News and Tech Update

DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience. Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers. Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others. The ten job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered. We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration. Greenhouse Ripple Chicago, IL Site Reliability Engineer, Observability $160,000 to $200,000 EarnIn Mountain View, CA Staff Site Reliability Engineer $252,000 to $308,000 Ashby Element 84 Ale...

GitHub has moved Copilot cloud agent’s integration with Linear out of preview and into general availability, and the update brings more than a stability label. Along with GA status, GitHub added a set of controls that let teams shape exactly how the agent behaves on a per-issue or per-team basis, without leaving Linear. The core workflow stays the same as it was in preview: Assign a Linear issue to Copilot cloud agent, and it analyzes the issue contents, opens a draft pull request, and works independently inside its own ephemeral development environment powered by GitHub Actions. Progress streams back to the Linear activity timeline, and the agent requests a pull request review once the work is done. What’s new at GA is the control layer sitting on top of that workflow. Teams can now pick which model Copilot uses for a given task, point the agent at a custom agent defined in their repository so it follows team-specific conventions, and set the base and working branch to co...

GitHub and the Python Package Index (PyPI) just added a new layer of defense against supply chain attacks, and it doesn’t involve scanning code or blocking uploads outright. It involves waiting. GitHub’s Dependabot now applies a default three-day cooldown before opening a pull request for a routine version update. PyPI, meanwhile, will no longer accept new files uploaded to a release once it’s more than 14 days old. Neither change stops a bad actor from publishing malicious code in the first place. Both are designed to shrink the window during which that code can quietly work its way into production systems before anyone notices. The reasoning behind both moves is simple. A newly published package version often gets pulled into build pipelines within minutes of going live, well before a person or a scanner has had time to review it. Security tools are usually fast at flagging something suspicious. But flagging isn’t the same as fixing. A maintainer still has to...

CodePen has spent 14 years as the place where front-end developers show off CSS tricks and JavaScript experiments. That reputation is about to change. The company just shipped CodePen 2.0, a full rebuild that turns individual “Pens” into deployable, file-based, version-controlled projects. For a platform best known for demos, that’s a meaningful shift toward production use. One Editor to Replace Three Until now, CodePen ran three separate editors: the Classic Pen editor, a Projects editor, and a Vue-specific editor. CodePen 2.0 merges all three into a single tool, and co-founder Chris Coyier says it was built to do everything the old editors could do, plus a long list of new capabilities. The company also says it kept backward compatibility with existing Classic Pens as a priority during the rebuild. The most notable addition is one-click deployment. Every Pen built in 2.0 can now be pushed to a live *.codepen.app subdomain, updated on save, or pointed at a custom ...

Continuous software delivery in the digital age has come to depend on CI/CD pipelines. They enable engineering teams to rapidly develop, test, and deploy code while keeping it highly usable and consistent across environments. CI/CD pipelines, however, can themselves become a source of friction as systems start at a small scale and become more complex. Pipelines set up to work for small projects have difficulty scaling with ever more repositories, ever larger test suites, and ever larger development teams. Slow feedback cycles, increased infrastructure costs, and decreased developer productivity are some by-products of bad pipeline design. Here are 10 CI/CD pipeline mistakes that you’ll often come across in your own environment, based on common patterns found across many engineering organizations. 1. Considering CI/CD Pipelines as “Set and Forget” Some teams set up a CI/CD pipeline at the beginning of a project and then rarely touch it afterward. The pipeline of an a...

When Photify AI was removed from the App Store, our team had about two weeks to preserve one of its core capabilities: AI-powered photo and video generation. The fastest path was to rebuild that functionality for the web inside Botify AI, a product built on a technology stack I had never worked with before. There was little time to learn the stack from scratch, yet the feature still had to meet production standards. Claude Code became an important part of that process. It accelerated my understanding of an unfamiliar codebase, generated working implementations and helped resolve issues that would otherwise have taken much longer to investigate. However, writing code turned out to be the easy part. The real challenge was deciding what the generated code should and shouldn’t become. AI could produce code that compiled, but it couldn’t judge whether it matched the existing system’s architecture, respected established patterns or introduced risks that would surface later in production. E...