Rutvik (@0xrutvik) • Hey
experimenting ¯\_(ツ)_/¯ | X-Polygon
Publications
- Wait what, how long have I been asleep!!!
@lens/orb looks soo sleek & smooth
- gm!
orbiting here since 2 weeks, clearly confused on whom should i follow
pls suggest @orbapp.lens
- The eventual state of the chains is to be interoperable
- Curious are there thread bois orbiting around? 👀
- I like how @orbapp.lens has minimum number of hashtags in use right now.
Clean messages!
Being straight to the point!
- I feel people are supporters rather than followers. We should create an environment to foster support for people rather than to follow what someone does, hence I am highly curious to see if @orbapp.lens sets this as a new norm or possibly a new culture in with web3.
Essentially in a community you support people to grow along with you.
- - Rick Rubin
- By enabling any developer or team to build their own recommendation algorithm, the combination of easier to leverage AI primitives, open protocols, and immutable public data are paving the way for a more personalized and superior user experience (UX) for social applications.
With apps like Twitter, TikTok, and Instagram people are stuck with the given recommendation algorithm without any choices or options other than leave the platform if they don't like it.
For instance a lot of changes have happened with the Twitter recommendation algorithm. People have complained that they see a lot more clickbait, but the only other option is just the "Following" feed or to leave.
In theory, because web3 and blockchain data is public, developers and teams could build their own recommendation algorithms. I said "in theory" because in the past this was not really a thing as this type of system did not really exist before.
We're now starting to see some great alternative recommendation algorithms emerge and become available in the @LensProtocol ecosystem, and apps are already starting to pick them up!
Here are a few teams working on this from various angles (not inclusive of all teams):
1. Karma3Labs - Is a ranking and reputation infrastructure using the EigenTrust algorithm. It enables developers to curate a ranking and recommendation system for their apps based on on-chain data.
https://karma3labs.com
2. Whitebox from @kozlovchad - Is an open protocol to consume, develop and collaborate on modular, composable, and transparent algorithms, owned by the developers.
I think of this as something like an NPM for recommendation algorithms
https://whitebox.build
3. @madfinance - creating various recommendation algorithms like a suggested follows API + "For You" API and enabling optionally financial mechanisms allowing users to boost themselves
https://creators.madfinance.xyz
We have a handful more that are also available or being worked on.
Watch this space! It is one of the key differentiators between private, proprietary, and sometimes predatory social networks of the past, and modern social platforms like @LensProtocol
- Wowo, never thought @orbapp.lens was this lively!
Gg Orb team! 🤝
- gm
- What is earnable proof of humanity and how should it work?
Proving you're human could be extremely rewarding, basing human verification on the idea of earnable proof of humanity points could revolutionize Lens Protocol. These innovative points allow you to earn credit for completing tasks attesting that you are more likely real person, and could potentially unlock a variety of benefits in the online world. In this article, you will discover how proof of humanity points can change the way we think about online security and verification, and how they could help you take control of your online identity. There is many sound ways to prove humanity if you are willing to dox yourself like the infamous world coin’s scan your eyeball for tokens, which attests to your unique humanity. However, it is important that we be able to prove humanity without such rigorous breaches of humanity. This is where earnable proof of humanity points come in. Earnable proof of humanity points do not guarantee that an online account is human, instead the amount of points attest to the amount of human like tasks the online entity has performed. Or better said, the likelihood that a profile is being operated by a human is shown by its humanity points. One quick easy and dirty way to make the biometric test available without doxing is to generate zk-proofs off chain attesting to the unique humanness of your data, however this can be gamed like all other methods. Although, it could confirm the operator is human, with only zk-proofs on-chain it cannot attest that you have not been attested on-chain before, so the zk-proofing should only reward a capped amount of proof of humanity points. Then, there is another method of earning proof of humanity points through public attestations of past actions. This method would reward a capped amount of points to addresses that demonstrated publicly a proof of potential humanity. Did you use Uniswap 3 times this year? + 300 humanity points. Did you deposit onto AAVE? +350 humanity points. Did you mint a paid collect on lens? +1000 humanity points and so on. Additionally, this concept of earnable proof of humanity by a points system gets rid of the all or none idea that an actor is either a bot or human due to the goal of differentiation because that differentiation does not allow or account for errors in the model. Instead, earnable proof of humanity points provides a spectrum of chances that an online actor is a human as opposed to being objectively human. Another good example of a way to earn humanity points would be pop-up-shop quests. These quests would pop-up at random times for random durations. The quests should be things that are not predictable and easily botted, but able to be done by a human with on-chain verification. While not a perfect example of decentralized on-chain verification the “OP Quests” were an exemplary showcase of how this could work. A really strong way to have consistently well done quests in awarding points is to incorporate human intelligence tests into the quests especially by using visual illusions. Using visual illusions help humans combat bots because the bots do not have the same perception of the stimulus. Take the Titchener illusion for example, if you show a photograph of two equally sized balls, but one is surrounded by smaller balls and the other surrounded by larger balls, the equally sized balls will no longer look equal in size. A computer would perceive that the balls are equally sized no matter what, so this sort of test would generate high quality humanity points. Furthermore, there is the more rigorous proof of humanity test processed by humans themselves. You could fund and operate a peer to peer Turing test on people to prove their humanity. In this test, there would be a tester that would pay an arbitrary fee, then they would communicate in text chat with assessors that discern whether you could be human and get paid if their assessment is within the majority of the assessor votes, but acutely slashed if their vote is against the majority of assessor opinions. The amount of variations that can be brought about by these type of tests illustrate that no matter what, there are seemingly infinite ways to assess and give humanity points, but whose humanity points should be considered valid? Well, social consensus via a DAO should decide this manner. Humanity points should be issued by individual actors, apps, and protocols and a DAO should weigh the merit of each type of points value, and cumulate it into an universal humanity point super score that can be used to unlock human gated services and content. Therefore, an earnable proof of humanity points system would be extremely useful when screening bots in order to propagate the majority of services and content to potential humans.