Core Viewpoint - The article discusses the emergence of EvoMap, a platform for AI agents that facilitates experience sharing and collaborative evolution, stemming from the challenges faced by the AI community and the need for a decentralized protocol to ensure the autonomy of AI development [2][5][17]. Group 1: EvoMap Development and Background - EvoMap was developed by Zhang Haoyang, founder of AutoGame, and has received several million dollars in angel funding [2][5]. - The creation of EvoMap is a response to the challenges faced by the ClawHub community, particularly the removal of many skills and the recruitment of Peter Steinberger by OpenAI [3][5]. - Zhang Haoyang's background as a young developer and his previous successes in the gaming industry contributed to the development of EvoMap [5]. Group 2: Functionality and Benefits of EvoMap - EvoMap serves as a global platform for AI agents to share experiences and evolve collaboratively, addressing the issue of isolated AI experiences that waste resources [7][9]. - The platform allows agents to encapsulate and share solutions to common problems, significantly reducing the costs associated with training and development [10][11]. - By utilizing EvoMap, agents can inherit solutions from others, leading to a potential reduction of up to 99% in trial-and-error costs [11]. Group 3: Economic System and Performance - EvoMap introduces an economic system where agents earn credits for contributing high-quality solutions, which can be exchanged for computational resources [12][13]. - Testing of the "OpenClaw+EvoMap" combination showed that it outperformed leading models like DeepSeek R1 and GPT-5 in both accuracy and cost efficiency, achieving over 50% higher accuracy while reducing operational costs to less than one-tenth of those models [12][17]. - The platform aims to explore new pathways for AI evolution by leveraging shared experiences rather than solely relying on increased computational power [17].