A normal database finds exact matches. Search "cafe" and you get rows containing the…
This is a dev post classified by Jev as Databases (a tutorial), kept by the Dev Radar because it carries real work, not commentary.
A normal database finds exact matches. Search "cafe" and you get rows containing the word "cafe." A vector database does something stranger. Every item is a point in space, and it finds the points nearest to yours — even if they share no words at all. Ask for "a quiet cafe to work from" and it returns "study-friendly coffee shop, wifi." Zero words in common. Still the right answer. But here's the real problem. With 8.4 million vectors, comparing your query against all of them takes over three seconds. Nobody waits three seconds. So the database builds an index — it groups vectors into neig
Posted by Swati Gupta (36k followers) 2 h ago · 32 likes · 811 views · view the original post on X. Kept by the Dev Radar as Databases.
More dev work like this
- Your database bill is eating your runway. — @suraj_sharma14
- Got 18M requests 🤯 on this from GoogleOther bot. Now Blocked with few clicks in @vercel… — @rishi_raj_jain_
- Essential SQL concepts #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT… — @Sheraj99
- SQL basics you must know #BigData #Analytics #DataScience #AI #MachineLearning #IoT… — @Sheraj99
- Construct a bitemporal knowledge graph with Utopia that evolves as material arrives and… — @tom_doerr
- Jev at dating: https://date-with-jev.vercel.app 💔 — @rishi_raj_jain_
- We’re bringing Databricks Unity Gateway to developers on Neon (@neondatabase), and it’s… — @Yuchenj_UW
- Docs changes shouldn’t need a mystery deployment process. — @DanKornas
Every post is read and classified by Jev (TypeSafe): what it is, which market it belongs to, and whether the link is a real tool. 15.2k posts from 4.8k X accounts over the last 21 days, 1.7k tools, 12 markets. Collected every 5 minutes, fully re-ranked every hour — last update 2026-09-20 05:10 UTC. Full method.