Operations | Monitoring | ITSM | DevOps | Cloud

Most organizations have responded to the rapid growth of AI by creating written policies that define acceptable use. A clear AI policy can establish expectations, assign responsibilities, and help employees understand how AI should and shouldn’t be used. But policy alone can’t provide oversight and is almost impossible to enforce at scale without the right tools.

How to export a Microsoft Teams chat - manual options, Purview eDiscovery for admins, what happens after a user leaves the organization, and why retention is not export.

We didn’t set out to build a new category of product. We set out to stop juggling. That’s the word Gyo, the senior engineer who built the first version of Kepler, keeps coming back to when he talks about where it started. “I have a lot of terminals opened, and with all those tabs, it was very difficult for me to keep focused on what I was doing,” he says. “I’m not a juggler.”

In April, Harness announced the Harness Cursor Plugin, a native integration that lets developers manage CI/CD pipelines, deployments, and security posture using natural language inside Cursor, governed by the same RBAC, OPA policies, and audit trails already enforced across the Harness platform. That experience has, until now, lived entirely at a desk.

Confidential computing is moving quickly. The foundation is already here: AMD SEV-SNP and Intel TDX have made it possible to run confidential virtual machines (VMs) with stronger protection for data in use. Ubuntu 26.04 Long Term Support (LTS) brings integrated host and guest support for both of these technologies, making confidential computing a native part of the Ubuntu virtualization offering..

For the past two years, the conversation around AI in software engineering has focused on one thing: productivity. Engineers are shipping faster, writing more code, and completing work in hours that once took days. Every new model promises another leap forward. What gets far less attention is what all that speed demands from the people using it. Guillaume Moigneu, Field CTO at Upsun, has spent the past year watching engineering teams adapt to AI-assisted development.

Business continuity planning has a quiet assumption built into most of it: that the infrastructure the plan runs on will cooperate. Backup systems will be accessible. Recovery procedures will work as documented. The provider whose services underpin the critical path will be available, or at least recoverable within the window the plan specifies. That assumption is tested every time a major provider has an outage, a regional failure, or a service disruption that cascades across dependent workloads.

A savings number tells you money is on the table, but it doesn’t tell you whether the finding holds up, what it’s based on, or what to do next. In that gap, recommendations pile up unactioned. When you’re staring at thousands of them, a title and a dollar figure isn’t enough to decide which are safe to act on.

Claude Opus 5 launched July 24, 2026 at $5 per million input tokens and $25 per million output tokens, identical to Opus 4.8. It delivers near Claude Fable 5 performance at half Fable's price and is now the default model on Claude Max. New effort settings let teams trade capability for token savings, which means two teams on identical pricing can now run up very different bills. Finance teams, that last part is your problem. Anthropic has shipped a model that costs exactly what the old one cost.

Codex pricing runs six tiers, from free to $200 a month, but the sticker price is not your real bill. OpenAI Codex pricing 2026 charges by the token, not the plan, a change that took effect in April. Plus is $20, Pro starts at $100, and everything past that depends on how many files you let the agent read. Most Codex pricing guides hand you a price list and call it done. That is like pricing a taxi ride by the door handle. The meter is what matters, and OpenAI put a real one on Codex this year.