<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>黄东旭 on MessageDaily</title><link>https://inkeast.github.io/MessageDaily/tags/%E9%BB%84%E4%B8%9C%E6%97%AD/</link><description>Recent content in 黄东旭 on MessageDaily</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Thu, 20 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://inkeast.github.io/MessageDaily/tags/%E9%BB%84%E4%B8%9C%E6%97%AD/index.xml" rel="self" type="application/rss+xml"/><item><title>从烧钱竞赛到精打细算：一个Token重度用户的Agent进化史</title><link>https://inkeast.github.io/MessageDaily/posts/2026-08-20-token-economy-agent-evolution-guigu101/</link><pubDate>Thu, 20 Aug 2026 00:00:00 +0000</pubDate><guid>https://inkeast.github.io/MessageDaily/posts/2026-08-20-token-economy-agent-evolution-guigu101/</guid><description>硅谷101对话黄东旭与张宏江：当Uber四个月烧穿全年AI预算、Meta给员工设token上限，&amp;ldquo;Token Maxing&amp;quot;的烧钱竞赛到达转折点。亲历者黄东旭讲述自己从日烧四五百美元的最强模型依赖，转向本地DeepSeek V4 Flash加云端Fable 5的混布组合；这场从token maxing到token efficient的转向，本质是模型能力跨过工程化门槛后，成本结构与企业KPI的重算。张宏江判断AGI奇点已至，而更深的分歧在于：单模型智商碾压与多agent蜂群，谁是终局。</description></item></channel></rss>