Let me explain why.

Why AI produces code that is “mediocre by default”

AI models learn from the code that’s available online. All of it. This encompasses the brilliant code, the average stuff, and the terrible. Of course, statistically speaking, most code out there is average. Exceptional developers are rare, and most repositories contain “good enough” solutions written under deadline pressure.

So when AI generates code, it gives you the most common patterns. Not necessarily the best ones—the most common ones.

Take React: the popular pattern you’ll find online is putting business logic directly into components using hooks. Need to fetch data? useEffect. Need to calculate something? Put useMemo right there in the component. Again, it works. And millions of tutorials teach it this way.

But this approach doesn’t scale. Your logic becomes tied to React. You can’t reuse it elsewhere. You can’t test it easily. And when you need to optimize performance, everything is tangled together.

An experienced developer knows when to extract logic into separate layers. But AI doesn’t suggest this unless you explicitly ask. (And if you don’t know these patterns exist, you won’t ask.)

This leads to a bigger problem: it’s hard to get AI to use anything uncommon. If a solution isn’t everywhere on the internet, AI probably won’t suggest it. You have to know what to ask for.

Common problems in vibe-coded projects

After working with vibe-coded projects, I’ve noticed the same problems appearing again and again. None of them are fatal on their own. But together, they make the codebase increasingly hard to work with. Death by a thousand paper cuts.

#1: No clear domain model. Business logic ends up scattered across React components. Some calculations live in one component, related validation in another, and state management is spread across hooks everywhere. There’s no clear place where the system’s behavior is defined.

Want to understand how something works? Good luck—it’s everywhere and nowhere.

#2: Everything in one place. Logic, API requests, UI concerns are all packed into a few “god” components. These components do everything, which means any change risks breaking something unrelated.

#3: Copy-paste instead of reuse. The same logic appears in multiple places with small variations. When you fix a bug in one place, you have to remember to fix it in five others.

You won’t remember.

#4: TypeScript by casting. When AI hits a type error, it solves it the way an inexperienced developer would, by casting. The code compiles, but you lose what types are for: documentation, safety, and confidence when refactoring.

#5: Security as an afterthought. The project works, but it’s full of subtle vulnerabilities. Raw SQL strings. Missing input validation. Sensitive data in logs. These issues don’t break your demo, but they’ll break your production.

#6: Technical debt that blocks progress. All these quick decisions pile up. Eventually, adding a new feature or refactoring safely becomes slower and more expensive than it should be. Sometimes it’s cheaper to rewrite parts from scratch.

Case study: Harmonizer

I’ll share a real example from my work.

Anton Lovchikov, a designer on our team, vibe-coded the first version of Harmonizer. It’s a tool for generating accessible, consistent color palettes. The idea is simple: you define lightness levels and hues, and it generates colors with consistent contrast using the OKLCH color model and APCA formula.

As a proof of concept, it worked. Anton validated the idea, tested it with real palettes, and proved it was useful. This is exactly what vibe coding is good for.

The problem: When we tried to use it for real work, issues appeared. Recalculating colors for a 10×6 palette took 200 milliseconds. Every column hover triggered a full re-render of the entire grid; headings, color cells, buttons, everything. The interface felt laggy and unresponsive.

The thing is, it just wasn’t built for performance. All the logic lived inside React components. Every state change caused unnecessary recalculations. There was no separation between UI and business logic.

Fixing this required knowing what to do, and naturally, it’s not easy to explain to an AI how to do something if you don’t already know yourself.

Developer steps in: I extracted the logic into signal-based stores, which allowed fine-grained reactivity without re-rendering entire components. I moved the heavy color calculations to a web worker to offload the main thread. And I restructured everything into a core package that could target multiple platforms.

The result: 60fps rendering, no unnecessary re-renders on hover, and an architecture that’s easy to extend. This structure also made it possible to build a Figma plugin version later—something that would have been much harder with the original code.

The vibe-coded POC was valuable. It proved the idea worked. But turning it into a real product required developer experience. I didn’t need to rewrite everything from scratch, but I did need to know which problems to solve and how to solve them.

Going long term

Vibe coding isn’t bad. It’s just an incomplete process.

It’s a fantastic tool for the first phase of any project: testing ideas, validating concepts, building something you can actually try. Anton’s Harmonizer proof of concept did exactly that. Without writing code himself, he proved the idea was worth pursuing. That’s valuable.

But a POC is not a product. And the gap between them isn’t something you can vibe-code away.

It’s important to note that the problems I’ve described above don’t appear on day one. They accumulate. The longer you continue developing without cleanup, the harder they get to fix. It’s like a codebase written by a team of juniors with no senior oversight.

It works …until it doesn’t.

If you want to turn your vibe-coded project into something sustainable (something you can extend, maintain, and rely on) you’ll need experienced developers to help you clean up, restructure, and make the right architectural decisions.

Actually, if you want to turn your vibe-coded project into something sustainable, consider having an experienced developer involved from the start.

An experienced developer can plan the architecture, guide technical decisions, and review the code that AI generates to catch issues before they compound. Vibe coding validates; developers make it last.

Here’s the thing: LLMs are great tools for those developers too. Maybe even better for experienced devs than for beginners, because experienced devs know what to ask for. They have the taste, the patterns, the understanding of how things should be built.

So go ahead and vibe code your idea. Test it. See if it works. But when you’re ready to make it real—bring in someone who can clean it up.