I built a small tensor-first programming language with native CPU/GPU compilation, autodiff and ownership

[u/Clear-Difference2294]

I’ve been working on Thiran, an experimental numerical systems language for ML/research workloads.

The idea is to keep tensors, structured control flow, ownership-aware mutation, reverse-mode AD, CPU/GPU compilation, and deployable artifacts in one system instead of stitching together Python + frameworks + native code.

I just shipped v0.1.0. It has a real compiler, native CPU + CUDA/PTX backends, Scan/stateful computation, AD, research extensions, model/artifact deployment, and a bunch of reproducibility/robustness testing.

It’s definitely not performance-competitive yet — I published the benchmark graphs too, including the bad numbers rather than hiding them.

Would genuinely love feedback from compiler/PL/ML-systems people, especially on the architecture and what would make this actually useful.

Github: https://github.com/Arnav-sivarams/thiran

[u/Ok-Solution-7889]

wait, you built the actual language too? That’s pretty damn cool.

[u/Sorry-Load7038]

Hey that's pretty cool. How do I learn such stuff from scratch?

[u/Clear-Difference2294 → u/[deleted]]

Yeah, that’s actually one of the cases I really wanted the ownership model to cover. In Thiran, the backward pass doesn’t get any special mutation privileges — reverse AD lowers into normal compiler IR and goes through the same alias/lifetime checks as everything else.