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Gym Training Optimized by Science-Driven AI

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Gym Training Optimized by Science-Driven AI

I've been lifting for 7 years, and my passion for understanding the 'why' behind things took me deep into the science of hypertrophy. After a year of diving into scientific papers, I identified a clear disconnect between how most people train and what the research indicates as optimal. \ So, I created Hyperficient: an AI trained on 1000+ scientific research papers on hypertrophy. It fine-tunes workout programs, aligning them with scientific principles for enhanced efficiency — meaning less time in the gym and more growth. Beyond that, it educates you on the science underpinning your program.

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Actual performance

2points
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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
81%81% 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
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
50%50% 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, 000, io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: growth, training · Missing: arr, mrr, revenue
27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
17%17% predicted probability of success on AppSumo, 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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