Some programming lessons come gently, like morning fog rolling in from Pacific. Others crash into you like a rogue wave during king tide, leaving you gasping and wondering how you'll ever trust solid ground again.

This is story of latter. day those four innocent words — "It works on my machine" — nearly washed away everything I'd built in my career. Sitting here in my Waldport home office, with sound of waves providing their eternal rhythm, I can finally laugh about it. But for a while there, I seriously considered switching careers to something safer, like bomb disposal or lion taming.

Perfect Storm: Setting Stage

It was three years ago, back when I was still figuring out this whole ADHD-programmer thing. I'd been working on a critical data processing pipeline for our biggest client — kind of project that makes or breaks quarterly reviews. My local development environment was a thing of beauty: customized, optimized, and perfectly tuned to my brain's particular way of working.

Past Ken's Confidence Level: Dangerously High

"This pipeline processes 50,000 records in under two minutes on my machine. client's going to love this!"

My ADHD hyperfocus had kicked in beautifully. For two weeks straight, I'd been in zone — that magical state where code flows like water and every problem has an elegant solution. I'd built something I was genuinely proud of: clean, efficient, and fast. Every test passed. Every edge case handled. Every performance metric exceeded expectations.

What could possibly go wrong?

🕊️ ⚡ 🕊️

Deployment Day: When Everything Falls Apart

I remember moment with painful clarity. It was a Thursday morning, 9:47 AM Pacific. client was scheduled to see demo at 10 AM. I'd deployed to production with confidence of someone who had never experienced true professional humiliation.

deploy went smoothly. Green checkmarks everywhere. Services started successfully. I even ran a quick smoke test — everything looked perfect. I grabbed my coffee ( good stuff from that little roastery in Newport) and joined video call, ready to watch jaws drop at my beautiful creation.

FATAL ERROR: Failed to process records. Timeout after 900 seconds.
STACK TRACE: OutOfMemoryException at line 247...
RECORDS PROCESSED: 847 of 50,000
STATUS: Complete system failure

silence on that call was deafening. Fifty thousand records that processed beautifully on my machine had ground production server to a halt after processing less than 900. client's data was stuck in limbo. Their entire workflow was broken. And there I was, staring at error logs that might as well have been written in ancient Sumerian.

"Ken, can you explain what's happening here?" client's voice was eerily calm, kind of calm that precedes volcanic eruptions.

I heard myself say it. Those four words that haunt every developer's nightmares: "It works on my machine."

Immediate Aftermath: Panic Mode Engaged

What followed was two hours of most intense debugging session of my life. My ADHD, which had been my superpower during development, suddenly became my worst enemy. Every notification, every sound, every movement in my peripheral vision felt like a personal attack on my concentration.

Differences That Broke Everything:

  • 🦀Memory: My machine: 32GB RAM. Production: 8GB RAM.
  • 🦀CPU: My machine: M1 Max with 10 cores. Production: 2-core Intel.
  • 🦀Database: My local SQLite vs. production PostgreSQL with different query optimizations.
  • 🦀Network: Local filesystem vs. network-attached storage with latency.
  • 🦀OS: macOS vs. Linux with different memory management.

worst part wasn't technical failure — it was realization of how naive I'd been. I'd built a Ferrari and tried to run it on a bicycle path. Every optimization I'd made, every performance assumption, every bit of confidence I'd built up was based on an environment that bore no resemblance to where code would actually live.

Emergency Rescue: Learning Under Fire

Sometimes necessity is most brutal teacher. With client breathing down our necks and my career hanging by a thread, I had to learn production debugging in real-time. Here's what saved day (and my job):

Emergency Fixes That Actually Worked:

# Before: Memory-hungry batch processing def process_all_records(records): return [heavy_transform(record) for record in records] # After: Streaming with memory limits def process_records_streaming(records, batch_size=100): for batch in chunked(records, batch_size): yield process_batch(batch) gc.collect() # Explicit garbage collection
  • 🦀Batch Processing: Instead of loading 50k records into memory, process 100 at a time.
  • 🦀Memory Monitoring: Added real-time memory usage tracking and automatic garbage collection.
  • 🦀Database Optimization: Switched from loading full records to streaming cursors.
  • 🦀Timeout Handling: Added proper timeout and retry logic for network operations.
  • 🦀Progress Tracking: Real-time progress reporting so failures could be resumed, not restarted.

patched version processed all 50,000 records in production in about 15 minutes — not blazing speed of my local machine, but stable, predictable, and actually working. client got their data. crisis was averted. But damage to my confidence was... significant.

Hard-Won Wisdom: What I Learned from My Disaster

In weeks that followed, I became obsessed with understanding what had gone wrong. Not just technical details, but fundamental assumptions that had led me astray. Here's what that painful experience taught me:

Environment Parity: Non-Negotiable

Your development environment should be as close to production as possible. Not "similar." Not "equivalent." As close as humanly achievable.

My New Setup:
✅ Docker containers matching production OS and versions
✅ Resource limits mimicking production constraints
✅ Same database engine with production-like data volumes
✅ Network latency simulation for external services
✅ Automated environment validation in CI/CD pipeline

Testing Assumptions: Question Everything

Every performance assumption I'd made was wrong. Every "this should work fine" turned out to be famous last words.

  • 🦀Load Testing: Always test with production-scale data
  • 🦀Resource Monitoring: Measure memory, CPU, and I/O under realistic conditions
  • 🦀Failure Scenarios: Test what happens when things go wrong
  • 🦀Performance Baselines: Document expected performance on actual production hardware

ADHD Factor: Working with My Brain, Not Against It

This disaster taught me something crucial about my ADHD programming style. My hyperfocus superpower had a dark side: when I'm in zone, I tend to optimize for my immediate environment and forget about constraints I can't directly observe.

Now I build production constraints into my development workflow from day one. I can't rely on remembering to test differently later — I have to make right thing easy thing.

Unexpected Gift: How Failure Became My Teacher

Here's thing about professional disasters: they're terrible when they happen, but they can become foundation for everything good that comes after. That humiliating Thursday morning became catalyst for most important growth period of my career.

difference between a junior developer and a senior developer isn't that seniors don't make mistakes. It's that seniors have made more interesting mistakes and learned more valuable lessons from them.

Six months after disaster, I was leading our team's infrastructure standardization project. A year later, I was go-to person for production debugging. Two years later, I was teaching other developers about importance of environment parity and production-ready code.

What I Do Differently Now:

  • 🦀Production-First Development: I start with production constraints, not local convenience
  • 🦀Continuous Integration Testing: Every commit runs on production-like infrastructure
  • 🦀Performance Budgets: Set resource limits early and test against them constantly
  • 🦀Failure Mode Analysis: Spend time thinking about what could go wrong before it does
  • 🦀Production Monitoring: Instrument everything and alert on performance degradation

irony is that this disaster made me a much better programmer. Not because I learned some esoteric technical skill, but because I learned humility. I learned to question my assumptions. I learned that "it works on my machine" is never good enough.

Reflecting from Shore: Wisdom from Waves

As I write this, I can hear Pacific Ocean through my office window. There's something about rhythm of waves that puts coding disasters in perspective. ocean doesn't care about our deployment schedules or our performance assumptions. It just keeps doing what it does, reminding us that nature operates on a different scale than our carefully constructed digital environments.

That's what production environments are like — they're closer to nature than to our development machines. They have their own weather patterns, their own resource limitations, their own unpredictable behaviors. Our job as programmers isn't to fight against these constraints, but to work with them.

Deeper Lesson: Humility in Code

Programming is fundamentally an act of communication — not just with computers, but with other programmers, with future maintainers, and with systems we may never directly interact with. When I said "it works on my machine," I was prioritizing my immediate experience over broader ecosystem my code would inhabit.

Now I try to code like I'm writing a letter to a programmer I'll never meet, running on hardware I'll never see, solving problems I may not fully understand. It's a more humble way to work, but it's also more robust.

Every time I'm tempted to take shortcuts in testing, or skip that extra validation step, or assume my local environment represents reality, I remember that Thursday morning. I remember silence on client call. I remember how small I felt, and how much I learned from feeling small.

Today: Turning Scars into Wisdom

These days, when junior developers on our team run into environment issues, I share this story. Not to scare them, but to normalize experience of failure and learning. Every experienced programmer has a version of this tale — day their assumptions crashed into reality and taught them something invaluable.

client from that disaster? They're still our client. relationship survived because we handled crisis with transparency, fixed immediate problem, and implemented processes to prevent similar issues. Sometimes showing your humanity in moments of failure builds more trust than never failing at all.

"Ken, remember when you broke our entire data pipeline on that demo call? That's when we knew you were going to become a great engineer. Great engineers learn from their disasters."

— That same client, two years later

Some lessons can only be learned through failure. Some wisdom can only be earned through humiliation. And sometimes, phrase "it works on my machine" becomes not a cop-out, but a reminder of everything we've learned about difference between what works and what actually works.

🌊 ⚡ 🌊

For Fellow Programmers Walking This Path

If you're reading this and nodding along because you've had your own "it works on my machine" disaster, know that you're in good company. Every programmer who's been in field long enough has a story like this. ones who don't are either lying or haven't been programming long enough.

key isn't to avoid all disasters — that's impossible. key is to fail forward, learn deeply, and share what you've learned with others who are walking same path. Our disasters become lighthouses that help other ships navigate safely to shore.

And remember: phrase "it works" is incomplete until you can answer question "where?" Production-ready code works everywhere it needs to work, not just where it's convenient to work.

From Oregon Coast, where every wave teaches us something about resilience,

Ken

Keep coding, keep learning, keep growing 🌊💻