Audio separation has recently become a mature technology, with state of the art reaching performance that permits wide audience application. While recent research continues to develop sophisticated methods to make results even better, we present our recent work in bringing the reference open-unmix model to the edge. This results in open.unmix.app, a web-based platform for audio separation. We demonstrate two contributions: First, we report on the difficulties of bringing a state-of-the-art deep learning-based model to javascript using TensorFlow.js including all necessary pre-and postprocessing. Solving them enables performing separation and enhancement of music and speech signals directly in the browser. Second, we present a platform that exploits this web-separation technology and allows users to share separated multitrack audio on the web using a custom, embeddable multi-track player.