{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/f885f1d2403047cf9d584fd98a1375dd\" 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/f885f1d2403047cf9d584fd98a1375dd-3fab05023d0ccf19.gif","duration":233.22,"title":"VibeFinder Music Recommendations Demo, Reliability Checks 🎵","description":"In this Loom I walk through VibeFinder, my final project music recommendation app. It takes a user’s genre, mood, and energy, then scores 20 songs using signals like genre match, smoothness, and energy closeness, and returns the top candidates with a plain English explanation of ranking. I demo Gargoyle conflict detection, where an adversarial profile scores only 3.76 out of 8 versus 7.8 for a low fi profile. I also run an automated reliability evaluator across 5 preset profiles, with 5 out of 5 passing, plus consistent checks showing identical output order. No action is requested from viewers."}