Generative AI | News, how-tos, features, reviews, and videos
While computer-use models are still too slow and unreliable, browser agents are already becoming production-ready, even in critical sectors such as healthcare and insurance.
The new reinforcement learning system lets large language models challenge and improve themselves using real-world data instead of curated training sets.
With agentic AI, the database must evolve from a passive ledger to an active reasoning engine that informs, guides, and enables autonomous action.
Jules performs better than Gemini CLI despite using the same model, and more like Claude Code and OpenAI Codex.
Using large language models to build applications that integrate large language models calls for new disciplines and techniques.
You train the model once, but you run it every day. Making sure your model has business context and guardrails to guarantee reliability is more valuable than fussing over LLMs.
Simulation environment called Magentic Marketplace is designed to safely examine how AI agents interact with humans and each other.
Open-source API development platform speeds API and MCP server development with native MCP clients, AI mock servers, and AI-powered commit suggestions.
From supercomputers to robotaxis, NVIDIA’s GTC 2025 made one thing clear — AI’s future is here, and it’s being built in America.
Generative AI models can carry on conversations, answer questions, write stories, produce source code, and create images and videos of almost any description. Here's how generative AI works, how it's being used, and why it’s more limi