My Principles

🔑 Simplicity is Key

I believe in building simple systems that are easy to understand, change, and debug. As Rich Hickey says, simple !== easy. Simple is the opposite of complex.

This approach gives us flexibility when requirements change (and they always do) and makes our code approachable for new team members. I'd rather spend extra time simplifying a solution than shipping complexity that haunts us later.

📈 Compounding Value of Type Safety

Strong typing dramatically reduces operational headaches and scales our team's productivity. When we catch errors at compile time instead of production, we sleep better at night.

Type systems also help us validate AI-generated code and provide guardrails as our codebases grow. The upfront investment in proper types pays dividends every time I refactor or extend functionality.

🛞 Don't Reinvent the Wheel

Before writing a single line of custom code, I research. Chances are, someone else has already solved your problem and open-sourced a battle-tested solution.

I'd rather spend time learning and adapting existing solutions than building everything from scratch. This isn't about being lazy, it's about being smart.

⚠️ Errors as First-Class Citizens

Errors aren't edge cases, they're part of the user experience. I design with failure in mind, considering error states during the design phase.

Whether it's network timeouts, validation failures, or loading states, I treat these scenarios with the same care as the happy path. Users notice when software handles problems gracefully.

++ Deliver Incremental Value

Big bang releases are expensive, risky, and often miss the mark. Instead, I break everything down into smaller chunks that deliver value along the way.

Stop-the-world migrations might look impressive on paper, but they're complexity bombs waiting to explode. I'd rather ship ten small improvements that users can benefit from immediately than one massive change that might not land as expected.

🔎 Stay customer focused

At the end of the day, we're not building software for ourselves—we're solving real problems for real people. This means understanding actual user needs, not hypothetical requirements or features that might be useful someday.

Every technical decision should ultimately trace back to customer value. Clean code and elegant architecture matter, but only insofar as they help us deliver better solutions to the people who depend on our work.