Agent Distillation. Same traces, different outcome. Turn them into training-ready datasets for smaller, task-specific models, built together with distil labs. Browse the Blueprints:
Who's Who of AI
datateam.bsky.social
Why follow
Founder with public evidence across AI business.
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- 13d ago
Data engineer & Cofounder @dlthub. Building out the tooling i wish i had.
What they're sharing
dltHub Blueprints dlthub.com
We are launching two dltHub Blueprints for agent spend: Agent Cost & Usage to understand it, Agent Distillation to optimize it dlthub.com
Ingesting Agent Traces with dlthub - Alena Astrakhantseva youtube.com
Articles & links
A few weeks ago we launched dltHub Pro. What surprised us was what people built with it. Teams didn't ask for primitives, they asked for complete solutions. So we built Blueprints.
How do you eval your agents? Agent traces are a good place to start. In this 1-hour workshop with @DataTalksClub, you'll learn how to ingest agent traces, model nested JSON into a queryable schema, and build dashboards to understand agent behavior.
Their own posts
Recent commentary
Gave an LLM a schema: 3/10. 📉 Gave it a schema + an ontology: 10/10. 🎯 Same model, same data. The difference? It finally understood what the columns actually mean instead of just vibing off the names.
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