I am something of a data scientist myself, I'd say that people who like data are my intended audience.
Regarding correlations I agree, I have a disclaimer that correlation is not causation in the About page / README. But it's still interesting to see correlations and you can make further investigations into whether there is a causal relation or it's a coincidence.
Hi all, I built this app (called Meetrics) to help me track my personal health data. I was diagnosed with colon cancer and have been on medical leave so lots of free time, and recently I read that survivors are usually people who get very involved in their treatment which pushed me to build this app and start logging everything like a maniac and doing data science on my health data.
However there is not enough data yet from the cancer to write an interesting blog post (I plan on writing one on how I use it for cancer in a month or so once I have more data). That's why I wrote this economics blog post with the help of Claude. It walks you through how to use the app and it's actually quite interesting if you're interested in macro-econ like me. The app is targeted for people who like data, dashboards, and metrics; and who want to have an easy way to run experiments on their own personal data (e.g. Thompson sampling is implemented for binary data).
There is also a premium subscription where you get an AI Analyst (powered by Claude) that has access to your (anonymized) data. But the app is very useful without the Analyst too, especially if you are into playing around and exploring data.
Regarding correlations I agree, I have a disclaimer that correlation is not causation in the About page / README. But it's still interesting to see correlations and you can make further investigations into whether there is a causal relation or it's a coincidence.