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Operationalizing digital employee experience is not about implementing another technology platform or launching another transformation project. It is about embedding new capabilities into the way IT and eventually the wider organization identifies problems, manages change and improves the employee experience. That cannot happen through implementation alone.

In the world of IT Asset Management (ITAM) and Digital Employee Experience (DEX) Management, staying ahead of system issues is a constant battle. IT teams are often flooded with data but starved for time, reacting to problems after they’ve already disrupted an employee’s workday.

Kubernetes workload rightsizing is one of those tasks everyone agrees is important and almost nobody enjoys doing by hand. Set requests too high and you pay for capacity that sits idle; set them too low and you invite CPU throttling, out-of-memory kills, and late-night pages. It’s no surprise, then, that many teams reach for the Vertical Pod Autoscaler (VPA) as their first step toward automated rightsizing.

Uptime Institute's Global Data Center Survey 2026 found that the average modal rack density exceeded 11 kW in 2026, up from 9 kW in 2025. “Modal rack density” means the rack density that is most common within a data center. Arguably, the more important trend is that rack densities are becoming more varied, with more facilities supporting a mix of conventional and high-density racks.

CVE-2026-74580 is a high-severity Linux kernel bug in vhost, the subsystem that accelerates I/O for virtual machines. It gives a local user a reliable path to root on the host, and in some VM setups it lets a guest write into host memory. The CVE record has existed since August 21, 2026, but the full technical write-up, nicknamed "Vghost", only went public on October 12. It came with working exploit demos for both a local root escalation and a VM escape.

Active-active means two or more locations serve live traffic at the same time, with the application running in each and its data replicated across them, so no location sits idle waiting for a failover. You build it in five layers, and data is the hardest.

A production inference layer needs GPU compute sized to the model, a serving runtime such as vLLM or SGLang, orchestration that autoscales on queue depth, one endpoint with health-aware routing and failover, model weights cached near the GPUs, and observability, security, and cost controls.

Developers use Bitbucket Cloud every day to turn plans into working software. Code lives in repositories, feedback happens in pull requests, and Bitbucket Pipelines helps teams validate changes before they move forward. For an AI assistant to help with this work, it needs access to the same information. Without a direct connection, developers have to gather pull request details, pipeline results, logs, and links before the assistant can help.

By Max Johansson, Vehicular Systems, Linköping University An AUV shown for illustration. Photo by Swedish Maritime Robotics Centre (SMaRC). At KTH, the Swedish Maritime Robotics Centre (SMaRC) is continuously developing an autonomous underwater vehicle (AUV) as a research platform for studying underwater environments. The AUV has traditionally been powered by batteries, but adding a fuel cell can extend its operating range.

You can now see which of your Multi-Region Cloud Storage buckets have cross-location read charges worth acting on, and start with the ones where a cache would save the most. It runs automatically for every organization with GCP Cloud Storage, so there’s nothing to set up. Google bills network data transfer when a Google Cloud service in one location pulls data from a Multi-Region bucket in another.