that's dodging the question a little bit? let's say Ruff is faster because it's so good, how much faster than type checking alternatives would this great tool be if it did type checking?

That depends on how you do type checking and more specifically type inference. Mypy infers types of local variables but not function boundaries as it expects you to annotate function arguments and return values.

In mypy a variable taking values of two types is an error (unless explicitly annotated) which means you can assume the type from the first assignment statement and then use that assumption every other time the variable is used/assigned. In pylint it allows variables to take multiple types and only emits errors when an operation is invalid against one of the types. E.g. `x[0]` is valid for lists and tuples so you could assign it a list or a tuple depending on some condition.

The number of possibilities it considers quickly multiplies. Eg if your function is of the form `if blue_condition: x = blue_action(x)` that's a doubling of the possibilities and ten flags -> 1024 possibilities. (pylint has some heuristics on when to give up.)

The advantage of pylint is that it doesn't require your code to fit a certain style. But nowadays people have type annotations and they prefer to use them even if it means changing their code style a bit here and there.

Other type inferers with different approaches are pyright, Jedi, Pyre, pytype etc with different tradeoffs.

Without building the tool to compare, it's really hard to tell. In my experience of rewriting some Python and Ruby code into compiled versions, you can expect 10-20x improvements. But it's both really dependant on the code itself and on how you're going to reimplement it - most of the time it doesn't make sense to do perfect 1:1 rewrites.