🔥 Hot Repo: Cut Claude Code Bills 92% — 40K Stars

One-liner — Headroom is a context compression layer that intercepts tool outputs, logs, and RAG chunks before they hit the LLM, cutting 60–95% of tokens with zero code changes. - Repo: chopratejas/headroomhttps://github.com/chopratejas/headroom - Stars: ⭐ 40,496 +3,786 today - Language: Python - License: Apache 2.0 --- What It Does Headroom sits between your AI agent Claude Code, Codex, Cursor, Aider and the LLM provider, compressing whatever the agent reads—tool outputs, log files, code search results, RAG chunks, and conversation history—before it reaches the model. It ships in three modes: a Python/TypeScript library compressmessages, a drop-in HTTP proxy headroom proxy --port 8787, and an MCP server with headroomcompress / headroomretrieve tools. Originals are cached locally and retrievable on demand via reversible compression CCR, so the model never loses information it might need. Why It's Blowing Up The repo gained 3,786 stars in the past 24 hours—its biggest single-day spike—driven by a Hacker News thread that surfaced 19 hours ago, alongside the v0.26.0 release just four days earlier. That release added a GitHub Copilot BYOK provider wrapper, an agent usage stats dashboard, and cross-provider streaming compression for AWS Bedrock. Each addition reinforced the argument that headroom is no longer a narrow Python hack but a universal proxy layer for any agentic stack. The deeper pull is economic. As Claude Code and Codex have become daily drivers for professional engineers, token costs on Opus-class models have become a real budget line. Headroom's author, Tejas Chopra Senior Engineer at Netflix, built the project because he was burning $200/day on tool-heavy agent runs. The README shows a live demo where 10,144 tokens compress to 1,260—an 87% reduction—with the same FATAL error surfaced intact. The benchmark table backs it up: GSM8K math accuracy holds at 0.870, SQuAD v2 hits 97% accuracy under 19% compression. Context engineering has quietly becom