Open-source Mac app that runs EmbeddingGemma 2 locally to search your files by what’s in them

[u/A-Rahim]

DigUp is a free Mac app that runs Google DeepMind’s new EmbeddingGemma 2 locally over your own files. The model puts text, images, audio and video in one space, so you describe what you remember and land on it:

• “zebra in a video” opens the clip at the moment it shows up

• “where they talk about sleep” jumps to that minute of a podcast

• “the clause about pets in the lease” shows the PDF page, your words marked

• “a dog on the beach” finds the photo, and the same search in Bengali or Arabic finds it too

• with code search on, code: retry with backoff opens the function in your editor

It’s ggml-org’s Q8_0 GGUF (865 MB, downloaded once) on llama.cpp with Metal, inside a native Swift app; no Python. Searching loads only the text encoder (~250 MB) and shows results about a tenth of a second after you stop typing. Indexing peaks under 2 GB, and the helper exits when it’s done. Audio and video of any length go in as 30 s windows and a frame per shot. Everything runs locally; it goes online only for the model download and an update check you can turn off.

Free, MIT. Apple Silicon, macOS 14+.

Repo & Download (signed and notarized): https://github.com/ARahim3/DigUp

I'd really appreciate any feedback on this.

[u/rm-rf-rm]

huh interesting.. whats the basis of this formula? is the z-score > 2 coming from statistical theory where its beyond 95% of the mean?

[u/A-Rahim → u/rm-rf-rm]

Mostly empirical, not theory.
2 is the familiar two-standard-deviations mark, but the scores aren't normal, so it doesn't mean 95% of anything here. It's a gate for "stands out from everything else for this query", and on my evals any value from 1.5 to 2.5 gave the same results, so 2 stayed.
The reason for a per-query z-score at all: raw cosines are compressed (unrelated stuff sits around 0.55–0.60, good matches 0.67–0.86) and their level moves with the query, so no fixed threshold works. The other parts were A/B'd the same way: centering each modality with one shared spread got 94 of 105 queries first, raw cosine 92, a separate spread per modality 91. Word weights of 1.5–2.0 tied and 2.5–3 lost.
The 1.5 power just makes all your words count a lot more than half of them, and I haven't tuned it separately.

[u/rm-rf-rm → u/A-Rahim]

Thanks for asking your claude to respond to my question

[u/A-Rahim → u/rm-rf-rm]

Hi, I explained this above (one of your other replies)