Show HN: Slim – 50% fewer tokens than JSON for LLM applications (github.com)

SLIM (Structured Lightweight Interchange Markup) has been introduced as a new data serialization format aimed at optimizing token usage in AI and LLM applications. By reducing the number of tokens by 40-50% compared to traditional JSON formats, SLIM enables more efficient data transmission to models like ChatGPT and Claude, which charge users based on token count. For instance, encoding a simple array of objects results in a reduction from 54 characters in JSON to just 31 in SLIM, showcasing significant space savings. This innovation is particularly important for developers working with large datasets, as SLIM can yield up to 56% savings in token count for user tables and 18% for nested configurations. However, there is a trade-off: while SLIM's encoding and decoding processes may be slower due to its custom parser and table overhead, these performance costs are outweighed by the monetary savings from reduced token usage. This makes SLIM an attractive option for data-heavy AI applications, especially as it evolves into a broader ecosystem that includes databases and PostgreSQL extensions tailored for SLIM storage.