How I Cut AI Coding Token Usage by 90% Using Local AST Pruning (At Age 15)

Hey developers! 👋

I'm Toprak, a 15-year-old software engineer from Turkey building open-source developer tooling at Siber Akademi.

Like many developers using AI coding assistants like Cursor, Claude Desktop, and Windsurf, I constantly hit a major wall on large codebases: Context Window Bloat.

The Core Problem: Implementation vs. Interface

When you @ tag directories in Cursor Composer, 85-90% of the ingested tokens are function execution bodies (loops, SQL queries, helper transforms) that the AI doesn't need to read just to understand your architecture.

The Solution: AST Skeletonization (T-Zero)

I spent the last several months engineering T-Zero (v11.11), an open-source local Model Context Protocol (MCP) server that prunes multi-language code down to typed AST interface skeletons.

📊 Real Benchmark on 481-File Repository:

• Raw Codebase Context: 10,998,573 tokens ($32.99 per single Claude 3.5 prompt fill)
• T-Zero 4-Tier AST Skeletons: 1,319,828 tokens (-88.0% reduction)
• Focus File Skeleton: -94.6% reduction
• Local Latency: <400ms (pure local CPU, 0 cloud telemetry)
• Security: In-memory Shannon entropy scanner (H >= 4.75) detects and redacts hardcoded API keys locally.

⚡ 30-Second Setup in Cursor or Claude Desktop:

Add this to your Cursor MCP settings (~/.cursor/mcp.json):

{
  "mcpServers": {
    "tzero": {
      "command": "npx",
      "args": ["-y", "tzero-mcp@latest"]
    }
  }
}

(Or install via pip: pip install tzero-mcp)

Everything is 100% free and open-source under the MIT License!

I'm currently on a mission to hit my first 100 GitHub Stars ⭐ — if this saves your AI context budget, dropping a star would mean the world to an independent 15-year-old engineer:

👉 GitHub Repo (MIT): https://github.com/toprakahmetaydogmus/TZeroAlgorithm

👉 Interactive Web Playground: https://tzero-indol.vercel.app