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Developer experience predicts what's coming 3 to 6 months before it shows up in your delivery metrics. So why are most engineering leaders measuring it last? In this session, GitKraken VP of Developer Research Jeremy Castile breaks down what developer experience (DevX) actually is, how to measure it across 6 key dimensions, and how it connects to velocity, code quality, and AI impact data your team is already tracking.

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.

AI writes code. But shipping to production? That still takes a software engineer. In this GitKon talk, Chris Kelly from Augment Code breaks down what it actually means to use AI-assisted development to write production-ready code, not vibe code. If you've been using AI coding assistants and wondering why the output doesn't always make it past code review, this is for you. Chris covers: Key takeaway: The engineers who will thrive aren't the ones who let AI do everything. They're the ones who know how to review, direct, and architect around what AI produces.

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.

DORA metrics in the AI era reveal a paradox: PR volume is climbing, but deployment frequency is staying flat. In this talk, GitKraken's Director of Product Jeff Schinella breaks down why AI-accelerated code generation is creating a review bottleneck that your DORA metrics can't fully explain on their own. Jeff walks through how PR metrics (cycle time, first response time, code churn, and PR size) serve as the leading indicators behind your DORA data. If your deployment frequency is flat while PR counts go up, the bottleneck isn't your devs. It's your review capacity.

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.