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Introducing Kepler | GitKraken's Agentic Development Environment (ADE)

Kepler is GitKraken's agentic development environment: mission control for running parallel coding agents at scale. Running one agent is easy. Running five of them across three repos is where things break: scattered terminals, no shared view, no idea what's done or stuck. Kepler puts every agent session on one surface so you can plan work, write code, and review what ships without losing track of anything.

Inside the AI Team Weekly: AI Observability workflows and Prometheus exemplars (May 19th, 2026)

The Grafana AI team (Engineers Ivana Huckova and Sonia Aguilar) share what's new in AI Observability this week: a new way to instrument and visualize agent workflows, plus a neat trick for jumping straight from a metric spike to the exact conversation that caused it using Prometheus exemplars. In this episode: We're showing parts of our team meetings to build in public in some small way and give you a sneak preview of what's to come. But not all features we show may make it to production! You've been warned. :)

AI is only one of four things driving the data center boom

Tony Rossabi, aka the Godfather, has spent 30 years in this industry. Car washes to Telx to building data centers. He sat down with our CEO Michael Reid to break down what’s actually happening underneath the AI headlines, from where the real demand is coming from, to why a single megawatt of power is so hard to find, and how a team of eight is building 19 ten-megawatt facilities across two continents in 24 months.

The Godfather of AI Ready Data Centers | OCOLO CEO & Founder Tony Rossabi

AI is reshaping digital infrastructure, but the biggest challenge isn't always building bigger data centers, it's finding the power to run them. In this episode of Uplink, Michael Reid sits down with Tony Rossabi, Founder & CEO of OCOLO, to discuss how AI is changing the data center industry and what it takes to deliver the next generation of infrastructure.

The bottleneck has moved. AI is rewriting the Software Development Lifecycle

If you've read our previous piece on the 8 stages of AI engineering maturity, you know where your team sits. Turns out adopting AI is the easy part; adapting to its consequences is where most organizations struggle. For more than a decade, software organizations optimized around a single assumption: implementation capacity was scarce.