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I start with domain modeling and API design, establishing clear boundaries between services. My architecture typically follows a layered approach: presentation (React/Next.js), business logic (Node.js/TypeScript), and data persistence (PostgreSQL/MongoDB). I prioritize type safety throughout the stack, implement comprehensive error handling, and design for horizontal scaling from day one. Key principles include dependency injection for testability, event-driven communication between services, and implementing proper caching strategies at multiple levels.

I use progressive enhancement as a core strategy - building robust server-side functionality first, then layering on client-side enhancements. For real-time features, I implement WebSocket connections with fallback to polling, always with proper reconnection logic. State management follows the principle of optimistic updates with rollback capabilities. I leverage service workers for offline functionality and implement circuit breakers on the backend to gracefully degrade service during high load or failures.

I follow mobile-first design with progressive enhancement, using CSS Grid and Flexbox for layout flexibility. My approach includes fluid typography with clamp(), logical CSS properties for international support, and comprehensive focus management. I test with screen readers, ensure proper ARIA labeling, and maintain 4.5:1 color contrast ratios. Performance is crucial - I implement lazy loading, optimize Critical Rendering Path, and use responsive images with proper srcset attributes. Every interactive element is keyboard accessible with visible focus indicators.

I implement the testing pyramid: comprehensive unit tests for business logic, integration tests for API contracts, and strategic E2E tests for critical user journeys. My test architecture uses Page Object Models with Playwright, implements proper test data management with factories, and includes visual regression testing. I focus on test reliability through proper waits, isolated test environments, and parallel execution. Tests are treated as first-class code with proper abstraction layers and maintainable selectors.

Test execution speed and reliability are paramount - I implement test parallelization, smart test selection based on code changes, and proper retry mechanisms for flaky tests. My CI strategy includes running fast smoke tests on every commit, comprehensive regression suites on PRs, and performance testing in staging environments. I use containerized test environments for consistency, implement proper reporting with screenshots and videos for failures, and ensure tests fail fast with clear diagnostic information.

QA experience has made me obsessive about edge cases and error conditions. I write defensive code with proper input validation, implement comprehensive logging for debugging, and design APIs with clear error contracts. During code reviews, I focus on testability, potential race conditions, and failure modes. I advocate for feature flags to enable safer deployments and implement monitoring that aligns with user-facing functionality rather than just technical metrics.

I use AI as an intelligent pair programming partner - Claude for architectural discussions and complex problem-solving, GitHub Copilot for boilerplate generation and test cases. My workflow includes AI-assisted code reviews, documentation generation, and refactoring suggestions. I've developed prompt templates for consistent code generation and maintain clear guidelines for when to accept vs. modify AI suggestions. The key is treating AI as a force multiplier while maintaining critical thinking about security, performance, and maintainability.

Effective prompt engineering is crucial for consistent, reliable AI outputs. I use structured prompts with clear context, examples, and constraints. For code generation, I specify coding standards, error handling requirements, and testing expectations upfront. I implement prompt versioning and A/B testing for critical AI interactions, and always include validation layers for AI-generated content. The goal is creating predictable, auditable AI behaviors that integrate seamlessly with human workflows.

I establish clear success criteria and validation pipelines for AI outputs. For generated code, this includes automated testing, static analysis, and peer review. I implement logging and traceability for AI decisions, maintain human oversight for critical paths, and use techniques like chain-of-thought prompting to make AI reasoning transparent. When debugging, I analyze prompt effectiveness, check for training data bias, and iterate on context and constraints to improve reliability.

I prioritize performance and accessibility even in rich experiences. My approach combines WebGL/Three.js for 3D content with progressive enhancement, ensuring core functionality works without advanced features. For AI-generated interfaces, I implement proper loading states, fallback content, and user control over generative elements. I use CSS transforms and Web Animations API for smooth transitions, implement proper motion preferences, and ensure immersive experiences don't compromise usability fundamentals.

I use rapid prototyping with user feedback loops, starting with low-fidelity mockups before investing in complex implementations. For XR experiences, I focus on comfort metrics like motion sickness and fatigue alongside traditional usability measures. I implement analytics for spatial interactions, gesture success rates, and user attention patterns. Iteration involves A/B testing different interaction paradigms and maintaining fallback options for users who struggle with novel interfaces.

Clear communication protocols are essential - I use structured documentation, async-first communication, and regular sync points for complex discussions. My toolkit includes Figma for design collaboration, Linear for project tracking, and Slack for real-time communication. I implement code review processes that include design and QA perspectives, maintain shared glossaries for technical terms, and use video recordings for complex technical explanations. Time zone awareness drives my planning and ensures no team member is blocked by geographic constraints.

I tailor technical depth to each audience - focusing on user impact for product discussions, implementation feasibility for design reviews, and edge cases for QA planning. With designers, I emphasize what's achievable within performance budgets; with PMs, I translate technical constraints into business impact; with QA, I discuss testability and failure modes. I use visual aids like architecture diagrams, create shared artifacts like technical RFCs, and always confirm understanding through follow-up questions and documentation.