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Sam Altman video clone trained on 100h of Sam's startup content

Hacker News

Sam Altman video clone trained on 100h of Sam's startup content

The idea: Make Sam Altman your personal startup mentor, always available to go deep on your startup and give you targeted advice. Our larger goal is to develop indistinguishable intelligent clones (IIC) so everyone can meet. We started with Sam Altman because we wanted to meet him as founders ourselves. We are reaching out to personalities directly for permission. Our plan with Sam is to provide something helpful to early stage founders in private beta so we can go to him with validation. Long term we see IIC as a world-changing event on the scale of AGI, but with natural interaction and greater connection between people. Anyone else thinking about IIC?

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Actual performance

2points
6comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
84%84% 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.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs
42%42% 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
38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% 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
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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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