post-rfc VS Exercism - Scala Exercises

That's what it's best for, but personally I use it for everything. If I ever get into low-level code I'll probably use Rust though.

You can confirm that parsers/tokenizers is ranked "best in class" here though:

https://github.com/Gabriella439/post-rfc/blob/main/sotu.md

Note that this is ported from here: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md which comes with more exposition.

State of the Haskell Ecosystem

According to State of the Haskell ecosystem, Haskell is THE language of choice for implementing compilers, and THE language of choice for writing parsers. Thus, it is not surprising to see more Haskell projects from those particular categories than from other categories.

This is great for understanding what libraries to use in the Haskell ecosystem: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md

In particular, I got comfortable reading hackage documentation to understand quickly how to use libraries (aeson, megaparsec, mtl, pipes, etc), got comfortable with the ecosystem (this helped: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md), got comfortable with the main language idioms and features (https://smunix.github.io/dev.stephendiehl.com/hask/tutorial.pdf) and got comfortable with simple things that for some reason had confused me before (case, \case, let).

Gabriella Gonzalez has a great doc that is reasonably up-to-date, sounds similar to what you're looking for? https://github.com/Gabriella439/post-rfc/blob/main/sotu.md

With that being said, Python is without a doubt the best option, and I'd also be very interested to read the articles you found that say that Python is not a good choice because it's been the industry standard for a long time now. Data science and machine learning are one of the areas where the Haskell ecosystem is not as strong as other languages, but libraries and tools do exist. There's a great list of Haskell resources by domain here, and as you can see, there are Haskell bindings to tensorflow and pytorch, along with other libraries that support common data science programming.

Exercism

Nice, this reminds me of Exercism, which I wish was more widely known since they seem to be good folks. (disclaimer, I donate to them)

https://exercism.org/

Exercism, the free programming learning platform has initiated a challenge named: 48in24.

Last year, Exercism put together the #12in23 challenge. The goal was to learn a new programming language each month throughout the year. I was one of 135 people who completed the challenge, and I learned a lot along the way!

The list of languages contains every language on Exercism, excluding ones that I've used before, web languages, or ones that I can't download for some reason.

You might like https://exercism.org/

Learning by doing, with the help of mentors. Excellent way to learn a next language (as you are already familiar with the programming concepts).

Exercism is pretty good for beginners with some programming language, they are open source and worth contributing to.

Exercism

> it might be more sustainable if courses were stored in a version controllable medium to facilitate multiple collaborators

My initial thought was to actually use GitHub to store the content. Either on Markdown or JSON - to have some version control. I like how Exercism [1] does it. But I thought it would be hard for teachers - unfamiliar with Git - to update lessons.

Then, I thought about implementing a version control system for the project but I felt I was overcomplicating things for an MVP. But I like the idea of having some kind of version control to improve collaboration.

[1] https://exercism.org/