Your Code Doesn’t Need to Be Perfect. It Needs to Ship.

The art of knowing when your code is ready to ship—and when you're just overthinking it

I once spent 6 hours refactoring a function that took me 20 minutes to write.

The code worked. Tests passed. 10,000 users were using it in production without issues.

But I kept thinking: “What if I restructure this into smaller functions? Maybe extract a utility class? Could this algorithm be more elegant?”

At 11 PM, staring at my fourth iteration, it hit me — I wasn’t improving the code. I was solving a problem that didn’t exist because it felt like “real engineering.”

The Perfectionism Trap

If you’ve ever reopened a PR “just to check one more thing” for the 8th time, you know this feeling.

Or spent two hours researching whether your O(n) solution could be O(log n)—when n is always less than 50.

Or refactored a working implementation into a “more elegant” design pattern, even though the original was clear and maintainable.

There’s a difference between engineering rigor and engineering paralysis.

Good engineers design things right. Great engineers also know when they’re solving interesting problems instead of business problems.

The Line Between Design and Polish

Design decisions (spend time here):

• API contracts and error handling

• Input validation and security

• Separating concerns when complexity demands it

• Database schemas and testability

• Clear interfaces that won’t break consumers

Polish decisions (ship first, revisit if needed):

• Restructuring working functions “for better organization”

• Refactoring to patterns when current code is maintainable

• Optimizations without performance problems

• Abstractions before you have multiple use cases

• Algorithmic improvements when current performance is acceptable

The first list prevents technical debt. The second list is where perfectionism hides as “craftsmanship.”

The Over-Engineering Tax

Here’s what nobody tells you: You’re not paid to solve interesting engineering problems. You’re paid to solve business problems with engineering.

I’ve watched engineers spend a week building:

• A generic caching layer “for future flexibility” (current use case: 3 API calls)

• An event-driven architecture “for scale” (current traffic: 100 requests/day)

• A plugin system “for extensibility” (current plugins needed: 0)

Meanwhile, the simple solution would have taken 2 days and solved the actual business need.

The trap: These feel like “proper engineering.” But optimizing for theoretical scale or theoretical flexibility is still perfectionism — just wearing architecture diagrams.

The Balancing Act

You need to find the middle ground:

Under-engineered (tech debt)
→ No error handling
→ Hardcoded values
→ Direct DB calls in controller

Right-sized (ship this)
→ Try-catch with logging
→ Config file
→ Simple repository pattern

Over-engineered (wasted time)
→ Full retry framework with circuit breakers
→ Plugin system with hot reload
→ CQRS + event sourcing

Ask yourself: What does the business need right now?

Not hypothetical scale. Not potential future requirements. Not what would be intellectually satisfying.

The Decision Framework

Before spending another hour on working code, ask:

Will this change:
  ✓ Prevent bugs or make debugging easier?
  ✓ Simplify future changes meaningfully?
  ✓ Fix a design flaw?

Or am I:
  ✗ Optimizing for hypothetical scale?
  ✗ Building flexibility nobody asked for?
  ✗ Refactoring because it’s more fun than moving to the next task?

First category → Good engineering. Do it.
Second category → Over-engineering. Ship the simpler version.

Design Once, Then Stop Optimizing

Phase 1 - Design (take your time):

• What’s the interface contract?

• How do errors propagate?

• Is this testable and maintainable?

• What scale/complexity do we actually need?

Phase 2 - Implement & Ship:

• Write clear, working code with appropriate tests

• Use patterns proportional to actual complexity

• Stop when it works and is maintainable

Your North Star: Design, Ship, Learn

Ship well-designed code quickly. Don’t ship poorly-designed code at all. Don’t over-engineer for problems you don’t have. And don’t optimize working code when the business needs you shipping the next feature.

Design right for today’s problem, test it with real users, then iterate based on actual data — not hypothetical improvements.

Every hour you spend optimizing a working solution is an hour you’re not delivering business value, learning from real users, or solving the next problem.

Tell me, what are you working on right now — and which category does it fall into?