Price terms as published

Each line is the vendor’s own wording, split from the price capture checked on 2026-10-25. Nothing here is rounded, converted or re-worded — the full sentences stay in the review below. Lines that carry only a price continue the plan named on the line above, in the vendor’s own order.

2 price terms for DigUp
#Plan / termChecked
1Free (MIT open source)2026-10-25
2865 MB one-time model download2026-10-25

One-liner: DigUp replaces Spotlight/Windows Search habits with semantic search — you describe what a file shows or says (“zebra in a video”, “the sleep discussion in a podcast”, “the clause about pets in the lease”) and it opens the exact photo, PDF page, or audio/video moment. Everything runs on-device; the README states indexed files never leave the computer.

Everything below was checked against the official repository (GitHub API + raw README, fetched 2026-10-25) and independent coverage.

Verified facts (GitHub API + raw README + third-party, 2026-10-25)

Attribute Value Source
GitHub stars 5,720 (428 forks) ARahim3/DigUp via GitHub API, 2026-10-25
License MIT (app code); model is Apache-2.0 GitHub API + README
Created / last push 2026-10-08 / 2026-10-22 GitHub API
Model Google DeepMind EmbeddingGemma 2 (released October 2026, Apache-2.0, 740M params, 100+ languages), run via llama.cpp (ggml 8-bit build) README + model release coverage
Platforms macOS (Apple Silicon, Metal GPU), Windows 10/11 and Linux (Vulkan GPU when faster, else CPU), Tauri app README
Model download 865 MB once: text part 310 MB + image/audio encoders 555 MB; app itself ~20 MB (Mac), 40–140 MB (Win/Linux) README FAQ
Search shortcut ⇧⌘Space (Mac) / Ctrl+Shift+Space (Windows/Linux), works in any app README
File types photos/screenshots, PDFs (page-level, up to 5,000 pages), docs (txt/md/typ/rtf/docx/odt/html), spreadsheets/slides (xlsx/ods/pptx/odp), audio of any length (30-second windows), video of any length (frame per shot + soundtrack), code search per project README “What it reads”
Code search code: prefix; opens results at the line in Zed, VS Code, Cursor, Sublime Text, JetBrains, Xcode (Mac), Notepad++ (Windows) README “Code”
Privacy No account, no telemetry; online only to download the model once and check for updates once per day (update check can be disabled); never reads keys/certificates/.ssh, skips datasets, never uploads README + third-party coverage
First pass Mac: minutes for a few hundred files, about an hour for a big Downloads folder; Win/Linux: GPU (Vulkan) when faster, CPU otherwise (~5 s per picture on a 2017 laptop Core i5) README
Version gaps (Win/Linux at launch) screenshot OCR text not read yet; old Word .doc, RAW photos, SVG pictures only on Mac README FAQ

This is an official-terms entry: we have not installed or run DigUp, so there is no score and no hands-on claims.

Getting it running

DigUp ships as signed Mac builds and unsigned Windows/Linux packages. On a Mac, the README’s install path is brew install --cask arahim3/tap/digup (or download the DMG from Releases); on Windows, download x64-setup.exe and choose “More info → Run anyway” for the unsigned installer; on Linux, install the .deb (Ubuntu 22.04+, Debian 12+, Mint 21+) or run the AppImage. First launch downloads the 865 MB model once, then you pick which folders to index — setup shows the estimated first-pass time for each folder, measured on your own machine. After indexing, press the shortcut in any app and type a description of what you want; exact-word boosts keep invoice numbers and error codes findable even though the model is semantic. The repo also ships a command-line tool (.build/release/digup) for indexing, searching, and running evals from the terminal.

What it costs

The app is free and MIT-licensed, and the model is Apache-2.0 — there is no subscription, no account, and no cloud bill. The real costs are one-time and local: the 865 MB model download, and indexing time on first pass (a large photo library on CPU-only hardware can take hours; the README says a picture takes about five seconds on a 2017 laptop’s Core i5, and the Mac GPU path is far faster). After the first pass, a new file becomes searchable a few seconds after it lands, and the index updates incrementally. The model runs in a separate helper process that quits when done, so the README says it should not slow down normal use.

How it differs from alternatives

  • Spotlight / Windows Search find files by name, text, and metadata. DigUp also finds files by what they show or say and takes you to the page or moment — the README positions them as working fine side by side. It is not a replacement for Spotlight’s speed on known names.
  • Exact-keyword file tools (Everything, ripgrep-style searches) are precise on filenames and text but blind to meaning. DigUp’s single 768-dimensional vector space lets one query rank a video frame against a PDF page, which exact-keyword tools cannot do at all.
  • Cloud semantic search services require uploading your files. DigUp is the local-first pole: the model downloads once, then everything runs on-device with no account and no telemetry — the privacy pitch the README and third-party coverage (e.g., Medium’s “Describe It, Dig It Up”, ProPakistani, r/LocalLLaMA) all emphasize.
  • MCP/local file tools on this site (e.g., Clipto for video memory) are agent-facing memory layers for specific media. DigUp is a general desktop search app aimed at a human pressing a shortcut in any app — broader file types, but not an agent integration by default.

Where to stay honest

  • First pass can be slow on big libraries, especially CPU-only Windows/Linux machines with many photos — the README says setup shows the time estimate before you commit.
  • Windows and Linux builds are newer: screenshot text isn’t read (screenshots are found by what they show, not by typed text), and old Word .doc, RAW photos, and SVG are Mac-only at the time of writing.
  • The 865 MB download is not optional — the text part (310 MB) plus image/audio encoders (555 MB) must be fetched once before any search works.
  • No affiliate or promotion angle: this is an independent directory entry for a free open-source tool, verified against the repo and coverage as of 2026-10-25.

Who it’s for

  • People whose files live across photos, PDFs, recordings, and videos and who remember what a file was about, not its filename
  • Privacy-minded users who want AI search without uploading anything to a cloud
  • Cross-platform users (Mac + Windows + Linux) who want the same local search everywhere, with the caveat that Windows/Linux features trail the Mac build
  • Not for: anyone who needs known-name speed first (keep Spotlight/Windows Search), or teams that can’t accept a one-time 865 MB model download per machine

Sources