Homer – A tool to build interactive textbooks

Hacker News

Hi HN, my name is Rahul Sarathy and I built Homer ( https://usehomer.app ), a tool to allow creators to build more interactive digital textbooks. Right now the software supports image based scrollytelling with a roadmap to add interactive components such as annotations, animations, backlinks, and more. You can check out a demo of an interactive history book here: https://usehomer.app/demo Here's another short example of how a personal finance related book could look like: https://usehomer.app/rahul/8 If you're interested in checking out the UI used to build the demo above, feel free to sign up at https://usehomer.app/signup and create a book.

Visit product View on Hacker NewsJul 9, 2021

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

59%59% 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 · Strong signals: io · Missing: https docs, excited, just released

52%52% 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 · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

45%45% 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

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

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

42%42% 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: active · Missing: arr, mrr, revenue

14%14% 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.

Correct prediction on native model

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