For an example of a review (picked pretty much at random) see: https://sashiko.dev/#/patchset/20260318151256.2590375-1-andr...
The original patch series corresponding to that is: https://lkml.org/lkml/2026/3/18/1600
Edit: Here's a simpler and better example of a review: https://sashiko.dev/#/patchset/20260318110848.2779003-1-liju...
I'm very glad they're not spamming the mailing list.
That's cool. Another interesting metric, however, would be the false positive ratio: like, I could just build a bogus system that simply marks everything as a bug and then claim "my system found 100% of all bugs!"
In practice, not just the recall of a bug finding system is important but also its precision: if human reviewers get spammed by alleged bug reports by something like Sashiko, most of which turn out not to be bugs at all, that noise binds resources and could undermine trust in the usefulness of the system.
I think the table might be slightly inside-out? The Status column appears to show internal pipeline states ("Pending", "In Review") that really only matter to the system, while Findings are buried in the column on the far right. For example, one reviewed patchset with a critical and a high finding is just causally hanging out below the fold. I couldn't immediately find a way to filter or search for severe findings.
It might help to separate unreviewed patches from reviewed ones, and somehow wire the findings into the visual hierarchy better. Or perhaps I'm just off base and this is targeting a very specific Linux kernel community workflow/mindset.
Just my 1c.
(Also tests can be focused per defect.. which prevents overload)
From some of the changes I'm seeing: This looks like it's doing style and structure changes, which for a codebase this size is going to add drag to existing development. (I'm supportive of cleanups.. but done on an automated basis is a bad idea)
I.e. https://sashiko.dev/#/message/20260318170604.10254-1-erdemhu...
Having said that, if it can save maintainers time it could be useful. It's worth slowing contribution down if it lets maintainers get more reviews done, since the kernel is bottlenecked much more on maintainer time than on contributor energy.
My experience with using the prototype is that it very rarely comments with "opinions" it only identifies functional issues. So when you get false positives it's usually of the form "the model doesn't understand the code" or "the model doesn't understand the context" rather than "I'm getting spammed with pointless advice about C programming preferences". This may be a subsystem-specific thing, as different areas of the codebase have different prompts. (May also be that my coding style happens to align with its "preferences").
>"good catch - thanks for pointing that out"
We've already seen how bug bounty projects were closed by AI spam; I think it was curl? Or some other project I don't remember right now.
I think AI tools should be required, by law, to verify that what they report is actually a true bug rather than some hypothetical, hallucinated context-dependent not-quite-a-real-bug bug.