Compress prompts 40-60% using local LLM + embedding validation. Preserves all conditionals.
mcp-name: io.github.base76-research-lab/token-compressor Semantic prompt compression for LLM workflows. Reduce token usage by 40–60% without losing meaning. Built by Base76 Research Lab — research into epistemic AI architecture. Intent Compiler MVP is now live and uses this project as part of the idea -> spec -> compressed output flow: token-compressor is a two-stage pipeline that compresses…
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mcp-name: io.github.base76-research-lab/token-compressor Semantic prompt compression for LLM workflows. Reduce token usage by 40–60% without losing meaning. Built by Base76 Research Lab — research into epistemic AI architecture. Intent Compiler MVP is now live and uses this project as part of the idea -> spec -> compressed output flow: token-compressor is a two-stage pipeline that compresses prompts before they reach an LLM: 1. LLM compression — a local model (llama3.2:1b via Ollama) rewrites…