A few technical details for those interested in the behind the scenes:
Why AST? -> Simple regex or string parsing fails as soon as you hit complex decorators or nested classes. By using Python’s native AST module, we can get an objective map of the logic flow that doesn't care about formatting or comments.
The Visualization Challenge-> Rendering a dependency graph for a repo with hundreds of files can quickly turn into a laggy hairball. We spent a lot of time tweaking the D3.js force-directed graph parameters to ensure that nodes self-organize in a way that highlights clusters rather than just creating a mess.
Why Server-side? -> We chose server-side processing to handle larger repositories without crashing the user's browser (Numpy, FastAPI etc.), but we've architected it to be non permanent. The code lives in RAM just long enough to be parsed into a JSON structure for the frontend, then it's gone.
Next steps: -> I’m looking into adding Javascript, html and css support to make debugging the frontend much easier.
A few technical details for those interested in the behind the scenes:
Why AST? -> Simple regex or string parsing fails as soon as you hit complex decorators or nested classes. By using Python’s native AST module, we can get an objective map of the logic flow that doesn't care about formatting or comments.
The Visualization Challenge-> Rendering a dependency graph for a repo with hundreds of files can quickly turn into a laggy hairball. We spent a lot of time tweaking the D3.js force-directed graph parameters to ensure that nodes self-organize in a way that highlights clusters rather than just creating a mess.
Why Server-side? -> We chose server-side processing to handle larger repositories without crashing the user's browser (Numpy, FastAPI etc.), but we've architected it to be non permanent. The code lives in RAM just long enough to be parsed into a JSON structure for the frontend, then it's gone.
Next steps: -> I’m looking into adding Javascript, html and css support to make debugging the frontend much easier.
Are you also planning on adding GO support anytime soon?
Would definitely be cool. Especially interesting for getting into large full stack codebases
How much load does the render engine put on the user end?
For big projects its quite noticable.. but this only really starts to become a probleme once project sizes exceed 200k LOC
My poor 2018 Mac is currently dead at around 10 FPS on numpy. Desktop works fine tho