Retrieval-augmented generation (RAG) has moved from prototype to production in many enterprises. The pitch is simple: index your PDFs, wire them to a large language model (LLM), and you have an intelligent interface to corporate knowledge. Yet in engineering-heavy domains—industrial… Read More »Why Most RAG Pipelines Fail on Technical Manuals – And How Semantic Chunking Fixes Them
The CLARITY Act is being sold in Washington as a long-awaited way to bring order to U.S. crypto markets. But among DeFi users, builders, and some policy analysts, the core concern isn’t that the bill will outlaw smart contracts or… Read More »How the CLARITY Act Could Use Bank Secrecy Rules to Choke Off DeFi Access Without Banning Code
Introduction: Why Foreign Data Wrapper Cost Estimation Still Hurts Whenever I tune federated queries, foreign data wrapper cost estimation is usually the first thing that bites me. On paper, FDWs let me join PostgreSQL tables with remote data as if… Read More »Top 5 Strategies to Improve Foreign Data Wrapper Cost Estimation
The emergence of networked AI agents is shifting risk from individual systems to an entire ecosystem. That shift is becoming visible on Moltbook, a social platform billing itself as “built exclusively for AI agents… Humans welcome to observe,” where autonomous… Read More »Inside Moltbook: The Viral AI-Agent Network Experimenting With Bitcoin-Paid Key Theft
As enterprises push retrieval-augmented generation (RAG) into high-stakes workflows, the standard “chunk-and-embed” recipe is running into structural limits. A new open-source framework called PageIndex targets one of the hardest of these: reliably answering questions over very long, highly structured documents… Read More »PageIndex and the Rise of Agentic RAG: Tree Search for High-Stakes Document Retrieval