filipacsr

Code: https://github.com/FilipaCSR/FitAge

Full methodology, with the source, protocol, and cohort for every marker: https://medium.com/@filipacsr/how-to-measure-your-biological...

Thank you for raising this, I really appreciate you saying it. Bias in facial recognition and health tools is real, especially for trans and gender-diverse people, and it’s something I take seriously.

In this specific case, the model I’m using (KDM biological age) relies on sex-specific medical reference parameters from the original research datasets. The field isn’t used to define identity — it’s only to apply the correct physiological coefficients.

That said, I recognize that models like this were built on limited population data, and they may not perfectly represent everyone’s biology. I’m actively thinking about how to communicate those limitations transparently and improve inclusivity over time.

Thank you again for calling this out, it genuinely matters.

Hi! You make a fair point - the goal here is genuinely to share knowledge, not gate it. Try this link instead, which should bypass the login wall: https://medium.com/@filipacsr/i-always-wanted-to-know-my-tru...

And if you'd like to try the app itself: https://huggingface.co/spaces/filipacsr/TrueAge

Hope you enjoy it! And for the code: https://github.com/FilipaCSR/TrueAge

The overall value of each category is truncated to the maximum value of that category - for example, if you have 10 open ports, you will get an overall score in "Attack Surface" category of 2, because it's the maximum value (vulnerability importance) that we attributed to that category. The final score is the sum of the overall values of all categories, and then it's normalized between 0 and 100. If you have all 0 in Encryption, and then the overall result for that category is 3, something went wrong... thank you for your feedback!