For a long time, running NumPy in the browser meant running it without an accelerated BLAS. Matrix multiplications fell back to plain loops (portable, but blind to cache and SIMD). That just changed. The Emscripten-forge NumPy package now links OpenBLAS in WebAssembly, and at n = 1024 square np.matmul jumps to about 30.92× faster ( float32 ) and 14.90× faster ( float64 ). The next OpenBLAS release, already available as an experimental package on Emscripten-forge, with kernels contributed by QuantStack, pushes it further, and an optional Relaxed SIMD build adds another step on engines that support it. What is Emscripten-forge? ​ Emscripten-forge is a software distribution for WebAssembly. Together with conda-forge , it rebuilds the conda ecosystem for WebAssembly, so compilers, runtimes, and shared libraries ship as redistributable conda packages with a coherent ABI (not Python wheels, not R packages, but native libraries that any language ecosystem can link).…