High speed graphics rendering research with tinygrad/tinyJIT
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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.
HN · launched Jan 22, 2026 · 31 upvotes (top 28%) · 0 views
Niche: graphics rendering engines and libraries · 149 launches, 10 in the last 7 days ▼ -23% · Explore this niche →
Category and tags
- Horizontal
- Dev tools
- Developer
- AI feature
- Hobby / open-source project
Visit website ↗ Source listing ↗
More like this: all 149 launches in graphics rendering engines and libraries →
Could you build this?
No Writing a custom real-time graphics and light-transport renderer on top of tinygrad's JIT and tensor primitives requires deep mathematical knowledge of ray tracing, shader compilers, and GPU compute kernels.
What it would actually take: The project requires implementing Monte Carlo path tracing and light transport algorithms directly using tensor operations, hooking into tinygrad's intermediate representation (UOps) and tinyJIT compiler. A developer needs specialized knowledge in computer graphics theory, radiative transfer equations, and low-level GPU compilation pipelines.
Discussion
10 comments analyzed.
Competitors mentioned: TypeScript (ts) for light transport implementation, JAX for differentiable path tracing
Concerns raised: Fork instead of importing tinygrad as dependency, No modifications to tinygrad itself, just a copy, Demo is bloated and doesn't follow stated design principles, Proliferation of forks without contributing back to original project
Feature requests: Blog post explaining the approach
Competitors
Other products that read as similar to this one : 426 launches clear the similarity bar, closest 8 shown.
Attention rank: #153 of 427 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 85 days after the earliest competitor.
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