Putting more AI processing into a pair of glasses or a smartphone creates a problem that faster silicon alone cannot solve: the heat has to go somewhere. xMEMS Labs is moving its solid-state micro-cooling technology into mass production, targeting devices too thin for a conventional fan and small data center components that need airflow close…

Eka Robotics may be the first robotic arm that is actually good at picking a wide range of objects up, and it is so smooth and human-like, it is wowing the industry.Læs hele historien hos techcrunch.com →

AGCO and Agility Robotics have signed a commercial agreement to bring Digit humanoid robots into AGCO's manufacturing operations, the companies announced in a joint press release on October 20, 2026. The U.S. pilot is expected to begin in 2027 at AGCO's production facility in Hesston, Kansas, and will support AGCO's efforts to create more flexible,…

Contributors to the llm-d project announced llm-d 1.0, a Kubernetes-native distributed inference serving stack, on October 20, 2026, at the PyTorch Conference North America, according to an IBM Research announcement. IBM said the release moves llm-d beyond demonstrating individual performance techniques and is meant to help organizations bring model deployment in-house as inference costs rise.…

Meta is suspending the sharing feature while it investigates its privacy.Læs hele historien hos arstechnica.com →

AI can help a team build a new product feature in hours. Finding out whether people actually want it can still take weeks. Vectorial is betting that human behavior models can narrow that gap, and has raised $3 million in seed funding to put simulated audiences inside the product development process. The San Francisco company…

Five self-hosted Docker containers replace the public utility sites where developer data quietly leaks. Learn which ones handle JWT decoding, data transformation, file conversion, API testing, and diagram rendering locally.Læs hele historien hos kdnuggets.com →

Self-supervised learning creates training targets from the structure of unlabeled data, allowing models to learn representations at enormous scale without manual labels for every example. This guide explains the mechanism, trade-offs, evaluation, and controls that matter in practice.Læs hele historien hos unite.ai →