I think I may have missed what alternative is being recommended instead, after scrolling through the whole article.
I thing a lot of them used “rstudio” to browse the data.
https://github.com/scipy/scipy/issues?q=is%3Aissue%20state%3...
Scrolling through this list, it’s clear that many are “correctness issues.”
I do not link this to argue that scipy bugs are more serious or more frequent. I don’t think that kind of statistical comparison is meaningful.
However, I think a motivated reasoner could write a very similar blog post to the OP, but about $arb_python_lib instead of $arb_julia_lib.
I suppose my position is closer to “only Kahan, Boyd and Higham write correct numerical algorithms” (a hyperbole, but illustrative of the difficulty level).
Regardless, overall, these are grossly of another complexity and seriousness than the base sum function being just wrong, or cultural issues among developers with not verifying inputs "for performance", or things of that nature. The scientific Python community has, in my experience, a much higher adherence to good standards than that.
Yes, of course. I am not conflating the two.
> The scientific Python community has, in my experience, a much higher adherence to good standards than that.
Not in my experience. Nor am I defending Julia.