Red Hat

As AI evolves from standalone models into agentic applications, enterprises need more than powerful AI models. They need a flexible foundation that connects everything, from infrastructure and models to AI agents. This means having the flexibility to adopt new technologies, integrate different solutions, and scale AI without being tied to a single vendor or technology. With its open approach, Red Hat helps organizations build a flexible foundation across the AI stack, making it easier to connect technologies, build AI solutions, and adapt as their AI needs evolve. Because the right foundation doesn’t just support AI today, it helps enterprises prepare for what comes next. 💬 Learn more about building a flexible foundation for enterprise AI in the article shared in the comments below!

As IT environments continue to grow more complex, organizations are looking for ways to make automation easier to manage, scale, and integrate across different systems. Modern IT teams are no longer dealing with isolated automation tasks. They need solutions that can connect existing automations, coordinate workflows, and simplify operations across increasingly complex environments. Red Hat Automation Orchestrator, available as an add-on for Ansible Automation Platform 2.7, helps organizations bring existing automation assets together into more complex, connected workflows. By building on existing automation investments, teams can streamline processes while scaling automation more effectively. With a more unified approach to automation, IT teams can reduce operational complexity, improve efficiency, and focus more on innovation rather than managing disconnected processes. As automation continues to evolve, the ability to orchestrate workflows effectively will become essential for organizations looking to build more agile and scalable IT operations. 💬 Learn more about Red Hat Automation Orchestrator in the article shared in the comments below!

"That’s what makes me so excited right now. At NC State, we have people in all the disciplines working at that interface of data-driven engineering, machine learning, and deep learning. We have autonomous and self-driving labs with incredible expertise. In biomedical engineering, we have exoskeletons trained by deep learning and AI models to enhance well-being and quality of life for people who might be paralyzed or healing. I could go on and on—some faculty member is going to hear me and say, 'Hey, you didn’t mention me,' but every department has these incredible strengths right now. We are by far the largest college of engineering and computer science in the state of North Carolina, and it brings together a massive amount of talent."

Token consumption is expected to increase 24x by 2030, making proprietary APIs and fragmented software tools an expensive long-term challenge. The "metal to agents" framework establishes an open, standard AI operating system across the hybrid cloud. Learn how an integrated open source stack gives you full control over inference workloads and infrastructure costs: https://red.ht/4g3byOU

From "harvest now, decrypt later" threats to scaling complex AI workloads, enterprise IT leaders are facing a whole new playbook. How do you keep innovating without trading off control and security? In this edition of Red Hat: Open Insights, we explore how Red Hat OpenShift provides the backbone for modern hybrid cloud strategies—touching on the latest OpenShift 4.22 release, agentic AI workflows, post-quantum cryptography, and real-world impact from global pioneers. Read the full edition below and join the discussion in the comments. 💬

The Red Hatt CEO, Matt Hicks, built his first AI project because nobody in his house could hear the doorbell from the mud room. He trained a machine learning model to recognize the specific sound of his doorbell and broadcast it through Sonos, filtering out everything else, including his kids. I was genuinely amazed hearing this because it was a real problem that had a focused solution, and a lesson that still applies to how the most effective AI work gets done today. That project taught him something that still holds: it was a very hard thing to program, but it was a very easy thing to train. If you've been waiting for the perfect project idea to start learning AI, you're thinking about it wrong. ↳ Start with something that genuinely bothers you. The motivation to finish a tutorial evaporates fast. The motivation to fix something that affects your daily life does not. ↳ Constraint is the point. A model interpreting two distinct sounds is not a complex problem on purpose. Small scope forces you to actually understand what the model is doing instead of running someone else's code. ↳ Personal projects teach you things work projects can't. There's no deadline, and no stakeholder to say good enough. You have to understand it because you're the only one who can debug it. Your bar to start is lower than you think. ♻️ Repost for your network so they can stop waiting for the perfect project!

Red Hat is proud to be an inaugural member of NVIDIA’s Open Secure AI Alliance, working alongside upstream maintainers and industry partners to safeguard the full AI stack. Red Hat CTO, Chris Wright, dives into Red Hat's commitment to securing the open source supply chain through our work with NVIDIA and the broader ecosystem: https://red.ht/4yJlNRa

Con Red Hat OpenShift Virtualization, unificas la gestión de VMs y contenedores en una misma plataforma para migrar sin riesgos operativos. 🛡️⚙️ 👇 Vota en la encuesta y conoce nuestras soluciones: https://red.ht/4vPo1MQ