Existing workout trackers log performance but offer no guidance on why progress stalls. This gap led to the creation of WhyRep, a workout tracker with an AI coach. The coach's decisions are not arbitrarily generated but derived from a pre-written and approved methodology. The developer, with a background in exercise science, first establishes this methodology before utilizing AI.At its core, WhyRep employs deterministic engines to implement the methodology, which are rigorously tested. The LLM, Claude, serves as a conversational interface, explaining pre-approved coaching decisions and facilitating program adjustments. This approach aims for a nuanced coaching experience grounded in scientific principles.For example, the coach can suggest program modifications to address specific muscle growth goals. It can even identify less obvious training opportunities, like emphasizing the brachialis with shoulder-flexed curls. Every recommendation is traceable to the underlying, validated methodology.The developed features include comprehensive workout tracking, progression detection, autoregulation, deload logic, and plateau diagnosis. Users receive basic alerts or detailed solutions, depending on their subscription. A Kotlin Multiplatform core ensures consistent performance across Android and iOS.The backend coach chat integrates Claude, with methodology documents cached for context. The methodology itself is considered the core product, meticulously crafted and validated. It accounts for fractional muscle contributions from various exercises, providing a more holistic approach to volume calculation.Unlike other AI fitness apps, WhyRep encodes an evidence-based methodology rather than relying on an LLM to invent training science. Marketing efforts focus on educational gym content on social media platforms. The developer is seeking advice on audience building for technical products and communicating correctness and trust effectively.