This, dear reader, is a hammer. It is almost entirely irrelevant to the article. Enjoy.
I presume everyone is familiar with the idiom “if all you have is a hammer, everything looks like a nail”. If
not, well, there it is. It’s generally used pejoratively about being single-minded, but I think it also gives
a glimpse into something more interesting: mental and perceptual sets.
Before I explain, let me tell you a story about a person who bought a 3D printer. When they were first
thinking about getting one, they weren’t sure if it was worth it. They could print a few board game
accessories, but then what? After they got it, though, a whole new world opened up: everywhere they looked
there were opportunities to improve things by adding 3D printed plastic. Broken appliances were repaired with
3D printed parts, all sorts of shelves, organisers, hooks and other things were made. But when they talked
about this to other people, most often the response was “That’s nice, but I don’t think I’d use one”. How
could they not see the truth in all its glorious layer lines?!
The answer is in the concept of a ‘set’, and instead of trying to explain it, I’m just going to quote
Wikipedia:
In psychology, a set is a group of expectations that shape experience by making people especially sensitive
to specific kinds of information. A perceptual set, also called perceptual expectancy, is a predisposition
to perceive things in a certain way. […] A mental set is a framework for thinking about a problem. It can be
shaped by habit or by desire. Mental sets can make it easy to solve a class of problem, but attachment to
the wrong mental set can inhibit problem-solving and creativity.
This perfectly captures what happened. They’d developed a ‘3D printing mental set’: a predisposition to see
problems that can be solved with their hammer 3D printer. Once I started noticing this pattern, I
started seeing it all over the place.
An app can be a ready meal
Published on Jun 11, 2025
It's not a home-cooked meal, but it does the job sometimes.
Three years ago I read “an app can be a home-cooked meal” by Robin Sloan. It’s a great article about how Robin cooked up an app for his family to replace a
commercial one that died. It’s been stuck in my head ever since. It’s only recently that I’ve actually done
anything like Robin described, though. Part of the reason was my brain got too hung up on the family aspect:
in my head, a home-cooked meal is one where your family or friends all gather around to eat it with you (in
much the same way as Robin’s app is used in the article). It took me an embarrassingly long time to realise
that you can apply all the same arguments to an app built just for you. And it doesn’t even have to be
difficult. In fact, it can be more like a ready meal than a family dinner.
Why not open source?
I love open source software. Almost everything I use day-to-day is open source, and most things I write for
myself I release as open source. I believe that should be the default stance for most software. So why would
you want to make something and keep it just for yourself?
Building a new Computer
Published on Jun 3, 2025
The finished PC. Cat pen for scale.
I recently built a new computer, after exclusively using a laptop for three years. It’s also the first time
I’ve departed from the usual combo of an Intel CPU and Nvidia GPU.
While the form factor of a laptop did make it amazingly handy for travelling and attending LAN events, it was
starting to show its age and there was basically no sane upgrade path. The main problem was its 3060 mobile
graphics card, which was okay for the first few years and then slowly descended into
painful. At the time, the (fairly disappointing) 50-series had just been released, but hadn’t yet
made it into laptops. Nvidia had stopped production on the 40-series beforehand, so there also weren’t any
compelling options there, either.
That’s not to say there were no laptops at all that I could have upgraded to. There were. Just not really any
that ticked all of my perfectly normal boxes like “run Linux”, “have at least 64GB of ram”, and “run modern
games well”. Having to upgrade the entire system just because the graphics card was showing its age was a bit
of a drag, too. So back to the land of desktop computers I went!
To try to preserve some of that convenience, I opted for a small form factor (SFF) case. The computer and all
the accessories can fit inside a hand-luggage-sized flight case. More on that later, though.
Components, Choices, and Cramming
So what, exactly, is in this computer? The case is a Fractal Design Terra, which is a lovely little 10.4L
case. My last desktop PC was a normal-sized Fractal Design case, which I liked a lot, and it seems like
they’ve upped their game since then. The Terra is both pleasant to look at (just look at that wooden panel!),
and a breeze to work in. The sides and top are fully and easily removable, giving you great access to
everything inside. I expected an SFF build to be fiddly, but the case made it feel about the same as a normal
full-sized build.
Coming around on LLMs
Published on May 28, 2025
Claude says hi.
For a long time I’ve been a sceptic of LLMs and how they’re being used and marketed. I tried ChatGPT when it
first launched, and was totally underwhelmed. Don’t get me wrong: I find the technology damn impressive, but I
just couldn’t see any use for it.
Recently I’ve seen more and more comments along the lines of “people who criticise LLMs haven’t used the
latest models”, and a good number of developers that I respect have said they use coding models in some
capacity. So it seemed like it was time to give them another shake.
The first decision to make was which model to try. OpenAI are no longer the only player in the game, every
tech company of a certain size is now also somehow an AI company. I looked a bit at some benchmarks, and then
mostly ignored them and went with the only company that I didn’t outright hate: Anthropic, and their model
Claude.
Initial impressions
The latest Claude models do feel a lot more “capable” than the earlier ChatGPT versions I remember, but they
also still have a lot of the same problems. At their heart, they’re still text-prediction models, and still
seem to be trained to predict text that will please the user rather than be factually accurate or useful.
Home Automation Without the Megacorps
Published on May 21, 2025
I first experimented with home automation in 2016, by picking up a Samsung “SmartThings” hub. It was terrible.
The UI to configure things was slow and clunky, firmware updates were applied whether you wanted them or not,
and everything stopped working if their cloud services stopped. You were also locked into whatever
integrations they deigned to support, of course. After that broke for the umpteenth time I scaled back and for
years the closest I got to home automation was a couple of Hue bulbs.
Recently I’ve been building it out again, though. This time using off-the-shelf components that interop using
Zigbee, open-source software, and some code I wrote myself. It’s great; it runs entirely locally and has had
basically zero downtime. The Zigbee ecosystem lets me integrate all sorts of things without having to spend
lots of money on “smart” alternatives. I think I’ve spent less on this incarnation than I did on the original
SmartThings hub all those years ago (even without adjusting for inflation!).
My current setup
I run everything on a Raspberry Pi 4, with a Sonoff USB Zigbee adapter based on the CC2652P chipset.
Interfacing with the Zigbee stack is handled by zigbee2mqtt (z2m for
short), an open-source project that basically bridges your devices to an MQTT broker. When a device reports
some data, it will send a new message over MQTT; when you want to make a device do something you just post a
message back. It’s incredibly lightweight, but supports a huge array of devices out of the box. And as it’s
just using MQTT, it’s trivial to integrate with other software or build on top of.
