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GenZD, a port of GZDoom for iOS, available on the App Store

Hacker News

GenZD, a port of GZDoom for iOS, available on the App Store

Hi everyone! I wanted to share the release of the app I've been working on in the past ~3 years, a port of GZDoom for iOS called GenZD: https://apps.apple.com/us/app/genzd/id6503916449 GZDoom is a source port of the Doom engine, and the app lets you play games using this engine (Doom, Heretic, Hexen, etc) as well as custom games that use GZDoom's enhanced scripting capabilities. This means you can run mods like Brutal Doom, Project Brutality and total conversions like Ashes 2063, Aliens Eradication and many more. The engine will render the games in full 3D and in widescreen, and it uses Metal as the graphics backend through the MoltenVK library. I've been working on and off on porting GZDoom to iOS for the past couple years, and then figuring out how to write a frontend that would be suitable for an app on the App Store. With the recent changes to Apple's App Store Review guidelines regarding emulators, I thought the app would have a better chance of getting approved. GenZD is technically not an emulator but is closer to something like ScummVM where you supply the game files and the engine can interpret them. ScummVM is also on the App Store and was approved recently. After a few initial rejections, the app went into an extended period of review (about a month) before it was approved last week. It's a paid app ($1.99 USD) but it's a one-time purchase and I do believe it's worth its value, but that's just me :) Here's a YouTube video of it if you just want to see what it does and how it use it: https://www.youtube.com/watch?v=a1HUJumkRAY Thanks!

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
88%88% 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: apple, apps, using · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs
55%55% predicted probability of success on TrustMRR, 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
49%49% 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 · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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