When WebMD failed me, I built LaDoctura.com

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Hi guys, using a throwaway account for reasons that will become obviously shortly.. some time back i was diagnosed with a severe and incurable form of male infertility (i'm 35, recently married). that shock and what followed thereafter made me realise just how difficult it is to get 'the full picture' when you're dealing with a rare and still not fully understood disease (in my case the doctors believe its a genetic issue, DNA deletions, but they can't prove it). this made me realise there may actually be a huge unsolved problem right now, problem in that its virtually impossible to get rich, good quality medical info on diseases in one place on the web right now.. WebMD, as we all know, is horrible. i built ladoctora.com to tackle this problem. right now it only has one patient guide for my condition on it, but i'm thinking if people like the format and think there's a need for it, i can start adding more and do this as a dedicated project.. Would love to get your feedback on the site and the overall concept.. thank you. The website address: http://www.ladoctora.com

Visit product View on Hacker NewsAug 11, 2015

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

11%11% 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.

Incorrect prediction on native model

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