QABounty – Make money answering questions

Hacker News

I recently finished my site QABounty. I would get annoyed when I could not get anyone to answer my questions on sites like Stack Overflow so I created this site, https://qabounty.com/. It is basically your standard QA site but you attach a USD $$ bounty to questions. For example I have a question up at: https://qabounty.com/questions/what-kind-of-database-should-i-use-when-trying-to-store-10-to-15-million-rows-a-day/ The best answer for that question will award that user 3 coins (which can be withdrawn for about $3). I plan on adding my own questions of the day for a while, basically giving money away for a while hoping people use it for their own questions eventually. If you want to make a quick $3 though and check out my idea please go to the site and try to answer the question! Let me know what you think! I am also looking for a designer as well after I see if people like the idea.. I used Stripe's new withdraw to debit card feature so you don't have to use bank account info to withdraw your "bounty" (money you get for answering questions)... Please give me some feedback on the site/idea!

Visit product View on Hacker NewsJun 25, 2014

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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