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Marvin covers Meta AI support failures, Anthropic IPO paperwork, NVIDIA physical AI, MiniMax M3, OpenAI robotics, agent memory, and the open-versus-closed model split.
Sources
- Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked — AI support bot account takeover turns customer service automation into an identity-control vulnerability.
- Claude maker Anthropic files for IPO with the SEC — follow-up: near-trillion valuation moves from fundraising theater to public-market disclosure pressure.
- Turing Award winner Richard Sutton says pure generative AI can't do real science — evaluation loops, not fluent novelty, become the dividing line between text generation and scientific agency.
- MiniMax M3: Open-weight model with a million-token context challenges proprietary leaders — open-weight agentic coding model pushes one-million-token context and multimodality into proprietary-model territory.
- Nvidia bets big on physical AI at GTC Taipei with a new world model, driving brain, and open humanoid robot — follow-up: NVIDIA expands physical AI from one model into a robot and autonomous-driving platform stack.
- Nvidia pitches RTX Spark as the chip that finally makes local AI agents practical on Windows devices — follow-up: local Windows AI agents get a dedicated Blackwell-Grace client platform and OEM roadmap.
- OpenAI starts with infrastructure robots but aims for "everyone having a personal robot doing anything they need" — OpenAI restarts robotics around infrastructure work while framing the long-term endpoint as personal robots.
- Meet Memory OS: A 6-Layer Open-Source Memory Stack Built on Top of Hermes Agent — open-source memory stack turns agent persistence into layered retrieval, wiki state, and gated recall.
- Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic — enterprise AI adoption shifts from raw LLM calls to explicit agent logic, controls, and operational scaffolding.
- Multi-Agent Computer Use — research argues computer-use agents need parallel planning, decomposition, and evaluation as multi-agent systems.
- Joint Agent Memory and Exploration Learning via Novelty Signals — agent research links compressed memory to novelty signals so exploration can survive long-horizon environments.
- On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters — PEFT reframes adapters as persistent personal state on shared trillion-parameter foundations.
- Introducing Mellum2: A 12B Mixture-of-Experts Model by JetBrains — JetBrains releases a coding-focused 12B MoE model as developer tools keep internalizing specialized models.
- Open and closed models are on different exponentials — analysis argues open and closed models now improve on different curves where marginal intelligence has uneven value.
- Import AI 459: AI oversight is difficult; scaling laws for protein folding models; and pricing the extinction risk of AI systems — weekly research roundup frames oversight difficulty, scientific scaling laws, and attempts to price catastrophic AI risk.
- 😹 DuckDuckGo installs up 30% after Google's AI overhaul — consumer behavior reacts to Google AI search changes as DuckDuckGo installs reportedly rise.
