Why Vector RAG Fails AI Agents: Building an Autonomous SRE Swarm with FalkorDB
Large Language Models depend on the context they are given. In the year the usual way to give context has been Vector RAG (embeddings plus cosine si…
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Large Language Models depend on the context they are given. In the year the usual way to give context has been Vector RAG (embeddings plus cosine si…
What we built JEVelric is an AI agent that investigates card fraud the way a human fraud analyst would: it takes a trigger — a risk score, a custome…
TigerGraph Hacker House Goa — Agentic Fraud Investigation Challenge Team / Co-Authors: Nikhil Kumar Panigrahi & Sai Manohari Godavarty Live Work…
Task #4 · TigerGraph problem statement · Hacker House Goa 2026 A risk score pings. Is it fraud, a holiday, or a new phone? We built SentinelGraph ,…
Open your Obsidian graph view and look at it honestly: every line on that screen is a link you typed by hand. The person mentioned in forty notes bu…
For a long time, I assumed building better AI applications meant using better LLMs. After learning about Neo4j , GraphRAG , Aura Agents , and LLM Me…
The promise of Enterprise AI is simple: give an LLM access to your company’s internal tools, and let it answer complex organizational questions. But…
Hello, tech innovators, data nerds, and health-tech visionaries! 👋 Welcome to the ultimate engineering deep-dive of Med AI . If you followed our jou…
The Phenomenon After building a knowledge graph with GraphRAG, you query a community node and discover it has no PARENT_OF relationships — neither a…
Building The Librarian: A Knowledge Graph-Powered RAG System for Medical and Scientific Literature How I built a conversational knowledge management…
LLM token costs explode at scale. The TigerGraph GraphRAG Inference Hackathon poses one question: can a knowledge graph make inference cheaper witho…
Hi, I'm Ryan , CTO at airCloset. In my previous posts I've introduced the full picture of our 17 internal MCP servers , an MCP server that searches…
The question that broke my RAG pipeline I had a solid RAG setup. Embeddings, vector store, top-k retrieval, the whole thing. It handled factual look…