Building EcoGraph AI: Why GraphRAG Beats Vector Search (My TigerGraph Hackathon

<p>Building an Autonomous GraphRAG Fraud Investigation &amp; Next-Best-Action Agent with TigerGraph</p> <p>A deep-dive into graph-native financial crime investigation,

<blockquote> <p>A suspicious transaction starts an investigation. The harder part is deciding what evidence to gather, what remains uncertain, and which action is justified.</p>

<p>This week’s headlines highlight the rapid evolution of AI in China, with models like Qwen and DeepSeek pushing the frontier of capability. But as these models grow more

<h1> SentinelGraph: Building a Graph-Powered AI Cybersecurity Investigation Agent with FalkorDB </h1> <p><em>How connected data can help security analysts investigate alerts,

<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked

<!-- SC_OFF --><div class="md"><p>Hi Reddit, we are a team of database researchers (including a PhD from MIT DB Group) and we just open-sourced an embedded vector database for

<h1> Moving Beyond Simple Vector Search: Why Hybrid Search is Essential for RAG </h1> <p>As LLMs continue to dominate the landscape, Retrieval-Augmented Generation (RAG) has

<h2> The Numbers Don't Lie </h2> <p>The AI Engineering Report 2026 analyzed telemetry from 22,000 developers across more than 4,000 teams. The headline metrics look impressive: