Curated articles, resources, tips and trends from the DevOps World.
This ongoing Docker Labs GenAI series explores the exciting space of AI developer tools. At Docker, we believe there is a vast scope to explore, openly and without the hype. We will share our explorations and collaborate with the developer community in real time.
Imagine boosting developer productivity by 30% while slashing project costs by 25%. That might sound impossible, yet it’s a realistic goal for organizations that adopt FinOps (financial operations), a data-driven approach to measuring software development life-cycle (SDLC) costs.
Discover Kubernetes' strict CPU manager for enhanced performance and reliability in resource allocation for workloads.
The future of enterprise-grade AI is facing a massive challenge: data. An AI model is only as good as the data it is trained on. Building models that yield meaningful results require enormous volumes of high-quality data for training and refinement.
There are so many Linux distributions on the market, each trying to create its own take on the open-source operating system. Some of those distributions don’t really venture that far from the norm and stick with the usual options: GNOME, KDE Plasma, Xfce, Cinnamon, Budgie, MATE, etc.
Many first heard of generative AI and large language models through late 2022 headlines on ChatGPT and its incredible ability to create original content and poetry. But as technology has evolved, so too have its use cases. Today, users across industries and departments can be found leveraging AI.
Few things are as easy to install as a new Mac application when using the GUI. However, not all software comes nicely bundled or relies on the graphical environment. In addition, some users may want to customize the installation of specific software.
Minko Gechev, who leads the Angular Team at Google, gave audiences at NG-BE 2024 a preview of what’s to come for Angular in 2025. The talk was recently posted to YouTube. The number one priority in the new year will be making zoneless stable.
Apache Pinot began life as a project within LinkedIn in 2013 as a way to run an analysis against a single metric captured across millions of users of all of the services. The company had already developed Apache Kafka to manage the millions of messages its systems were producing each day.
Have valuable insights to share with the DevOps community? Submit your article for publication.
Get the latest DevOps news, tools, and insights delivered to your inbox.
Made with pure grit © 2026 Jetpack Labs Inc. All rights reserved. www.jetpacklabs.com