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dbForge 2026.2 is out, and for anyone who writes SQL every day the headline is simple: dbForge AI Assistant now has a free Express edition. You can generate, explain, fix, and optimize SQL with AI directly inside your dbForge tool, working against your actual schema, without buying a subscription…
Vector Database Memory Sizing and Cost Math: How Much RAM Do 1 Million Vectors Need? Estimate vector database memory, disk, and cost with embedding dimension, precision, metadata, index overhead, replicas, and capacity planning.
Everyone's building agents right now. But a better question than "can an agent do this?" is "when does an agent actually beat something simpler?" For the TigerGraph Agentic GraphRAG Hackathon, I built three question-answering pipelines side by side to find out.
Disclosure: I build Schemity, a desktop ERD tool - this post is from our blog and uses it for the examples. TL;DR: For schema work an AI agent needs the structure, not the rows, so give it a database MCP server that exposes the schema and no SQL tool.
The fastest way to connect an AI agent to application data is often a generic SQL tool. Give the model a schema, accept a SQL string, run it, and return the rows. That is also where a prototype can quietly become a production security and maintenance problem.
The useful part of a deployment agent isn't telling me what a PostgreSQL upgrade is. It's remembering what happened the last time I tried one. That became the central idea behind DeployMind, a DevOps pipeline agent I built around Hindsight.
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