not much happened today

**ARC-AGI-3** benchmark introduced by **@arcprize** and **François Chollet** resets the frontier for general agentic reasoning with humans solving 100% of tasks versus under 1% for current models, focusing on zero-preparation generalization and human-like learning efficiency. The scoring protocol sparked debate over its harsh efficiency-based metric compared to prior ARC versions and other benchmarks like **NetHack**. The community acknowledges the benchmark highlights weaknesses in current LLM agents in interactive, sparse-feedback environments. Concurrently, agent infrastructure advances with **LangChain** launching Fleet shareable skills for reusable domain knowledge, and **Anthropic** revealing **Claude Code auto mode** for classifier-mediated approval balancing autonomy and manual confirmation. Browser and coding agents are evolving into trainable systems beyond prompt wrappers, exemplified by **BrowserBase** and **Prime Intellect** collaboration.