Angeldust – a fast and efficient video game

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After years of solo development I'm presenting: Angeldust, an omni-compatible action RPG and building game! Website: http://angeldu.st My single server hosts 250+K active players in one giant, dynamic fantasy world. Angeldust's game server is pervasively multithreaded, implementing John Carmack's dream of processing an immutable world state each tick. Both world handling and network traffic routing uses any number of threads. For networking, a typical game session only uses ~3Kbps of bandwidth, enabling decent play even over 2G/EDGE networks. The client is programmed in C++ with bits of C, Objective-C, Java and PHP. It runs really well on very low-end and obsolete hardware, all the way up to modern systems with fast GPUs. Angeldust works perfectly on Windows XP+, even without hardware OpenGL; Mac OS X 10.6+, even on the first 2006 32-bit Intel GMA945 MacBook1,1; Linux 64-bit glibc 2.17+; Android v2.2.3+, supporting Bluetooth game controllers; and iOS 6+, though iTunes App Store forces iOS 8+ (any way around this?). I live stream weekly on YouTube and Twitch, even right now! Come ask me your development questions! I often see developers playing together with their kids. YouTube channel: https://www.youtube.com/AngeldustLive One more thing: the Angeldust website ( https://angeldu.st ) uses plain HTML and CSS to offer interactive, game-related actions. It's fully functional even without JavaScript enabled.

Visit product View on Hacker NewsDec 22, 2019

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

96%96% 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: mac, single, open · Missing: agents, macos, agent

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, video, way · Missing: mobile apps, personal, entrepreneurs

63%63% predicted probability of success on TrustMRR, 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

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.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, efficient · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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

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

Correct prediction on native model

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