Operations | Monitoring | ITSM | DevOps | Cloud

For decades, digital accessibility in state-funded higher education has largely been a "reactive" game. If a student with a visual impairment reported an issue with a tuition portal, the university would scramble to provide an accommodation. As long as the institution could show "meaningful progress" toward compliance, it was generally shielded from significant legal repercussions. That era is officially ending. The U.S.

We’ve got a boatload of features to show off this week. We knocked out some of the most common requests, user management, single-sign on, and my personal favorite: the weekly email summary.

Post-mortems are one of the most consistently underperforming rituals in software engineering. Most teams do them. Most teams know theirs aren't working. And most teams reach for the same diagnosis: the templates are too long, nobody has time, and nobody reads them anyway. These aren't wrong observations. But they're symptoms, not causes. The actual problem is that somewhere along the way, the post-mortem stopped being a piece of communication and became a compliance artifact.

Developers and security professionals have struggled to get on the same page for what seems like forever and AI is only making that divide larger, according to results from our State of AI-Native Application Security 2025 research report.

Ahead of the DevOps Modernization Summit, Matthew Skelton, CEO & CTO of Conflux shares his takes on output-driven AI, how DORA metrics aren’t enough, and why governance and compliance must be built into the platform. ‍ Matthew Skelton is the CEO & CTO of Conflux and a featured speaker at this year’s DevOps Modernization Summit. Ahead of our annual summit, Matthew has shared his hot takes on AI, DORA, and the key to successful automation.

Years in the FinOps trenches taught me one thing: The data has never been the problem. The data exists. It’s out there, scattered across provider invoices, buried in tagging gaps, locked behind dashboards that maybe three people in your org actually know how to navigate. The real problem? Nobody can get to it when they need it. Engineers ship features without understanding what they cost the business, let alone whether they improved margin.

Somewhere between a model’s first demo and its first production workload, the cost conversation changes completely. Training is a big number, but it’s a finite one. Inference isn’t. Every user interaction, every query, every API call triggers compute behind the scenes — and unlike training, inference never stops billing. That shift from one-time expense to ongoing operational cost is where inference economics begins.

Seven developer-requested features. Tighter control over branches, history, and large repos. No CLI detours required. If you have been using GitKraken Desktop in a complex repo, you already know what it feels like when the commit graph turns into a wall of branches. When rebasing requires more ceremony than it should. When you just need one file back from three commits ago but have to orchestrate a whole checkout to get it. GitKraken Desktop 11.10 is built for those moments.

Continuous delivery is only as good as your ability to understand what’s happening inside your pipelines. When a build is slow, flaky, or burning through capacity, you need more than a green/red status and a wall of logs — you need traces. Bitbucket Pipelines now exposes pipeline execution as OpenTelemetry (OTel) traces via webhook events. This lets you stream detailed pipeline spans into your own observability stack and correlate them with the rest of your system. This post walks through.