Comment by nindalf | Hacker News Reader

Comment by nindalf | Hacker News Reader

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The author of Ruff mentioned that many people told him exactly what you're saying. He went ahead and built it anyway. I'm puzzled though. Those naysayers were naysaying about an attempt at rewriting. You appear to be naysaying a successfully built project.

It's true that the folks who find it most helpful are the ones with large Python projects. But these are sometimes very important, widely used projects. See the testimonials from users (https://beta.ruff.rs/docs/#testimonials)

> widespread adoption

This makes little sense in the context of a linter. As long as my linter implements all the popular rules and my code is kept tidy, what's the issue? What does it matter if there's widespread adoption or not?

> still limited to a subset of functionality

You have a link to some functionality you're missing and necessary for your workflow? Or is this more of a theoretical concern?

Sorry if this comes across as rude.

I am happy if this tool works for you but I think writing off my comment as naysaying is not constructive.

There is no support for all syntax features of the current language specification and reference implementation. There is also no legacy support. Again, especially in projects concerned with code quality and analysis, building is not more important than long-term maintenance: Both language specification and reference implementation are constantly evolving. The current approach relies on an upstream interpreter and thereby inherits idiosyncratic divergences and limitations.

There is no extensibility. The majority of projects are neither public nor frameworks. Often projects employ a number of frameworks each with associated dialects, with both frameworks and dialects also constantly evolving. The current approach to extensibility is approximately one person manually rewriting rules individually from heterogeneous projects that span several contributors over several years. There is already outstanding maintenance work on manually introduced rules, only a subset of which gets flagged by a fraction of adopters. There is even outstanding work on feature parity.

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