LaterAI
AI-powered reading, 100% on your device
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Worathiti Pung

LaterAI - AI-powered reading, 100% on your device

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LaterAI
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LaterAI turns saved articles into a personal reading experience powered entirely by on-device AI. No sign-ups. No cloud. No data leaving your phone. Save from any app with one tap. Listen with built-in TTS engines offline. Get AI summaries, daily digests with quizzes and hot takes, and personalized recommendations, all running on-device. Explore 150+ curated sources, track reading streaks, and highlight what matters. No account. No tracking. Your reading habits are nobody's business but yours.

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Worathiti Pung
Maker
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Hey Product Hunt! 👋 I built LaterAI because I was tired of read-later apps that need an account, sync to someone else's cloud, and treat my reading habits as data to monetize. I wanted something different: an app where AI actually helps you read, summarize, text-to-speech, smart digests, but the best part is that everything stays on your phone. No servers. No analytics. No "sign in to continue." A few things I'm proud of: 🔇 Two TTS engines that run offline: they handle abbreviations, numbers, and natural pausing so articles actually sound good. Looking forward for you to try it out. 🧠 On-device AI digests: every day you get theme insights, quiz cards, and hot takes generated from your saved articles, all without an API call 𝕏 Supports social media platforms such as X, Reddit, or even LinkedIn 🔒 Truly private: thanks to Apple Silicon, there's no backend. I couldn't see your data even if I wanted to. But it’s still synced across all devices (through private iCloud CloudKit). This is a solo project and I'd love your feedback. What features would make LaterAI your go-to reader? Happy to answer any questions!
Sviatoslav Dvoretskii

Running AI summaries and daily digests entirely on-device with zero cloud dependency is the privacy gold standard that most "private" apps claim but don't deliver. The quiz cards generated from saved articles are a clever retention mechanic that actually reinforces what you read. How large is the on-device model, and does it noticeably impact battery life during longer digest generation sessions on older iPhones?

Worathiti Pung
Maker
@svyat_dvoretski Great question and thanks for noticing the on-device approach, that was really important to me. The model is relatively small and optimized for Apple’s on-device ML stack. In practice, generating a digest or quiz cards only takes a few seconds and runs as a short burst of processing rather than a long session. So far I haven’t seen any noticeable battery impact in testing, even on older iPhones. It might take a bit longer to generate, but the workload is brief and stops immediately after the digest is created.