OSS + ELC EU 2026

Large Language Models often dominate headlines, but many real world AI applications don't need billions of parameters to be effective. Tasks such as classification, information extraction, intent detection, summarization and domain specific assistants can often be served effectively by smaller open source models with lower latency, reduced infrastructure requirements and improved privacy.

This session introduces Small Language Models (SLMs), explores the trade-offs between model size, accuracy, cost and performance and discusses techniques such as quantization, distillation and domain adaptation that make these models practical for deployment on laptops and edge devices.

Attendees will leave with a framework for selecting the right model for a given use case, understanding why bigger is not always better and how open-source SLMs are making AI more accessible to developers and organizations with limited compute resources.

Creating your own AI platform sounds like a job for a funded team and a matching cloud bill. Surprisingly, it isn't. I want to show how a handful of open source tools combine into a minimal viable AI platform that runs on one machine from a single Docker Compose file.
Five layers are all needed for a valuable platform: LiteLLM as the gateway (one API for any model, with keys and cost tracking), Open WebUI as the chat interface, n8n for automation and agents that take real actions, Ollama for private local models, and Langfuse for request tracing.
Then we look at how to grow it (RAG, authentication, scaling) without adding a pile of disconnected tools. This setup can be advanced for every demand.
The setup is indeed not perfect, but it is a valid starting point for organizations of any size.
No enterprise budget needed, just you, some OSS tools and a little bit of interest.

"Foundation" is a word that carries weight: endowments, permanence, institutional resources. In the philanthropic world, foundations often have millions or billions in their endowments. In open source, the same word describes organizations that range from trademark holders and event hosts to genuine funders of development work.
That gap between expectation and reality matters. When developers, companies, and governments hear "foundation," many assume someone already has it covered. That assumption is one reason so many critical open source projects remain underfunded: the name signals security that often doesn't exist.
This talk unpacks the spectrum of what open source foundations actually do and don't fund, why the word creates a misleading picture of available resources, and what genuine infrastructure funding looks like in practice. Drawing on the work of the Sovereign Tech Agency, which funds critical open source projects and the ecosystem infrastructure sustaining them, this session offers a clearer picture of where the money is, where it isn't, and what that means for the long-term health of open source.

The discussion on quantum computers often focuses on abstract physics, but for the engineers manipulating those systems, the underlying software stack is becoming a very pressing reality. As the field matures, we are witnessing the emergence of a collaborative, open-source ecosystem.

This session provides a comprehensive tour of the current quantum open source landscape, and will aim to demystify programming for quantum computers. Community-led projects are currently providing the necessary plumbing (compilers, simulators, verification tools, ...) to ensure that quantum technologies are not only scalable and useful, but also transparent and interoperable.

No prior background in quantum physics or quantum computing is required. The session will actually aim to argue that most quantum software challenges are very known problems in software engineering and computer science, and that one can contribute to quantum open source projects without any knowledge of quantum physics.

In 1967, Seymour Papert, Wally Feurzeig, and Cynthia Solomon built a language based on studies of Piaget whose radical premise still hasn't been fully cashed in: children should program the computer, not be programmed by it. Logo gave them a turtle an "object to think with" and a place to live in mathematics by walking it, drawing it, and making it their own.

Half a century later, most kids meet software as something to consume, not construct. This talk argues that Papert's constructionism and the free-software ethos are the same idea one generation apart, both are about the freedom to understand and reshape your own tools and that open source is exactly how we finally deliver Logo's promise at scale.

I'll show TurtleGrove: a UCBLogo compatible interpreter we created that runs in any browser, with a Rust core and a vanilla-JS frontend, fully open source and self-hostable.

In the OpenSource mindset children can even show-and-share their code with other children. Working on their own canvas or creating art together. Children learn the best, when learning together. We use this also in the Living OpenSource project to introduce kids in African countries into programming.

I've given several talks & workshops on various GenAI tools in the past couple of years. Generally the goals of the presentations are to demonstrate the how - how to fine tune a model, how to prepare data for RAG systems, how to use this framework or optimize that pipeline - I find that a lot of the discussions which happen during and after the workshop are around the why.

Why is structured data important? Why do we care about open source and open weights in models? Why does context size matter? Why can't I run/reproduce the same AI set up on my laptop? Why do I even have to use GenAI??

These and many more questions have popped up and quickly drowned in the rush to keep up with the next new AI tool or agent. I'd like to provide a space for everyone, especially those new to GenAI and/or open source, to pose these questions and discuss them without worring about missing out. Whether you are a skeptic or have your fully agentic workflow set up, we might share similar concerns and doubts. Bring your ideas and opinions!

During the BoF, I will also share various open source resources that may be relevant and useful in answering your questions and better prepare you on the AI journey.