Javier Albarracín V.·Aug 22Introducing EliteSQL: An Embedded Database for AI-Native Applications“SQLite-fast” reads, better concurrent writes, and native vector search — without assembling an entire database stack.A response icon1A response icon1
Npranay·Aug 7BM25 vs. Dense Vector Search: A Practical Guide for Engineers Building Search and RAG Systems1. Introduction
Andreeawade·May 18Embedding interoperability is becoming its own categoryFor the last few years, most of the attention in AI infrastructure has gone toward the models themselves: bigger models, cheaper models…
Satyam Yadav·Apr 15Dynamic Embeddings and Blue-Green Vector RolloutsHow enterprise RAG systems stay semantically fresh without downtime
Juan D. Rodriguez·Mar 16Building a Semantic Image Search Engine with FastAPI, Celery, and SupabaseA tiny example of a distributed system
Alexander Shereshevsky·Feb 17Vector Store for Graph RAG: Can We Use the Existing Analytical DB?Can your analytical warehouse double as the vector layer for enterprise AI memory? I tested it.
Sabih Hasan·Feb 10The Great Vector Migration: Migrate Your Vector Database with Zero DowntimeGoogle recently deprecated its legacy text embedding models (text-embedding-004, text-embedding-005) in favour of gemini-embedding-001. If…
Vedhashree Sampath·Jan 2AI for Full-Stack Engineers (Part 4): Embeddings & Vector Databases — The New “Index”You already know how to index data, optimize queries, and design for scale. Embeddings aren’t a new concept — they’re a new way to index…
InOpenVINO-toolkitbyAaryap·Nov 7, 2025Bridging Vision and Language: My GSoC 2025 Journey Building the Multimodal Embedding Explorer…How I spent my summer visualizing intelligence, detecting noisy captions, and learning to see AI differently.
