#Embeddings

My longtime labmate-turned-student/friend1 Dhruva Karkada recently wrote a sick paper on data statistics which deservedly went viral on Twitter, in part beca...

When we shipped Auto Embeddings — the feature that turns any text column into a vector automatically, with no separate model service to run — the most common piece of feedback was about speed.

Most vector database discussions happen at small scale. A few thousand documents. A few hundred...

At 620 million monthly users, calling a frontier model for every image recommendation isn't a strategy — it's a bill. Pinterest CTO Matt Madrigal solved it by gutting Qwen3-VL's vision layer and rebuilding it with proprietary embeddings, cutting costs 90%…

In this article, you will learn how to build a context-aware semantic search engine in Python that combines embedding-based similarity with structured metadata filtering.

Explore vector embeddings, their function in AI, types like Word2Vec and BERT, applications in semantic search and NLP, and implementation considerations for de

Learn how to integrate semantic search with embeddings in a Spring Boot application for more accurate search results

Optimize Retrieval-Augmented Generation pipelines: chunking strategies, embedding selection, retrieval tuning, and evaluation.

Learn embeddings techniques for semantic search, clustering, and similarity matching with vector databases.

Cut embeddings 1536 → 256 without breaking recall. Matryoshka native truncation, PCA on legacy models, recall curves, decision rule.

From Dev.to - machinelearning: Understanding Encoder-Only Transformers: The Foundation of BERT and RAG Retrieval

Local embeddings vs hosted APIs in 2026. Crossover math, where each one wins, and the cases where shipping your own model is the cheaper bet.

In this article, we’ll explore the basics of Vector Databases (Vector DBs) and why they are the...

This is my Day 2 of learning AI fundamentals where I will be covering the following...

From Dev Community: Understanding Text Similarity with Embeddings and Cosine Similarity

LeBonCoin's ML team built a custom late-fusion transformer that uses pre-computed visual embeddings and character n-gram text vectors to predict ad at

Hey DEV community, CallmeMiho here. I’ve been auditing AI architectures all week, and I keep seeing...

AI in Practice, No Fluff — Day 8/10 I built a memory system for one of my AI projects. Every...

A few months ago, I built a chatbot that sounded very smart… Until it started confidently giving...