RSS3 aims to be a bridge between Web3 and AI technologies by providing a decentralized information flow that is free, efficient, and secure. It also aims to promote the development of AI-driven applications in social, financial, and e-commerce fields through technological iterations.
Currently, both artificial intelligence (AI) and Web3 based on blockchain technology are among the hottest investment tracks. Many industry experts believe that the combination of the two will redefine the next generation of the Internet and tirelessly seek opportunities for transformation.
However, AI and Web3 have common goals but also inherent contradictions. On the path of bridging the two, RSS3 has begun to explore and try to meet the value demands of maximizing the combination of Web3 and AI through its open innovation solutions.
01. Introduction to RSS3
RSS3 is an open network information distribution protocol designed to provide decentralized information indexing infrastructure for AI, search, and social networking.
This protocol can directly index data from various blockchains and decentralized networks. Users can easily access the data sources of decentralized networks through a set of simple and user-friendly APIs. The standardized data format provided by the RSS3 API ensures that users can conveniently and quickly access various Web3 content sources.
RSS3 aims to be a bridge between Web3 and AI technologies by providing a decentralized information flow that is free, efficient, and secure. It also aims to promote the development of AI-driven applications in social, financial, and e-commerce fields through technological iterations.
02. Background of RSS3
RSS3 was created by a developer team focusing on the concept of open networks in 2021, with the initial intention of solving the issue of information ownership brought by centralized social media platforms.
Founder Joshua is a serial entrepreneur and the founder of one of the first ClubHouse-like products in China, “JuJu”.
With his continuous technological innovation and deep understanding of technology capital in the industry, Joshua believes that to achieve true technological transformation, a single innovative product is not enough to bring adaptive solutions to users and the market. It is necessary to start from the underlying environment that carries the technology and promote the implementation of open networks.
Therefore, he founded the Web3 ecosystem project RSS3, hoping to bring a revolution from the inside out to the decentralized ecosystem.
RSS3 aims to create a decentralized open information layer and build an open Internet by improving the free flow of open information. In early 2022, the project’s white paper has been released.
The development team has collaborated with mainstream Web3 platforms such as Ethereum, Arweave, Polygon, Binance Smart Chain, Arbitrum, Avalanche, Flow, and xDai Chain to promote the protocol to major decentralized networks.
The name RSS3 comes from “RSS” (Really Simple Syndication), the first widely used information distribution protocol.
RSS is a type of web feed that allows users and applications to access updates from websites in a standardized, machine-readable format. RSS3 co-founder DIYgod stated that the “RSS” in the name is a tribute to the history of this protocol. The “3” in the name of RSS3 represents Web3.
03, RSS3 Concept
RSS3 is a pioneer in the combination of Web3 and AI artificial intelligence. As early as 2021, during the downturn in the AI field, RSS3 attempted to deeply integrate Web3 and AI by decentralizing the complex information flow of Web3 and breaking down the information barriers between various protocols.
In 2023, as the market focus shifted to AI, RSS3 became even more determined to make AI its development direction and explored a business model that combines AI and Web3 that is suitable for itself. It also pointed out a feasible development path for the Web3 industry.
RSS3 founder Joshua has publicly shared his insights on the combination of Web3 and AI business models. In Joshua’s view, there are three feasible ways to combine Web3 and AI:
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The first is to solve the problem of AI training datasets through game theory. The core of AI is intelligence, while the narrow definition of Web3 is blockchain, which is fundamentally based on game theory. The words intelligence and game seem to have no connection, but fundamentally, the ultimate goal of intelligence is to directly derive the result of a complex game. For example, the market can reflect the price of bread through the game of supply and demand. However, AI is far from being able to predict market prices. The access to Web3 data may help AI reach this new height.
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The second is that AI’s computing power comes from the computing power and data network under a Web3 game mechanism. For example, using a large amount of GPU computing power or obtaining more decentralized datasets, the supply-demand relationship between this computing power and data needs to be reflected by the mechanism of Web3. As a result, the economic model of Web3 will drive a completely open and decentralized market, where resources are no longer monopolized.
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The third is that Web3 serves as a source of information as a whole and feeds back into AI models. This direction is RSS3’s expertise. AI can read data from the blockchain and user behavior from Web3 through RSS3, which is likely to enable AI to access the new continent of the blockchain and eventually train the ability to predict the market to a certain extent. In the era of Web3 + AI, RSS3 practices the concept of an open network, embraces open source, adheres to the principle of data openness, and promotes the free flow of decentralized data. RSS3 enables users and developers to easily access data on decentralized networks to achieve efficient, free, and secure data flow.
At the same time, RSS3 actively releases open source code to attract more like-minded developers and accelerate industry recognition of its concepts and new product AIOP (AI Open Platform).
By indexing and standardizing support, building consensus for an open network ecosystem, RSS3 aims to incorporate as many decentralized networks as possible to achieve inclusiveness.
04. The Path of Web3 + AI Integration
Artificial Intelligence (AI) and Web3 based on blockchain technology are both the hottest investment tracks, and the AI trend led by ChatGPT has exerted a certain suction effect on the external funds of the Web3 track.
Many Web3 communities have also noticed this phenomenon, and discussions on the combination of AI and Web3 have gradually emerged. However, most of them are either for speculative purposes or lack technical reserves and cannot find the connection between AI and their core business. As a result, the majority of Web3 teams that announced their entry into AI have ended up fading out of the stage.
AI and Web3 can be combined because they share common goals in enhancing user experience and optimizing processes.
The target audience of both fields is committed to leveraging technological progress to drive innovation and create value in the digital realm. At the same time, AI and Web3 both emphasize the importance of data, although from different perspectives.
AI relies on large datasets for model training and decision-making, while Web3 focuses on decentralized data governance and user control over personal information.
AI and Web3 have common goals but also inherent contradictions.
AI tends to be centralized, relying on algorithms and models to determine outcomes, while Web3 advocates for decentralization and user participation in decision-making, achieved through concepts like gamification.
For example, algorithm-based platforms (such as TikTok) embody algorithmic decision-making, while Web3 aims to establish decentralized governance models. These conflicting paradigms present challenges for seamless integration.
However, the aforementioned contradictions have not prevented some Web3 projects from involving AI. In the earlier period of this year, when the market was crazy about AI, any project related to AI experienced a noticeable price reaction. However, this does not truly reflect the value enhancement brought by the combination of AI + Web3 in the two highly valued tracks. It was more driven by financial speculation.
After the hype, the values of these projects have fallen. As Warren Buffett said, “Only when the tide goes out do you discover who’s been swimming naked.” However, if one can persist and combine the cutting-edge narratives of AI and Web3, it will bring about a valuation shock, and this is exactly what RSS3 is exploring and gradually realizing.
Since its establishment, RSS3 has been actively exploring the integration of Web3 and AI. It is worth mentioning that the protocol has a significant first-mover advantage. It has deeply cultivated the direction of decentralized data indexing (currently supporting almost all mainstream platforms in the blockchain) and has accumulated a wealth of technical experience, as well as assembled an excellent team of developers.
At the current stage, RSS3 has incorporated indexed decentralized network/on-chain data into AI training. The advantage of this technology lies in breaking down the information barriers between protocols, enabling more data to be used for AI model training.
On this basis, RSS3 continues to explore the potential value of decentralized data and utilizes the deep development capabilities of its AI open platform AIOP. AIOP empowers RSS3 with the ability to deeply mine decentralized data and maximize the information value of Web3.
With the ability to access and process large amounts of data, RSS3 can collaborate with different project parties to develop different AI models and provide powerful data support for Web3-related fields.
These models can provide analysis results with higher accuracy and faster speed, thereby bringing better user experience to Web3 users.
Currently, RSS3 serves as a bridge between Web3 and AI, and when the day comes for Web3 to enter thousands of households as desired, RSS3 may become a bridge for AI to understand the world.
05. RSS3 AI Training Platform and ChatGPT Plugin
AIOP
In early 2023, RSS3 combined AI technology with the information distribution capabilities of decentralized networks and launched the first Web3 low-code AI open platform, AI-Training Open Platform (AIOP).
AIOP, as a Web3 open model training platform developed by RSS3, integrates cross-chain data information distribution and natural language processing (NLP) model training technologies.
The platform’s language logic and data analysis capabilities can precisely serve the crowd participating in Web3 co-construction. By accessing cross-chain data sources on decentralized networks, it assists developers in training their own unique AI models in an open and efficient manner. These models can be applied to various scenarios, including providing personalized conversations, search recommendations, and cryptocurrency price predictions.
The platform provides decentralized developers with an open, scalable, and long-term valuable infrastructure.
Through continuous user training and data indexing, the knowledge boundaries of AIOP can be infinitely expanded. Based on this platform, RSS3 has launched the first AI assistant Model 1 trained based on AIOP, and the beta version of this product will be officially opened to Waitlist users.
Model 1 is a hybrid model that integrates multiple AI models. By identifying and understanding the questions input by users in the dialogue box, it provides corresponding suggestions and solutions to help users efficiently process information and tasks related to chain data.
This technology will accelerate the transformation of Web3 and the AI industry. The high-speed iterative decentralized information flow is of vital importance to project development and innovation. Model 1 can help developers overcome the difficulties of cross-chain data distribution and data timeliness, providing better technical support for Web3 projects and developers.
The current version of Model 1 has the following three major functions:
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Analysis Task: Model 1 builds an analysis component that can comprehensively analyze decentralized data indexed. This enables users to extract valuable insights and make decisions based on the abundant available data on the RSS3 data network. Real-time results including on-chain transaction data and market dynamics can be provided through data analysis techniques.
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Prediction Task: RSS3 integrates prediction tasks into its internal model, which can generate reasonable predictions related to the cryptocurrency market (not financial advice). Users can use this model to predict trends in the cryptocurrency market.
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Information Retrieval: Users can effectively retrieve feed information from the blockchain and external sources through Model 1. Model 1 can also provide information summaries.
Web3 User Activity
In addition to AIOP, RSS3’s self-developed plugins are also available in the ChatGPT plugin store. This Web3 User Activity plugin allows users to directly access Web3-related data on Open AI’s GPT-4.
Users can view the activities of any Web3 player on various chains by entering 0x addresses, ENS, Lens, and other name services.
This plugin supports users to directly query diverse data including NFT/DeFi through multiple chains and cross chains on Chat GPT.
For example, by entering V God’s address, you can use the Web3 User Activity to obtain Vitalik’s behavior in Web3 and track his activities on various platforms, such as posts on the Farcaster social platform.
In addition, Web3 User Activity also supports advanced features, such as directly interpreting the corresponding address transaction behavior.
From the above use cases, we can see that under the efforts of RSS3’s information distribution, its developed AI model is not only simply acquiring Web3 information but also has certain analytical capabilities.
At the same time, the RSS3 team is also constantly exploring more innovative ways, with the current focus on developing AI models to enable users and developers to efficiently utilize the vast decentralized data in the open network.
06. Summary and Outlook
The recent Twitter access restriction controversy has once again triggered public thinking about data ownership, highlighting the importance of free access to data.
In contrast, RSS3 provided a solution to the problem of centralized data flow two years ago, which can provide data to AI without restrictions, showing its forward-thinking concept. RSS3 practices its concept of an open network, bringing together data scattered in different protocols, with the aim of liberating the monopolized production elements of AI in the future.
From an economic perspective, we cannot simply equate the AI trend with previous trends such as mobile internet and new energy using conventional thinking.
Because the latter only improves the original productivity through technological development, while AI actually creates a new productivity, with the core being data and computing power.
“How to scientifically allocate this new productivity of AI?” This may be a more difficult problem to solve in the long run than “How to train better AI models?”
From the perspective of academia, this problem involves complex games among geopolitics, capital, manufacturing, and other factors, which will become very tricky to solve.
However, if we consider Web3, which inherently has attributes such as decentralization, fairness, freedom, and market-based pricing, everything becomes clear. Web3 can deliver the unlimited productivity of AI to any corner of the world through dynamic games.
Admittedly, there is competition for resources and differences in technical philosophy between AI and Web3 (currently AI is centralized), which brings great resistance to the seamless integration of the two. However, RSS3 has brought inspiring explorations on the road of combining Web3 with AI.
From decentralized data feeds, to AI plugins Web3 User Activity, to the AI open platform AIOP, one step at a time.
This reflects not only the development process of a project itself, but also the possibilities of industry development – creating a free and unconstrained market for AI, liberating monopolized AI resources, moving towards liberalization and marketization, and ultimately exploring the greater potential of the AI industry.
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