Spoiler: The algorithm wasn’t impossible... but the environment was.
Not long ago, I faced a set of technical challenges from BairesDev, mostly focused on ETL validation, SQL reasoning, and clean Python logic. Individually, none of the problems were unmanageable. The tricky part wasn’t what I had to solve — it was where and how I had to solve it.
When the platform becomes the bottleneck
No local testing
No console output to debug
No documentation on expected structure
Harsh handling of minor mistakes (like NZEC from missing parentheses)
One of the challenges had me convert IPv4 addresses to integers. A tiny issue with operator precedence (<< +) caused a syntax error that took longer to debug than the whole algorithm itself.
It wasn’t the logic, it was the validation
Beyond writing functions like:
Counting IPs between two addresses
Ranking arrays with repeated values
Sorting unique characters alphabetically
The real test was surviving a system that didn’t tell me why it failed.
Lessons I documented
Every challenge became a mini resource: a README, a blog post, or just a reminder of what to watch out for next time. Because honestly:
Debugging inside a silent box is a skill too.
✅ Tips I’d give anyone taking similar tests:
Run your logic locally before submitting
Use try/except to catch invisible errors
Add extra parentheses when in doubt
Simulate inputs the platform expects
Document edge cases, even if they seem obvious
