Best learning resources for AI
Open-source AI-powered browser-automation framework that exposes a website’s interactive elements in a simple, text-like format so LLM agents can read pages and complete multi-step tasks automatically.
The `q` CLI lets developers chat with Amazon Q Developer from the command line, generating commands and code with AWS context.
Official Cloudflare MCP servers that let AI agents read, analyse and even change Cloudflare resources through natural-language requests.
Distributed KV-cache store & transfer engine that decouples prefilling from decoding to scale vLLM serving clusters.
Provides cloud-browser automation so LLMs can navigate, scrape and interact with the web via Browserbase & Stagehand.
Volcano Engine Reinforcement Learning library for efficient LLM post-training—open-sourced HybridFlow.
RAGFlow is InfiniFlow’s open-source Retrieval-Augmented Generation engine focused on deep-document understanding and scalable multi-format ingestion.
Cherry Studio is a cross-platform desktop client that unifies cloud and local LLMs, knowledge-base RAG, AI drawing and translation inside one extensible interface.
This paper introduces DeepSeek-V3, a 671B-parameter Mixture-of-Experts (MoE) language model that activates only 37B parameters per token for efficient training and inference. By leveraging innovations like Multi-head Latent Attention, auxiliary-loss-free load balancing, and multi-token prediction, it achieves top-tier performance across math, code, multilingual, and reasoning tasks. Despite its massive scale, DeepSeek-V3 maintains economical training costs and outperforms all other open-source models, achieving results comparable to leading closed-source models like GPT-4o and Claude-3.5, thereby significantly narrowing the open-source vs. closed-source performance gap.
OpenHands is an open-source platform for building full-stack AI software-development agents.
This paper introduces DeepSeek-R1, a large language model that improves reasoning purely through reinforcement learning (RL), even without supervised fine-tuning. It shows that reasoning skills like chain-of-thought, self-reflection, and verification can naturally emerge from RL, achieving performance comparable to OpenAI’s top models. Its distilled smaller models outperform many open-source alternatives, democratizing advanced reasoning for smaller systems. The work impacts the field by proving RL-alone reasoning is viable and by open-sourcing both large and distilled models, opening new directions for scalable, cost-effective LLM training and future development in reasoning-focused AI systems.
Aider is a terminal-first AI pair-programming tool that edits your local Git repo through chat.
