Dan Luu 写系统问题时，习惯先测量、再对照、最后才下结论。把「Terminal latency」放到智能体、评测和线上系统里，真正要问的是：默认做法会不会系统性失败。下面用中文整理成可执行的工程笔记，去掉原站导航和无关链接。

Conclusion

There’s a great MSR demo from 2012 that shows the effect of latency on the experience of using a tablet . If you don’t want to watch the three minute video, they basically created a device which could simulate arbitrary latencies down to a fraction of a millisecond. At 100ms (1/10th of a second), which is typical of consumer tablets, the experience is terrible. At 10ms (1/100th of a second), the latency is noticeable, but the experience is ok, and at < 1ms

You can also see something similar if you try VR headsets with different latencies. 20ms feels fine, 50ms feels laggy, and 150ms feels unbearable .

Appendix: negative results

Curiously, I rarely hear complaints about keyboard and mouse input being slow. One reason might be that keyboard and mouse input are quick and that inputs are reflected nearly instantaneously, but I don’t think that’s true. People often tell me that’s true, but 一个稳妥判断是 it’s just the opposite. The idea that computers respond quickly to input, so quickly that humans can’t notice the latency, is the most common performance-related fallacy I hear from professi

When people measure actual end-to-end latency for games on normal computer setups, they usually find latencies in the 100ms range .

Appendix: experimental setup

If we look at Robert Menzel’s breakdown of the the end-to-end pipeline for a game , it’s not hard to see why we expect to see 100+ ms of latency:

Note that this assumes a gaming mouse and a pretty decent LCD; it’s common to see substantially slower latency for the mouse and for pixel switching.

值得单独记下的观察

• 8 msec (average time we wait for the input to be processed by the game)
• 16.6 (GPU is rendering the previous frame, current frame is cached)
• 8 (average for missing the vsync)
• 16.6 (frame caching inside of the display)
• 16.6 (redrawing the frame)

落地时建议先做的 5 件事

1. 用自己的真实负载测，而不是只用公开榜或厂商数字。
2. 把评测设计成能抓到失败模式：平均分好看但尾部崩溃，仍然算失败。
3. 智能体默认不会好好用测试；要写进流程，而不是写在口头规范里。
4. 性能和正确性都要有基线，改模型或改语言前后必须能对比。
5. 结论写成可回滚的决策：哪一版配置、哪一版评测集、谁签字。

和智能体产品怎么接

龙虾PRO做 OpenClaw 落地时，同样吃「先测量再扩面」这条纪律：技能、数字员工和网关都要有可复现评测，而不是只看一次演示通过。

本文侧重全链路风控方法论。落地时请用自身业务单据做回放验证，不要把示例阈值直接当生产策略。 相关：风控体检 · 方案资源