> For the complete documentation index, see [llms.txt](https://docs.batching.ai/codecaching/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.batching.ai/codecaching/code-caching-guide.md). # CODE Caching Guide **Welcome to CODE Caching!** Batching.ai created CODE Caching, an original treasure hunt-style puzzle game that challenges your problem-solving skills while providing intriguing prizes. In this game, players embark on a one-of-a-kind experience by solving AI-generated visual puzzles. These puzzles are meticulously crafted, with certain strings ingeniously buried throughout the visuals. Your job is to find these secret strings and decode the code. What actually distinguishes CODE Caching is its interaction with blockchain technology. The game runs mostly on the XPLA mainnet, which ensures a safe and transparent gameplay experience. Additionally, CODE Caching supports the Havah and BNB chains, giving gamers the freedom to choose their preferred blockchain network. This multi-chain capability makes the game more accessible, allowing a larger audience to partake in this exciting and rewarding experience. Whether you're a puzzle enthusiast seeking for a new challenge or a blockchain enthusiast hoping to discover creative gaming solutions, Code Caching provides the ideal balance of cerebral stimulation and cutting-edge technology. Join us on this trip to see if you have what it takes to become a master code solver in the realm of CODE Caching! Happy CODE Caching! 📯 --- # 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, and the optional `goal` query parameter: ``` GET https://docs.batching.ai/codecaching/code-caching-guide.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.