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X: @ZaggyGoKrazy 多些理解少些角度
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Will AI Agents Come Back?
In essence, the vast majority of AI agent projects that issued tokens were rushing ahead. These tokens can be seen as a quantitative representation of market and RWA (real world attention) expectations for the project's future under those conditions. However, due to the market's emotional fervor and the extreme concentration of RWA, the delivery capabilities of these projects have been greatly overlooked.
Looking back to January, the market automatically formed a valuation path: Oh, this is a framework project, so it benchmarks AI16Z; Oh, this is a defai project, so it benchmarks xxx, and so on. As long as there is a somewhat decent official website, and if there are real-name devs and a GitHub repo with good star/fork performance, it basically starts with a market cap of several million. Plus, at the beginning of the year, a bunch of star projects graduated from the Solana hackathon. I remember a teacher made a table of all the hackathon projects, opened a scraper, and monitored to directly buy CA to fully capture the profits.
Looking back to January, the market automatically formed a valuation path: Oh, this is a framework project, so it benchmarks AI16Z; Oh, this is a defai project, so it benchmarks xxx, and so on. As long as there is a somewhat decent official website, and if there are real-name devs and a GitHub repo with good star/fork performance, it basically starts with a market cap of several million. Plus, at the beginning of the year, a bunch of star projects graduated from the Solana hackathon. I remember a teacher made a table of all the hackathon projects, opened a scraper, and monitored to directly buy CA to fully capture the profits.
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(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.
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.
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If you missed $TRUMP or made a big profit on $TRUMP , what should you do?
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Buy AI concept coins with strong rebound. I have observed that $anon $buzz $moby have made some profits from Trump. Hot spots are hot spots. Stay rational and protect profits.
For anon and buzz, please check my homepage for detailed investment research content.
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