Tools: If Your Code Works on the First Try, There’s a Massive Mistake Somewhere

Why This Quote Resonates With Developers ## Why First-Try Success Is Suspicious ## A Simple Example ## The “Try to Break It” Mindset ## Why Production Is Where Bugs Are Born ## When First-Try Success Is Actually Fine ## Turning First-Run Success Into Confidence ## Final Thoughts I read this line somewhere on Reddit and it stuck with me: If your code works on the first try, there is a massive mistake somewhere. It sounds like a joke, but every experienced developer knows there’s a painful truth hidden inside it. When code works on the first run, it usually means you tested only the happy path. Missing edge cases, untested assumptions, environment mismatches, or silent failures are often hiding underneath. First-try success should trigger skepticism, not celebration. Every developer has lived through this moment: The quote isn’t saying good engineers write broken code. It’s saying real software lives in messy environments, not in ideal inputs and local setups. If your code works immediately, one or more of these is likely true: Passing once proves almost nothing. This works perfectly for: First-run success only proves your input matched your assumption. When code works immediately, do this next: If you can’t break it, write a test that proves why. Production introduces things your laptop never will: Code that works once in isolation hasn’t earned trust yet. There are exceptions: But the moment code becomes reusable, shared, or deployed, it needs tests and defensive thinking. A healthy workflow looks like this: Confidence comes from evidence, not luck. That Reddit quote isn’t pessimistic — it’s pragmatic. When your code works on the first try, don’t assume you’re done. Assume you haven’t looked hard enough yet. The best engineers aren’t the ones whose code works instantly — they’re the ones who expect it to fail and prepare for it anyway. If your code survives your attempts to break it, then you can trust it. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: from datetime import datetime def parse_date(s): return datetime.strptime(s, "%Y-%m-%d") Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: from datetime import datetime def parse_date(s): return datetime.strptime(s, "%Y-%m-%d") CODE_BLOCK: from datetime import datetime def parse_date(s): return datetime.strptime(s, "%Y-%m-%d") CODE_BLOCK: 2026-01-23 Enter fullscreen mode Exit fullscreen mode - Code runs perfectly on your machine - You push it - Production breaks in ways you never imagined - You only tested the happy path - Inputs were overly clean or hard-coded - Error handling was never exercised - The environment matches your local machine too closely - Concurrency and load were never tested - Latency and failure scenarios were ignored - Success criteria were too shallow - Jan 23 2026 - Different locales or time zones - Add unit tests for the happy path - Add tests for invalid and edge inputs - Run property-based or fuzz testing - Test against real dependencies, not mocks - Simulate production-like environments - Introduce latency and partial failures - Run concurrent requests - Verify logging and error reporting - Add static analysis and type checks - Get a second set of eyes in code review - Network delays - Partial outages - Race conditions - Different CPU architectures - Locale and encoding differences - Unexpected user behavior - Scale and concurrency - Pure deterministic functions - Well-specified algorithms - One-off scripts - Throwaway experiments - Code works on first run - Add tests that assert that behavior - Add tests that try to break it - Automate those tests in CI - Observe behavior in staging - Monitor and log in production - Iterate when reality disagrees