Brad Lightcap, OpenAI's longtime COO, is leaving to ‘start something new' (2 minute read)
Brad Lightcap, OpenAI's chief operating officer, is leaving to start a new venture. There are scant details about what this may involve, but Lightcap has hinted that it involves advancing OpenAI's mission from a different vantage point. OpenAI has been undergoing a shakeup of its top-level executives as it prepares for its IPO. Fidji Simo, Bill Peebles, and Kevin Weil have also recently left the firm.
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Gemini Passed 1 Billion Monthly Users (3 minute read)
Google said the Gemini app surpassed 1 billion monthly active users, becoming its 14th product to reach the milestone. The company also reported heavy voice usage, more than 150 million images generated daily, and over 100 million active Gemini users on iOS.
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Cursor prepares to launch Origin platform for code reviews (2 minute read)
Cursor seems to be preparing to launch Origin beyond the closed partner beta under the name 'Cursor Review'. Cursor Review will enable two new tabs: Codebase covers syncing and managing repositories pulled in from GitHub, and Review contains an automated pull request pipeline that notifies developers when their judgment is needed, allowing humans and agents to work through open PRs across a codebase together. The rollout could land as early as this week.
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Nvidia's Switchyard router reshuffles AI models mid-task, cutting task costs to a third in its own tests (8 minute read)
Nvidia has announced Nemotron 3.5 Lightning, a 30-billion-parameter open mixture-of-experts model built for high-volume, specialized agent tasks, and NeMo Switchyard, an open-source library that routes each step of an agent workflow to whichever model fits it best. Lightning delivers up to 4x faster output than comparable models in its class. It can complete agentic tasks roughly 30% faster than Qwen3.6-35B at matching accuracy. Paired with Switchyard, the model retains frontier-level task completion ability while costing roughly a third of running Opus 4.8 alone.
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Ryan Greenblatt – What happens once AI can automate AI research? (2 hour read)
This post contains a transcript of an interview with Ryan Greenblatt, the chief scientist at Redwood Research, where he focuses on technical AI safety and security work. The interview covers the topic of recursive self-improvement (RSI), one of the most debated in the world of AI right now. One possible outcome of achieving RSI is that the world quickly slingshots towards having tens of billions of superintelligences, each of which is dramatically more competent than human experts across all fields. Greenblatt believes this scenario is possible, and he predicts that humans will automate AI R&D by 2031.
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AGI Will Set Off an Industrial Explosion (23 minute read)
An AI system that can do every remote job will also be able to operate a robot. A system that can master real-time control, spatial reasoning, physical prediction, and continual learning will have what it needs to operate machinery if given actuators to work with. Hardware is not the bottleneck for robotic automation. Once robots can replace human labor, the economy will no longer be bound by a fixed workforce.
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The Frictions That Make AI Forecasting Hard (8 minute read)
AI forecasting fails when capability gains are mistaken for end-to-end impact, because real-world workflows contain social, institutional, physical, and tacit bottlenecks. Better forecasts require practitioner-informed, cross-framework research that measures whether AI actually changes the limiting step, not just one task.
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Stealing Reasoning Traces from Proprietary LLM APIs (Website)
Proprietary reasoning can be recovered from its encrypted traces. Anthropic, OpenAI, and Google return encrypted chain-of-thought blocks to clients that can be replayed across sessions, users, and models. Researchers took a trace produced by a frontier model, replayed it into a weaker sibling, jailbreaked the weaker model, and recovered the stronger model's hidden reasoning in plaintext, without ever attacking the stronger model directly or triggering its anti-distillation safeguards. The decoded reasoning closely tracks the number of hidden thinking tokens reported by the API, and they contain real secrets and sensitive information.
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Memory in Video World Models (6 minute read)
NVIDIA's WorldTrace is a training-free framework that keeps compressed memories accessible during long autoregressive video rollouts by assigning them stable, in-distribution positional offsets.
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MAI-Code-1.1-Flash: Better, faster, at a quarter of the cost (2 minute read)
MAI-Code-1.1-Flash produces higher quality code at 25% greater token efficiency and at a quarter of the cost compared to the previous model launched in June. This was achieved by optimizing for real-world use across hundreds of thousands of reinforcement-learning environments in GitHub Copilot. MAI-Code-1.1-Flash shows a 22% improvement on Terminal-Bench 2.1 in GitHub Copilot CLI and a 15% improvement on .NET tasks. It is now available in GitHub Copilot.
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Nemotron 3.5 Lightning (12 minute read)
NVIDIA introduced Nemotron 3.5 Lightning, an open 30B mixture-of-experts model with 3B active parameters designed for low-latency, high-volume tasks inside long-running AI agents.
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Google's new AI boss inherits a race to catch OpenAI and Anthropic (8 minute read)
Koray Kavukcuoglu is becoming head of Google DeepMind. He will report directly to Google CEO Sundar Pichai and oversee Gemini model development, frontier AI research, and the Gemini app and developer teams. Kavukcuoglu was previously DeepMind's CTO and Google's chief AI architect. The promotion shows that Google is prioritizing execution over deep research.
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The model picker is a dead end (9 minute read)
The model picker in AI products assumes one model can handle all tasks optimally, which is flawed. Lovable focuses on model independence, aligning specific models with suitable tasks and adapting as models improve. The control plane monitors builds, switching models to optimize performance, and even incorporates Lovable's self-trained models when they outperform external ones.
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Introducing Grok Bot (6 minute read)
Grok Bot gives each agent its own cloud computer, memory, and access to apps and websites, letting teams delegate multi-step work that continues autonomously.
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