Operations | Monitoring | ITSM | DevOps | Cloud

Harness FME continues its investment in OpenFeature, building on our early support and adoption of the CNCF standard since 2022. Evaluate flags consistently across languages and environments, and integrate them seamlessly into your applications without modifying your code. Feature flags are table stakes for modern software development. They allow teams to ship features safely, test new functionality, and iterate quickly, all without re-deploying their applications.

Shipping application code, especially for LLM-based applications, can be a stressful and complex task. These applications demand intricate model management, careful resource allocation, and manual handling of dependency conflicts. Traditionally, preparing such applications for deployment involves integration tests, containerization, and updating image registries: all time-consuming manual steps. This is where an automated CI pipeline becomes invaluable.

With the ACME protocol, to issue a certificate you have to prove you control the domain. The CA gives you a challenge, you complete it, and they issue your cert. The trouble is that every validation method has tradeoffs. And as certificate lifetimes get shorter, those tradeoffs will get more painful. DNS-PERSIST-01 is a new approach coming in 2026 that trades proof-of-freshness for easier operations.

Developers and SREs who rely on GitLab to develop their services often face significant friction when troubleshooting errors or fixing issues that degrade code quality. To understand the context of a problem, they resort to tab-hopping between observability tools and GitLab, connecting stack traces, spans, and profiles back to the right files and commits.

2025 was a tough year for businesses, with slow growth, high costs, cyber risks and geopolitical uncertainties all contributing to a challenging climate. More than ever, businesses must innovate to survive and grow, and digital infrastructure will play a key role in 2026. Last year I predicted a pivotal year for cloud strategy, with repatriation gaining momentum due to shifting legislative, geopolitical, and technological pressures. This trend has accelerated, with a growing focus on data sovereignty.

In the past, our yearly recaps were mostly about numbers. What we shipped, how much Spike grew, and a long list of stats. See past recaps: 2023, 2024. But 2025 felt different to me. It had many moments that shaped how Spike as a product and the company looks today. Some of them were exciting. Some were uncomfortable, and all of them changed how I think about building Spike. We’re still bootstrapped and operating lean, with a team of fewer than ten people.

You’ve probably used AI earlier today. Maybe you asked it to debug a function, generate a test case, or explain a legacy codebase you just inherited. But here’s the thing: you didn’t just type a question and get an answer. You explained your problem, shared background context, pasted code snippets, clarified what you meant, then refined the output until it was actually useful. In other words, you were context engineering.

We’ve written before about the advantages of training an AI SRE on real telemetry data rather than generic Kubernetes documentation. We’ve explained why RAG augmentation based on actual high-scale workload patterns produces better results than LLMs trained on generic scenarios or forum threads. The theory makes sense, the architecture is sound, and the approach is defensible.