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Official tutorial hub teaching how to code effectively with AI agents inside Cursor, from AI foundations to working with agents and reviewing their output. Lessons cover rules, tools, context as working memory, and which tasks agents handle well.
Runnable starter projects for the Claude API you fork and adapt: a knowledge-base customer support agent, a financial analyst that charts results in chat, plus computer-use, browser-use, and autonomous-coding-agent reference implementations.
Readable, minimal-dependency Python implementations of core robotics algorithms — localization (EKF, particle filter), SLAM (ICP, FastSLAM), path planning (A*, RRT*, PRM), and path tracking (LQR, MPC) — written to be studied, not just run.
Curates step-by-step, hands-on tutorials for reimplementing technologies from scratch—covering everything from OSs and compilers to neural networks, LLMs, and vision systems—so learners learn by rebuilding real systems across languages.
Collects 60+ PyTorch implementations of neural network papers — transformers, diffusion, GANs, RL, optimizers — each annotated line-by-line and rendered beside the code at nn.labml.ai, so you study the math and a runnable implementation together.
Official collection of example notebooks and guides for building with the OpenAI API — text generation, embeddings, function calling, RAG, fine-tuning, and more. Mostly runnable Jupyter notebooks (~93%); mirrored at cookbook.openai.com.
Hands-on lecture series that teaches neural networks from first principles up to building a GPT: each lecture pairs a YouTube video with Jupyter notebooks and exercises so you code models (micrograd → MLPs → WaveNet-like convs → GPT) while learning training and debugging.
Teaches generative AI app development through 21 lessons covering LLM basics, prompting, chat, search, image generation, agents, RAG, fine-tuning, small models, and responsible AI.
AI Engineering Hub is a comprehensive GitHub repository offering in-depth tutorials and 93+ production-ready projects on LLMs, RAGs, AI agents, and real-world AI applications for all skill levels.
Teaches agent harness engineering — the permissions, memory, persistence, and coordination layer that lets an LLM act — across 20 progressive lessons, each adding one mechanism with standalone runnable code. Chinese-first, plus English and Japanese.
A template and workflow for feeding AI coding assistants structured context — project rules, code examples, and validation gates — instead of one-off prompts. Centers on Product Requirements Prompts (PRPs) that an agent generates, then executes.
Collects Claude Code commands, agents, skills and engineering best practices with ready CLAUDE.md templates and orchestration examples. Focuses on reusable agent workflows, hooks, and MCP integrations for productionizing Claude-based coding/automation.