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Name That Blue - How well do you know your digital brand colors?

Hacker News

Name That Blue - How well do you know your digital brand colors?

Can you tell Facebook's blue from Instagram's blue? Know your Twitter from your Dropbox? Test your skills using my latest hack: Name That Blue http://colin-gourlay.com/namethatblue/ Most of the hex values are sourced from http://brandcolors.net/ (thanks @galengidman!), but I'm slowly adding others to make the challenge more engaging. I'd love to know what you think! Thanks

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Actual performance

12points
8comments
Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
33%33% predicted probability of success on Hacker News, 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: using · Missing: mac, agents, macos
28%28% 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
20%20% 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
8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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