{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/10aeeb46485649b081d685bef706d88a\" frameborder=\"0\" width=\"1280\" height=\"960\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":960,"width":1280,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":960,"thumbnail_width":1280,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/10aeeb46485649b081d685bef706d88a-e1f0556851f941c7.gif","duration":174.015,"title":"Music Recommender Reliability and Consistency Testing","description":"I ran the full music recommender pipeline via source.main to show how it loads songs, scores them against user preferences, and returns top ranked recommendations for multiple profiles like Conflicting Vibe, Intense Rock, Chill Lo Fi, and High Energy Pop. I also added a reliability and consistency testing system that automatically re-runs checks to ensure the same inputs produce consistent output. The log shows consistency testing for 18 songs per genre and reports whether the check passed. I demonstrated an intentional inconsistency by altering scoreSong with randomness, and the system detected it. No viewer action was requested."}