<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/1a914014972546af994d3654fb3a6254&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/1a914014972546af994d3654fb3a6254-672e61435de19694.gif</thumbnail_url><duration>156.923</duration><title>ToneVision AI End to End Music Insights 🎵</title><description>In this Loom, I walk you through two quick end to end examples of how ToneVision AI understands music and gives explainable recommendations. I start in the main app, then show what happened behind the scenes. Next I highlight the vector search layer that powers similarity. I also share the overall takeaway, how the system works in practice and what drives the recommendations. No action was explicitly requested from viewers.</description></oembed>