Missing: write static python and transpile to rust pyO3 which is at the top of the ladder.

Some nuance: try transpiling to a garbage collected rust like language with fast compilation until you have millions of users.

Also use a combination of neural and deterministic methods to transpile depending on the complexity.

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zahlman 4 hours ago | parent | next [-]

> a garbage collected rust like language with fast compilation

I don't know what languages you might have in mind. "Rust-like" in what sense?

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adsharma 13 minutes ago | parent | next [-]

It's not a popular thing to say on social media.

V-lang is the one I'm tinkering with. It's like rust in terms of pattern matching as an expression, sum types, ?T instead of exceptions.

Like golang, it has shorter compile times.

I try to keep my argument abstract (that you need to lower python to something intermediate before rust) for that reason.

	▲	adsharma 11 minutes ago \| parent [-]
		Here is a python AST parser written in V. It's targeting a dialect that's mostly compatible with a static subset of python3, but will break compatibility where necessary. In this case pattern matching, possibly elsewhere. https://github.com/py2many/v-ast

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pjmlp an hour ago | parent | prev [-]

Probably OCaml, Standard ML, Haskell, MLton, F#, Scala,....

If going to complain about some of those being slow, remeber that they have various options between interpreter, bytecode, REPL, JIT and AOT.

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tda 4 hours ago | parent | prev [-]

One thing with python is that usually I will use one of the many c based libraries to get reasonable speed and well thought out abstractions from the start. I architect around numpy, scipy, shapely, pandas/polars or whatever. So my code runs at reasonable speed from the start. But transpiling to rust then effectively means a complete redesign of the code, data structures, algorithms etc. And I have seen the AI tools really struggle to get it right, as my intent gets lost somewhere.

So what I do now (since Claude Code) is write really bare bones (and slow) pure python implementation (like I used to do for numba, pypy or cython ready code), with minimal dependencies. Then I use the REPL, notebooks and nice plotting tools to get a real understanding of the problem space and the intricacies of my algorithm/problem at hand. When done, I let Claude add tests and I ask it to transpile to equivalent Rust and boom! a flawless 1000x speed upgrade in a minutes.

The great thing is I don't need to do the mental gymnastics to vectorize code in a write only mode like I've had to do since my Matlab days. Instead I can write simple to read for loops that follow my intent much better, and result in much more legible code. So refreshing!

And with pyO3 i can still expose the Rust lib to python, and continue to use Python for glue and plotting

	▲	adsharma 5 minutes ago \| parent [-]
		Cython and all the libs you mention use the c-api, which is the #1 thing python needs to lose to be competitive. I wish someone writes a stdlib without using it. My attempt from a few months ago in a repo under the py2many org.