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

Platform Engineering vs DevOps: How a Software Engineering Platform Unites Both | Harness Blog

DevOps is a culture and practice that gets development and operations teams to collaborate, automate, and ship software faster and more reliably. Platform engineering is the discipline that builds the internal tooling and self-service infrastructure that makes those DevOps practices repeatable at scale. Put simply: DevOps is the goal; platform engineering is one of the most effective ways to reach it across many teams. Your developers are shipping code faster than ever.

What the Platform Team Actually Does When Everyone is an AI-Assisted Builder

An AI model can write a fully functioning microservice in about fifteen seconds. If you hook it up to a pull request pipeline, it can generate migrations, write unit tests, and suggest refactors before your lead engineer has finished their first cup of coffee. We are entering an era of unprecedented code velocity. But code is not an application, and shipping is not operating.

Digital Sovereignty: Is your data really yours?

Digital sovereignty is one of the most talked-about terms in cloud and AI right now. But what does it actually mean in practice? Civo Product Director Russ Smith gives his honest take: sovereignty isn't just about where your data is stored. It's about whether someone else can switch it off, access it, manipulate it, or determine what you can do with it. If they can, it's not sovereign.

IT Automation: What It Is and How to Get Started

Key Takeaways IT teams are supporting more users, systems, alerts, and services without a matching increase in headcount. Employees also expect immediate support, while businesses want critical services to remain available around the clock. Manual handoffs cannot reliably meet both demands. This guide explains what IT automation includes, how its approaches differ, and how to choose a practical starting point. It also links to deeper resources for each major category.

DevOps Cost of Ignoring Bad Bots on Your Infrastructure

A traffic spike used to mean good news. Now, it's just as likely to mean a scraper found your pricing page or a credential-stuffing script started hammering your login endpoint at 3 a.m. Most teams treat this as a security problem and hand it off accordingly. That's a mistake, because by the time it reaches security, it has already cost engineering time, compute budget, and a fair amount of sleep.

dbForge Studio for SQL Server: AI-Powered IDE for the Full Database Lifecycle

SQL Server development involves much more than writing and executing queries. There are other core aspects such as database design, debugging, data management, performance analysis, administration, deployment, and workflow automation. In this video, you will see how dbForge Studio for SQL Server lets you manage the entire database lifecycle from a single AI-powered SQL Server IDE. Instead of switching between separate tools for SQL coding, database design, test data generation, comparison, reporting, and administration, you can use one application for everything.

From Plan to Main: Why GitKraken Is The Code Flow Company

Code Flow is what we call the shift happening across every engineering team right now: AI can generate code faster than ever, but that doesn't mean it ships any faster. In this clip from our Code Flow Live stream, our team unpack why adding AI coding agents to a team is a lot like adding lanes to a highway that's already jammed. More lanes, more cars, same traffic.

Every AI Agent You Add Leaves Something Behind to Clean Up

Adding a second AI agent to a project feels like doubling your output. In practice, it usually means doubling your bookkeeping too. Every agent needs its own worktree so it can work without touching the branch someone else, human or otherwise, is using. Multiply that by five agents across three repos, and the isolation that made parallel work possible starts generating its own kind of work: which worktree goes with which branch, which ones are stale, which upstream nobody remembers creating.

How to Manage AI Infrastructure in Your Traditional Enterprise Data Center

Managing AI infrastructure in a traditional enterprise data center comes down to validating that sufficient capacity exists before hardware arrives, then maintaining accurate infrastructure data to support planning, deployment, troubleshooting, and ongoing operations. This is because AI has changed what enterprise data centers were built to handle.