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Reading long text is exhausting — your eyes fatigue, your mind wanders, and plain TTS sounds like a robot reading a manual. ModernReader is a multi-sensory AI reading system on the CRA model: parse Markdown/EPUB/TXT, read aloud with rhythm-aware voice, tag emotions (EN & ZH), and AI-summarize. Runs as PWA, desktop, or Capacitor shell — self-host via Docker. Built for deep readers, students, and anyone with visual fatigue. Finalist — 2026 Cross-Strait Entrepreneurship Competition.
First time here? 👋 I'm TeWei, the maker of ModernReader.
ModernReader started from a simple frustration: long-form reading online is exhausting — your eyes fatigue, your mind wanders, and default TTS sounds like a robot reading a manual. So I built a reading system around the CRA model (Comprehension-Rhythm-Affect): it parses Markdown/EPUB/TXT, reads them aloud with rhythm-aware voice (ElevenLabs or local gTTS/ffmpeg), tags the emotional tone of every passage (joy, sadness, anger, fear, surprise, disgust — in EN & ZH), and summarizes with AI when you're short on time.
It runs anywhere: as a PWA in your browser, a desktop demo, a Capacitor mobile shell — or fully self-hosted with Docker in one command.
If you're a deep reader, a student, or someone whose eyes need a break, I'd love your feedback: - What's the first thing you'd want to import? - Which emotion tags feel most useful to you? - Would you use voice-guided reading on your commute?
Happy to answer any question about the architecture (TypeScript core, CoreML LSTM, FastAPI) — ask away in the comments. ⭐ a star on GitHub helps a lot too!
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About ModernReader on Product Hunt
“Read with rhythm. Feel the words.”
ModernReader was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #129 on the daily leaderboard. Reading long text is exhausting — your eyes fatigue, your mind wanders, and plain TTS sounds like a robot reading a manual. ModernReader is a multi-sensory AI reading system on the CRA model: parse Markdown/EPUB/TXT, read aloud with rhythm-aware voice, tag emotions (EN & ZH), and AI-summarize. Runs as PWA, desktop, or Capacitor shell — self-host via Docker. Built for deep readers, students, and anyone with visual fatigue. Finalist — 2026 Cross-Strait Entrepreneurship Competition.
ModernReader was featured in Open Source (69.1k followers), Education (79.5k followers), GitHub (41.5k followers) and Inclusivity (561 followers) on Product Hunt. Together, these topics include over 90.4k products, making this a competitive space to launch in.
Who hunted ModernReader?
ModernReader was hunted by Te-Wei Ko. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how ModernReader stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Te-Wei Ko
First time here? 👋 I'm TeWei, the maker of ModernReader.
ModernReader started from a simple frustration: long-form reading online is exhausting — your eyes fatigue, your mind wanders, and default TTS sounds like a robot reading a manual. So I built a reading system around the CRA model (Comprehension-Rhythm-Affect): it parses Markdown/EPUB/TXT, reads them aloud with rhythm-aware voice (ElevenLabs or local gTTS/ffmpeg), tags the emotional tone of every passage (joy, sadness, anger, fear, surprise, disgust — in EN & ZH), and summarizes with AI when you're short on time.
It runs anywhere: as a PWA in your browser, a desktop demo, a Capacitor mobile shell — or fully self-hosted with Docker in one command.
If you're a deep reader, a student, or someone whose eyes need a break, I'd love your feedback:
- What's the first thing you'd want to import?
- Which emotion tags feel most useful to you?
- Would you use voice-guided reading on your commute?
Happy to answer any question about the architecture (TypeScript core, CoreML LSTM, FastAPI) — ask away in the comments. ⭐ a star on GitHub helps a lot too!