
transcript
show notes
What if the best way to build something is to start with the entire finished thing, even when you know it's going to be wrong?
Most people approach creation sequentially. Finish the first part, move to the next, and gradually work toward the final product.
But there's a fundamental problem with this approach: you can't understand how the pieces should work until you see how they interact.
In this episode of NonTrivial, I explain why your first draft should always be the entire thing, whether you're writing a book, building software, starting a business, or developing an idea.
Using concepts from machine learning, Fisher information, and sensitivity analysis, I explore how starting with a naive version of the final product helps you identify the information you actually need, rather than wasting time perfecting pieces that may not belong.
The goal isn't to get it right the first time. It's to build something complete enough to reveal why it's wrong.
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