P-AI-R Programming

Sound Familiar?

You tried Copilot, weren't impressed, and moved on
You use AI for boilerplate but still write most code yourself
Your AI tools generate plausible-but-wrong code you spend hours debugging
You've heard about MCP, skills, and rules—but don't know where to start
You're watching early adopters ship 3-5x faster while you fall behind

Here's the truth: Most developers use 10% of what these tools can do. The difference isn't the model—it's the configuration.

What Changed in Two Years

In 2023, I wrote the first edition of this book. ChatGPT was new. Copilot was impressive. AI pair programming felt like the future.

I was completely wrong about how fast that future would arrive.

By early 2026, I shipped 49 pull requests in Q1—over 95,000 lines of code. Three epics in 6 days that would have taken 8 weeks. Not because I got better at coding, but because I learned to configure AI tools properly.

This book is the completely rewritten second edition—full of real stories, honest mistakes, and the workflows that actually work when you stop treating AI like autocomplete and start treating it like a pair programming partner.

What You'll Learn

MCP Integrations

Connect AI tools to GitHub, Jira, databases, and APIs—so they work with your real workflow, not toy examples.

Rules & Skills Systems

Configure behavior that persists across sessions—testing standards, code review checklists, and project-specific workflows.

Delegation Workflow

The mental model shift: stop writing code, start designing systems. Let AI handle implementation while you focus on architecture.

Avoid Common Pitfalls

TypeScript bugs that look valid but don't work. Calendar logic that forgets past dates exist. Real mistakes from real projects.

Team Adoption

How to introduce AI tools to skeptical teams—and what to do when resisters create bottlenecks.

Ethics & Safety

Privacy checking, skill maintenance, responsible automation—the unglamorous work that keeps teams safe.

Imagine Shipping Features Like This...

Design → implement → test → demo in an afternoon instead of days
Write tests first, let AI generate passing implementations
Review pull requests with automated security and quality checks
Delegate git workflows, Jira updates, and shell scripting to AI
Focus on architecture and judgment while AI handles mechanics

This isn't speculation. This is what I do every day at my day job. The stories in this book are real. The workflows are tested. The configuration files are included.

About the Author

Michael D. Callaghan is a software developer with 40+ years of experience at HP, Compaq, Dell, Disney, and startups.

He's an advocate for AI-assisted development, technical writer, and educator. His courses have been published on Udemy and Pluralsight.

In Q1 2026, he shipped 49 PRs and 95,405 lines of code while working full-time. His team adopted AI tools enthusiastically—except for a few resisters who are visibly falling behind.

Other books include Slow Down to Get Ahead (communication skills for developers) and the original 2023 edition of P-AI-R Programming.