Operations | Monitoring | ITSM | DevOps | Cloud

Open source software (OSS) is a cornerstone of modern technology. According to the Linux Foundation, it powers up to 90% of software tools used today. Unlike proprietary software, OSS is developed collaboratively, meaning its code is available for anyone to use, change, and distribute. Because OSS projects have historically been driven by developers, they tend to be highly flexible and functional, but they can lack critical usability considerations.

Canonical Livepatch now officially supports Arm64, further expanding its security patching automation capabilities. For the first time, Ubuntu on an Arm64 machine can apply critical kernel updates, without service interruption or rebooting. Starting with Ubuntu Core 26 for Arm64, and for Ubuntu Core 20 and onwards for AMD64 machines, a wider range of devices and cloud virtual machines can achieve timely vulnerability remediation through Canonical Livepatch.

Your Ubuntu Core fleet is running beautifully. OTA updates roll out in minutes. Every device is strictly confined, cryptographically attested, and carrying a 10 to 15 year long term support (LTS) commitment. The operational team sleeps soundly. Then the product roadmap meeting happens. The industrial floor needs vibration sensors on every motor. The smart building needs temperature nodes in every room. The cold chain system requires dozens of low-power Bluetooth tags. And someone just said the words.

In an increasingly volatile job market, standing out from the competition is vital. For many in the open source community, formal recognition for self-taught skills is a significant challenge. These skills are often built through hands-on hobbies, side projects, and deep community contributions. While the market is flooded with certificates and certifications, most fail to reliably measure practical execution, or fall behind the rapid pace of industry changes.

As designers working at Canonical, we’re always thinking about open source. We believe that encouraging more designers to contribute to open source benefits everyone, from the project maintainers to the end users themselves. In the 2025 edition of FOSSBackstage conference, we presented our research findings on why designers don’t get involved in open source projects and found a particular breakdown between designers and project maintainers.

Canonical’s security philosophy has always been built on the premise that vulnerabilities exist and will be discovered. Our response relies on defense-in-depth architecture, rapid patch deployment, and strict adherence to Coordinated Vulnerability Disclosure (CVD). AI changes vulnerability discovery volume and speed. We have a robust vulnerability management process that is backed by rigorous compliance certifications.

The telco industry is undergoing a fundamental change. Over the past few years, the increasing maturity of cloud-native infrastructure has accelerated the movement from manually operated and hardware-centric systems to automated, software-defined platforms. Underpinning this change are open source initiatives such as the Sylva project. Sylva is hosted by Linux Foundation Europe and heavily backed by major telecom operators and vendors.

Legacy infrastructure was not designed for the requirements of the AI era. While large-scale model training remains centralized in data centers, test-time inference is rapidly shifting to the edge to reduce latency and bandwidth consumption. This shift creates a new frontier for enterprise AI, but deploying at the edge introduces significant manual complexity, interoperability issues, and security vulnerabilities.

Previous articles walked through RDMA (Remote Direct Memory Access) as a programming model and InfiniBand as the fabric that was built around it. Both led to the same conclusion, even if it was never stated outright: moving data, not compute, becomes the bottleneck once systems scale. So what happens when you want RDMA, but you’re already running an Ethernet network you’re not keen to replace? That’s usually where RDMA over Converged Ethernet (RoCE) enters the conversation.

Tokens per watt (TpW) – the measure of useful AI work produced per watt of energy consumed – is the metric at top of mind for CEOs, heads of AI, and infrastructure teams alike. With the tremendous cost of GPU clusters, extracting as much value as possible from the expense is critical. But in the pursuit of tokens, it’s important to remember that hardware efficiency isn’t the only factor influencing data center operating costs, or the output of useful, revenue-generating AI work.