ACOS: Build and share competitive turn-based browser games

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Hi everyone, This is a little tech demo I've been working on the past 2 years. It's been up for 1 year now, and has since had a few major updates. I have a soft spot for games with ranking systems, and I wanted an easy way for developers to create games with the competitive element abstracted away. The current games are built with ReactJS, so it's pretty easy to onboard if you are a web developer. All games developed for ACOS come with these features: - Real-time turn-based for browser - Simulator for game development - Client / Server architecture - Easy multiplayer with simple JSON game state update - Single player, Team-based, Battle Royale, and Free-for-all games supported - Matchmaking for Rank / Casual play - Room Scoreboard - Game Replays of every match - Error Logs - Global Rankings - Global Top Scores - GitHub Integration with acosgames organization - Discord Integration for matchmaking / game events - Simple deployment and version management It's a ridiculous endeavor, but I'd like to invite the world to my playground and help me drive features and technology forward. I may allow AI bot battles in the future against any of the hosted games, but I will prioritize based on the will of the people. Read our documentation at https://sdk.acos.games/ Hope you enjoy it!

Visit product View on Hacker NewsJan 7, 2023

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

80%80% 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: single · Missing: mac, agents, macos

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

41%41% 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 · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% 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

13%13% 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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