My first android game. MongoDB/Tornado backend

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After graduation, my friend and I decided to build mobile games instead of finding jobs. Today we released our first game called Monster Rivals. It is a fighting game with simple RPG elements such as stats and items with unique powers. We have a lot more planned for it in the future, but wanted to get the game out as soon as possible. The game also allows you to fight your friends through Facebook. The game data including the user/character profile is all on my servers hosted on linode. We have 3 servers- [primary mongo], [secondary mongo], [mongo arbiter, redis(for leaderboards), nginx forward proxying to 4 tornado processes]. I chose to use mongodb because it seemed to fit our needs very well for a database. Our user/char data is always queried together, and therefore we don't need to do many joins. The data is sent and parsed on the phone as a json, therefore it is extremely convenient to keep the data in a collection instead of constructing it every time. Link to the game: https://play.google.com/store/apps/details?id=com.pixelmaji.monsterrivals

Visit product View on Hacker NewsFeb 6, 2013

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

88%88% 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: nginx, ide, io · Missing: https docs, excited, just released

61%61% 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: apps, google, way · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, soon · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: google, apps, user · Missing: mac, agents, macos

33%33% 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, revenue

15%15% 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.

Incorrect prediction on native model

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