MicroHs VS hy

> expand into lambda calculus statement that could then be compiled down to different sets of combinators

This approach can be reasonably efficient for implementing Haskell, as shown in [1] and the much more concise [2].

[1] https://github.com/augustss/MicroHs

[2] https://crypto.stanford.edu/~blynn/compiler/

Not exactly the same (doesn't embed into the source like this did), but I believe Hylang[0] is the best Lisp package available for modern Python.

[0] https://github.com/hylang/hy

Isn't that a bit what hy (https://hylang.org/) tries to do ? AIUI it is a lisp interacting directly with the AST of Python, allowing seamless interop: Python modules can be used from hy and vice versa, everything is transparent.

I’ve been keeping loose tabs on this and Hy[1] for a while, but I’ve had some trouble figuring out the major differences between them and the use-cases for either. Would love to see an in-depth comparison in the form of a blog post sometime (though maybe the answer here is to do the research and write one up myself).

1: https://hylang.org

“Python is for scientists. Lisp is for engineers.”

Then what does that make Hy language?

https://hylang.org/

Re Languages with lots of example code and LLM’s

With translators or things like Hy lang, one could get the LLM’s to solve your problem in Python before converting it to another form. Then, you just need a translator. If lacking one, it’s easy to translate by hand.

The practicality of this concept will probably vary by use case. My experiments had GPT doing sketching, implementations, boilerplate, and even porting Python to Rust. A legally-clear LLM trained on multiple languages could probably be fine-tuned to do Python to LISP conversions. If not, Hy might be a stepping stone, too.

You could say so: I've been maintaining the compiler since 2016 ;). Infinitesimal Quest 2 + ε (SQ) exists more to advance Hy than for its own sake.

If you are using Python - you might find Hylang (https://hylang.org) interesting.