Operations | Monitoring | ITSM | DevOps | Cloud

Infrastructure cost visibility has become a critical part of IT decision-making. Performance still matters, but for many infrastructure leaders, that’s no longer the full conversation. Leadership teams increasingly want clarity around cost movement, upgrade exposure, underutilized resources, and whether infrastructure decisions are financially defensible. That creates a different requirement for operations teams: visibility that connects technical behavior to business impact.

Engineering leaders need more than raw metrics, they need actionable insights they can trust with their data. When evaluating engineering intelligence platforms, privacy controls and centralized repository oversight should top your criteria list. The platforms on this list each offer distinct approaches to tracking DORA metrics, developer productivity, and code quality while keeping your data secure.

Tracking down who made a specific change in your codebase can feel like detective work. Whether you’re debugging an issue or trying to understand why a particular piece of logic exists, knowing the history behind each line is invaluable. GitKraken makes this process simple with tools like GitLens for VS Code and GitKraken Desktop, which bring blame annotations directly into your workflow.

Your pull request has been open for three days. Your reviewer hasn’t commented. You’re starting to wonder if anyone will ever look at it—and whether the code you wrote on Monday still makes sense on Thursday. This feeling is common. PR lifecycle time—the duration from first commit to merged code—directly impacts how quickly you ship features, how fresh your code stays, and how engaged your reviewers remain.

Key Takeaway: Harness AI Test Automation now runs existing Playwright suites without code changes, adds AI-powered failure triage, and integrates test results directly into build and deployment pipelines. ‍

Today, Canonical announced the release of Workshop, a solution for launching development environments with a single command. These environments are configured once, and can be reproduced on different machines. This means consistent workflows across development machines and deployment pipelines, and less time managing dependencies.