There is also numpy-ts[1] which is measured to be faster than numpy proper if you want to skip the webassembly.

[1] https://numpyts.dev/latest/performance/overview

Wouldn't AI rewrite of these legacy Fortran packages like BLAS be easier at this point than trying to keep Fortran alive?

Very cool too see. So happy we actually have SIMD in WASM

The dynamic-linking detail is the part I find most interesting. On PyPI, NumPy wheels vendor a BLAS snapshot, so a BLAS improvement means waiting for the next NumPy release. Here OpenBLAS is its own conda package, so a JupyterLite / notebook.link environment picks up 0.3.35 by adding a channel and nothing else changes. That's a real payoff of treating wasm32 as a conda platform instead of a wheel target.

Question for the authors: Appendix F puts 0.3.35 at roughly 0.4-0.5x of single-thread linux-64 on GEMM. How much of the remaining gap do you attribute to wasm codegen vs. the missing threads? And is a pthreads build on the roadmap at all? A lot of JupyterLite deployments are static hosting (GitHub Pages and the like) where you can't set the COOP/COEP headers SharedArrayBuffer needs, so I'd guess single-thread stays the default for a long time regardless.