Operations | Monitoring | ITSM | DevOps | Cloud

GitKraken Desktop in 6 Minutes: Open a Repo, Run an Agent, Ship the Change

The fastest way to get up and running in GitKraken Desktop. In this tutorial, you'll open a repo, start an AI coding agent in its own worktree, review the agent's changes against your own work, and ship a pull request without leaving the app. What you'll learn: Chapters Help Center: help.gitkraken.com.

Jira GitHub Integration: The Complete Guide

Most teams use Jira to plan work and GitHub to build it. The problem is those two tools don’t talk to each other by default. Developers end up manually copying commit references into tickets, project managers hunt through GitHub to answer basic status questions, and sprint reviews become archaeology expeditions through two disconnected systems. Git Integration for Jira closes that gap.

90% AI Adoption. Still Failing. DORA Explains Why.

AI adoption is nearly universal. So why are most teams still struggling? In this session from GitKon, Nathen Harvey, head of DORA at Google Cloud, shares findings from the 2025 DORA State of AI-Assisted Software Development report, drawing on data from nearly 5,000 developers worldwide. The answer isn't more AI. It's what surrounds it.

Why Mandating AI Tools Backfires on Engineering Teams

Responsible AI adoption for engineering teams starts with culture, not compliance. In this GitKon talk, Rizel Scarlett (Tech Lead of Open Source DevRel at Block) shares how Block helped thousands of engineers actually want to use AI tools, including Goose, Cursor, Claude Code, and more, without mandates, vibe coding disasters, or security gaps.

Git Sync: Observability as code built for scale | Demo | Grafana Labs

In this video, Fabrizia Rossano and Roberto Jiménez demonstrate Git Sync, a feature that provides you with the power of Git version control right in your Grafana instance. Git Sync enables you to submit changes in your dashboards as pull requests and get them reviewed by your team directly from Grafana or from Git.

You're Running Agents. Your Tooling Is Still Catching Up.

Introducing GitKraken Desktop 12.0. At some point in the last year, the question shifted. It stopped being “should I use AI coding agents?” and became “how do I run more than one at a time without losing my mind?” If you’ve been there, you know what the management layer looks like. A terminal per agent. A worktree created by hand before each session.

Your Developers Feel More Productive. Your Codebase Disagrees.

AI adoption is up. Developer confidence is up. So why is code duplication up 10x since 2022? GitKraken VP of Developer Research Jeremy Castile shares the frameworks we built after analyzing 211 million lines of code and talking to hundreds of engineering teams. This is the playbook version of the research — practical, not theoretical. In this session, you'll learn: The gap between how productive developers feel and what's actually happening in the codebase is real. If you can't measure it, you're just guessing. Nobody wants to be guessing with this stuff.

GitKraken Desktop 12.0 Release: Agent Sessions, Terminal Performance Boosts, and More!

If you're running Claude Code, Codex, or Gemini, managing multiple sessions means one terminal per agent, status checks by window-switching, and worktree setup from scratch every time. GitKraken Desktop 12.0 adds structure to that workflow. What's new: Works with Claude Code, Codex CLI, Copilot CLI, Gemini CLI, and OpenCode.

AI Enablement for Dev Teams: The 6-Pillar Flywheel

AI adoption is already happening on your team, whether you have a strategy or not. Tracy Lee (CEO of This Dot Labs, Microsoft MVP, Google Developer Expert) breaks down the AI Enablement Flywheel — a 6-pillar framework used by successful engineering organizations to move from scattered experimentation to scalable, ROI-positive AI workflows.

How to Catch AI Code Mistakes Before They Reach Production

AI can write code fast, but it makes mistakes humans often don't. In this session from Ole Lensmar, CTO of Testkube, breaks down the real quality risks of AI-generated code and how engineering teams can build guardrails before those bugs hit production. What you'll learn: Common mistakes LLMs make (and which ones are unique to AI) Whether you're a developer leaning on AI to ship faster or a QA lead trying to keep up with the pace of AI-generated code, this talk gives you a practical framework for staying ahead of quality issues.