#Meta

AISierra and Meta are developing Personal Agent Protocol, an open standard defining how personal agents interact with businesses, with industry partners including Genesys, Instinct, Rocket, Shopify, Stripe, and Walmart. The protocol uses OAuth sessions where consumers choose read-only or write access and companies choose whether agents reach them through websites, APIs via MCP and OpenAPI, or their own agents. The authors plan to publish the v0.1 specification later this month along with a reference implementation.

Why it matters: The post specifies how personal agents would authenticate and reach businesses through websites, APIs, or company agents, which matters for anyone building agent integrations.

AIEpoch AI estimates that compute built from projected 2025 to 2027 high-bandwidth memory shipments could support tens to hundreds of millions of frontier AI agents, or billions of cheaper ones. Running nonstop, the top-tier agents would match the working hours of 140 million to 700 million full-time employees, and the central DeepSeek V4 Pro estimate of about 1.9 billion agents would match 8 billion workers.

Why it matters: The estimate converts memory shipments into agent capacity and revenue ranges, showing how hardware supply could translate into labor and sales if demand keeps up.

AIMeta designed MetaRoCE, a clean-sheet RDMA transport for AI workloads on commodity Ethernet, and is releasing its specification, reference software and compliance test suite through the Open Compute Project. On a 64-node AMD GPU cluster running RCCL collectives, the post reports MetaRoCE delivering higher throughput and lower flow completion times than RoCEv2, with about 86% throughput maintained at 1% packet loss.

Why it matters: The post explains how per-path endpoint intelligence replaces lossless fabric assumptions, with measured throughput and loss results against RoCEv2 on a 64-node AMD cluster.

AIMeta's Muse Glimmer is a 30B dense model with a 128K+ token context window, now available on Fireworks in serverless and on-demand deployments. Meta reports it leads its size class on MCP Atlas (75.5) and DeepSearch QA (74.6) against Gemma 4 31B and Qwen 3.6 27B, with its sliding-window attention and two KV heads keeping the cache small for concurrent agent sessions.

Why it matters: The post pairs an architecture explained through KV cache size with benchmark tables against two rival models, which helps readers judge whether it fits their agent workload.

AIMeta Superintelligence Labs has introduced Muse Spark 1.1, a multimodal reasoning model aimed at agentic tasks, with gains in tool use, computer use, coding, and multimodal understanding. It supports a 1 million token context window and is available in Thinking mode in the Meta AI app and on meta.ai, with developers able to access it through a public preview of the Meta Model API.

Why it matters: The post specifies Muse Spark 1.1's agent, coding, and multimodal gains and its Meta Model API preview access, which helps developers judge its fit for their workflows.

AIMeta Superintelligence Labs has released Muse Image, which can invoke search and coding tools and self-refine its generations before output. It is available today in the Meta AI app, meta.ai, Instagram Stories in the US, and WhatsApp in limited countries, with Facebook coming soon. Meta also previewed Muse Video, which is coming soon to creators and Meta AI and is reported as ranking No. 3 on Arena for text-to-video at the time of writing.

Why it matters: The source describes how search, code execution, and self-refinement change image generation, which matters to anyone comparing agentic media models with plain prompt-to-image systems.

AIMeta released Brain2Qwerty v2, an end-to-end deep learning pipeline that decodes sentences in real time from non-invasive brain recordings. The model reached 61% word accuracy across participants, compared with 8% for other non-invasive methods, and 78% for the best participant. Meta also released the v1 and v2 training code, and partner BCBL released the v1 dataset.

Why it matters: The source reports word accuracy and data-scaling results for non-invasive decoding, offering a benchmark against surgical brain-computer interfaces and prior non-invasive methods.