$ cat post/apt-get-from-the-past-/-a-rollback-took-the-data-too-/-it-boots-from-the-past.md
apt-get from the past / a rollback took the data too / it boots from the past
Title: Navigating AI Copilots in a Post-Hype Kubernetes World
November 10, 2025. I woke up to another day of debugging and coding with my trusty LLM copilot, which has become an indispensable part of my daily routine since the era of AI-native tooling took off. This blog post is about the challenges and triumphs that came with it.
Yesterday was particularly eventful. A minor outage hit our Cloudflare infrastructure, and despite the fact that we’ve been running this setup for years, it felt like an invasion from a different decade. The outages in the news—like the one at Cloudflare on November 18—echoed concerns I’ve had about resilience and dependency. We all know that multi-cloud is now default, but it’s not without its pitfalls.
I spent much of my morning arguing with my copilot (yes, it’s a real argument, just in text form) over the best way to handle edge cases during our daily pipeline run. The LLM was suggesting we use a newer, less battle-tested eBPF feature for tracing and monitoring, which seemed like an exciting idea until I pointed out that we hadn’t actually benchmarked its performance under heavy load. It’s moments like these where the pragmatic side of me takes over, reminding us to stick with what works.
Later in the day, our platform team hit a snag while deploying some updates using Kubernetes. The usual process was pretty straightforward: bump the version, push the Docker image, and watch it roll out. But something went awry when we switched to the new AI copilot-driven workflow. I started wrestling with the eBPF side of things again, trying to get it to work seamlessly with our Wasm + container setup. The integration was smooth in theory but proved tricky in practice. By lunchtime, I had a good workaround, which involved tweaking some Kubernetes manifests and leveraging the new kubectl features that let us pass in custom annotations.
The AI copilots are great at suggesting code changes or identifying potential bottlenecks before they become issues. But they can also be a bit too optimistic about what’s possible with our current infrastructure. It’s like having a co-worker who always thinks everything should be easy and doesn’t understand the real-world constraints we have to deal with.
In the afternoon, I attended an internal hackathon where some of my team members were exploring new ways to integrate AI copilots into their workflows. One idea that caught my eye was using them for on-the-fly performance tuning of our microservices. They could monitor system metrics and automatically adjust service configurations in real time based on predicted load patterns. It’s a concept I’ve been toying with myself, but it’s always challenging to balance the benefits of automation against the overhead of constantly changing configurations.
By the end of the day, we managed to get our pipeline running smoothly again and the copilot was integrated into the process in a way that felt more natural than before. The eBPF tweak worked well, and I even got a chance to play around with some AI-powered debugging tools that my team set up. It’s not magic, but it does help catch issues faster.
Reflecting on today, I’m reminded of how far we’ve come since the early days of Kubernetes being seen as this bleeding-edge, complex technology. Now it’s boring and essential—just like any other tool in our stack. The real challenge lies in leveraging these tools without getting carried away by the hype. We need to stay grounded, focus on what works, and continuously iterate based on actual experience.
As I wrap up for the day, I’m looking forward to diving into some of those AI copilot suggestions tonight—perhaps with a more critical eye than usual. After all, it’s always good to challenge assumptions and push past the boundaries that these tools seem so eager to define for us.