Semantic search

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.

My notes could answer what I decided but not what I actually did, so I built a second searchable layer over the working history itself, and a rule for when it is allowed to be used.

Prospect got meaning-based search fused with its keyword search, and a semantic check for reposted listings. Both had to be designed so they could fail without taking anything down, and the duplicate threshold was calibrated on a copy before it touched the real database.

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.

I taught my notes to answer questions by meaning, so I can ask what I decided about X and why and land on the right note even without remembering a single word from it.

Pulled fleet health and my entire notes vault onto one gated dashboard, then added semantic search over my own work by borrowing an embedding model already running elsewhere on the network.