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Replying to @johnathan27
I’m Johnathan - a full-stack engineer who ships web applications from ground-up to production. Next.js for web, Supabase for backend, and AI/LLMs where they add real value. 8+ years building AI-powered products for healthcare, fintech, real estate, and SaaS teams.
Exactly - Everything looks fine until a CarePlan references an Observation that got dropped in normalization, and now a clinician is making decisions on incomplete data.
The part that changed how I think about data pipelines generally: in most software, bad data is a bug you fix. In clinical systems, bad data has a downstream human consequence. That constraint forces a level of defensiveness in the schema design you don't always apply elsewhere.
Co-Founder building ambitious software at the intersection of AI, automation, visualization, and creative systems. Currently building HelioraAI, a celestial intelligence platform, and working on autoretto, an autonomous music production pipeline. Shipping fast, learning faster.
that defensiveness mindset is so real. i hit a softer version of it with ephemeris data, if a planetary position is off even slightly the whole chart reading is wrong and people make life decisions on it. forces you to treat the data layer as sacred not just functional