Stop Solving Problems That Don't Exist

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Have you ever found yourself spending too much time creating code to solve problems that do not currently exist? Writing unnecessary code can clutter your codebase and make it harder to maintain. This becomes even more of a time-wasting exercise when building a minimum viable product (MVP) that hasn’t yet launched. During the MVP phase, the focus should be on making it work and testing it in the market. Even with a product roadmap, detailed requirements for future features are often unknown.

Most overengineering mistakes can be attributed to two areas: speculative generality and premature optimization.

Speculative Generality

Speculative generality involves writing unused code that might be useful in the future. When we think about potential future uses, we tend to add unnecessary complexity and reduce code clarity. It’s crucial to distinguish between genuine requirements and speculative needs. Writing unnecessary code can create more problems than it solves, leading to confusion and unnecessary maintenance work.

Premature Optimization

Premature optimization occurs when we optimize for performance before the need arises. Often, we overengineer solutions to achieve a perceived performance gain that may not be necessary. It’s essential to focus on creating functional code that meets current requirements before diving into performance optimization. Optimizing too early can introduce increased complexity, longer development cycles, and other complications.

Striking the Balance

While it’s important to build a codebase that is extensible and scalable, it’s equally important to avoid overengineering for speculative future uses. The reality is that we often don’t know what the future holds in terms of requirements and challenges. Understanding the difference between overengineering and necessary flexibility is crucial to prevent unnecessary complexity. Striking a balance between building for the future and meeting current requirements is key.


In conclusion, it’s crucial to avoid the pitfalls of overengineering, which can result in unnecessary complexity, longer development cycles, and maintenance headaches. By focusing on creating functional code that meets current requirements and avoiding premature optimization and speculative generality, we can develop cleaner, more maintainable codebases that are easier to manage and scale over time.


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