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Perfectionism before shipping is procrastination with better PR. Get something real in front of real users, then fix what actually matters — not what you imagined would matter.
You've been working on the same feature for two weeks. It's almost ready. You find one more edge case to handle. You refactor the data model because you realized it won't scale. You add a loading state. You improve the error messages. You wonder if the prompt should be different.
Meanwhile: zero users. Zero feedback. Zero signal.
This is the perfectionism loop. It feels like diligence. It's actually fear — fear of shipping something imperfect to real people, of finding out your assumptions were wrong, of facing the uncomfortable feedback that the thing you built might not be what people need.
Ship a working, honest thing. Then watch what actually breaks.
"Working" means it does the core task correctly for the main case. Not all cases. The main case.
"Honest" means it doesn't pretend to do things it doesn't do. If it only handles English, say so. If it sometimes fails on complex inputs, say so.
Then ship it. Then watch.
In two weeks of solo development, you will discover approximately zero of the failure modes that your first 10 real users discover in 48 hours. This is not because you're bad at thinking through edge cases. It's because users don't use software the way builders imagine they will.
They paste in malformed input. They start a task and close the tab. They misread your UI and think a feature does something it doesn't. They combine your feature with other tools in ways you never considered. They have use cases you didn't anticipate.
None of this is knowable from the inside. It requires contact with reality.
Ship then audit does not mean ship and forget. The audit is the point. One week after shipping:
The audit converts shipping into learning. Without it, you're just accumulating features without accumulating understanding.
A prioritized list of actual problems, ranked by frequency and severity. Not the problems you imagined. Not the problems that are intellectually interesting. The ones that actually stopped real users from getting value.
Fix those. In order. The rest goes on the backlog.
AI agents fail in complex, context-dependent ways that are nearly impossible to anticipate. Your eval suite covers the cases you thought of. Real users bring the cases you didn't.
Ship the agent with a modest task scope. Watch the traces. Find the failure patterns. Update the prompt. Rerun evals. Ship again.
This cycle — two or three days each turn — is faster and more productive than two weeks of pre-shipping speculation. And the resulting agent is better calibrated to actual usage, not imagined usage.
If you've been building something for more than a week without shipping to anyone, you owe real users a working version right now. Not next week. Not after that one more thing.
Ship it today. Fix it tomorrow.