Maginet, a cross-platform strategy game with Rust/WebAssembly

Hacker News

I hereby present my little tiny pet project that entertained me for the past six months. In this post, I briefly present Maginet and its technical components. It is a turn-based strategy game that is built using Rust and compiles to WebAssembly with HTML5 bindings, which works on mobile and desktop alike! It comes with many game mechanics and components, including an AI opponent and online cross-platform multiplayer. There is so much to discuss even for a project of a smaller scale, but I am nevertheless excited to share a little bit about how you can actually build a game in Rust, practically from scratch, and run it anywhere! I already chopped this article in half to only focus on technical components, and I have a separate one on the design aspects of it. Moreover, if people are interested, I am happy to work on something that is more akin to a tutorial on building a complex web application with shared client-server logic too. I hope this post is juicier than your usual ‘make a game in X framework of Y language’ post, as this one comes with a real game that’s launched on Steam. Thanks, and enjoy!

Visit product View on Hacker NewsDec 12, 2023

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

79%79% 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: excited, io, including · Missing: https docs, just released, exist

73%73% 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 · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% 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

20%20% 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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