Show HN: Lowfat – pluggable CLI filter that saved 91.8% of my LLM tokens (github.com)

A new tool called lowfat has been introduced as a pluggable command-line interface (CLI) filter, designed to significantly reduce token costs associated with AI interactions by filtering unnecessary output before it reaches the agent. The lightweight, extensible design allows users to customize filters while maintaining full data ownership, thanks to its local-first approach without telemetry. Users can easily integrate lowfat into their existing setups with instructions for various environments, making it a versatile option. The significance of lowfat lies in its potential to save users a remarkable 91.8% on LLM token costs, which is crucial for developers and researchers working with AI models that charge based on token usage. With features such as UNIX-style composability and customizable plugins, it allows flexibility in managing and optimizing command outputs. Users can track savings, adjust filter aggressiveness, and even create their own plugins, empowering them to enhance their AI interactions efficiently. The tool also supports integration with popular platforms like Claude and OpenCode, streamlining its adoption across different workflows.