news
Fedora/Copr and Red Hat Hype Articles
-
Copr: Generating dataset from Fedora packaging guidelines
Recently we have published a dataset of sourced questions and answers about Fedora packaging guidelines to Hugging Face. With the intention of using it to fine tune LLMs.
In this blog post, I will describe the approach I have taken to generate it. Focusing on matters of prompt construction, and response constraints.
-
Red Hat ☛ Quantum computing 101 for developers [Ed: Boosting the next hype wave (fake valuation) for "daddy IBM"]
Quantum computing sounds like it comes straight out of science fiction. But it's quickly becoming a real-world strategy. It promises to solve problems that today's most powerful supercomputers can't handle. We're still early in the game, but now is the time for developers, architects, and technology leaders to start understanding what's coming.
How will it affect your tech stack, your workloads, and your customers? What does it mean for your business? Is it just hype, or is this a technology you and your clients should start thinking about today?
This post gives you a functional baseline. We'll skip the heavy physics and focus on what quantum means for real-world software development, platform engineering, and how it connects to the Red Bait technologies you already use (such as Red Hat OpenShift, AI/ML pipelines, and secure software supply chains).
Quantum 101: A quick crash course
Your laptop or phone runs on bits. Think of each bit like a tiny light switch: it's either off (0) or on (1). Everything your computer does—streaming videos, running spreadsheets, browsing memes—is just a super-fast game of flipping lots of these switches.
-
Red Hat ☛ One model is not enough, too many models is hard: Technical deep dive [Ed: "AI" hype again]
This post is a practical, end-to-end guide to running hundreds to thousands of machine learning models without chaos. It assumes you already know what your models do; the focus here is how to operate them at scale with consistency, traceability, and security.
We'll show how to turn the model lifecycle into an assembly line (define → train → package → deploy → monitor → retrain) using configuration-driven pipelines, versioned artifacts, and GitOps promotion. The goal isn't any specific vendor stack; it's a set of repeatable patterns you can re-create with cloud-native, open tools.