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Meta pauses Muse conversation sharing feature

Meta has paused the conversation sharing feature of its Muse AI agent. The reason is that researchers at security firm Claroc found that the URLs of these shared links could be retrieved on the public web. Claroc said it found more than 6,800 Muse links through public DNS records and reviewed hundreds of conversations, with content involving marketing company revenue reviews, small business owner loan applications, workplace year-round employee calendars, and notes from job seekers' calls with AI labs. Muse spokesperson Tracy Clayton said users can share specific Muse content and generate pages accessible via links, and the company is reviewing the report and considering adjusting how links are discovered. According to Unite.AI, on October 20 Claroc rechecked 40 shares that previously could be opened without an account, and none could be opened, but hostnames containing conversation titles remained in passive DNS history; Muse launched on September 8, and share addresses consist of the conversation title plus a random ID, and when first opened, DNS records the full hostname.

English translation is being prepared.

Meta has paused the conversation sharing feature of its Muse AI agent. The reason is that researchers at security firm Claroc found that the URLs of these shared links could be retrieved on the public web. Claroc said it found more than 6,800 Muse links through public DNS records and reviewed hundreds of conversations, with content involving marketing company revenue reviews, small business owner loan applications, workplace year-round employee calendars, and notes from job seekers' calls with AI labs. Muse spokesperson Tracy Clayton said users can share specific Muse content and generate pages accessible via links, and the company is reviewing the report and considering adjusting how links are discovered. According to Unite.AI, on October 20 Claroc rechecked 40 shares that previously could be opened without an account, and none could be opened, but hostnames containing conversation titles remained in passive DNS history; Muse launched on September 8, and share addresses consist of the conversation title plus a random ID, and when first opened, DNS records the full hostname.

View story:Meta pauses Muse conversation sharing feature

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English translation is being prepared.

Fireworks Launches Meta's Open-Weight Model Muse Spark Open

On October 20, 2026, Fireworks launched Meta's open-weight model Muse Spark Open on the day of its release, making it available to users through its serverless API. The model is an 816B-parameter MoE model that activates 42B parameters per token, has a 1M-token context window, and is aimed at long-horizon coding and agentic tasks. Fireworks said the model ranks first among 75 models on Vals AI's Harvey legal agent benchmark with 25.4%, nearly four times that of Claude Opus 5 (6.7%); on the Rogo Big Finance Bench, it surpasses Claude Opus 5.5 at every difficulty level. Fireworks also said it validated the weights with its own production stack, scoring 91.1% on AIME26, 89.7% on GPQA Diamond, and 84.3% on SWE-bench Verified, with consistent results when rerun using SGLang. On Fireworks, the model has zero data retention enabled by default, uses only compute located in the United States with no additional surcharge, and supports fine-tuning with serverless LoRA.

Meta Releases Muse Glimmer Technical Report

Meta has released the Muse Glimmer technical report. The report introduces a 30B open-weight model aimed at agentic tasks on consumer-grade hardware. According to the report, at 4-bit precision the model can run at practical speeds on a single consumer GPU in a Mac or PC, and it leads open-source models of the same scale in tool use and multi-step planning. The report covers aspects including model architecture, distillation recipe, and quantization. There are no further developments at present.

Meta releases Muse Glimmer technical report

Meta has released the Muse Glimmer technical report. The report introduces a 30B open-weight model aimed at agentic tasks on consumer-grade hardware. According to the report, at 4-bit precision the model can run at practical speeds on a single consumer GPU in a Mac or PC, and it leads open-source models of the same scale in tool use and multi-step planning. The report covers aspects including model architecture, distillation recipe, and quantization. There are no further developments at present.

View story:Meta Releases Muse Glimmer Technical Report

Meta compresses its Agent model Muse Glimmer 30B to run on-device

On October 20, 2026, Meta released the technical report for Muse Glimmer, a 30B open-weight model aimed at agent tasks on consumer-grade hardware, running at 4-bit precision on a single consumer GPU. The reporter adds that the model leads open-source models of the same scale in tool calling and multi-step planning, surpassing Qwen3.6-27B and Gemma4-31B on some metrics; after 4-bit quantization it is about 17GB, targeting devices with 24GB of VRAM, and uses DFlash speculative decoding to reach about 233 tokens/s on an RTX 5090, roughly 3.1 times faster than ordinary decoding. There are currently no subsequent developments.

Fireworks launches Meta's open-weight model Muse Spark Open

On October 20, 2026, Fireworks launched Meta's open-weight model Muse Spark Open on the day of its release, making it available to users through its serverless API. The model is an 816B-parameter MoE model that activates 42B parameters per token, has a 1M-token context window, and is aimed at long-horizon coding and agentic tasks. Fireworks said the model ranks first among 75 models on Vals AI's Harvey legal agent benchmark with 25.4%, nearly four times that of Claude Opus 5 (6.7%); on the Rogo Big Finance Bench, it surpasses Claude Opus 5.5 at every difficulty level. Fireworks also said it validated the weights with its own production stack, scoring 91.1% on AIME26, 89.7% on GPQA Diamond, and 84.3% on SWE-bench Verified, with consistent results when rerun using SGLang. On Fireworks, the model has zero data retention enabled by default, uses only compute located in the United States with no additional surcharge, and supports fine-tuning with serverless LoRA.

View story:Fireworks Launches Meta's Open-Weight Model Muse Spark Open

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Meta is developing Confidential VMs functionality on the Muse web client, allowing users to run Muse tasks in an encrypted confidential environment. Meta previously previewed that this feature would launch before the end of 2026. A newly surfaced hidden setting shows that the feature will initially be available only to Meta AI subscribers; after toggling it on, voice mode, image and video generation, and wearable device support will be temporarily unavailable. Meta says conversations run in a secure encrypted environment and that Muse data will not be used to improve Meta's AI. The feature is currently still in development, and there is no further evidence of an official launch date.

View story:Meta is developing Confidential VMs functionality on the Muse web client

Meta says it is training the Muse foundation model on the Prometheus supercomputing cluster

Meta announced that the first phase of its gigawatt-scale supercomputing cluster Prometheus has been completed in New Albany, Ohio, and is being used to train the Muse series of foundation models, including Muse Wonder. The cluster currently consists of hundreds of thousands of GPUs, with a full-scale target of about 1 million, and has deployed two generations of platforms, NVIDIA GB200 NVL72 (Catalina) and GB300 NVL72 (Clemente), with Vera Rubin NVL72 to be introduced later. Meta says Muse Wonder's pre-training goodput exceeds 85%, reinforcement learning goodput exceeds 90%, and the interruption rate has been reduced by 99% from its mid-2025 peak. Subsequent reports added that during Muse Wonder training, an interruption occurred on average only about every 47K GPU hours; Meta said that in a cluster of this scale, even a single host failing to meet performance standards would slow down an entire training round, so Prometheus was designed for large-scale fault tolerance.

English translation is being prepared.

Meta announced that the first phase of its gigawatt-scale supercomputing cluster Prometheus has been completed in New Albany, Ohio, and is being used to train the Muse series of foundation models, including Muse Wonder. The cluster currently consists of hundreds of thousands of GPUs, with a full-scale target of about 1 million, and has deployed two generations of platforms, NVIDIA GB200 NVL72 (Catalina) and GB300 NVL72 (Clemente), with Vera Rubin NVL72 to be introduced later. Meta says Muse Wonder's pre-training goodput exceeds 85%, reinforcement learning goodput exceeds 90%, and the interruption rate has been reduced by 99% from its mid-2025 peak. Subsequent reports added that during Muse Wonder training, an interruption occurred on average only about every 47K GPU hours; Meta said that in a cluster of this scale, even a single host failing to meet performance standards would slow down an entire training round, so Prometheus was designed for large-scale fault tolerance.

View story:Meta says it is training the Muse foundation model on the Prometheus supercomputing cluster

Meta compresses its agent model Muse Glimmer 30B to run on-device

On October 20, 2026, Meta released the technical report for Muse Glimmer, a 30B open-weight model aimed at agent tasks on consumer-grade hardware, running at 4-bit precision on a single consumer GPU. The reporter adds that the model leads open-source models of the same scale in tool calling and multi-step planning, surpassing Qwen3.6-27B and Gemma4-31B on some metrics; after 4-bit quantization it is about 17GB, targeting devices with 24GB of VRAM, and uses DFlash speculative decoding to reach about 233 tokens/s on an RTX 5090, roughly 3.1 times faster than ordinary decoding. There are currently no subsequent developments.

View story:Meta compresses its Agent model Muse Glimmer 30B to run on-device

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Meta updated its commercial terms, removing language that had been interpreted as allowing it to scrape website content to train AI. Neiman Lab and Cornell University law professor James Grimmelmann previously argued that the clause was intended to let Meta use scraped content for free to train AI, and could bypass blocking instructions in robots.txt. Meta Vice President of Communications Andy Stone and spokesperson Daniel Roberts had responded that Meta's use of web crawlers had not changed, and this terms update directly removed the disputed language. Other reports said that when Meta updated its business services terms, it had stated it had the right to use content from business account domains, web pages or URLs to train AI models, and deleted that language after controversy; Meta said the update was merely a consolidation of existing advertising terms, not an expansion of crawler behavior, and that the revised terms limit crawlers and data collection to advertising and business products only, while site owners can still control crawler permissions via robots.txt.

View story:Meta updates its commercial terms, removing language that could have allowed scraping of news website content

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English translation is being prepared.

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Meta releases open-weight model Muse Spark Open

On October 15, 2026, Meta released the open-weight model Muse Spark Open, with the weights going live on Hugging Face the same day under the Muse Model License. According to the PyTorch Blog, the model has 816B total parameters, 42B activated parameters, and a 1M token context window; it is natively multimodal and aimed at coding and agentic tasks, adds safety post-training on top of Muse Spark, and can run on SGLang and vLLM. On October 16, the model launched on Amazon SageMaker JumpStart with two variants, BF16 and NVFP4; the same day it entered live testing in Arena's Agent Arena and launched in Code Arena's WebDev and other projects. On October 17, Arena announced that Muse Spark Open (Max) ranked first among US open models on the Code Arena WebDev leaderboard with a score of 1511, placing 13th among all open models.

Meta updates its commercial terms, removing language that could have allowed scraping of news website content

Meta updated its commercial terms, removing language that had been interpreted as allowing it to scrape website content to train AI. Neiman Lab and Cornell University law professor James Grimmelmann previously argued that the clause was intended to let Meta use scraped content for free to train AI, and could bypass blocking instructions in robots.txt. Meta Vice President of Communications Andy Stone and spokesperson Daniel Roberts had responded that Meta's use of web crawlers had not changed, and this terms update directly removed the disputed language. Other reports said that when Meta updated its business services terms, it had stated it had the right to use content from business account domains, web pages or URLs to train AI models, and deleted that language after controversy; Meta said the update was merely a consolidation of existing advertising terms, not an expansion of crawler behavior, and that the revised terms limit crawlers and data collection to advertising and business products only, while site owners can still control crawler permissions via robots.txt.