post-rfc VS rlua

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.

There's the rlua package for running lua in your rust app: https://github.com/amethyst/rlua

Some sys crates do embed the source files, like rlua for example.

[1] - https://github.com/amethyst/rlua/blob/master/examples/guided_tour.rs

Yes though, given that a CCleaner/BleachBit-style situation is going to be I/O-bound, I'd probably go with rlua, PyO3, or rust-cpython for plugins.

I wrote up some rust low-level bindings for guile [1]. The problem is that Guile liberally uses setjmp/longjmp, which breaks rust destructors. It might be possible to fix this by wrapping every call the way rlua does [2], but I'm not familiar enough with Guile to know.

1: https://github.com/ysimonson/guile-sys

2: https://github.com/amethyst/rlua/issues/21

And.. another binding finally called rlua https://github.com/amethyst/rlua

hlua or rlua are what you want for Lua, rust-cpython or PyO3 for Python, rutie for Ruby, and possibly deno_core for JavaScript.

Janet is what introduced me to Fennel. Conjure [1] sold me on Fennel over Janet. The Neovim community is rallying around Lua.

(And in fact, what sold me on Rust was its truly excellent Lua FFI support [2].)

[1]: https://github.com/Olical/conjure

[2]: https://github.com/amethyst/rlua