MaybeFace – Facebook groups based matching

Hacker News

Find people through group members and 'like them' for a purpose (romance, going out, and that other thing :) why? because you can comment/like to their posts in the group so they'd be aware of your existance, and hope they will do so too! So if your shy (or smart, considering the 3rd option), that's the tool for you! It's a side project, fully functional MVP...ish. Will continue the work if the world shows interest. https://maybeface.com

Visit product View on Hacker NewsSep 19, 2017

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1points

Did not reach leaderboard

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

61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

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

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

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

32%32% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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