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MTL-style circuit simulator
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Script dependencies with Nix
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NixOS Is Making Me Less Afraid
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The geometric model of maintenance
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Swemak: Colemak for Swedish
I have written before about my Swedish Colemak hack, but I never explained what makes it so good. This is a better image what it looks like:
If you’re struggling to find the difference against normal Colemak, look at the cluster of five keys next to the return key.
This layout makes room for the three Swedish letters Å, Ä, and Ö, by only
changing five keys in total, and they have changed very thoughtfully:
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Predicting Peace
I have long struggled with predicting when peace will occur. Actually, I’m struggling with predicting anything relating to war, but let’s focus on when peace arrives for now. There are two such questions in the current ACX 2026 competition:
- Will there be a cease-fire in Ukraine in 2026?
- Will there be a cease-fire in Sudan in 2026?
When I entered my forecast for these questions, I did it based on Laplace’s rule of succession, i.e. as a function of their duration only. This is sort of justified if war durations are heavy-tailed, which would mean that the longer they go on for, the less likely it is that peace comes soon. But I don’t know if that is true.
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Donating to open source
I have decided to start donating a small amount of my fun money to open source projects. After some research, the initial set of recipients are
- Magit
- Perl 5 Core Maintenance Fund
- XMonad
These are important to me, they need more money, and I believe they can use donations well. If you disagree, I’d genuinely like to hear what mistakes I’ve made in the analysis below. I hope it shows that this is important to me.
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Shrinking a Fedora virtual disk
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Cheaper LLM labelling
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Tag index for Org mode blog
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Better AI code comment detector
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Claude comment detection
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A curmudgeon tries a language server
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Snakes and Ladders
It is common for promotional pamphlets aimed at children to contain a themed variant of snakes and ladders. This is the “game” where the players toss a die, move as many places as the die says, and then some spots on the board have an event that send the player forward, or back, or give them an extra toss or whatever.
The theme is typically related to whatever the promoter wants to promote, but styled for children. I get the appeal – for children. They can probably imagine they’re really travelling along the path! In the variant pictured below, there’s forest and water and tunnels and dogs and everything!
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A noob learns FFT
I have never done any time–frequency transformations. I understand the general idea, but I have never worked with the details. Today I had a reason to use it, but that means learning the basics first.
The following is three seconds of a single 5 Hz sine wave, recorded at a sample rate of 100 Hz.
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Purely functional digital circuit simulator (SICP 3.3)
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Digital circuit simulator in Haskell (SICP 3.3)
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Kuiper Q-Q plot: are these the same?
When we tried to get an intuition for the differences in distribution functions, we learned that children subjected to a treatment had greater variation in their score than the reference group.
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Intuition for distribution differences
Imagine every person in a country is assigned some sort of score, and a higher score is better. I can’t share the specifics around the score I’m investigating right now, but on the population level it is collected by a government agency. Here’s the distribution of these scores for the population in my country.
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A full body MRI earns you a year of smoking
Alternative titles:
- … earns you a high-risk pregnancy
- … earns you an ascent of Matterhorn
- … earns you 10,000 km on a motorcycle
- … earns you two BASE jumps
- … earns you a day on the frontline in Ukraine
These are all about equivalent to the risk of one year of smoking.
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Data-directed programming in Haskell (SICP 2.4.3)
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Tagged data in Haskell (SICP 2.4.2)
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GLM 5.2 playing text adventures
I’ve heard some buzz around the new GLM 5.2 open-weights model. They say it’s very capable! I won’t run a full comparison benchmark, but I have some credits sloshing around on OpenRouter so I figured I might compare GLM 5.2 to the similarly-priced Gemini 3 Flash, and see where things land.
This uses the same setup as the previous benchmark: each LLM gets a few attempts at playing the game, with each attempt being limited to a fixed budget of around $0.15. The LLM doesn’t know it, but the harness tracks achievements for each game, and counts how many the LLM earns in each attempt.
Here are the number of attempts for each game in this run.
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Lean, not backpressure
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LLMs and almost good code
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Is the Monaco Grand Prix decided at qualifying?
A Formula One driver triggered my fact-checkitis. They claimed that
Winning the Monaco Grand Prix in Monte Carlo is determined nine out of ten times by which position one starts in.
That makes intuitive sense, because the Monte Carlo track is a narrow street track with few opportunities for overtakes. But … really? Is that an off-the-cuff remark or an accurate statistical prediction of the race?
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90 % of the t distribution
William Sealy Gosset was great. He improved beer at Guinness by using the statistics that existed at the time. Not happy with that, he invented new statistics to brew even better beer. The things he invented are used all over the place now, but Guinness wanted to keep him a secret weapon, so they made him publish his results under the fake name Student.
One thing Gosset realised is that it is wrong to compute 90 % confidence intervals for the mean by taking the standard deviation of the sample, and assume a normal distribution, like-a-so:
\[\hat{\mu} \pm 1.645 \hat{\sigma}\]
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The stock market returns 4 %
People assume all sorts of wild stock market returns when they make their financial calculations. Here are some numbers that show up on web searches:
| 6 % | 8.4 % | 10 % | 10.1 % | 11.3 % | 11.5 % | 13.6 % | 16 % |
These are all correctly computed under their respective assumptions, but they are very misleading because whatever those assumptions were, they’re not relevant for most calculations. You should assume 4 % in your calculations. Here’s one way to arrive at that:
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Pythagorean Addition
TL;DR: Instead of labouriously computing \(c = \sqrt{a^2 + b^2}\), we can mentally calculate using the alpha-max plus beta-min algorithm, by estimating
\[\hat{c} = \mathrm{max}\left(a, 0.9a + 0.5b \right)\]
and this will be very close to the actual \(c\). This is useful for adding up sources of variance, or figuring out radiuses, or other such things.