Avoid making multiple attempts at bug fixes without resetting, as this can lead to layers of unnecessary code. Instead, reset and start again to maintain a clean codebase.

Use version control religiously, like Git, to manage your code. Always start with a clean slate before adding new features to ensure you can revert to a known working version if needed.

Don't worry about scaling your code initially; instead, prioritize getting in front of users and validating your product.

Start vibe coding by crafting your test cases first, then let the LLM generate the code based on those tests, ensuring quality control.

Monitor the LLM's responses closely; if it keeps generating incorrect code, reassess the context you're providing to improve its output.

If you're unsure about a bug, try switching models. Different AI models may succeed where others fail, and if you find the source of a bug, reset all changes and give specific instructions to fix it on a clean codebase.

Frequent refactoring is essential once the code is functional and tests are in place, as it helps maintain a clean and manageable codebase.

An iterative approach is essential in working with AI; continuously debugging and refining prompts can lead to more reliable outcomes.

The vibe coding approach, where you code without fully understanding the code, doesn't work well in complex projects with many contributors.