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Secure Your Dev Team > Releasing the Power of Git
Every so often we sit down with someone from our support team and turn their week into a blog post. This time, Roberto walks us
Running AI coding agents in parallel across repositories is no longer experimental. It’s how high-performing engineering teams ship faster. But the tools you pick to
Pull request queues keep growing, reviewers lose context between rounds of feedback, and merges stall for days. If your team’s code review process has become
Jellyfish helped define engineering intelligence for enterprise teams, but it’s far from the only way to track software engineering intelligence alternatives like DORA metrics, delivery
AI coding agents can generate pull requests at a pace your team has never seen. The bottleneck has shifted from writing code to everything that
We put a blunt question to developers on a recent live webinar: right now, could you actually prove AI agents are paying off for you
Your code review collaboration platform probably does a decent job showing diffs. But for distributed teams, the toughest problems rarely live inside the diff itself.
Running one coding agent is manageable. Running four of them across three repos, each at a different stage, each waiting on something different from you,
Giordano “Gyo” Piazza, GitKraken’s Director of Engineering, has spent the past month heads down on the next version of Kepler. When he got on a
Choosing a software engineering intelligence platform is one of those decisions that gets more complicated the longer you wait. The category has grown rapidly, and
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