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Unit Calores – Calculate TDEE from First Principles

Hacker News

Unit Calores – Calculate TDEE from First Principles

I mainly built this for the UI/UX and the problem just happened to be something that interested me as someone who counts their calories. If your familiar with this area you'll know about the debates and misnomars around these components and how they should be calculated. Typical calorie counters can't handle the combo of a desk job and a rigorous workout routine. Unit Calories calculates your TDEE from first principles, giving you an accurate daily calorie target tailored to your unique lifestyle and exercise routines. If anyone is curious, the underlying calculation discounts active calories by 28%. I did this because of this research paper ( https://www.sciencedirect.com/science/article/pii/S096098222... ) I found which concluded that extra calories burned get offset by a reduction in your bmr (basal metabloic rate).

Visit product View on Hacker NewsOct 29, 2023

Actual performance

3points

Did not reach leaderboard

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77%77% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

44%44% 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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

18%18% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

18%18% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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