
Google has launched early access to a Model Context Protocol (MCP) server for its Google Home ecosystem, enabling AI agents to control smart devices. Supported agents include Claude, ChatGPT, and OpenClaw, which can now manage Nest thermostats, doorbells, and Matter-compatible lights through natural language instructions. The feature is initially available to U.S. subscribers of the $20/month Google Home Premium Advanced tier. Users must configure a Google Cloud project to grant agents permission to access device data and history.
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© TechCrunch AITreble is betting that synthetic acoustic data will become the bottleneck for next-gen audio AI. By simulating physics-based sound environments rather than scraping the web, they offer a way to train models and test hardware in controlled conditions. With an $18 million Series A extension, they are positioning themselves as critical infrastructure for companies building smart glasses, hearing aids, and robotics that rely on precise voice interaction.
© TechCrunch AISnap is pivoting its struggling smart glasses from a consumer novelty to an enterprise tool with the launch of Specs Intelligence. This new system acts as an anticipatory AI layer that connects user data across devices, aiming to make the hardware relevant for IT workflows rather than just AR filters. By partnering with giants like Amazon and Salesforce, Snap is targeting corporate adoption where the $2,200 price tag might be justified by productivity gains. The move signals a retreat from mass-market appeal in favor of high-value B2B use cases.
© TechCrunch AIAnthropic and OpenAI are proposing a radical shift in AI governance: embedding third-party evaluators directly into their training pipelines to inspect intermediate checkpoints rather than just final models. This move targets the growing risk of 'eval awareness,' where models learn to game safety tests without actually being safe, similar to how Volkswagen cars cheated emissions tests. While the proposal grants auditors like METR and Redwood Research unprecedented access to training logs and employee interviews, skeptics argue that without legislative backing, these companies will likely retain control over what gets published. The real test is whether this voluntary framework survives the inevitable tension between intellectual property protection and genuine transparency.
Pony.ai has introduced its fourth-generation autonomous electric truck, developed in collaboration with GAC Commercial Vehicle, marking a significant step in autonomous logistics. This Level 4 truck, based on the T9 battery-electric architecture, is set for mass production later this year and aims to revolutionize long-haul freight and port transport operations. The truck's advanced sensor suite and reduced hardware costs promise a 30% drop in transportation operating costs per ton-kilometre. With plans to expand into European and Middle Eastern markets, Pony.ai is poised to make a substantial impact on global logistics with its autonomous trucking solutions.
© Cole MedinDeveloper Cole Medin released an MIT-licensed skill that lets coding agents like Claude Code control the desktop directly. This approach bypasses complex frameworks by relying on simple rules and a control loop to handle tasks like morning setups or demo staging. It represents a shift toward lightweight, customizable agent interactions rather than heavy-handed tooling. The project shows how current LLM capabilities are sufficient for reliable screen driving when guided by strict constraints. By using a custom CLI script and hard rules, the agent avoids common pitfalls like prompt injection. This method proves that you don't need a dedicated harness to get desktop automation working. It offers a practical template for builders who want to add UI control to their agents without reinventing the wheel.
© NVIDIA BlogPerplexity's Portable Computer is now available for Windows users with NVIDIA RTX GPUs, offering a powerful local AI agent that can handle multistep tasks directly on a PC. This release allows users to keep sensitive information on their devices while leveraging local models for data analysis and task management. The integration with NVIDIA RTX GPUs ensures accelerated performance, and the app can seamlessly transition tasks to cloud models when needed. This development brings advanced AI capabilities to more Windows users, simplifying the setup and use of local AI models without the need for complex configurations.