I was previously using Bluefin fairly extensively. And I was mostly happy — aside from a few niggles, I was able to mostly stay in Linux and only boot into Windows occasionally. Some of the niggles were just Microsoft clashing with Linux — Outlook required for work, etc. Other niggles — sleep not working, high battery use, and lack of HDR — were, I thought, inherent to running Linux on my particular laptop, the…

I've had Gigabyte's version of Nvidia's DGX Spark, the AI TOP ATOM, on my desk since July, and the two numbers that actually matter about it are 128 and 50: 128GB of unified memory, and roughly fifty watts at the wall while it processes. The box itself It's a lovely little box. Dark grey, 150mm square, about 1.2kg, small enough to live beside the monitor rather than under the desk.

Ugh. It pains me to say it, but this is probably the best laptop in the world. And it's not even close. Shame about macOS, though. I've been lucky enough to be generously provided with a MacBook Air (thanks Beth!) primarily so XMALab gets compiled and built properly for macOS. It's the base model — 16GB of unified memory, 512GB SSD, and the base M5 chip.

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I've been using Windows Hello face unlock on my desktop for years, and it's one of those things you don't realise how much you rely on until it's gone. You sit down at the machine, look at the camera, and you're in. No faffing about with passwords, no keyboard mashing while half-asleep. It just works. On Linux? Not so much.

Epic have just released RealityScan 2.2, and the headline feature is one that's been on people's wishlists for a long time: full AMD GPU support on Windows. For years, photogrammetry has been effectively CUDA-only. Most serious reconstruction software -- Metashape, Colmap with GPU acceleration, the lot -- all lean heavily on NVIDIA's CUDA ecosystem. That means if you want to do photogrammetry at any scale, you're locked into buying NVIDIA hardware.

I've been running Hermes Agent — an open-source AI agent framework by Nous Research — for a few weeks now, and I'm genuinely impressed with it. If you haven't seen what it's been doing for me, I wrote about that recently. What I haven't mentioned is that I've got it running on four machines simultaneously, all talking to the same model, all sharing the same memory and personality.

Oh dear. Found this option in wordpress experimental features. Gave it a try. Is this useful to anyone? It weirds me out a bit. Anyway, transcript below: Pip: Prof. Peter Falkingham has been doing what academics do best: turning a simple question into a multi-week infrastructure project, then writing about it thoughtfully enough that you're glad he did. Mara: This episode covers his work getting local AI models running for coding and research — from the tooling inside VS Code all the way to a full personal research assistant built around his own data.

A systematic sensitivity analysis of a six degree-of-freedom range of motion (ROM) analysis workflow was conducted to evaluate how variation in the subjective steps of the reference pose assembly aff...

I've been writing posts here about getting set up with local AI, agents, harnesses, and what I've done with them. It's fun, and it stretches my computer legs in a way they haven't been stretched recently. But I can't shake this nagging feeling that it's all a colossal waste of time, because everything's moving so fast it's taking all my free time just to keep up, and within weeks or months, something will come out that makes it all obsolete.

I've been running Linux as my primary desktop for a while now — specifically Bluefin (the immutable Fedora-based distro with dinosaurs) on an ASUS ProArt P16 laptop with an RTX 5090 Mobile and AMD HX 370 processor. The migration from Windows is basically done, but there are still a few edge cases that remind me I'm not quite there yet (

I've been spending the last few weeks setting up Hermes Agent as a personal research assistant, and it's gone from "chatbot I talk to on Telegram" to "actually useful thing that does stuff" pretty quickly. Thought it was worth documenting what we've achieved so far, because some of it's genuinely useful and I suspect other academics might find bits of it helpful too.

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I've been using Copilot in VS Code for a while now, and it's genuinely useful for the kind of small scripts I write regularly – visualising DEM output in Blender, sorting through CT scan data, that sort of thing. With an education account I get access to the top-end models, which is brilliant. But there are times when you don't want your data going to the cloud.

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I really rather loved my Asus ProArt P16 with Nvidia 4070. A beautiful and powerful laptop. But I was bumping up against RAM limitations with some CT data; 32GB was proving limiting, as was the 8GB VRAM on the otherwise fine nvidia 4070 GPU. So I solved that by upgrading the the newer version of the model, released in late 2025.