Red Hat and SUSE Selling Buzzwords and Hype

posted by Roy Schestowitz on Nov 26, 2025

• IBM

  • Red Hat Official ☛ Beyond the bot: How Red Hat Training makes you a better IT professional [Ed: The "certification" diploma mill]

    This raises a valid question for many IT practitioners—with so much knowledge at your fingertips, is a guided training and certification path still necessary?

  • Red Hat Official ☛ 9 strategic articles defining the open hybrid cloud and AI future [Ed: Selling buzzwords instead of products/services]

    Red Hat AI 3, generally available in November, delivers production-ready capabilities across the AI portfolio for greater enterprise efficiency and scale. The release focuses on delivering speed and predictable scale for gen AI applications, primarily through SLA-aware inference capabilities. Key features include the generally available llm-d for reliably scaling Large Language Models (LLMs) and support for the emerging Model Context Protocol (MCP) and Llama Stack API (in Developer/Technical Preview) to accelerate agentic AI development. The platform also offers an extensible toolkit for model customization, enhanced RAG (Retrieval-Augmented Generation) capabilities, and intelligent GPU-as-a-Service (GPUaaS) features for maximizing hardware efficiency across the hybrid cloud.

  • Red Hat ☛ Introducing Models-as-a-Service in OpenShift AI [Ed: Trying to sell OpenShift using buzzwords and not technical merits]

    This article explains how to deploy and manage Models-as-a-Service (MaaS) on Red Bait OpenShift, now available in developer preview. We'll begin by discussing the benefits of MaaS, highlighting how it enables organizations to share Hey Hi (AI) models at scale. Then, we'll guide you through the process of setting it up on OpenShift, deploying a sample model, and demonstrating how rate limiting protects your resources.

    What is Models-as-a-Service (MaaS)?

    With Models-as-a-Service (MaaS), you can deliver Hey Hi (AI) models as shared resources that users within an organization can access on demand. MaaS provides a ready-to-go Hey Hi (AI) foundation using standardized API endpoints, enabling organizations to share and access private, faster Hey Hi (AI) at scale.

  • Red Hat ☛ Building domain-specific LLMs with synthetic data and SDG Hub [Ed: More buzzwords]

    As high-quality human text reaches its limits, synthetic data generation has become a core technique for scaling and refining large language models (LLMs). By leveraging one model to produce training examples, instructions, and evaluations for another, teams can expand datasets, target domain-specific gaps, and improve controllability without relying on scarce or sensitive human data. With advances in open source LLMs, fast inference frameworks like vLLM, and reproducible generation pipelines, synthetic data is now a practical foundation for modern model development. 

• SUSE

  • Silicon Angle ☛ SUSE’s MCP Server tech preview lays foundation for AI-assisted GNU/Linux infrastructure [Ed: Selling buzzwords]

    Enterprise GNU/Linux company SUSE SE today announced a milestone in its mission to create an artificial intelligence-assisted computing infrastructure, where complexity is brushed aside in favor of simple, natural language commands.