The Lemonade Machine Problem
Automation in medicine should not be judged against an ideal clinician. It should be judged against real human variance, machine variance, supervision, and the cost of failure.
· 4 min read
I Gave an AI Agent 94 Skills and Let It Help Run My Clinical, Coding, and Theology Workflows
Three weeks into running Hermes Agent in production, I can say this: the real value is not the model. It is the workflow ecosystem wrapped around it. Here is what 94 specialized skills looks like in a real physician-developer stack.
· 12 min read
The Architecture Behind My 24/7 AI Operations Partner
The model is not the product. The workflow is the product. Here is a technical look at the production stack behind a physician-built AI operations partner: Docker, Traefik, model failover, rate limits, logs, HIPAA boundaries, and the failure modes I watch for every week.
· 13 min read
When AI Told a Dying Man What He Wanted to Hear
Joe Riley trusted AI over his oncologist and died of a treatable cancer. His tragedy wasn't naivety — it was earned distrust, amplified by a machine that had no way to know the difference.
The PDF Wall: Why Your Clinical RAG Keeps Hallucinating
Your clinical RAG system is not hallucinating because the model is bad. It is hallucinating because your document pipeline is broken. Here is what clinical PDF parsing actually requires, and why Docling is the fix.
· 11 min read
Logs Before Intelligence: Why Data Discipline Must Precede AI Insight
Before you build any AI feature, you must first build the log. The principle every physician-developer needs to internalize before writing a single line of intelligence code.
· 8 min read
Skill Loss Is the Wrong Fear. Here's the Right One.
88% of physicians fear AI will erode their clinical instincts. That fear is real but misdirected. The greater risk is intellectual dependency on systems we didn't build and cannot interrogate.
· 8 min read
The AMA Is Right About Augmented Intelligence — They're Wrong About Who Should Build It
The AMA's 2026 survey shows 81% of physicians now use AI in practice. But read the fine print. Physicians want a seat at the table. The best way to earn that seat is to be the person who wrote the code.
· 9 min read
From the ICU to the Marathon Course: What AI-Driven Glycemic Control Teaches Us About Athletic Performance
A physician-developer explores the powerful parallels between AI-driven glycemic control in the ICU and metabolic management for endurance athletes with Type 2 diabetes, introducing the Performance Glycemic Intelligence System (PGIS) as a real-world n-of-1 framework.
I Didn't Download an App. I Described My Problem to an AI and It Built One for Me.
A Maternal-Fetal Medicine specialist describes how his personal AI health system identified low HRV, recommended breathing exercises, and prompted him to build a custom evidence-based breathing app in a single afternoon. A case study in disposable software, physician agency, and the future of personal health technology.
· 9 min read
Part 1: The $50 Billion Bait-and-Switch
Maternal-Fetal Medicine Specialist & Founder, CodeCraftMD
Part 2: What We Should Actually Build
Maternal-Fetal Medicine Specialist & Founder, CodeCraftMD
The Era of the Clinical Scratchpad: Why Every Doctor Should Build "Disposable" Software
For the past two decades, medical software has been synonymous with monolithic platformsEpic, Cerner, and their ilkmassive systems designed to serve e...
📊 Scalars and Vectors: The Building Blocks of Machine Learning in Medicine
When clinicians hear about artificial intelligence, we often jump straight to buzzwords like deep learning or neural networks. But behind every powe...
Python Programming for Physicians: A Complete Guide to Healthcare Analytics
Meta Description: Learn how Python programming can revolutionize your medical practice. From patient data analysis to AI diagnostics, discover practical...