Operations | Monitoring | ITSM | DevOps | Cloud

Discover how Docker is empowering developers in the GenAI era with tools that simplify AI application development. Docker VP of Product Michael Donovan shares how containers are critical for building, testing, and scaling GenAI applications, plus real solutions for the biggest challenges developers face today.

Problem statement → Outcomes → Show your work → Get sign-off. This engineer's 4-step framework turns vague ideas into fundable proposals. No fluff. Just the same discipline you'd use for a design doc. If your last proposal got ghosted, this might be why.

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.

Ever wish you could turn a quick Loom recording into Jira work items without all the manual typing? Now you can! In this Demo Den episode, Pierre walks through a new Rovo AI feature that automatically converts your Loom videos into actionable Jira work items. Whether you're recording bug reports, feature requests, or project updates, Rovo handles the data entry for you. What Pierre covers: Turning Loom videos into work items with Rovo How it works in your AI-enabled Jira instance.

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.