Booky – app to organize notes from books you read

Hacker News

Hi folks, I've launched "Booky" - app for organizing notes from books you read. App Store: https://apps.apple.com/pl/app/booky-organize-books-notes/id1492092565 With the app you can: 1. Snap picture of the book while you're reading it 2. Quickly highlight your favorite quotes 3. Sync your book shelf with Goodreads 4. Search in the notes you scanned Promo video: https://www.dropbox.com/s/lfnwb8cvi5oyfd9/preview.mov?dl=0 Those features are similar to what you might find in ebooks apps but the app enables you to do that with physical books. I'm not sure how to build any business model around it so I'd love to hear your suggestions :)

Visit product View on Hacker NewsDec 20, 2019

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

76%76% 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: model, apple, apps · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, video · Missing: mobile apps, ios, personal

64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% 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

35%35% 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

21%21% 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.

Incorrect prediction on native model

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