Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Get more value out of your Cortex catalog with our MCP prompt library

You've set up the Cortex MCP and connected it to your AI assistant and IDE. You ask about service ownership, check a Scorecard or two, and it works. You're impressed by how much faster this is than clicking through the web UI. Now you're wondering what else you can do with it. I'm willing to bet we've hit a nerve with that "hypothetical" scenario. The Cortex MCP works exactly as designed, but it's deceptively difficult to know which questions to ask and when to ask them.

Sanitizing HTTP/1: a technical deep dive into HAProxy's HTX abstraction layer

HTTP/1.1 is a text-based protocol where the message framing is mixed with its semantics, making it easy to parse incorrectly. The boundaries between messages are very weak because there is no clear delimiter between them. Thus, HTTP/1.1 parsers are especially vulnerable to request smuggling attacks.

Intro to Jira Plans | Release management

Discover how Jira Plans simplifies release management, helping you coordinate complex projects with confidence. In this video, Product Manager Joe Nguyen demonstrates how you can use releases in Jira Plans to track progress, manage timelines, and ensure every release delivers value to your customers. Achieve smoother, more predictable releases that boost business outcomes. Timestamps.

The War Room of AI Agents: Why the Future of AI SRE is Multi-Agent Orchestration

We’ve all been there. It’s 2 AM, your phone is buzzing with alerts, and you’re suddenly thrust into an incident war room with a dozen other bleary-eyed engineers. The production environment is on fire, customers are affected, and everyone’s trying to piece together what went wrong. But here’s what makes these moments fascinating from a systems perspective – it’s rarely just one person silently fixing the issue in isolation.

How to launch a Deep Learning VM on Google Cloud

Setting up a local Deep Learning environment can be a headache. Between managing CUDA drivers, resolving Python library conflicts, and ensuring you have enough GPU power, you often spend more time configuring than coding. Google Cloud and Canonical work together to solve this with Deep Learning VM Images, which use Ubuntu Accelerator Optimized OS as the base OS. These are pre-configured virtual machines optimized for data science and machine learning tasks.

Capture and Use Network Response Data in AI Powered Testing

Learn how to capture and use response data from network calls to build smarter and more reliable AI-driven tests. This walkthrough covers the full workflow from configuring user actions to extracting backend responses, validating data, and creating dynamic test flows. You will also see how response data improves debugging visibility and supports data-driven automation. The video includes Ideal for developers, testers, and platform engineers looking to improve the accuracy and resilience of AI-powered test suites.

Gamifying FinOps (And CloudZero) For Better Adoption

In our increasingly online world, managing cloud, AI, and other tech spend has shifted from a good idea to an absolute necessity. But even when cost management is a priority, how do you get busy development teams and engineers actively engaged in the new practices? New initiatives are often viewed as more work on the team’s plate, which is an understandable deterrent to adoption. That leaves FinOps proponents struggling to get others on board.

The AI Cost Crisis: 'AI Cost Sprawl' Is Crashing Your Innovation (AI Cost Sprawl Explained + How To Fix It)

AI should speed up innovation, not inflate your cloud bill. But today, the biggest GenAI challenge for SaaS teams isn’t model quality; it’s cost. And increasingly, that cost comes from AI cost sprawl. That’s not because anyone is doing something wrong, but because AI operates differently from the cloud services we’ve all spent a decade learning how to manage.

Accelerating Our Mission to Bring AI to Everything After Code

Since launching Harness in 2017, we’ve been on a mission to unlock faster innovation by removing the bottlenecks that slow software engineering teams down. From day one, we believed that the biggest obstacles in engineering weren’t in writing code — they were in everything that followed.