This repository is a structured, comprehensive AI engineering curriculum spanning 20 phases and over 500 lessons, from math foundations and ML fundamentals to deep learning, computer vision, NLP, speech, transformers, generative AI, reinforcement learning, LLMs from scratch, agent engineering, multi-agent systems, production infrastructure, and ethics/safety. It is an educational resource, not a production application or data research project. The code consists of self-contained pedagogical implementations (primarily Python, with some TypeScript, Rust, and Julia) that demonstrate core algorithms from scratch—such as attention mechanisms, BPE tokenization, diffusion models, RLHF, and agent loops—using synthetic data and deterministic stubs. Capstone projects include small web servers (Hono/TypeScript) and test harnesses. The curriculum is actively maintained with CI tooling, certification prep materials, and translation support. While the code is simplified for learning, it exhibits clean patterns, consistent lesson structure, and testing practices that make individual components (e.g., tool registries, state machines, evaluation harnesses) reusable in other contexts.
This repository is a curated, interactive learning roadmap for building AI agents. It is not a software library or framework, but a single-page web application (index.html) that renders a comprehensive todo list, project ladder, and curated resource collection. The application allows users to track progress via checkboxes stored in localStorage, take Markdown notes per stage, search content, toggle dark mode, and navigate via a sidebar. The underlying data is a static JSON structure embedded in the JavaScript. The repo's primary value is its meticulously organized learning path covering agent loops, tool use, RAG, multi-agent coordination, skills/protocols, browser agents, evaluation, and production deployment. It references dozens of high-quality external resources (official docs, papers, open-source projects) and provides clear learning principles. While the repo contains no runnable agent code, the interactive frontend is a well-crafted tool for self-paced learning. The codebase is a hybrid: it is an application (web UI) but its content is a curated research/learning resource.