Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes is an open-source container orchestration engine that is used to automate containerized application deployment, scaling, and administration. It is an open-source management platform that can be used to manage containerized workloads and services, as well as declarative configuration and automation. Kubernetes is a framework for running distributed systems in a resilient manner. It handles scaling and failover for your application and provides deployment patterns and other features.

Application security testing tools promise coverage and accuracy, but teams often struggle just to get started. One of the biggest friction points in dynamic application security testing is configuring authentication correctly so a scanner can even access a target application, let alone API endpoints that power the functionality. Whether it’s API keys, bearer tokens, or custom auth flows, setting up authentication for scans frequently requires trial-and-error and engineering support.

llm-d is an open source solution for managing high-scale, high-performance Large Language Model (LLM) deployments. LLMs are at the heart of generative AI – so when you chat with ChatGPT or Gemini, you’re talking to an LLM. Simple LLM deployments – where an LLM is deployed to a single server – can suffer from latency issues, even with just one user. This can be because of lack of memory-bandwidth on the server, or because of KV cache pressure on system memory.

Most teams use Jira to plan work and GitHub to build it. The problem is those two tools don’t talk to each other by default. Developers end up manually copying commit references into tickets, project managers hunt through GitHub to answer basic status questions, and sprint reviews become archaeology expeditions through two disconnected systems. Git Integration for Jira closes that gap.

If you’re running infrastructure on Azure, you’ve probably dealt with the split between your Azure-native monitoring and the rest of your stack. Your VMs, databases, and Kubernetes clusters generate platform metrics through Azure Monitor, but those metrics live in a separate world from the OS-level, application, and on-prem metrics you’re already watching in Netdata.

So far in this deployment series, you’ve: At this point, you can deploy your app. The question is: do you really want to keep doing it manually? Welcome to CI/CD — where deployments stop being an event and start becoming a system.

It's 2:47 AM. PagerDuty fires. You open a Slack alert and see: p99 latency spike on checkout-service. You SSH into the host, check dashboards in four tabs, grep logs for the last 20 minutes, and eventually find a slow query introduced in a deploy six hours ago. It took 34 minutes. You resolved it, w 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.

> Standard OTel.NET instrumentation captures headers, status codes, and timing — not request or response bodies. Here's how to add body capture to your traces while keeping PHI out of your observability backend. 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.