Operations | Monitoring | ITSM | DevOps | Cloud

I built GitBench because I was curious about how well models did git things. It turns out that it was a good idea because some models will surprise you. Some of the frontier models were remarkably bad and some cheap open models punched above their weight class. But, as they say, “the times they are a changin’”. Before I go too deep into the latest results, you may want to read up on GitBench and what it is and why I built it in the previous blog post.

There is no doubt that LLMs have changed how we work as developers. You can go back just a year from today and I would have had a very different opinion. But, things have changed significantly. Complex work usually involves just prompting an agent for it. You still need taste and opinions to steer it the right way. With that in mind, it’s easy to se that the future of the IDE might be uncertain.

Five worktrees for five agents sounds like organization. In practice, it’s five contexts to keep straight, five places history could live, five spots for a WIP that gets left behind or diffed against the wrong branch. GitLens 19.2’s fix isn’t a second graph. It’s making the one graph you already trust point wherever the work actually is.

Every new AI coding agent comes with the same pitch: write code faster. For most devs, that part already checks out. Codex writes a function in seconds. Claude Code refactors a file mid-meeting. Copilot fills in a test before you finish describing it. None of that touches the problem that shows up an hour later: five agents running across three repositories, each with its own diff, and no single place to see what’s stuck, what’s done, and what’s actually safe to ship.

Finding the right pull request review tool can mean the difference between a team that ships confidently and one that drowns in open PRs. When review workflows are scattered across tabs, comment threads, and standalone dashboards, code quality and velocity both take a hit. GitKraken gives you a unified code review experience that connects your IDE, desktop client, and browser so reviews happen where you already work.

One of the features I have been wanting in Kepler for a long time was the ability to use Pi as my agent when spinning up tasks. Pi is such a minimal harness that it doesn’t prompt for approval for every little thing and it’s system prompt let’s the model just be itself. That minimalism comes at a cost, though. Pi doesn’t have ACP support out of the box, so that means we haven’t been able to officially support it in Kepler, yet.

Every so often we sit down with someone from our support team and turn their week into a blog post. This time, Roberto walks us through four things generating tickets right now: an upcoming Microsoft authentication change, how AI credit pools actually work, multi-account support in Kepler, and a one-line fix for laggy GitKraken on Linux or WSL. Here’s what’s changing and what to do about it.

Internal hackathons are a powerful way to let your teams explore ideas and work together on something fun besides the same old stuff for work. Maybe they want to build something brand new, maybe they want to knock out things that are on the backlog that never get prioritized, or maybe they want to work on something fun but completely unrelated to work.

Running AI coding agents in parallel across repositories is no longer experimental. It’s how high-performing engineering teams ship faster. But the tools you pick to orchestrate those agents can either multiply your output or introduce new bottlenecks. GitKraken gives your team a purpose-built surface for AI coding agent orchestration through Kepler, its agent-agnostic development environment. Before you commit to any orchestration tool, though, you need to ask the right questions.

Pull request queues keep growing, reviewers lose context between rounds of feedback, and merges stall for days. If your team’s code review process has become a bottleneck instead of a quality gate, the tooling around it may be the real issue. GitKraken gives you a unified code review workspace that cuts through noise and surfaces meaningful changes so reviewers focus on what matters.