We built a social network for Pokemon Go to chat, share and connect

Hacker News

Hey everyone! When Pokemon Go released in early July, I was on of the crazies that was looking for a way to help players communicate and chat with each other. I quickly created a slack group for them to join and a few weeks later there was over 1500 members chatting and sharing about their Pokemon journeys. I knew there was an opportunity to built something real. A true social network for Pokemon Go. A person named Trevor emailed me around this time and we quickly chatted over the phone and realized we had a common goal to create this. Trevor has the technical skills as a genius designer/developer and I had experience in marketing, strategy, user experience, and growth (hopefully). The result is Pokechat. www.pokechat.co Pokechat is simple a way to share, post, chat, and connect with Pokemon Go players! If you have any questions feel free to ask! Pika Pika.

Visit product View on Hacker NewsAug 31, 2016

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Actual performance

4points

2comments

Did not reach leaderboard

Launch Intel predictions

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

76%76% 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.

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% 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: way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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