video1.0<iframe src="https://www.loom.com/embed/54683866a75b4fe5bf4342d85c958a45" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/54683866a75b4fe5bf4342d85c958a45-f069ceeab6c8a164.gif345.09This Loom walks through Shaurya’s approach to tracking persona drift in a fully offline-first adaptive rack system. The raw dataset lacked real-time timestamps, so Shaurya implemented a synthetic chronological clock that bucketed every 15 messages into a simulated day to analyze sentiment and tone across 734 continuous days. To detect drift triggers without heavy LLM summarization, he used a lightweight TF-IDF keyword extractor combined with relationship tags to isolate what caused tone shifts.