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Newsbump – a news platform with annotated and versioned reports

Hacker News

Newsbump – a news platform with annotated and versioned reports

Hi HN! I have been toying with this idea for about a year and half and have built a prototype. Long story short: - A platform to request news updates about anything that traditional media has long forgotten. - A platform where anybody can post news updates - A news report format that can be annotated by the editor instead of ML programs extracting pieces of information e.g. facts and opinions - Explicit association between claimed facts and supporting evidence. - Versioned news reports - A place to discuss / chat with the reporter. Website: https://newsbump.in/ What do you think?

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
58%58% 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.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
48%48% 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
29%29% 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
29%29% 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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