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Build a fully local AI coding agent on Ubuntu using Canonical Workshop and the Qwen3-Coder Inference Snap. In this step-by-step demo, you'll set up an isolated development environment, connect your coding agent to a locally hosted LLM, and run coding tasks without an internet connection. Follow along to recreate the setup on your own machine, using CPU or GPU inference. Chapters.

When hardware operates unattended in remote locations, a failed update could be an operational disaster. Bricking a device hundreds of miles away turns a simple software patch into a costly physical recovery mission. Without built-in self-healing mechanisms, one bad deployment can paralyze your entire field operation. Canonical offers solution.

Why scale AI, and how do you do it? From training and fine-tuning models to delivering AI-powered services to thousands of users, every step brings new infrastructure demands. How can organizations keep AI running 24/7, maintain performance, and control costs? That's where AI factories come in.

Updates are the easy part. Or are they? When thousands of connected devices are involved, organizations need to manage updates at scale, without disrupting critical operations. What to update, when to push it, and how to respond when things go wrong become key decisions.

Linux security is built for environments where resilience matters. From vulnerability management to long-term maintenance, securing open source at enterprise scale requires a robust approach across the software lifecycle. Robustness comes from how those practices work together and hold up over time.

AI now powers critical workloads across increasingly complex infrastructure. The question is, how do you keep the data used and generated by AI secure? For many, it’s a question of maximizing control over their own AI stacks, from training data and inference, to infrastructure operations and the underlying software.

Curation plays an important role in enterprise Linux security. Knowing what enters your environment, how it's maintained and what you can rely on over time helps manage open source securely at scale. Understanding these considerations helps inform a deliberate approach to Linux security.

Linux security benefits from transparency. Open source makes code available for scrutiny, allowing vulnerabilities to be identified, understood and addressed. At enterprise scale, that transparency plays an important role in how Linux security is managed.

Linux security is evolving rapidly, with critical implications for enterprise environments. As operational complexity grows, so do the nuances of securing and managing Linux and open source at scale.

Every space mission begins long before launch day. Ideas become spacecraft. Thousands of decisions are made before a single engine ignites. The European Space Agency (ESA) knows what that journey looks like. As the agency prepares to increase the missions it supports by 2030, it is laying the groundwork for decades of future exploration. Working with Canonical, ESA is building a common platform for mission operations based on open source. Kubernetes, Ceph, PostgreSQL, and Kafka form ESA’s infrastructure foundation.