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Jaskirat Singh
Jaskirat Singh

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

#ai #programming #coding #buildinpublic

Meme

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.

TL;DR

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.

Why This Quote Resonates With Developers

Every developer has lived through this moment:

  • Code runs perfectly on your machine
  • You push it
  • Production breaks in ways you never imagined

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.

Why First-Try Success Is Suspicious

If your code works immediately, one or more of these is likely true:

  • 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

Passing once proves almost nothing.

A Simple Example 

from datetime import datetime

def parse_date(s):
    return datetime.strptime(s, "%Y-%m-%d")
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This works perfectly for:

2026-01-23
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But what about:

  • 23-01-2026
  • 2026/01/23
  • Jan 23 2026
  • 2026-02-30
  • Different locales or time zones

First-run success only proves your input matched your assumption.

The “Try to Break It” Mindset

When code works immediately, do this next:

  1. Add unit tests for the happy path
  2. Add tests for invalid and edge inputs
  3. Run property-based or fuzz testing
  4. Test against real dependencies, not mocks
  5. Simulate production-like environments
  6. Introduce latency and partial failures
  7. Run concurrent requests
  8. Verify logging and error reporting
  9. Add static analysis and type checks
  10. Get a second set of eyes in code review

If you can’t break it, write a test that proves why.

Why Production Is Where Bugs Are Born

Production introduces things your laptop never will:

  • Network delays
  • Partial outages
  • Race conditions
  • Different CPU architectures
  • Locale and encoding differences
  • Unexpected user behavior
  • Scale and concurrency

Code that works once in isolation hasn’t earned trust yet.

When First-Try Success Is Actually Fine

There are exceptions:

  • Pure deterministic functions
  • Well-specified algorithms
  • One-off scripts
  • Throwaway experiments

But the moment code becomes reusable, shared, or deployed, it needs tests and defensive thinking.

Turning First-Run Success Into Confidence

A healthy workflow looks like this:

  1. Code works on first run
  2. Add tests that assert that behavior
  3. Add tests that try to break it
  4. Automate those tests in CI
  5. Observe behavior in staging
  6. Monitor and log in production
  7. Iterate when reality disagrees

Confidence comes from evidence, not luck.

Final Thoughts

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.

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