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Moonshot AI founder Yang Zhilin released a 40-minute video detailing the training process of the Kimi K2 model, which cost only $4.6 million. In an 8-model real-time programming competition, Kimi K2 took first place, defeating GPT-5.5 and others, demonstrating how a small team can overturn the traditional compute-stacking paradigm through architecture optimization.
This article introduces a framework for using seven specific Claude sub-agents to automate roles such as research, editing, project management, and financial analysis, effectively replacing a high-cost team.
RAO (Recursive Agent Optimization) is an end-to-end reinforcement learning approach for training LLM agents to spawn, delegate to, and coordinate with recursive copies of themselves, turning recursive inference into a learned capability.