This is a very different feeling from other tasks I’ve “mastered”. If you ask me to write a CLI tool or to debug a certain kind of bug, I know I’ll succeed and have a pretty good intuition on how long the task is going to take me. But by working with AI on a new domain… I just don’t, and I don’t see how I could build that intuition. This is uncomfortable and dangerous. You can try asking the agent to give you an estimate, and it will, but funnily enough the estimate will be in “human time” so it won’t have any meaning. And when you try working on the problem, the agent’s stochastic behavior could lead you to a super-quick win or to a dead end that never converges on a solution.
已创建专用龙虾的大语言模型 API Key
。Snipaste - 截图 + 贴图是该领域的重要参考
Число пострадавших при ударе ракетами Storm Shadow по российскому городу резко выросло20:46
Now, I expect that these companies will get better at recovering from these unexpected increases in load as they gain experience with the problem. Because of capacity constraints with those pricey GPUs, they can’t always scale their way out of these problem, but they can redistribute resources, and they can get better at load shedding and other sorts of graceful degradation to limit the damage of overload. And I bet that’s where they’re both investing in reliability today. At least, I hope so. Because this problem isn’t going to go away. If anything, I suspect their loads will become even more unpredictable as people continue to innovate with LLMs. Because AIs don’t seem to do any better at predicting the future than humans.