On April 21, Shu Jun, Chief Technology Officer of MuleRun, the world's first self-evolving personal AI project, shared at the offline event themed "Decoding Web 4.0: When AI Agents Take Over On-Chain Permissions" that traditional methods of data acquisition relying on search engines or free APIs are inadequate for high-precision needs, especially in financial trading scenarios. Free market data interfaces often suffer from delays and data omissions, making them unsuitable for high-frequency or specialized decision-making. To address this, MuleRun integrates high-quality data sources and specialized tool interfaces, lowering the barrier for users to access without needing to procure or configure APIs themselves, allowing them to utilize a data system that includes on-chain data, U.S. stock data, and multi-dimensional analytical capabilities. The platform also introduces various 'Skills', including trading strategy evaluation, multi-role simulated debates, and quantitative backtesting functionalities. Backtesting tasks are executed on external servers to resolve issues of insufficient computational power in local and general cloud environments, enhancing the actual user experience for quantitative users. Additionally, MuleRun is expanding its diverse data sources, such as integrating cross-border e-commerce product data, to enhance the Agent's information acquisition capabilities in different business scenarios. In terms of 'intelligent decision-making', Shu Jun emphasized that the core of the Agent lies in its ability to handle complex tasks, including multi-period data analysis, indicator calculation, and strategy execution. Cost control has also become a key metric. Currently, AI systems generally face high token consumption issues, which directly affect user costs. In response, MuleRun has systematically optimized token usage at the engineering level, significantly reducing costs while ensuring task completion quality, thereby improving task execution efficiency under the same budget. Shu Jun stated that data quality and model efficiency will directly determine the performance of the AI Agent in tasks, and in the future, the platform will continue to seek a balance between high-quality data access and low-cost intelligent computation to support more complex on-chain application scenarios.