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AI Summary Helper 2.0
Summarize web content, map your reading graph & Kindle
AI Summary Helper enhances your browsing experience.
I’m Phil, a product designer and open-source builder. I originally created AI Summary Helper to solve a problem I deal with every day: digital fatigue, tab overload, and the sheer volume of unread articles accumulating in my browser.
💡 Why AI Summary Helper 2.0? We don’t have an information shortage—we have a processing crisis. True digital literacy in 2026 isn't about hoarding 50 open tabs; it's about absorbing what matters, from your preferred angle, in your native language, when it suits you best.
🚀 What’s New in 2.0: • 100+ AI Models via OpenRouter / ByPhil Cloud: As a European designer, vendor lock-in was a non-starter for me. Whether you prefer European models like Mistral, global standard-bearers (OpenAI, Gemini, Claude), or emerging regional LLMs, you can now toggle between 100+ models seamlessly or bring your own API key. • Send to Kindle Integration: Inject summaries into web content and forward formatted briefings directly to your e-reader for distraction-free, off-screen reading. • Local Knowledge Graph & Analytics: Visualize your reading history as an interactive network map. Discover recurring topics, track your reading streaks, and measure your time saved. • Save for Later & Speed-Reading (RSVP): Assign review timeframes to tabs (Tomorrow, Weekend, Research Session) with optional notes, speed-read streaming summaries, and let tabs auto-close. • 100% Local-First Privacy: All your article history, knowledge graphs, decision notes, and text highlights stay stored locally on your device.
I’d love to hear your feedback, feature ideas, and how you manage your daily reading queue. I'll be in the comments all day to answer any questions!
Happy reading,
Phil 🚀
About AI Summary Helper 2.0 on Product Hunt
“Summarize web content, map your reading graph & Kindle ”
AI Summary Helper 2.0 was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #152 on the daily leaderboard. AI Summary Helper enhances your browsing experience.
On the analytics side, AI Summary Helper 2.0 competes within eBook Reader, Productivity, GitHub and Tech — topics that collectively have 1.3M followers on Product Hunt. The dashboard above tracks how AI Summary Helper 2.0 performed against the three products that launched closest to it on the same day.
Who hunted AI Summary Helper 2.0?
AI Summary Helper 2.0 was hunted by Phil Wornath. 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.
Phil Wornath
Hey Product Hunt! 👋
I’m Phil, a product designer and open-source builder. I originally created AI Summary Helper to solve a problem I deal with every day: digital fatigue, tab overload, and the sheer volume of unread articles accumulating in my browser.
💡 Why AI Summary Helper 2.0?
We don’t have an information shortage—we have a processing crisis. True digital literacy in 2026 isn't about hoarding 50 open tabs; it's about absorbing what matters, from your preferred angle, in your native language, when it suits you best.
🚀 What’s New in 2.0:
• 100+ AI Models via OpenRouter / ByPhil Cloud: As a European designer, vendor lock-in was a non-starter for me. Whether you prefer European models like Mistral, global standard-bearers (OpenAI, Gemini, Claude), or emerging regional LLMs, you can now toggle between 100+ models seamlessly or bring your own API key.
• Send to Kindle Integration: Inject summaries into web content and forward formatted briefings directly to your e-reader for distraction-free, off-screen reading.
• Local Knowledge Graph & Analytics: Visualize your reading history as an interactive network map. Discover recurring topics, track your reading streaks, and measure your time saved.
• Save for Later & Speed-Reading (RSVP): Assign review timeframes to tabs (Tomorrow, Weekend, Research Session) with optional notes, speed-read streaming summaries, and let tabs auto-close.
• 100% Local-First Privacy: All your article history, knowledge graphs, decision notes, and text highlights stay stored locally on your device.
I’d love to hear your feedback, feature ideas, and how you manage your daily reading queue. I'll be in the comments all day to answer any questions!
Happy reading,
Phil 🚀