{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/23435db8309a450c91ef0c9eeca93246\" frameborder=\"0\" width=\"1986\" height=\"1489\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1489,"width":1986,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1489,"thumbnail_width":1986,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/23435db8309a450c91ef0c9eeca93246-c918e62a91dc05b0.gif","duration":507.719,"title":"Vide Finder: AI Music Recommender Demo Pipeline 🎵","description":"Hi, I am Youngmin Kim, and this Loom covers my project BDFinder, an AI Music Recommender built on the Cloud API. I explain the architecture for turning free text into game and mood analogy and a score, using my rule engine catalog and a nearly empty evaluator. I show live demos for happy pass and unhappy pass, with competence and energy results, and I demonstrate tuning with a few shot example. I also run pytest with 9 predefined and 5 normal profiles plus 2 categorical gap profiles, finishing in under 1 second. No action was requested from viewers."}