Crowd Sourcing Pain Points

Hacker News

I made an MVP last night for a site that would allow people to start topics about annoyances they have that, presumably, developers/hackers would be interested in solving if they knew there was an audience out there that would appreciate the solution. I had written a web app that I never released a few months back that would, if modified, be perfect for such a discussion site. However, as it would take a bit of reworking, I wanted to see if there was any interest out there for such a site first. Any advice as to what direction you would like to see such a project take or what would make this project useful to you would be highly appreciated. Regards, Nick

Visit product View on Hacker NewsFeb 9, 2012

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

60%60% 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.

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

45%45% 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: month · Missing: mobile apps, ios, personal

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

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

38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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.

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