Large language models (LLMs) are machine-learning models specialised in understanding natural language. They became famous once ChatGPT was widely adopted around the world, but they have applications beyond chatbots. LLMs are suitable to generate translations or content summaries. This blog will explain large language models (LLMs), including their benefits, challenges, famous projects and what the future holds.

Kubernetes 1.28 is currently planned to be released on the 15th of August 2023. Canonical Kubernetes 1.28 is scheduled to release swiftly afterwards, with MicroK8s 1.28 expected to be generally available within 24 hours of upstream, and Charmed Kubernetes expected to follow within days. We consistently follow the upstream release cadence to provide our users and customers with the latest improvements and fixes, together with security maintenance and enterprise support for Kubernetes on Ubuntu.

In the traditional computing threat model, privileged system software like the hypervisor, host OS, firmware, and DMA-capable devices were all granted access to the data and code of your workloads. This was widely accepted because it seemed necessary for the system managing VM resources (memory, execution, and hardware access) to also have access to the workload’s data. How else could it manage it after all?

Smart homes are changing. A modern smart home typically isn’t a self-contained, set-it-and-forget-it, custom installation. It’s a hyper-connected set of devices that work together to automate and enhance the users’ living space. Today’s smart homes contain devices from many manufacturers, running dozens of protocols, interfacing with multiple cloud backends.

In the constantly evolving world of software development and IT management, choosing the right operating system for your enterprise developers is a strategic choice. Ubuntu vs macOS, which is right for you? Our latest whitepaper helps you answer this question and make an informed decision. This blog post provides a sneak peek into our findings covering both the developer landscape, and the key considerations for IT administrators. Considering macOS or Linux for your organisation?

Note: This post is co-authored by Simon Aronsson, Senior Engineering Manager for Canonical Observability Stack. AI/ML is moving beyond the experimentation phase. This involves a shift in the way of operating because productising AI involves many sophisticated processes. Machine learning operations (MLOps) is a new practice that ensures ML workflow automation in a scalable and efficient manner. But how do you make MLOps observable?

The Canonical Data Fabric team is pleased to announce the first beta release of Charmed Spark, our solution for Apache Spark. Apache Spark is a free, open source software framework for developing distributed, parallel processing jobs. It’s popular with data engineers and data scientists alike when building data pipelines for both batch and continuous data processing at scale.

When people ask about the negative points preventing higher electric Vehicles (EV) sales, two points are raised systematically: range anxiety and the availability of charging stations. Range anxiety refers to the fear of running out of battery while driving. Of course, with more range or more charging stations, range anxiety decreases. Unfortunately, most countries lack EV charging infrastructure that meets consumer expectations.

Complying with US government security standards such as FIPS, FedRAMP, and DISA-STIG is essential for federal agencies and any business that deploys systems and services for U.S. government use. However, maintaining a compliant IT ecosystem is a major undertaking, as each regulation brings a host of specialised requirements. And dealing with the never-ending stream of security vulnerabilities that require patching only adds to this task.

In Ernest Hemingway’s novel “The Sun Also Rises,” there is a memorable exchange between the author and the main character, Mike. When asked how he went bankrupt, Mike responds with a concise yet profound answer: “Two ways. Gradually, then suddenly.” Innovation happens much in the same way. Gradually, then suddenly. Ideas simmer and evolve, gaining traction until they reach a tipping point.