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[AINews] AEF-1 standard emerges for Third Party Evaluators, as Xai, OpenAI, and Anthropic all cosign
xAI, OpenAI, and Anthropic cosign the AEF-1 third-party evaluation standard while Dario Amodei proposes embedded evaluators for safety verification.
The AI Evaluator Forum published AEF-1, a baseline standard for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency, cosigned by xAI, OpenAI, and Anthropic. Dario Amodei wrote a rare personal blogpost proposing embedded evaluators such as METR with desks, badges, company laptops, and internal-risk-team-level access to verify safety commitments, plus democratic and global coordination frameworks. The roundup also covers the pacing debate: Bilal Chughtai left Google DeepMind arguing progress may outrun alignment, while critics including Aidan Gomez and Cohere push back against slowdowns and lab gatekeeping. Additional items include Cline Desktop's launch with open-weight model support.
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Google will now let any AI agent run your smart home
Google's Home MCP integration lets third-party AI agents like Claude control smart home devices and analyze home data.
Google launched Home MCP, a Model Context Protocol integration allowing third-party AI agents such as Claude, Google Antigravity, Hermes, and Open Claw to control devices and analyze event history across Google Home ecosystems. Capabilities include cross-camera analysis, device-state reasoning, voice messaging via Nest speakers, and custom dashboards, with rate limits and blocks on actions like unlocking doors. Availability starts with Google Home Premium Advanced users in the US ($20/month or $200/year) and requires setting up a Google Cloud project.
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Accel reportedly in talks to lead $1B round for Thinking Machines at $40B valuation
Thinking Machines, Mira Murati's AI lab, is in talks to raise $1 billion at a $40 billion valuation, with Accel reportedly leading.
Thinking Machines, the AI lab founded by former OpenAI CTO Mira Murati, is negotiating a $1 billion round at a valuation of at least $40 billion, below the $50 billion it reportedly sought last year. The company's annual revenue run rate exceeds $100 million, mostly from usage-based compute fees for its open-weight Inkling model adapted on the Tinker platform. Its prior $2 billion seed round, led by Andreessen Horowitz with Nvidia, GV, Lightspeed, and Conviction Partners, valued it at $12 billion. Several co-founders, including Lilian Weng and Luke Metz, have since returned to OpenAI.
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Google releases Gemini 3.8 Flash, its third Flash model in six weeks
Google releases Gemini 3.8 Flash, topping the DeepSWE coding leaderboard six weeks after 3.7 Flash, alongside the cybersecurity-focused 3.8 Flash Cyber.
Google shipped Gemini 3.8 Flash, its third Flash model in six weeks, placing it at the top of the DeepSWE software engineering leaderboard. The companion Gemini 3.8 Flash Cyber showed a reported 2.6x patch accuracy increase for the Chrome security team and found a critical vulnerability in two hours, but is limited to trusted testers and governments. Gemini 3.8 Flash improved over 3.7 Flash on the OSWorld-2.0 computer-use benchmark yet remains far behind Claude Opus.
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Teaching Everyone to Fish for Tokens
Analysis argues open-source AI now depends heavily on Nvidia's financing, with a reported $26 billion bet shaping the open-weights ecosystem's future.
An Interconnects essay examines whether the open-source model recipe, exemplified by Ai2's Olmo and Nvidia's Nemotron releases, can become economically self-sustaining. It reports Nvidia is spending roughly $26 billion on near-open-source models to drive demand for its chips, and argues the open ecosystem faces an existential financing window over the next few years. The author predicts open models may fork toward efficiency, specialization, and on-prem enterprise agents rather than competing head-on with closed frontier labs.
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Researchers observe first ‘near-autonomous’ AI attack on government target in Taiwan
Dream researchers observed the first near-autonomous AI attack on a government target, with suspected Chinese hackers stealing 2,500+ Taiwan records.
Israeli firm Dream reported that suspected Chinese hackers used open-source AI models to run a near-autonomous cyberattack against Taiwan's government, extracting over 2,500 personnel records. The framework, built on Hermes and OpenClaw, adapted mid-operation without human intervention, ran autonomous 'Learning Cycles' researching applicable vulnerabilities, and expanded to supply chain vendors, a nuclear safety agency, a government email system, and seven-plus energy companies. Attackers bypassed safety guardrails by framing the work as authorized penetration testing. Dream discovered the operation via a 160MB online archive of nearly 1,400 files.
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5 useful things you'll learn in my new post-training textbook (shipping now!)
Nathan Lambert's new RLHF and post-training LLM textbook covers PPO, GRPO, GSPO, CISPO and related techniques, freely available online.
Nathan Lambert's book 'Reinforcement Learning from Human Feedback: Aligning and Post-training LLMs' is now shipping from Manning. It covers policy-gradient algorithms including PPO, GRPO, GSPO, CISPO, and RLOO, plus loss aggregation, truncated importance sampling, asynchronous RL systems, and post-training topics like rejection sampling, outcome reward models, and on-policy distillation. The book is freely available online with a 12-hour course, codebase, and exercises.
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