Machine learning

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.

Reskinned davidgomez.cc into a Swiss/International typographic system, stood up a GPU box and proved face-embedding on CUDA, and hand-built a regenerable wiki over every one of my notes.

Built a reusable image generator to manufacture a synthetic photo library for the public cull demo, wired the static demo to behave like the real app across several views, and ran a graphify pass to map the app's architecture and data flow.