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I made an API endpoint 13 times faster. Then I realized my first verification only checked the status, headers, and response schema. I had not checked the totals. I had made the bug faster. That is the problem with giving an AI coding agent one kind of evidence. A CPU profile can show where the application is slow, but not whether an optimization preserves behavior. A traffic replay can prove that behavior stayed stable, but not explain why the code burns CPU.

Megaport has signed the Open Weights and American AI Leadership letter, supporting open-weight AI, customer choice, and the infrastructure that powers AI innovation. Artificial intelligence is reshaping how organizations build, deploy, and scale digital infrastructure. As AI adoption accelerates, the conversation is evolving beyond model performance alone to the broader ecosystem needed to support long-term innovation.

Every AI coding tool ships the same promise: your developers, faster. Autocomplete in the IDE, agents in the terminal, a working prototype before lunch. And it delivers, at least for the person holding the keyboard. The problem is that most of what it takes to ship software was never a solo activity, and that is the part the market keeps skipping.

An Azure cost calculator is a tool that estimates your monthly Microsoft Azure bill before you deploy anything. Microsoft's official Azure pricing calculator covers 600+ services and produces free, exportable estimates. It is excellent at telling you what Azure charges and terrible at telling you what your teams will consume. This guide covers how to use it, what real workloads cost in 2026, and why estimates and invoices keep breaking up.

FP&A stands for financial planning and analysis. It is the corporate finance function responsible for budgeting, forecasting, variance analysis, and decision support. If you're asking what is FP&A in practice: FP&A teams build the annual operating plan, project revenue and expenses, explain gaps between plan and actuals, and give leadership the numbers behind strategic decisions. Accounting reports what happened. FP&A models what happens next.

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.

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.

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!