AI research, skincare science, and company news
From the Tincyglo team
Why skin tone matters in AI skin analysis
Why we think about fairness across skin tones as we build and test our models.
Hydration vs. oil: why your skin can be both dehydrated and oily
Breaking down a common skin-type misconception, and what your scan results can reveal.
Why we're building Tincyglo free, in the open, first
How staying user-first while we build shapes our roadmap, from the AI to the API.
How we think about accuracy in AI skin analysis
What "accuracy" should mean for an AI skincare product, and how we're approaching it.
The ingredient list red flags our AI catches most often
A breakdown of the top five ingredient conflicts flagged by our Ingredient Intelligence engine.
What we're building next: the Tincyglo API
A first look at the developer API we're designing for clinics and brands.
Why generative AI needs guardrails in skincare advice
How we constrain our AI Skin Coach to stay grounded in your actual scan data.
What a 'skin health score' actually measures
A transparent look at how multiple skin markers combine into one number — and what it doesn't capture.
How we're thinking about privacy and security as we grow
A look at our approach to data privacy and security as we build toward clinical and enterprise use.
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