Brief News Daily

Brief News Daily

Indie Hackers

AI-powered news briefs for minimalists, no noise, no ads.

I built BriefNewsDaily to stay informed without ads, clutter, or manual AI prompts. It finds top news and summarizes it in 100 words. Daily read time: 5 minutes. Perfect for minimalists who just want the facts.

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
24%24% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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BetaList
Ha
HackerNews News Recommender59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

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News for AngelList.43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

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