My first ever e-book, Hacking Obamacare

Hacker News

This is my first-ever e-book, www.hackingobamacare.co I was lurking on HN for a while reading comments and suggestions on how to write one. Thank you to everyone and their deep knowledge on what does and doesn't work. I wrote hacking obamacare this because I kept seeing lots of my friends, family and loved ones struggling to figure out how to make the health care system work for them. I've been fortunate given that I've been working in the industry for nearly a decade and know where to look and who to talk to if I'm unsure about something. This e-book is by no means a comprehensive review of Obamacare and a commentary on its merits as a cure for a broken healthcare system. It's simply for 20-30 somethings who don't know about the health care system, who use the Internet and their phones for everything and want a leg up on everyone else. Hope you pick up a copy. Drop me a line at david@hackingobamacare.co to let me know what you think. I'd love any feedback. Oh and 25% of the proceeds go to organizations that make healthcare suck less.

Visit product View on Hacker NewsDec 19, 2013

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

81%81% 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: mac · Missing: agents, macos, agent

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

68%68% 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 · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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

19%19% 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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