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Mariocraft.com - A Community Based SMB

Hacker News

Mariocraft.com - A Community Based SMB

After three years of joyful experiments I'm glad to present you the result of them. Mariocraft is a SMB web project based solely on HTML5/CSS/JS, without canvas. The whole idea behind it was to create a community in order to build and share new kind of SMB experience. To achieve this goal, I focused most of my efforts on the editor, making it as easy to use for beginners without forgetting about advanced users (shortcuts, etc.) I tried to create overall design which matches subject of my nostalgia – the 80's. Hence, you'll have the pleasure to encounter less fancy effects and more 8 bits images. Once you have created a world, it is serialized in a database. At this point you can share your world with anybody just by sending a link. I must precise that anybody can play without a need to register. Registration is only required to perform editing or creation of a new world. Here is an example of a world I made for the HN community: http://mariocraft.com/show_hn/s/xumi and if you want a little challenge, here it is: http://mariocraft.com/nfXdir/s/xumi (beat my time if you dare: 346) Oh, and please note that for this demo sounds and musics are available only to registered users to spare some bandwidth. If you are interested in the project you can visit a little blog about it which I started: http://blog.mariocraft.com. Naturally, all the client side code will soon be available on Github. Thanks for your time, I hope to see you soon jumping on Goombas with me!

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: soon, users · Missing: plus, platform, intuitive
69%69% predicted probability of success on AppSumo, 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
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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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