Re

Readwise Reader, an all-in-one reading app

Hacker News

Readwise Reader, an all-in-one reading app

Hey HN, cofounder of Readwise here. We've been working on this cross-platform reader app for about 2 years, excited to finally share it in public beta. Probably the most notable thing that makes Reader unique is that it supports almost any content type you could want to save/read/highlight: * web pages * emails/newsletters * PDFs * ePubs * twitter threads * youtube videos (with transcripts) * RSS feeds With all of your knowledge content in one place, we built powerful reading and highlighting, as well as a bunch of novel triage/organization features, so you can actually consume & stay on top of that content! There are also a lot of advanced features too, such as text-to-speech, GPT3 questions/summaries, super powerful highlighting (that includes markup and images), complex filtering/search (with our own query language), sleek mobile triage UI, keyboard shortcuts for reading/everything, integrations with note-taking apps, a browser extension for both saving pages and highlighting them, and much more. If anyone's interested in more product details, as well as our business model, etc, we wrote a detailed launch post: https://blog.readwise.io/the-next-chapter-of-reader-public-b... Predicting a common question: Reader is part of the Readwise subscription pricing right now in beta -- there's a 30 day free trial and then it's paid at ~$8usd/month. We also promise to not raise this price for existing subscribers. Reader is also fairly technically interesting -- our iOS, Android and webapp all work fully offline and sync your reading data/progress with eachother. Our search on web is built with wasm sqlite. We have a fairly intense pipeline for cleaning web articles (removing ads/styling). We share lot of modules around syncing/highlighting across all platforms, etc... Happy to answer any questions :)

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, ios · Missing: reddit linkedin, podcasting, created
90%90% 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, apps, new · Missing: mac, agents, macos
88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription, subscribers · Missing: arr, mrr, revenue
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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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