Show HN: Create LLM-optimized random identifiers (github.com)

A recent announcement from the AI community features a tool called "tokeydokey" designed to generate random identifiers optimized for large language models (LLMs). This library allows users to create identifiers using a specified number of non-overlapping LLM tokens, enhancing both efficiency and compatibility. For example, users can generate a default identifier or specify a length, with the output comprising diverse formats that cater to developers' needs, from camel case to dot-separated structures. This innovation is significant for the AI/ML community as it improves upon traditional identifier generation techniques, offering a more token-efficient alternative. The tool allows for the generation of identifiers with high entropy while requiring fewer tokens—approximately 50% more efficient than random base64 identifiers. Additionally, the library includes various customizable formats and ensures compatibility with standard concatenation practices, making it a versatile resource for developers looking to streamline their workflows in AI applications.