tvmv VS post-rfc

https://github.com/keithfancher/tvmv (or check out the quick-start to get the rough idea)

For example, I wanted to build a tool to pull down TV episode data from an API and bulk-rename files. Super IO-heavy, figured it would be a P.I.T.A. to write in Haskell. But I did it anyway, haha. And it was fine! Fun, even.

And now I'm working on something a bit more complex: lots of IO, filestystem stuff, API calls, etc. It's basically a FileBot replacement (tentatively) called tvmv. And after that, I kinda want to try a small game.

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).

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.

State of the Haskell Ecosystem lists libraries for many of its domains.

Recently saw this repo that shows the state of Haskell in a lot of programming applications. It classifies it as Immature for Numerical Programming; I would like to know what are the challenges in it.