How Christianity Went Viral, and Mad Madras

“He who has never sinned is less reliable than he who has only sinned once.” - Nassim Taleb

How Christianity Went Viral: In my previous careers, I created and grew software, including viral applications. The insights on viral growth are what allowed me to quickly understand COVID, and when I look at the rise of Christianity, I see the exact same patterns: a piece of software hyperoptimized for growth. The Christian pitch was so much better than that of the competition that good missionaries could convert thousands of people... Women in general are a better target than men for early conversion to a new religion: They tend to make up the biggest share of new members in cults, are more religious, more prone to believe in the supernatural (e.g. astrology), and in certain circumstances are more easily persuaded. And in the Ancient World, they were the most underserved audience... Peter Turchin has this theory that lots of revolutions are caused by secondary elites, who are close enough to power that they can see it and experience it, but for whom full access is barred. They become the angriest and tend to lead revolutionary movements. That’s indeed the segment that Christianity targeted.

Mad Madras: Patchwork Madras has its roots in traditional Indian handwoven cotton cloth from the Chennai region (formerly Madras). The fabric is known for its lightweight feel and yarn-dyed colour, which naturally softens over time. In its original form, Madras often features slight irregularities in tone and texture, which is part of its character. Patchwork interpretations take that idea further, combining multiple Madras fabrics into a single garment for a more expressive finish.

AI Protein Design: Proteins are the tiny molecular machines that run every living thing. Insulin, antibodies, enzymes that digest food, the spikes on viruses — everything important in biology is a protein. A protein is a long string of amino acids (think of them as 20 different Lego colours). The exact order of those colours determines how the string folds into a precise 3D shape. That shape decides what the protein can do: lock onto a cancer cell, break down plastic, neutralise a virus, etc. For decades, designing a brand-new protein was almost impossible. Scientists had to guess the right sequence, build it in the lab, test whether it folded correctly, and repeat — thousands of times. It was slow, expensive, and mostly blind luck. Then came AlphaFold (2021–2024), which solved half the problem: given a sequence, it could predict with stunning accuracy how it would fold. The new generation — generative AI protein design platforms — solves the other, much harder half: you describe the function you want (“Make me a protein that sticks perfectly to this cancer target and ignores everything else”), and the AI invents the exact amino-acid sequence that will fold into that perfect shape. It doesn’t copy nature; it creates new proteins that have never existed before.