🚨 A Netflix engineer just open-sourced the fix for the most expensive problem in AI: wasted tokens It's called Headroom, and it might be the smartest fix to high token usage Your agent reads a 10,000-token log file to find one error. You paid for all 10,000 tokens. The answer needed 1,200. Headroom sits between your agent and the LLM and compresses everything before the model sees it. JSON, code, logs, RAG chunks, each gets its own specialized compressor. And it's reversible: the originals stay on your machine, so nothing is lost. The results speak for themselves: → Up to 95% fewer tokens → Same accuracy on benchmarks → Zero changes to your code Setup takes one minute: 1. pip install "headroom-ai[all]" 2. headroom wrap claude Done. Works with Claude Code, Cursor, Codex, and anything OpenAI-compatible. Everything runs locally, fully open source. The cheapest token is the one you never send. 🔗 Github repo: github.com/headroomlabs-ai/h… So, are you compressing your context, or just paying the bill?