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arxiv-sanity lite: tag arxiv papers of interest get recommendations
Arxiv Sanity Lite is a lightweight rewrite of Karpathy’s original arXiv discovery tool for tracking machine-learning research. It periodically polls the arXiv API for new papers and stores them locally for browsing. Users can tag papers they find relevant and receive recommendations tailored to each tag. Recommendations are generated with support vector machines trained on TF-IDF features extracted from paper abstracts.
Web interface for browsing, search and filtering arxiv submissions
...Personalized recommendations are generated through models trained from user preferences. The web interface uses Flask, Tornado, and SQLite, while the indexing pipeline relies on scientific Python libraries. A newer rewrite called arxiv-sanity-lite later became the recommended successor.