DigUp for Product Managers

Download DigUp for Mac EmbeddingGemma 2 All roles

On-device flow Schematic: DigUp uses EmbeddingGemma 2 for Product managers local search

Original schematic — DigUp indexes local files with EmbeddingGemma 2 (llama.cpp).

Sourced snapshot (2026-10-15)

Fact	Value	Source
Model	EmbeddingGemma 2 · ~740M params · ~865 MB Q8 via llama.cpp	Google AI · DigUp README
Vector space	768-d shared multimodal space	Google AI · DigUp README
Mac / Win·Linux	0.6.1 · 0.7.0-beta.1	GitHub Releases
Stars / forks	~3,768 / ~254	GitHub API

Pain → DigUp workflow → tip

Pain	DigUp + EmbeddingGemma 2 workflow	Tip
Where did we write the non-goal?	PRD PDF/docx search	Open at passage/page
User interview clip about onboarding	Audio/video moments	Jump near quote
Competitive screenshot set	Image embeddings	Describe the UI
Ship week laptop thrash	Schedule indexing hours	Battery guide

Numbered checklist

Install DigUp; index Product / Research / Demos locally.
Allow ~865 MB EmbeddingGemma 2 first run.
Search outcome language (“reduce time-to-value”), not only ticket IDs.
Cross-link engineering code: search when needed.
Read multimodal guide for modality matrix.

Why EmbeddingGemma 2 matters here

Google’s EmbeddingGemma 2 (~740M, unified 768-d multimodal space) is what DigUp runs via llama.cpp (~865 MB Q8 package). One query can rank images, PDF pages, audio windows, video frames, and optional code — see the local multimodal guide and model page. digup.run is not Google-official.