usecases.sh
usecases.sh
Games & Simulations

Decode the unreadable Super Mario 64 courtyard plaque with a forward model

A Redditor had Claude (Opus 5.5) build a GPU font search and simulation harness to read the 32x32 Mario 64 plaque; it landed on "Eternal Star / We hope so" in Palatino Bold Italic.

Done with

What they did

u/decorator set out to settle a fan argument from the 1990s: what the tiny 32x32 plaque texture in Super Mario 64's castle courtyard says. Upscaling cannot recover it, so the poster had Claude (Opus 5.5) reverse the process instead. Claude wrote a model of how a 1996 artist would have produced the texture (set text in a font, draw it on a canvas, shrink it, blur it, put it on stone) and a GPU search over fonts, sizes, kerning and candidate words. For the second line it built a dictionary search over about 17,700 words, plus a test harness that plants known text in fake plaques to measure how often the method picks the wrong answer. It also wrote a 40-page LaTeX report and packaged the code as an open-source repo.

How it went

The search settled on "Eternal Star" for line one and "We hope so" for line two, set in Adobe's 1990 Palatino Bold Italic. Along the way the poster caught a word-gap bug that let overlapping words count the same ink twice, and an early claim about a capital S that the joint test did not back up. In the thread the poster showed that "for" ranks 28th of 37 possible endings and that the popular "L is real 2401" reading fits about twice as badly.

Worth knowing

A commenter pointed out that fans already read line one as "Eternal Star" in 2019, so the new part is the second line and the method. Others argued a heavily degraded image may have several equally good answers; the report has a section on what could still be wrong. The post names the model but not the harness. Held-out tests mattered more than anything else, by the poster's account.

Try it withClaude

I have a tiny, low-resolution image of text: [the image file]. Upscaling cannot read it. Build a forward model of how it was likely made: [font candidates], render, downscale to [width x height], blur and add the background texture. Then search fonts, sizes, kerning and words from [a word list] to find the text whose render best matches the pixels. Before trusting any result, build a test harness that plants known text in fake images and reports how often the search is fooled. Stop and show me the top 10 candidates with their error scores and the false-positive rate before writing any conclusion.

Square brackets are the parts to fill in. The prompt is ours; the story is theirs.

Where they said it

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