It was supposed to be a joke. But weirdly it turns out there's a correlation between how good a model is and how good it as at my stupid joke benchmark.
Always loved this example, what do you think of ASCII art vs SVG?
Since it's not a formal encoding of geometric shapes, it's fundamentally different I guess, but it shares some challenges with the SVG tasks I guess? Correlating phrases/concepts with an encoded visual representation, but without using imagegen, that is.
Do you think that "image encoding" is less useful?
It's a thing I love to try with various models for fun, too.
Talking about illustration-like content, neither text-based ASCII art nor abusing it for rasterization.
The results have been interesting, too, but I guess it's less predictable than SVG.
I've had disappointing results with ASCII art so far. Something I really like about SVG is that most models include comments, which give you an idea of what they were trying to do.
For me, it shows if LLM are generalising from their training data. LLM understand all of the words in the prompt. they understand the spec for svg better than any human. They know what a bird is. They know what a bike is. They know how to draw (and given access to computer-use could probably ace this test). They can plan and execute on those plans.
Everything here should be trivial for LLM, but they’re quite poor at it because there’s almost no “how to draw complex shapes in svg” type content in their training set.
Kinda feel like the content is a much better reason to visit than the pelicans. Though I suppose the pelicans are part of the content.
I'm quite happy that there's someone with both the time to keep up with all the LLM/AI stuff, that is also good enough at writing amusing stuff that I want to keep reading it.
It was supposed to be a joke. But weirdly it turns out there's a correlation between how good a model is and how good it as at my stupid joke benchmark.
I didn't realize quite how strong the correlation was until I put together this talk: https://simonwillison.net/2025/Jun/6/six-months-in-llms/
Always loved this example, what do you think of ASCII art vs SVG?
Since it's not a formal encoding of geometric shapes, it's fundamentally different I guess, but it shares some challenges with the SVG tasks I guess? Correlating phrases/concepts with an encoded visual representation, but without using imagegen, that is.
Do you think that "image encoding" is less useful?
It's a thing I love to try with various models for fun, too.
Talking about illustration-like content, neither text-based ASCII art nor abusing it for rasterization.
The results have been interesting, too, but I guess it's less predictable than SVG.
I've had disappointing results with ASCII art so far. Something I really like about SVG is that most models include comments, which give you an idea of what they were trying to do.
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For me, it shows if LLM are generalising from their training data. LLM understand all of the words in the prompt. they understand the spec for svg better than any human. They know what a bird is. They know what a bike is. They know how to draw (and given access to computer-use could probably ace this test). They can plan and execute on those plans.
Everything here should be trivial for LLM, but they’re quite poor at it because there’s almost no “how to draw complex shapes in svg” type content in their training set.
It’s been useful though given the authors popularity I suspect it’s only a matter of time new LLMs become “more aware” of it
It's useful because it's SVG so it's different than other image generation methods.
I think in 5 years we might have some ultra-realistic pelicans and this benchmark will turn out quite interesting.
And then the author will try the "Pelican tries to swallow the capybara as-is". And it will fall apart again.
It's this part where it gets interesting... how exactly it falls apart :D
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Kinda feel like the content is a much better reason to visit than the pelicans. Though I suppose the pelicans are part of the content.
I'm quite happy that there's someone with both the time to keep up with all the LLM/AI stuff, that is also good enough at writing amusing stuff that I want to keep reading it.
> Kinda feel like the content is a much better reason to visit than the pelicans.
That's how the pelicans get ya.
Scored a whole two upvotes here, my scheme is clearly working great!
Leave some upvotes for the rest of us