Odyssey Systems released Odyssey-3, a world model trained on visual observations that can control robots, drive cars, pilot drones, and train other AIs with minimal task-specific data. The same base model adapts across these diverse physical and virtual systems by learning general physics and cause-and-effect relationships rather than being specialized for each task.

• Odyssey-3 learns robot arm control with tens of hours of demonstrations and shows recovery behaviors not in training data, suggesting it grasps underlying physics rather than memorizing examples.
• With only 20 hours of simulated driving data, it autonomously drove cars in India, performing 77% as well as policies trained on real footage.
• The model can generate simulated environments where AI agents learn and discover world model failures, creating a feedback loop where each intelligence improves the other.

Periodic trained an AI model called Neon that outperforms frontier models like GPT-6 at analyzing X-ray diffraction data—a task materials scientists spend hours on—using less compute and lower cost. The model learned from experimental lab data through reinforcement learning with expert judgment, achieving a 55% success rate on their hardest internal benchmark.

• Neon reached 55.3% success on FrontierXRD (134 complex samples), a 20x jump from the base model's 2.7%, while costing less per analysis than GPT-6 Astra or Claude Fable 5.1
• Periodic built a custom scientific harness that achieved 3.8x higher success rates than Claude Code with standard tools, showing that model capability depends heavily on available databases and software
• The company used an LLM-judge ensemble calibrated to human expert ratings (74.6% agreement with humans, 84% with consensus) to generate training signals for reinforcement learning on tasks without ground-truth answers

Major record labels are suing Anthropic for allegedly using torrented music and songs to train Claude, arguing the $1.5 billion settlement with authors doesn't adequately punish the company. They claim Anthropic founders personally downloaded millions of copyrighted works and plan to keep using them indefinitely.

• Sony, EMI, and Warner Chappell allege Anthropic illegally downloaded "thousands upon thousands" of copyrighted songs via BitTorrent starting in July 2021, including works by Taylor Swift, Eminem, and the Beatles.
• Anthropic co-founder Benjamin Mann personally conducted the torrenting, with CEO Dario Amodei's approval; both are named as individual defendants.
• Publishers argue the $1.5 billion book settlement is insufficient deterrent given Anthropic's $2 trillion valuation, and that Claude can now generate songs mimicking artists' styles and reproduce lyrics verbatim, directly harming songwriters competing against AI-generated music.

Meta paused its Model Capability Initiative after an internal leak exposed employees’ private conversations, performance metrics, and keystroke logs across the company. The breach was rated SEV 2 on Meta’s 0–5 severity scale, prompting an investigation and a temporary suspension of the program.

• Meta paused its Model Capability Initiative (which recorded employee keystrokes/mouse movements for AI training) after a leak exposed private conversations, performance reviews, and transcription logs to all employees, rated SEV 2 on Meta's severity scale.
• Meta says there's no evidence the exposed data was abused, but is investigating how access controls failed.
• Employees are angry, saying Meta broke its promised privacy safeguards for a program that was already mandatory and controversial when launched in April.
• This follows other recent Meta security failures: an AI chatbot flaw that let attackers hijack Instagram accounts in May, and a rogue AI agent incident in March.

Jane Street builds its trading systems in OCaml, relying on the type system to catch errors at compile time and avoid run-time crashes. They snapshot every successful build to train an in-house AI model, rewarding only code changes that compile and pass tests.

• Jane Street runs its trading systems almost entirely on OCaml, betting on compile-time type checking (e.g., mandatory Option handling) to prevent costly runtime bugs.
• They rejected off-the-shelf tools like Copilot and Claude in favor of an in-house AI model trained on their own workspace snapshots.
• Developer workspaces are snapshotted every 20 seconds, and reinforcement learning rewards only edits that compile and pass tests.
• This snapshot-trained system underpins infrastructure supporting roughly $40 billion in annual trading volume.

Anthropic released a two-hour course led by the engineer behind Claude Code that walks you through building self-managing Claude agents. It covers terminal integration, file-system memory, hallucination-blocking hooks, and scaling to large codebases. Whether you’re a beginner or advanced user, you’ll finish ready to use Claude professionally.

• Anthropic released a free two-hour course on building Claude agents, led by the engineer who writes Claude Code
• Covers terminal integration, file-system-based memory, and hooks designed to catch and block hallucinations
• Includes guidance on scaling agents to large codebases via isolated environments and multi-file task orchestration
• Shows real failure examples (missing dependencies, misread logs) and how to harden pipelines against them