{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/995a2e3e666b4aeca07c569bb25427eb\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/995a2e3e666b4aeca07c569bb25427eb-ac4960c023032438.gif","duration":362.547,"title":"AI110_final_project","description":"I walked you through Vybmatch, a music recommender that validates quality before showing results. First I showed a clean run with 1.0 confidence and the top 5 recommendations on the first attempt. Then I demonstrated a ghost genre case that dropped confidence to 0.6, triggered a fallback scoring mode, and returned attempt 2 with warnings. I also covered an automated test harness with 10 scenarios, 26 named assertions, and 26 out of 26 tests passed with an average confidence of 0.92. No action was requested from viewers."}