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.
