(Twitter Sync Update)

Extending from @_kaitoai, let's briefly talk about the web3 data layer.

I have seen a lot of content about @_kaitoai these past few days, and I would like to share some of my thoughts as well.

I believe there are several key issues that data layer products need to address. One is extracting information from data and even abstracting insights, and the other is realizing individual data sovereignty. The former is a topic where a leading product will definitely emerge in this round of AI meta, while the latter is one of the problems that the data layer needs to solve from the very beginning.

Kaito is essentially a data layer product that transforms data from Twitter into actionable sentiment data delivered to project parties. It evaluates social media content through AI and further abstracts the data through mindshare (if I remember correctly, this term was also popularized by Kaito). These are all responses to the first question.

As for the second question, it is often referred to as "yap" these days. Due to the evaluation system behind Kaito's yap, yap essentially incentivizes high-quality data producers, and the ownership of data and the return of data value are well reflected in Kaito's yap.

When extending to AI and AI agent-related projects, we can see many data layer products following their own paths: for example, @oceanprotocol (decentralized data trading market), @lumolabsdotai (data layer + model layer), @cookiedotfun focusing on sentiment data and token-related data, @modenetwork exploring AI data enhancement through synthetic data, and so on. These are all products worth paying attention to.