Fplyr – Adult Entertainment Tool for playing moaning sounds and music

Hacker News

Fplyr is a background audio sample and music player specialized in playing moaning sounds and relaxing music for adult entertainment purpose. With fplyr you can define audio samples like lustful moans and (if you like) rubber clothing squeeching which are extracted from your video files and played back in a defined random fashion on multiple audio tracks. Of course you can define totally different sound setting. It depends on which video files you have in your collection and which one of these you wanna hear. What are your favorite sounds you would like to mix in? ~ Please also visit my other projects for your entertainment pleasure: Want to organize your collection of adult images and videos? -> fapel-system [1] Want to browse all your adult images on a single huge canvas? -> rugivi [2] [1] https://github.com/pronopython/fapel-system [2] https://github.com/pronopython/rugivi

Visit product View on Hacker NewsJul 27, 2023

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

68%68% 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.

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

55%55% predicted probability of success on AppSumo, 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

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.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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