A small browser game with a skydiving Santa

Hacker News

This is a silly little game I built over the last couple of days. You launch Santa into the air, then use your arrow keys to glide him to houses to deliver the presents. I've been playing with React Three Fiber all year, but this is my first game and the first time using the physics engine Rapier. I had a blast, and surprisingly everything turned out pretty much as I envisioned it, from the game loop to the glide mechanic. There is so much jank and it's desktop browsers only (sorry phone users), but I think it's pretty fun. Enjoy! (Cheat codes: press 3 to bypass the first 3 houses, press 4 to skip to the end scene!)

Visit product View on Hacker NewsDec 25, 2024

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

78%78% 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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

50%50% 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: users · 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 · Strong signals: arr · Missing: mrr, revenue, profit

18%18% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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