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PagePal – An eBook reader for ADHD and crap memory

Hacker News

PagePal – An eBook reader for ADHD and crap memory

Hey HN! I've been working on PagePal, an eBook reader designed to help people with ADHD and/or poor memory actually follow and finish books (of course, many people with ADHD can read fine, I just happen to have the kind that gives me Dory-like working memory). A little over a year ago, I was trying to get into reading. I’d be enjoying a book, then hit a wall as soon as too many characters or place names came up (it doesn't take much — five character names and I’m lost). I remember thinking: “If I could just tap on a name and see who this is...”. Then I realised AI could make this possible. A few years earlier, I left my career to learn software, hoping to make a cool educational app one day, but I wasn't sure what form that would take. This, combined with my desire to read more easily is how PagePal was born. I’ve since teamed up with another developer, and we’re now looking toward investment and early traction. There are plenty of book summary products out there, but most are about skipping the book, not helping you read it. PagePal is about helping more people actually read. A few core features: - Instant, spoiler-free summaries up to your current page: Last Page Recap, Story So Far, and Character/Story Elements - Simplify and explain tricky text or passages - 100+ classic books in a curated library - Available on iOS & Android The summaries are AI-generated and pre-generated for each book, so they load instantly. There’s a short demo video under the “No more re-reading pages!” section on the site. Beta is starting soon. Would love your thoughts. Feedback, questions, or critiques are all welcome. https://www.pagepalapp.com

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
95%95% 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: adhd, ide, io · Missing: https docs, excited, just released
64%64% 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: plain · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, video, education · Missing: mobile apps, personal, entrepreneurs
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
28%28% 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.

Correct prediction on native model

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