On-device semantic file search for Mac power users
Package a polished macOS menu-bar app that lets users semantically search everything on their Mac (photos, PDFs, recordings, videos, code) using on-device embeddings, with no cloud upload. Target privacy-conscious professionals, researchers and developers drowning in local files who don't trust cloud indexing tools like Rewind or Spotlight's weak search. Sell as a one-time license or low-cost subscription through the Mac App Store or a landing page with Product Hunt launch.
What to build
A polished macOS menu-bar app that lets a user semantically search everything on their Mac — photos, PDFs, voice recordings, videos, and code — using on-device embeddings, aimed at privacy-conscious developers, researchers, and professionals who refuse to upload their files to cloud AI tools.
DigUp proves fully on-device, multimodal semantic search on macOS works and already has strong organic pull (1717 GitHub stars); the opportunity is to turn that open-source proof-of-concept into a polished, sellable app for people who won't trust a cloud-indexing tool with their local files.
Demand
Developers, researchers, and privacy-conscious professionals sitting on years of unsorted local files want to search by meaning instead of filename, and specifically distrust cloud-backed tools like Rewind with sensitive personal/work data — the repo's star count shows this want is already active in public.
- GitHub trendingProduct
ARahim3/DigUp has 1717 stars on GitHub (Swift), described as searching your Mac by what's in your files (photos, PDFs, recordings, videos, code) fully on-device using Google DeepMind's EmbeddingGemma — high engagement at the source.
- ARahim3/DigUp (GitHub repository)Product
Repository positions itself explicitly around on-device, multi-file-type semantic search as its core differentiator, suggesting the author identified this as the key unmet need over cloud alternatives.
Stack
- Swift/SwiftUI
- EmbeddingGemma (via MLX or llama.cpp)
- Core ML / MLX for on-device inference
- SQLite-vec or usearch for the local vector index
- FSEvents API for incremental file watching
- Paddle or Gumroad for licensing/payments
Solo + AI difficulty
The hard algorithmic work (multimodal on-device embeddings via EmbeddingGemma) is already proven in the open-source reference, so a solo builder can study or fork it rather than invent it; the real effort is UX polish, making incremental indexing of huge video/PDF libraries fast and battery-friendly, and handling notarized distribution outside the sandboxed App Store. Rough MVP: 3-6 weeks for a builder comfortable with Swift and on-device ML.
- Entry threshold
- Low capital barrier since it wraps an open on-device model (EmbeddingGemma) and llama.cpp, but requires real Swift/macOS engineering skill to build a stable menu-bar indexing app with good UX, likely 2-4 weeks for a solo builder with AI assistance.
- Window
- 6-12 months
Where to find first users
- Product Hunt launch
- Show HN on Hacker News
- r/macapps and r/privacy
- Direct outreach to DigUp's GitHub stargazers and issue commenters
Competitors
Counter-signals & risks
Apple's built-in Spotlight and native on-device semantic search (Photos app search, Siri Suggestions) are improving quickly and could close the gap, reducing willingness to pay for a third-party tool.
On-device embedding indexing of large local corpora (video, PDFs, code) can be CPU/RAM/battery/storage-intensive, creating UX friction versus cloud-backed competitors like Rewind.
As an open-source GitHub project, DigUp shows no existing monetization strategy, team, or App Store presence, making the 'package and sell' thesis speculative rather than validated.
Original title: ARahim3/DigUp: Search your Mac by what's in your files: photos, PDFs, recordings, videos and code. On-device, built on Google DeepMind's EmbeddingGemma 2.
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