$ cat post/the-kernel-panicked-/-the-socket-never-closed-right-/-the-cron-still-fires.md
the kernel panicked / the socket never closed right / the cron still fires
Title: Debugging DevOps Nightmares in the Era of AI
October 31, 2022. Another Halloween, another year of grappling with the intricacies and challenges that come with running a platform in this ever-evolving tech landscape. As I sit down to write this, my thoughts are scattered across the multitude of issues we faced in the last few months, from AI integrations to cost optimization.
It all started when our team decided to explore the latest wave of AI tools. GitHub Copilot was one of them. We were excited about its potential to speed up development cycles and make our codebase cleaner. However, like many others, we quickly ran into a series of issues that turned out to be more than just teething problems.
The first major issue was performance overhead. We noticed a significant drop in our CI/CD pipeline’s efficiency, which led to longer build times and delayed deployments. This wasn’t surprising given the resource-intensive nature of running AI models on every pull request. We had to start profiling and optimizing our builds, which turned into an ongoing battle against the AI’s desire for compute resources.
On another front, we were experimenting with the new wave of LLMs like ChatGPT. We built a prototype to integrate it with our internal ticketing system to help us answer common questions faster. The initial results were impressive, but as soon as more users started using it, we saw a spike in API costs. This was a clear reminder that cost management isn’t just about budgeting; it’s about understanding the underlying infrastructure and its behavior.
Speaking of cost, FinOps has become an increasingly important part of our culture. We’re now working closely with finance teams to understand our cloud spend better. DORA metrics have made their way into our daily standups, and we’re constantly looking for ways to improve deployment frequency, lead time, and the number of bugs released.
One of the projects I spent a lot of time on this month was related to WebAssembly (Wasm) on the server side. We’re using it as part of our microservices architecture to run some compute-intensive tasks more efficiently. It’s been a fascinating journey, but we had to overcome several hurdles. One of the biggest was ensuring compatibility with our existing stack and debugging issues that popped up due to Wasm’s limitations.
Another area where I spent a considerable amount of time was developer experience (DX). We’re always looking for ways to make our developers more productive and happier. This month, we released an internal tool that integrates Obsidian with our existing documentation setup. It’s been great to see how this has helped streamline knowledge management across the team.
On the infrastructure side, Kubernetes continued to be a workhorse, but we’re also exploring other container orchestration tools like Helm for better management and deployment of Wasm services. We’ve hit a few snags with Kubernetes’ stability, especially during our recent upgrades. It’s been a learning experience in understanding how to best use and configure the platform.
Lastly, I’ve been reflecting on the industry landscape and how it’s changing. The CNCF ecosystem is overwhelming, but we’re finding ways to navigate through all the options. Platform engineering has become mainstream, and that means more focus on tools like Prometheus for monitoring and Grafana for visualization.
Halloween might be a day of superstition and magic, but in tech, it’s just another day full of bugs, deadlines, and learning curves. As we move into November, I’m looking forward to seeing how these technologies evolve and how our team can continue to adapt and thrive.
That’s the state of affairs as of October 31, 2022. Debugging DevOps nightmares is an ongoing process, but it’s also incredibly rewarding when you see progress. Here’s to another year in tech!