A remake of my 2004 PDA video game

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My background project for the last two years has been re-implementing my 2004 C++ shoot'em up game in TypeScript + WebGL, and it's finally done (just in time for the 20th anniversary!) Play the game online: https://nicollet.net/blog/darklaga/darklaga.html Technical article about the remake: https://nicollet.net/blog/darklaga/remake.html I have tested Firefox, Chrome and Edge on desktop and mobile (no access to a device capable of running Safari). It's amazing how much difference 20 years makes: the hardware is so much more powerful, the web as a deployment platform is so much easier than side-loading onto a PDA through a serial cable or sharing .exe files through e-mail, and my experience as a professional developer makes almost everything so much easier... but at the same, it didn't feel that the language, editor or debugger (TypeScript on Visual Studio Code) were significantly better than good old Visual C++ 6. Repository with the code of the remake: https://github.com/VictorNicollet/Darklaga (sadly, I cannot provide the video and audio assets themselves under any open license).

Visit product View on Hacker NewsDec 31, 2024

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

81%81% 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: visual, code, open · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

59%59% 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

34%34% 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

21%21% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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