Inspired by Karpathy's autoresearch, I taught VibeHQ to self-evolve, not just evolving a single agent, but evolving the collaboration methods of the entire Multi Agents. 7 fully automated runs, zero human intervention: • Token usage: 7.2M → 5.7M (peak reduction of 62%) • Coordination-related issues reduced (occurrences of duplicate work, etc.): 4 → 0 • PM token waste: -91% Loop: benchmark → collaborative quantification and LLM analysis of failure modes → /optimize-protocol rewrite coordination code → rebuild → repeat. AI observes the agents' team collaboration failures, analyzes why they failed, and then modifies its own source code to coordinate collaboration logic, all with zero human input, completely allowing AI to organize its own team dynamics. After looking into related topics, autoresearch focuses on automatically optimizing model training. The previous Ralph was a single agent's autonomous loop, while Gastown runs 20-30 Claude Codes simultaneously for orchestration but lacks evolutionary capability. These are impressive, but in the end, they all focus on evolving the capabilities of a single agent. No one is evolving team collaboration itself—how to divide labor, how to avoid conflicts, how to share context, how to unblock each other—just like in the real world, AI teams also need to develop synergy. Imagine what this could evolve into: • Agents develop their own team culture and work synergy. • Adaptively organized by project, allocating a 3-person team or a 7-person team based on project development levels. • The more projects undertaken together, the stronger the team. • Agents can onboard new teammates during project execution, automatically reallocating tasks. Honestly, what it will ultimately evolve into? I don't know, but that's the most exciting part.