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Am 01.06.26 um 12:03 schrieb Kieren MacMillan:
Hi all,
there is no.guarantwe that the AI agents actually reflect appropriate
understanding of the code base.
Adding them to LilyPond will add cognitive debt, which I believe is much
worse than technical depth. When AI -generated code is created, there is a
strong likelihood that nobody in the world understands why that particular
code works and is an appropriate solution for the problem under consideration.
A valid and important point. To paraphrase the brilliant Cory Doctorow: “Code
is a liability — not an asset (as most people seem to believe) — and AI lets us
generate that liability at scale.”
I haven’t yet interacted too deeply with LLMs+Lilypond (just working on a
collection of skills files right now!), but I *have* worked a fair bit with
LLMs in the context of mathematics (number theory), and here’s a process I’ve
discovered to be REALLY helpful “pre-submission”:
1. Use LLM1 (your preferred agent) and “best practices” (good prompts,
iteration, etc.) to generate Solution A.
2. Ask LLM2 and LLM3 to review the code.
3. Return to LLM1 with these “referee’s comments”, and see what gets
changed/improved.
4. Iterate, if appropriate/necessary.
How about granting presumption of innocence to people who hand in some
code and *might* have had *and* applied a similar idea? (Not to speak
that a harness not doing this should be discarded immediately)
How about judging by the code,or in math the proof or whatever, instead
of declaration of "how much" or "which"?
Each LLM has its strengths and weaknesses. At least in my math work, putting
multiple “minds” on the problem often reveals gaps, uncovers more elegant
solutions, etc. Having a clear understanding of not only “how much” an AI was
used in the creation of a given block of Lilypond code, but exactly *which* AI
[!!], may be useful.
Out of topic but out of curiousity, in math: How about lean4 & friends
as "referee" there? And how mcuh equivalence in code about compilers and
regression tests?
Best,
Kieren.
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