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.
> 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.