Dustpan for macOS – declutter your desktop with ease

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hey HN. I'm here to plug my most recent side project, dustpan. I wanted to make something that would help me clean up the clutter on my Mac's desktop without automatically deleting anything or messing with my folders. The idea originally was for ivy to grow around files on your desktop as they go untouched to help promote a less cluttered desktop by pointing out rarely used items. This idea was not my own, but I thought it was awesome and immediately went into research mode. After spending a good chunk of my free time for a while tinkering, I decided that the idea in its purest form is not something I'm capable of, but that I could definitely create an app to the same effect. My macOS app is called dustpan. It periodically dusts your desktop, moving all files that have not been recently used (recently is by default a week, but is customizable) to the dustpan, where they can be accessed or moved at will. Dustpan declutters your desktop without deleting anything, messing with your desktop folders, or affecting your important, frequently-used files at all. Please check out what I made here! HTTPS://dustpan.github.io I apologize that it's not completely free. I made it 99 cents for now as otherwise I would be losing money due to hosting and etc., and as a college kid that's not really an option right now. thanks!

Visit product View on Hacker NewsFeb 21, 2017

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

81%81% 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: mac, macos · Missing: agents, agent, cursor

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

24%24% 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

14%14% 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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