8 days ago
- Perfection and over-engineering are often conflated but differ: perfection solves the right problem with clear requirements, while over-engineering solves the wrong problem.
- Over-engineering arises from ambiguous or incorrect requirements, leading to complexity and solutions for problems that never existed, such as unnecessary microservices causing data inconsistency.
- A perfect solution exists when requirements and constraints are tightly defined, resulting in the only viable option that fits all needs, like choosing Python or a specific framework based on context.
- Systems should be treated as products with users and needs; understanding these helps shape the solution, whether it's a service, library, or API, to avoid over-engineering.
- To prevent over-engineering, focus on gathering accurate requirements and constraints, as perfection becomes achievable and practical when the real problems are addressed.