HypraFap – Commodore 64 adult image viewer

Hacker News

I was wondering how a stash of digital adult images would have looked like in the 1980s, how slow everything was and how much disks would have been lying around. With my new weekend project "HypraFap" you can experience retro adult fun! Watch up to 16 glorious colorful images per disk on your Commodore 64 :-) I wrote HypraFap to learn a bit 6502 assembly and of course to learn a bit about the Commodore 64! On the github project page you find both, the TMPx assembly files and a demo floppy disk d64 image to try out HypraFap. I also explain how to create your own disks and images in Koala Painter format. And to be fair: It starts to make fun when you use a Floppy Fast Loader (I linked one in the readme). Does it work on your side? Ideas for cool features or how to improve it?

Visit product View on Hacker NewsFeb 12, 2024

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

67%67% 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.

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

61%61% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

58%58% 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

44%44% 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

40%40% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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