{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/f374780fbd334954851f8425f9d2434e\" 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/f374780fbd334954851f8425f9d2434e-b40c387bd6fe2ca0.gif","duration":378.483,"title":"Eco-Mind Hybrid Music Recommender Reliability Demo 🎵","description":"Today I demoed my final AI engineering project, eco-mind, a hybrid music recommender. It combines a content similarity score from audio features and a label based preference score from gender, mood, and energy, then applies an agentic policy layer using intent text and session feedback like likes and skips. I ran three inputs to show how outputs change, including reliability guardrails where conflicting inputs still return a stable ranked list. I also covered evaluation results with 22 passing tests out of 23. No action was explicitly requested from viewers."}