Personas agents built from engagement data, scored on predictions (github.com)

A new system has been announced that constructs user personas based on actual engagement data, allowing for more reliable predictions about audience behavior before launching marketing assets. Unlike traditional personas that often rely on demographic information and assumptions, this method focuses on behavioral evidence, enabling personas to judge the effectiveness of content and make predictions that are then scored against real outcomes. The iterative process improves persona accuracy over time, revealing biases and refining predictions with continuous feedback loops. Built on Claude Code, the system utilizes markdown files to create persona agents that include documented behaviors, preferences linked to real engagement, and a "known biases" section that updates based on prediction scoring. This approach not only enhances the reliability of marketing strategies—applying to various outputs such as blog posts, ad copy, and product features—but also empowers developers and marketers to validate their assumptions and optimize content strategies systematically. The resulting framework fosters a more data-driven approach to audience understanding, promising to enhance the effectiveness of AI tools in understanding and engaging with users.