Living Museum of Learning
When the Bug Isn't Yours
Last week, while waiting for the seaplane in Vancouver, I invited Cindy and our daughter An to play a three-player game of Golden 24.
It should have been a perfect demonstration of nearby multiplayer without Wi-Fi.
Instead, it was embarrassing.
The devices struggled to connect, and even when they did, the connection often broke shortly afterward. There was no obvious error message—just a game that quietly failed to stay together.
As the developer, I naturally assumed the bug was mine.
Back in Toronto, I thoroughly reviewed our Multipeer Connectivity implementation.
We traced packets, examined state synchronization, simplified the architecture, and searched for race conditions. Yet nothing explained why code that had worked before was suddenly unreliable.
Then I came across numerous reports from iPhone users after upgrading to iOS 26.
Tesla digital keys disappearing while driving.
Bluetooth headphones disconnecting.
Wireless CarPlay failing repeatedly.
Completely different applications.
The same underlying wireless technology.
Maybe the problem wasn't our code after all.
The discovery didn't prove that Multipeer Connectivity has a bug in iOS 26.
But it changed the investigation itself.
Instead of asking, "Where is the mistake in our code?"
I began asking, "What assumptions are we making about the platform beneath our code?"
Sometimes software engineering isn't only about finding bugs in what we wrote.
Sometimes it's about recognizing that we're standing on someone else's foundation.
Good debugging isn't about proving yourself right.
It's about eliminating possibilities until the remaining explanation—even an unexpected one—deserves serious consideration.
When our software behaves strangely, we naturally look inward first.
But engineering also means understanding where our responsibility ends and where the platform begins.
The hardest bugs are often the ones that don't belong to us.
A failed experiment can still produce valuable knowledge by revealing where the real problem may lie.
Systematically eliminate your own hypotheses before considering failures in the underlying platform.
Understanding the boundary between your code and the platform leads to better engineering decisions—and less time chasing ghosts.
