Open Source Summit + Embedded Linux Conference North America 2026

In 2026, Kubernetes is the engine behind every modern app and AI model you use. This beginner-friendly session breaks down "K8s" into simple concepts, showing you how it automatically runs, scales, and repairs your software so you don’t have to. We’ll explore the 2026 essentials—from how it manages AI workloads to its role in saving companies millions in cloud costs. Whether you're a developer or just curious, you'll leave knowing exactly why Kubernetes is the most important skill to have in the cloud-native era.

AI is all around us. In this talk, I will discuss ways maintainers can also leverage AI to combat AI slop, improve maintainer experience and avoid burnout. I will mention my experience on using AI to aide in development of Kubernetes features, maintaining testing environments and providing a good experience for users of AI assistants.

AI, for better or worse, is here to stay and maintainers should embrace the tools to aide development. In this talk, I will highlight using AI pull request review tools, creating AGENTS.md to streamline dev experience of using tools, leveraging the tools to do those cleanups you know are needed but nobody wants to do and mention ways your project can better support AI tooling.

Learning to architect robust cloud infrastructure often comes with high barriers: expensive monthly cloud bills and a lack of access to senior mentorship. How can we train the next generation of SREs and Platform Engineers without financial gatekeeping? This session explores a novel pedagogical approach: combining the physicality of a Raspberry Pi "Micro-Data Center" with the instructional power of AI coding assistants.

We will demonstrate how to use low-cost edge hardware to make abstract concepts (clustering, database sharding, and network security) physically tangible. We will then show how to utilize LLM-based chatbots to act as real-time "Senior Architects," guiding learners through complex configuration and troubleshooting tasks that usually require years of experience to master.

Attendees will leave with:

- A blueprint for building a low-cost, enterprise-grade learning lab.

- Strategies for using AI agents to accelerate learning in Kubernetes, networking, and security.

- A method to simulate "Catastrophic Failure" (e.g., pulling a plug) to learn resilience safely.

All the quantum computing talks I've seen so far are about qubits, spin, superposition, and complicated math. Things that may not be entirely in the realm of software developers.
However, the reality is that there are several resources to aid software developers to simulate, test, and run quantum-based algorithms. Simple applications can be developed to explore the functioning of this new realm of computing.
The talk will consist of a section that explores all the available resources for software engineers to program simple quantum circuits and observe results. The second part will be sample code and runs that demonstrate several of these. Samples written in Python and Rust will be run and demonstrated.
In addition, several Linux Foundation projects and courses will be highlighted during the talk.

Quantum computing is often described as the next frontier of computation, but for most engineers, it remains abstract and disconnected from practical systems such as AI and distributed infrastructure.

This session introduces the fundamentals of quantum computing in clear, accessible terms. We will explain core concepts such as qubits, superposition, and entanglement without heavy mathematics, and compare them to classical computing models used in today’s AI workloads.

We will then explore realistic intersections between quantum computing and AI, including optimization problems, simulation, and potential long-term impacts on edge and cloud architectures. The session will also briefly review open source quantum development toolkits and how engineers can begin experimenting today.

Attendees will leave with a grounded understanding of what quantum computing can and cannot do, and how it may influence the future of AI systems.

As a Kubernetes user, have you ever wondered how clusters from different vendors and distributions remain consistent? Whether running in the cloud or on-premises, workloads behave the same way without modification, and applications can move between platforms without being rewritten. This consistency is made possible by the Certified Kubernetes Conformance Program run by CNCF, which ensures distributions meet upstream Kubernetes standards.

However, conformance validates runtime behavior, not how clusters are created. Cluster provisioning approaches varies widely across providers, leading to configuration drift, inconsistent upgrades, and operational complexity at scale.

To address these challenges, the Kubernetes Special Interest Group Cluster Lifecycle introduced Cluster API, bringing declarative APIs to cluster provisioning and lifecycle management.

In this beginner-friendly session, I will walk you through the full landscape for Kubernetes cluster creation, and share insights from a contributor’s perspective on why Cluster API exists, the problems it solves, and how community-driven effort helps make cluster creation more consistent and sustainable.