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AI wasn't supposed to move this fast. Twelve months ago, the H100 was still the benchmark everyone measured themselves against. Six months ago, the B200 changed the calculus for serious inference workloads. Now there's the B300, NVIDIA's Blackwell Ultra GPU, and it doesn't just move the goalposts. It takes them off the pitch entirely. The B300 is the highest-performance GPU in the Blackwell family.

July has been one of our most structurally significant releases yet for Konstruct. With 0.6, we've shipped a second git provider, a completely reworked authorization model, new tooling for AI-assisted troubleshooting, and a handful of improvements that are smaller in scope but large in impact for day-to-day platform operations. Let's walk through what shipped and why it matters. You can explore the full 0.6 release notes directly in the docs.

Most engineering leaders are drowning in data but starved for insight. We have dashboards full of metrics, but they often create more questions than answers and rarely tell us what to do next. In the age of AI, where development velocity is accelerating at an unprecedented rate, this problem is only getting worse. Shipping code faster than you can fix it is an existential risk, and a dashboard that doesn't lead to action is just a distraction.

We are excited to announce support for Ubuntu 26.04 LTS Resolute Raccoon on all Cloud 66 products, including registered servers. From this point onward, new applications are provisioned on Ubuntu 26.04 by default, on both x86_64 and ARM64. Don't forget, you can control your target Ubuntu version via the selection dropdown when scaling up via the UI, or through your manifest!

A faster car doesn’t get you home faster if the freeway is still jammed. That is the problem most teams run into once they add a second, third, or fourth AI coding agent to the mix. More agents generate more code. They do not automatically generate more finished work, because someone still has to track which agent is waiting on input, which one just opened a pull request, and which one has been quietly stuck for twenty minutes. Kepler is GitKraken’s answer to that traffic jam.

LLM cost optimization is the practice of cutting what you spend on large language models, mostly inference, without losing the quality that makes the AI worth running. The biggest levers are routing requests to cheaper models, caching repeated tokens, batching anything that can wait, trimming prompts, right-sizing models, cutting calls you do not need, and putting one gateway and cost view in front of all of it.