Interesting. Again - what's the usecase you have in mind? Showing relevant who follow?
For precomputed to work you'd have to generate ranks for all possible permutations of pubkeys? nostr:nprofile1qyt8wumn8ghj7ct4w35zumn0wd68yvfwvdhk6tcpz4mhxue69uhhyetvv9ujuerpd46hxtnfduhsz9mhwden5te0wfjkccte9ehx7um5wghxyctwvshsz9mhwden5te0wfjkccte9ec8y6tdv9kzumn9wshszxthwden5te0wfjkccte9eekummjwsh8xmmrd9skctcpzemhxue69uhk6mr9dd6juun9v9k8jtnvdakz7qpq76p7sup477k5738qhxx0hk2n0cty2k5je5uvalzvkvwmw4tltmeqrtmus0 do you have any idea how many those would be for Personalized Pagerank?
We have a DVM that sorts authors. You give it a bunch of authors of comments, and it returns them sorted by rank. How do you do that with NIP-85?
Any given service - Grapevine, Vertex, etc - will have customers. For each customer, let’s assume on the order of 10^5 to 10^6 kind 30382 notes. Each note contains multiple WoT scores. Your service updates these notes periodically and stores them in a relay server. Different levels of service exist with higher levels of service meaning more notes per customer, more scores per note, more frequent updates.
Future: all of the above, but managed by a personalized WoT relay.
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