Open Devdocs VS Skimrr

Skimrr's answer:

It grew out of a very ordinary problem: the photo folder nobody opens, years of imports nobody sorted, full of duplicates and near-misses nobody's confident enough to delete by hand. (There's a longer writeup on this: https://medium.com/@admin_53263/the-folder-nobody-opens-1b897eb33eed)

Skimrr's answer:

Most duplicate-finder tools either ask you to upload photos to a cloud service, rely purely on hashing (missing edited near-duplicates or flagging unrelated similar shots), or use a one-size-fits-all blur cutoff that misfires constantly. Skimrr keeps everything on your machine, adds a local vision-model sanity check most competitors skip entirely, and never deletes anything without a review grid and an undo-able trash.

Skimrr's answer:

Every proposed duplicate group is checked by an on-device vision model before it's shown, not just matched by hash, so near-duplicates (cropped, recompressed, lightly edited) get caught without a flood of false positives. The blur score is calibrated per-folder rather than using a fixed threshold, so a soft-focus portrait or a busy, detailed scene doesn't get misjudged. Everything runs 100% locally in Rust, with no account, no cloud, no telemetry, and nothing is ever deleted outright: files go to a reversible local trash first.

Skimrr's answer:

Tauri 2 and Rust for the desktop app and the core scanning, hashing, and clustering engine; a local MobileNetV2 ONNX model run through Candle (a pure-Rust ML framework, keeping the whole app free of Python or native ML dependencies) for duplicate verification; and React with TypeScript and i18next for the six-language UI.

Skimrr's answer:

People sitting on large personal photo libraries built up over years, with multiple phones, cameras, and import sessions, who want to reclaim disk space without handing their personal photos to a cloud service. Photography enthusiasts, people who regularly import from SD cards, and anyone privacy-conscious about where their photos go.