Operations | Monitoring | ITSM | DevOps | Cloud

This guide covers battle-tested chaos experiments for Kubernetes, AWS, Azure, and GCP to help you validate production resilience before real failures happen. Start with low blast radius experiments (pod-level) and gradually progress to higher impact scenarios (node/zone failures), always defining clear hypotheses and using probes to measure results. Building reliable distributed systems isn't just about writing good code. It's about understanding how your systems behave when things go wrong.

Heroku makes it easy to deploy and operate applications without managing servers, but understanding how your application behaves internally still requires instrumentation. Platform metrics like CPU usage, memory consumption, and router request/status counts are useful, but they don’t tell you how long your code takes to run, when your app throws errors, or whether users are interacting with key features.

The open-source Kubernetes platform is designed to help simplify application deployment through Linux containers. It supports tasks like deploying workloads in the form of pods, clustering nodes, managing container runtimes, and tracking resources. The Kubernetes microservices system has risen in popularity over the last several years as an easy way to support, scale, and manage applications.

Today, we are pleased to announce the release of CFEngine 3.27.0! The code word for this release is exploration. This release also marks an important event, the beginning of the 3.27 LTS series, which will be supported for 3 years. Several new features have been added since the release of CFEngine 3.24 LTS, in the form of non-LTS releases.

Top Tips is a weekly column where we highlight what’s trending in the tech world and list ways to explore these trends. This week, we’ll be talking about how we can improve our retrieval-augmented generation (RAG) systems using contextual engineering. Prompt engineering has gained a lot of attention in the past year, and it’s finally time to move on to a better experience that transforms the way AI results are provided to us.

As IT organizations enter 2026, many of the assumptions around monitoring and observability have already been tested. Throughout 2025, infrastructure teams made it clear that visibility alone is not enough. Alerts without context, short data retention, and fragmented tools limited teams’ ability to explain behavior, validate changes, and plan with confidence. This article looks at what emerged from those experiences and how observability expectations continue to shift.

Why strong reviews, accountability, and monitoring matter more in an AI-assisted world Artificial intelligence has become the latest fault line in software development. For some teams, it’s an obvious productivity multiplier. For others, it’s viewed with suspicion. A source of low-quality code, unreviewable pull requests, and latent production risk. One concern we hear frequently goes something like this: It’s an understandable fear; and also the wrong conclusion.