Peer-sampling protocols constitute a fundamental mechanism for a number of large-scale distributed applications. The recent introduction of WebRTC facilitated the deployment of decentralized applications over a network of browsers. However, deploying existing peer-sampling protocols on top of We-bRTC raises issues about their lack of adaptiveness to sudden bursts of popularity over a network that does not manage addressing or routing. Spray is a novel random peer-sampling protocol that dynamically , quickly, and efficiently self-adapts to the network size. Our experiments show the flexibility of Spray and highlight its efficiency improvements at the cost of small overhead. We embedded Spray in a real-time decentralized editor running in brow-sers and ran experiments involving up to 600 communicating web browsers. The results demonstrate that Spray significantly reduces the network traffic according to the number of participants and saves bandwidth.