Re: Merging AI-generated program code
Arno Waschk via Discussions on LilyPond development <[email protected]> Mon, 1 Jun 2026 13:30:35 +0200
| Newsgroups | gmane.comp.gnu.lilypond.devel |
|---|---|
| Message-ID | <[email protected]> |
Arno Waschk Gubener Str. 44 10243 Berlin +49 172 3149605 arnowaschk.de <https://arnowaschk.de> *[email protected]* current and upcoming projects: *Lesungen Klaus Maria Brandauer im Burgtheater, Prinzregententheater, Metropol-Theater Bremen, Neuhardenberg, etc.*2025 *Die Dreigroschenoper: Berliner Ensemble, Berlin Schauspiel Dresden u. a. * *Beethoven, die drei letzten Klaviersonaten* wieder ab 2026 *Buch der hängenden Gärten George/Schönberg* *Verklärte Nacht und Forellenquintett* ab Frühjahr 2026 *Jede Menge Unsterblichkeit* Musiktheater im Revier Gelsenkirchen *Die weisse Rose von Udo Zimmermann Kammerfassung der zweiten Version* Theater Erfurt 2025 *Die weisse Rose von Udo Zimmermann Kammerfassung der Version 1968-72* Theater Hof Regie: Lothar Krause ab Februar 2023 *Zukunftsmusik* von Jelena Schulte Regie: Antje Thoms *Der Idiot* Deutsches Theater Berlin Regie: Sebastian Hartmann *Beichte* mit Markus Öhrn Schweden, on tour seit Dezember 2020 *Häusliche Gewalt* Wiener Festwochen, Biennale Wiesbaden, u. v. a. mit Markus Öhrn on tour *Schlingensief und die Avantgarde* Publikation ZiF Bielefeld *Fräulein Else* Hörbuch mit Elisabeth Trissenaar *u. v. a.* 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. > __________________________________________________ > > 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.