Operations | Monitoring | ITSM | DevOps | Cloud

Security operations teams depend on telemetry from everywhere: endpoints, identity systems, cloud platforms, networks, applications, and security tools. This data underpins detection, investigation, compliance, and response.

Throughout 2025, we watched AI transform from a novelty into a non-negotiable requirement for engineering teams. Leaders moved quickly to roll out coding assistants, driven by the promise of unprecedented velocity. But as we settle into this new reality, it’s becoming clear that there is a massive difference between buying a tool and successfully scaling it. You can't just drop AI into a complex organization and expect it to work without a solid foundation.

As organizations increasingly embrace digital transformation, cloud agility has become a critical priority. Yet, the promise of cloud-native speed and flexibility often comes with trade-offs: loss of control, unpredictable costs, and operational complexity. Many companies find themselves stuck between the desire for agility and the reality of legacy infrastructure or regulatory constraints. At Civo, we don't think you have to choose. We’ve spent years helping teams navigate this tension.

As enterprise IT continues to evolve, networking is becoming a strategic lever – one that directly impacts agility, resilience and the ability to compete. In a recent podcast episode, our CTO Paul Gampe shares the technologies he thinks will matter most for the foreseeable future. Three themes stood out: the rise of Network-as-a-Service (NaaS), the shift toward transmission on-demand at layer one and the rapid evolution of Model Context Protocol (MCP).

Whenever service management has struggled, the conventional response was to add processes, tools, and people. That worked for a little while; at the very least, it worked well enough to keep the lights on. However, today’s environments don’t fail quietly or occasionally. They fail noisily and across tightly coupled systems. The end result is an operational model where human effort is consumed reconstructing context instead of resolving problems. More processes don’t fix that.

TINYINT is one of the simplest numeric data types you can ever work with in SQL databases. It stores small numeric values, saves space, and is commonly used for flags, statuses, and boolean-like fields. But the moment you bring C# into the picture, things get intriguing. There is no TINYINT keyword in C#, and no one-to-one mapping you can use. Instead, you are left asking an important question: What is the correct C# equivalent of the TINYINT data type in SQL?

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.