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Fclones-GUI – a fast duplicate file finder with GTK4 interface

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Fclones-GUI – a fast duplicate file finder with GTK4 interface

Some time ago, as an exercise in writing parallel Rust, I created fclones [1] - a command line utility to find and remove duplicate files. It was fun to develop, and soon it became quite advanced. It's probably also one of the most efficient duplicate finders available - some users reported using it for cleaning terabytes of data. But, being a CLI app, it probably doesn't offer the best possible user experience for those who want an easy to use program. In particular, the typical workflow of finding duplicates and then picking some of them to remove is hard to execute smoothly in a batch-oriented program. Hence, I thought a GUI would be a nice improvement. And here it is! An old-school desktop app. In GTK+. Some say I'm crazy to do this in 2023. It doesn't offer all the functionality available in its CLI cousin yet, but it already does the job: it finds duplicates and can remove the selected ones. It also shares the majority of code with CLI fclones, so it is also quite fast. This is obviously a very early version. Let me know if you like it and which features you'd like to see next.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: interface, soon, efficient · Missing: plus, platform, intuitive
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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