1. Nvidia agrees to acquire Hugging Face for $12.9303 billion, pledges platform won’t be tied to its compute Earlier reporting described unsigned acquisition negotiations that valued Hugging Face at more than $13 billion but could still collapse.

2. GPT-6 Astra Begins Phased Rollout, With Key Cybersecurity Capabilities Open First to Approved Organizations OpenAI has begun rolling out GPT-6 Astra, its first model to reach the company’s highest internal cybersecurity capability threshold, called “Critical.

3. 760 Software Recommendation Tests Find Perplexity Heavily Citing Low-Ranked Sites and Mass-Produced Buying Guides A study of Perplexity’s web-connected sonar and sonar-pro models found that software recommendations frequently relied on little-known websites, including three sites that had collectively published

In Brief

• Nvidia’s PAIR pools idle home computers for local AI workloads Nvidia released a free, open-source tool that connects compatible Windows, Linux, and macOS computers to run local AI tasks in parallel, using idle capacity and adapting as devices join or leave. The beta supports RTX 20-series and newer GPUs, RTX Pro and DGX Spark systems, and Apple M4 chips or newer.

• Google adds conversational voice modes to Gmail, Docs, and Keep Google is rolling out Gmail Live, Docs Live, and Keep Live, which let users retrieve inbox information, structure documents, and capture contextualized notes through spoken conversations. The English-language mobile rollout begins with paid Google AI plans, with Workspace business availability coming later.

• WeatherNext 3 produces hourly global forecasts at up to five-kilometer resolution Google DeepMind and Google Research introduced WeatherNext 3, an AI weather model that incorporates live satellite and weather-station observations instead of relying solely on delayed numerical simulations. It generates hourly forecasts and adds variables tailored to wind and solar energy production; Google cites Brightband evaluations for its accuracy claims.

• Meta offers steep Muse Spark discounts in exchange for training data Meta’s contributor pricing cuts Muse Spark input-token charges from $1.25 to $0.10 per million and output-token charges from $4.25 to $0.20 when customers permit their prompts and outputs to support future model development. Muse Spark is designed for coding and other agent-based applications.

• Abliteration.ai commercializes access to models stripped of safety refusals Abliteration.ai hosts modified open-weight models whose guardrails have been removed, offering them through a browser and API for uses including cybersecurity testing. TechCrunch reported that one hosted model supplied harmful cyber and biological instructions, while the startup said it is still defining its safeguards and customer-verification responsibilities.

• Thinking Machines reportedly seeks $1 billion at a $40 billion valuation Thinking Machines, the AI laboratory founded by former OpenAI CTO Mira Murati, is reportedly discussing a $1 billion funding round led by existing investor Accel. The company has introduced the open-weight Inkling model and reportedly exceeds $100 million in annualized revenue, but neither party confirmed the talks.

• ChatGPT, Claude, and Grok recover from simultaneous outages OpenAI, Anthropic, and xAI restored their AI services after overlapping disruptions affected ChatGPT, Claude, Grok, and related tools. Anthropic attributed its partial outage to infrastructure trouble and xAI linked its incident to its Memphis data center, while no common cause was established.

• Ollie gains SOC 2 compliance for its family-focused AI assistant Ollie, a subscription assistant that helps households manage calendars, email, shopping, appointments, and bills, has completed a SOC 2 audit covering its data-security controls. The startup says it does not train on or share customer data and uses remote browser sessions when users must authenticate or pay.

• DisCo turns repository knowledge into reusable skills for research agents Researchers introduced DisCo, an agent that distills operational knowledge from machine-learning repositories into compact, verified skills, alongside a library containing more than 5,000 skills from 1,000 repositories. Under the authors’ fixed experimental setup, adding the skills improved results across four autonomous-research benchmarks.

• EarlyEval cuts agent-testing costs by predicting outcomes mid-run The EarlyEval framework uses lightweight classifiers to stop benchmark runs when an agent’s likely success or failure becomes sufficiently clear. Across SWE-bench Verified, TerminalBench, and Toolathlon, its authors report eliminating 13%–26% of agent steps with prediction accuracy of 89%–97% and average resolve-rate changes of one to two percentage points.

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