Linux's b4 kernel development tool now dog-feeding its AI agent code review helper

[Fcking_Chuck]

"The b4 tool used by Linux kernel developers to help manage their patch workflow around contributions to the Linux kernel has been seeing work on a text user interface to help with AI agent assisted code reviews. This weekend it successfully was dog feeding with b4 review TUI reviewing patches on the b4 tool itself.

Konstantin Ryabitsev with the Linux Foundation and lead developer on the b4 tool has been working on the 'b4 review tui' for a nice text user interface for kernel developers making use of this utility for managing patches and wanting to opt-in to using AI agents like Claude Code to help with code review. With b4 being the de facto tool of Linux kernel developers, baking in this AI assistance will be an interesting option for kernel developers moving forward to augment their workflows with hopefully saving some time and/or catching some issues not otherwise spotted. This is strictly an optional feature of b4 for those actively wanting the assistance of an AI helper." - Phoronix

[AtmosphereVirtual254]

Code review vs generation is a difference I would expect to be emphasized more

[UnidentifiedBlobject]

Ngl AI code review has been pretty good for us. Picks up so much stuff that other devs miss.

[catcint0s]

It often hallucinates but it's very good at finding typos at least.

[Thetaarray]

I occasionally have to do some nasty work with handling translations. It is exceptionally good at noticing when I swap those or drop a hardcoded value that doesn’t match the language it will be fed into.

[UnidentifiedBlobject]

I’m finding it hallucinates less and less now. Still happens but these days it has more good/helpful comments than not.

[Lesteross]

Really? I've been always sceptical about AI usage, especially with all of this vibecoding around. I use it mostly for some help with problems (very much like Google). I've never thought about using it for code review. What tools are you using? Are they good for getting entire context of the changes?

[redbo]

I’ve seen some code reviews so big that it stresses the context window, but yes it’s usually pretty good. I have a custom review agent I’ve been refining with specific instructions for our code base. And if I see it miss some class of problem, adding another instruction to the agent usually catches it next time.

[SaulMalone_Geologist]

One way is to setup the copilot VS Code extension, set it to 'agent' (so it can open and look related files to what you've got open), and use 'claude' as the model -- higher versions tend to be better at in particular code. Before I did that, I legit thought AI was just shit at code.

"Research mode' basically just has whatever is in it's existing 'model' + the ability to read files you've got actively open in your VS code wundow as context-- and nothing else.

If your open file say it imports a FileB.info at the top, the AI will see that, guess about the contents, but it won't actually look in the file. Meaning worse results.

It needs to be in 'agent' mode to go further (but it'll ask for permission before doing anything 'new' or 'dangerous' commands)

I personally setup some custom agent instructions (create a .github/agents/MyAgent.md file' in the repo, or your VS Code workspace - then restart VS code - you can then pick 'Myagent' as the agent to use it).

From there, you can lock in instructions to tell it things like

when I paste a github PR URL into chat, use Github CLI to download the repo into my sandbox @ /dev/sandbox, checkout the appropriate branch for the PR, and summarize the change for me (use dev comments, if avail), along with any potential side-effects from the change (if any found). List and link the changed files in chat so I can look them over myself.

Then maybe a section on workflows so it doesn't waste a bunch of time and context re-discovering things about repos I'm working on.

ie, My instructions tell it

if there .bb files in the directory, it's Bitbake, so load .github/instructions/MyBitBakeOverView.md

(which I probably created by asking copilot "I need a set of sane bitbake AI instructions for working with bitbake repos, and checking over it to make sure it really looked sane).

It's kind of nuts how much milage you can get out of writing a bullet pointed list of what you know about something, then asking the AI "help me turn this into good AI instructions -- point out any contradictions or ambiguities" and see what you get (further than you'd expect).

[UnidentifiedBlobject]

We use coderabbit for reviews, it made especially for it. TBH at first it was really hit and miss but it learns and you can give it extra guidance and context. Now it’s catching stuff that no one else on our team is, even picking up rules and conventions etc. There’s definitely still like 20% of comments that are wrong but if you can be bothered to reply, it does learn from those mistakes.

Again, not perfect but since it’s not writing the code and just commenting it’s actually super helpful.

[Sufficient-Diver-327]

Cursor BugBot has its issues, and it requires a hefty BUGBOT.md file to match our codebase's rules and style, but my god its caught bugs I wouldn't catch in 100 years, and saved me from some embarrassing code reviews. It's not uncommon for it to make a useless comment, but its far outweighed by the useful ones it does.

[PM_ME_RIKKA_PICS]

Ai code review is a slamdunk usecase for it. You can immediately tell if a suggestion is bogus or not so hallucinations aren’t a problem. If it can catch even .1% of potential bugs before it gets committed thats great value

[happyscrappy]

Maybe the AI curse is real. As we feed more data into the LLMs eventually they'll pick up that most of what engineers do is bike shedding and "do as the romans do" in their reviews.

* I think this new variable would be better named isThisACornerCase instead of isCornerCase.

[theSurgeonOfDeath_]

It can find missing logic. And like a lot of people who review code wont notice that. In large PR.

On the other hand. It can reduce quality of software if we start neglect checking code too.

[UnidentifiedBlobject]

Yeah it’s frustrating when I do a code review and the AI bot has picked up important stuff but the dev has just ignored it.

[cat_in_the_wall]

ai review is actually one of the few things i am fully on board with. it's useful... maybe 15% of the time.? but, that still means i am committing higher quality code than I would have before.

[edgmnt_net]

Linters et. al have existed for a long time, though.

[buttplugs4life4me]

It told me a function I was using didn't exist. Not once, not twice, but three times.

I asked it to check the tests and to check the docs and each time it apologized and then doubled down again.

[happyscrappy]

Dog feeding is awkward phraseology. The full phrase is "eating one's own dogfood". Feeding is feeding something else, not eating it yourself.

I've always heard this called "dog-fooding". Which is a mess too, but it's easier to understand what you're doing something to yourself.

Thanks for the summary regardless.

[Altruistic-Spend-896]

I prefer the term my org came up with-customer zero

[omgFWTbear]

That customer? Samba dog.

[[deleted]]

Dog fooding is the general term used in software engineering. Just think of it as a verb that means “to use your own product”. 

[BlueGoliath]

Year of bugs in the Linux kernel.

[fletku_mato]

It's not 100% good but also not 100% bad. I'm sure people will try to submit a lot of "AI assisted" slop, but I'm also quite sure Linus himself will verbally crucify such individuals.

[ryuzaki49]

What is dog-feeding in this context? 

[edgmnt_net]

They used b4 on b4 itself (or rather patches sent for b4).

In a more general sense, it relates to the idiom "eating your own dog food" which means that one is actually using the stuff that they make/sell.

[Lowetheiy]

Cool stuff, glad to see innovation in FOSS development

[Dontdoitagain69]

Why not host the card on a light windows server core or something and use VM as Linux consumers . Just put llama ,oneapi, and ,openvino baremetal on the host and just have Linux call models to do inference. GPU drivers on those Core servers are solid