Comment on page
Web2 has too much data for users. For a query, there are millions of relevant web pages. Redundant information, outdated data, and spam are everywhere. Users don’t need and cannot digest so much overloaded information.
Throwing infinite information directly to users is not that helpful. People are forced to waste unbelievable time organizing and extracting knowledge from all the mess. However, current search engines focus on listing all information than summarizing information into knowledge. MetaphoricallyIt is like a bookstore without categorizing books. Readers have to find everything on their own.
Developing and maintaining a search-engine product is labor-intensive in the Web2 era. Each development team has to do everything on its own, e.g., collect raw data, ETL data, design user interface, etc. Too much repetitive work causes a surprising waste of financial, time, and human resources. What’s even worse, the way that Web2 organizes and stores data is only readable to human users but not to machines, and therefore developers could not be relieved from the dirty works of cleaning and sorting information.
Search engines were born and developed because of an open Internet, but they soon “betrayed” the “open” spirit. Everything is closed now. Closed index, closed algorithm, closed user data, and closed ads system. A closed ecosystem might benefit giants to make money but is hurting the overall industry.
Google indexes only less than 10% of all content on its website. Because of the drawbacks of Web2, developers, and users are looking for somewhere else to carry their data. The unchanged mechanism from finding data in other ecosystems, e.g., deep web, and dApps on Web3.
- Transform chaotic information into orderly knowledge.
- Expand the data coverage to Web3, deep web, and other sources that are usually ignored in traditional search engines. Fortunately, Web3’s mechanism empowers search engines to cover a broader range of data and therefore enrich the whole ecosystem.
- AI-friendly. Web3 revolutionizes how back-end data is stored. Data stored in Web3 is well-structured and readable to the machines. Thus, Web3 is an ideal playground for AI. Innovative search engines should incorporate the advantages of Web3 and AI to feed humans more targeting outputs and assist them to make smarter decisions.
- Open-source. Web3 search engines need to open their data to avoid the redundant work of developers, open their technologies to avoid being abused by tech giants, and open their revenue to developers who contribute data and users who contribute their time.
- A decentralized search engine that provides a user experience surpasses that of traditional Web2 search engines.
- 1.Each user’s contribution to data input will be recognized and rewarded fairly.
- 2.Incorporate frontier AI and NLP technologies, for example, sentiment analysis and smart tagger, to improve the search results with more accurate, personalized, interactive, and even humanized outputs.
- 3.Revolutionize the current sorting algorithm, query processor, and other techniques to provide unprecedented searching experiences.
- 4.Launch composable search-engine applications that enable users and developers to lego their personal search engines to fulfill various use cases.
- Full coverage of Web3 data, both on-chain and off-chain. All the data indexed by Adot will be openly available to developers and users.
- Intelligent search engines that return sorted knowledge to assist users to make decisions.