How I Built a Zero-Dependency Token Compressor for AI Coding Agents (During My High School Exams)

as developers, we are spending more and more time working alongside AI coding agents like Cursor, Claude Code, GitHub Copilot, Windsurf, or Cline.

But as your session grows, you quickly run into two major problems:

Context Window Inflation: Long-running loops, verbose model reasoning, and unfiltered terminal log dumps clog the context window, causing the LLM to get "lost in the middle" and start hallucinating.

Financial Overhead: Large context windows mean higher token usage, which translates directly to higher API costs.

To solve this, I built TITAN (Token Intelligence Through Agent Narrowing): a universal, zero-dependency CLI framework designed to compress AI agent token consumption by 70% to 85% without degrading reasoning quality.