Fi

Filenest – Desktop File Organizer

Hacker News

Filenest – Desktop File Organizer

Hey everyone! I built Filenest to solve a problem we all have (at least me): a messy Downloads folder. It's a lightweight desktop app that sits in your system tray and automatically organizes your files into folders based on their type. How it works: - Set up custom categories (Documents, Images, 3D Models, etc.) with the file extensions you want - Enable the watcher, and Filenest automatically moves new downloads to the right folder - Or trigger a manual sort whenever you want No more hunting through hundreds of random files to find that PDF you downloaded last week. Also, its customizable so you don't have to use the Downloads folder if you don't want to you can point it to anywhere you want and when you add files to that folder they will be automatically sorted.

Visit product View on Hacker NewsDec 26, 2025

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

56%56% 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

41%41% 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 · Missing: mobile apps, ios, personal

26%26% 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

18%18% 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.

Correct prediction on native model

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