A collaborative pixel drawing game for when you're bored

Hacker News

Hello HN. This is a project me and some friends built at university in 2017. It was originally built in a rush, but we've added a few improvements over the years. It's a fun game to host at a demoparty and project on a big screen - We did that at Instanssi many times. The implementation is still rough, and suffers from the fast pace of the modern web - The frontend is likely very outdated, and the backend is built with Vapor 2 and Swift 3.1, both no longer supported. You can host this yourself, but it might be a bit of a pain to set up. The WebSocket protocol is fairly trivial to work out and write a bot for to ruin the whole canvas, but I trust that the HN crowd can come up with clever things to draw with a script, should they choose to do so. The server is recording draw events into a log, and I will be posting a timelapse here in the comments after some time. Have fun!

Visit product View on Hacker NewsFeb 1, 2022

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

70%70% 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.

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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

57%57% 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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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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