[maxnoe]

Numba essentially does the same as julia, compile to llvm bytecode, in julia, that's a language design decision, in python it is a library.

You can get very far with these approaches I python, but having these at the language level just has more potential for optimization and less friction.

The debugability of numba code is very limited and code coverage does ot work at all.

Having a high level language that has scientific use at its core is just great.

Python has the maturity and community size on its side, but Jul is catching up on that quickly.

[spenczar5]

I agree that numba's JITted code needs debuggability improvements. I've been working on getting it to work with Linux's perf(1) for that reason.

The Julia-for-astronomy community is just microscopic right now, so it's hard to find useful libraries. Nothing comes close to, say, Astropy[0].

I'm not a huge fan of the current numpy stack for scientific code. I just don't think anyone should get too carried away and claim that Julia is taking the entire scientific world by storm. I don't know anyone in my department who has even looked at it seriously.

[0] https://www.astropy.org/