vibecoding.phd — Mastering AI-Driven Development

Launched vibecoding.phd — a structured curriculum for developers who want to master the art of building with AI coding assistants. The site covers the full spectrum: from basic prompting patterns to advanced multi-agent orchestration, from single-file scripts to production monorepos. Each module is designed as a hands-on exercise, not a lecture — you ship something real at the end of every chapter.

The content is organized into progressive levels. Fundamentals cover prompt engineering and context management. Intermediate modules tackle multi-file refactoring, test-driven development with AI, and debugging workflows. Advanced topics include autonomous agent design, skill creation for Claude Code, and building AI-native development pipelines.

Traditional coding tutorials assume you're writing every line yourself. The new reality is different: developers spend more time directing AI assistants than typing code, and the skill set for that is completely undocumented. Knowing React doesn't help you write a good prompt for refactoring a React component. Knowing TypeScript doesn't teach you when to trust the AI's output and when to verify manually.

vibecoding.phd fills a gap that no other resource covers — the meta-skill of building software through AI collaboration. It's not about any specific tool or model. It's about the intuition for when to give the AI more context, when to constrain it, when to split a task into smaller pieces, and when to let it run autonomously.

Capturing tacit knowledge was the biggest challenge. Vibecoding is pattern recognition — experienced practitioners "feel" when a prompt is wrong or when the AI is heading down a bad path. Turning that intuition into teachable, repeatable steps required deconstructing dozens of real building sessions, identifying the decision points, and articulating what was actually happening at each fork. The result is less like a tutorial and more like a field guide: "when you see this, do that."