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quantbagel

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投稿

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1 ポイント·投稿者 quantbagel·3 か月前·0 コメント

Show HN: Robot Astral Projection

tryreflex.ai
3 ポイント·投稿者 quantbagel·4 か月前·0 コメント

[untitled]

1 ポイント·投稿者 quantbagel·5 か月前·0 コメント

Show HN: High speed graphics rendering research with tinygrad/tinyJIT

github.com
31 ポイント·投稿者 quantbagel·6 か月前·10 コメント

Gust a fast, rust based Swift package manager

github.com
2 ポイント·投稿者 quantbagel·7 か月前·1 コメント

Claude was used for a cyber campaign – Im making it open-source

trysonder.ai
1 ポイント·投稿者 quantbagel·7 か月前·1 コメント

Omniclip – universal clipboard free from the Apple ecosystem

github.com
2 ポイント·投稿者 quantbagel·7 か月前·1 コメント

コメント

quantbagel
·5 か月前·議論
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
quantbagel
·6 か月前·議論
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
quantbagel
·7 か月前·議論
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
quantbagel
·7 か月前·議論
near 300x speed improvement on warm build and resolved, parallelized installs, etc
quantbagel
·7 か月前·議論
Also made an abstraction for tryhackme, hackthebox and pwncollege. So you can solve machines straight from the terminal with hints.
quantbagel
·7 か月前·議論
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!
quantbagel
·7 か月前·議論
Sakana.ai improved on this by honing in on sample efficiency iirc with shinkaevolve (which is open source and not an ai slop project)