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Dstort – utility to "organize" randomly a directory

Hacker News

Dstort – utility to "organize" randomly a directory

So this utility given a directory or path, will move randomly files in subdirectories and will move subdirectories randomly too. CAREFUL, if given system directory it will probably trash the system, equivalent of doing `rm -rf /`, except all the files are still there! :) I don't know why I came up with this idea nor if its original but its mine. You might ask, whats the use case, well that's an interesting question, you know why? Because... (Of course, I'm open to feedback.)

Visit productView on Hacker NewsJun 14, 2024
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Launch Intel predictions

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Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% 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
26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
23%23% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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
10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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