The one-year-old startup is now valued at $300 million.
Runpod has officially achieved SOC 2 Type II certification, validating that its enterprise-grade security controls not only meet strict design standards.
As distributed AI training scales, failures across hardware and software dependencies increasingly disrupt training progress, making fast, reliable recovery...
RSI is the point at which AI grows so good at training itself that it spawns a dramatic acceleration of AI capabilities. To the AI…
New audio-to-audio model. Tags: mamba-ssm, speech-enhancement, universal speech enhancement, multiple input sampling rates, language-agnostic, audio-to-audio, arxiv:2603.02641, arxiv:2606.25621
The website builder said that the job losses are part of a broader restructuring of its organization.
The New York Times is suing privacy-focused web browser company Brave for allegedly scraping the outlet’s copyrighted and unlawfully distributing it to its own customers…
General-purpose mobile manipulation requires a combination of capable robot hardware, scalable demonstration collection, expressive whole-body control, and effective policy learning. Yet these components are often developed independently, creating mismatches that limit the capabilities of the complete system. We present Pixel, a co-designed full-stack system for learning general-purpose mobile manipulation. Pixel combines a low-cost wheeled humanoid with human-aligned kinematics and compliant grippers, multiple teleoperation and robot-free human data-collection interfaces, a unified whole-body controller, and a common policy learning pipeline across human and robot demonstrations. A hierarchical learning stack further enables language-conditioned visuomotor control and long-horizon task execution. We evaluate the system across hardware, data collection, and policy learning, and demonstrate mobile-manipulation behaviors requiring dexterity, active perception, navigation, and whole-body coordination. We hope that Pixel provides an integrated and extensible foundation for studying how robot hardware, data, control, and learning can be designed together for robot learning. More results at https://pixel-robot.com/pixel
