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Reveal-App: AI Summarization of latest news

Hacker News

Reveal-App: AI Summarization of latest news

Hello Hacker News Community, I have created a website/application for aggregation and summarization of the latest news articles ( https://reveal-app.net ). How it works: It is a simple concept really. The backend grabs the news content (uses RSS) and then passes it through an LLM to create a summary of the news. The front end web app allows viewing the summaries. It’s built using NodeJS / NestJS for backend, Angular for the frontend. It’s hosted using Firebase and GCP, and uses Gemini Vertex AI for the LLM. Why I built it: Basically today there is just so much information, It’s hard to keep up. Many reputable news publications are now behind paywalls. It’s impractical to have news subscriptions to multiple publications. And news publications always seem to have a particular viewpoint. So it is difficult to get different viewpoints. And even If you really make an effort to go to different news sites, the whole ecosystem of online advertising and tracking makes the experience highly unsatisfactory (to me atleast). So I built this application to summarize the latest news from a variety of sources. You can read the summary and then decide if it's worth your time to go to the original article. So hopefully much fewer click baits. My target audience is folks who want to efficiently catch up with the latest goings on. What I am looking for: Any feedback positive or negative. Any suggestions on how to increase awareness in a cost effective manner. If it is even worth trying to increase awareness too. Please let me know what you think. – Jantric

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94%94% 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: new, gemini, using · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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Correct prediction on native model

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