This article has been written a hundred times. "We abandoned a fast language for a language that is slow but can use fast libraries, and so the result is fast.

This article has been written a hundred times. "We abandoned a fast language for a language that is slow but can use fast libraries, and so the result is fast. It's faster because the programmer discovered existing libraries that do a better job of what they were doing already." There's so many convolved factors here I don't even know where to begin, so I guess I'll just say that I'm glad Julia exists. The author glosses over many decades of programming language and compiler research -- which makes sense, because this is not their specialty. However, what I see is the field of scientific computing migrating from a dinosaur language (Fortran isn't, actually. It just is used this way) and dinosaur practices of writing everything oneself, to one of the slowest interpreted languages that happens to be the most difficult to JIT, and saying this or that about how interpreted languages are slow but library calls are fast to justify this. At the same time they're learning to build a functioning library ecosystem. Basically, grad students are learning proper programming practices and collaboration after switching to a more expressive language, they just managed to pick the slowest and most difficult to optimize one. Maybe they just managed to wipe some of the slate clean by switching away from Fortran and its culture (the culture being the bad part), and the culture of Python filled the space, creating a net positive but somewhat unfortunate situation. Just one more time -- the idea that you can call a "faster" language to do the heavy lifting is true of every language and does not justify the choice of Python in particular. The justification for Python is the momentum, and this is in my opinion the only one.