DevOps
fromInfoQ
12 hours agoGitLab Adds Flat-Rate Code Reviews, Free-Tier AI Access, and Spending Caps
GitLab's new pricing model for automated code reviews aims to reduce backlogs and improve efficiency for development teams.
The new feature via the Actual Result field enables you to record precise outcomes for each test step, improving traceability, audit readiness, and collaboration across your teams.
The most dangerous assumption in quality engineering right now is that you can validate an autonomous testing agent the same way you validated a deterministic application. When your systems can reason, adapt, and make decisions on their own, that linear validation model collapses.
Dependabot sounded the alarm on a large scale. Thousands of repositories automatically received pull requests and warnings, including a high vulnerability score and signals about possible compatibility issues. According to Valsorda, this shows that the tool mainly checks whether a dependency is present, without analyzing whether the vulnerable code is actually accessible within a project.
Industry professionals are realizing what's coming next, and it's well captured in a recent LinkedIn thread that says AI is moving on from being just a helper to a full-fledged co-developer - generating code, automating testing, managing whole workflows and even taking charge of every part of the CI/CD pipeline. Put simply, AI is transforming DevOps into a living ecosystem, one driven by close collaboration between human judgment and machine intelligence.
DBmaestro is a database release automation solution that can blend the database delivery process seamlessly into your current DevOps ecosystem with minimal fuss, and without complex installation or maintenance. Its handy database pipeline builder allows you to package, verify, and deploy, and gives you the ability to pre-run the next release in a provisional environment to detect errors early. You get a zero-friction pipeline, which is often not the case with database delivery process.
Custom agents in GitHub Copilot are tailored versions of the Copilot coding agent that you can define once to follow your own workflows, coding conventions, and tool preferences. They act like specialized teammates that consistently apply your team's standards instead of you repeating the same instructions each time. You configure custom agents using Markdown-based agent profiles that specify prompts, tools, and behaviors.