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The Jeff Bezos-Washington Post Conflict-of-Interest Crayon

Hacker News

The Jeff Bezos-Washington Post Conflict-of-Interest Crayon

I made this bookmarklet to highlight potential conflicts of interest related to Jeff Bezos in Washington Post stories. It's basically a quick fork from an existing GitHub repo. Anyone can suggest keywords at a wiki linked to from the GitHub page here. Tested and works in Chrome and Safari; doesn't work in my Firefox but I'm too ignorant to know why. Any help with that would be appreciated, as would any and all contributions to the wiki.

Visit product View on Hacker NewsAug 7, 2013

Actual performance

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

59%59% predicted probability of success on Product Hunt, 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 · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

37%37% 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 · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

23%23% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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