6 ms·

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 finit

by jarodrh 2mo ago

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. Good thread. Give frugon a try on your own logs, and tell me what you think.

westurner 2mo ago
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.
- jarodrh 2mo ago
  > 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

by jarodrh 2mo ago

westurner 2mo ago

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.

jarodrh 2mo ago
> 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