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Person: "caspar_br"
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cosmos-3 nemotron-3-ultra minimax-m3 nvidia runway novita vercel cloudflare openclaude flowith omnimodal-models mixture-of-experts autoregressive-models diffusion-models structured-prompts fine-tuning open-weight-models multimodality agent-models benchmarking model-serving context-windows token-efficiency kimmonismus clementdelangue artificialanalysis scaling01 ctnzr caspar_br eliebakouch pbdtokenrouter rauchg gitlawb notjazii lostinlatencyx zhihufrontier
NVIDIA led open-source AI model releases with Cosmos 3, a comprehensive omnimodal world model unifying language, image, video, audio, and action using a Mixture-of-Transformers design, and Nemotron 3 Ultra, a 550B parameter open-weight model noted for high serving speed and strong evaluation performance. The Cosmos Coalition was launched to foster an open ecosystem for physical AI world models. Meanwhile, MiniMax M3 debuted as a multimodal agent/coding model with 1M context and strong benchmark scores, gaining rapid ecosystem support from vendors like Novita and Vercel AI Gateway. However, MiniMax M3 showed some inefficiencies such as high token consumption and verbose self-check loops. These developments highlight advances in open physical AI, multimodality, and agent models with significant community and infrastructure engagement.
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codex chatgpt openai github microsoft nous-research moonshot-ai langchain prime-intellect agent-infrastructure agent-first-ux remote-ssh programmatic-access-tokens sandboxing continual-learning agent-trace-data multi-agent-workflows ide-integration browser-extensions hwchase17 caspar_br bentannyhill jakebroekhuizen willccbb
OpenAI expanded Codex integration with the ChatGPT mobile app enabling remote task management and introduced Remote SSH, hooks, and programmatic tokens for enterprise automation. The IDE ecosystem is shifting to "agent-first" UX with GitHub Copilot App preview and VS Code launching a multi-agent workflow window. Open-source agents like Nous/Hermes integrated Codex runtime, and Kimi released a web bridge extension supporting multiple coding agents. LangChain released significant agent infrastructure including SmithDB for agent trace data and LangSmith Engine for trace analysis and continual learning, launching LangChain Labs to improve agents via production trace feedback loops.
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arc-agi-3 claude-code anthropic langchain arcprize primeintellect agentic-reasoning interactive-environments benchmarking efficiency-metrics zero-preparation-generalization agent-infrastructure trainable-agents classifier-approval fchollet mikeknoop scaling01 _rockt mark_k andykonwinski bradenjhancock jeremyphoward togelius bracesproul hwchase17 caspar_br _catwu
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.