Operations | Monitoring | ITSM | DevOps | Cloud

Every engineering organization measures success differently. A dashboard that’s perfect for one team might be meaningless for another. While out-of-the-box views for DORA are a great starting point, leaders need the ability to define and share the specific combination of metrics that matter most to their business. Without this, you're either forcing your teams to conform to generic reports or wasting time rebuilding the same views every week.

Rolling out a powerful AI tool like GitHub Copilot is a big win for any engineering leader. But because it’s such a significant investment, leadership will inevitably ask if it was worth the cost. Until now, answering this was nearly impossible. While GitHub provides adoption stats, connecting that data to real-world performance metrics like cycle time or code quality has been a manual, frustrating process. We built the Cortex AI Impact Dashboard to provide a clear answer.

At every turn, IT leaders are required to prove the value of every technology investment. Technology business management (TBM) practices encourage connecting tech spend directly to business outcomes, demanding accurate data about what’s in your network and how it supports the organization.

Amazon EKS on Fargate is a managed service that reduces the operational overhead of maintaining a Kubernetes cluster by abstracting away the underlying infrastructure. In a serverless Fargate environment, each pod is assigned its own isolated compute resources; there is no direct host-level access.

Today, Obkio’s Network Performance Monitoring solution is announcing the release of our all-new Visual Traceroute Tool integrated into Obkio’s application. This feature is a re-invention of Obkio’s standalone Visual Traceroute Tool (Obkio Vision), and has been transformed to help users better understand network path performance and the source of network issues.

Recent industry research shows that AI is accelerating code creation, but having mixed results downstream. They also show that better platforms and pipelines yield better outcomes for teams adopting AI for coding. Every engineering leader I talk to is asking the same questions about AI coding assistants: How much faster can we ship? How much more productive can my developers be? On the surface, the answers look pretty good.

Transitioning LLM agents from intriguing prototypes to reliable, production-grade solutions introduces a unique and significant challenge: the inherent stochasticity of LLMs. Unlike conventional software, where inputs predictably yield precise outputs, an LLM’s response can exhibit variability even when presented with identical prompts. To ensure the dependability of your LLM agent, you will need a rigorous validation strategy.

Integrating artificial intelligence (AI) into web applications can significantly enhance user experience. AI offers features like image recognition to process and analyze user-uploaded images. Combining this with a robust continuous integration and continuous deployment (CI/CD) pipeline using CircleCI ensures seamless updates and reliable delivery. In this article, you will learn how to build a React app that uses TensorFlow.js for client-side image recognition and set up automated testing with CircleCI.

When it comes to storing your files or photos online, the number of choices can be overwhelming. To make things more confusing, some services focus on cloud storage and photos, and others offer both, such as Google Photos and Google Drive. But what’s the difference? Although both appear to be the same, they have some differences based on whether you need photo or file storage. Both have advantages and features to meet your needs as a photographer or for general cloud storage.