James Read's Code, Containers & Cloud blog

OliveTin has been getting a lot of security scrutiny this week - as there’s been a relatively huge surge of security advisories come for the first time. I imagine that the project has been listed in a security forum or similar, and AI tools (and human eyeballs) are being used to looking for security vulnerabilities. This is a good thing, and I welcome more security reports, that will be patched, and will make OliveTin better.

AI tooling for software engineering has moved quickly from fancy autocomplete to the verge of fully autonomous engineering. Most people can see that AI adds value. The real question is no longer whether to use AI, but how much autonomy to give it.

Capability alone doesn’t justify maximising autonomy everywhere. You have to match autonomy to risk. This post defines a simple scale of autonomy levels and where I draw my own lines.

I’ve been trying to wrap my head around NVidia’s portfolio to get a better understanding of the company. I’m very familiar with the consumer GPUs and parts of the data centre portfolio, but it’s taken me much longer to understand than expected! I kept coming up against marketing buzzwords and confusing naming - so this is a quick blog post to document my current understanding. I was particularly motivated to write this after reading about NVidia’s announcement in December 2025 - about reprioritising their DGX Cloud offering. I wanted to understand what that meant in context of their wider portfolio.

A quick snapshot of the cloud providers I’m still using in 2026—what I use, why, and a few recommendations that may be relevant to single developers, self hosting enthusiasts, and similar.

This is a post about making OliveTin more resilient in 2026, by adding betas, adding a Codeberg mirror, increasing Matrix usage, improving documentation, testing and tools. None of OliveTin’s principles are changing, it’s license isn’t changing, and it’s maintainer aren’t changing. Just making OliveTin stronger.

This article started life in October 2024 as a “Initial reaction to using Cursor.AI for a few hours” - as the provisional title suggests, I was trying out Cursor.AI for the first time - and it was not love at first sight! As an eager user of vim (and now, neovim more recently) I was curious to see how well this AI-powered code completion tool would integrate into my existing workflow.

I’ve been doing tech for… quite a while now, but it’s important to be humble and admit when the simple things catch you out. Today, a simple task of copying files to a MicroSD taught me to pay attention to the details. All I was doing was copying some video files back onto a MicroSD card, from my Linux workstation. It’s an exfat file-system, so -a spews errors with rsync;

This is just a super simple post to encourage you to check out Ventoy, a fantastic open-source tool for creating bootable USB drives. With Ventoy, you can easily create a multi-boot USB drive that can hold multiple ISO files, allowing you to boot into different operating systems or utilities without the need to reformat the drive each time.

A new class of cloud providers is already here, and they’re called “neoclouds”, but you’ll be forgiven if this is the first time you’re hearing about it. You see, unless you’re in the AI space, these could have passed you by, that’s because ’neocloud’ refers a new class of cloud providers that are specialised in AI. A neocloud provider specifically focuses on the infrastructure services that are needed for AI, such as GPU-as-a-service (GPUaaS), or bare-metal-as-a-service (BMaaS), that are optimised for AI workloads.

Many people are familiar with the concept of using markup languages to develop diagrams, like GraphViz’s dot Language, or PlantUML. These languages allow users to define diagrams in a text-based format, which can then be rendered into images. If you are not familiar with these tools, check them out - they’re seriously faster for diagram editing and updates than manually drawing boxes in diagramming tools, like some sort of neandertal!