TL; DR: There are a lot of great and nuanced features of Julia, but if you're writing off Julia without understanding the potential power of "multiple dispatch", I'd really encourage you to read up on multiple dispatch and its benefits. Also, if your programming is "laughably mundane", watch out that a machine or machine-learned model doesn't take your job. I'm surprised this got upvoted to a 5. One of the aims of Julia is to 'solve' or 'avoid' the "two-language problem". Interestingly, you just embraced the two-language problem. You can call and use C-code from Python, but that's not necessarily simple. At that point, anything that is "fast" from a computational standpoint now needs someone who's expert at both languages to provide functionality. Most Julia packages provide "fast" code, while allowing any Julia user to help and fix things by learning Julia - i.e. the "one-language satisfaction" ? Julia code is generally "fast" while being very "expressive" - i.e. an aim at trying to avoid the 2-language problem. Crystal looks very interesting and is an "LLVM-language" like Rust and Julia, but I don't see any mention of "N-Dimensional Arrays". To quote another poster, "dealing with ND Arrays with for loops is like pulling teeth". Python has NumPy, but all the good syntax was taken by the time NumPy came along (mid-2000s), so the ND Array syntax can be quite cumbersome with Python, as much as I like the NumPy/SciPy stack. I've been working almost exclusively with Julia over the past 3 months. I'm parsing large-ish (~1GB) binary files generated by arduino boards and a lot of signal analysis work. I'm absolutely loving it, but Julia has been on my radar for a few years now. A few years ago I did a bunch of the Euler challenges with Julia while trying to write as little code as possible and that was a fun exercise to see how powerful some of the syntax is.