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User experienced AI reinforcing bugs, requiring manual intervention to provide a 'working commit' and reset AI's understanding. This points to a need for AI prototyping tools to better handle and correct persistent errors, possibly by allowing users to mark specific states as correct or providing better bug detection.
How I Build Prototypes Fast with Vibe Coding. Top 5 tips that I want to share: 1️⃣ Define your tech stack before you start. Spend a few hours selecting the most convenient and widely supported stack for AI development. Avoid weird and rare tech solutions, stick to common ones that everyone uses and AI knows best. 2️⃣ Keep requirements clean and clear. Focus on requirements, not code. Code will change and break, but requirements will stay with you until the end of the project. And skip code review of the prototype, just test for requirements manually. 3️⃣ Prioritize the core features. At the start, implement only what shows the concept works. Plus, at this stage, the context is still small, which makes AI work much better. 4️⃣ Commit as soon as you achieve results. When something works, commit it. Then ask AI for simplification or refactoring. If the refactored version is stable, commit again. If something breaks the prototype, just roll back to the last working commit. 5️⃣ Don't fight the AI. If AI isn't giving you the result, stop pushing on the same request. Start over or switch to another task for a while. Maybe later, there will be enough context for AI to complete the task. 🛠️ Final note: Once you have a working prototype, bring in a software developer to turn the MVP into a production-ready application. Don't rely on AI alone for that step.