Over time, I’ve noticed that tools which promise to do everything often end up doing nothing particularly well.
Simple tools are easier to understand, easier to debug, and easier to leave when they stop serving you.
This isn’t an argument against complexity — it’s an argument for clarity.
“If a tool is hard to explain, it’s usually hard to maintain.”
Quick takeaways
- Prefer small tools with clear boundaries.
- Optimize for readability and debuggability.
- Document decisions while they’re fresh.
Checklist
- Can I explain it in one paragraph?
- Can a teammate debug it in one sitting?
- Can I replace it without rewriting everything?
Demo code
# simple, boring, and clear
def estimate_cost(age, bmi, smoker):
base = 120
base += (age - 18) * 4
base += (bmi - 20) * 3
base += 200 if smoker else 0
return max(base, 0)
print(estimate_cost(28, 23.4, False))
Comparison table
| Approach | Clarity | Maintenance |
|---|---|---|
| Small tools | High | Low effort |
| All-in-one suite | Medium | High effort |