If They Only Knew…What the Founders Would Think About Excessive Fines and Financial Punishment

What would the Founding Fathers think about excessive fines, court fees, and financial punishment that can ruin a life without a jail cell? In Federalist #84, Hamilton argued the Constitution was already a kind of bill of rights—but the ratification fight also exposed a quieter danger: punishment that becomes profit. In these imagined letters, Madison, Jefferson, Mason, Hamilton, and Adams trace how money can become a chain—how penalties, forfeitures, and fee-driven justice can turn law into a revenue engine. Neither prison nor pardon is needed when the ledger can do the work. Read, disagree, and measure our modern practices against the old constitutional warnings.

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The Mathematics of the Doom Loop – You Think It’s Simple? Think Again!

You think the Doom Loop is simple? Think again. For over forty years, critics have dismissed the Doom Loop as “too simple”—just a 2×2 matrix with a curve. They’re wrong. The Doom Loop is a qualitative partial differential system that tracks two variables (preference and performance) over time while holding all others constant. This article presents the complete mathematical framework: the human state function, the constraint equations, the satisfaction derivative, and the precise conditions that define the “Doomed” point. It explains why smarter people reach boredom faster, why quantification would destroy the model’s usefulness, and why reduction is not naïveté. For engineers, mathematicians, and systems thinkers: here is the proof that the napkin drawing was calculus all along.

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If They Only Knew…What the Founders Would Think About Trial by Jury in Civil Cases

What would the Founding Fathers think about the vanishing civil jury trial? In Federalist #83, Hamilton defended the Constitution’s silence on civil juries—but privately worried that “efficiency” might someday justify their abolition. Federal Farmer warned that without juries, government would cease to be republican and become merely “managerial.” Brutus predicted that courts would erode jury rights “in the most certain, but yet silent and imperceptible manner.” Two centuries later, civil jury trials have declined from over eleven percent to less than one percent of federal cases. The founders feared we would lose this right by conquest. They did not imagine we would sign it away in the fine print.

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What Is an AI Protocol, Anyway?

Many people ask me about AI protocols now. They want to know what an AI Protocol is and what it actually contains.
A protocol is not a better prompt. It is a governing document—a written agreement that defines how human-AI collaboration will work before execution begins.
I learned this building The Federalist Papers podcast, where AI assists with research and script development for episodes examining the eighty-five essays that defined American constitutional government. The challenge: producing episodes that were consistent in format but different in content, without drift, fabrication, or wandering tone.
A prompt could not solve this. A protocol could.
This article walks through what protocols actually contain—purpose, invariants, constraints, verification—using a real case study. No theory. No jargon. Just the architecture that makes AI output trustworthy.

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What’s the Professional AI Jockey’s Big Secret?

What’s the professional AI Jockey’s Big Secret? Everyone wants to know the secret to making AI work. The prompt. The hack. The technique that unlocks everything. I’ll tell you the secret/ There is only the skill that has always mattered most—the one people overlook because it’s not new, not clever, not proprietary. That skill is communication. In thirty years of studying what makes people succeed, I found eight critical skills. One stood above the rest. It hasn’t changed. What’s changed is who we’re communicating with. AI is like a thoroughbred—powerful, fast, capable of extraordinary performance. But you don’t prompt a thoroughbred. You ride it. That’s the professional AI Jockey’s job. And it’s simpler than you think—once you understand how.

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