Re: GR: Ban LLM contributions from Debian

Lucas Nussbaum <[email protected]> Fri, 24 Jul 2026 10:01:43 +0200
Newsgroups	gmane.linux.debian.devel.vote
Message-ID	<[email protected]>
Hi,

On 24/07/26 at 00:03 +0200, Pierre-Elliott Bécue wrote:
> Lucas Nussbaum <[email protected]> wrote on 23/07/2026 at 21:12:44+0200:
> > On 23/07/26 at 19:17 +0200, Pierre-Elliott Bécue wrote:
> >> What I want to do is to demand those using these tools to not give up
> >> their skills and intellect in favour of letting an LLM do all the
> >> job.
> >
> > Which skill am I giving up by asking my agent to 'git commit && git
> > push' for me, instead of doing it manually?
> 
> Do you still review all the atomic changes your LLM does? Do you check
> that it doesn't produce redundant and overly verbose code? Do you still
> evaluate if it answers to the algorithmic problem you're trying to
> solve?
> 
> Most of the people I know working with a generative AI asking it to
> commit and push read less than half the code it writes. I already see
> the difficulties they have when they have to code on their own.
> 
> Any easy path is a path the brain loves to take, and it has a price,
> whether you like it or not.
> 
> I don't want to be there when the next generation will push software,
> as I fear it might be atrocious.

No, I don't always read the source code produced by AI. It depends on
what I am trying to achieve. For example, sometimes I'm just trying to
build a throw-away tool to solve a specific problem, and there's no
reason to care about code quality.

Also, over time, I've read a lot of code, and missed lots of problems.
So I'm convinced that the focus should be on engineering practices and
the whole environment around code (documentation, tests, etc.) more than
on the specific practice of reading the code. Put bluntly, I feel that
if one really needs to read the code to be convinced that it works, then
one probably does not trust the scaffolding that should have been built
around the software project.

That's something I find very interesting with AI-assisted software
engineering: it puts specification, documentation and testing back at
the center of the picture. LLMs are very good at detecting
inconsistencies between a codebase and a specification/documentation, at
improving a test suite, or even at detecting gaps in a specification
(like a rubber duck with superpowers). Putting energy into specification
is no longer a loss of time, because specification can be used as input
by LLMs to verify code (or even to produce code).

How far this can be taken in the context of Debian is still to be
explored. But that's why I find it more important to responsibilize
humans, rather than to focus on making specific practices mandatory.

Lucas
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