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I made an AI-digested news to help you survive information overload

Hacker News

I made an AI-digested news to help you survive information overload

Hi HN, I made an AI-digested news platform called Tailored News to help people efficiently consume content in today’s information-overloaded world. Our platform uses AI to curate and summarize the most important stories, providing concise insights and weekly audio briefings. This approach ensures that you stay informed without feeling overwhelmed. Key Features: - AI-Digested Content: We use AI to summarize news, highlight key points, and generate an audio version - Full Context in a Nutshell: I created the Minimum Context Understanding (MCU) concept to provide brief context summaries for each piece of content, delivering a complete or most important understanding of long texts in just a few sentences. - Comprehensive Summaries: More detailed summaries to give you in-depth understanding. - Q&A Section: Questions and answers to help clarify key aspects of the news. - Weekly Audio Briefings: Listen to concise summaries of the week’s top stories. I’m looking forward to your feedback and any suggestions you might have!

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Actual performance

6points
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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, efficiently · Missing: supports, reddit linkedin, podcasting
69%69% 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, context · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, efficient · Missing: plus, intuitive, reviews
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: answers · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
8%8% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · 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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