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Our HTML5/CSS3/GSAP-JS/SoundJS/letterpress holiday card
Hacker NewsOur HTML5/CSS3/GSAP-JS/SoundJS/letterpress holiday card
For our holiday card this year, we made a site where anyone can create and send their own holiday card in the form of a word puzzle. The art was all created by well known letter press print studio Firecracker Press (you know, movable lead type!) and the code uses bleeding edge libraries from Greensock ( http://www.greensock.com/gsap-js/ ) and CreateJS (http://www.createjs.com/#!/SoundJS ). The link is to our blog post about it. You can play the game itself at http://www.xmaspie.com!
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released57%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.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos18%18% 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, revenue16%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, crypto5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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