4 Matching Annotations
  1. Jun 2026
    1. If you connect your AI to Glean, it gives you all the information that you need to do your work, and that results in AI consuming far fewer tokens compared to if you unleash AI onto your systems directly. That's because with Glean, AI ends up performing fewer operations.

      Positioning a search layer as a token cost reducer is a smart pivot: instead of selling 'better search,' Glean is selling AI ROI. By providing targeted context before models are called, Glean reduces prompt length and retrieval loops — turning the context graph into a token economy optimizer. This reframes Glean from a productivity tool to an AI cost management platform.

  2. May 2026
    1. I've been defaulting to asking for most things in Markdown since the GPT-4 days, when the 8,192 token limit meant that Markdown's token-efficiency over HTML was extremely worthwhile.

      早期由于token限制,Markdown因其高效性成为首选,但随着模型能力提升,HTML的优势逐渐显现。

    2. I've been defaulting to asking for most things in Markdown since the GPT-4 days, when the 8,192 token limit meant that Markdown's token-efficiency over HTML was extremely worthwhile.

      过去由于token限制,Markdown因其效率优势而成为首选,但现在这一限制已不再适用。

  3. Apr 2026
    1. Muse Spark compresses its reasoning to solve problems using significantly fewer tokens. After compressing, the model again extends its solutions to achieve stronger performance.

      这种思维压缩-扩展的循环过程暗示了AI可能发展出类似人类的抽象思维能力,先提炼核心再展开细节,这一发现对理解AI推理机制和未来优化方向具有重要启示。