September 15, 2025. Today marks another day of managing the chaos that is my team’s infrastructure. As a platform engineer with a focus on AI and automation, I’ve seen a lot of changes over the years, but this era has brought some of the most significant shifts in how we operate.

The last few months have been filled with excitement and frustration as we continue to integrate copilots and LLMs into our workflows. The hype around these tools is hard to ignore; everyone talks about the magic they bring to engineering teams. But the reality of making them work in a production environment? That’s a whole different ballgame.

Slack Price Increase: A Wake-Up Call

One day, I got an email from my admin team with a stark reminder of what it means to be in tech these days: our Slack bill had increased by $195k per year. The cost alone is insane, but what’s even more concerning is the ongoing service issues that come with such a dependency.

We’ve been using Slack as an AI copilot for various tasks like code review and meeting scheduling. But it’s become increasingly clear that while these tools can be incredibly helpful, they’re also prone to outages and unexpected behavior. The price increase just underscores how much our productivity depends on third-party services.

Debugging the Latest LLM Integration

One particularly frustrating day, we were trying to integrate a new LLM for automated code reviews into our CI/CD pipeline. We’re using Claude as one of the copilots, but getting it to work seamlessly with our existing tools has been a challenge. The integration went smoothly at first, but then came the inevitable bugs.

We found that the LLM was misinterpreting some comments in our codebase and suggesting changes that weren’t quite right. After hours of debugging, we realized that the context wasn’t being properly passed between the LLM and the rest of our pipeline. It’s a classic case of the AI not understanding the nuances of human coding.

Wasm + eBPF: A Promising Duo

On the brighter side, we’ve been experimenting with WebAssembly (Wasm) alongside extended Berkeley Packet Filter (eBPF). The combination is looking promising for some of our performance-critical tasks. We’re using Wasm in containers to offload some of the heavy lifting from our main application stack and then leveraging eBPF for deep packet inspection at the kernel level.

The key benefit here is that we can write performant, sandboxed code directly on the edge without having to worry about traditional VM overhead or the complexity of managing a full-blown container. This has allowed us to optimize certain parts of our system with minimal impact on overall performance and security.

Multi-Cloud as Default

Speaking of optimization, multi-cloud is now default for us. With Kubernetes becoming increasingly boring and essential, we’ve been expanding our use cases across multiple cloud providers. This isn’t just about cost savings; it’s also about resilience and flexibility.

We’re using different clouds for different workloads based on their strengths: AWS for managed services, Google Cloud for machine learning, and Azure for specialized networking tasks. This approach has allowed us to take full advantage of the best tools available without being locked into a single vendor.

NPM Package Compromise: A Scary Realization

Lastly, we had an incident where our internal package manager, NPM, was compromised. Some of our projects were affected by hacked packages that contained malicious code. The lesson here is clear: no matter how much automation and AI we throw at the problem, human oversight can’t be replaced.

We’ve since tightened up our security protocols, including more rigorous vetting of dependencies and implementing stricter access controls. This incident reminded us that while technology can help, it’s ultimately people who need to stay vigilant.

Conclusion

Tech moves fast, but so do the problems we face. AI copilots have undoubtedly made life easier in many ways, but they also bring new challenges. From managing massive price increases and unexpected outages to debugging tricky integrations and ensuring security, there’s a lot to keep track of.

The future is exciting, but it’s not without its hurdles. We’re learning every day, both as engineers and as individuals trying to navigate this ever-evolving landscape. For now, I’ll continue to roll up my sleeves and figure out how to make these tools work for us—because at the end of the day, that’s what matters most.

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