Embedding Models

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Scoured 38 posts in 9.2 ms

Building Semantic Search with Transformers.js and Sentence Embeddings

 📌Embedding Retrieval

hanxiao/omni-macos: Native macOS semantic search over your local files - text, images, audio, video in one vector space, on-device on Apple silicon.

 🔢Algebra  Content type: Code
github.com··Hacker News

Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing

 🤖ML  Content type: Academic
arxiv.org·

Generalizable self-supervised learning for imaging flow cytometry on multi-dataset leukocyte differential

 🤖ML  Content type: Academic
nature.com·

Best practices for building a modern app with vector search

 🔍Search Indexing  Content type: Blog
elastic.co·

The Reliability Stack for AI Agents [Part 2]

 ⚙️Adaptive Execution  Content type: Blog
medium.com·

CLASPP: A unified model for predicting post-translational modifications

 🧮Constraint Solvers  Content type: Academic
biorxiv.org·

How I benchmarked a 100% local RAG pipeline to 9/9 (zero API keys)

 📌Embedding Retrieval
buy.polar.sh··DEV

Local-first multimodal file search on macOS

 🔢Algebra

Casual experiment hint that models seem to search for different stuff

 🔍Information Retrieval
spock.is··Hacker News

Implicit Data Synthesis for Contrastive Unsupervised Data Augmentation

 📉Embeddings Optimization  Content type: Academic
arxiv.org·

Kyros-494/kyros-ai: Kyros — The Memory OS for AI Agents Give your AI agents secure, self-correcting, persistent memory in 3 lines of code. Three memory types (episodic, semantic, procedural) with built-in forgetting curves, cryptographic integrity, and automatic contradiction resolution. Model-agnostic REST API with Python and TypeScript SDKs.

 📌Embedding Retrieval  Content type: Code
github.com··r/CLine
Less-relevant results

Designing Memory for a Minimal Rust Coding Agent, Without a Vector Store

 📌Embedding Retrieval

Your AI agent reads the fine print: building a RAG pipeline over EU regulations with Elasticsearch and OGX

 🔍Search Indexing  Content type: Blog
elastic.co·

Vibe Coding Specificity Foundation Models

 🤖ML  Content type: Academic
biorxiv.org·

Machine Learning Methods for Studying Latent Neural Activity Dynamics

 🤖ML  Content type: Academic
arxiv.org·

Lighting-Aware Representation Learning under Controllable Lighting Variation

 🔍SPLADE  Content type: Academic
arxiv.org·

test: fence embedding provider secrets env · openclaw/openclaw@6bb91b2

 🌐XDP  Content type: Code
github.com·

FIGMA: Towards FIne-Grained Music retrievAl

 🔍Information Retrieval  Content type: Academic
arxiv.org·

shoo99/paper-rag: A private, fully-local RAG over your own PDFs: BGE-M3 + embedded Qdrant + a local LLM via Ollama. ~150 lines, nothing leaves your machine.

 📌Embedding Retrieval  Content type: Code
github.com··DEV

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