Ask HN: I built an AI-native codebase framework–could you evaluate it?
I built this open-source project and would really appreciate technical feedback from people here:
https://github.com/xodn348/ai-native
The goal is to make AI-assisted development more reliable through clearer project structure, explicit contracts, and verification workflow.
I made this because applying these patterns from scratch in every project was repetitive and hard to maintain, so I wanted a reusable framework.
If you have time, I’d love your evaluation on:
1. What is useful in practice?
2. What is unclear or unnecessary?
3. What should be improved first?
4. What evidence/tests would you want to see for stronger credibility?
If you find it useful, I’d appreciate a GitHub star as well. I’m open to direct criticism.
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3 comments
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It's a waste of my time and yours.
Practical feedback:
1. The verification workflow is the most useful part. Most 'AI coding' frameworks focus on generation but skip validation. This addresses the actual bottleneck.
2. The project structure conventions feel heavyweight for small projects. Consider a 'lite' mode that only enforces contracts + verification without the full directory scaffold.
3. For credibility: add a before/after comparison on a real task. Show the same feature built with and without the framework — error rates, iteration count, final correctness. That'd be more convincing than any README.
4. The contract format could benefit from examples of failure cases — what does a bad contract look like, and what goes wrong downstream?
Interesting project. The structured validation angle has legs.