About the Book

What if you could see how AI actually thinks?

Not metaphorically. Not theoretically. But through a working demonstration you can build yourself using physical objects—the same approach Donald Michie used in 1961 to prove machines could learn, by teaching matchboxes to win at tic-tac-toe.

The Wizard’s Lens reveals something that does not exist elsewhere in AI literature: a complete Large Language Model you can construct and operate with terrain maps, tokens, and attention mechanisms made tangible. This is not a metaphor for understanding AI. This is AI, demonstrated through physical implementation.

While others struggle to use AI effectively, this book teaches you to think like AI—to see the patterns, understand the mechanisms, and apply insights that enable you to accomplish what has never been done before.

The Hidden Knowledge

What Cray Research accomplished is well documented: we built the world’s fastest supercomputers and changed what was computationally possible. How we did it has never been written down—until now.

This book shares the systems thinking approaches and revolutionary mindset from that era, applied to modern AI. Not as history, but as practical methods you can use today. The same approaches that created computational breakthroughs in environments where second place was not survivable now reveal how to work with AI in ways others cannot replicate.

For those who recognize the significance of Cray Research: yes, this is that knowledge. For everyone else: you’re learning approaches that have already proven they enable the impossible.

What You’ll Discover

Through hands-on demonstrations and clear explanations, you’ll learn:

Token Context and Embeddings - Build a working model that shows how AI represents and processes information, using physical tokens and terrain maps that make abstract concepts concrete.

Attention Mechanisms - Understand how AI focuses on relevant information through a demonstration you can manipulate yourself, revealing why certain approaches work and others fail.

The Ping Pong Effect - Move beyond one-shot prompting to develop collaborative relationships with AI that grow more effective with each interaction.

Pattern Recognition at Scale - Learn to see how AI connects disparate information, and how to structure your work to leverage these connections.

Revolutionary Thinking - Develop the mindset that enables you to take on challenges others consider impossible, treating “it can’t be done” as an invitation rather than a boundary.

Why This Book is Different

Most AI books teach you to write better prompts or explain transformer architecture with equations. This book shows you how the mechanisms actually work through physical demonstration, then teaches you to apply that understanding in ways no one else can.

You’ll learn to use AI like nobody before you—not through tricks or techniques, but through genuine understanding of how Large Language Models process information, maintain context, and generate responses. This understanding transforms how you collaborate with AI, opening possibilities that others believe require technology that doesn’t exist yet.

Who This Book is For

This book serves multiple audiences:

Professionals and knowledge workers who need to accomplish complex tasks with AI and recognize that basic prompting has fundamental limitations.

Developers and technical practitioners who want to understand LLM internals without drowning in mathematics, gaining practical insight that informs better implementation decisions.

Strategic thinkers and innovators who need to solve problems that have never been solved before, and recognize that revolutionary results require revolutionary approaches.

Anyone who suspects there’s more to AI than what current tutorials and guides reveal, and wants to develop capabilities that create genuine competitive advantage.

The Author’s Background

Edward W. Barnard spent years at Cray Research during the era when we were accomplishing what had never been done before in computing. This book shares the systems thinking approaches and revolutionary mindset from that time, now applied to AI collaboration.

These aren’t theoretical frameworks. These are battle-tested methods from environments where results mattered more than credentials, where impossible challenges were solved through clear thinking and revolutionary approaches to complex systems.

The cryptographic origins of modern computing—from Alan Turing through Seymour Cray—created specific ways of thinking about information, context, and computational possibility. This book teaches those approaches in a form you can apply immediately.

What’s In It For You

Immediate capability - Learn to accomplish tasks with AI that others consider beyond current technology, through understanding how LLMs actually process and generate information.

Deep understanding - Build a working LLM yourself, making abstract concepts concrete and revealing why certain approaches succeed while others fail.

Transferable skills - Develop systems thinking approaches that work across all AI platforms and remain valuable regardless of how technology evolves.

Revolutionary mindset - Learn to approach impossible challenges the way they were approached at Cray Research: as interesting problems to solve rather than boundaries to accept.

Long-term mastery - Create a foundation for continually improving your AI collaboration skills, based on understanding rather than memorized techniques.

Whether you’re drowning in operational complexity, struggling with projects that seem beyond AI’s current capabilities, or simply know there’s a better way to work with these tools, this book will transform not just what you can accomplish with AI, but how you think about what’s possible.

The revolution isn’t coming. It’s here. The question is whether you’ll be among those who can see it.

About the Author

Edward W. Barnard

Edward W. Barnard brings unique implementation expertise from programming Cray supercomputers at the hardware level, debugging systems at classified facilities, and solving critical Y2K infrastructure problems. His 20+ years at Cray Research included hands-on work with assembly language, operating systems, and I/O subsystems. He has published over 100 technical articles, helping developers implement solutions to complex problems. His cross-domain experience, from NSA troubleshooting to Saudi Aramco installations, provides practical insights into applying advanced problem-solving techniques across diverse technical environments. 

Edward has transferred his skill of bare-metal programming the Cray I/O Subsystem (with only 131,072 bytes of local memory) to novel ways of managing Large Language Model token context windows, unlocking capabilities not yet taught in AI literature. When a skill dormant for 35 years becomes suddenly relevant again, he calls this The Time Travel Pattern.