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An Introduction to AI Knowledge Sets

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An Introduction to AI Knowledge Sets

The paper linked to here (1) provides a simple introduction to AI knowledge sets that you can run in GPT4o. (1) https://github.com/ersatzdais/ai_study?tab=readme-ov-file#kn...

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TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
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52%52% predicted probability of success on Product Hunt, 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
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
30%30% predicted probability of success on BetaList, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
25%25% predicted probability of success on AppSumo, 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.

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