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By Chris Griffing
If you are a software developer or engineer, you most likely have to do code review. At the bare minimum, you probably have had your pull requests reviewed. If you haven’t, then you are probably curious about how the rest of the world deals with the process. In general, we use code review to make sure we are shipping high quality code that does what it’s supposed to and is easy to maintain. That’s the goal, at least. In practice, code review can get messy.
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By Jade Nangah
Choosing the right Git extension for your VS Code setup can make the difference between a smooth workflow and hours lost hunting for context. GitLens, developed by GitKraken, and VS Code Git Graph both aim to enhance your Git experience, but they approach the problem differently. This article ranks both extensions across key workflow scenarios – merge conflicts, commit history, code review, debugging, UX, and performance – so you can pick the right tool for how you work.
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By Jade Nangah
Measuring AI’s impact on your engineering team is harder than it sounds. Headlines claim AI writes 30% of code and doubles productivity, but those numbers rarely match what you see on the ground. Without a dedicated dashboard that blends leading indicators, anti-gaming safeguards, and ROI reporting, you cannot answer the question that matters most: is AI helping your team ship better software faster?
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By Jade Nangah
Engineering leaders face a common challenge: too much data scattered across too many tools, and no clear picture of how software delivery is actually performing. A software engineering intelligence platform pulls together metrics from your Git repositories, CI/CD pipelines, and issue trackers into a single view – helping you make decisions based on evidence rather than intuition.
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By Jonathan Silva
If you spend most of your day in branches and pull requests, the platforms you pick decide how much friction you carry. The “version control platforms” label covers two different things: the hosting service where your code lives, and the client you use to interact with it locally. They both matter, and they don’t always pull in the same direction.
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By Jade Nangah
Merge conflicts waste hours of development time every week. The Git branching strategy you pick directly shapes how often these conflicts appear and how painful they are to fix. GitKraken simplifies conflict resolution with visual tools that help you spot problems before they become blockers. This guide walks you through a step-by-step decision process for selecting between GitFlow and trunk-based development.
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By Jade Nangah
Picking the right VS Code Git extension can shape how you move through your codebase every day. GitLens and Git Graph both add visual Git tools to your editor, but they take different paths to get there. GitLens gives you deep context about every line of code – who wrote it, when, and why. Git Graph focuses on visualizing your commit history in a branching timeline. This article breaks down each extension so you can decide which one fits your workflow.
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By Jade Nangah
Merge conflicts can bring a small team’s momentum to a grinding halt. You’re working on a feature, ready to push your changes, and suddenly Git throws up conflict markers that demand your attention. For smaller teams where everyone touches the same codebase, these interruptions stack up fast. This guide walks you through the root causes of frequent merge conflicts and gives you actionable tactics to prevent them.
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By Jade Nangah
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.
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By Jade Nangah
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.
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By GitKraken
AI developer productivity, agentic workflows, and the lessons learned running engineering tools for 100,000+ software engineers at Google. John Montgomery, CCO at GitKraken, sits down with Asim Hussain, co-founder of Alterion AI and former Google VP of Engineering Productivity, to get real about what AI actually changes for engineering teams in 2025.
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By GitKraken
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.
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By GitKraken
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.
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By GitKraken
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.
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By GitKraken
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.
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By GitKraken
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.
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By GitKraken
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.
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By GitKraken
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.
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By GitKraken
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.
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By GitKraken
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.
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GitKraken is on a mission to make Git easier, safer and more powerful across multiple surfaces and environments that development teams use.
Over 10 million developers from more than 100,000 organizations worldwide rely on GitKraken to get their work done. Since 2014, we've been rapidly developing the legendary cross-platform tools while reimagining an intuitive, visual approach to Git. Our team is dedicated to making tools that help software developers be more productive using Git, it's truly our passion. We develop software that's in use by the world's most elite companies like Apple, Google, Microsoft, Amazon, and thousands of other leading organizations.
We Make Git Tools Devs Love.