A local-first eBook reader with a journal for each book you read

Hacker News

I made this eBook reader because I was looking for something (especially, after I got a foldable phone) that would allow me to effectively: 1. have a journal for every book I read. Taking notes is easier when I can mark up the book + insert my own pages as needed 2. have an RSVP (Rapid serial visual presentation) reading mode, not for the speed reading perspective, but for the focus 3. have all my data be stored locally on Windows or Android and be exportable as plain text whenever I wanted This was deeply personal, and I've been using Ms. Penrose to read more. There's a bunch of bugs, it is still an alpha build (for example, the TTS tokenization is rough right now and reads aloud awkwardly). The stack, for those curious: Flutter + Flame foliate‑js (MIT) pdfium for PDF sherpa‑onnx (offline Piper TTS) for read‑aloud If you try it out and have any features/feedback in mind, please share! EDIT: Here are some videos of the app and features: https://forgottenmachine.substack.com/p/the-making-of-ms-pen... Thank you!

Visit product View on Hacker NewsJun 29, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, visual, using · Missing: agents, macos, agent

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

58%58% 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

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

TrustMRRLess likely to generate early MRR · Strong signals: personal, video · Missing: mobile apps, ios, entrepreneurs

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

22%22% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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