> For the complete documentation index, see [llms.txt](https://aigora-tech.gitbook.io/aigora-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aigora-tech.gitbook.io/aigora-docs/welcome-to-aigora..md). # Welcome to AiGora. ## Overview Even though the term artificial intelligence appeared in 1956, only in the last decade have new technologies made it possible to fill it with meaning. Starting with simple algorithms of chess robots and human assistants, AI technologies have been further developed by Internet search engines and online grammar correctors in writing. But the real explosion came with the advent of the ChatGPT application, which introduced AI with the knowledge of a freshman student in all branches of human knowledge and applied sciences at the same time. However, being the most famous ChatGPT is not the only application in the market, which is increasingly filled with applications that offer opportunities such as interaction that provides transformation: • text-to-text (T2T) • text-to-NFT (T2N) • text-to-code (T2C) • text-to-image (T2I) • text-to-audio (T2S) • text-to-video (T2V) And many others. How can a simple user understand all this? Download all apps in AppStore or Google? We offer a simple and elegant solution - a decentralized AI application marketplace that contains both Web2 socialization elements and Web3 monetization capabilities. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://aigora-tech.gitbook.io/aigora-docs/welcome-to-aigora..md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.