What's Great about Julia?
Everytime Julia is mentioned on HN, I see a surprising amount of people who dislike the language for whatever reason. I understand a lot of their concerns (time to first plot, debugging sucks, IDE support isn't amazing yet). However, I don't see a lot of alternatives for the niche that Julia occupies. If you want to do high performance scientific computing, there aren't a lot of options in terms of languages.<p>Python is the de facto but it's so slow for anything that can't be well represented as vectorized NumPy operations. There are ways around that like Numba, Jax, Cython, etc. but their use cases are pretty limited, and they don't work well with other Python packages.<p>There is, of course, C and C++ which are commonly used to speed up Python or as standalone packages. However, C++ is such a complicated beast that writing performant and correct code takes forever. C is much more manageable, but I find that there are not a lot of scientific packages written in pure C. This isn't even touching on the horrendous build system that is CMake.<p>Fortran is a pretty simple looking language and would probably be the closest to Julia in terms of speed and expressiveness, but the writing is on the wall and Fortran's days are numbered. I am not aware of many new packages being developed using it.<p>Other than that, there's languages like Rust and Go but those have ecosystems that are so small, they make Julia's ecosystem look like Python's. I really don't want to spend my time as a grad student writing basic numerical libraries from scratch.