Coding agents need to understand a repository before they can safely modify it, a process that can consume a significant portion of their context window as they search for symbols,
Vector databases are becoming part of the infrastructure that sysadmins, DevOps teams and developers need to understand and operate. Behind many Retrieval-Augmented Generation (RAG) systems, semantic search engines and AI
Visual FoxPro has been without official Microsoft support for more than a decade, yet companies still run applications built with the platform to manage billing, inventory, orders, accounting, and internal
A developer has managed to run GLM-5.2, a mixture-of-experts model with roughly 744 billion parameters, on a consumer computer with around 25 GB of RAM and no graphics card. The
DBeaver has held a very comfortable place for years in the daily workflow of many developers, system administrators and data teams. It is complete, free in its Community edition, cross-platform
One of the biggest hidden costs in retrieval-augmented generation is not the model itself, but the storage burden of the vector index sitting underneath it. That is the problem LEANN
The market for backend services for web and mobile apps has long been dominated by a small group of familiar names, especially Firebase and, more recently, Supabase. But alongside those
MariaDB has signed a definitive agreement to acquire GridGain Systems, the company best known for its in-memory computing technology and for being the original creator of the open-source Apache Ignite
Over the past two years, “GenAI text-to-SQL” became one of the most repeated promises in enterprise AI: describe your question in plain English and get perfect SQL, ready for production.
For years, plenty of architecture discussions have leaned on a convenient idea: that SQL databases “don’t scale,” and that beyond a certain size the natural move is to abandon the
