How I Solve Problems
A systematic approach to engineering complex systems, from problem analysis to lessons learned.
Problem Analysis
Understanding the real problem is key. Before writing code, I clarify constraints, stakeholders, risks, and success criteria.
- Requirements & constraints mapping
- Stakeholder and use-case analysis
- Success metrics definition
- Risk identification
Hypothesis & Assumptions
Every solution starts with a hypothesis. I state assumptions clearly and validate them early to avoid building on weak foundations.
- Hypothesis formulation
- Assumption documentation
- Validation plan
- Risk mitigation strategies
System Design
I design systems—not just interfaces. This means defining boundaries, flows, contracts, and scalability from the start.
- Component architecture
- Data flow diagrams
- API contracts
- Performance considerations
Architecture Overview
I create high-level architecture maps that show how everything fits together with clarity and intention.
- System architecture diagram
- Stack selection and rationale
- Infrastructure and deployment strategy
- Integration points and dependencies
Implementation Strategy
I deliver incrementally: small, reliable steps that compound into well-architected systems.
- Incremental delivery plan
- Dependency mapping
- Tooling and workflow setup
- Testing at each layer
Validation & Iteration
Continuous validation ensures quality. When something doesn’t work, I iterate quickly and refine.
- Automated testing
- Performance monitoring
- User feedback analysis
- Iteration based on data
Lessons Learned
Every project teaches something. I document learnings and patterns to continuously improve my engineering approach.
- Post-mortem and reflection
- Pattern identification
- Knowledge sharing
- Process refinement
Continuous Improvement
This approach isn't rigid—it adapts to the context. Some problems need more analysis, others need faster iteration. The key is maintaining clarity, precision, and a focus on building systems that evolve gracefully over time.