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StayUpAI – A daily curated feed of AI news, research, and videos

Hacker News

StayUpAI – A daily curated feed of AI news, research, and videos

Hi HN! I just launched StayUpAI, an all-in-one platform to stay ahead in the AI world. It aggregates AI news from multiple sources (articles, research papers, YouTube), then uses AI to summarize all news daily or weekly. Core features: - Unified AI news feed (with source/language/theme filters) - Daily & weekly AI summary emails - Summaries for each news item (papers, YouTube, articles) - Multilingual news (with translation) - Free to start, no personal info needed for a quick test I built this because tracking AI news is overwhelming, and I wanted a tool that does the curation (and summarization) for me. Would love feedback, especially on: - What info/filters/features are most useful to you as a reader? - Design/UX tips for the feed or summaries? - What’s missing for your ideal AI news aggregator? You can try it here → https://stayup.ai Thanks!

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Actual performance

3points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
68%68% 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, email · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, video · Missing: mobile apps, ios, entrepreneurs
31%31% 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
24%24% 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
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · 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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