You’ve got a local LLM setup — Ollama, LM Studio, whatever. Now which model do you actually run? That’s the question nobody’s really answering well. HuggingFace shows you download counts. Ollama search tells you what fits in VRAM. But “fits” and “best” are two very different things. I’ve spent way too many afternoons downloading model after model, testing them one by one, only to wonder if there’s something better I missed. ...
You know that feeling when you’re watching Claude Code or Cursor explore a big codebase, and it just keeps… digging? One grep, one find, one Read file — over and over. Meanwhile your token counter ticks up like a taxi meter. I’ve been there. Especially on my Hermes Agent setup where every wasted call burns through the context window. So when I saw CodeGraph rocketing up GitHub with 42k stars and +9.3k in a single week, I had to find out if it lives up to the hype. ...
Headroom Review 2026: Cut AI Agent Token Costs by 60-95% Without Losing Accuracy Running AI coding agents daily? You’ve probably noticed the token bills. Every tool output, every log line, every RAG chunk gets fed to the LLM — and you pay for all of it. Headroom is a context compression layer that sits between your agent and the LLM, shrinking inputs by 60-95% while preserving answer quality. Meta Description: Headroom compresses AI agent inputs by 60-95% without losing accuracy. Tested with Claude Code, Codex, Cursor, and more. Includes benchmarks, quick start guide, and honest comparison. ...