Technology will keep changing faster than anyone can keep up with it. That's not a reason to stop learning — it's a reason to change what you learn. Specific tools expire. The thinking patterns underneath them don't. A leader who understands feedback loops, incentives, and compounding can reason about a technology that doesn't exist yet.
So we built a collection of sixteen interactive mental models — each one a simulation you can watch, then a system you can drive, then a deck of everyday examples that make it stick. They're organised as a journey through six domains, from simple foundations to applied method.
The six domains
Foundations — how change works. Entropy (order decays without energy), compound interest (small gains multiply), feedback loops (outputs become inputs and amplify).
Engineering — build more with less. Leverage (one hour of effort becomes a thousand, from hand tools to AI), second order thinking (the consequences have consequences — a decision that feels like +30 today can add up to −35), and the Pareto principle (four well-aimed hours beat sixteen sprayed ones).
Information — find truth in the flood. Signal vs noise (averaging reveals the message hiding in static) and compression (a good summary stops at the sweet spot; past it, meaning collapses into slogans).
Games — win without war. Network effects (value grows with the square of users), the trust game (betrayal wins rounds, trust wins games — play it against tit-for-tat yourself), and opportunity cost (every yes is a no to everything else, and the no has a price).
Mind — know your own machine. Confirmation bias (watch inconvenient evidence bounce off a closing mind while certainty rises and accuracy doesn't) and incentives (reward bugs fixed, and someone will farm bugs — the cobra effect, live).
Method — think like a scientist. The hypothesis loop (being wrong quickly beats being sure slowly), first principles (a battery pack "costs" $600/kWh; its materials cost $82 — the rest is history and habit), and inversion (forward search wanders; backward from the goal, the path is obvious).
Why this matters now
AI compresses the cost of execution. What it doesn't compress is judgment — knowing what to build, what to measure, what to ignore, and what the second-order effects will be. Mental models are how judgment scales. Collect enough lenses and the chaos starts to look like a system.
Start simple. Go deep. The app tracks which models you've explored — the funnel runs from a two-minute simulation to driving the parameters yourself.