Operations | Monitoring | ITSM | DevOps | Cloud

Tired of manually updating Jira issues after every change? Want to avoid the dreaded "what's the status of XYZ ticket" question? In this video, we’ll show you how to automate Jira updates using Smart Commits and Jira Automation Rules — so your issues always stay up to date without you lifting a finger. We’ll cover what they are and how they work, then walk through how to incorporate them into your workflows.

Managing 50 repos with different Git workflows is like conducting an orchestra where every musician is playing a different song. We built GitKraken CLI to fix this: multi-repo actions, a single command for all things Git, and team-wide standardization that doesn't sacrifice speed. Watch the full video on how we're simplifying complex workflows without dumbing down Git.

Why do developers stop using AI coding assistants? According to Carnegie Mellon research, the top reason is unhelpful suggestions. Tabnine's Principal Architect John Feeney explains how context transforms AI coding tools from generic to genuinely useful. Learn the 4 Cs framework for maximizing AI assistant value: Context (workspace indexing), Connection (repo integration), Coaching (rules-based guidance), and Customization (fine-tuning). Discover how Retrieval Augmented Generation (RAG) helps AI understand your codebase, not just open source patterns.

We never talk about pregnancy in the workplace. Maybe it's time to change that. Tech is still male-dominated, which creates a ripple effect: poor maternity policies, overworked expecting developers, and privacy-invasive apps that fail the people who need them most. But what happens when a developer decides to solve this problem themselves? Rizel built a pregnancy app she could actually trust. Not because existing solutions didn't exist, but because they weren't built with real privacy, real needs, and real developer insight in mind.

GitKraken MCP (Model Context Protocol) brings repository intelligence directly into VS Code, Cursor, Windsurf, and other AI-powered IDEs so your agent stops guessing and starts understanding your actual workflow. Instead of manually explaining your branch structure or digging through issues in your browser, MCP connects your AI agent to the actual state of your repositories. It understands your branches, your issue tracker, your pull requests, and your commit history—then helps you start work, resolve conflicts, and review code without leaving your editor.

Git worktrees let developers work on multiple branches simultaneously without stashing or losing context. Learn how to use Git worktrees to handle parallel development, urgent hotfixes, and code reviews without the single-threaded pain of constant branch switching. In this tutorial, GitKraken Senior Product Director Justin Roberts shows you how to set up Git worktrees using both the command line and GitKraken Desktop. You'll see real-world examples including handling critical bugs, reviewing pull requests without disrupting your current work, and managing multiple feature branches at once.

GitHub Copilot won't replace you. But developers who master it might have an edge over those who don't. Frank Boucheros dropped serious wisdom at GitKon about this exact reality. His take? Copilot will enhance developers, but only if we learn to use it properly.

Most software intelligence tools take months to implement, cost a fortune, and end up collecting dust. GitKraken Insights is different. It helps engineering leaders measure what matters: AI impact, code quality, delivery performance, and developer experience, all in one place. It’s the latest evolution of the GitKraken DevEx platform, trusted by over 40 million developers. Insights connects data from across your GitKraken tools to give you a complete picture of engineering health and value. We're talking DORA metrics, pull request metrics, and AI impact.

Ever feel like you're drowning in documentation scattered across Confluence, Slack, Jira, and Git commits? Kyle Fransham, Senior VP of R&D at Superna, shares why every developer should run their own local LLM and shows you exactly how to do it. In this GitKon talk, Kyle reveals how to turn your personal "master document of knowledge" into a queryable AI assistant running directly on your laptop. No cloud dependencies, no organization bottlenecks...just your own development copilot that understands your unique workflows, tips, and tribal knowledge.