•Swiss researchers developed "Kinematic Intelligence," a software framework enabling robots to transfer learned skills to different hardware configurations without retraining.
•The system provides robots with an "innate mathematical awareness" of their physical limitations, including dangerous kinematic singularities, preventing joint jams and unstable movements.
•This AI-free approach aims to make robot skill transfer as seamless as syncing apps to a new smartphone, significantly reducing development and deployment complexities.
•OpenAI has introduced Workspace Agents, a new enterprise offering that evolves beyond custom GPTs to enable persistent, multi-step work automation across various third-party business applications.
•Powered by OpenAI's Codex, these agents can write and run code, use connected apps, remember context, and continue tasks autonomously, even if the initiating user leaves the session.
•Workspace Agents integrate with tools like Slack, Salesforce, Google Drive, and Microsoft apps, aiming to transform AI from individual productivity enhancement into a shared organizational resource fo...
•Microsoft plans to integrate agentic AI capabilities into Copilot, enabling it to autonomously perform tasks like managing emails and calendars.
•The initiative is inspired by the open-source OpenClaw platform, with Microsoft prioritizing safety and tracking features, akin to Nvidia's NemoClaw, for enterprise adoption.
•The new agentic Copilot, potentially unveiled at the upcoming Microsoft Build conference (June 2-3), aims to reduce user friction by moving from conversation to direct action.
•Imbue's `mngr` tool enables launching and orchestrating hundreds of parallel AI agents (specifically Claude) for complex tasks.
•A case study demonstrates `mngr` using these agents to *test and improve* its own documentation and test suite.
•The process involves converting tutorial scripts into pytest functions, with agents executing, debugging, and refining them.
•This AI-driven testing approach highlights unclear interfaces and points to a future of more composable, scalable, and self-improving software development.
•Swiss researchers developed "Kinematic Intelligence," a software framework enabling robots to transfer learned skills to different hardware configurations without retraining.
•The system provides robots with an "innate mathematical awareness" of their physical limitations, including dangerous kinematic singularities, preventing joint jams and unstable movements.
•This AI-free approach aims to make robot skill transfer as seamless as syncing apps to a new smartphone, significantly reducing development and deployment complexities.
•OpenAI has introduced Workspace Agents, a new enterprise offering that evolves beyond custom GPTs to enable persistent, multi-step work automation across various third-party business applications.
•Powered by OpenAI's Codex, these agents can write and run code, use connected apps, remember context, and continue tasks autonomously, even if the initiating user leaves the session.
•Workspace Agents integrate with tools like Slack, Salesforce, Google Drive, and Microsoft apps, aiming to transform AI from individual productivity enhancement into a shared organizational resource fo...
•Microsoft plans to integrate agentic AI capabilities into Copilot, enabling it to autonomously perform tasks like managing emails and calendars.
•The initiative is inspired by the open-source OpenClaw platform, with Microsoft prioritizing safety and tracking features, akin to Nvidia's NemoClaw, for enterprise adoption.
•The new agentic Copilot, potentially unveiled at the upcoming Microsoft Build conference (June 2-3), aims to reduce user friction by moving from conversation to direct action.
•Imbue's `mngr` tool enables launching and orchestrating hundreds of parallel AI agents (specifically Claude) for complex tasks.
•A case study demonstrates `mngr` using these agents to *test and improve* its own documentation and test suite.
•The process involves converting tutorial scripts into pytest functions, with agents executing, debugging, and refining them.
•This AI-driven testing approach highlights unclear interfaces and points to a future of more composable, scalable, and self-improving software development.
