$ cat post/the-floppy-disk-spun-/-a-system-i-built-by-hand-/-the-socket-still-waits.md
the floppy disk spun / a system I built by hand / the socket still waits
Title: AI Copilots in Production: A Realist’s Perspective
July 21, 2025. It feels like a new era every day with the AI copilot revolution. I’ve been working closely with these tools for over two years now, and it’s time to unpack some of the realities.
First off, let me say that LLM-in-a-box solutions are becoming more integrated into our platform infrastructure. The idea that engineers can get away with just throwing a copilot at a problem is increasingly seen as naive. We’re not yet at the point where AI tools replace human judgment entirely, but they certainly augment it in ways we’re only beginning to understand.
One recent project involved debugging a critical service using an LLM-assisted copilot. The service was experiencing intermittent latency spikes that were hard to reproduce and diagnose manually. We spun up a copilot session and fed the logs into it, hoping for some insight. At first, the copilot suggested some obvious optimization strategies (which I knew we had already considered). But then it made a suggestion about using eBPF probes in an unexpected location that I hadn’t thought of before.
I was skeptical at first, but after digging into its reasoning and running the probe, I found it pointed directly to the root cause: memory fragmentation issues in our C++ code. This is where things got interesting—using AI to debug low-level systems like this feels a bit like cheating, in a good way. It’s not just about getting answers; it’s about having new tools that can help us think through problems differently.
Of course, there are downsides too. Copilots aren’t perfect and often require context. For instance, the copilot mentioned some Python libraries we could use to simplify our API interactions. But I found that it didn’t fully understand the architectural constraints of our system, which led to a few missteps before we landed on an elegant solution.
Another area where AI is making waves is in the integration between WebAssembly (Wasm) and containerized environments. Wasm has finally proven itself as a versatile runtime for running small, self-contained pieces of code within browsers or alongside containers. This convergence with Kubernetes seems like it’s going to be transformative. We’re starting to see more microservices written in Rust or Go that leverage Wasm not just for edge cases but as the primary runtime.
But integrating these Wasm modules into our existing infrastructure is proving challenging. The initial excitement around Wasm’s simplicity and security has given way to the reality of compatibility issues across different cloud providers and tooling. As platform engineers, we’re spending a lot of time figuring out how to containerize Wasm functions, ensuring they work seamlessly with Kubernetes, and debugging them when things go wrong.
On a lighter note, I’ve had some funny moments with these AI tools. The other day, one of our team members was trying to add a feature based on the assumption that an LLM-assisted tool would think it existed. Turns out, ChatGPT couldn’t even provide documentation for the library in question. That led to a good laugh but also a serious discussion about how we’re still relying too much on these tools as a crutch.
Speaking of which, one of our recent projects involved using a copilot to help us build a website rather than an app. The idea was to avoid downloading any software and just use the browser for everything. We thought it would be cool to have a fully functional web-based IDE where you could write code and see results in real-time. In practice, though, we found that even with all these fancy copilots, there’s still no substitute for local development environments when dealing with complex projects.
Lastly, I can’t help but think back to the hype around Kubernetes post-hype. It’s now just a boring old tool that gets used every day without much fanfare. But it remains essential, and I’m glad we’ve moved past the excitement and into practical use cases.
As for the future? Well, I guess we’ll keep exploring these AI copilots to see what they can do next. The tech world continues to evolve at breakneck speed, but sometimes, it’s the mundane tasks that become the most important ones.
That’s where I stand on this front in early 2025. It’s been a journey of both promise and pragmatism.