They're generated from source code using a combination of static analysis + llm analysis. They're not editable per se, but you can update them when you edit your code.
It identifies telemetry, accessors, and other trivial code and strips them out of the graph. The idea is to retain architecturally relevant parts of the code.
You might be interested to look up the Williamson-Van der Mark model. It says that an electron is just light that loops in on itself and shows how properties like mass, charge and spin can emerge from such a geometry. It's fascinating because it opens up the question whether every particle could be described in terms of different ways light can loop in on itself.
Talking to insurance agents I realised, they don't bother to read the policy documents and have a very superficial knowledge of the policies they are selling. You can glean lot more information feeding the docs to an LLM and asking questions.
Really cool! I'm also dabbling with this idea. The biggest challenge I find is to reduce noise. Large codebases come with a lot of cruft. Surfacing every small detail in the visualization tends to make it messy and less useful. I've not seen contemporary tools tackle this, but think can be useful.
People said the same thing about the internet, that you should be getting your information from actual books and that the internet will make you lazy and complacent. There was a time when you were encouraged to write code on paper first instead of typing it directly because it made you think clearly. Some time before calculators apparently dulled your mental faculties, so you should be hand rolling all your calculations. Go back in time far enough and you'll find Socrates disparaging writing because it weakens your memory and destroys your mind. And yet humanity is here and seems to be doing all right. Every generation has managed to produce smart people that have been able to push the boundaries of scientific and technological progress. If anything we may be getting smarter. What history has repeatedly shown is that when you reduce friction for the human brain, it goes and finds more complex things to do. Such a periodic removal of friction, may very much be a necessity for progress, because it allows the paradigm of thought to shift to a higher level. The same should happen with AI as well.
Layout is one of those things humans do so easily and intuitively, yet you couldn't write an easy algorithm for it. I wonder if there's potential to use gen ai to achieve human like results. Anyone has any thoughts on feasibility and complexity of such an approach?
Now exploring on extending this to general programming languages. Software is getting increasingly complex, but I don't think we have figured out many ways of navigating large codebases. Would love to hear about your favourite tools in this space and what would you like to see?
This is a cool problem to solve! especially as more and more code is being generated with AI. But are you pivoting away entirely from code visualization? Curious to hear why it didn't work out.