"domain": "feishu",
我們需要對AI機器人保持禮貌嗎？
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Returning back to the Anthropic compiler attempt: one of the steps that the agent failed was the one that was more strongly related to the idea of memorization of what is in the pretraining set: the assembler. With extensive documentation, I can’t see any way Claude Code (and, even more, GPT5.3-codex, which is in my experience, for complex stuff, more capable) could fail at producing a working assembler, since it is quite a mechanical process. This is, I think, in contradiction with the idea that LLMs are memorizing the whole training set and uncompress what they have seen. LLMs can memorize certain over-represented documents and code, but while they can extract such verbatim parts of the code if prompted to do so, they don’t have a copy of everything they saw during the training set, nor they spontaneously emit copies of already seen code, in their normal operation. We mostly ask LLMs to create work that requires assembling different knowledge they possess, and the result is normally something that uses known techniques and patterns, but that is new code, not constituting a copy of some pre-existing code.
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The solution is building your own tracking system using no-code automation tools. This approach requires more initial setup but provides ongoing monitoring at a fraction of commercial tool costs. The system I built uses Make.com, a no-code automation platform, to query AI models systematically, analyze responses, and track mentions over time. Make offers 1,000 operations monthly on their free tier, making it possible to start tracking without any monetary investment.。业内人士推荐爱思助手下载最新版本作为进阶阅读
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