Operations | Monitoring | ITSM | DevOps | Cloud

When AI Writes the Code, Who Pays the Cloud Bill?

This is part two of a series of the implications of AI generated code becoming mainstream. We recently wrote about how AI-generated code is overwhelming SRE teams with production complexity they can’t manage. Turns out that’s only half the problem. The other half shows up on the cloud bill. A prospect reached out to us last month. They’d been using Cursor and Claude Code for six months, shipping features at unprecedented velocity. Product was thrilled.

Humanized AI Text for Stronger DevOps and Operations Content

You create content for operations teams, DevOps engineers, SREs, and IT decision-makers. Topics include monitoring, incident management, cloud infrastructure, ITSM processes, and observability tools. AI generates initial drafts quickly. The results frequently come across as mechanical. Sentences follow predictable patterns. Technical explanations lose nuance. Readers in this field expect precise, practical language from experienced practitioners. They detect generated text easily. Engagement drops when content feels detached from real-world ops challenges.

How Fabrix.ai Agents Ensure Data Privacy & Security

As Agentic AI moves into enterprise environments, IT and security leaders face a critical challenge on how to leverage advanced LLMs without exposing sensitive data, intellectual property, or proprietary configurations to the cloud. You cannot build a self-driving, autonomous IT infrastructure if your security team blocks the deployment, and that’s exactly why the Fabrix.ai platform features an Enterprise-Grade LLM Integration architecture anchored by our built-in Data Security layer.

Canonical and Ubuntu RISC-V: a 2025 retro and looking forward to 2026

2025 was the year that RISC-V readiness gave way to RISC-V adoption. It’s been quite a journey. What began years ago as early architectural exploration and enablement has matured into real silicon, systems, and deployments. In particular, RVA23 provides a stable and predictable baseline we can align on with our wider ecosystem of partners. At Canonical, we’re committed to making RISC-V a viable option for anyone who wishes to adopt it.

Why Evidence-Backed RCA in Edwin AI Starts With Logs

A step-by-step look at how Edwin AI uses native LogicMonitor logs, topology, and context to turn root cause analysis from alert-driven inference into evidence-backed investigation. Most root cause analysis today starts with alerts and ends with explanations that sound reasonable but can’t be verified. An alert is fed into a language model, and the output looks like an answer. It often isn’t.