The recent breakthroughs in advanced AI models are reshaping how traders approach cross-asset analysis. Running GLM-4.7 alongside Claude Code, I built a scanning tool that identifies outperformance opportunities across different asset classes simultaneously. The workflow starts with Claude mapping out the implementation architecture, establishing the framework for multi-class asset comparison. From there, the coded function executes systematic scans—comparing performance metrics, tracking volatility patterns, and flagging relative strength positions across crypto, equities, commodities, and alternative assets. This blend of planning-first approach with AI-assisted coding accelerates the process from conception to execution, making it feasible to monitor broader portfolio correlations in real time. The key insight: letting AI draft the blueprint first, then code the logic, substantially reduces iteration cycles and improves accuracy in identifying genuine alpha signals versus noise.