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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.

AI coding agents can generate pull requests at a pace your team has never seen. The bottleneck has shifted from writing code to everything that follows: reviewing, iterating, and merging. AI code-to-merge platforms are the category of tools built to manage that entire lifecycle, from the moment an agent starts working to the moment code lands in your main branch. This article walks through ten questions you should ask before committing to a platform.

We put a blunt question to developers on a recent live webinar: right now, could you actually prove AI agents are paying off for you or your team? Only 24% said yes. The other 76% were guessing, unsure, or already suspicious that agents are costing more than they’re saving. That gap between adoption and proof is the real story in agentic development right now. Teams aren’t behind on running agents. They’re behind on knowing whether it’s working.

The recent mystery around Ox Alpha last week and the week before was a fun slice of what makes social media fun. The hunt for the model provider and how people did that discovery should be studied. But, this post is more about stealth models in general. You might be wondering what the phrase “stealth model” even means and if you already know, you might still be curious about why companies release stealth models.