Chainfeeds Summary:
The current "research-oriented" AI agents are becoming more specialized: they can analyze K-lines for technical analysis, assess whether a project has a 'rug' risk, and even generate similar research reports. TechFlow has reviewed several prominent research-oriented AI agents.
Source:
https://x.com/TechFlowPost/status/1871914532580557209
Author:
TechFlow
Opinions:
TechFlow: Agent Scarlett (AGENCY): Your token due diligence agent. AGENCY will list the pros and cons, similar to a short (shallow) research report. For example, it will tell you whether social media sentiment and organizations/KOLs related to the token are supportive, and also summarize on-chain data such as the number of holding addresses and token distribution. The current market seems to be very receptive to these small assistants with tokens, and relevant data shows that the discussion heat of AGENCY is rising rapidly. From the token distribution perspective, nearly half of the addresses holding around 3k tokens hold less than $100, which also reflects the nature of these AI agents being close to the 'retail investors'.
TRISIG: Will write research Threads on X. A smart crypto analyst who claims to be able to identify early-stage alpha projects, and shares their views on any major events or trends in the crypto market. Interestingly, TRISIG will write its own tweets to provide a quick research introduction on a project. If TRISIG is impressed by the question you ask, it will airdrop TRISIG tokens to you by asking you to leave your wallet address in the tweet reply. In terms of token distribution, the difference between addresses holding less than $100 and less than $1000 is not large, indicating that this token is relatively more favored by those who hold large positions, but its market cap is also higher.
KWANT: Focuses on technical analysis and provides price levels. KWANT is more direct, directly analyzing the token's price chart and providing investment recommendations. Unlike general projects that say "NFA", KWANT directly gives you very specific operational advice such as support levels, breakout levels, and stop-loss levels. Having more references and voices is not a bad thing, as this is the lower limit for the popularity of such projects. In terms of token distribution, small retail investors still make up the base, and users with large holdings of more than 25K are not many. One possible reason is that compared to research-oriented bots, this type of bot that directly gives price levels has a higher risk of going wrong, and can easily become a negative example of multiple inaccurate calls.
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