June 23, 2025. Just another day in tech where Kubernetes continues to do its boring but essential job of keeping our services running smoothly, while the AI copilots are taking over more mundane tasks.

This morning started like any other—coffee, code, and a quick review of my Slack notifications. I had just settled into my chair when an email from the team popped up. It was urgent: one of our microservices had gone down, causing a ripple effect across multiple applications. A quick glance at the logs showed a 500 error, but no immediate indication of what caused it.

Typically, I would have started diving deep into the code or digging through recent changes, but this time felt different. The AI copilot in my head suggested checking the eBPF traces instead. It’s been a while since I’ve used them extensively, but they’re production-proven and can give us quick insights into what’s happening under the hood.

I fired up the eBPF tooling and watched as it gathered data on our service. The logs showed nothing out of the ordinary, but the eBPF trace revealed some unexpected patterns in network latency spikes. It looked like there was a bottleneck in one of our network libraries that had somehow been introduced during the last update.

This was where my AI copilot’s advice really shone. I quickly spun up an interactive graph using Gemini CLI to visualize the data and noticed a clear pattern—a slight but significant increase in latency exactly when the service updated its dependencies. It seemed the new version of the library wasn’t optimized for our environment, causing this issue.

The solution was straightforward—rolling back the update and doing some optimizations ourselves. However, the debate started within my team. Some argued that we should let the copilot handle everything to avoid human error, while others pointed out that AI tools can only do so much; sometimes, real understanding of the system comes from human insights.

I found myself mediating this argument, which had become somewhat heated. In the end, I decided to take a hybrid approach. We’d use the AI copilot for initial diagnostics and suggestions but retain the ability to dive deeper into specific areas when necessary. This way, we could leverage both the speed of the AI and the depth of human expertise.

The service was back up within an hour, and as I sat back from my desk, reflecting on the day’s events, I couldn’t help but think about Bill Atkinson’s passing mentioned in Hacker News. His work on HyperCard and other tools had undoubtedly influenced much of what we take for granted today. It made me wonder about our own legacy and how AI would shape the future of software development.

As I closed my laptop and headed home, I realized that even with all the AI assistance available, there was still a place for old-fashioned problem-solving skills. The AI copilot is a tool, not a replacement for human ingenuity and experience.

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That’s what I learned today: sometimes, you need to put down the AI copilot and pick up an eBPF trace or two. Technology evolves, but the core principles of good engineering still matter.