北京日报的一篇报道标题很扎心：《刷屏的OpenClaw“AI小龙虾”，为啥银行不敢碰？》。答案很简单：承载海量客户敏感数据的银行，对数据安全的红线意识远超一般企业。开源代码的公开性、远程控制带来的内网安全隐患，与银行闭环管理、内外网严格隔离的核心建设思路相悖。
for more details.，详情可参考wps
。谷歌对此有专业解读
https://feedx.site，更多细节参见WhatsApp Web 網頁版登入
The total encoding cost includes all the work that goes in to writing a prompt, and all of the compute required to run the prompt. If the task is simple to express in a prompt, the total encoding cost is low. If the task is both simple to express in a prompt, and tedious or difficult to produce directly, the relative encoding cost is low. As models get more capable, more complex prompts can be easily expressed: more semantically dense prompts can be used, referencing more information from the training data. An agent capable of refining or retrying a task after an initial prompt might succeed at a complex task after a single simple prompt. However, both of these also increase the compute cost of the prompt, sometimes substantially, driving up the total encoding cost. More “capable” models may have a higher probability of producing correct output, reducing costs reprompting with more information (“prompt engineering”), and possibly reducing verification costs.
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