DIYPS.org

I recently swanned past 24 years of living with type 1 diabetes.

Recently I was asked to give a talk to a group of endocrinologists and other diabetes providers about exocrine pancreatic insufficiency, and while building my presentation I was thinking through everything I wish endocrinologists knew about EPI.

I have previously written about how I work with AI and the kinds of things I do with it.

One of the critiques I hear from others about AI is lack of credit for work.

I originally built Protocol Pilot for myself, to keep track of a complicated medication trial that involved slowly ramping up my dosing and keeping track of side effects & my resulting experience (hopefully, improvements).

That time I built a project… to keep track of my projects.

It’s awesome and terrible at the same time in this day and age to be handed a medicine with a complicated protocol and given no ideal way to visualize or track the schedule and the feedback from the medicine.

Why is everything a titration problem; why does the traditional healthcare / research system not solve titration for so many therapies; and why do I always find myself having to do it?

Reader, I bought longer shorts.

When you’re diagnosed with celiac disease, it is nice in the sense that you have a known issue (consuming gluten causes an autoimmune reaction and damage to your body) and that the solution is known: eating 100% gluten free.

Note: I’m assuming if you are reading this post that you’ve first read this post, summarizing the concept.

We need more signal, which means we want more noise.

I talk a lot about the data we leave behind.

I thought I would share some of how I am using AI.

I have to ask myself these days, is this a software-shaped feeling?