Hu

Humans Did What? - Index of humans.txt files

Hacker News

Humans Did What? - Index of humans.txt files

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Actual performance

1points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
63%63% 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.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
55%55% 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
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
34%34% predicted probability of success on BetaList, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
22%22% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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Hashtags for Humans by Humans

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Aioserial, pyserial-asyncio for humans62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

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UChicago admissions asked me to find Waldo. I did.63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

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Did you ever stuck between two dresses?53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

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no
nomadlist.txt41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

nomadlist.txt

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Base24 binary-to-text encoding for humans52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Base24 binary-to-text encoding for humans

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