> Have benchmarks for every single task and sub-task that will happen everywhere a LLM is involved, so you can measure improvements
exactly. are there standard approaches or tools specifically for instrumenting LLM utilization, for applications rather than model development?
i use claude code a bit. i relentlessly push it to keep its work ephemera in source controlled and discoverable locations, following a lot of ozbrain's motivation. i also have claude generate task logs for all spawns. the theory with the task logs is to try and build some standard measures so i can see whether a thing i do (writing skills, experimenting with third-party MCP tooling, etc) actually helps in a way that is more robust than gut. i have yet to turn that effort into something useful. i'm just starting to get enough data to try to do some diving.
the problem is that this feels like the classic problem of "how do we measure productivity"? i was thinking very basic efficiency like "tokens in per tokens out" (maybe with a domain-specific value weighting function on the output tokens, e.g. code tokens > memory tokens > chat tokens) could be an possibility.
the point being, seems like you'd want to become pretty sophisticated about measurement to sell a product like ozbrain, as a basis for continuous improvement across major versions. or convincing people to use it, quantitatively rather than qualitatively.
curious if you're using something mostly-out-of-the-box to layer on visualizations for your dashboards?
relatively new to duckdb, love it so far, looking at alternatives for downstream visualization. so far just exporting datasets and piping into python scripts.
For a schema-first (vs. code first) approach (which I think would be a sweet spot for agent driven dashboarding), I'd suggest looking at https://vega.github.io/vega-lite/ or https://vega.github.io/vega/. A little higher level than full D3 but gives you a little higher level approach.
exactly. are there standard approaches or tools specifically for instrumenting LLM utilization, for applications rather than model development?
i use claude code a bit. i relentlessly push it to keep its work ephemera in source controlled and discoverable locations, following a lot of ozbrain's motivation. i also have claude generate task logs for all spawns. the theory with the task logs is to try and build some standard measures so i can see whether a thing i do (writing skills, experimenting with third-party MCP tooling, etc) actually helps in a way that is more robust than gut. i have yet to turn that effort into something useful. i'm just starting to get enough data to try to do some diving.
the problem is that this feels like the classic problem of "how do we measure productivity"? i was thinking very basic efficiency like "tokens in per tokens out" (maybe with a domain-specific value weighting function on the output tokens, e.g. code tokens > memory tokens > chat tokens) could be an possibility.
the point being, seems like you'd want to become pretty sophisticated about measurement to sell a product like ozbrain, as a basis for continuous improvement across major versions. or convincing people to use it, quantitatively rather than qualitatively.