General
The paper reframes large language model (LLM) teams as distributed systems and offers a principled framework to study when teams help, how many agents to deploy, and how team structure affects performance. It demonstrates parallels with classic distributed computing and provides insights for building scalable AI-powered workflows and automation.
NVIDIA announces Vera CPU, a purpose-built processor for agentic AI and reinforcement learning, delivering twice the efficiency and 50% faster performance than traditional CPUs. Th…
The article explains the central limit theorem, its origins with de Moivre and Laplace, and how averaging independent random effects yields a universal bell curve. It discusses pra…
An in-depth look at Stavros' workflow for building software with LLMs, including a multi-model harness and defined roles (architect, developer, reviewers). It includes a real-world…
Popular Science reports that Niantic Spatial used billions of Pokémon Go player images to train Coco Robotics' Visual Positioning System, enabling centimeter-level localization for…