Engineering Philosophy
The principles that guide every engineering decision I make.
Technologies evolve. Frameworks come and go. The principles behind good engineering remain.
Throughout my career I've found that the best technical decisions come from understanding problems, trade-offs and systems — rather than from preferring specific technologies.
Software exists to solve problems.
Software is never the goal — it's the mechanism for creating value.
Software is never the goal. React is not the goal. Architecture is not the goal.
The goal is creating value by solving meaningful user and business problems. Every technical decision should ultimately contribute to that objective.
Engineering creates value in two ways.
Every contribution either improves the product directly, or improves the team's ability to keep improving it.
Every engineering contribution belongs to one of two categories. Some improve the product directly. Others improve the team's ability to continuously improve the product.
Both ultimately create value for users — one now, one compounding.
- Reduced LCP by 3× through virtualization and lazy loading.
- Optimized component rendering — memoization and eliminating unnecessary re-renders — across complex, high-traffic interfaces.
- Delivered user-facing features across SaaS products.
- Improved responsiveness and overall user experience.
- Owned the Design System powering ~80% of a new React application.
- Built a reusable Data Grid reducing implementation time by 50–70%.
- Introduced AI-assisted development workflows that tripled engineering throughput.
The teams that get the most from AI-assisted development have the clearest context, the strongest guardrails and the highest existing engineering bar. AI amplified throughput 3× because the review process, documentation and skills around it were built first.
Optimize for outcomes, not technologies.
Technology is one possible solution — never the starting point.
I don't begin with technology. I begin with the problem.
Technology is simply one possible solution. Every architecture is an optimization for a particular set of constraints.
- Wrote a decision framework for choosing between CSR, SSR, SSG, ISR and React Server Components based on the problem, not the platform's default.
Every engineering decision is a trade-off.
Engineering decisions are context-dependent — the right answer changes with the question.
Consumption optimizes the initial render. Manipulation optimizes the interaction loop.
Yes → SSG or ISR become candidates. No → you need SSR or CSR.
Rarely → SSG. Regularly → ISR. Effectively every request → SSR.
Decided per feature, not per page — some parts of a page can be interactive while the rest isn't.
Server Component by default. Client Component only when the interaction demands it.
Instead of presenting technologies individually, I organize them around questions. The visitor — like the engineer — should understand that engineering decisions are context-dependent.
This is the five-step framework from my rendering-strategies article, applied question by question.
- Weighed delivery speed, maintenance cost and long-term adaptability explicitly — not just the 'best' solution in isolation.
- Chose SSR for authenticated, personalized pages and a client-owned architecture for a manipulation-heavy, AG Grid-style surface in the same product.
I don't optimize for the 'best' solution in isolation — I weigh delivery speed, maintenance cost and long-term adaptability against the constraint that actually matters for the business right now.
Great architecture increases engineering capacity.
Architecture's purpose is increasing a team's ability to keep improving a product, not elegance for its own sake.
Architecture should not exist for elegance. Its purpose is increasing a team's ability to continuously improve a product.
- Owned the Design System and a reusable Data Grid that cut implementation time for new product pages by 50–70%.
- Standardized reusable abstractions shared across multiple applications and libraries.
- Improved documentation and Developer Experience to shorten the feedback loop for the whole team.
Better tooling reduces friction. Less friction increases the rate of shipping. More shipping surfaces the next tooling gap sooner. I invest here first because it accelerates everything built after it.
A one-off feature solves one problem. A system solves the next ten. Systems thinking is slower in week one and faster than the alternative by week eight — and the gap keeps widening.
Simplicity scales.
Complexity compounds — and so does simplicity, in the opposite direction.
Complexity compounds silently until it stalls a team. Simplicity compounds too — in the other direction.
Good engineering removes accidental complexity instead of managing around it.
- Reduced technical debt through continuous architectural improvements while migrating a legacy Ember platform toward React.
- Eliminated unnecessary rendering and redundant network requests across complex user interfaces.
- Embedded performance and simplicity into shared components instead of relying on one-off optimization passes.
Every unmanaged abstraction, undocumented workaround and special case is a small tax on the next person who touches that code. Reducing complexity is compounding interest paid back to the whole team.
Measure impact, not activity.
Lines of code, PR counts and hours worked measure motion. They don't measure whether anything got better.
Engineering success should be measured by outcomes rather than output.
- 3× LCP improvement on the platform's most-used pages.
- 50–70% reduction in implementation time for new product pages.
- 3× frontend engineering throughput after leading an AI-adoption initiative.
Learn from first principles.
Understand why something exists before learning how to use it.
I try to understand why something exists before learning how to use it. That's what lets me adapt quickly as technologies evolve — the specifics change, the reasoning underneath doesn't.
- The rendering-strategies framework starts from what the user is doing on a screen, not from a list of technologies.
- Applied the same question → constraints → trade-offs → decision approach to performance, architecture and AI-adoption decisions alike.
I don't believe great engineers are defined by the technologies they know.
I believe they're defined by the principles they consistently apply.
Technologies will change. Good engineering principles endure.