OSS + ELC EU 2026

With the advent of large language models, looking up human-made software documentation might seem like an antiquated and inefficient way of getting information. On top of that, Claude Code and similar tools can now crunch information straight from your JIRAs and conjure new pull requests at speeds that cannot be matched by even the most veteran documentation maker.

So where does this leave you as a technical writer?

Let us come together as professional technical communicators to grumble and bellyache, but mainly to share tips and insights on how to make the best of the paradigm shifts and upheavals that AI advancements now introduce to our work-life on a regular basis.

Many communities reach a point where knowledge has naturally concentrated in the hands of a few dedicated people. It happens organically. Someone steps up, carries the weight, and keeps things moving. But without a process to capture and distribute that knowledge, the community becomes fragile without ever meaning to. Contributors arrive eager and leave quietly. Decisions get made but are never recorded. Onboarding repeats itself because nothing was ever written down.
This talk is about what happens when a community decides to change that. Drawing on experience as a maintainer and community manager, I will walk through the journey of building a documentation process from the ground up, not by writing more docs, but by shifting how the community thinks about knowledge itself. From auditing where knowledge lived, to creating structures for knowledge capture and decision documentation, forming a working group that distributed ownership, and building triage and mentorship pathways that made contribution genuinely accessible.

Producing documentation in multiple languages and formats often sounds like a simple promise... until it is time to maintain translations, review changes, publish consistent versions, and avoid diverging copy-pastes.

Drawing from the real-world experience of building a structured multilingual glossary for embedded AI, we will explore how to apply a Docs-as-Code approach to documentation using AsciiDoc. We will cover modular design, conditional content, preprocessing, and automated HTML and PDF generation, all while leveraging the full power of Git for versioning, reviews, and reproducible deployment pipelines.

The goal isn't merely to "write multilingual docs," but to treat documentation as a fully-fledged software artifact. Through this concrete example, we will demonstrate how to organise and maintain a shared structure across languages, centralise common elements, and automate the production of your deliverables.

We will highlight the core principles, best practices, and reusable patterns required to build an industrialised, multilingual technical documentation system, completely freeing you from word processors and manual publishing workflows.

For 4 years, the documentation team of Red Hat Czech has been teaching the fundamentals of technical writing to university students in Brno.

The courses have been attended by students of IT as well as non-technical majors, and have been well received by both

In this talk, we will share how we approached constructing the courses, what worked, what didn't, and how we refined the syllabus year-over-year to adapt to the recent turbulence in the world of technology.

AI-powered security models have fundamentally changed vulnerability discovery. What once took months of expert auditing now runs continuously at scale, flooding maintainers with vulnerability reports, coordinated disclosure requirements, patch demands, and CVE assignments. The industry celebrates AI finding more bugs; maintainers are drowning in the reports.

This talk examines how maintainers of critical open-source infrastructure can adapt to handle the incoming wave. This requires new processes at all levels of the development process. Maintainers can use AI tooling to help differentiate slop requests from genuine vulnerabilities, use them to help automate and coordinate disclosures, and start proactively searching for these vulnerabilities so they never entered production in the first place. We'll give concrete examples and patterns that you can use to apply to your own works as a maintainer or security researcher.

Quietly over the course of 25 years, open source software evolved from a domain perceived as that of only hobbyists into the invaluable backbone of modern digital infrastructure. Sustaining that success for the future will require more than code. It requires resilience: a trait not of technology. but of people. Of community. With increasing regulation around the world, evolving cybersecurity requirements, burnt out contributors, and stagnant corporate participation and funding, how do we ensure the ecosystem’s continued success? The things that have worked for the first decades will not be the things that keep us going. Let’s look together at sustainability not only as a funding problem, but also from the perspectives of global policy changes, security, and other intertwined issues that face open source in the coming years.