Coconut VS mojo

> to be part of the Python ecosystem

I'd rather use Python if I'm in the Python ecosystem. So many attempts were made in the past to make a new language compatible with the Python ecosystem (look up hylang and coconu -- https://github.com/evhub/coconut). But at the end of the day, I'd come back to Python because if there's one thing I've learnt in recent years it's this:

    minimize dependencies at all costs.

Have you had a look at Coconut? I don't know if it'll push all your buttons but whenever I hear someone who's reasonably content with Python but wants more FP goodies I always think of it. https://github.com/evhub/coconut . It's basically a superset of Python3 that transpiles into Python3 and is compatible with MyPy. I don't think I'd code Python w/o it ever again assuming I had the choice. The biggest negative for me is that there's no IDE support for the language last I looked, though of course you can work with the transpiler output (plain Python) in your favorite Python IDE. It might be fun to play around with, I know that I really enjoyed it but then I got spoiled by the language+tooling of Scala3, but if you don't have that option ...

If you're looking for suggestions I want to put forward zig lang if you like C/C++ and Coconut Lang if you like Python!

F# and also the fun, compile-to-Python, functional language called Coconut.

https://github.com/modularml/mojo/blob/main/LICENSE Is this not it?

Interesting, but the intent seems similar to Chris Lattner's new Mojo language which arguably has similar characteristics and is further along in its development.

https://docs.modular.com/mojo/

Mojo is a programming language that combines the performance and control inherent in systems languages like C++ and Rust with the flexibility and simplicity of use typical of dynamic languages like Python. Because of its combination of performance, extensibility, and usability, its design makes it possible to construct high-performance systems, which makes it a good option for AI development.

If you take a look at the optimized Mojo code doing the matrix multiply [1], it takes an expert to understand. It’s not just some simple for-loops in Mojo they’re comparing against.

[1] https://github.com/modularml/mojo/blob/5ce18c47a27c0c4123de1...

Mojo truly unlocks a world of possibilities in high-performance computing.

For a concrete example of Liquid Haskell, see how Gabriella Gonzalez safely removed bound checks of high-performance protocol parsing, in "Scrap your Bounds Checks with Liquid Haskell" [1].

With Liquid Haskell, the bound checks are moved from runtime to compile time, semi-automatically handled by SMT-solvers. With static types, programmers can write correct programs faster, and the programs also run faster.

As an aside, speeding up programs with static analysis (constrained dynamism) are also present in Mojo (a variant of Python) or Swift [2].

[1]: https://github.com/Gabriella439/slides/blob/main/liquidhaske... "Scrap your Bounds Checks with Liquid Haskell"

[2]: https://github.com/modularml/mojo/discussions/466 "Mojo and Dynamism"