We made a game this week-end!

Hacker News

Hi Hacker News! My friend and I [1] would like to share with you something we did this week-end. We really like the idea of game jams like the Ludum Dare, and we’d like to participate, but it’s often complicated for us, as we are often limited by the rules inherent to this kind of competition: we have to be available at specific dates, and the rules doesn’t always work for us. For example: if you want to participate as a team in a Ludum Dare compo, you have 72h instead of 48h, and you are not required to publish your code. We realized that what we really like is: - To work together. - The 48h stress (two days is perfect because it fits in a week-end). - To work with a theme. - To play with Open Source enthusiasts, so we can all learn from each other. That’s why we decided to create a quick manifesto, the Selfish Game Challenge [2]. It’s just a really small set of rules that we will follow for our game sessions from now on. It’s really just for us, but we thought it would be nice to share it with people. The game we made is called “REVERSIBLE” [3]. We rushed a bit to finish in time, and it needs a lot of polish, but it feels good to have something playable at the end. It’s about cells and evolution, and we quite like it! It’s open source, so don’t hesitate to have a look at the repository too [4]. [1] http://lisezmoi.org/ [2] http://lisezmoi.org/selfishgame/ [3] http://lisezmoi.org/reversible/ [4] https://github.com/lisezmoi/reversible

Visit product View on Hacker NewsJul 21, 2014

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

84%84% 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.

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

74%74% 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: new, code, open · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% 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

25%25% 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

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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