Meta unveiled Muse, a consumer personal AI agent that can carry out tasks (including email, travel booking, bill renegotiations, and purchases) and continues operating after the app is closed until approval is needed. The rollout starts in the US and Muse offers basic use free up to 100 million tokens per week, after which subscriptions begin at $20/month for Power and $100/month for Maximum. Meta says Muse data is isolated in a dedicated “Secure VM,” Sentinel enforces approvals and outbound access, and “Confidential VM” encryption only under the user’s key is planned before the end of the year.
DeepSeek announced it is hiring about 150 engineers to build and maintain the server-side infrastructure for running and evaluating AI agent sandboxes, rather than doing AI model research. The hiring targets September 7, with roles focused on Agent Elastic Compute and related systems like scheduling, storage, and virtualization. As workloads scale to hundreds of thousands of concurrent sandboxes, DeepSeek plans upgrades and rewrites to its backend, including DSec and its sandbox environment and recovery mechanisms.
llm-d, an open-source inference framework led by IBM Research, Red Hat, and Google, demonstrated efficient serving of agentic LLM workloads on existing enterprise GPUs by avoiding redundant context processing and scaling different inference stages separately. It deployed GLM-5.2, about 753B parameters (39B active), on 544 NVIDIA H100 GPUs and reached over 6.6 million output tokens per minute at peak with up to 3,000 concurrent coding agents and zero preemptions. The result is that operators can self-host an open-weight frontier-scale mixture-of-experts model for long-context agents on H100 fleets with software changes that improve latency/throughput and cut per-token costs versus equivalent commercial API pricing.
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