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🚀 AI can help you build an MVP in days—but production-ready software still requires human judgment.
Over the past year, AI coding tools have completely changed the way developers build software. Whether you're using ChatGPT, Cursor, Claude, GitHub Copilot, or another assistant, it's now possible to go from an idea to a working prototype faster than ever before. That speed is exciting, especially for indie hackers, startups, and small teams trying to validate ideas quickly.
However, I've noticed a recurring pattern: the code that gets you to an MVP isn't always the code you should ship to production.
AI-generated code often contains subtle issues that don't become obvious until real users start interacting with your application. Duplicate logic, inconsistent naming conventions, weak error handling, unnecessary dependencies, security oversights, unoptimized database queries, and dead code can quietly accumulate throughout the project. Individually these problems may seem minor, but together they can impact performance, maintainability, and the overall user experience.
That's why I believe the final stage of any AI-assisted project shouldn't be adding more features—it should be cleaning up what AI has already generated.
To help developers and founders avoid common post-launch issues, I put together a practical guide covering 10 critical cleanup tasks I recommend completing before deploying an AI-built application. It's based on patterns I've consistently encountered while working with AI-assisted development workflows.
📖 Read the full article here:
https://medium.com/@thomasshellby.tm/vibe-coding-cleanup-before-launch-10-problems-you-shouldnt-ignore-b42406a93e02
I'd love to hear how others approach this. Do you have a checklist for reviewing AI-generated code before shipping, or have you learned any lessons the hard way? Let's share experiences and help build better AI-powered software together.
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