Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

Throughput is one of the most foundational metrics in application performance monitoring. It tells you how many requests your app is handling over time and offers a direct look at system load, responsiveness, and scalability. But throughput rarely speaks for itself. The key is knowing how to interpret it, and when to act. In this post, we’ll look at how throughput works in the real world: what healthy looks like, what broken looks like, and what lives in between.

Setting up a local Deep Learning environment can be a headache. Between managing CUDA drivers, resolving Python library conflicts, and ensuring you have enough GPU power, you often spend more time configuring than coding. Google Cloud and Canonical work together to solve this with Deep Learning VM Images, which use Ubuntu Accelerator Optimized OS as the base OS. These are pre-configured virtual machines optimized for data science and machine learning tasks.

Remote workers depend on VPNs to access corporate resources. When VPN performance tanks, productivity stops. The problem? Most IT teams troubleshoot blindly. They can't tell if slow performance is caused by VPN encryption overhead, ISP issues, or corporate infrastructure problems. Here's the reality: Your remote workers are calling the help desk, saying "the VPN is slow", but you have no visibility into what's actually happening on their end. You're guessing. Maybe you ask them to restart their router.