Operations | Monitoring | ITSM | DevOps | Cloud

When working with Kubernetes, pods are the fundamental building blocks of deployment. But not all pods are created equal. Understanding the different types of pods and their use cases is crucial for optimizing workloads, ensuring reliability, and maintaining efficiency in your cluster. Let's break it all down.

A great retrospective isn’t just about documenting what happened — it’s about bringing your team together to uncover the insights that lead to real improvements in your process, roles, and technology. But to make that happen, retrospectives need to be structured enough to be effective, flexible enough to fit your team, and easy to collaborate on. That’s exactly what we set out to build.

As systems grow more distributed and complex, having a reliable way to monitor and understand what's happening across your infrastructure becomes essential. Telemetry data provides the visibility needed to keep everything running smoothly, whether you're managing microservices, cloud environments, or sophisticated AI systems. In this guide, we’ll break down what a telemetry data platform is, why it’s so important, and how you can choose the right one to meet your needs.

Across regions, the regulatory landscape is shifting dramatically: These regulations signal a new era where energy waste is no longer just an economic concern but a compliance risk. Failure to adhere can result in hefty penalties, restricted operations, and reputational damage.

We here at JFrog have long advocated for promoting – never rebuilding – release candidates as they advance across the stages of your SDLC. For many JFrog customers, that meant using JFrog’s “Build Promotion” capabilities. Now you can level up your CI/CD game with promotions using Release Lifecycle Management (RLM)! In this article we’ll show you why promotions with RLM are more simple, secure, and scalable than our legacy build promotion API.

Engineering’s gotten really complicated. The rush to “innovate or die” created more code, more tools, and more teams—without a single, unifying way to keep it all healthy and moving. The result? Orphaned services, slow releases, and endless overhead... just to name a few.

In bridging traditional enterprise environments with modern GPU resources, we're helping organizations build AI infrastructure that's truly ready for production workloads. Co-authored by Duncan Ng, Vice President Solutions Engineering, Vultr As enterprises move from AI experimentation to production deployment, most are realizing a fundamental truth: Successful AI adoption requires more than just access to GPU computing power.

2024 was the GenAI year. With new and more performant LLMs and a higher number of projects rolled out to production, adoption of GenAI doubled compared to the previous year (source: Gartner). In the same report, organizations answered that they are using AI in more than one part of their business, with 65% of respondents mentioning they use GenAI in one function.