OSS + ELC EU 2026

The Zephyr USB host stack is still evolving and remains an experimental subsystem for many real-world applications. This session presents practical experience in enabling key USB host classes in Zephyr, including UVC (USB Video Class), CDC-ECM, and USB Hub support, along with a series of architectural improvements to the host stack.
The presentation highlights the challenges and lessons learned while bringing these features to Zephyr, including attach/detach handling, dynamic class matching for composite devices, device lifecycle management across class drivers, the core stack, and UHC drivers, dynamic configuration selection, and limitations in current APIs.
In addition, the session identifies gaps in the current USB host subsystem and outlines potential directions for future evolution. It aims to provide practical insights and guidance for developers working on USB host support in Zephyr.

Spot instances reduce cloud bills by up to 90%, but choosing which to use is a multi-dimensional puzzle where price, performance, interruption risk, and instance heterogeneity all interact. Kubernetes provisioners like Karpenter optimize a narrow slice of this problem, tightly coupled to a single cloud vendor, AWS.

KubePACS is an open source project that separates the decision from the execution. A vendor-neutral optimizer computes the best mix of instance types for a workload's cost-performance-availability profile, then translates that into native configuration for the user's provisioner

The talk goes deep into the system: the ILP-based selector for cost-performance-availability trade-offs, the GSS-driven optimizer that tunes them automatically, multi-node spot placement awareness for robust capacity across cloud providers, and clean integration with upstream Karpenter.

Attendees see measured gains over baseline configurations and take away an architecture pattern for performance-aware spot instance provisioning. We close with our intent to grow KubePACS as an open source community effort and welcome contributors to shape what comes next.

For decades, the Open Source Software world has run on a simple, elegant legal hack: standard open-source licenses (like GPL, BSD, or Apache 2.0) use copyright law to enforce openness and collaboration. But as the industry shifts to AI models are we are hitting a catastrophic legal wall? AI models and weights aren't code, they aren't traditional "data" either. Is the licensing regimes we have been reliant on for so long no longer an adequate solution for how to license AI Models and wights when we want to make them available and keep them as Open Source, and if so why do we keep on licensing them this way?

This panel hopes to brings together experts from various fields and industries to discuss some of the challenges and potential risks you may run into then using or sharing AI models and wights under Open Source licenses, and explore some high level ideas of what mechanisms could be used instead of "traditional" Open Source licenses to keep the openness and collaboration we all depend on to further innovation.