This hands-on guide helps you use AI-assisted development to create apps, automate workflows, and turn ideas into working software
You no longer need traditional programming experience to create real applications. This book introduces you to building software using Claude Code, where you will collaborate with AI to design, develop, and deploy applications from scratch.
Starting with the basics, you’ll learn how to work effectively with AI assistants, structure prompts, and translate ideas into working software. As you progress, you’ll build a range of real-world projects from personal websites and portfolio apps to interactive tools and AI-powered applications. Each project is designed to reinforce practical skills while exposing you to modern development workflows.
The book emphasizes a structured, step-by-step learning approach, guiding you through planning, iteration, debugging, and deployment. You’ll also explore advanced capabilities such as API integration, automation workflows, and AI agent-based development. Later chapters introduce MCP, plugins, and skills to extend Claude Code into a powerful development environment.
By the end of this book, you won’t just understand AI-assisted development, you’ll confidently build, customize, and deploy your own applications.
Whether you’re a beginner or a professional, this book equips you with the skills to turn ideas into real, working software. It is ideal for early-career software engineers, marketers, researchers, educators, entrepreneurs, and anyone interested in AI-assisted development. If you’ve ever wanted to build applications but felt limited by coding skills, this book provides a practical and accessible path to get started.
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Taeho Jo is a Professor in the Department of Radiology and Imaging Sciences at the Indiana University School of Medicine, where he leads research on the early diagnosis of Alzheimer's disease by applying AI and deep learning to the medical field. His work focuses on identifying the causes of disease through the integrated analysis of genomics, multi-omics data, and medical imaging using AI.
He earned his Ph.D. in protein structure prediction from Tokyo Medical and Dental University before moving to the United States, where he was among the early researchers to introduce deep learning into this field. Since 2018, he has been conducting research at Indiana University on deep learning–based early diagnosis of Alzheimer's disease, tracking causative proteins, and predicting genetic mutations.
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