I know literally all programming languages.
Nothing here yet.
I definitely wanted .NET assembly. F# for .NET was also a maybe as that's where |> was introduced before Elixir popularized it. And there were a few .NET-based small languages. None of that happened for technical reasons. Piet looks fun, but I don't think I could a post write about it, it would probably require a lot of images with annotations on them, much more work than a day. Overall I was especially interested in esoteric languages with unusual computation model (AsciiDots, Whenever, Thue), not just unusual looks (Rockstar, Chef, LOLPython etc.) If Hashnode is not serving you well, this series is double-posted to dev.to as well . I don't think Ruby gems would just work in Crystal. It looks very similar, but everything except maybe some really trivial ones would take some adjustment.
7 languages in 7 weeks is a good start, 100 languages in 100 days is a bit much, and I don't think anyone else even attempted that. If you follow exercises from the book, you can even get a bit deeper than I was able to, due to space limitations.
I still use old style cygwin+sshd setup on my Windows gaming machine. I set that up long before WSL was a thing, and now I just copy the same setup to every new Windows machine I get, as it still works. Nowadays WSL is probably the way to go.
Yeah, Windows support is weak for most of the languages I review. Pretty much all of them are Unix-first. Ruby used to have good one-click installer with JRuby but they foolishly removed that, and now even that is a multistep process (find the right Java version etc.). Terrible decision.
Yes, this is a review of just the base language, not any additional tooling. As for particular points: According to the surveys, overwhelming majority of developers of most languages use language-independent tooling like VSCode, Jupyter, Vim, Notepad++ etc. , so I think it's reasonable to not review language based IDEs. Similar trend seems to be happening with data scientists , and RStudio lost more than half its use share from 2018 to 2021 according to Kaggle surveys. It would be more accurate for me to run these in a Jupyter environment than with VSCode and terminal like I did, I didn't do this as it would make formatting it for the post more difficult. The working theory for this series is that language that struggles a lot given very simple tasks will likely struggle even worse given more realistic tasks. This isn't really provable, and it might not work for some highly specialized languages (like let's say Verilog or WebGL Shader Language), but I think it's very reasonable for data science. According to yet another survey data preparation, which is basically a very large number of such simple tasks, is the most time consuming part of the job, so I think it's fair to see how well the language is doing on such tasks. And in the end, I rate languages relative to others in similar domain. So I'm not saying it's impossible to do a decent job with R, just that other languages (Julia and Python in this case) seem to be a lot better at the same thing.
I'm trying to balance showcasing the language with keeping the posts approachable. Your single expression fib makes sense for an introductory post, but the generator sequence would need a lot of explaining, as few languages have something like that.