prompt-compression

Headroom for macOS — cut Claude Code and Codex token costs by ~50%

Drop-in prompt compression for production LLM apps. Cut your token bill 40-60% without changing your code. Python SDK, LLMLingua-2, MIT.

Local proxy that compresses your LLM API requests so you pay less, with no change to the answers. Trims wasted tokens from prompts, history, tool output, and code before they're sent: -31% input / -74% output, measured live. Any provider, no extra model calls. Also an MCP server and embeddable library (Rust, Python, Ruby, Kotlin, Swift, JS/TS).

HEWN 2.0 2026: AI Output Router for Precision Summaries & Polished Code

A curated list of strategies, tools, papers, and resources for reducing LLM token costs and improving efficiency in production.

VL-JEPA inspired pipeline — compress images/text locally via Ollama, send compact payloads to any LLM API. Cut token costs by ~80%.