Anyone trying to sell one-size-fits-all expertise waves a huge red flag for us. Random lists masquerading as comprehensive explanations are orange, or at least a bright yellow.
We believe that a valuable framework needs to walk on two legs: algorithms and archetypes. One without the other stumbles when you try to run. Pure data lacks relevance. Pure storytelling lacks rigor. But when you combine them into something testable and memorable? That’s where the magic happens.
The Two Legs
Archetypes are proven patterns. They’re dynamics, explanations, and stories that we’ve seen validated across multiple time periods and domains. These include the proverbial “first principles” of biology or economics. We also include the ancient truths down to the grandmotherly wisdom that’s earned the right to be an eye-roll-inducing cliche.
Think of archetypes as the “left foot” (or maybe left brain?). They give us patterns that we can recognize, stories we can remember, and wisdom we can trust. They resonate in our guts because they jive with our personal experience.
Algorithms provide a clear delineation of components and their interactions. What’s a “thing” (a static element) versus what’s a “dynamic” (how things interact)? When we define clear actors and actions, we have a tool that we can test, falsify, audit, and translate across scenarios.
Algorithms as the “right foot.” They give us consistency, proportionality, and transferability to apply frameworks across different situations without losing meaning.
Multiplication, Not Addition
When we distill a framework, we map things out objectively using clear definitions and logical relationships (the algorithm), then test those findings against proven patterns and tell them as memorable stories (the archetypes).
A framework that’s all algorithm feels sterile and academic. You might understand it intellectually, but it doesn’t stick because it lacks human texture. You can audit the definitions and test whether relationships hold up in different contexts, but you can’t remember it or feel its relevance. And here’s the trap “experts” fall into: they keep drilling down into the data, adding variables and complexity, until their models become so big and so fragile that they loop back on themselves. Overfitting disguised as rigor.
When you harness both, you get high impact. Rigorous enough to trust, memorable enough to consistently apply. Our goal isn’t comprehensive, academic perfection. It’s sparking engagement and experiential, personal learning. Direction matters, but action matters more.