wangxiaoxiang55

I'm pretty new to this. I've built a few websites, but none of them have gotten much traffic yet.

My current understanding is that there are roughly two approaches:

1. Find new/emerging demand, such as new Google search trends, build early, and let users discover you through search.

2. Build for an existing demand, where competition is already strong, and actively promote the product and build a brand through Reddit, LinkedIn, YouTube, communities, etc.

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.