video1.0<iframe src="https://www.loom.com/embed/4ff3971917c44a1aa2284bc48fd044b4" frameborder="0" width="1114" height="835" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>8351114Loomhttps://www.loom.com8351114https://cdn.loom.com/sessions/thumbnails/4ff3971917c44a1aa2284bc48fd044b4-5d18abe394ecc7db.gif300.19I built on the Module 3 Music Recommendation Simulation with a reliability layer. Every recommendation now includes a confidence score based on high genre match, mode match, and energy similarity, and the CLI prints reasons plus a reliability check. I also run an evaluation harness on predefined user profiles, so we get pass rate and average confidence, like 3 out of 4 passing and an average confidence of 0.94. The harness showed a key failure case where mood and energy can conflict, and the engine favors energy closeness. No viewer action was requested.