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ACOS.games – Middleware platform for ReactJS multiplayer web games

Hacker News

ACOS.games – Middleware platform for ReactJS multiplayer web games

Hi HN, I engineered this platform to support building multiplayer games for competitive gamers. Game development uses JavaScript for both client and server, and focuses on Trivia and Board game genres, but all played in real-time. The platform manages all the functionalities outside of your gameplay and user interface, which includes: networking, matchmaking, rankings, timing, replays, deployment, and many other features. There is an SDK and Simulator that helps speed up development tremendously, but you as a developer will remain focused on authoring gameplay via the JSON game state and displaying the changes to that state on the browser. Many hours were spent ensuring the website is supported on most devices, with tough resiliency to ensure users can get back into matches when traveling on degraded cell service. This will allow perhaps having a battle royale with 100s of people playing in a single match of a trivia game. It always bothered me how online trivia games only allowed up to like 8 or 16 players. I'm about halfway on my roadmap, but the core foundation of the platform is settling in. Now, its time to expand into the more community focused features. That is where you can help, by pushing the limits of my systems. Break things, enhance things, all is welcome. The platform behind the website will be open-sourced in the near future, but you can access the ACOS simulator and existing games on GitHub. Play or develop a game on: https://acos.games Read the documentation at: https://sdk.acos.games Learning to code? We can help you build your first game. Simply join us on Discord. https://discord.gg/ydHkCcNgHD Happy Coding!

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Actual performance

3points
3comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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.
AppSumoStrong fit for a featured deal · Strong signals: platform, interface, users · Missing: plus, intuitive, reviews
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
54%54% 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: users, way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, single, coding · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
9%9% 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.

Incorrect prediction on native model

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