Performance

If you list the tools that made Python feel fast lately, a pattern jumps out. The dataframe library everyone is switching to, Polars, is written in Rust. The linter that replaced flake8, Ruff, is Rust. The packaging tool eating pip and virtualenv, uv, is Rust. Pydantic v2 rewrote its core in Rust; Hugging Face’s tokenizers is Rust; orjson is Rust; even cryptography moved its guts to Rust years ago. Python isn’t going anywhere — it’s still the interface. But the engine underneath the performance-critical parts is quietly being rewritten in Rust. This post looks at that shift from two angles I’ve actually measured: Polars vs Pandas (the library you use) and PyO3 vs Cython (how you’d build such a library yourself). All numbers below I ran on one 8-core Linux x86-64 box. ...

Python is the most popular programming language in the world, and one of the slowest in wide production use. That combination should be catnip for anyone building a faster runtime — and for fifteen years, people have. There is a version of Python with a tracing JIT that is genuinely several times faster. There are compilers that turn it into native code. There are ports to the JVM and the .NET CLR. Big companies have funded forks. And after all of it, the interpreter almost everyone actually runs is still plain CPython. ...

I came across pon, a project that sets out to be “the bun/v8 of Python”: a from-scratch native compiler and runtime for Python 3.14, written in Rust, with no interpreter and no bytecode. The pitch is ambitious enough to be worth taking seriously — and the repository is unusual in another way. It was built in about a week: 475 commits from a single author between June 30 and July 7, with an AGENTS.md file and conventional-commit discipline that make it fairly clear the bulk was produced with AI coding agents. ...