Alright! "Loop Engineering" is the term we're using? Works for me. Here's my latest loop engineering project. Built on @NimaSiboni's recent "The Heuristic Scientist: Open-Ended Algorithm Discovery with LLMs" workshop at @TUBerlin.
I think my take on it came out nice AND I found some nifty new levers that proved to be super useful, especially the auto-pivoting facility.
"Auto-Pivoting on patience"
Give the agent a patience — how many rounds it may go without a new best. Spend it, and it must pivot: drop the current line for a genuinely different groundrule. Our main guard against rabbit-holes.
"Runs that build on runs"
A search isn't one fixed run. Each new run inherits the champion — the best groundrule so far — and climbs from there, while you change the model, the patience, the library around it.
"Hand-curated inspo"
The library is an optional, human-written input — notes, framings, even a personality handed to the agent before it thinks. It doesn't fix the answer; it shapes the frame the search starts from.
"Run it on your own hardware"
The loop talks to any OpenAI-compatible endpoint, so the proposing model can run locally — LM Studio, or anything serving the same API. Point it at your own hardware and the search runs unmetered: no rate limits, no quota walls.
