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Transform your workflow with Raygun's remote MCP

We're happy to announce Raygun's new remote MCP server, giving AI tools direct access to live error data so they can investigate issues, surface root causes, and take action with real context, not guesses. It's been nearly a year since Anthropic released the Model Context Protocol (MCP), and a lot has changed in the AI space. Since then, almost all major players now support MCP, allowing them to tap into the massive and ever-expanding catalogue of MCP servers. When MCP first launched, we shipped our own Raygun MCP within 48 hours of the spec dropping, which was an early step toward giving LLMs visibility into Raygun data.

October 2025 Azure outage: How StatusGator detected it first

When Azure Front Door began to fail on October 29, 2025, hundreds of downstream services, including Microsoft 365, Teams, SharePoint, and Azure SQL, went dark. While Microsoft didn’t publicly acknowledge the issue until 12:35 PM ET, StatusGator dashboards were already lighting up nearly 50 minutes earlier. StatusGator notified its subscribers of an Azure outage 42 minutes prior to the official status page at 11:53 AM ET.

The Top Five Business Continuity Software

Disaster can strike any business at any time. Businesses must be prepared to continue critical operations with minimal disruption, whether it’s a flooded server room, a data breach, or any other kind of exploit. That’s why it’s essential to have strong measures in place—including a business continuity plan (BCP)—and the right tools to support these measures.

Harness patent for hybrid YAML editor enhances CI/CD workflows

Harness earned a patent for it's unified pipeline editor which makes it easy to configure pipelines whether they are for CI, CD, IaC, database migrations, service onboarding or other DevSecOps activities. ‍ We're thrilled to share some exciting news: Harness has been granted U.S. Patent US20230393818B2 (originally published as US20230393818A1) for our configuration file editor with an intelligent code-based interface and a visual interface.

How to Optimize GPU

The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU as a static commodity, ignoring model intent, workload shape, and sharing capabilities.

Why Simplicity Beats Sprawl in Modern IT

In enterprise boardrooms today, what was once an arms race to adopt more tools and chase every new capability has now crystallized into a single mandate, “Make the platform work harder without spending more.” The industry has reached a saturation point. The buyers who once greenlit expansions now demand efficiency. And the ones who built the stack? They’re rethinking it entirely. It’s no wonder platformization is taking off.