I’ve passed Calculus three times. But I never actually understood it.

I could follow the steps, pass the tests, move on to the next thing. But passing isn’t the same as mastering the underlying logic. And I knew if I kept building on a shaky foundation, it would eventually limit what I could build.

This isn’t about capability. By all measures, I am “good” at math. But I’ve recognized a gap in my foundational understanding that would eventually constrain what I could create.

So I’m taking a term break from school to fix it properly - not just pass calculus again, but build the foundation I need for the complex systems work I’m moving toward.

The problem wasn’t that I’m bad at math. The problem was I was trying to learn it the wrong way.

I’d been approaching math like I’m supposed to memorize formulas and regurgitate steps. That doesn’t work for me. It never has. I learn by doing, by breaking things, by understanding the system beneath the surface.

Recently, while switching between calculus practice and LeetCode problems, something clicked. The approach that actually works for me is:

1. Attempt the problem (even if I have no idea)
2. Get stuck (this is the point - find the edge of what I know)
3. Look at the solution (not to copy, but to understand the pattern)
4. Try again without looking (rebuild it from understanding, not memory)
5. Repeat with similar problems (train pattern recognition)

This approach felt wrong at first. Shouldn’t I struggle through it on my own? Isn’t looking at the solution “cheating”?

That’s the memorization mindset talking. Looking at solutions isn’t cheating when you’re using them to understand patterns, not copy steps. The goal isn’t to prove I can suffer through problems - it’s to build actual understanding that I can apply to new problems.

And the repetition isn’t just about practice - it’s training my subconscious mind to recognize problem types and automatically recall solution strategies. The same way I don’t consciously think through every debugging step anymore, or how I can recognize code smells without actively analyzing. Pattern recognition becomes intuitive through repeated exposure.

This isn’t “taking shortcuts.” It’s deliberately exposing gaps, learning the underlying logic, testing if I actually internalized it, then training my brain to recognize those patterns automatically.

Since applying this to calculus, problems that felt impossible two weeks ago now feel manageable. Not because I memorized more formulas, but because I understand the why behind them. More importantly, I’m starting to recognize problem types instantly - my brain pattern-matches before I consciously think through the approach.

I’m finding I actually enjoy the process now. Not because math suddenly became easy, but because I’m engaging with it the way my brain actually works - through systems thinking, pattern recognition, and iterative building.

This matters because I’m planning to do grad school. I need this foundation to be solid - not just enough to get by. The stakes are higher than just passing a class. I’m building toward something specific, and I can’t afford to fake the fundamentals.

But here’s the thing - this pattern works everywhere. I’m finding the same approach applies to algorithm problems, embedded systems debugging, even organizational design work. Find the edge of what you know, study the underlying pattern, rebuild from understanding, repeat until pattern recognition becomes automatic, then apply to new contexts.

Whether it’s calculus derivatives, firmware debugging, or designing team interfaces - the learning process is the same. Find the edge, understand the pattern, rebuild from first principles, train the intuition, apply broadly.

Turns out learning calculus properly isn’t just about math. It’s about learning how to learn the things that actually matter. Right now, that matters a lot. I’m building toward renewable energy systems work, climate resilience planning, complex systems design. The foundation has to be solid.

Not for the credential. For the capability.