April 28th, 2025. I sit back and reflect on the past year, which has been a whirlwind of AI copilots and platform teams owning AI infra pipelines. It’s funny to look at the Hacker News headlines from this month; they paint a vivid picture of our current tech landscape. Let me share my journey through some real ops work that landed in my lap.

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I can’t help but chuckle every time I think about the debate we had over whether we should really be allowing AI copilots to take over critical infrastructure tasks. The team argued vehemently, with some advocating for a more hands-off approach and others pushing hard for full automation. In the end, it came down to a simple test: deploy an AI copilot to manage our eBPF production pipelines.

The first few weeks were like watching a toddler learning to walk—full of stumbles and falls. The copilot would crash the build with its own overzealous refactoring, or it would get sidetracked by optimizing something that wasn’t bottlenecked in our system. But slowly, it started to learn. It began to recognize patterns in how we handle edge cases and make decisions based on historical data.

One of the most frustrating episodes was when the copilot tried to optimize our WebSocket connection code. It decided to replace everything with a new protocol stack, thinking it would be more efficient. We had to roll back quickly because it broke compatibility and caused cascading issues. The lesson? AI isn’t perfect, and human oversight is still necessary.

We also wrestled with the convergence of WebAssembly (Wasm) and containers. Our platform team was tasked with figuring out how to make these technologies work together seamlessly. We started by setting up a Wasm environment in Kubernetes clusters but ran into some unexpected issues. The Wasm modules had to be compiled for each architecture, which posed a challenge since our infrastructure supported multiple architectures.

After much trial and error, we landed on using containerd as the runtime manager for both containers and Wasm modules. This allowed us to share the same runtime environment and made it easier to manage dependencies across different languages and frameworks. The team was ecstatic when they saw how smoothly everything worked together, but there were still some teething problems with performance overhead.

As I reflect on these experiences, I can’t help but think of the other Hacker News stories from this month. The debate over advertising legality was interesting, but it’s clear that AI is changing our world in profound ways. The CVE program issues highlighted the ongoing battle against security vulnerabilities as we integrate more complex systems. And then there’s the Microsoft controversy—how does a monolith like them decide to fork and how does it affect us?

In the end, I believe the most significant takeaway from this era is that AI copilots are here to stay. They’re not perfect, but they are incredibly useful when used correctly. We’ve learned to embrace their strengths while managing their weaknesses. And as engineers, we continue to navigate the complex landscape of multi-cloud and Kubernetes in our quest for reliability and efficiency.

As I wrap up my day, I’m already thinking about what new challenges lie ahead. The journey is far from over, but I’m excited to see where this AI-driven future takes us next.

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That’s my take on AI copilots in DevOps. What do you think? Leave a comment below or find me on [Twitter handle] if you have any thoughts!