A Small SHMUP Game, Frequent Flyer

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Gameplay Gyfcat: https://gfycat.com/PastelRadiantHornedtoad Basically, quit my job to develop games (something like a sabbatical). The game is simple: fly around, try to survive, unlock new planes by picking stars and new weapon bonuses by simply playing (and accumulating score). The current version took me about 45 days to make, my girlfriend was doing the design/drawing parts. Going to update it eventually, will add more enemy animations and juice up the bullets / projectiles (working on how to make those better - don't know yet). I really messed up with marketing (kept some kind of dev diary, but was not publishing it anywhere) so I don't think I can recover from that, but it was a valuable lesson. I'll be posting my daily progress everywhere when I'll be working on my next project. Google play (runs smoothly, offers achievements and leaderboards): https://play.google.com/store/apps/details?id=com.coldwild.frequentflyer&referrer=utm_source=hn Try it online (albeit with some lag, working on fixing it): http://coldwild.com/flyer/ Vote on Greenlight (if you liked it): http://steamcommunity.com/sharedfiles/filedetails/?id=744697163 Any feedback is appreciated (don't hold back). Thanks!

Visit product View on Hacker NewsAug 31, 2016

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Launch Intel predictions

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

80%80% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, new · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, 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

46%46% 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 · Missing: arr, mrr, revenue

20%20% 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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