neatroff VS pyodide

I recommend sticking with Groff. There are other implementations of Troff—some older (Heirloom Doctools), some newer (Neatroff)—but most of the documents you'll encounter in the wild will have been written with Groff in mind.

Plan 9 troff might work! It works with utf8 out of the box[0], and while I haven't used it for complex math typesetting, there is a command (eqn [1]) that was developed for it. I'd recommend Ali Rudi's port (neatroff [2][3]) for a minimalist implementation. There's also Heirloom Documentation Tools [4] which is an implementation of *roff-and-friends that uses Knuth's paragraph-at-once algorithm (instead of the original line-wise one) for typesetting, plus some other interesting features.

The authors of eqn wrote a paper about it: "Typesetting Mathematics" by Brian Kernighan and Lorinda Cherry. Kernighan also wrote two manuals (one in 1976 with a revision in 1992, and one in 2007 with updates for the Plan 9 version). [5].

[0] utf8 was developed by Ken Thompson and Rob Pike during the creation of Plan9. The entire OS is compatible. Story here: http://doc.cat-v.org/bell_labs/utf-8_history

[1] http://man.cat-v.org/plan_9/1/eqn

[2] https://github.com/aligrudi/neatroff

[3] PDF manual for neatroff: http://litcave.rudi.ir/neatroff.pdf

[4] https://n-t-roff.github.io/heirloom/doctools.html

[5] These (and more) can be found here: http://www.kohala.com/start/troff/troff.html

We implemented an in-browser Python editor/interpreter built on Pyodide over at Comet (our users are data scientists who need to build custom visualizations quite often, and the most familiar language for most of them is Python).

One of the issues you'll run into is that Pyodide only works by default with packages that have pure Python wheels available. The team has developed support for some libraries with C dependencies (like scikit-learn, I believe), but frameworks like PyTorch are particularly thorny (see this issue: https://github.com/pyodide/pyodide/issues/1625 )

We ended up rolling out a new version of our Python visualizations that runs off-browser, in order to support enough libraries/get the performance we need: https://www.comet.com/docs/v2/guides/comet-ui/experiment-man...

Thank you! Yes, one of the items in the Roadmap is support for Pyodide (https://github.com/pyodide/pyodide) for running in-browser python on the results of each of the code blocks! This should allow most ML libs to be usable in-browser! This is pretty high-up on our priority list.

If I understand correctly, WASM only makes sense for compiled languages, you can run the python interpreter in WASM of course[1], but that will be at a significant performance disadvantage to the native javascript interpreter, and it's also something that has to be loaded every time you load the website.

[1]: https://github.com/pyodide/pyodide

If you absolutely need something comparable to Sympy, then one option might be to figure out how to best call Sympy from Rust. e.g. - RustPython, although it seems like Sympy isn't supported yet - Pyodide, and figuring out how to run it outside of a web browser. Probably also not very easy. - PyPy, and having a pretty simple Python binary for every platform - ...

For running programming languages other than JavaScript in the browser there is Emscripten and WebAssembly. There is v86, where a Linux build is compiled to WASM. Folks have written QuickJS into a Linux build compiled to WASM, Node.js into the Linux buildroot https://github.com/cemalgnlts/now, so Python or CPython can be written to the image and loaded into the browser as WASM as well https://github.com/pyodide/pyodide.

There's a Python runtime that runs on WebAssembly (https://github.com/pyodide/pyodide). I have no idea what it's like, I've never used it.

Still in a quest to provide some tooling to quickly compose documentation websites: https://github.com/synw/docdundee . As I have tons of libs to document and was tired of managing restructured language for readthedocs I started with this, and now it has executable Python examples in the frontend via a Pyodide wrapper composable: usePython

This project's brand new and a lil hacky, but I've already reached out to the scikit-learn team, and they recommended that I experiment with using Pyodide as an alternative backend for the Python bridge.