hangrymoon01

you will need to collect data for every decision/usecase and then train a model. But this can be used for different use cases with just a prompt.

Founders response to a similar question on X: https://x.com/CompleteSkeptic/status/2100067328620896408?s=2...

pasting it here: zero-shot + general == programmable

I would assume any extreme scale narrow task could then be fine-tuned for, but we'll see - I suspect putting it all in shared cognitive core has bit maintainability/generalization benefits

Re: savings - it depends on the use case. For example, one of our users set up a small runbook to run a group-by-IP query for high-throughput alerts, since that was their most common first response to those alerts. That alone cuts out a couple of minutes of exploration per incident and removes the variability of the agent deciding what data to investigate and how to slice it.

In our experience, runbooks provide a consistent, fast, and reliable way of investigating incidents (or ruling out common causes). In their absence, the AI does its usual open-ended exploration.

Re: custom harnesses, imo maintaining them can be time consuming especially when things are changing very fast with AI. Bringing up a prototype is easy but a robust harness that handles the edge cases needs time and effort.

Interesting. Nice to see someone publishing actual cost numbers.