Loupe

A phone screenshot of an error turned into a day of storage work: an audit of 4,921 pulled photo edits found 3,777 byte-identical to their originals, 108 GB reclaimed, a 3.4 TB library moved with an instant rename, and five separate ways a copy or a delete can lie to you.

Loupe got its first real test suite. Writing it found two things: one of the new tests could not fail, and the search feature had been silently broken since a move two days earlier while the service reported healthy the whole time.

The timer that snapshots Loupe's databases reported success while the newest snapshot in the destination was three days old. Two scheduled runs had failed and a manual test run had overwritten the status. Then a real restore drill, which checked four properties and first failed on my own script's assumption.

I set out to train a small local model to reproduce Apple's photo-quality score so Loupe could rank photos for anyone. It matched Apple almost perfectly. Then a cheap probe showed what the score actually measures, and it was not beauty.

Loupe can now search photos by describing them, with every model running on my own hardware. The interesting work was not the model. It was proving that the index machine and the query machine compute exactly the same numbers, and finding out the plan's premise was false.

For an app that looks at your private photos, the machine learning cannot phone home, so every model in Loupe runs on your own machine, on a single consumer graphics card.

Culling a hundred-thousand-photo library should never come down to one mysterious score, so I split the decision into three plain roles that each answer a different question.