Operations | Monitoring | ITSM | DevOps | Cloud

Cloud computing was already booming before 2020. But in the following two years, remote work flourished, and cloud adoption soared. The trend continues to accelerate — even faster now. Some companies have since returned to the office. Others are adopting hybrid models, balancing work-from-home and in-office. Yet, there’s more to the rise of cloud computing than remote working.

At IBM’s Think conference this week, the company made a bold announcement: 94% of its HR functions are now handled by AI, a shift they claim will generate $3.5 billion in savings over the next two years. These are staggering numbers. And while the cynic in me can’t ignore that this announcement was made at what is, effectively, a sales conference – especially one that coincided with the launch of IBM’s AI Agent Store – the scale of those numbers deserves attention.

We are standing at the threshold of one of the most transformative technological shifts in modern enterprise history. AI is no longer on the horizon – it’s here, it’s powerful, and it’s already reshaping the way businesses think about productivity, creativity, and competitive advantage. OpenAI’s recent report, ‘AI in the Enterprise‘, offers a concise and thoughtful roadmap for leaders seeking to implement AI within their organizations.

Let’s be honest, most AI (Artificial Intelligence) tools today are relatively smart but not always super useful. You ask them to explain something, write an email, maybe generate some code, but the moment you want them to do something real, like schedule a meeting, file a bug report, or move stuff across your tools, they just stare blankly. That’s where MCP comes in. MCP stands for Model Context Protocol.

Troubleshooting failed cron jobs without proper logging can be frustrating. Ubuntu cron logs record the execution of scheduled tasks, helping you identify what's working and what isn't. This guide covers what engineers need to know about Ubuntu cron logs – from finding them to analyzing their contents and setting up effective monitoring solutions.

Implementing observability in Angular applications presents unique challenges. Understanding how users experience your application and identifying performance bottlenecks requires specialized tools and approaches. This guide covers implementing OpenTelemetry in Angular applications, with practical code examples for instrumentation, data collection, and integration with observability backends.

As AI adoption increases, the race to real, durable AI value intensifies. Almost every organization that can use AI is using AI — but one of the most troubling trends I’ve observed revolves around talent. Right now, most organizations use AI to increase internal efficiencies — do quick research, write quick emails, start a new project at the 50% mark rather than the 0% mark, etc. But some executives I’ve talked to are taking a more aggressive approach.

The software supply chain has become the backbone of modern IT environments. It powers applications, underpins operational processes, and drives innovation within organizations across industries. Securing the software supply chain has moved from a peripheral concern to a central element of cybersecurity: The vast network of code, dependencies, integrations, and third-party tools that comprise the supply chain is increasingly under siege from cyber threats and at risk of exploitation.