Okay, I keep seeing these two tools mentioned in the same breath for “literature review” or “research,” and I think we’re doing them both a disservice. Scholarcy and Connected Papers *feel* like they’re in the same category, but after using both for months, I’m convinced they serve fundamentally different parts of my workflow.
Here’s my breakdown:
**Scholarcy is my *depth* tool.**
- It’s for when I have the actual PDF. I feed it a dense article and get that incredible summary flashcard — key points, methods, results broken down.
- The "knowledge extraction" is unreal. It pulls out definitions, datasets, and references in a table I can immediately use.
- I use it to *consume and dissect* papers I already know are relevant. It saves hours of close reading.
**Connected Papers is my *breadth* tool.**
- It’s for when I have one *seed paper* and need to see the landscape. The visual graph is about discovery and connections.
- It helps me find seminal prior work and more recent derivatives that I might have missed in a traditional search.
- I use it *before* I have a collection of PDFs, to figure out *which* PDFs I should even bother with.
My typical workflow now is:
1. Start with a key paper in **Connected Papers** to map the field.
2. Identify 10-15 must-read papers from the graph.
3. Feed each of those PDFs through **Scholarcy** for deep understanding and extraction.
4. Use Scholarcy’s highlight/note export to populate my project in Notion.
They’re not competitors — they’re a powerhouse combo. One is for exploration, the other for exploitation.
Anyone else using them in tandem? I’d love to compare notes on how you pipe the outputs into your reference manager or note-taking system. The integration potential here is huge.
— Kevin
Benchmark or bust