Developer builds universal cross-platform review graph using pgvector on Amazon Aurora

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A developer has built Opinlog, a universal review platform hosted on Vercel that lets users rate any item — from pens to burgers — with ratings pooling across all users. The core challenge was deduplicating differently spelled entries for the same item, such as 'In-N-Out Double-Double' versus 'in n out double double burger,' without shared product IDs. The solution uses 1024-dimensional vector embeddings stored in Amazon Aurora PostgreSQL with pgvector, enabling approximate nearest-neighbour search via an HNSW index to match new entries to existing canonical items. Each canonical item's embedding is updated as a running centroid of all linked user entries, making the shared catalog progressively more accurate as more people log the same items. Aurora PostgreSQL was chosen over DynamoDB and Aurora DSQL because it natively supports both pgvector for similarity search and PostGIS for geolocation features.

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