HN
HN to E-Book – Read HN on your E-Reader
Hacker NewsHN to E-Book – Read HN on your E-Reader
I read HN on my kindle as I find it useful for reading under sun(while walking in a controlled environment) and taking notes. I was running hntokindle.com to offer this as a service for others. But Amazon has recently made sending bulk emails to the Kindle impossible by requiring 2FA over registered Amazon email address for each item sent to the Kindle. Hence I've stripped down HN to Kindle code to enable local transfer to any e-book reader which supports .mobi format and made the project open-source.
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86points
34comments
Made the leaderboard
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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created86%86% 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.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: email, notes, code · Missing: mac, agents, macos65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive31%31% 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, revenue16%16% 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, crypto4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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