Brothers. Found a great open-source project that teaches you how to do vibe coding. It already has over 15,000 stars on GitHub.
The most interesting part of this project is that it uses a philosophical framework to explain programming, divided into four levels: Dao, Fa, Shu, and Qi.
Dao is about thinking clearly about what you want to do first, ensuring each step has a clear direction.
Fa is about architecture-level matters - modules should be independent, interfaces should come first, and overall structure should be clear.
Shu focuses on the debugging phase. The core idea is to compare expected results with actual results, then have AI target and fix the issues.
Qi refers to tool combinations - how to optimize the pairing of IDE, AI models, and various auxiliary tools to their best state.
Beyond this methodology, it's also very solid in practical implementation. The project comes with a built-in prompt library and reusable skill modules that you can use right out of the box. The context is fixed, so AI won't forget the project background as the conversation goes on. The code uses modular design - clean and easy to maintain. There's also a recursive optimization mechanism where the generator handles output and the optimizer handles refinement. The more you use it, the better it works.
The overall approach is to transform AI from randomly generating code into a stable, usable development partner that gets more seamless the more you use it.
Brothers. Found a great open-source project that teaches you how to do vibe coding. It already has over 15,000 stars on GitHub.
The most interesting part of this project is that it uses a philosophical framework to explain programming, divided into four levels: Dao, Fa, Shu, and Qi.
Dao is about thinking clearly about what you want to do first, ensuring each step has a clear direction.
Fa is about architecture-level matters - modules should be independent, interfaces should come first, and overall structure should be clear.
Shu focuses on the debugging phase. The core idea is to compare expected results with actual results, then have AI target and fix the issues.
Qi refers to tool combinations - how to optimize the pairing of IDE, AI models, and various auxiliary tools to their best state.
Beyond this methodology, it's also very solid in practical implementation. The project comes with a built-in prompt library and reusable skill modules that you can use right out of the box. The context is fixed, so AI won't forget the project background as the conversation goes on. The code uses modular design - clean and easy to maintain. There's also a recursive optimization mechanism where the generator handles output and the optimizer handles refinement. The more you use it, the better it works.
The overall approach is to transform AI from randomly generating code into a stable, usable development partner that gets more seamless the more you use it.
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