Operations | Monitoring | ITSM | DevOps | Cloud

When a monolithic app starts to hit its limits, microservices are often the next step forward. They let you scale only what’s under pressure, keep changes local, and give teams the freedom to deploy on their own schedule. It’s no wonder the market for microservices is growing fast, from $1.93 billion in 2024 to a projected $11.36 billion by 2033. So how do you build microservices? In.NET, the process is surprisingly straightforward.

On the morning of December 10, 2025, Microsoft Teams experienced a service disruption affecting users across Australia. Although Microsoft 365 users reported issues across several apps, the hardest hit service was Microsoft Teams which became completely unusable for many organizations. While Microsoft did not acknowledge the incident until 03:46 UTC StatusGator identified the issue at 02:52 UTC through incoming outage reports and delivered an Early Warning Signal at 03:01 UTC.

Your analytics dashboard is green, which indicates that your application is up 99.9% of the time, pages load in under three seconds on average, and conversion rates are stable. But here’s the uncomfortable reality, you’re probably missing 40% to 60% of the actual performance problems which impact real customers every day.

Organizations see AI’s transformative potential, but success requires more than technology – it demands a clear strategy led by IT. A structured AI implementation roadmap aligns initiatives with business goals, establishes governance, and enables measurable ROI, while improving employee and customer experiences. Yet, 66% of organizations view AI as critical, but only 38% report meaningful competitive advantage, highlighting the need for disciplined adoption.

In our increasingly online world, managing cloud, AI, and other tech spend has shifted from a good idea to an absolute necessity. But even when cost management is a priority, how do you get busy development teams and engineers actively engaged in the new practices? New initiatives are often viewed as more work on the team’s plate, which is an understandable deterrent to adoption. That leaves FinOps proponents struggling to get others on board.

AI should speed up innovation, not inflate your cloud bill. But today, the biggest GenAI challenge for SaaS teams isn’t model quality; it’s cost. And increasingly, that cost comes from AI cost sprawl. That’s not because anyone is doing something wrong, but because AI operates differently from the cloud services we’ve all spent a decade learning how to manage.

Choosing the proper Home Assistant hardware can be overwhelming. Whether you’re new to home automation or a seasoned pro, the hardware you select can make or break your experience. This comprehensive guide will demystify the requirements, delve into the various options, and help you make an informed decision. From the compact Raspberry Pi to the powerful Intel NUC, we’ve got you covered. So, strap in, and let’s dive into the world of Home Assistant hardware!

Most AI programs don’t fail on model quality. They fail because the experience makes people either over-trust or quietly avoid the system. Employees often use AI more than leaders realize, frequently without training or guardrails. Interfaces that just “show an answer” without confidence, provenance, or recourse create two risks: blind reliance and shadow use.

If there’s one thing clear from our AI-powered observability webinar, it’s that observability has officially graduated from a “nice-to-have” to a business-critical discipline, and AI is helping lead that charge. Our webinar brought together guest speaker Stephen Elliott, Group VP at IDC, and Ranbir Chawla, former SVP of Engineering at RB Global, for an hour of insights that mixed data, experience, and hard-won lessons from the trenches.