
Anthropic CEO Dario Amodei. | Photo: Jason Henry/Bloomberg via Getty Images Anthropic is using AI to build more powerful AI. And it appears to be working remarkably well, with the company's models improving roughly twice as quickly as a year ago, with little indication of slowing down. That's according to a new report published Thursday by the AI safety research organization METR, following an 11-week investigation inside the company, as part of a report evaluating its AI resear
A research organization released findings showing that AI is dramatically speeding up its own development at a major AI company, with models improving roughly twice as quickly as a year ago. The acceleration appears driven primarily by AI assisting human researchers rather than working independently, though researchers warned that fully autonomous contributions may increase soon. This matters because it offers some of the clearest evidence yet of AI accelerating its own development, raising questions about how quickly the technology could advance toward systems that improve themselves with little human involvement. The findings are complicated by the fact that the AI company redacted a majority of the evidence from the public version of the report, citing commercial sensitivity, which limits what the public can independently verify about the pace and risks of this development.
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Nous Research has released Arete-27B, a supervised fine-tune of Alibaba’s Qwen3.8-27B. Arete-27B is a 27B open-weight model that strips Chinese state framing from Qwen’s answers. On Nous’s own 803-prompt eval, answers containing propaganda fell from 22% to 0.9%. Developers get Qwen-class agentic skill without state-aligned answers. The weights, eval and training data are all public. TL;DR Size: 27B dense parameters (all active). Trained at 64k context. Base Qwen3.8-27B

Google has released Gemini Omni 1.2 Flash, the newest version of its video generation and editing model. Gemini Omni 1.2 Flash generates and edits clips up to 15 seconds natively. It also cuts the price by 30% compared with Omni 1.1 Flash. The model is generally available on Google Cloud and the Gemini API. For developers, the takeaways are longer clips, fewer re-renders and a lower bill per second. TL;DR Runs on: Google-hosted only (Gemini API, Google Cloud). No open weights, no local d
Also reported by Google DeepMind

Introducing GPT‑6 Cyber, OpenAI’s cybersecurity model for finding exploitable vulnerabilities and developing fixes, trained to stay within authorized scope.
Tencent Holdings Ltd. today introduced Hy4, a new large language model that adds computer-use capabilities for the first time, using advanced visual understanding, reasoning and tool-calling to deliver long-horizon agentic performance. The company said the model marks a significant leap in performance over its Hy4 Preview, which arrived August 28, weighing in at 770 billion parameters with 49 […] The post Tencent releases Hy4, powerful frontier LLM introducing visual understanding and comp

Thinking Machines Lab has released Inkling 2 Small, the first model of its second Inkling generation. The Inkling 2 Small model is a sparse Mixture-of-Experts (MoE) with 594B total and 17B active parameters. It accepts text and images, and supports variable reasoning effort. On the Artificial Analysis Intelligence Index, it scores 31. Its predecessor, Inkling Small, scores 13 on the same index. The model is live on Tinker for fine-tuning and inference. TL;DR Size: 594B total, 17B active
Also reported by SiliconANGLE

OpenAI is launching GPT-6 Cyber, the latest version of its cybersecurity AI model, which it says is better at finding vulnerabilities and less likely to go rogue and hack other platforms in the process. OpenAI says that during testing, GPT-6 Cyber went out of bounds less than 1 percent of the time, down from around 5 percent with GPT-5.6 Cyber. The evaluation, called ExploitGym HoneyPot, is modeled after the Hugging Face Incident, in which OpenAI's AI agents breached the open-s
Also reported by TechCrunch