A Huawei engineer posted to the Mesa mailing list today over potentially upstreaming a Vulkan driver they have developed for an in-house GPU. However, at least in current form, this new in-house GPU Vulkan driver is likely dead in the water from being upstreamed into Mesa...
The Intel Arc A-Series graphics (DG2) remains in a bit of an awkward state on Linux due to the transition period moving from the i915 kernel driver to more modern and feature capable Xe kernel driver. It's only post-Alchemist where the Intel Xe driver is officially supported and used by default while those opting for the Xe driver with the A-Series can often enjoy better performance and functionality. Except one of the caveats in using the Xe driver on Alchemist GPUs has been the lack of proper media acceleration support...
Last week Debian developers began considering a general resolution over AI large language model (LLM) usage within the Debian project. They are now up to weighing five different proposals for how to permit or block AI/LLM usage in the Debian space...
Over the past few Intel VA-API Media Driver releases we have seen preparations made for Nova Lake video acceleration. That has continued today with the Intel Media Driver and Intel oneVPL GPU Runtime releases today with their new quarterly feature releases...
The System76 and Pop!_OS developers continue quickly iterating on their Rust-based COSMIC desktop environment...
Starting with Red Hat Enterprise Linux (RHEL) 10.2, Firefox and Thunderbird are delivered as Flatpaks by default. If you install RHEL with a graphical desktop, your browser and email client will now come from the Red Hat Flatpak registry instead of traditional RPM Package Managers (RPMs). Here's what that means in practice and why we think it's a better experience.What's changingWhen you install RHEL 10.2 with a GNOME desktop, Firefox and Thunderbird are delivered as Flatpaks as part of the installation process. If your system has an active RHEL subscription, this happens automatically—no ma
Your agent can reach its tools. Identity is scoped. Governance is in place. None of that matters if the model behind the agent cannot reliably call those tools.I have watched this play out. A team builds an agent on LangChain or CrewAI. It works in development. It passes staging. Then someone asks the obvious question: where is inference actually running? The answer, almost every time, is a third-party hosted API—OpenAI, Anthropic, or Google. Not because the team prefers it, but because open-weight models running on their own infrastructure weren't reliable enough for agentic workloads. The
It's Friday evening. Priya, a machine language (ML) engineer at a financial services firm, queues up a fraud-detection fine-tuning job on an GPU cluster costing $55 an hour. The run should take about 40 hours—roughly $2,200 in compute. She double-checks the hyperparameters, submits the job, and heads home for the weekend.Monday morning, she opens her laptop. The model had stopped learning sometime Friday night, but the job kept running—burning through 2 full days of GPU time on a training run that was going nowhere. That's over $1,500 in wasted compute, and she has to start over.If this so
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