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Open source thrives on engineering-driven processes. Fast feedback loops, terminal tools, Git workflows: they’re the lifeblood of how we build software in the open. But for software to truly excel, we need to create user experiences that empower people to use them. I wanted to bring this conversation into the spotlight as part of Canonical’s Open Design initiatives. What better way than at FOSS Backstage 2026 Berlin?

Canonical, the publisher of Ubuntu, today announced the general availability (GA) of Managed Kubeflow on the Microsoft Azure Marketplace. This solution enables AI teams to get a fully managed, production-ready MLOps platform in their own tenant. Upstream Kubeflow is a powerful tool for machine learning, but it remains notoriously challenging to deploy and maintain.

I’ve yet to meet a developer that enjoys working with metered AI APIs. The need to pay for every API call in development works in direct opposition to the ethos of rapid iteration, and it’s easy for the costs to get out of hand. That’s why Canonical has created a different approach to building AI-powered applications; one where the model lives inside your app, not behind a pay-per-token HTTP call.

Welcome to this blog series which explores innovative uses of Ubuntu Core. Throughout this series, Canonical’s Engineers will show what you can build with this Core 26 release, highlighting the features and tools available to you.

The recent unveiling of Anthropic’s Claude Mythos preview has radically shifted the cybersecurity landscape. We are now in an era where AI can autonomously discover and exploit zero-day vulnerabilities in mature codebases at machine speed.

BYOD (bring your own device) has always looked better on paper than it does in real life. The promise is clear: let people use the gadgets they already own. Less friction, lower costs, and more freedom. But when security and privacy are non-negotiable, the conversation around BYOD usually ends quickly. Not because BYOD is a bad idea, but because the model behind it doesn’t quite work. With BYOD, you’d be trying to secure something that isn’t meant to be trusted.

Why run Ubuntu on Windows? It’s about getting the best of both worlds. Many organizations rely on Windows applications, enterprise software, and policy configurations; but for developers and system administrators, Ubuntu’s native command-line tools, package managers, and server environments are invaluable. Likewise, with its broad ecosystem of machine learning tools and libraries, and silicon optimizations, Ubuntu is ideally suited for AI workloads.

llm-d is an open source solution for managing high-scale, high-performance Large Language Model (LLM) deployments. LLMs are at the heart of generative AI – so when you chat with ChatGPT or Gemini, you’re talking to an LLM. Simple LLM deployments – where an LLM is deployed to a single server – can suffer from latency issues, even with just one user. This can be because of lack of memory-bandwidth on the server, or because of KV cache pressure on system memory.

Today Canonical announced the release of Ubuntu 26.04 LTS, codenamed “Resolute Raccoon,” available to download and install from ubuntu.com/download. Resolute Raccoon builds on the resilience-focused improvements introduced in interim releases, with TPM-backed full-disk encryption, improved support for application permission prompting, Livepatch updates for Arm– based servers, and Rust-based utilities for enhanced memory safety.

The evolution of Ubuntu’s toolchains story goes beyond just providing up-to-date GCC, LLVM, and Python. It is also about opinionated openJDK variants, task-focused devpacks, FIPS compliant toolchains, and snaps, like the new.NET snap and Snapcraft plugin. These are enhancements that collapse half a day of setup into a single command or two, demonstrating what a frictionless developer experience means in practice for framework and application developers on Ubuntu.