Firstly, not everyone doing performance-sensitive work is doing numeric work (that seems to have been a motivator for writing this article), so numpy isn't alwa

Firstly, not everyone doing performance-sensitive work is doing numeric work (that seems to have been a motivator for writing this article), so numpy isn't always practical. Secondly, I think the "if it's slow just rewrite the hard parts in C" is generally out of step with more modern options. Python gets you a really nice environment that's super pleasant to work with... until suddenly it doesn't and you're backed into a performance corner, and then you potentially need to conquer a major learning curve, take a huge usability hit, sacrifice memory safety, etc. There are increasingly common options now that let you get near-C performance for many applications while also totally avoiding that cliff. For applications that have even a decent chance of eventually needing that kind of work, I think it's reasonable to ask why you'd want to chance it when you could just write it in something both reasonably fast and much more pleasant than C from the get-go.