Driftmania – an open source PICO-8 racing game

Hacker News

I've been spending a lot of my spare time over the last year creating this little racing game. It's built in PICO-8, which is a really fun “fantasy retro console” that's been mentioned on HN several times. The console has strict limits and I wanted to see how far I could push them The source code for the game is over here: https://github.com/maxbize/PICO-8/tree/master/Driftmania . It's a bit of a mess, but I'm happy to answer any questions on it or development of the game. Cheers! EDIT: For those not familiar with PICO-8, there's only a few inputs: arrow keys, Z/C/N, and X/V/M

Visit product View on Hacker NewsFeb 16, 2024

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

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

58%58% 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

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% 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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