Red Hat Puff Pieces and More
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Red Hat Official ☛ Platform engineering and self-service: simplifying complexity with Red Hat Developer Hub
For development teams tasked to develop and maintain software solutions, waiting days or weeks for resources to provision disrupts workflows and delays value delivery. For IT operations, the constant flood of requests, combined with the need to maintain compliance and governance, creates a resource-intensive burden. It’s a challenge of speed versus control, innovation versus regulation—a delicate balance that often leaves both sides frustrated.
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Red Hat Official ☛ Connecting your systems using the Insights proxy
To help showcase this new capability and how it works, we released a video that shows you how to set up the Insights proxy and how to configure your hosts to use the proxy.
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Red Hat Official ☛ Beyond the AI pilot project: Building a foundation for generative AI [Ed: Buzzwords, not substance]
There are a lot of factors to consider in this process, but it's important to remember that all this AI stuff is still software. The skills and disciplines you and your teams have developed over the years will all come to bear in this new era of AI, with a few new factors thrown in to keep things exciting.
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Red Hat Official ☛ Accelerating Azure AI with Ansible Automation Platform: from subscriptions to AI models [Ed: Promoting Microsoft]
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Network World ☛ Linux containers in 2025 and beyond
The use of Linux containers has shown no sign of slowing down since their emergence in the early 1980s. One exciting thing to look forward to in 2025 and beyond is the integration of AI (artificial intelligence) and ML (machine learning) as in RedHat’s RamaLama project, which aims to make it easy for developers and administrators to run and serve AI models.
When first launched on a system, RamaLama determines whether GPU support is available (and falls back to CPU support if it isn’t) and then uses a container engine – such as Podman or Docker – to download a container image from RamaLama that contains everything that you need to run an AI model. RedHat has claimed that this makes working with AI “boring,” but that isn’t meant to imply it isn’t very exciting, just that it’s quite easy to work with. Sounds good to me. RamaLama currently supports llama.cpp and vLLM for running container models.