Glowstick – type level tensor shapes in stable rust
Hi HN, In the past few years I've become more interested in machine learning. Since I'm sure the same is true for many here, I wanted to share this project I've been working on: glowstick uses type-directed metaprogramming to keep track of tensor shapes in Rust's type system and determine which operations are permitted or not at compile time. I find Rust has a lot of strengths when it comes to ML applications, but waiting until runtime to find shape related issues feels a bit strange since normally I don't run the code all that often while developing. Given Rust has fancy types available, I…
In plain words
Glowstick is a Rust library that uses type-level programming to track tensor shapes at compile time, enabling developers to catch shape-related errors before runtime. It integrates with machine learning frameworks like Candle and Burn, allowing developers to write ML code where tensor operations are validated during compilation rather than execution. This approach leverages Rust's type system to prevent dimension mismatch bugs early in development.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, In the past few years I've become more interested in machine learning. Since I'm sure the same is true for many here, I wanted to share this project I've been working on: glowstick uses type-directed metaprogramming to keep track of tensor shapes in Rust's type system and determine which operations are permitted or not at compile time. I find Rust has a lot of strengths when it comes to ML applications, but waiting until runtime to find shape related issues feels a bit strange since normally I don't run the code all that often while developing. Given Rust has fancy types available, I figured I'd try my hand at using them to address this. I've added integration crates for the two ML frameworks I use most frequently, candle and burn, and included examples of implementing llama 3.2 in each using typed shapes for much of the model internals and inference loop. Mixtures of static and dynamic dimensions should be supported well enough for most applications at this point, though there are of course still improvements to be made. Any feedback is appreciated!
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