post-rfc VS purescript-native

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.

Before 2012, all of my websites were made using HTML, CSS and a sprinkling of JS. Then, I went all-in on AngularJS, followed by React. I started using Typescript and then PureScript and learned more frameworks like Halogen and Concur. I even wrote my own UI framework called purescript-deku.

PureScript native back-ends manage memory by reference counting too https://github.com/andyarvanitis/purescript-native/blob/cpp/README-cpp.md

I hope you get a chance to try PureScript out in your JS projects! For more learning resources, you can check out PureScript website.

Did you look into purescript-native, the purescript-to-c++ transpiler? Just curious what your reasons were to avoid that option.

Typeclasses are the big one I'd say. User defined operators are missing. Direct foreign function access is missing as well - for js stuff you have to communicate through "ports", you can't call js functions striaght from elm code. Also its not a general purpose language, you can't write a commandline tool with it or compile it to native code.

Another I thought of: Purescript (a haskell-like language that compiles to Javascript) has a native backend that compiles to either C++ or Go.

Purescript. A Haskell like language that is translate to Javascript. You can run it in NodeJS. There is also native versions that translate to Go or C++.