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Sticky News Headlines Without Photos

Hacker News

Sticky News Headlines Without Photos

I put this together a few weeks ago. It polls for news headlines every hour and the ones that are there again and again rise to the top: https://wolfschedule.com/news So if you make it all the way down to the score of "1" those are very fresh, only seen in the last hour and there is no guarantee they will rise. But some do rise and if they are at the very top they have been seen over and over for the past 24 hours. Max high score is 24. Similar to legiblenews, there are no photos, just the text to scan. I like the freshness of this system. Every time I want to know "ok so what are the BIG stories" I scan the top score ones and sometimes I make it down to low score ones.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · 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.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
29%29% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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