Operations | Monitoring | ITSM | DevOps | Cloud

Previous articles walked through RDMA (Remote Direct Memory Access) as a programming model and InfiniBand as the fabric that was built around it. Both led to the same conclusion, even if it was never stated outright: moving data, not compute, becomes the bottleneck once systems scale. So what happens when you want RDMA, but you’re already running an Ethernet network you’re not keen to replace? That’s usually where RDMA over Converged Ethernet (RoCE) enters the conversation.

The Harness VS Code Extension is now on the Marketplace. Monitor pipelines, debug logs, approve deployments, and query failures with Claude Code, Copilot, or Cursor, without leaving VS Code. Your Harness pipelines, logs, and deployment approvals are now a sidebar panel away inside VS Code. The Harness VS Code Extension is live on the VS Code Marketplace today, no.vsix download, no manual install.

‍ Learn how to master Azure deployment with CI/CD pipelines, progressive delivery, and feature flags. See how Harness helps engineering teams ship faster and safer on Azure. Azure deployment sounds straightforward. Push code, it runs in the cloud. But if you've managed a 2 a.m. production incident because a deployment went sideways on AKS, you know the gap between "it deploys" and "it deploys safely at scale" is significant.

The promise of agentic AI is dead simple to understand. Instead of waiting for a human to draft every instruction, an AI agent can interpret a goal, take action, and work across systems until the task is done. For IT teams, that motion sounds like the next logical phase of automation. That promise is real... but it’s also where the risk starts. Traditional automation followed instructions. Agentic AI, by contrast, pursues outcomes. That difference turns the entire governance model on its head.

There's a version of this conversation that plays out in engineering organizations everywhere. Leadership pushes for standardization. Developers push back. The argument from developers is reasonable on its face: every codebase has different needs, every team has tools they're good at, and adding process feels like slowing down to go faster. It's a genuine tension, and it's also a false one. The teams that ship the most aren't the ones with the most infrastructure freedom.

A few months ago, Steve Yegge published his 8 levels of AI-assisted development, and it clicked the moment I read it, because I had lived that exact progression myself, moving from autocomplete to running agents one step at a time. Framed as an AI trust gradient, it finally gave the industry a vocabulary for something most of us were already going through without a name for it. If you haven’t read it, save it for later.

AI changed how code gets written before it changed how code gets operated. Generation accelerated; the downstream controls that turn that output into reliable, secure software at a reasonable cost did not keep pace. The result is elevated risk, distributed unevenly across engineering organizations. A recent survey explains why the distribution is so uneven.

This edition of the Pulse is shifting lanes. We’re calling it the AI Economics Pulse now, because the question on every finance leader’s mind is whether AI spend and the returns on it can be made to pair at all. That question came to a head over the last few weeks. The bills came due, and they came due in public. Uber burned through its entire 2026 AI budget in four months and capped employee spending on Claude Code and Cursor at $1,500 a month.

Choosing a Data Center Infrastructure Management (DCIM) platform is one of the more consequential decisions a data center team will make. Get it right, and you gain an accurate digital twin of your physical infrastructure, a single source of truth across teams, improved operational visibility, and a platform for planning, reporting, and automation. Get it wrong, and you risk a failed deployment, a platform that doesn't fit your needs, or a shelfware investment that's hard to justify renewing.