news
Fedora and Red Hat Leftovers
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Fedora Project ☛ Fedora Community Blog: Fedora Forge Usage Policy
After extensive review and discussion on the ticket request and in recent council meetings (see meetbot for 29 July and 15 July 2026), the Fedora Council would like to initiate the policy change policy process for ratifying the Fedora Forge Usage policy. This document is open to public feedback (if any) for a minimum of two weeks. If there are no significant changes to be made to the policy based on community feedback after Thursday, 13 August 2026, this policy will go to a formal ticket vote for Council to approve or reject.
If there are significant changes to be made to the document, Council will review the policy and, if necessary, extend the feedback period before calling for an official vote. Please provide feedback on the discussion post. -
Red Hat Official ☛ Firefox and Thunderbird as RHEL Flatpaks: What it means for you
When 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 manual login, no credential management, no extra configuration. Your existing subscription entitlement handles registry access.
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Red Hat Official ☛ Why self-hosted inference is essential: Building a reliable, sovereign inference layer [Ed: Slop slop slop.]
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 framework handles orchestration. The hosted API handles inference. Neither is under the organization's control.
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Red Hat Official ☛ Make every GPU-hour count: Progress tracking in Red Hat OpenShift AI [Ed: Red Hat peddling slop nonstop]
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.
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Red Hat ☛ Optimize GPU efficiency with OpenShift Hey Hi (AI) and llm-d flow-control
Modern model serving is highly optimized using inference frameworks like llm-d and vLLM. These serving tools allow organizations to share a single model instance across hundreds or even thousands of concurrent users and applications, each with unique requirements.
Imagine a model being used by two development teams, one to routinely summarize documents, and the other as a chatbot for debugging code. When the first team's routine summarizations are taking place, the noisy-neighbor problem occurs and the request time for the second team's chatbot could rise exponentially, potentially degrading the expected level of service.
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The Register UK ☛ High hardware costs see Red Hat offer a two-server edge rig, no mini-PC required
Red Hat has come up with a way to reduce the quantity of servers needed for high-availability edge deployments of its OpenShift platform, because its customers worry about high hardware costs.
The IBM business unit’s Daniel Froehlich and Paul Lancaster last week introduced Red Hat’s approach in a post that saw the pair observe interest in “alternative topologies for large-scale edge deployments” due to “the prohibitive cost of powering, maintaining, and deploying a third node across hundreds or thousands of sites – a motivation that has only grown stronger in light of the recent steep increase in hardware prices.”