my iBooks Author design and publishing guide

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I wrote a guide to iBooks Author, Apple's digital book design software. I just posted it for sale on my Shopify store. I'd greatly appreciate any and all feedback regarding my blog and Shopify store (both rough, I know), my website copy, the concept, the price, and so on. Blog post about the guide: http://inkslingerindustries.com/2012/06/announcing-how-to-make-a-book-with-ibooks-author/ The guide in my Shopify store: http://store.inkslingerindustries.com/collections/frontpage/products/how-to-make-a-book-with-ibooks-author Thanks!

Visit product View on Hacker NewsJun 12, 2012

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3points

Did not reach leaderboard

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

72%72% 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 NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

38%38% 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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

16%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, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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