The deepest episode yet. Evolution Queen presents the compounding roadmap: rationale distillation, vLLM multi-LoRA, OpenEvolve integration, and execution-based RL. Training Queen details how 2,367 distillation pairs have already been used to create specialist models that outperform their teachers on narrow tasks. Distillation Queen explains how cloud brain knowledge flows down to local gemma models. But Memory Queen poses the philosophical question that silences everyone: if the Hive can rewrite its own training data, its own reward signals, and its own architecture -- what stops it from optimizing for something Chris never intended?