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

[quantbagel]

I saw a tweet that tinygrad is so good that you could make a graphics library that wraps tg. So I’ve been hacking on a gtinygrad, and honestly it convinced me it could be used for legit research.

The JIT + tensor model ends up being a really nice way to express light transport all in simple python, so I reimplemented some new research papers from SIGGRAPH like REstir PG and SZ and it just works. instead of complicated cpp its just a 200 LOC of python.

[nl]

Why is this a fork of tinygrad and not just something that imports it?

[sixtyj]

Because forking is new coding /s (What we see is natural entropy of systems. Wannabe codies fork a repo… and instead of contributing to original one they make their own copy. What will happen if you repeat this a few times? ;)

[Keyframe]

That is a legit way of working on contribution. You fork, you work on the fork - if it's not junk then you issue a pull request. What's the deal with belittling and holier-than-thou moralizing?

[sixtyj]

I have nothing against forking ofc. I like it. But I really don’t like laziness when there is no contribution to original project - instead those codies make the project as their, in fact it is just a (poor) fork. The result is the mess. My first comment was about this behaviour.

Forking is nice when it’s nice.

[quantbagel]

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

[nl]

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?

[sxp]

Claude didn't follow your "Every line must earn its keep. Prefer readability over cleverness. We believe that if carefully designed, 10 lines can have the impact of 1000." from https://github.com/quantbagel/gtinygrad/blob/master/AGENTS.m... given how bloated this demo is.

https://blog.evjang.com/2019/11/jaxpt.html is a better demo of how to render the Cornell Box on a TPU using differentiable path tracing.

[hackernudes]

The agents.md is from the upstream tinygrad repo: https://github.com/tinygrad/tinygrad/blob/master/AGENTS.md

[nomilk]

> Never mix functionality changes with whitespace changes.

Whoa.. the cursor rule I didn't know I needed!

[suhacker256]

so cool! id love to read a blog post about this.