Context That Dreams
summary
Claims a ceiling on filing-cabinet context layers and demonstrates a working alternative: a pipeline that pulls Zoom, Outlook, Jira and Confluence into a knowledge graph via the Nodeex library, runs acquire→distill→extract→resolve→schema→human review, and continuously consolidates nodes to generate new, provenance-backed inferences. A Smitty AI app built on this graph turns 6 repositories and ~100 conversations into a live second brain that emits scored ideas (idea scorer 0–10) while enforcing grounding, provenance and RBAC; the system runs in production and surfaces derived bugs and opportunities.
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chapters (10)
- 0:00Why Do AI Systems Need a Context Layer?
- 1:06How Do RAG and Context Engineering Work?
- 4:40What Comes After Traditional Context Engineering?
- 7:46Can AI Generate New Insights Without Hallucinating?
- 8:46How Context Can Reveal Hidden Software Issues
- 10:55How Do Knowledge Graphs Turn Information Into Context?
- 17:22How Does Multi-Hop Retrieval Find Unwritten Answers?
- 21:33How Can AI Continuously Generate and Validate New Ideas?
- 23:07Building an AI Second Brain With Smitty AI
- 30:51How Do You Control Hallucinations, Permissions, and Context Quality?
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