但在可预见的未来，有一些人类的底层能力是算法难以企及的。比如，复杂真实环境下的柔性适应力与韧性。AI能在给定规则里找到最优解，但真实社会的运转往往是模糊的、多变的，甚至是不讲逻辑的，这种时候人类的灵活性无可替代。
Compare this with Rust, Go, Swift, or even Java: the variable is either initialized to a known value at declaration, or the program doesn’t compile. Period. There’s no “erroneous behavior” category because the error is prevented structurally. In C++23, you can still write int x; return x; and get a program that compiles, runs, and returns garbage. The garbage is just more predictable now.
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APPSO：国家互联网应急中心针对 OpenClaw 发出了安全预警，多家国有机构也开始限制员工使用 OpenClaw。WorkBuddy 同属龙虾品类，这顶帽子会不会也扣过来？
Smaller models seem to be more complex. The encoding, reasoning, and decoding functions are more entangled, spread across the entire stack. I never found a single area of duplication that generalised across tasks, although clearly it was possible to boost one ‘talent’ at the expense of another. But as models get larger, the functional anatomy becomes more separated. The bigger models have more ‘space’ to develop generalised ‘thinking’ circuits, which may be why my method worked so dramatically on a 72B model. There’s a critical mass of parameters below which the ‘reasoning cortex’ hasn’t fully differentiated from the rest of the brain.