Nostr Web Client

Oh that's kind of awesome! The same networkx library is in use for both noswot and this project so I suspect there are many possibilities for cross-pollination. Dude, imagine if neurons had reputation. I'll put the presentation in my queue.

david 2y ago

The model I present in that presentation I now call the tapestry model and I conjecture 1) we need to employ it if we’re ever going to build a decentralized web (ie a functioning WoT) and 2) the brain already implements the model.

And the brain of any social organism has got to have an internal representation of the external WoT — one could consider that to be part of the definition of “social.” If a cortical column (or some other patch of neurons) represents an individual in one’s WoT, and contextual trust ratings are calculated for that individual, then would that qualify as neurons having reputation? Perhaps so!

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david 2y ago

That presentation btw is from before I discovered nostr. And what I call the “principle of Loki” I’ve decided to rename the “class thread” principle, with “class” a reference to object oriented programming.

STERRY 2y ago

I'm not sure if you've looked further into it but noswot's WoT is initially built from follows and reports. The WoT algorithm is based on one from freenet which instead of 6 degrees of separation uses an exponential falloff of influence. If you're interested in trying a different source than follows and reports I can point you to where that's defined in code or maybe add it to noswot.

I started with follows and reports for simplicity. It operates on the least amount of data and allows us to see if some kind of WoT can prove out the value of this general approach. That being said intake can be expanded to get reactions and replies for example if you wanted to attempt to detect topics and rank by bucketed engagement. It can quickly get complicated.

david 2y ago

idk why I’m just now seeing your reply … that’s very interesting, and another way that we seem to be thinking along the same lines! The WoT in my own desktop app Pretty Good does the same thing: influence (which is contextual) is the quantity being calculated by the WoT, and the fall off is exponential. There’s a scrollbar to adjust how steep the falloff occurs (a constant between 0 and 1, which is factored in to the weight of each rating). If Alice endorses (not a follow, but similar) Bob in some context, then her influence determines how much weight to give to her endorsement. There are scrollbars so users can adjust the default influence of users about whom no ratings exist. There are multiple other variables to adjust in the control panel, designed to mitigate various sybil attacks, so yes it’s also quite complicated, but not intractably so. Most of the user-adjustable variables can be set to default values and hidden from the user, to be discovered later only if they need adjusting.

STERRY 2y ago

Thanks for mentioning cortical columns. I feel like nature is the ultimate guide so was fascinated to research in this direction. I understand cortical columns are interconnected in potentially heirarchical ways. I'm not sure if my project is closer to develop a column or to model a small network of them. I saw that Hawkin's company has cultivated a community around their work and has also released a lot of code so plenty to get into here.