This article, part of a series, explores how organizations often pay twice for the same LLM response due to constant re-computation. It highlights the necessity of rethinking how work is reused to optimize AI costs and efficiency.
Semble, a new open-source tool, dramatically cuts AI code search tokens by 98% compared to traditional grep methods. It achieves this by intelligently extracting only necessary code snippets and stripping irrelevant elements, drastically reducing LLM prompt costs.
The release of Claude Opus 4.7, featuring a new tokenizer that increases token usage by up to 35% for the same text, prompted the author to seriously address token waste management. The article will detail how they measured and differentiated token waste from inefficient usage across over 133,000 turns.