Developers often rely on various online resources, such as blogs, to keep themselves up-to-date with the fast pace at which software technologies are evolving. Singer et al. found that developers tend to use channels such as Twitter to keep themselves updated and support learning, often in an undirected or serendipitous way, coming across things that they may not apply presently, but which should be helpful in supporting their developer activities in future. However, identifying relevant and useful articles among the millions of pieces of information shared on Twitter is a non-trivial task. In this work to support serendipitous discovery of relevant and informative resources to support developer learning, we propose an unsupervised and a supervised approach to find and rank URLs (which point to web resources) harvested from Twitter based on their informativeness and relevance to a domain of interest. We propose 14 features to characterize each URL by considering contents of webpage pointed by it, contents and popularity of tweets mentioning it, and the popularity of users who shared the URL on Twitter. The results of our experiments on tweets generated by a set of 85,171 users over a one-month period highlight that our proposed unsupervised and supervised approaches can achieve a reasonably high Normalized Discounted Cumulative Gain (NDCG) score of 0.719 and 0.832 respectively.