I spend a lot of my time rescuing vibe coded apps that started out as a useful personal project and ended as a mentally draining production problem. The demo works and the landing page looks great. But when real users start to sign up, suddenly things start to break.
I’ve observed that the patterns are almost always the same. The app was built fast, but nobody built the safety rails around it. Yes, AI can write a working feature in minutes, but it cannot know your deployment setup, your users, or the mistakes it made three prompts ago.
After fixing enough of these projects, I’ve learned that most of them fail for the same reasons.
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Missing development environment
No save point before the AI changes something
One of the biggest issues I have when working on these projects is that I don’t even have a way to download the source files. Clients have to manually send me folders, and sometimes they are not sure where those folders are.
When I do get the project files, I often find everything crammed into one folder, or spread across so many folders that I cannot tell what belongs where. The tools the app needs may be installed all over the client’s computer, which means I have to manually recreate their setup on my machine.
An easy fix is to ask your AI to create a virtual environment in a specific folder. Tell it to install all future dependencies there, keep track of them, and record the exact versions it uses. Whether you use Claude Code or other alternative agentic coding services, they should be able to handle this without a problem.
Pair that with version control, such as Git. GitHub is optional, although backing up your project online is a very good idea. Git is the non-negotiable part because it records every change your AI makes, lets you roll back a disaster, and shows you exactly what changed.
If an AI hallucinates and wrecks your codebase, a clean commit history is your undo button.
The workflow is simple. Whenever the AI fixes a bug or adds a feature, prompt it to “commit all changes”. Before it starts a large experimental feature you’re not sure will work, prompt it to “make a new branch and implement the feature there”. If something goes wrong, prompt it to “switch back to the main branch and revert the experimental changes”. Once you have tested the feature and confirmed it works, prompt it to “merge the changes into the main branch”.
That tiny habit gives you a safe version of your app, a place to test risky ideas, and a way back when an AI-generated change goes wrong.
3
No logging and poor error handling
Your app will have bugs, and you will need to know what caused them
No matter how carefully you test an app in development, it will still have bugs. You can review every prompt, click through every page, and think you caught everything. Then a real user uploads a strange file, loses internet access, or registered an unusual username, and suddenly something breaks.
Once real people start using your product, new failure modes and edge cases show up constantly.
That is why logging matters.
A useful log tells you what happened, when it happened, and what error message appeared. Without it, you are guessing why the problem happened. But with logging you can see that a feature failed because a rare race condition happened when a client pushed a button at exactly 2:30 in the early morning.
Good error handling matters just as much. Don’t show users a raw stack trace or database error. Catch the failure, show them a clear message, and keep the the detailed technical logs private.
Prompt the AI to create a logging system before you build your tenth feature. Add logs for failed requests, login problems, database errors, and third-party API calls. Then test failure on purpose. Disconnect the database, enter bad input, and expire a token. If your app cannot explain its own failure, it is not ready for users.
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Security is an afterthought
Security is where vibe-coded apps fail most often
Security is where vibe-coded apps fail most often. I once reviewed an app where the owner had hardcoded their OpenAI API key into the project. Nothing had happened yet because it was not public on GitHub, but anyone who found it could have used their credits for free.
I did not find it because I was running a sophisticated security audit. I found it because I opened the project folder and saw the key sitting in a config file.
API keys, database passwords, and access tokens should never sit in your code, screenshots, browser files, or public repositories. Keep them in environment variables or a secrets manager instead. If you use GitHub, make sure your .env file is listed in the .gitignore file. If you accidentally pushed a secret before removing it, change that key immediately.
Once your secrets are protected, the much bigger question is who can use the app. Do not rely on a hidden button to protect admin pages, payments, or personal account information. Anyone can open the browser’s developer tools and send a request directly to your app.
Before launch, ask your AI to check that every private page and action verifies the user on the server, not just in the browser. Have it validate user input, use safe database queries, turn off debug mode, remove default passwords, and limit access to your database and cloud storage.
If possible, you’ll want to use the strongest frontier models you have. I’ve tried using local AI agents but they really struggle with agentic coding.
Also update your app’s tools and libraries, then scan them for known security problems. OSV-Scanner is a free option that checks the packages your app uses for known vulnerabilities.
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Feature creep with no tangible seams
Fast features need boundaries
Feature creep was already a problem before AI coding. With AI coding, it has become an even bigger problem. You ask for one feature, it arrives in a few minutes, and suddenly you think of three more worth adding. Then you add the nice-to-haves and repeat. A few days later, you have a messy app that breaks in unexpected places and leaves you stuck trying to remember how everything works.
The core issue is that you are building faster than you can understand the app. Every new feature gets attached to the last one. The part people see starts doing jobs it should not do, the same rules get written in several places, and different parts of the app start relying on each other in ways nobody planned.
That makes every change risky. Change one form field and three pages stop working. Fix one bug and accidentally change how payments work. This is where clear boundaries matter.
You don’t really need an enterprise architecture for a small project. But you do need enough structure that a new feature has an obvious place to live.
For example, keep user input validation at the edge, put business rules in a service, and let the database handle storage. If your app has roles, define them early. If it handles money, define the rules before the AI writes the checkout flow.
If you’re not sure about anything, there are AI apps made to answer your coding questions. You can even ask your coding agent to help you decide. What matters is that you read each recommendation, understand it, and think it through before accepting it.
Even a simple separation between the user interface, the app rules, and saved information can save you from a codebase where every file is connected to every other file.
Clear boundaries make the app easier to fix, maintain, and grow.
Build the rails, then vibe
I don’t want to talk anyone out of building with AI. I use it constantly, and it has never been easier to turn an idea into something real. But shipping an app means accepting responsibility for the parts users never see, such as logs, permissions, backups, and error messages.
Before you add another feature, spend an extra hour on the boring foundation. Set up Git, pin dependencies, add logging, secure your secrets, and create clear boundaries. Yes, those steps may not make your app viral, but they will make sure it is still running when people finally start using your app.