part two of convex centric data; looking at a couple markets one may ask "if this money ever wants to leave, where does it go?" heres every exit route priced, and a verdict for each book (of 6 i randomly chose):
Took a proper look at convex through the xerberus engine. A full risk monitor of the protocol: where the money actually sits, who's really holding it, who steers the governance, and what needs attention. all rebuilt from raw chain data in the @xerberus engine:
Took a proper look at convex through the xerberus engine. A full risk monitor of the protocol: where the money actually sits, who's really holding it, who steers the governance, and what needs attention. all rebuilt from raw chain data in the @xerberus engine:
@cow_ada Great data scientists like you are always on my mind, funnily enough I have been wanting to reach out to you about the risk model and its open sourcing⦠your analysis would be a great addition
@TheUnpopularEL Dude keep up with the python too, I bet you can make some great subscores for the risk model once we open source. Also kudos to you for working so hard and embracing the new field of development <3
I know many skeptics of prediction markets. I don't have an ideological faith in them (OK, maybe quasi ideological). But the empirical evidence is they have worked really, really, really well. And did again on Tuesday night.
A short π§΅ about this remarkable picture.
@JesperFluX@xerberus Our risk ratings will be completely open source and changes / edits to those scores over time need to pass a governance vote through the community, lastly, we are distributing the compute of the score into a distributed node network to ensure consensus on the calculations