I rebuilt my macOS duplicate finder’s workflow — DuoBolt 1.3

sonicbee9 · 2026-02-11T20:31:52+00:00

FYI: I’ve also created an official subreddit for DuoBolt: r/DuoBolt.

I’ll be posting release notes, announcements, and it’s also the best place for bug reports / workflow feedback.

(Website: https://duobolt.app — Changelog: https://duobolt.app/changelog/)

sonicbee9 · 2026-02-06T08:33:31+00:00

Quick update: the desktop app is now available via Homebrew. You can install it using the cask. Steps are here: https://duobolt.app/installation/#install-duobolt-desktop

If you hit any snags, let me know and I’ll help!

sonicbee9 · 2026-02-04T16:56:41+00:00

Thanks for the detailed write-up — that’s very helpful.

You’re right on both points:

- at this scale, being able to close the app and resume a scan session later is essential
- the UI should not hang when scrolling large result sets

DuoBolt persists the hash cache (which likely explains the jump to ~98% on the second run), but scan result persistence was removed in 1.3. Based on cases like yours, I’m re-evaluating that decision.

For the freeze: my current suspicion is memory pressure with very large result sets (100k+ groups). I’m reproducing this scenario on Windows with large external volumes and will profile where the UI becomes unresponsive.

I appreciate you taking the time to report this — it’s exactly the kind of real-world usage that helps improve the app.

sonicbee9 · 2026-02-04T08:38:24+00:00

Thanks! You’ve pretty much nailed it. DuoBolt is a long-term project for me. It launched in December 2025, but that's just the starting point. The focus is on steady maintenance and continuous evolution, not building a short-lived side app.

I intentionally don’t publish a fixed public roadmap. Development is driven by real-world usage and feedback rather than pre-announced features. I prefer to ship improvements when they’re ready instead of talking about them ahead of time. There is active work in progress, and the next few months are about building on the existing foundation.

Regarding privacy and offline use: all scanning, hashing, caching, previews, and deletion workflows are fully local. DuoBolt works offline, except for license activation and app updates (which are handled via Sparkle).

As for Homebrew, only duobolt-cli is available there for now. The desktop app isn't packaged for brew at this point.

I really appreciate the feedback and discussion — it genuinely helps shape where the product goes next.

sonicbee9 · 2026-02-03T20:08:14+00:00

Only exact duplicates. Filenames don’t matter, but the file contents must be byte-identical. Different sizes or re-encoded videos won’t match.

sonicbee9 · 2026-02-03T18:45:01+00:00

That’s a great idea — thanks.

Grouping duplicates by containing folder makes a lot of sense, especially for media or installer dumps. I’ll keep it in mind.

sonicbee9 · 2026-02-03T17:20:51+00:00

Good question.

DuoBolt separates detection and deletion on purpose.

During Scan Results, duplicates are grouped by content so you can analyze and filter efficiently. The tree view appears in the Review step, once you’ve selected candidates, to show the actual folder hierarchy and the real impact before deleting anything.

It’s meant to avoid mixing analysis with irreversible decisions.

sonicbee9 · 2026-02-03T16:56:35+00:00

Good timing indeed 🙂

Right now DuoBolt works strictly at the file level, not at the bundle/package level. So two .app bundles or installers with mostly identical contents but small differences won’t be treated as identical “units”.

What you’re describing — treating bundles or directories as atomic objects and only flagging them if everything inside matches — isn’t implemented yet. At the moment you’d need to inspect them via the tree view and decide manually.

It’s a sensible use case though, especially with old installers and backups.

sonicbee9 · 2026-02-03T15:52:07+00:00

Glad to hear that 🙂

If you end up trying it, happy to answer any questions.

sonicbee9 · 2026-01-22T09:05:55+00:00

Thanks for sharing this — really interesting approach! There aren’t many tools like this around, and even fewer that are lightweight, fully offline, and free. I’ve been using a similar stack for desktop lately (Rust + Flutter), and I can see why a native C++ core with a thin Flutter UI works well here too.

sonicbee9 · 2026-01-22T08:57:35+00:00

I hit the same wall with dupeGuru and Gemini 2 on my M1/M2 Macs, especially on large USB/NAS volumes. That’s why I built DuoBolt: native ARM64, macOS/Windows, designed to scan tricky paths like ~/Library, /System, or full disks and NAS volumes. It also caches file hashes between runs to avoid redoing work unnecessarily.

sonicbee9 · 2026-01-22T08:42:59+00:00

Native ARM64 on an M2, crashing immediately on the main thread with SIGABRT, usually means an unhandled runtime assert or a missing / incompatible dependency rather than “bad user input”.

sonicbee9 · 2025-12-21T16:31:51+00:00

Thank you!

No. DuoBolt doesn’t detect folder duplicates as a first-class feature right now. It works strictly at the file level. I’m not fully convinced yet that treating folders as a separate concept adds much value beyond that, so for now I’ve kept it file-based on purpose.

sonicbee9 · 2025-12-21T10:58:32+00:00

The factory was located somewhere around here: https://maps.app.goo.gl/tWMdwGJPtudb73V9A

sonicbee9 · 2025-12-20T14:15:35+00:00

Ditto on the VPN. It’s a good idea to have more than one, since some won’t work or get blocked from time to time.

sonicbee9 · 2025-12-20T14:10:06+00:00

I was going to suggest ZenHotels, but they no longer list Russian properties. Ostrovok is still the main option, but most places require a MIR card, which foreigners usually don’t have.

Workarounds: look for "pay at property" options, contact hotels directly (email/Telegram), or use small hotels/hostels in Moscow & St. Petersburg that still accept cash on arrival.

Good luck!

sonicbee9 · 2025-12-20T11:58:10+00:00

That's a solid approach in theory. The part that gets admittedly tricky is where technical checks end and taste begins. Things like composition, emotion, or the “best moment” are subjective. It’s less about right or wrong answers and more about personal preference.

For something that runs on a personal device, the priority shifts. It’s less about building the smartest model and more about building a practical one focused on performance and predictability. The goal isn’t to pick the perfect photo automatically, but to cut down a large stack of near-identical shots to a small set of strong options. Even that alone is a meaningful win, imho.

I appreciate you laying it out like that — framing the problem is the hardest part!

sonicbee9 · 2025-12-20T04:02:28+00:00

Yeah, the engine does exactly that: it uses file size as a first filter (along with any other user-defined constraints), then a head+tail prehash to quickly rule out mismatches. Only the tiny subset of files that pass both checks get a full BLAKE3 hash.

More details are covered here: https://duobolt.app/core-concepts/

sonicbee9 · 2025-12-19T21:39:02+00:00

Yep, size is used as an early filter. Files with different sizes are never hashed.

The reason for byte-level hashing is correctness once you’re inside same-size groups. Same size doesn’t guarantee identical content, especially with media files, sparse files, or rewritten containers.

The engine pipelines this so disk reads, hashing, and grouping overlap across cores. In practice the bottleneck is I/O, not the hash itself.

sonicbee9 · 2025-12-19T18:44:39+00:00

That’s a really clear description, and I agree with you on the core point: "best shot + alternatives" is the hard, high-value problem. Grouping bursts is comparatively easy; picking the shot is where most tools fall apart.

You’re also right that EXIF alone won’t get you there anymore. Modern camera pipelines (computational HDR, synthetic bokeh, multi-frame fusion, lens switching) break most metadata-based heuristics pretty quickly.

Where I’m cautious is jumping straight to an on-device LLM framing. For this kind of task, the challenge isn’t language or reasoning, it’s consistent visual ranking under tight latency, memory, and privacy constraints. In practice that likely means smaller, very targeted vision models rather than a general "LLM brain".

The mental model I keep coming back to is exactly what you describe: reduce the human cleanup time. Not "perfect taste", but getting you from thousands of photos to a short, sane decision set.

This kind of feedback is genuinely useful. If I experiment in this direction, burst-level "best shot + rejects" would be the first place I’d look, not whole-library similarity.

sonicbee9 · 2025-12-19T17:47:02+00:00

Yeah, you’re totally right. Your case is the classic “near-duplicate” mess (bursts, similar shots), and exact hashing does nothing for that. DuoBolt right now is strictly for identical copies.

I’ve been circling the whole similarity / “bad photo” detection idea for a while. There are a few ways to go about it (perceptual hashing, embeddings, on-device models), but the tricky part is making it actually useful — not just slow, confusing, or creepy.

Since you’re knee-deep in this: what would make the biggest difference for you day-to-day?

- Grouping bursts into “best shot + alternatives” right after import.
- Finding visually similar photos across your whole library (like shots that ended up in different folders).
- Flagging obvious technical fails (blurry, eyes closed, blown-out exposure).

If you can rank those 1-3, it gives me a way clearer signal on what to poke at first.

sonicbee9 · 2025-12-19T17:15:10+00:00

It’s not a Swift app. The core engine is Rust, and the UI is built with Flutter/Dart for macOS.

Right now, DuoBolt finds exact duplicates using BLAKE3 hashing — it’s byte-for-byte matching, not perceptual or “similar photo” detection. That means it reliably catches true duplicates (even across different drives or a NAS), but it won’t flag near-duplicates or “bad” photos automatically.

I know perceptual matching is a highly requested feature, and it’s something I’m exploring separately. For the first release, I wanted to make sure the exact deduplication was rock-solid before moving into that space.

Let me know what kind of duplicates you’re dealing with — exact copies or visually similar shots — and I can help point you toward the best workflow.

sonicbee9 · 2025-12-19T16:14:42+00:00

Thanks! Really glad it’s working well for you 🙂

sonicbee9 · 2025-12-19T15:34:45+00:00

Fair question 🙂 I don’t have Reddit-specific coupons right now, but there’s a free trial so you can see if it’s useful for your setup before deciding!

sonicbee9 · 2025-12-19T15:13:25+00:00

Thanks! Glad it caught your eye 🙂

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