GitHub has introduced a new feature called Topics, which allows users to tag their repositories with descriptive words or phrases to enhance discoverability on the platform. This feature leverages a topic extraction framework, Repo-Topix, developed using techniques from text mining, natural language processing, and machine learning. The framework processes human-readable text from repository names, descriptions, and READMEs to generate, select, and refine candidate topics, addressing challenges such as noise filtering, topic similarity, and canonicalization. The system ranks topics using a tf-idf-based scoring system, aiming for a balance between uniqueness and relevance, while also experimenting with methods to improve topic variety and avoid redundancy. The initiative aims not only to improve the discoverability of public repositories but also to contribute to an evolving GitHub knowledge graph, mapping relationships among concepts, code, people, and projects. Future plans include refining the model with user feedback and potentially extending topic suggestions to private repositories, all while ensuring privacy and data security.