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All digests tagged Graph Databases
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The Data Context Layer provides a graph-based solution to give AI agents semantic understanding of data and code. By computing lineage, grain, and dependencies, it moves agents beyond ambiguous text search and unreliable SQL.
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Jeremy Adams details building a wearable AI agent (NanoClaw) on a Raspberry Pi 4B. The system uses Neo4j and the POLE schema to create offline-first, graph-based memory, demonstrating robust edge computing capabilities.
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In complex engineering domains like chip design, alignment is the biggest bottleneck. The solution requires building a shared nervous system—a living graph that tracks intent and constraints to prevent costly systemic failures.
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This workshop details how building reusable 'graph shapes' (Trees, Communities, Connections) on top of lakehouse data provides critical context for AI agents. It moves beyond simple vector search to solve complex, estate-level querying challenges.
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LLM synthesis destroys the paper trail. Learn how to build provenance into knowledge graphs using Graphiti, ensuring every derived fact can be traced back to its original source for compliance and debugging.
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