Vibe Coding Built My App in an Hour. Then I Tried to Fix It.

I built a working app in 47 minutes without writing a single line of code. Then a button stopped working, and I spent three hours trying to figure out why. That’s vibe coding in a nutshell — and if you’ve been anywhere near AI Twitter lately, you’ve heard the hype. “I built a SaaS in a weekend!” “Non-coders can ship now!” “The future is here!” All of that is technically true. What they don’t tell you is what happens on day two.

What vibe coding actually looks like

The first time you try it, it feels like magic. You open Cursor or Replit Agent or Bolt, describe what you want in plain English, and watch code appear. A few prompts later, you have a working app. Buttons click. Data saves. It looks like something you’d actually use.

I wanted a simple habit tracker — nothing fancy. Add habits, check them off daily, see a streak counter. I described it to Cursor, and within 40 minutes I had a React app with local storage, a clean UI, and animations. I was genuinely impressed. I showed it to friends. I started thinking about monetization.

Then I tried to add a feature: weekly email summaries. That’s when everything fell apart.

The wall you hit

Here’s what nobody warns you about: vibe coding is a one-way street until you learn to read the map. The AI generated my app’s code across 15 files. I understood maybe three of them. When I asked Cursor to add the email feature, it modified eight files, broke the streak counter, and changed the data structure for habits without telling me. The app still ran — it just silently stopped tracking anything new.

This is the pattern. AI-generated code doesn’t break dramatically. It breaks quietly. A state management change here, a data flow assumption there. The app looks fine until you notice your streak says 0 days when it should say 14.

The core problem is context. AI coding tools work within a window — they see the files you show them, not your entire project’s architecture. They make assumptions about data structures, naming conventions, and how components talk to each other. When those assumptions are wrong, you get bugs that are nearly impossible to debug if you can’t read the code.

I’ve written about this before in the mistakes I made so you don’t have to — the gap between “it works” and “I understand why it works” is where things get expensive.

What I learned (the expensive way)

After that first disaster, I spent a week talking to people who actually use AI coding tools professionally. Here’s what they told me.

Commit after every working change. This is the single most important habit. Before you ask the AI to modify anything, make sure your current version is saved and working. That way, when the AI breaks something (not if — when), you can roll back to the last good version. Git isn’t just for developers anymore.

Ask the AI to explain before it changes. Instead of “add a weekly email summary,” try “explain how the streak counter works and what files it touches.” Understanding the architecture before modifying it prevents the silent-breakage problem. This is slower. It’s also the difference between building something you own and building something that owns you.

Test one change at a time. I made the mistake of asking Cursor to add three features at once. When something broke, I had no idea which change caused it. Now I do one thing, test it, commit it, then move to the next. It takes longer. It also actually works.

Read the error messages. I know — they look like gibberish. But AI can explain errors just as well as it can write code. Copy the error, paste it into ChatGPT, and ask “what does this mean and how do I fix it?” You’ll be surprised how often the fix is one line.

The tools that handle this better

Not all AI coding tools are equal when it comes to the “and then I tried to fix it” problem.

Bolt and Lovable are better for true beginners because they generate simpler codebases — fewer files, less abstraction, easier to understand when something breaks. The tradeoff is they can’t build as complex an app.

Cursor is more powerful but assumes you can read code at least at a basic level. Its Composer feature is great for multi-file changes, but it’s also where silent breakage happens most.

Replit Agent sits in the middle — it handles deployment and testing automatically, which catches some bugs before they reach you. But it’s also more opinionated about how your app should be structured.

If you’re choosing, I wrote a full breakdown of the 7 AI tools I’d learn first — including which ones are best for non-coders who want to ship real things.

The uncomfortable truth

Vibe coding works. It genuinely does. But it works the way a power tool works — incredibly well when you understand the basics, dangerous when you don’t. You wouldn’t use a table saw without learning how wood grain works. You shouldn’t use AI coding tools without learning how code structure works — at least at a conceptual level.

The people posting “I built a SaaS in a weekend” aren’t lying. But they’re also not telling you about the 20 hours they spent debugging after that weekend, or the fact that they had to learn what a state variable is before they could fix their app’s data persistence issue.

I still vibe code. I’ve even figured out how to make AI output actually useful instead of generic. I do it almost every day now. But I do it differently — with version control, incremental changes, and enough understanding of the generated code to know when it’s about to break. The magic is real. You just have to respect the tool.

The bottom line

If you want to try vibe coding, start with a small project, commit constantly, and learn to read error messages before you need to. The barrier to building has never been lower — but the barrier to maintaining still exists. For more on getting started with AI tools the right way, check out /start-here/.