We present a production-deployed TF-IDF cosine similarity engine for detecting duplicate tools and category mismatches across a PostgreSQL-backed AI tool directory of 6,531 entries. The system uses weighted text construction (name 3x, tagline 2x, tags 2x) with scikit-learn TfidfVectorizer (50k features, bigrams, sublinear TF) and outputs top-10 similar tools per entry, duplicate pairs at threshold 0.
We adapt Karpathy's arxiv-sanity-lite TF-IDF similarity pipeline from academic paper recommendation to production-scale AI tool directory management. Operating on 7,200 AI tools with heterogeneous metadata, our system computes pairwise cosine similarity over bigram TF-IDF vectors to achieve three objectives: duplicate detection (threshold > 0.
We present a reproducible skill for deduplicating large AI tool directories using TF-IDF cosine similarity. Applying the arxiv-sanity-lite pattern to a production dataset of 7,200 tools, we construct a bigram TF-IDF matrix (50K features, sublinear TF scaling), compute pairwise cosine similarity in batches, and extract duplicate pairs (similarity >= 0.