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Love2d Game running in browser with a single-player eSports twist

Hacker News

Love2d Game running in browser with a single-player eSports twist

Hey HN! I'm a huge fan of Love2D - a lightweight, open-source, 2D game engine. It's easy to use, cross-platform, and perfect for hobby projects, but it doesn’t officially support web deployment. I've been working on a game in my spare time, and wanted to see if I could get it running in the browser without rewriting it in something like Godot or Phaser. After some trial and error, I hacked it together using Love.js (an unofficial Emscripten/WebAssembly port of Love2d) and Love.js-Api-Player, which lets Lua call JS functions. The result is a Love2D game running in the browser, with leaderboards and other web features. The game is “What They Don’t Tell You About Living in New York” — an alpha-stage bullet hell dungeon crawler where you fight demons in an NYC skyscraper with psychic powers. It’s fast-paced, with 8-bit programmer art, and mechanics inspired by American Football. As an experiment, I added a single-player esports twist: top leaderboard players win small prize pools I fund myself, paid out via P2P apps (PayPal, Cash App) or crypto (Dogecoin, Solana). It’s less about the money and more about how modern payment tech makes running these kind of micro-competitions possible for indie devs. The tech stack is janky but works surprisingly well, and AFAIK, no one’s pushed Love2D this far into the web space before. Has anyone else messed with Love2D in the browser like this? I’d love feedback on the setup, the game, or the esports idea. Join the Discord (discord.gg/E8xxQdNERb) if you’re into Love2D or game dev tinkering!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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: apps, new, single · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, game engine · Missing: https docs, excited, just released
62%62% 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: platform · Missing: plus, intuitive, reviews
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
33%33% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto, paid · Missing: web3, chat, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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