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nanochat is a full-stack, minimal codebase for training, fine-tuning, evaluating, and deploying a ChatGPT-like large language model (LLM) from scratch on a single 8xH100 GPU node for under $100.

12 Lessons to Get Started Building AI Agents

Agent Lightning is an open-source framework developed by Microsoft Research for optimizing and training AI agents using reinforcement learning (RL) and other techniques, supporting integration with any agent framework with minimal code changes.

Learn how to use Cursor, an AI-powered code editor designed to make software development faster and more efficient.

LightRAG is an open-source framework designed for simple and fast Retrieval-Augmented Generation (RAG), integrating knowledge graphs, vector search, and efficient LLM-based processing to enhance question-answering over large document collections.

nanoGPT is the simplest, fastest repository for training/finetuning medium-sized GPTs. It is a rewrite of minGPT that prioritizes practicality over education. Still under active development, but currently the file train.py reproduces GPT-2 (124M) on OpenWebText, running on a single 8XA100 40GB node in about 4 days of training.

KTransformers is a flexible framework for experiencing cutting-edge optimizations in LLM inference and fine-tuning, focusing on CPU-GPU heterogeneous computing. It consists of two core modules: kt-kernel for high-performance inference kernels and kt-sft for fine-tuning. The project supports various hardware and models like DeepSeek series, Kimi-K2, achieving significant resource savings and speedups, such as reducing GPU memory for a 671B model to 70GB and up to 28x acceleration.

Claude Quickstarts is a collection of projects by Anthropic to help developers quickly build deployable applications using the Claude API. It includes quickstarts for a customer support agent, financial data analyst, computer use demo, and autonomous coding agent, demonstrating Claude's capabilities in natural language processing, data analysis, computer control, and automated coding.

An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

MiniMind is an open-source GitHub project that enables users to train a 26M-parameter tiny LLM from scratch in just 2 hours with a cost of 3 RMB. It provides native PyTorch implementations for Tokenizer training, pretraining, supervised fine-tuning (SFT), LoRA, DPO, PPO/GRPO reinforcement learning, and MoE architecture with vision multimodal extensions. It includes high-quality open datasets, supports single-GPU training, and is compatible with Transformers, llama.cpp, and other frameworks, ideal for LLM beginners.

ML for Beginners is a free, open-source curriculum developed by Microsoft, spanning 12 weeks with 26 lessons and 52 quizzes, focusing on classic machine learning using primarily Scikit-learn, avoiding deep learning. It uses a project-based approach with global cultural themes to explore topics like regression, classification, clustering, NLP, and time series forecasting.

X-AnyLabeling is a powerful annotation tool integrated with an AI engine for fast and automatic labeling. Designed for multi-modal data engineers, it offers industrial-grade solutions for complex tasks. Supports images and videos, GPU acceleration, custom models, one-click inference for all task images, and import/export formats like COCO, VOC, YOLO. Handles classification, detection, segmentation, captioning, rotation, tracking, estimation, OCR, VQA, grounding, etc., with various annotation styles including polygons, rectangles, rotated boxes.