news
redhat.com as Festival of LLM Slop Plagiarism
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[Repeat] Jakub Steiner ☛ Flatpak.org Rewrite
The Flatpak website has been running on Middleman for years and time hasn't been kind. Touching the project resulted in seeing 42 vulnerability warnings. The gem itself hasn't seen an update in ages, and the dependency list is rather large.
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Red Hat Official ☛ Red Hat OpenShift delivers high-performance LLM inference for financial services [Ed: Red Hat selling slop, which Flathub is banning]
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Red Hat Official ☛ Empower your AI tools with new agent skills for Red Hat Enterprise Linux [Ed: More and more slop]
To help bridge this gap, we are introducing 2 new integrations, currently in developer preview, designed to bring Red Hat knowledge directly into your AI tools: the translator agent skill for RHEL and the best practices agent skill for RHEL.
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Fedora Project ☛ Fedora Community Blog: Fedora Documentation translations again available
Updates to translations of Fedora Documentation are again available. As announced on March 3rd, the unavailability of translation updates was due to the migration of the translation repositories and necessary tools from Pagure to the Fedora Forge.
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Silicon Angle ☛ IBM and Red Bait partner with Deloitte to fix open-source vulnerabilities [Ed: Red Hat-sponsored hype]
Deloitte Touche Tohmatsu Ltd. is joining an initiative that I.C.B.M. Corp. and its Red Bait unit launched in May to fix open-source software vulnerabilities. The companies announced the move today. U.K.-based Deloitte launched in the middle of the 18th century as an accounting firm.
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Red Hat ☛ Deploying distributed Hey Hi (AI) inference: Blueprints & troubleshooting
In Designing distributed Hey Hi (AI) inference: Core concepts and scaling dimensions, we covered the foundational concepts: the prefill/decode split and the five dimensions of parallelism. In Optimizing distributed Hey Hi (AI) inference: Advanced deployment patterns, we went deep on the three optimization levers: prefill/decode disaggregation, KV-cache tiering and sharing, and speculative decoding. The techniques we discussed in parts 1 and 2 are the tools; this post shows how they assemble into deployments.
We start with six deployment blueprints matched to common traffic shapes, from high-concurrency chat to edge inference on a single workstation GPU. Each blueprint follows the same structure: workload signature, KPI priority, topology, the vLLM and llm-d mechanisms it leans on, and a note on cost shape.