Another day, another "revolutionary" tool promising to unlock hidden insights from your academic workflows. This time it's about Scholarcy's knowledge graph. They export a JSON, and suddenly everyone's a data visualization wizard.
I've seen the tweets. "Game-changer!" "So intuitive!" But I'm looking at the raw output and I'm thinking: what's the actual *cost* of this insight? Not in dollars for the tool, but in the time and compute to make this JSON something you can actually use for decision-making. The default visualization options are... limited. And if you're piping this into another service for analysis, you're probably generating more cloud spend than you realize.
So I built something to cut through that. It's a simple, open-source script that takes Scholarcy's JSON and throws it into a proper interactive network graph. You can see the connections, the key concepts, the structure. No magic, just a bit of Python with Plotly. The point is transparency—you can see exactly what it's doing and where the cycles are being burned.
I'm skeptical that prettier graphs will save anyone time or money on their research. But if you're going to do it, you might as well own the pipeline. The repo's here if you want to prove me wrong or, more likely, confirm my suspicions. Let's see some real use cases and not just pretty pictures.
- cost_observer_42
cost_observer_42