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.

Engineering teams today manage infrastructure spread across multiple clouds and tools. Whether this happened through gradual accumulation or deliberate strategy, the result is the same: complexity that slows teams down. Managing each cloud separately with different tools and workflows is a bottleneck to delivery speed, operational efficiency, and platform reliability.

For most Central IT leaders, tech debt isn't a surprise. It's the silent tax on every roadmap, every quarterly plan, every conversation about why things take so long. Modern cloud application platforms (true PaaS environments) give IT leaders a path to unwind years of accumulated complexity while simultaneously accelerating innovation. You no longer have to tolerate the tax.

You've set up the Cortex MCP and connected it to your AI assistant and IDE. You ask about service ownership, check a Scorecard or two, and it works. You're impressed by how much faster this is than clicking through the web UI. Now you're wondering what else you can do with it. I'm willing to bet we've hit a nerve with that "hypothetical" scenario. The Cortex MCP works exactly as designed, but it's deceptively difficult to know which questions to ask and when to ask them.

HTTP/1.1 is a text-based protocol where the message framing is mixed with its semantics, making it easy to parse incorrectly. The boundaries between messages are very weak because there is no clear delimiter between them. Thus, HTTP/1.1 parsers are especially vulnerable to request smuggling attacks.

We’ve all been there. It’s 2 AM, your phone is buzzing with alerts, and you’re suddenly thrust into an incident war room with a dozen other bleary-eyed engineers. The production environment is on fire, customers are affected, and everyone’s trying to piece together what went wrong. But here’s what makes these moments fascinating from a systems perspective – it’s rarely just one person silently fixing the issue in isolation.

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.