Hi HN, I made a JIT compiler so that I can easily setup VLA experiments and iterate fast for my robot arms using a python interface but have cuda kernels and metal kernels in the background. I prefer the platform agnostic DX and the uv native simplicity whilst also gaining performance, Its a cool idea please give a few stars, im sure it will help the ecosystem

Well I wanted to implement light transport papers without having to deal with cpp. I think tinygrad, and more specifically tinyJIT are super useful abstractions. This is def not available in ts

My question was more why a fork instead of doing the conventional "import tinygrad" into your own project.

I don't think there is anywhere you are modifying tinygrad itself is there?

When I made a swift package manager as Rust rewrite I realized that the language wasn't the issue, design is a lot more important. Rust just gave a boost to everything else. you can try Gust here https://github.com/quantbagel/gust a lot better than using SwiftPM but there's room for improvement! Make issues with your ideas

near 300x speed improvement on warm build and resolved, parallelized installs, etc

Also made an abstraction for tryhackme, hackthebox and pwncollege. So you can solve machines straight from the terminal with hints.

works iOS->linux, windows, Mac. Android->linux, windows, Mac, and vice versa. open source, feel free to add issues, star or check out some if my other projects!

Sakana.ai improved on this by honing in on sample efficiency iirc with shinkaevolve (which is open source and not an ai slop project)

Yep, ShinkaEvolve described here: https://sakana.ai/shinka-evolve/ and available here: https://github.com/SakanaAI/ShinkaEvolve