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Caveman: Why use many token when few token do trick

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Why This Matters

This innovation demonstrates how simplifying language in AI prompts can significantly reduce token usage, leading to faster and more cost-effective interactions without sacrificing accuracy. It highlights a practical approach for optimizing large language model efficiency, benefiting both developers and consumers by lowering operational costs and improving response times.

Key Takeaways

caveman

why use many token when few do trick

Install • Benchmarks • Before/After • Why

A Claude Code skill/plugin and Codex plugin that makes agent talk like caveman — cutting ~75% of tokens while keeping full technical accuracy.

Based on the viral observation that caveman-speak dramatically reduces LLM token usage without losing technical substance. So we made it a one-line install.

Before / After

🗣️ Normal Claude (69 tokens) "The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle. When you pass an inline object as a prop, React's shallow comparison sees it as a different object every time, which triggers a re-render. I'd recommend using useMemo to memoize the object." 🪨 Caveman Claude (19 tokens) "New object ref each render. Inline object prop = new ref = re-render. Wrap in useMemo ." 🗣️ Normal Claude "Sure! I'd be happy to help you with that. The issue you're experiencing is most likely caused by your authentication middleware not properly validating the token expiry. Let me take a look and suggest a fix." 🪨 Caveman Claude "Bug in auth middleware. Token expiry check use < not <= . Fix:"

Same fix. 75% less word. Brain still big.

Benchmarks

Real token counts from the Claude API (reproduce it yourself):

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