🔗 RAG - hugonoss · Scour

Apple researchers built an AI that tests several ideas in parallel before answering 🦙Ollama

9to5mac.com·1d

OlmPool: How small architectural choices compound to undermine long context extension 🦙Ollama

allenai.org·16h

Stash - AI 에이전트를 위한 지속형 메모리 계층 📘how to use AI

news.hada.io·4d

机器人终局：NVIDIA Jim Fan 谈 Embodied AI 的下一步 🌀Immersive Art

bestblogs.dev·20h

Start With Context: Building the Retrieval Core for Agentic Apps 🧮Vector Databases

alexbevi.com·2d

[ASAP] Predicting Peptide Aggregation with Protein Language Model Embeddings 🧮Vector Databases

What are embeddings? 🧮Vector Databases

vickiboykis.com·4d

DeepSeek v4 🧮Vector Databases

dsebastien.net·4h

Diagnosing protein sequence search in the era of language models 🧮Vector Databases

biorxiv.org·16h

AI 101: What’s So Magical About Embeddings? 🧮Vector Databases

turingpost.com·1d

Vector Database 🧮Vector Databases

newsletter.systemdesign.one

·6d

Adaptive Thinking: Large Language Models Know When to Think in Latent Space 🦙Ollama

machinelearning.apple.com·2d

DeepSeMS: revealing the hidden biosynthetic potential of the global ocean microbiome with a large language model 🧮Vector Databases

·1d

How to Build Your Own Language-Specific LLM [Full Handbook] 📘how to use AI

freecodecamp.org·6d

Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG 🧮Vector Databases

Show GN: 완전하게 로컬에서 돌리는 AI 페르소나 시뮬레이터 🦙Ollama

news.hada.io·2d

Rethinking Personalization for the Agent Era, Semantic Recall for Vector Search, and More! 🦙Ollama

recsys.substack.com

LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning 🦙Ollama

machinelearning.apple.com·1d·Hacker News

neuml/txtai: 💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows 📘how to use AI

github.com·18h

N-grams Near Links: Identify recurring phrases in the context around your backlinks 🔎Search engine

blog.majestic.com·2d

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