post-rfc VS cxx

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 are cxx and autocxx, what else do you propose to do?

I would like to see a comparison of how this compares to Rust. In terms of interoperability it has Cxx (https://cxx.rs) to offer safe bindings to C++ but also has great support for Android, Linux and many other systems. You don't even need to hack together Windows bindings (as explained in the blog post) because Microsoft offers official bindings (https://crates.io/crates/windows). I'm not sure if I'd call it a superpower if any potential interoperability has to be written to be used (compared to it already being available). Or rather, in comparison to what is interoperability a Swift superpower? Certainly not C++ or C which can be used in a far wider set of targets.

We selected Qt as a cross-platform solution. The C++/Rust interface is the clunkiest and ugliest part of the application, and rather complex because some state is shared between several windows in the GUI and several threads in the backend, and any component might modify that state at any time, and updates have to be transmitted to the other components without introducing inconsistencies. Using cxx [1] helped a little, though.

The project began in 2020, and I'm not sure what I'd choose as a GUI framework today – definitely not Qt Widgets, though.

[1] https://cxx.rs/

If the build process for the C library isn't too involved I recommend using cxx bridge (https://cxx.rs/) and letting cargo handle the build and linking. cxx basically allows you to describe the bidirectional interface (although it sounds like you only need 1 direction, which is fine too) in Rust code and it provides a "good enough" API for compiling C code inside the build.rs file.

The tooling for the first kind -- calling Rust from another language -- is a bit less developed, and tends to rely on code generation that doesn't necessarily produce a natural C API. cbindgen, uniffi, cxx, and Diplomat all take this course.

I would like to utilize OMPL's functionality in Rust code, so I want to call into OMPL C++ code somehow in Rust. I've seen two (non-mutually-exclusive) options so far: - rust-cpp, which allows you to write C++ code in Rust within the cpp!() macro. - cxx, which allows you to define both sides of the FFI boundary manually (as opposed to bindgen's automatic generation).

I'm not sure how to do this in cxx; issues like https://github.com/dtolnay/cxx/issues/447 suggest that this isn't settled yet?

I use non-vendored dependencies for the Buck build in https://github.com/dtolnay/cxx.

There's also the cpp and cxx crates for doing C++/Rust interop, but they probably aren't appropriate to use in all cases. The C ABI is definitely the safest way to go unless you're really trying to marry Rust and C++ code bases, not just writing library bindings.

There's also cxx (can't vouch for it personally but it claims to make things a lot easier) https://github.com/dtolnay/cxx