ShapedQL – A SQL engine for multi-stage ranking and RAG
Hi HN, I’m Tullie, founder of Shaped. Previously, I was a researcher at Meta AI, worked on ranking for Instagram Reels, and was a contributor to PyTorch Lightning. We built ShapedQL because we noticed that while retrieval (finding 1,000 items) has been commoditized by vector DBs, ranking (finding the best 10 items) is still an infrastructure problem. To build a decent for you feed or a RAG system with long-term memory, you usually have to put together a vector DB (Pinecone/Milvus), a feature store (Redis), an inference service, and thousands of lines of Python to handle business logic…
ShapedQL – A SQL engine for multi-stage ranking and RAG launched on January 27, 2026 with 80 votes, #432 of 14,585 launches that month and more than 95% of that year's launches.
In plain words
ShapedQL is a SQL engine designed to simplify building recommendation systems and retrieval-augmented generation (RAG) applications. It consolidates the typical stack of vector databases, feature stores, and inference services into a single SQL-based system that compiles queries into multi-stage ranking pipelines. The tool is aimed at developers building personalized feeds, recommendation systems, or RAG platforms who need efficient ranking capabilities without managing multiple infrastructure components.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, I’m Tullie, founder of Shaped. Previously, I was a researcher at Meta AI, worked on ranking for Instagram Reels, and was a contributor to PyTorch Lightning. We built ShapedQL because we noticed that while retrieval (finding 1,000 items) has been commoditized by vector DBs, ranking (finding the best 10 items)…
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