The Palmdoc Chronicles

I did not set out to become a programmer. I wanted to solve a problem. Doctors need simple, clean ways to share their professional identity. Patients need easy access to reliable contact details. And most existing “digital business card” tools are either bloated, generic, or too rigid. So I decided to build my own. Starting Point: Almost No Coding Background I have minimal coding experience. Enough to understand structure, not enough to build a full platform from scratch. In the past, this would have stopped me. This time, I used Claude Code. That changed everything. The Shift: From Coding to “Vibe Coding” Instead of thinking like a traditional developer, I approached it differently. I described what I wanted. I iterated quickly. I tested constantly. I refined based on feel and usability. This is what I call “vibe coding.” You are not writing every line manually. You are guiding the system. You are the product owner, QA tester, and designer at the same time. The AI becomes your developer. What I Built I ended up building a multi-user virtual business card platform with two key products: 1. vCard for Doctors 2. vBizCard for Everyone This was not just a static page…

Like many clinicians, my mornings are compressed. Ward rounds, clinics, calls, meetings. The day starts fast, and the last thing I want is to manually scan my calendar while rushing out. I wanted my phone to simply tell me my schedule, clearly and automatically. Not just the event titles, but the start times as well. So I decided to build a Shortcut on my iPhone that would read out my appointments each morning. The problem? Apple Shortcuts is powerful, but not always intuitive. That is where ChatGPT helped. The Objective Create a Shortcut that would: Pull all calendar events for the day Exclude irrelevant entries Speak each appointment Include the start time Sound natural when read aloud In short, I wanted something that behaved like a quiet personal assistant. Where I Got Stuck Initially, I made a common mistake. I used “Get Text from Calendar Events.” It looked logical. But it flattened the data into messy text. Once that happens, you lose access to structured fields like: Event title Start time Location ChatGPT immediately pointed out the flaw. Do not convert structured data into text too early. Instead, iterate through the events properly. This single correction saved me a lot…

Patients are saying something the medical system should not ignore. Some credit AI tools like ChatGPT for flagging danger when humans did not. Not because AI made a diagnosis, but because it pushed them to act when the system stalled. In these stories, people pasted symptoms, lab results, or scan reports into a chatbot. The AI highlighted red flags and urged urgent care. Clinicians later confirmed serious disease. The value was speed, pattern recognition, and persistence. Not certainty. This does not make AI a doctor. It makes it a gap-filler. Short consults, fragmented records, delayed follow-up. These are real failures. A language model that reads everything and responds instantly can change the timeline. In emergencies, that matters. Doctors should be skeptical but not dismissive. Yes, AI can hallucinate. Yes, it lacks examination and accountability. But it also listens without interruption and never gets tired. Ignoring that advantage is risky. The bigger risk is not patients using AI. The bigger risk is pretending they are not. Patients will use these tools anyway. The smart move is to guide them. Position AI as triage and augmentation, not diagnosis. Build pathways for rapid human review when AI raises alarms. When a patient says…