In the past few years of observing the Web3 industry, I have been troubled by a question: my industry analysis always seems to lag behind the market rhythm. Often, an article about the 'RWA sector trends' takes a week to polish, and by the time it is published, new projects have already launched, and previous viewpoints have become 'outdated content.' Readers often comment, 'The content is useful, but the timeliness is too poor.' I have tried using traditional AI to assist in researching materials, but it can only organize past data, cannot connect to real-time on-chain information, nor can it update content based on market changes—until I used Holoworld AI, which transformed a static analysis into a 'dynamic content ecosystem' that can iterate in real-time.
The initial attempt started with training the 'Web3 Dynamic Observation AI.' In Holoworld's AI Studio, I uploaded my industry notes and past analysis articles from the past three years and connected several commonly used on-chain data interfaces, hoping to enable the AI to automatically capture real-time information. However, the first time the content was generated, the result was very poor: the AI mixed the BTC price from three days ago with the latest RWA data, leading to chaotic analysis logic and even instances of 'data contradictions.'
I realized that having data and materials alone is not enough; I also need to provide AI with a clear 'analytical logic framework.' So I reorganized my thoughts and broke down my analytical method into three steps: 'data screening - logical deduction - viewpoint output,' and used the 'rule setting' function of AI Studio to embed these steps into the AI model. For example, I set 'prioritize on-chain data within 24 hours' and 'must relate to the corresponding project’s asset confirmation information when analyzing RWA.' At the same time, I found a friend who develops on-chain data through Holoworld's 'creator community' to help me optimize the way data interfaces are connected, allowing AI to capture effective information more accurately.
The adjusted 'Web3 Dynamic Observation AI' has slowly taken shape. It can not only update market data in real time but also generate 'daily short comments' and 'weekly in-depth summaries' according to my analysis logic. What's more convenient is that I no longer need to modify it word by word; I just spend 10 minutes each day checking whether the data captured by the AI is accurate and supplementing some personal judgments on market sentiment, and the content can be quickly published. Once, there was a sudden large inflow of funds into the RWA sector, and the AI generated relevant analysis within an hour. After I added my observations on the fund flow and published it, the viewership that day tripled compared to previous static articles. Many readers said, 'Finally, we can see analysis that keeps up with the market.'
Later, I tried to add interactive attributes to this 'dynamic analysis.' Through Holoworld's 'Connector SDK,' I bound the AI to the 'user demand module' in the ecosystem, allowing readers to leave comments under the article, proposing the specific directions they want to learn about, such as 'on-chain risks of a certain RWA project' or 'new cases of AI combined with DeFi.' The AI automatically collects these demands and prioritizes covering them in subsequent analyses. One reader repeatedly commented wanting to know about 'on-chain asset management by AI.' The AI not only included related content in the analysis but also generated a simple operation guide. This reader later became my regular fan and actively shared their practical experience, which was incorporated into the AI analysis material library.
What was even more surprising was that three creators who also observe the industry contacted me after seeing this dynamic analysis, wanting to optimize the AI model together. We formed a small co-creation group in Holoworld, with each person responsible for a specific sub-field: some provided related data about AI and DAO, some optimized the dimensions of market sentiment analysis, and some improved the risk warning module. The contributions of the group would be recorded on-chain, converted into corresponding rights, and the revenue generated by AI content would be distributed according to each person's contribution ratio.
Now, this 'dynamic analysis' has become a small content ecosystem: AI is responsible for real-time data capture and basic content generation, while the four of us creators are responsible for controlling the direction and supplementing in-depth viewpoints, and readers are responsible for proposing demands and sharing experiences. It is no longer just my 'work' but a 'value carrier' that everyone participates in together.
Looking back on this experience, I found that Holoworld AI has brought me not just an increase in creative efficiency, but also a change in creative thinking—originally, creation does not have to be 'one person completing everything,' nor does it have to be bound by 'timeliness.' Through AI partners and the on-chain ecosystem, we can turn a single piece of content into an iterative, interactive, and co-constructed ecosystem. And this change might be the most precious aspect of creation in the Web3 era.@HoloworldAI
