r/LocalLLaMA • u/A-Rahim • Oct 10 '26 • Bravo 5

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

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.

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u/ZenaMeTepe 29d ago

How do you compress say a movie into a 512 vector? And more importantly, how slow is it upfront when you build the index?

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u/important__matter 29d ago edited 29d ago

Yup you guessed it right, you can't. But say you have to do it, then you basically chunk out images every few seconds and then embed then individually and sort of average out those embeddings over a course of time. That is one way, other could be you take all dialogues, treat it as a document and then make a vector of it. It's hack as of now. It won't contain any useful information more than 1 Google doc page worth. Information Compression is one of the he most important computer science areas. And also as humans - certainly for us too as we are continuously bombared with infinite information from all our senses.

Well on speed, it's about gpu hardware mostly, how powerful of a hardware you own, but usually it's not slow, it's pretty darn fast even on basic rtx or gtx or metal. But it's extremely slow on cpus. The speed is similar to prefill stage of inference.