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redhat.com: Slop Plagiarism, Faking Data Protection, and More
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Red Hat Official ☛ Introducing asago: Open source AI safety and governance orchestration [Ed: IBM Red Hat trying to sell slop, not Linux, and openwashing along the way (plagiarism is "open")]
The open source AI safety ecosystem is a rich one. There are plenty of excellent and mature projects across areas such as guardrails, evals, red teaming, and agentic security, and these are complemented by comprehensive risk frameworks, ontologies, and mappings. These are important projects that need continued development. At Red Hat, our AI Safety team collaborates with, and contributes to, many of these upstream communities, and we'll continue to do so.
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Red Hat Official ☛ Beyond the hype: What financial leaders need to know about enterprise AI [Ed: IBM is swallowing the pyramid scheme and vomiting it out via the "Red Hat" brand]
According to McKinsey’s estimates highlighted in Red Hat’s executive guide, Beyond the Hype: AI in Financial Services, AI represents a $1.2 trillion value opportunity for the global banking industry alone—with generative AI responsible for up to $340 billion of that potential. Furthermore, industry projections suggest that nearly 30% of operating profits in banking will soon be directly linked to AI-driven capabilities.
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Red Hat Official ☛ What's new in Red Hat OpenShift confidential computing and sandboxing [Ed: Well, "confidential computing" means outsourcing to back doors, surveillance, and a mere delusion of data protection]
This release advances confidential computing on 2 fronts: [...]
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Red Hat Official ☛ The Oracle estate you can control
The work Red Hat and Everpure have done together exists to break that coupling. Every layer of the estate—hypervisor, operating system (OS), storage, and the database itself—should modernize on its own timeline, which you control. Over the past 2 years we’ve built and published the validation to make that real for Oracle workloads specifically. This post covers what we tested, what the results were, and what they mean for how you sequence the next 3 years.
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Red Hat ☛ Testing modern hash table designs in OVN and OVS
During my Red Hat internship on the Open Virtual Network (OVN) team, I was assigned a task I found especially interesting: Investigate whether OVN and Open vSwitch (OVS) hashmap performance could be improved by using a different hashmap design. This work turned into a deeper look at modern hash table algorithms, OVS's existing hashmap implementation, OVN's large-scale workloads, and performance profiling in C.
My goal with this article is to make the work useful to anyone interested in hashmap design and performance, while also giving OVN and OVS developers enough context to understand what was tested and what the results mean for the codebase.
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Red Hat ☛ AutoRAG: Optimizing RAG for small models
Retrieval-augmented generation (RAG) is a well-worn path by now. Parse the data, chunk it, embed the chunks, retrieve the ones closest to the question, stuff them into the prompt, and let the model answer. You've built this. The shape is familiar. Or if you haven't, this Red Bait primer on RAG and tuning is a good place to start.