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Project Title: Physical AI & Humanoid Robotics — Interactive Course Book
Short Description:
An AI-generated educational course book on Physical AI and Humanoid Robotics, built and deployed as a fully structured documentation site.
Project Details:
I built this interactive course book using the Claude AI Agent SDK and Speckit to autonomously generate structured, technical educational content — and deployed it as a live Docusaurus site on Vercel.
The book covers a complete learning path from foundational concepts to advanced robotics implementation, organized into the following modules:
Introduction to Physical AI — what it is, why it matters, and how it differs from traditional AI
Module 1: The Robotic Nervous System (ROS 2) — architecture and robot coordination
Module 2: The Digital Twin (Gazebo & Unity) — simulation environments and sensor integration
Module 3: The AI-Robot Brain (NVIDIA Isaac) — perception, synthetic data, and reinforcement learning
Module 4: Vision-Language-Action (VLA) Systems — integrating speech, LLMs, and robotic actions
Capstone Project — end-to-end Physical AI integration
Supporting sections: Glossary, Hardware Considerations, Software Stack, Roadmap, and References
Tech Stack:
Claude AI Agent SDK, Speckit, Docusaurus v3, Vercel
What makes it notable:
The entire course content was generated and structured by an AI agent pipeline — not written manually. This demonstrates the practical use of agentic AI for content generation, technical writing automation, and documentation at scale.
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With AI coding assistants, it’s easy to fall into the trap of letting the LLM drive the entire development workflow. You prompt, copy, paste, and pray.
But our value as software engineers has never been about how fast we type syntax. It’s about system design, critical thinking, and code ownership.
Our role is evolving from builders to technical pilots. If you completely delegate the workflow, you lose control of the project.
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Design First, Prompt Second: Define your architecture, data flow, and edge cases before touching an LLM. Use AI to implement, not to invent.
Deconstruct: Break features into micro-steps. Guide the AI through isolated, high-context tasks (schema ➡️ logic ➡️ error handling).
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