High speed graphics rendering research with tinygrad/tinyJIT
Details
- External ID
- 46714916
- Source
- HN
- Company
- —
- Product
- High speed graphics rendering research with tinygrad/tinyJIT
- Website domain
- github.com
- Launched
- Jan. 22, 2026
- Cohort
- —
- Upvotes
- 31
- Upvotes percentile
- 0.7200263504611331
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
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.
Enrichment
- Theme
- lightweight and on-device AI runtimes
- Vertical
- Horizontal
- Function
- Dev tools
- Audience
- Developer
- AI stance
- Not AI
- Project type
- Hobby / open-source project
- Normalized one-liner
- graphics rendering research
- Manually corrected
- False
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
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Attention rank: #75 of 214 (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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Other launches for this product
- No other launches for this product.
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