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Meta launches Muse Wonder and Muse Wonder Full models
On October 13, 2026, Meta released the Muse Wonder series, comprising two models, Muse Wonder and Muse Wonder Full, which it called its most capable models to date, achieving SOTA on personal and professional agent tasks, covering professional domains such as law, finance, and health as well as frontier coding capabilities. Both models feature Active Memory and Active Focus, and were trained, evaluated, and released according to the Meta Superintelligence Scaling Framework. According to Artificial Analysis data, the two models scored 52 and 56 respectively on Intelligence Index v4.3 at maximum reasoning effort, higher than the 48 of Muse Spark 1.3 (max); Muse Wonder is already available on Meta's first-party API and Muse Code, while Muse Wonder Full is available only to Muse Code users. On the same day, Arena added the two models to Agent Arena testing, and Simon Willison released the llm-meta-ai 0.2 plugin supporting the new models. On October 14, Arena announced Muse W
eased the Muse Wonder series, comprising two models, Muse Wonder and Muse Wonder Full, which it called its most capable models to date, achieving SOTA on personal and professional agent tasks, covering professional domains such as law, finance, and health as well as frontier coding capabilities. Both models feature Active Memory and Active Focus, and were trained, evaluated, and released according to the Meta Superintelligence Scaling Framework. According to Artificial Analysis data, the two mod
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Gemini 4 Argon appears on the GCP quota page
On October 13, 2026, Gemini 4 Argon appeared on the GCP quota page. Testing Catalog, citing AiBattle, said the model was expected to launch within a few hours and called it a precursor to an imminent API model release; earlier that day, a Google employee had also teased an upcoming release. That evening, Testing Catalog reported that the model had begun rolling out and was already live in GCP's Agent Studio, where users could test it, with Google AI Studio and the Gemini app expected to follow. On October 14, Google officially released Gemini 4 Argon, open to developers and enterprises worldwide, usable via AI Studio and the Gemini platform API, and supported in Google Antigravity as well as the Gemini app and Search AI Mode for Ultra and Pro subscribers; reports said it supports long decoding output of up to 1,048,576 tokens and announced limited-time introductory pricing.
earlier that day, a Google employee had also teased an upcoming release. That evening, Testing Catalog reported that the model had begun rolling out and was already live in GCP's Agent Studio, where users could test it, with Google AI Studio and the Gemini app expected to follow. On October 14, Google officially released Gemini 4 Argon, open to developers and enterprises worldwide, usable via AI Studio and the Gemini platform API, and supported in Google Antigravity as well as the Gemini app an
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Meta launches personal AI agent Muse
On September 9, 2026, Meta released the personal AI agent Muse, powered by the Muse Spark model. It can be used for conversations in the Muse app or on WhatsApp, and can carry out tasks such as sending emails, booking itineraries, filling out forms, and negotiating prices, and it comes back to confirm when approval is needed. Muse runs on a dedicated cloud virtual machine, Muse Secure VM, with an independent Sentinel agent governing its networking behavior. Users can choose which apps to connect and the scope of authorization, and can ask it to forget specific information. The product has launched in the United States on iOS, Android, and muse.ai, and will soon arrive on AI glasses; most features are free, with a subscription plan also offered. Subsequently, on September 11, David Singleton said the personal avatar feature was the first feature he fell in love with; on September 18, Tarek Sheasha shared a user's experience and invited users to edit the Muse feed prompt; on September 23, Alex Cornell looked back at the "status" status pill in Muse's earliest design drafts and its design philosophy; on September 28, in response to a user's claim that Muse disclosed a buyer's home address to a Facebook Marketplace seller, David Singleton said he had replied to the person on Threads and sent a direct message, and was willing to assist with the investigation.
On September 9, 2026, Meta released the personal AI agent Muse, powered by the Muse Spark model. It can be used for conversations in the Muse app or on WhatsApp, and can carry out tasks such as sending emails, booking itineraries, filling out forms, and negotiating prices, and it comes back to confirm when approval is needed. Muse runs on a dedicated cloud virtual machine, Muse Secure VM, with an independent Sentinel agent governing its networking behavior. Users can choose which apps to connect
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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.
nse. 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
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MaaS Has Arrived: Anthropic Turns an Operating Profit for the Quarter, China's Public Cloud MaaS Revenue Reaches 10.63 Billion Yuan in Half a Year
Anthropic's revenue in the second quarter of this year was 11.555 billion US dollars, achieving an operating profit of 974 million US dollars for the first time, with about 80% of revenue coming from pay-as-you-go billing. IDC data shows that in the first half of 2026, China's public cloud MaaS market revenue reached 10.63 billion yuan, up 889.4% year on year, and its share of public cloud PaaS revenue rose from 5.2% in the second half of last year to 21.0%. Zhipu's open platform and API revenue was 825 million yuan, accounting for 86.5% of total revenue; Volcano Engine ranked first with a 45.1% share. The article argues that model services can now generate gross profit, and the unit of competition has shifted to inference optimization and delivery capability.
Anthropic's revenue in the second quarter of this year was 11.555 billion US dollars, achieving an operating profit of 974 million US dollars for the first time, with about 80% of revenue coming from pay-as-you-go billing. IDC data shows that in the first half of 2026, China's public cloud MaaS market revenue reached 10.63 billion yuan, up 889.4% year on year, and its share of public cloud PaaS revenue rose from 5.2% in the second half of last year to 21.0%. Zhipu's open platform and API revenue
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NVIDIA uses a 12,000-line Megakernel to optimize Kimi K3 inference, and vLLM says AI code is now faster than human review
Benjamin Chislett, an engineer at NVIDIA CentML, used a 12,000-line Megakernel written in CuTe DSL to optimize Moonshot AI's Kimi K3 inference. The vLLM blog reports over 840 tokens/s at batch 1 and a 1.5x to 2x increase in decoding throughput across multiple configurations, while SemiAnalysis estimates 423 tok/s/user on GB300 NVL72, a 1.68x improvement. The vLLM blog says agent-optimized inference code is now outpacing human review and maintenance speed, so the team proposed a fast-track model optimization RFC that lets reviewers judge designs from high-level abstractions rather than reviewing code line by line. The article also mentions that NVIDIA is developing optimizations for other models for vLLM, such as DeepSeek V4.1.
nd a 1.5x to 2x increase in decoding throughput across multiple configurations, while SemiAnalysis estimates 423 tok/s/user on GB300 NVL72, a 1.68x improvement. The vLLM blog says agent-optimized inference code is now outpacing human review and maintenance speed, so the team proposed a fast-track model optimization RFC that lets reviewers judge designs from high-level abstractions rather than reviewing code line by line. The article also mentions that NVIDIA is developing optimizations for other
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Latent Space announces AIE CODE coding agent conference curation themes
Latent Space host swyx introduces the curation approach for the annual flagship conference AIE CODE, which is single-track, invitation-only, and aimed at about 1,000 agent engineers. This year's three themes are Scaling without Slop, System One models, and Increasing Ambition, among which Jarred Sumner will share Bun's rewrite in Rust and a Bun AOT preview. The article also rounds up that day's AI news, including Mistral's open-weight release and OpenAI's frontier model monitoring.
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Kimi K3.1 Pro hands-on test: Why does quota consumption still spark controversy after the API price cut?
Kimi released K3.1 Pro on October 24, cutting the API input price from 20 yuan to 13 yuan per million tokens and the output price from 100 yuan to 68 yuan, but users questioned the cost-effectiveness due to rapid token consumption; on October 26, the company apologized, compensated quotas, and adjusted the model configuration. The author used a Pro subscription account to test five creative tasks, which consumed a total of about 25.48% of the monthly quota, with "The Peach Blossom Spring" at 6.91% the highest and the mechanical keyboard at 1.46% the lowest; consumption mainly came from Plugin entries and multiple rounds of revisions. The article also mentions the controversy over overseas bloggers being asked to hide paid partnership labels in promotions, and that former miHoYo international president Jin Wenyi joined Moonshot AI to oversee overseas expansion.
Kimi released K3.1 Pro on October 24, cutting the API input price from 20 yuan to 13 yuan per million tokens and the output price from 100 yuan to 68 yuan, but users questioned the cost-effectiveness due to rapid token consumption; on October 26, the company apologized, compensated quotas, and adjusted the model configuration. The author used a Pro subscription account to test five creative tasks, which consumed a total of about 25.48% of the monthly quota, with "The Peach Blossom Spring" at 6.9
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SemiAnalysis Analyst Interprets Anthropic S-1: The Ledger Behind the Valuation and AGI Narrative
The Verge's Decoder podcast invited SemiAnalysis analyst Joey Brookhart to break down the S-1 filed by Anthropic, discussing its potential Nasdaq listing at a valuation of $1.5 trillion to $2 trillion. Brookhart said Anthropic has achieved over $70 billion in ARR, with inference gross margins turning from negative 94% two years ago to positive 85%, and revenue per megawatt of inference at about $65 million, but revenue is highly concentrated in coding scenarios, and about 70% of compute is used for training and research. He also mentioned that Anthropic has $500 billion to $600 billion in compute commitments over the next 7 to 10 years, and compared OpenAI's more consumer-oriented business structure.
The Verge's Decoder podcast invited SemiAnalysis analyst Joey Brookhart to break down the S-1 filed by Anthropic, discussing its potential Nasdaq listing at a valuation of $1.5 trillion to $2 trillion. Brookhart said Anthropic has achieved over $70 billion in ARR, with inference gross margins turning from negative 94% two years ago to positive 85%, and revenue per megawatt of inference at about $65 million, but revenue is highly concentrated in coding scenarios, and about 70% of compute is used
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Day 130 of Xiaowei's gray release: WeChat breaks mini programs into parts and hands them to AI
It has been 130 days since WeChat's native AI assistant "Xiaowei" launched. Its entry points have expanded from the 16 officially disclosed to 28 measured by third parties, covering chat, search, official accounts, Channels, and PC. Through the developer integration guide released on June 8, WeChat requires developers to break mini programs into atomic interfaces and atomic components and write SKILL documentation; mini programs that have not integrated will be unable to be recommended and invoked by WeChat AI. The article also mentions that the development model must pass platform evaluation (80 points or above in the three items of service delivery, interaction experience, and performance quality, and 60 points or above in scenario coverage) to be eligible for invocation, while the invocation ranking rules have not yet been made public.
It has been 130 days since WeChat's native AI assistant "Xiaowei" launched. Its entry points have expanded from the 16 officially disclosed to 28 measured by third parties, covering chat, search, official accounts, Channels, and PC. Through the developer integration guide released on June 8, WeChat requires developers to break mini programs into atomic interfaces and atomic components and write SKILL documentation; mini programs that have not integrated will be unable to be recommended and invok
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