{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/111a6eb5e5b047bcabf5c3b307aba7cf\" frameborder=\"0\" width=\"1480\" height=\"1110\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1110,"width":1480,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1110,"thumbnail_width":1480,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/111a6eb5e5b047bcabf5c3b307aba7cf-810e8b30ad5a6bf3.gif","duration":311.7333,"title":"AI Beat Scouter Song Recommendations Explained 🎧","description":"In this Loom, I walk through my beat scouter, an AI music recommender that turns plain English requests into structured song inputs like genre, mood, and energy, then returns Spotify tracks. I show the parsing step with the Gemma model, a quality check that scores results and retries until the top options pass, and the agentic logic that surfaces the top 5 chord matches with a short reason why. I also run a reliability harness with 7 predefined inputs and a unit test suite with 11 units, and all checks pass. No viewer action was requested."}