BadgeFace – user badges for Facebook groups

Hacker News

A Chrome extension that allows you to add badges, beside your user image, in Facebook group discussions. Badges are visible to other BadgeFace users. You can also add your own badges that are specific to the group (your favorite soccer team, etc) via image url. * Side Project. * I'm currently adding more badges to the repository. * Also recently added the option to add credits to image authors (still figuring the UX, but until then, it's saved in the DB). * Submitted this project to HN 3 months ago, but that was a POC, and has been 90% rewritten. https://chrome.google.com/webstore/detail/badgeface/jlpioeph...

Visit product View on Hacker NewsJan 24, 2019

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

61%61% 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.

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

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% 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, io · Missing: https docs, excited, just released

34%34% 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

17%17% 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.

Correct prediction on native model

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