27/01/2026
🚀 We just shipped a cleaner, faster way to group news headlines at scale.
Our service pulls ~600 headlines per run (from 5 sources) and needs to group “same story” headlines together. Doing this with a reasoning model sounded nice, but it gets costly and slow when you run it constantly.
So we switched to vector similarity:
• Generate embeddings for each headline (765-d vectors)
• Store them in Supabase (pgvector)
• Use cosine similarity to find and group related titles
Result: faster, cheaper, and way more consistent. We’re currently hitting ~90% correct grouping, and we’ll push it further with hybrid matching (semantic + keyword + category checks).
Read the full post 👇
https://elobyte.com/optimizing-content-aggregation-from-llm-based-grouping-to-vector-similarity-search/
If you’re building an AI-powered SaaS or content-heavy product and want help scaling features like this safely, you can book a free SaaS plan & quote: https://calendly.com/dev-elobyte/30min
Al Mustarik This article was originally published on Medium The Problem I Was Trying to Solve My task was to […]
