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Remake of 9M+ downloaded app (Yahoo News Digest)

Hacker News

Remake of 9M+ downloaded app (Yahoo News Digest)

Years ago Yahoo has shut down Yahoo News Digest. It was an app loved by millions. The idea is simple, a once daily news digest that delivers and unbiased 6-10 stories. Each story broken down into atoms; summary, quote, images, maps. With the advancement in LLM's it made it possible to bring this back. Its called Quill News Digest and it follows nearly the same format. Everyone is sick of scrolling through endless feeds or watching people yell at each other on tv. Quill delivers an unbiased and succinct digest that you can complete. Just like a newspaper. Once you're done you can get on with your day. It has really helped me out and I think it could be useful to you too. Check it out below. https://apps.apple.com/us/app/quill-news-digest/id1669557131

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apple, apps, new · Missing: mac, agents, macos
37%37% predicted probability of success on Product Hunt, 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.
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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