This demonstration presents MeowsReader, a real-time news ranking and filtering prototype. MeowsReader illustrates how a general class of continuous top-k queries offers a suitable abstraction for modeling and implementing real-time search services over highly dynamic information streams combining keyword search and realtime web signals about information items. Users express their interest by simple text queries and continuously receive the best matching results in an alert-like environment. The main innovative feature are dynamic item scores which take account of information decay, real-time web attention and other online user feedback. Additionally, a trends detection mechanism automatically generates trending entities from the input streams, which can smoothly be added to user profiles in form of keyword queries.