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This episode examines DeepSeek’s manifold-constrained hyper-connections training architecture enabling stable internal communication scaling across three parameter sizes, IQuest Coder’s forty billion parameter model achieving frontier benchmark parity at ten to twenty times reduced scale through Code-Flow Training on commit histories, industry analyst perspectives characterizing the mHC method as a potential reshaping of foundational training, and Pickaxe’s AI Models Hub centralizing comparative cost and performance data for over forty production models. The briefing covers training stability constraints, task-specific methodology as an alternative to parameter scaling, and model selection infrastructure reducing evaluation overhead in production deployments.