Launching our first iOS game, BAMF

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I am from Double Stallion Games, and yesterday was the global launch of our first iOS game, Big Action Mega Fight!, or BAMF! for short. It's now sitting at #70 overall for iPad games on the US App Store and we're pretty stoked about it. Meanwhile, an Android version is in the works and coming soon. Most notably, we developed the game thanks to the support of an incubator/accelerator called Execution Labs, which was founded in Montréal and accepted its first cohort this past January (2013). Its model is similar to a miniature Y Combinator, focused specifically on mobile games. We get seed money, office space, mentor advice, and valuable connections over the 6 to 9 month program, in exchange for temporary profit share and equity. I would love to get some feedback about the game from everyone, or answer any questions regarding our development, launch or marketing, efforts as well as our incubator experience. Landing page: http://bigactionmegafight.com/ Trailer: http://www.youtube.com/watch?v=UPkuRGbjJyw App Store page: https://itunes.apple.com/app/id672277660?mt=8

Visit product View on Hacker NewsNov 8, 2013

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

93%93% 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.

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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: model, apple · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: profit · Missing: arr, mrr, revenue

23%23% 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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