AI/TLDR — New AI Releases Daily: Models, Tools, Repos & PapersA high-volume feed of new AI releases — models, open-source repos, developer tools, papers, datasets, and benchmarks — refreshed every 8 hours. Each release is explained in plain English so you actually understand what shipped.This site uses JavaScript to render the interactive feed. Enable JavaScript, or visit the source repo for the raw JSON.

AI/TLDR

Retrieval-Augmented Generation (RAG)

Grounding model answers in your own data — chunking, retrieval, reranking, GraphRAG, and evaluation.

RAG Fundamentals

What RAG is, when to choose it, and your first working pipeline.

BEGINNERWhat Is RAG? Retrieval-Augmented Generation Explained for Beginners BEGINNERHow the RAG Pipeline Works, Step by Step

Chunking & Ingestion

Where most RAG quality is won or lost: how documents become chunks.

BEGINNERWhat Is Chunking in RAG? Why Document Splitting Matters INTERMEDIATERAG Chunking Strategies Compared

Retrieval & Reranking

BM25 meets embeddings: hybrid search, RRF, rerankers, and query rewriting.

BEGINNERWhat Is a Retriever? How RAG Finds the Right Documents INTERMEDIATEWhat Is Reranking in RAG?

Advanced RAG Architectures

Agentic RAG, GraphRAG, self-correcting pipelines, and retrieval beyond plain text.

INTERMEDIATEWhat Is Agentic RAG? When the LLM Decides What to Search INTERMEDIATEWhat Is Hybrid RAG?

RAG Evaluation

Proving your pipeline works — and debugging it when it doesn't.

BEGINNERHow Do You Evaluate a RAG System? The Metrics That Matter INTERMEDIATERAG Evaluation Metrics Explained