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.
This repository is a self-paced learning roadmap for algorithmic trading, structured as a hierarchical collection of markdown files. It curates resources, certifications, and project ideas across artificial intelligence, computer science, mathematics, finance, cloud engineering, and software engineering. The repository is entirely documentation-driven; there are no executable applications, data pipelines, or machine learning models. Its primary value lies in its organized curriculum, reusable topic templates, and role-based learning paths (e.g., quant developer, algorithmic trader). While not a software project, it demonstrates high organizational craftsmanship through consistent documentation structure, templates, and CI/CD for link checking and diagram rendering.