I see your point but respectfully disagree with the conclusion. Comparing to numpy, not native python, would be the right move. Again, I don't write matmuls in

I see your point but respectfully disagree with the conclusion. Comparing to numpy, not native python, would be the right move. Again, I don't write matmuls in pure python. I do compute matmuls in numpy. I don't actually know whether numpy is 90,000x faster than pure python, so this benchmark is not useful for me. If they want to show all 3: mojo, numpy, and pure python, then that might be the best of all worlds. They could brag about being 90,000x faster than python, while at the same time showing the actual slowdown of using a pure python-like language (mojo) compared to a compiled numpy library. Let's say the mojo code ends up being 0.5x as fast as numpy; that would still be a pretty great tradeoff for being able to do it all in one language. If you're a python programmer and want to do something that isn't possible with the existing compiled libraries, this would be a good sell. To me, that's still the real comparison of interest.