Figuring it out

On trial and error and my perpetual pursuit

I’ve always been a fan of trial and error.

I remember 5th grade fondly when we were first introduced to π and told to divide 22/7. That same shorthand in use since the old kingdom (c. 2700) supposedly.

Divide I did. On paper and pencil. It was soon clear that it was a larger number than π if only moderately so.

But what numerator might get me closer?

21.99? 21.98?

I kept going. Trying to see how many digits I could match. It was laborious work back then with such crude instruments. Yet strangely satisfying.

Richard Feynman, in "The Pleasure of Finding Things Out," describes exactly this sensation—a profound satisfaction born not from knowing something, but from the journey of discovering it yourself.

That exercise cemented in me an essential truth: trial and error wasn’t just a method; it was how I learned best.

As I’ve grown, I’ve engaged in a lot of solitary learning. Primarily through reading and reflection.

Its efficient to read about the errors of others so you don’t repeat them. But there's something formative about experiencing error firsthand—it leaves a mark that other’s advice never replicates.

I guess what I mean to say is that trial and error — beyond being a great learning method — has defined most of of my empirical world view.

Not to feel exasperated, or defeated, or despondent because your days aren’t packed with wise and moral actions. But to get back up when you fail, to celebrate behaving like a human—however imperfectly—and fully embrace the pursuit that you’ve embarked on. — V. 9

I resonate deeply with Feynman’s view that nature doesn't yield its secrets easily; they must be coaxed out through persistent questioning, experimentation, and a tolerance for uncertainty and failure.

During my post-graduate years at Berkeley, my favorite class was "Field Experiments." I still have text by Gerber and Green on my shelf.

Immersing myself in the language, design, and nuanced caveats of randomized controlled trials—and then applying them confidently at work—was deeply rewarding.

It was that adult manifestation my childhood fascination: meticulously varying conditions, observing outcomes, and refining understanding through trial and error.

In recent meditations I’ve written at some length about The Unreasonable Effectiveness of Compressed Cycle Times.

AI enables unprecedented scale and speed in trial and error experimentation across nearly every domain imaginable. The feedback loop accelerates learning and is deeply intoxicating.

Nowhere is this more evident to me than in building software. AI facilitates the translation of ideas into reality at the "speed of thought" where previously so many barriers existed.

With a few modest subscription fees and determination, most anyone can engage in iterative experimentation, rapidly prototyping, testing, and refining their ideas into tangible tools.

This democratization is so powerful and new that its challenging to quantify its impact.

Yet, amidst all this change, the core of the thrill remains unchanged from my fifth-grade desk to today.

Trial and error isn’t merely about finding the right answers: it’s that embrace of uncertainty, relishing the unknown and cultivating curiosity as a lifelong companion.

For me true pleasure lies not just in the discovery but the perpetual pursuit of figuring it out.