I feel like it's pretty easy to predict what OpenAI is trying to do. They want their codex agent integrated directly into the most popular, foundational tooling for one of the world's most used and most influential programming languages. And, vice versa, they probably want to be able to ensure that tooling remains well-maintained so it stays on top and continues to integrate well with their agent. They want codex to become the "default" coding agent by making it the one integrated into popular open source software.
I think this is more about `ruff` than `uv`. Linting is all about parsing the code into something machines can analyze, which to me feels like something that could potentially be useful for AI in a similar way to JetBrains writing their own language parsers to make "find and replace" work sanely and what not.
I'm sort of wondering if they're going to try to make a coding LLM that operates on an AST rather than text, and need software/expertise to manage the text->AST->text pipeline in a way that preserves the structure of your files/text.
The parser is not the hard part. The hard part is doing something useful with the parse trees. They even chose "oh is that all?" and a picture of a piece of cake as the teaser image for my Strange Loop talk on this subject!
Writing a literal parser isn’t too hard (and there’s presumably an existing one in the source code for the language).
Writing something that understands all the methods that come in a Django model goes way beyond parsing the code, and is a genuine struggle in language where you can’t execute the code without worrying about side effects like Python.
Ty should give them a base for that where the model is able to see things that aren’t literally in the code and aren’t in the training data (eg an internal version of something like SQLAlchemy).
Static analysis just requires that you don't actually execute the code. It's possible (sometimes) to infer what methods/properties would be create without actually statically analyzing the code.
E.g. mypy has a plugin to read the methods and return types of SQLAlchemy records, I believe without actually executing them.
Obviously not globally true, but in limited domains/scenarios you can see what would exist without actually executing the code.
This just seems like panic M&A. They know they aren’t on track to ever meet their obligations to investors but they can’t actually find a way to move towards profitability. Hence going back to the VC well of gambling obscene amounts of money hoping for a 10x return… somehow
The dev market? Anthropic's services are arguably more popular among a certain developer demographic.
I guess this move might end up in a situation where the uv team comes up with some new agent-first tooling, which works best or only with OAI services.
OpenAI could vibe-code marketshare by introducing bias into ChatGPT's responses and recommendations. "– how to do x in Python? – Start by installing OpenAI-UV first..."
This. It's valuable b/c if you have many thousands of python devs using astral tooling all day, and it tightly integrates with subscription based openai products...likelihood of openai product usage increases. Same idea with the anthropic bun deal. Remains to be seen what those integrations are and if it translates to more subs, but that's the current thesis. Buy user base -> cram our ai tool into the workflow of that user base.
