My Frontend Philosophy
I believe the frontend constitutes the product itself. For most users, it represents the company: the interface they interact with, the system they trust, and the aspect that fails them when issues arise. This intent navigates my approach to component design and performance matters.
I focus on being intentional constraint. I only add abstractions when they’re truly needed. (Beware of overengineering.) A hard-to-use or black-box design system will keep people from adopting it. Every abstraction I introduce must be justified. Design system components that are difficult to adopt are often circumvented. API boundaries that expose implementation details can introduce persistent cross-team defects. Pipelines with excessive runtimes disrupt developer productivity and impede organisational efficiency. I prefer solutions that are reliable, composable, and efficient rather than those that are too complex or comprehensive.
I am particularly wary of overengineering. It’s a habit I have struggled with in many projects and has triggered refactors, big and small, on many occasions.
I favor progressive disclosure of complexity: keeping things simple for basic use cases, while still making it possible to deal with complex situations when needed. I apply this idea to component APIs, state management, and deployment strategies.
How I Think About Architecture
Every problem in frontend, like most other Computer Science layers, has many different solutions. And our job as engineers is to make well-informed tradeoffs at each layer. Before major design decisions, I consider the following questions:
- Where is state managed, and who owns it? Managing state at the wrong layer can create technical debt that persists throughout the product lifecycle.
- What is the likely impact of failure? Can a component fail without affecting the entire page ?
- How will this decision affect the team in six months? Architectural choices are both technical and organizational. A micro-frontend boundary defines team boundaries, and a shared library creates shared responsibility.
- Allow room for mistakes. As architects, we should remember that what we build today will be used in ways we cannot predict. People working on this project will probably question the choices we make now. The best thing we can do is make sure the system can adjust to these changes.
In practice, I prefer explicit over implicit solutions, keeping systems cohesive rather than centralized unless scaling demands otherwise, and focusing on measurable outcomes. I adopt patterns like monorepos, microfrontends, or SSG only when they solve a particular problem.
Performance and Performance Scalability
Performance is a core product requirement. Cutting load times from minutes to seconds directly increases traffic and revenue. My approach to performance engineering follows a consistent methodology:
- Measurement comes before optimization. I instrument systems before making improvements. Tools like Lighthouse, Web Vitals, bundle analysis (webpack-bundle-analyzer, source-map-explorer).
- Optimize at the appropriate layer. Network latency, render-blocking resources, loops, and JavaScript parse time typically have a bigger impact than component-level optimisations. I address the largest bottlenecks first.
- Integrate performance into the pipeline. Without enforcement, performance regresses. I include bundle size budgets and Lighthouse CI gates in CI/CD to catch regressions before production.
- Select rendering strategies based on context. SSG, SSR, ISR, and CSR are tools, not fixed rules. The appropriate strategy depends on content update frequency, SEO needs, audience scale, and personalization complexity. I have experience with all of them and understand when each is suitable.
One thing I’ve learned is that these priorities need to be balanced with business goals. If we can’t clearly show the impact, these tasks often stay in the backlog.