A collaborative pixel drawing game for when you're bored (Attempt #2)

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Previous thread: https://news.ycombinator.com/item?id=30167391 Hello again HN! It's been 3 months since I last submitted this project and the back-end server software melted down. I was really bummed that many people didn't get to play, so I've spent most of my spare time since that last post completely rewriting the back-end server software using small, carefully selected dependencies (C99, sqlite, mongoose.ws & cJSON) I've done some basic performance testing and I'm cautiously optimistic that it can now handle up to ~1000 concurrent users. If you have any ideas for improvements, do let me know! Bots are permitted, but try and do something creative instead of just filling the canvas with junk :^) You can view the source code for the new back-end here: https://github.com/vkoskiv/nmc2 You can also check out a recent time-lapse here: https://www.youtube.com/watch?v=FUmnUvTqn6M The time-lapse gets generated from the server log output using this tool: https://github.com/zouppen/pikselipeli-parser/ (Note to HN staff: It wouldn't let me repost this unless it had a 'slightly different url', hence the sneaky redirect there. If this is a big no-no, I apologize, otherwise feel free to swap out the URL to the redirected one :^))

Visit product View on Hacker NewsMay 14, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

84%84% 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 · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

28%28% 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

14%14% 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.

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