Operations | Monitoring | ITSM | DevOps | Cloud

Application security & engineering teams are under pressure to move faster, cover more, and reduce the operational drag that often comes with security testing. But in practice, two problems keep slowing teams down and adding friction.

When you launch a new service in production, you’re working with a lot of unknowns. You don’t yet know how it behaves under real traffic or which incidents are worth waking someone up for. That makes alerting for a new service a little different from what you’re used to with an established one. The goal in the early days isn’t to get everything perfectly configured. It’s to learn enough about the service to get your alerting right.

A two-person startup signs up for the obvious hyperscaler because their last company used it, because Stripe runs on it, because the documentation is exhaustive, and because the free tier looks generous. Eighteen months later, with a small team and a healthy seed round, they discover they're spending $18,000 a month, and they don't quite know where most of it is going. Three engineers can describe the architecture in detail. Nobody can describe the bill.

Why ECS containers collapse under service.name = aws_ecs and how to fix it for both EC2 launch type and Fargate, including the resource-vs-log-record pitfall that quietly breaks log filtering. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

What actually works for Kubernetes monitoring at scale — not what looks good in a vendor demo with a five-pod cluster.

So far in this deployment series, you’ve: Now we arrive at the layer that quietly determines whether your app thrives… or throws mysterious 2am errors. Step 5 is cloud configuration. This is where your application gets its infrastructure, its environment, and its ability to scale without drama.

AI coding tools eliminated the 'writing code' bottleneck. But deploying that code? Still a mess. Here's how Claude Code + Qovery Skill lets you go from idea to production in a single prompt - with enterprise-grade guardrails. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

It’s becoming increasingly clear that AI-generated code can create real challenges once it reaches production. At Harness, we’ve been focused on innovating fast and solving those problems, so teams can move quickly without sacrificing reliability. In the past 30 days, we delivered 70+ new features.

‍Summary: Google Cloud Next ’26 focused on the future of software delivery, emphasizing that AI, platform consolidation, and an urgent push toward efficiency are reshaping the Software Development Life Cycle (SDLC). The key takeaway from the event was that organizations are moving from AI experimentation to operationalization, actively consolidating fragmented tools onto end-to-end platforms that embed AI for control, intelligence, and speed. ‍

Before I worked in tech, I worked in logistics. I saw a specific pattern repeat itself at office supply companies over and over, until I could see it coming before the customer did. The pattern went like this. A small office supply company would sell paper and pens to local businesses. One day a customer asked, "can you deliver a box of paper?" The salesperson said yes, drove the box over in their car after work, and thought nothing of it. The customer told their friend.