dltHub

@dltHub

dltHub is the creator of data load tool (dlt)

Berlin

dlthub.com/docs/intro?utm…

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dltHub
@dltHub
May 14
Building pipelines with AI usually means losing context between tools. dltHub AI Workbench runs the full 12-step workflow as a continuous session, schemas, incrementals, traces, transformations, and notebooks share context across the stack. dlthub.com/blog/agentic-d…
dltHub
@dltHub
Jul 7
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.
dltHub
@dltHub
Jul 6
AI writing pipelines is cool. AI remembering the entire workflow is better. dltHub Pro carries context from ingestion to deployment to maintenance instead of starting over every step. Try it: dlthub.com/products/dlthub
dltHub
@dltHub
Jun 29
AI agents generate traces, but are you analyzing them? Join Alena Astrakhantseva to learn how to turn tool calls, token usage, and outcomes into structured, queryable data with dltHub Pro. July 6, live on YouTube with @DataTalksClub Register ↓
Ingesting Agent Traces with dlthub · Luma
From luma.com
dltHub
@dltHub
Jun 26
AI memory just got smarter!
Vasilije
@tricalt
Jun 26
Introducing Cognee v1.0: a major breakthrough in agentic intelligence. It is 145% better than Opus 4.8 and GPT 5.5 at long context memory retrieval. Cognee allows a 100 BILLION token context window 100,000x more than Claude. It's: - 6.9x cheaper than GPT 5.5 and Opus 4.8 -
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Readers added context
Cognee's website and GitHub do not provide benchmarks or evidence supporting claims of 145% better long-context memory retrieval than GPT-5.5/Opus 4.8, a 100 billion token context window, or 6.9x cheaper costs. cognee.ai github.com/topoteretes/co…