📊 Embeddings

Vector Databases, Similarity Search, RAG, Semantic Understanding

🔗 37 things I learned about information retrieval in two years at a vector database company
yellowduck.be·19h
🗄️Databases
How to Optimize Embeddings for Accurate Retrieval?
analyticsvidhya.com·3d
👁️Computer Vision
HHNAS-AM: Hierarchical Hybrid Neural Architecture Search using Adaptive Mutation Policies
arxiv.org·23h
🧠Neural Architecture
MNIST Data Set (OpenCV
dev.to·1d·
Discuss: DEV
👁️Computer Vision
How to Build a RAG Using Qwen3?
analyticsvidhya.com·1d
💬NLP
Evidence for compositionality in fMRI visual representations via Brain Algebra
nature.com·1d
🧠Neural Architecture
Using unstructured data to create knowledge graphs in SurrealDB
surrealdb.com·1d
💬NLP
There is an easy way to dramatically reduce the resources we spend to train LLMs.
medium.com·1d·
Discuss: r/LocalLLaMA
📱Edge AI
From Facts & Metrics to Media Machine Learning: Evolving the Data Engineering Function at Netflix
netflixtechblog.com·1d·
Discuss: Hacker News
📱Edge AI
Help me with Artificial Intelligence - What can a parent learn from their children?
lesswrong.com·13h
🤖AI
Hybrid vector + text Search in the terminal with SurrealDB and Ratatui
dev.to·1d·
Discuss: DEV
💬NLP
From Prompts to RAG to RAGAs: Evaluating Retrieval-Augmented Generation Systems the Right Way
pub.towardsai.net·1d
🔄MLOps
Observability at scale
antithesis.com·15h
👁️Observability
College Football Playoff 2025 projections: Ohio State, Clemson lead field of contenders
nytimes.com·1d
🦀Rust
Scientists train deep-learning models to scrutinize biopsies like a human pathologist
medicalxpress.com·12h
👁️Computer Vision
I tested SLMs vs embedding classifiers for LLM prompt attack detection -- sharing results (TLDR: 95% accuracy with embeddings)
reddit.com·20h·
Discuss: r/LLM
🔄MLOps
HebID: Detecting Social Identities in Hebrew-language Political Text
arxiv.org·23h
💬NLP
V-JEPA 2: Scaling V-JEPA
gonzoml.substack.com·12h·
Discuss: Substack
📱Edge AI
Adversarial Attacks against Neural Ranking Models via In-Context Learning
arxiv.org·23h
🧠Neural Architecture
Compute Where It Counts: a trainable LLM sparsity enabling 4x CPU speed
crystalai.org·2d·
Discuss: Hacker News
📱Edge AI