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. 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. Best, Kieren. __________________________________________________ My work day may look different than your work day. Please do not feel obligated to read or respond to this email outside of your normal working hours.
