why I'm excited about the Physics Derivation Graph

A few months ago I realized that rather than try to figure out what the best storage format was (MathML, Latex, Sympy, etc) and what database should store that representation (CSV, SQL, XML, etc), the "easy" solution was to simply store strings in dictionaries as a Python Pickle. No translation needed -- simply save the internal representation to disk and read it in as a Python variable.

Then I had an insight about how the front-end was supposed to work using the Model-View-Controller (MVC) approach. Now I felt comfortable about both the back-end and front-end aspects of the PDG! I had a backlog of features which were now easy and intuitive to implement. However, that didn't result in the excitement and motivation I now feel.

In the process of reviewing my hand-written notes from graduate school, I realized I now have actual hope of converting the notes to an electronic format. My reinvigorated interest in implementing the Physics Derivation graph is because I now have the relevant aspects figured out and see a well-defined end point.

That shifted my view of what I should be working on in the PDG code. Rather than working on arbitrary features, I am now focused on addressing aspects that are blocking me from converting my paper notes into PDG content.