Rethinking Query Complexity for Adaptive RAG: Architectural Comparison and Beyond

Lighteval is your all-in-one toolkit for evaluating LLMs across multiple backends

Repository for Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions, ACL23

Model Context Protocol Servers

Agent framework and applications built upon Qwen>=3.0, featuring Function Calling, MCP, Code Interpreter, RAG, Chrome extension, etc.

LEAKED SYSTEM PROMPTS FOR CHATGPT, CLAUDE, GEMINI, GROK, PERPLEXITY, CURSOR, LOVABLE, REPLIT, AND MORE! - AI SYSTEMS TRANSPARENCY FOR ALL! 👐

Corrective Retrieval Augmented Generation

Forward-Looking Active REtrieval-augmented generation (FLARE)

This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.

280+ free n8n automation templates — ready-to-use workflows for Gmail, Telegram, Slack, Discord, WhatsApp, Google Drive, Notion, OpenAI, and more. AI agents, RAG chatbots, email automation, social …

MMLU-Pro Turkish Evaluation Benchmark

An AI-powered task-management system you can drop into Cursor, Lovable, Windsurf, Roo, and others.

Anthropic's educational courses

Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflo…

Curated list of awesome Cursor Rules .mdc files

📄 Configuration files that enhance Cursor AI editor experience with custom rules and behaviors

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM

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🟣 LLMs interview questions and answers to help you prepare for your next machine learning and data science interview in 2026.

A Retrieval Augmented Generation example with Azure

The code and data for "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark" [NeurIPS 2024]

A framework for few-shot evaluation of language models.