In-depth analysis of RSS3 The bridge between Web3 and AI
RSS3 The bridge between Web3 and AI
Original Author: Fishery Isla, Biteye Core Contributor
Currently, both artificial intelligence (AI) and Web3 based on blockchain technology are the hottest investment tracks. Many industry professionals 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 to bridging the two, RSS3 has begun to explore its own open innovation solutions to meet the current demand for maximizing the value of the combination of Web3 and AI.
01 Introduction to RSS3
RSS3 is an open network information distribution protocol designed to provide decentralized information indexing infrastructure for AI, search, and social networks.
- When the post-2000s who entered the circle in 2020 are called OG, l...
- 80% of revenue comes from token trading taxes, the security is ques...
- Report Digital marketing will become the next major use case for Web3.
The protocol can directly index data from various blockchains and decentralized networks, and users can easily access data sources of decentralized networks through a set of simple and easy-to-use APIs. The standardized data format provided by the RSS3 API ensures that users can conveniently and quickly access various Web3 content sources.
By providing a free, efficient, and secure decentralized information flow, RSS3 is committed to becoming a bridge between Web3 and AI technologies and hopes to promote AI-driven application development 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 original intention of solving the issue of information ownership brought by centralized social media platforms.
Founder Joshua is a serial entrepreneur and the founder of “Juju,” one of the first batch of ClubHouse-like products in China.
With his continuous technological innovation and deep understanding of capital in the technology track, Joshua believes that to achieve true technological change, 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 ecological project RSS3, hoping to bring a revolution to the decentralized ecosystem from the inside out.
RSS3 aims to create a decentralized open information layer and build an open Internet by enhancing the free flow of open information. In early 2022, the project’s whitepaper has been released.
The development team has cooperated 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 network information source that allows users and applications to access website updates in a standardized, machine-readable format. DIYgod, co-founder of RSS3, stated that the name RSS in the name is a tribute to the history of this protocol. The “3” in the name RSS3 represents Web3.
RSS3 is a pioneer in the integration of Web3 and AI artificial intelligence. As early as 2021, during the low tide period of the AI track, 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 more determined to make AI its development direction and explored a business model that combines AI and Web3. It 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:
First, use a game-theoretic approach to solve the problem of AI training datasets. The core of AI is intelligence, and the essence of Web3, or blockchain, is game theory. The words intelligence and game theory may seem unrelated on the surface, but fundamentally, the ultimate goal of intelligence is to directly derive the outcome of a complex game. For example, the market reflects the price of bread through the game of supply and demand. However, AI is far from being able to predict market prices. The integration of Web3 data may help AI reach this new height.
Second, AI’s computing power comes from the computing power and data network of a Web3 game mechanism. For example, using a large amount of GPU computing power or obtaining more decentralized datasets, the supply-demand relationship of this computing power and data needs to be reflected by the mechanism of Web3. As a result, Web3’s economic model will drive a completely open and decentralized market, where resources are no longer monopolized.
Third, Web3 serves as the overall information source and feeds back into the AI model. This is RSS3’s expertise. AI can read blockchain data and Web3 user behavior through RSS3, which may allow AI to access the new continent of blockchain and ultimately train its ability to predict the market. 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 will include as many decentralized networks as possible to achieve inclusiveness.
04The Path of Web3 + AI Integration
Artificial intelligence (AI) and Web3 based on blockchain technology are both the hottest investment tracks, and the AI boom led by ChatGPT has had a certain suction effect on the external funding of the Web3 track.
Many Web3 communities have also noticed this phenomenon, and the discussion of the combination of AI and Web3 is gradually rising. However, most of them are either for speculative purposes or lack technical reserves and cannot find the connection between AI and their core business. In the end, most Web3 teams that announced their entry into AI have faded out of the stage without achieving much.
The combination of AI and Web3 is possible because they share common goals in enhancing user experience and optimizing processes.
The audiences in both fields are committed to leveraging 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 a large amount of data sets 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, often through gamification concepts.
For example, algorithm-based platforms like TikTok demonstrate algorithm-driven decision-making, 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 engaging in AI. Earlier this year, during the frenzy for AI-related projects, the prices of any projects related to AI had a noticeable response. However, this did not truly enhance the value of the combination of AI and Web3, but rather had more financial implications and speculative motives.
After the hype, the value has declined. As Warren Buffett once said, “Only when the tide goes out do you discover who’s been swimming naked.” However, if we can persist and organically combine the most cutting-edge narratives of AI and Web3, it will bring about a valuation impact, and this is precisely what RSS3 is exploring and gradually achieving.
Since its inception, RSS3 has been exploring the integration of Web3 and AI. It is worth mentioning that this protocol has a significant first-mover advantage. Its deep cultivation in the direction of decentralized data indexing (it currently supports almost all mainstream blockchain platforms and its API can provide standardized data formats for partners to access data and desired information) has accumulated a wealth of technical experience and assembled an excellent development team.
At this stage, RSS3 has already incorporated indexed decentralized network/chain data into AI training. The advantage of this technology is that it breaks down information barriers between protocols, allowing more data to be used for AI model training.
Based on this, RSS3 continues to delve into the potential value of decentralized data and utilizes the capabilities of its AI open platform AIOP for in-depth development. AIOP empowers RSS3 with the ability to deeply mine decentralized data, maximizing the information value of Web3.
After having the ability to access and process large amounts of data, RSS3 can collaborate with different project parties to develop different AI models, providing strong data support for the Web3-related 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 from Web3 to AI, and on the day when Web3 enters thousands of households 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 capability of decentralized networks to launch the first Web3 low-code artificial intelligence open platform – AI-Training Open Platform (AIOP).
AIOP, developed by RSS3, is a Web3 open model training platform that integrates cross-chain data information distribution and natural language processing (NLP) model training technology.
The language logic and data analysis capabilities of this platform can accurately serve the crowd participating in the co-construction of Web3, assist developers in training their unique AI models in an open and efficient manner through access to cross-chain data sources on decentralized networks, and the models can be applied to various scenarios, including personalized conversations, search recommendations, and coin price predictions.
This platform provides developers in the decentralized field with an open, scalable, and long-term valuable infrastructure.
By continuously training and indexing data, the knowledge boundary of AIOP can be infinitely expanded. Based on this platform, RSS3 has launched Model 1, the first AI assistant trained based on AIOP, and the beta version of this product will be officially open to Waitlist users.
Model 1 is a hybrid model integrating multiple AI models. By identifying and understanding the questions input by users in the dialogue box, it provides corresponding suggestions and solutions, helping users use on-chain data to process information and tasks more efficiently.
This technology will accelerate the transformation of Web3 and the AI industry. The fast-paced 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 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 the 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 models and 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 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 have also been launched on the ChatGPT plugin store. This Web3 User Activity plugin can help users 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 the 0x address, ENS, Lens, and other name services.
This plugin supports users to directly query various data, including NFT/DeFi, through multiple chains and cross-chains on Chat GPT.
For example, by entering Vitalik’s address, you can use Web3 User Activity to obtain Vitalik’s behavior in Web3 and track his dynamics on various platforms, such as posts on the Farcaster social platform.
In addition, Web3 User Activity also supports advanced functions, 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 not only simply obtains information from Web3 but also has certain analytical capabilities.
At the same time, the RSS3 team is also constantly exploring more innovative ways. Currently, the 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 sparked public thinking about data ownership, highlighting the importance of free access to data.
In contrast, RSS3 provided a solution to the problem of data centralization in the data stream as early as 2 years ago, providing data to AI without restrictions, which was a very forward-looking concept. RSS3 practices its open network concept by aggregating data scattered across different protocols, aiming to liberate 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 has actually created 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 future than “how to train better AI models?”
From the perspective of academic circles, this problem involves multiple complex games such as geopolitics, capital, and manufacturing, and will become very tricky.
However, if we consider Web3, which inherently has attributes of decentralization, fairness, freedom, and market-based pricing, everything becomes clear. Web3 can deliver the limitless productivity of AI to any corner of the world through dynamic games.
Indeed, there is a competitive relationship in terms of resources and a philosophical divide 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 exploration and achievements on the path of combining Web3 and AI.
From decentralized data feeds, to AI plugins for Web3 user activity, to the AI open platform AIOP, step by step.
This not only reflects 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, shifting towards liberalization and marketization, and ultimately tapping into the greater potential of the AI industry.