RuGiVi – Adult media landscape browser

Hacker News

RuGiVi enables you to fly over your image collection and view thousands of images at once. Zoom in and out from one image to small thumbnails with your mousewheel in seconds. All images are grouped as you have them on your disk and arranged in a huge landscape. RuGiVi can work with hundred thousand of images at once. Fly over thumbnails and zoom in on any image anytime in an instant! Tested with around 700.000 images, that's a RuGiVi Pixel size of 4.600.000 x 4.400.000 pixels or 20.240.000 Megapixels or 10.120.000 Full HD Screens to be scrolled through. RuGiVi works on Windows and Ubuntu Linux RuGiVi saves all generated thumbnails in a database and can load these on the fly at the next session. Oh and regarding my other project: RuGiVi integrates fully into the Fapel-System [1], so you can travel trough your collection and tag images as you fly over them! Have fun! [1] https://github.com/pronopython/fapel-system

Visit product View on Hacker NewsMay 9, 2023

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

75%75% 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.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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