Geez πŸ€¦β€β™€οΈ

Hey is this why you switched npubs?

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I was being earnestly threatened by a crazy person and took a break, and then tried to start over. Didn't work. There are always more crazy people. Wish muting really worked.

Got hit with a botswarm on the new npub. I had to get off all public relays and unfollow a bunch of people who were following the bots, to get rid of it. No more public relays, for me. Full of npub viruses, basically.

Npub viruses, good label there. Make efforts to not catch them

How can we fix this?

Configurable web of trust

Think WOT is the most straightforward path. Cant stop people from viewing, following, commenting on your public account but you can filter them from your view and put bad actors into a sinkhole where they need a new npub to build trust.

Parameters for wot score:

nostr:nevent1qqst0r4ky5565r6t8wk9eqgxt3slken8af5z5nlmh6xtx7cckwad85qpz3mhxue69uhhyetvv9ujuerpd46hxtnfdupzphzv6zrv6l89kxpj4h60m5fpz2ycsrfv0c54hjcwdpxqrt8wwlqxqvzqqqqqqyt8lwu5

Multiply by a scaling factor and throw into tan function. At least thats my initial run at it.

coracle uses followers- log(mutes)^2 but im not sure where that formula comes from.

I think a formula for this should be standardized

You mean there should already exist a formula that is standardized or that it should be formally defined for nostr as a nip?

Second one, need to create one.

m'brain. Tan is interesting, can you explain for non-mathy people like myself?

Actually curious about the reasoning for it πŸ˜€

We care about some sort of threshold classification, with some sort of "pending"/leniancy state for newcomers.

Maybe even a sinkhole point-of-no-return-make-a-new-npub-bitch for spam/noted toxic individuals given your network.

A sigmoid is standard practice for classification, but asymptotic bounds of 0 and 1 don't really help those that have been in the game for a while. So we want to classify yes and no, in between state, and also note the very (un)trustworthy individuals.

Tan and cubic curves accomplish that. They grow very fast after a certian point.

With minimal assumptions, you can just encode every data point as Β±1 for positive/negative interactions, and put the average * scaling constant into the function.

Or you can weight the interactions by type (mutes>follows> sentiment classification of comments > reaction classification, or weight based on if the points are coming from your follows) and compute the weighted average.

Coracle's WOT formula, where mutes are the argument and follows are the parameter.

I'm actually learning about these functions in my algebra 2 class rn 😭😭😭

Feel like i didnt understand math till Calc 3 πŸ€£πŸ€£πŸ˜…πŸ˜°πŸ˜­πŸ˜­ even more so when i took Dynamic Systems

proof-of-work, this is what NIP-13 is for. Filter out any npubs without a kind-0 rated at a difficulty of at least 20.

POW is not a meaningful metric when you remember that people are using Phones

What do you mean? We're talking about people randomly generating new keypairs to flood accounts, right? Phones have enough compute to run basic PoW on a metadata event