Operations | Monitoring | ITSM | DevOps | Cloud

How Implementing Medical AI Scribe Transforms Patient Care

Medical AI scribes are dramatically changing the landscape of healthcare delivery by reducing the administrative burden on clinicians and improving patient interactions. Recent studies suggest that AI scribes can decrease the time physicians spend on documentation by up to 50%, allowing more time for patient care. This technology not only enhances the quality of interactions between doctors and patients but also improves diagnostic accuracy. Below, we explore how AI-powered scribes are playing a pivotal role in modern healthcare environments.

From Reactive to Proactive: AI-Driven Automation for Shopify Infrastructure Monitoring

Operations teams manage Shopify infrastructure with their eyes half-open most days. You're monitoring system health across multiple layers, responding to alerts when they fire, and hoping you catch problems before customers notice. The whole setup is reactive by design. Something breaks. You get paged. You investigate. You fix it. But here's what most ops leaders don't realize: your Shopify operation generates enough signals to predict problems hours (sometimes days) before they actually occur. The data's there. You're just not analyzing it at the right scale or speed.

Reality Bytes Is BACK: ft. Marc Petter on the Future of IT Jobs

Reality Bytes is back—and this time, we’re diving straight into the future of IT jobs. Tom, Oriana, and Dina are joined by Marc Petter (Senior Product Manager, Nexthink) to explore how AI is reshaping roles, workflows, and career paths. From automating repetitive tasks to the rise of AI agents handling entire processes, the conversation tackles what’s changing, what still requires a human touch, and how IT professionals can stay ahead. They unpack the difference between what can vs. should be automated, and what the new IT career ladder might look like in an AI-driven world.

What Metrics to Monitor in Your Vibe Coded App

These days, using a tool such as Cursor, GitHub Copilot, Zed, or Claude makes it easier than ever to develop and deploy applications. You express your requirements, receive the completed project back as output, and there you have it! You now have an application that is in production and functioning. However, the surprise comes after the app has been deployed. When your app breaks or behaves abnormally, it may not be immediately obvious what is wrong or how to fix it.

AI Is an Amplifier, Not a Shortcut

There’s a version of the AI story that engineering leaders want to hear. It goes like this: adopt AI coding tools, watch output multiply, ship faster, do more with less. Clean. Simple. Boardroom-ready. The data tells a different story. Not a worse one. Just a more honest one. We recently analyzed 2,172 developer-weeks of real coding activity across teams using GitHub Copilot, Cursor, and Claude Code. The headline numbers are striking: power users show 4-14x higher activity than non-users.

Defeating Context Rot: Mastering the Flow of AI Sessions | Harness Blog

In Part 1, we argued that most dev teams start in the wrong place. They obsess over prompts, when the real problem is structural: agents are dropped into repositories that were never designed for them. The solution was to make the repository itself agent-native through a standardized instruction layer like AGENTS.md. But even after you fix the environment, something still breaks. The agent starts strong.

Secure and Compliant DevOps in an AI-Enabled World

Is Your DevOps Strategy Ready for the AI Era? AI is accelerating modern software delivery—but it’s also raising the stakes for security, compliance, and auditability. As AI-driven change increases, many organizations are discovering that incomplete DevOps practices are creating new risk. Based on insights from 800+ global IT professionals, the 2026 State of DevOps Report reveals why vendor‑backed, enterprise‑grade DevOps platforms are becoming critical for managing AI‑driven risk and meeting evolving regulatory demands.

Agno Monitoring & Observability with OpenTelemetry and SigNoz

Learn how to implement end-to-end monitoring and observability for Agno-based AI systems using OpenTelemetry and SigNoz. In this video, we walk through instrumenting your Agno workflows, collecting traces, metrics, and logs, and visualizing everything in SigNoz to gain real-time visibility into performance, failures, and bottlenecks. You'll see how to move from basic logging to production-grade observability—so you can debug faster, optimize latency, and confidently run AI systems at scale.