Operations | Monitoring | ITSM | DevOps | Cloud

Your Dynamic Host Configuration Protocol (DHCP) server already knows what’s connected to your network. The problem is that DHCP data rarely stays aligned with the rest of your infrastructure systems. Instead, it becomes fragmented across Windows servers, branch offices, spreadsheets, and disconnected operational tools. Lease data ages, assignments go untracked, and teams lose confidence in their network inventory.

Claude Mythos is now available to the public through Claude Fable 5, released June 9, 2026. Claude Fable 5 pricing is $10 per million input tokens and $50 per million output tokens, exactly 2x Claude Opus 4.8 ($5/$25). Claude Mythos 5 (the restricted Project Glasswing version) has identical pricing. Prompt caching cuts input spend by 90%. Batch API pricing is $5/$25 (50% off). In April 2026, Anthropic announced a model it said was too dangerous to release.

AI spend has no ceiling. An engineer can burn $5,000 in an hour, and a team that spins up an agent on Friday can loop it on a bad prompt all weekend. You find out when the bill lands: the money is already gone, the damage pieced back together from logs. Cloud spend had a natural limit. Tokens don’t. Now you see it as it happens. Connect a source and the calls stream in within seconds. Within minutes they’re broken out by model, provider, agent, and user.

Your engineers run Claude Code. Your designers are in Cowork. Half the company has Claude open in a browser tab, and a few are on Cursor. It’s on their laptops, each person authenticated a different way, and none of it touches your gateway. The only record you get is one lump-sum bill at the end of the month. Now you can capture it where it happens – on the laptop.

Something unusual happened in the first half of 2026: the most productive AI coding tool on the market became the most financially dangerous. And the companies that discovered this the hard way read like a Fortune 50 roll call.

Microsoft Copilot cannot see archived SharePoint content in either Microsoft 365 Archive or standard third-party archives. Here's why - and how to close the gap.

‍ Learn how to master Azure deployment with CI/CD pipelines, progressive delivery, and feature flags. See how Harness helps engineering teams ship faster and safer on Azure. Azure deployment sounds straightforward. Push code, it runs in the cloud. But if you've managed a 2 a.m. production incident because a deployment went sideways on AKS, you know the gap between "it deploys" and "it deploys safely at scale" is significant.

There's a version of this conversation that plays out in engineering organizations everywhere. Leadership pushes for standardization. Developers push back. The argument from developers is reasonable on its face: every codebase has different needs, every team has tools they're good at, and adding process feels like slowing down to go faster. It's a genuine tension, and it's also a false one. The teams that ship the most aren't the ones with the most infrastructure freedom.

A few months ago, Steve Yegge published his 8 levels of AI-assisted development, and it clicked the moment I read it, because I had lived that exact progression myself, moving from autocomplete to running agents one step at a time. Framed as an AI trust gradient, it finally gave the industry a vocabulary for something most of us were already going through without a name for it. If you haven’t read it, save it for later.