Down the rabbit hole we go.

> Is this a convex optimization or non-convex optimization problem?

- Neither, frugon sidesteps that issue by having a small finite candidate selection that it evaluates completely. No search needed because the problem is discrete and small.

> OpenInference and OpenTelemetry have a schema for agent sessions.

- Good to know for when I look into it.

> Accuracy is sensitive to nonce and temperature without variance in the model hyperparameters

- Yes, this is a current weakness in frugon...Hmm, I see, so I'd essentially need to establish a baseline (with whatever the default temp is) + nonce, then compare both spreads. Noted.

As well. Someday costed opcodes and sharded redundancy for all of this;

> No search needed because the problem is discrete and small.

If an algorithm selects according to rank, there's a cost/score/fitness/survival/error function (that assumes or imposes a distance metric to make a metric space).

Awhile back I had to match exterior paint to printed paint swatches and I'm stereotypically not good at colors, so finally I arrived at just A/B; this one or this one and discard.

> As well. Someday costed opcodes and sharded redundancy for all of this;

Hmm, noted.

> If an algorithm selects according to rank, there's a cost/score/fitness/survival/error function.

- Yes, what I'm saying is that frugon has a small enough candidate pool that it can all be evaluated at the same time. It doesn't need to search through it.

> Awhile back I had to match exterior paint to printed paint swatches and I'm stereotypically not good at colors, so finally I arrived at just A/B; this one or this one and discard.

- This is exactly the shape the judge prompt takes in frugon.

See: src/frugon/measure.py

Then search: JUDGE_PROMPT_TEMPLATE