Charlie Marsh

Today, we're announcing our first hosted infrastructure product: pyx, a Python-native package registry. We think of pyx as an optimized backend for uv: it’s a package registry, but it also solves problems that go beyond the scope of a traditional "package registry".

Today, we're announcing our first hosted infrastructure product: pyx, a Python-native package registry. We think of pyx as an optimized backend for uv: it’s a package registry, but it also solves problems that go beyond the scope of a traditional "package registry".

The goal of pyx is to meet that growing demand and growing complexity.

Today, we're announcing our first hosted infrastructure product: pyx, a Python-native package registry. We think of pyx as an optimized backend for uv: it’s a package registry, but it also solves problems that go beyond the scope of a traditional "package registry".

pyx is also a good example of our strategy and philosophy. Our tools remain free, open source, and permissively licensed. Nothing changes there. Instead, we want to offer hosted services that represent the "natural next thing you need" when you're already using our tools.

Today, we're announcing our first hosted infrastructure product: pyx, a Python-native package registry. We think of pyx as an optimized backend for uv: it’s a package registry, but it also solves problems that go beyond the scope of a traditional "package registry".

Our deep focus on Python enables us to build better Python infra than anything else out there; and integration with our open source tools enables us to build experiences that otherwise wouldn't be possible at all.

Today, we're announcing our first hosted infrastructure product: pyx, a Python-native package registry. We think of pyx as an optimized backend for uv: it’s a package registry, but it also solves problems that go beyond the scope of a traditional "package registry".

A "private registry" typically connotes security, compliance, etc. pyx gives you those things, but the ideas behind it are actually much bigger. pyx is about improving the developer experience of working with Python. Solving user problems with our own infrastructure.

Today, we're announcing our first hosted infrastructure product: pyx, a Python-native package registry. We think of pyx as an optimized backend for uv: it’s a package registry, but it also solves problems that go beyond the scope of a traditional "package registry".

"Why did the latest setuptools release break our build? Can't we harden against that?"

Today, we're announcing our first hosted infrastructure product: pyx, a Python-native package registry. We think of pyx as an optimized backend for uv: it’s a package registry, but it also solves problems that go beyond the scope of a traditional "package registry".

"Why is it so hard to install PyTorch, or CUDA, or libraries like FlashAttention or DeepSpeed that build against PyTorch and CUDA?"

Today, we're announcing our first hosted infrastructure product: pyx, a Python-native package registry. We think of pyx as an optimized backend for uv: it’s a package registry, but it also solves problems that go beyond the scope of a traditional "package registry".

With pyx, uv becomes even faster, more secure, and hardware-aware (PyTorch, CUDA, FlashAttention, etc. become first-class citizens). You can use pyx to host private packages (it's much faster than other private registries), or as a frontend to public sources like PyPI.

Today, we're announcing our first hosted infrastructure product: pyx, a Python-native package registry. We think of pyx as an optimized backend for uv: it’s a package registry, but it also solves problems that go beyond the scope of a traditional "package registry".

The uv build backend is now stable, and considered ready for production use. An alternative to setuptools, hatchling, etc. for pure Python projects, with a focus on good defaults, user-friendly error messages, and performance. When used with uv, it's 10-35x faster.

Try it out by setting your project's build system, or running `uv init --build-backend uv`: ```toml [build-system] requires = ["uv_build>=0.7.19,<0.8.0"] build-backend = "uv_build" ``` (In a future release, we'll make this the default.)

Today, we’re announcing the preview release of ty, an extremely fast type checker and language server for Python, written in Rust. In early testing, it's 10x, 50x, even 100x faster than existing type checkers. (We've seen >600x speed-ups over Mypy in some real-world projects.)

And second: thank you (and congratulations) to the folks on the Astral team that made this release possible: @alexwaygood.bsky.social, @burntsushi.net, @carljm.me, @dcreager.net, @gankra.bsky.social, @ibraheem.ca, @michareiser.bsky.social, @sharkdp.bsky.social, @zanieb.bsky.social...

Today, we’re announcing the preview release of ty, an extremely fast type checker and language server for Python, written in Rust. In early testing, it's 10x, 50x, even 100x faster than existing type checkers. (We've seen >600x speed-ups over Mypy in some real-world projects.)

From here, we'll be implementing the rest of the typing spec, and iterating closely with users as we push towards a production-ready release. If you're interested in helping us get there, give it a try :)

Today, we’re announcing the preview release of ty, an extremely fast type checker and language server for Python, written in Rust. In early testing, it's 10x, 50x, even 100x faster than existing type checkers. (We've seen >600x speed-ups over Mypy in some real-world projects.)

Alongside the preview release, we're also shipping an official VS Code extension. And you can use `ty server` directly in any editor that supports the Language Server Protocol (Neovim, etc.).

Today, we’re announcing the preview release of ty, an extremely fast type checker and language server for Python, written in Rust. In early testing, it's 10x, 50x, even 100x faster than existing type checkers. (We've seen >600x speed-ups over Mypy in some real-world projects.)

Instead, this release is about giving users a _preview_ of what to expect from ty, and a call to follow along as we work towards a stable release later this year.

Today, we’re announcing the preview release of ty, an extremely fast type checker and language server for Python, written in Rust. In early testing, it's 10x, 50x, even 100x faster than existing type checkers. (We've seen >600x speed-ups over Mypy in some real-world projects.)

Our goal with ty is to build the best static analysis toolchain for Python -- to push the boundaries of how helpful a type checker can be in a highly dynamic ecosystem. And we want it to be capable of scaling to tens or even hundreds of millions of lines of code.

uv is one year old today. Hard to believe its only been a year. The growth, adoption, and impact surpassed my wildest expectations. Happy birthday, uv! 🥳

We’re building a new static type checker for Python, from scratch, in Rust. From a technical perspective, it’s probably our most ambitious project yet. We’re about 800 PRs deep!

Warning: this project is _not_ ready for real-world user testing, and certainly not for production use (yet). The core architecture is there, but we're still lacking support for some critical features. Right now, I'd only recommend trying it out if you're looking to contribute.

We’re building a new static type checker for Python, from scratch, in Rust. From a technical perspective, it’s probably our most ambitious project yet. We’re about 800 PRs deep!

Another goal: minimizing false positives, especially on untyped code, to make it easier for projects to adopt a type checker and expand coverage gradually over time, without being swamped in bogus type errors from the start.

We’re building a new static type checker for Python, from scratch, in Rust. From a technical perspective, it’s probably our most ambitious project yet. We’re about 800 PRs deep!

Like Ruff and uv, there will be a significant focus on performance. The entire system is designed to be highly incremental so that it can eventually power a language server (e.g., only re-analyze affected files on code change).

Incredible: PyCharm now ships with first-class support for uv!

You can now choose uv when setting the Python interpreter for your project, which will then invoke uv to create the virtual environment.