In this paper, we present ZoRRO (Zero-Weight Personalized Recommender System), a zero-weight and training-free framework for personalized news recommendation designed for scalable real-world deployment. We show that ZoRRO outperforms strong neural baselines in offline ranking evaluations and delivers click-through rate performance in online A/B testing that is nearly on par with a state-of-the-art deep learning model, while operating more than (600x) faster. Our experiments reveal gaps between offline and online performance, and show that models with similar click-through rate (CTR) outcomes can produce markedly different recommendation distributions, influencing the overall news flow. These findings position ZoRRO as a practical and efficient solution for large-scale news recommendation and highlight the importance of evaluating recommender systems using metrics beyond accuracy alone. Our code is available at https://github.com/johanneskruse/zorro.