#kubernetes-and-cloud-native

#kubernetes-and-cloud-native

The most dangerous assumption in quality engineering right now is that you can validate an autonomous testing agent the same way you validated a deterministic application. When your systems can reason, adapt, and make decisions on their own, that linear validation model collapses.

"In the legacy cloud, too many custom modifications to OpenStack made upgrades difficult. Flava adopts an architecture that stays aligned with upstream OpenStack. We keep custom patches to a minimum, and when functional changes are needed, we proactively contribute them upstream so they can be merged into the main project."

If Ingress is the Legacy Path, then the Gateway API is the modern highway. In this guide, I will walk you through a complete migration demonstrating how to swap out your old Ingress controllers for Envoy Gateway. We won't just move traffic; we'll leverage Envoy's power to implement seamless request mirroring and more robust, path-based routing that was previously hidden behind complex annotations.

Red Hat AI Enterprise provides a foundation for modern AI workloads, including AI life-cycle management, high-performance inference at scale, agentic AI innovation, integrated observability and performance modeling, and trustworthy AI and continuous evaluation. Tools are provided for dynamic resource scaling, monitoring, and security.

Almost a quarter of those surveyed said they had experienced a container-related security incident in the past year. The bottleneck is rarely in detecting vulnerabilities, but mainly in what happens next. Weeks or months can pass between the discovery of a problem and the actual implementation of a solution. During that period, applications continued to run with known risks, making organizations vulnerable, reports The Register.

Over the past decade, software development has been shaped by two closely related transformations. One is the rise of devops and continuous integration and continuous delivery (CI/CD), which brought development and operations teams together around automated, incremental software delivery. The other is the shift from monolithic applications to distributed, cloud-native systems built from microservices and containers, typically managed by orchestration platforms such as Kubernetes.

An observability control plane isn't just a dashboard. It's the operational authority system. It defines alert rules, routing, ownership, escalation policy, and notification endpoints. When that layer is wrong, the impact is immediate. The wrong team gets paged. The right team never hears about the incident. Your service level indicators look clean while production burns.

For years, reliability discussions have focused on uptime and whether a service met its internal SLO. However, as systems become more distributed, reliant on complex internet stacks, and integrated with AI, this binary perspective is no longer sufficient. Reliability now encompasses digital experience, speed, and business impact. For the second year in a row, The SRE Report highlights this shift.

Industry professionals are realizing what's coming next, and it's well captured in a recent LinkedIn thread that says AI is moving on from being just a helper to a full-fledged co-developer - generating code, automating testing, managing whole workflows and even taking charge of every part of the CI/CD pipeline. Put simply, AI is transforming DevOps into a living ecosystem, one driven by close collaboration between human judgment and machine intelligence.

The Harness Resilience Testing platform extends the scope of the tests provided to include application load and disaster recovery (DR) testing tools that will enable DevOps teams to further streamline workflows.

Steve Yegge thinks he has the answer. The veteran engineer - 40+ years at Amazon, Google and Sourcegraph - spent the second half of 2025 building Gas Town, an open-source orchestration system that coordinates 20 to 30 Claude Code instances working in parallel on the same codebase. He describes it as "Kubernetes for AI coding agents." The comparison isn't just marketing. It's architecturally accurate.

Docker builds images in layers, caching each one.When you rebuild, Docker reuses unchanged layers to avoid re-executing steps - this is build caching. So the order of your instructions and the size of your build context have huge impact on speed and image size. Here are the quick tips to optimize and achieve 2 times faster speed building images: 1. Place least-changing instructions at the top

If you've ever struggled with running multiple docker run commands for a complex application, Docker Compose is your solution. It's a tool that allows you to define and manage multi-container Docker applications using a single, declarative configuration file. Instead of a long list of commands, you describe all your services, networks, and volumes in a docker-compose.yml file. With one command, you can spin up your entire application stack.

Blue/green deployments on Amazon Elastic Container Service (Amazon ECS) have long been a go-to pattern for shipping zero-downtime deployments. Historically, the recommended approach in the AWS Cloud Development Kit (AWS CDK) was to wire ECS to AWS CodeDeploy for traffic shifting, lifecycle hooks, and tight integration with AWS CodePipeline. In July 2025, Amazon ECS launched built-in blue/green deployments. This allows you to operate directly within the ECS service, without requiring the use of Amazon CodeDeploy.