Proprietary vs open source models [LWN.net]

Proprietary vs open source models

Posted May 26, 2026 12:48 UTC (Tue) by csamuel (✭ supporter ✭, #2624)
In reply to: Proprietary vs open source models by marcH
Parent article: Reviewing kernel patches with LLMs

> It's good to start somewhere. Also, it sounds like what already exists is good and valuable enough to be shared and used, even if that's just for a few months.

Strong agree, and I think one definite advantage to getting things in now is made by Jon here:

> Kernel documentation maintainer, Jonathan Corbet, said that the prompts looked like ""a whole lot of very useful documentation on how to understand and review kernel patches"", though it is ""really sad that we couldn't write it until we were writing it for a machine"". He wondered what that kind of documentation might have enabled had it been added to the kernel long before now.

If what is written helps humans do this work, then we are benefiting the community, not (just) the machines.

All the best,
Chris

to post comments

Proprietary vs open source models

Posted May 26, 2026 15:23 UTC (Tue) by kleptog (subscriber, #1183) [Link] (1 responses)

> "really sad that we couldn't write it until we were writing it for a machine"

To some extent I can understand this. Writing documentation is also a process that requires feedback. You write documentation, you want to know from users whether this helped them or not, you improve it. But the feedback process for documentation is not easy to arrange and slow.

If you write the documentation for an LLM you can get direct feedback as to whether it understands it or not, whether important things are missing. This makes it way more interesting to write. I'm not sure we could have done it earlier.

Proprietary vs open source models

Posted May 26, 2026 18:12 UTC (Tue) by iabervon (subscriber, #722) [Link]

Also, humans tend to be affected by the previous version of the documentation and your reactions to the inaccurate statements they made about your code after reading it, in a way that prevents really checking whether it will work to educate new humans yet. With LLMs, you can just start a new session, and you don't need to worry about having confused and then discarded a potential contributor last time.