City Defense Video Game

Hacker News

Link to video demo: https://youtu.be/kN9vukVWsLI This is a new game that I'm experimenting with. It is a game in the genre of Tower Defense but with cities. Basically the goal is to destroy your enemy's city before they destroy yours. What is happening in the video is that there are two groups of aircraft, one group is fighting on your behalf and the others are your enemies. Still in the early stages. I will have something playable in the near future. Let me know your thoughts.

Visit product View on Hacker NewsOct 4, 2017

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Actual performance

2points

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Launch Intel predictions

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

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

45%45% 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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

20%20% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

18%18% 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

15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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