We tested @ASIMOV_Protocol with a 1-hour parliamentary debate and got accurate answers with reference to moments down to the second. With Knowledge Graphs you can reduce the chance of your AI hallucinating! Here is how they work🧵👇

2/5: Knowledge Graphs store structured facts as entities & relationships. LLMs generate fluent text from patterns in data but can hallucinate. • Knowledge Graphs = precise, deterministic queries over explicit facts. • LLM = flexible, contextual language understanding/generation.

3/5: In Asimov, every data source from Telegram, LinkedIn, X, etc is wrapped in its own module that extracts a mini-knowledge graph. Because “a graph plus a graph is still a graph,” you can merge them seamlessly. By unifying these per-app graphs into one personal knowledge graph, you only need one query to explore everything.

4/5: That unified graph becomes your personal data warehouse. Want to know which contacts mentioned “AI” last month? One graph query answers it. And with your LLM of choice—cloud or local—you can navigate, summarize, and reason over your entire graph in natural language.

5/5: This is the power of Knowledge Graphs in Asimov: modular ingestion + graph merging + LLM access = flexible, privacy-preserving, and infinitely extensible data insights. Dive in at #KnowledgeGraph #AI