Vector Indexing

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HNSW vs LSH: How Elasticsearch hits 0.99 recall@10 at 15,000 QPS — and what it costs

 🎯Vector Search  Content type: Blog
elastic.co·

Understanding HNSW: The Engine Behind Fast Vector Search

 🗂️Vector Indexes
chimchim89.github.io·

HNSW-MS: Hierarchical Graph Indexing Enables Accurate Real-Time Mass Spectral Similarity Search at Repository Scale

 📌Embedding Retrieval  Content type: Academic
biorxiv.org·

Rayforce

 🗄️Vector Databases  Content type: Code

ASH: Asymmetric Scalar Hashing With Learned Dimensionality Reduction for High-Fidelity Vector Quantization

 📉Embeddings Optimization  Content type: Academic
arxiv.org·
Less-relevant results

New comment by yorktanaka2024 in "Ask HN: Who wants to be hired? (June 2026)"

 🎯Vector Search  Content type: Discussion

MongoDB as a Vector Database for AI Agents-MongoDB

 🎯Vector Search
foojay.io·

Redis vs Memorystore: key differences in 2026

 🎯Vector Search  Content type: Blog
redis.io·

Bridging Multi-Vector and Learned-Sparse Retrieval, A Diagnostic Framework for Robust Semantic IDs, and More!

 🔍SPLADE  Content type: News  Content type: Blog

Postgres 19 Beta 1 is here

 🐘PostgreSQL
postgresweekly.com·

ANN Search: Recall What Matters

 📏ANN Benchmarks  Content type: Academic
arxiv.org·

Enterprises Are Quietly Moving Their AI Back On-Premises. Here Is Why.

 📌Embedding Retrieval  Content type: Blog
medium.com·

Neo-X7/Neo-AI: A fully offline AI assistant powered by Ollama. Stores and retrieves conversations using SQLite + LanceDB vector search. No cloud. No API keys. Runs entirely on your machine.

 💾SQLite  Content type: Code
github.com··DEV

AI Agent Mastery & Coaching

 🎯Vector Search
ruv.io·

Aperon Technical Report: Hierarchical No-Pointer Tangent-Local Search for High-Dimensional Approximate Nearest Neighbors

 🎯Vector Search  Content type: Academic
arxiv.org·

Elasticsearch simdvec deep-dive: Walking the memory tightrope to 2x better vector throughput

 SIMD Vectorization  Content type: Blog
elastic.co·

I wondered how big platforms detect stolen images. So I built the whole system myself.

 📌Embedding Retrieval  Content type: Code

ColBERTSaR: Sparsified ColBERT Index via Product Quantization

 📉Embeddings Optimization  Content type: Academic
arxiv.org·

Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering

 🎯Vector Search  Content type: Academic
arxiv.org·

GoodQ02/goodq4all: Local-first multimodal epistemic memory for scene-level video, audio, and text intelligence.

 📦In-process Databases  Content type: Code
github.com··Hacker News

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