Data

GM

Did a deep dive into channels and wrote out key findings:

- How much overlap is there between users?
- What are the different flavors of chaneloooors?
- What's the dispersion of topics (crypto native vs general purpose)?

Hope you enjoy 🙏

GM

Did a deep dive into channels and wrote out key findings:

- How much overlap is there between users?
- What are the different flavors of chaneloooors?
- What's the dispersion of topics (crypto native vs general purpose)?

Hope you enjoy 🙏 

https://paragraph.xyz/@castaway/channels-and-crypto-natives

cooking @askgina.eth | prev: blockchain research @coinbase | sidshekhar.com | askgina.ai

The channel overlap data looks like it holds more insight than can be readily observed from a table. Would be interesting to perform hierarchical clustering to reveal the underlying topology

Physicist, quant, & Art Deco advocate |                                        

Moderating /science and /artdeco |     

instagram.com/johncamkiran

Definitely. The taxa could even be assigned by an LLM based on channel cast data

Yes good idea. The classification of the kinds of channels involved (with some form of taxonomy) would also help here