Ranking narrows candidates from git history, knowledge base and rules. A decision model judges each one’s relevance, then an LLM curates memory for context.
So it covers all kinds of memory: the decision history in git (episodic), the knowledge base (semantic) and rules (procedural).
@skelz0r@marmotize_@JoshARosen Git commit messages are prefect for this, I’ve been doing this for a while, but now after adding basic ranking and a decision model the results are beyond what I expected
Yes they’ll experience state of the art inference, but what if you want them to follow clear process every time without forgetting something? That’s just not possible right now, and skills don’t enforce it either. Workflows that drive existing harnesses/models in the answer, they progress with the frontier harnesses not instead of them.
@hanakoxbt This is great! I gave this to my agent and had it built the workflow with https://t.co/KBUgehk2HV, this aligns very closely with my thinking when I built it, although in my opinion the loop lives outside the node.
@arscontexta Yeah, the harness launches a lightweight micro ui automatically, I do what I need to do, then close it and it feeds back to the agent and the workflow continues.
Can launch manually too.
Some basic docs for it here - https://t.co/bzdU04W8ZE