The weights were MIT. The training data wasn't.
Accuracy came second when I picked models for krites. The licence file got there first, and one of those models is still in my lockfile with no licence at all...


Accuracy came second when I picked models for krites. The licence file got there first, and one of those models is still in my lockfile with no licence at all...

A wedding-photo culler ran out of memory compiling an LLM SDK it has never once called. The release had been running for thirty-eight minutes at that point.

A cull is worthless if it cannot follow you into Lightroom. Writing XMP sidecars out of krites, and being careful about what not to write.

A standard-library security fix forced a Go version bump, and then the release failed because the build image shipped an older toolchain.

The one real user of my photo tool was already faking a feature with a workaround. That workaround was the specification.

A desktop-first photo tool reviewed mostly from a phone, and the CSS grid that refused to behave on the one screen it was being judged on.

A correction to my own post: the culler does contain machine learning, and the distinction I drew between ML and AI does not survive scrutiny.

Dropping the Windows build of a photo tool rather than fixing it, and why that is the right call for a one-person project.

A photo tool that is non-destructive by construction rather than by intention, because the shoot it works on can never be taken again.

A wedding photo culler that contains no machine learning at all, by deliberate choice. Blur, exposure and eyes, done with plain arithmetic.
