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Perfection is not over-engineering

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Why This Matters

This article emphasizes that striving for perfection in tech solutions is often misunderstood; over-engineering stems from solving the wrong problems due to unclear or overly broad requirements. Recognizing that a 'perfect' solution depends on specific constraints can help teams avoid unnecessary complexity and focus on effective, tailored solutions. Clear requirements lead to optimal, context-specific outcomes rather than generic perfection, benefiting both developers and consumers by streamlining development and improving product relevance.

Key Takeaways

Perfection is not over-engineering

2026-07-19

"We don't want to do perfect." "We don't want to build the perfect solution." I've heard versions of that line more times than I can count, delivered as if "perfect" were a dirty word. And I understand the caution — over-engineering burns teams, and people have learned to treat anything that smells like perfection as the same risk.

It isn't. The industry has quietly conflated the two.

Over-engineering is solving the wrong problem. That's the whole definition. Not "caring too much." Not "making it too good." Solving the wrong problem. Often with good intentions, and almost always with a growing pile of incidental complexity.

There is a perfect solution

I believe a perfect solution exists. With one big caveat: you need a very clear set of requirements. Every constraint on the table. Tighten those enough and something interesting happens, you end up with only one possible solution. And that solution is, somewhat ironically, the perfect one. It's perfect because it's the only one that fits.

Start a new project. Every language, every tool, every hosting model available. You pick serverless. Python is a strong choice: no compilation step, upload your files to Lambda, ship. For someone else it's the wrong choice, they don't know Python, or they need to optimize for a different set of requirements, such as performance. Different constraints, different answer. Same problem space, different "perfect."

Or you chose Python and you're building a web app. Django or Flask? You can reach similar results with both. They're still different tools with completely different philosophies. Which one wins? It depends. Set clearer requirements, set stricter constraints, and the solution follows. That solution is the perfect one for you, for that case.

Systems are products

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