I built a deep learning framework in Rust from scratch — Part 3: the road to crates.io

In Part 1 I argued why a graph-based DL framework in pure Rust was a project worth doing. In Part 2 I wrote the GPU backend on wgpu and figured out how to make TransformerBlock train on it. Both posts ended with the same honest admission: the code was fine, but the project wasn't ready for other humans. This post is about closing that gap. Six phases of work, a v0.2.0 → v0.3.1 bump, and a crate that now looks like something you'd actually reach for. Here's the plan I committed to at the start: Phase 1: cleanup and consistency - get to 0 warnings.