5 Matching Annotations
  1. Last 7 days
    1. The rankings, set up by a Meta employee on its intranet using company data, measure how many tokens — the units of data processed by AI models — employees are burning through.

      这一观点揭示了‘tokenmaxxing’作为衡量员工AI使用能力的新趋势,暗示了数据消耗成为衡量生产力的一种方式。

    2. Employees at Meta Platforms who want to show off their AI superuser chops are competing on an internal leaderboard for status as a “Session Immortal”— or, even better, “Token Legend.”

      这个引用揭示了“Tokenmaxxing”作为一种新的竞争和显摆形式在Meta内部的兴起,员工通过使用AI令牌的数量来竞争地位。

  2. Apr 2026
    1. Members have been using Mythos regularly since gaining access — providing screenshots and a live demonstration of the model as evidence to _Bloomberg_ — though reportedly not for cybersecurity purposes in an attempt to avoid detection by Anthropic.

      人们通常认为黑客使用高级 AI 模型是为了进行网络攻击,但作者指出,这些黑客似乎并没有使用 Mythos 进行网络安全目的,而是为了避免被 Anthropic 发现,这表明了黑客行为可能并不总是出于恶意。

    1. Codex just hit 3 million weekly active users, our APIs process more than 15 billion tokens per minute, and GPT‑5.4 is driving record engagement across agentic workflows.

      令人惊讶的是:OpenAI的Codex代码助手每周活跃用户已达300万,API每分钟处理超过150亿个token,GPT-5.4在代理工作流程中创造了参与度记录。这些数字展示了AI工具在企业中的大规模采用和惊人处理能力。

    1. Consequently, they cannot verify if tools were actually invoked, applied correctly, or used efficiently.

      主流观点认为只要AI模型给出正确答案,其工具使用过程就是合理的。但作者尖锐指出现有评估方法根本无法验证工具是否被真正调用、正确应用或高效使用。这一论点挑战了AI领域对'结果导向'评估的依赖,暗示我们可能正在高估当前AI系统的实际能力,尤其是工具使用方面的能力。