In-depth Analysis of RSS3 Decentralized Information Distribution Protocol, a Frontier Explorer Combining Web3 and AI.
Analysis of RSS3 Decentralized Information Distribution Protocol, merging Web3 and AI.
Author: Fishery Isla, Core Contributor of Biteye
Editor: Crush, Core Contributor of Biteye
Currently, artificial intelligence (AI) and Web3 based on blockchain technology are the hottest investment tracks. Many industry insiders 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 attempt to meet the value needs of maximizing the combination of Web3 and AI with its open innovation solution.
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.
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This protocol can directly index data from various blockchains and decentralized networks. With a set of simple and easy-to-use APIs, users can easily access data sources of decentralized networks. The standardized data format provided by the RSS3 API ensures that users can access various Web3 content sources conveniently and quickly.
By providing a free, efficient, and secure decentralized information flow, RSS3 aims to become a bridge between Web3 and AI technologies. It also hopes to promote the development of AI-driven applications in fields such as social media, finance, and e-commerce through technological iterations.
02. Background of RSS3
RSS3 was created by a developer team focused on the concept of open networks in 2021, with the original intention of solving the issue of information ownership brought by centralized social media platforms.
The founder, Joshua, is a serial entrepreneur and the founder of “Juju,” one of the first Clubhouse-like products in China.
With his continuous personal technological innovation and deep understanding of technology-oriented capital, Joshua believes that to achieve true technological revolution, 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 promoting the free flow of open information. In early 2022, the project’s whitepaper 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 web feed that allows users and applications to access updates from websites in a standardized, machine-readable format. DIYgod, co-founder of RSS3, stated that the use of “RSS” in the name is a tribute to the history of this protocol. The “3” in the name RSS3 represents Web3.
03. RSS3 Concept
RSS3 is a pioneer in the integration of Web3 and AI. As early as 2021, during the low tide of 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, the market focus shifted to AI, and RSS3 became more determined to take AI as its development direction. It explored a business model that combines AI and Web3, and also pointed out a feasible development path for the Web3 industry.
RSS3 founder Joshua has publicly shared his insights on the integration of Web3 and AI business models. In Joshua’s view, there are three feasible ways to combine Web3 and AI:
- The first is to use a game-theoretic approach to solve the problem of AI training datasets. The core of AI is intelligence, and the narrow definition of Web3 is blockchain, which fundamentally follows the logic of games. The words intelligence and games may seem unrelated, but fundamentally, the ultimate goal of intelligence is to directly derive the outcome of a complex game. For example, the market can reflect the price of bread through the game of supply and demand. However, AI is still far from being able to predict market prices. The integration of Web3 data may help AI reach this new height.
- The second is that AI’s computing power comes from a Web3 game mechanism and a data network. For example, by using a large amount of GPU computing power or obtaining more decentralized datasets, the supply and demand relationship between this computing power and data will be reflected by the Web3 mechanism. As a result, the economic model of Web3 will drive a completely open and decentralized market, where resources are no longer monopolized.
- The third is for Web3 to serve as a source of information and feed it back into AI models. This is where RSS3 excels. AI can read blockchain data and user behavior from Web3 through RSS3, which may enable AI to access the new continent of blockchain and ultimately develop the ability to predict the market to some extent. In anticipation of the Web3 + AI era, 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 concept and new product AIOP (AI Open Platform).
By indexing and standardizing support, building consensus for an open network ecosystem, RSS3 aims to include as many decentralized networks as possible to achieve inclusiveness.
04. The Road to Integration of Web3 and AI
Artificial Intelligence (AI) and Web3 based on blockchain technology are both the hottest investment tracks. However, the AI boom led by ChatGPT has exerted a certain suction and drainage effect on the external funds of the Web3 track.
Many Web3 communities have also noticed this phenomenon, and discussions about the combination of AI and Web3 have gradually emerged. However, most of them are either for speculative purposes or lack technical reserves, unable to find the connection between AI and their main business. As a result, the majority of Web3 teams that announced their entry into AI eventually faded out of the stage without achieving much.
The combination of AI and Web3 is possible because they share common goals of enhancing user experience and optimizing processes.
The audience in both fields are committed to utilizing technological advancements to drive innovation and create value in the digital realm. At the same time, both AI and Web3 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 share common goals but also have 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, often through gamification concepts.
For example, algorithm-based platforms like TikTok demonstrate the decision-making power of algorithms, while Web3 aims to establish decentralized governance models. These conflicting paradigms pose challenges for seamless integration.
However, these contradictions have not prevented some Web3 projects from getting involved in AI. Earlier this year, during the AI frenzy in the market, any project related to AI experienced significant price reactions. However, this did not bring about a real increase in value from the combination of AI and Web3, but rather belonged more to the financial aspect and speculative nature.
After the hype, the value has fallen back, as Warren Buffett said, “It’s only when the tide goes out that you learn who’s been swimming naked.” However, if one can persist and organically combine the cutting-edge narratives of AI and Web3, it will bring about a valuation explosion. And this is precisely what RSS3 is exploring and gradually realizing.
Since its inception, RSS3 has been exploring the integration of Web3 and AI. It is worth mentioning that the protocol has a significant first-mover advantage. With its deep cultivation in decentralized data indexing (currently supporting almost all mainstream blockchain platforms and its API providing standardized data formats for partners to retrieve data and information they need), RSS3 has accumulated a wealth of technical experience and assembled an excellent development team.
At this stage, RSS3 has already incorporated indexed decentralized network/on-chain data into AI training. The advantage of this technology lies in breaking the information barriers between protocols, allowing more data to be used for AI model training.
Based on this foundation, RSS3 continues to tap into the potential value of these decentralized data by utilizing the capabilities of its AI open platform, AIOP. AIOP empowers RSS3 to deeply explore decentralized data and maximize the information value of Web3.
After gaining the ability to access and process large amounts of data, RSS3 can collaborate with different project parties to develop different AI models, providing powerful data support for the Web3 field.
These models can provide analysis results with higher accuracy and faster speed, thereby bringing a better experience to Web3 users.
Currently, RSS3 serves as a bridge between Web3 and AI, and when the day comes when Web3 enters every household as desired, RSS3 may become a bridge for AI to understand the world.
05. RSS3’s 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 artificial intelligence 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.
This platform’s language logic and data analysis capabilities can accurately serve the people participating in Web3 co-construction. By accessing cross-chain data sources on decentralized networks, it assists developers in training their unique AI models in an open and efficient manner. These models can be applied to various scenarios, including providing personalized conversations, search recommendations, and coin price predictions.
This platform provides a open, scalable, and long-term valuable infrastructure for developers in the decentralized field.
By continuously training and indexing data, the knowledge boundary of AIOP can be endlessly expanded. Based on this platform, RSS3 launched the first AI assistant, Model 1, trained based on AIOP. 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 users input in the dialogue box, it provides corresponding suggestions and solutions, helping users efficiently process information and tasks using on-chain data.
This technology will accelerate the transformation of Web3 and the AI industry. The rapidly iterating decentralized information flow is of vital importance for project development and innovation. Model 1 can help developers overcome the difficulties of cross-chain data distribution and obtaining data timeliness, providing better technical support for Web3 projects and developers.
The current version of Model 1 has the following three major functions:
- 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.
- 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 cryptocurrency market trends.
- Information Retrieval: Users can efficiently 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 plugin, the ChatGPT plugin, is also available in the 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 their 0x address, 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 Vitalik’s address, you can use the Web3 User Activity to track Vitalik’s behavior in Web3 and follow his activities on various platforms, such as his posts on the Farcaster social platform.
In addition, Web3 User Activity also supports advanced features such as directly interpreting corresponding address transaction behavior.
From the above use cases, we can see that under the efforts of RSS3’s information distribution, its developed AI models are not just simply obtaining information from Web3, but also have certain analytical capabilities.
At the same time, the RSS3 team is also constantly exploring more innovative ways. Its current focus is 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 prompted the public to think about data ownership and highlighted the importance of freely accessing data.
In contrast, RSS3 provided a solution to the problem of centralized data flow two years ago, allowing data to be provided to AI without restrictions, which was a very forward-thinking concept. RSS3 practices its open network concept by aggregating data scattered across different protocols, with the aim of liberating the monopolized AI production factors 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 existing productivity through technological development, while AI actually creates a new type of productivity, with data and computing power at its core.
“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 multiple complex games such as geopolitics, capital, and manufacturing, and will become very tricky to solve.
However, if we consider Web3’s inherent attributes of decentralization, fairness, freedom, and market-based pricing, everything becomes clear. Web3 can deliver this productivity of AI to any corner of the world through dynamic games and without restrictions.
Indeed, there is a competitive relationship in terms of resources and a difference in technical philosophy between AI and Web3 (currently AI is dominated by centralization), which brings great resistance to the seamless integration of the two. However, RSS3 has brought inspiring explorations on the road to combining Web3 and AI.
From decentralized data feeds, to AI plugins for Web3 user activity, to the AI open platform AIOP, one step at a time.
This not only reflects the development process of a project itself, but also the possibilities for industry development – to create a free and unconstrained market for AI, liberate the monopolized AI resources, shift towards liberalization and marketization, and ultimately explore the greater potential of the AI industry.
