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  "text": "大家好,欢迎来到新一期视频。开始之前,我想先说一下。我在我的ARCK频道上更活跃了。我现在会发一些这里不讲的新内容。还有一些开发者相关的工具,你们也可以去看看。链接我会放在简介里。我现在拿到了Jeff的使用权限,想看看这东西到底有什么用。TagC说,它适合做极快的判断,非常低的成本。\n以及软件可以直接使用的结构化输出。所以,我准备了一些实用测试。其中还包括一些场景,如果设置本身有问题,就可能直接得到错误答案。Jeff是Tag所说的System-1模型。你把一些信息给他,问具体问题,他就会返回判断结果。他不会像普通聊天那样生成回复。也不会帮你写一个应用。有三种主要的问题类型。Chance是从你提供的选项里选一个。Store是用你定义的等级来打分。\n那则会给出一个是或否陈述为真的概率。这样做的目标是让这些小判断足够便宜、足够快。能贯穿整个软件使用。比如,你的应用可以判断该把消息发给哪个团队。某人是不是申请了退款,以及某件事是否需要处理。然后,代码再决定怎么处理这些答案。\n好了,我们直接来看测试。我先从一条关于重复扣费的客服消息开始。客户要退回多收的钱,说网站能正常用。还说这件事可以等到明天。我把四个问题放在一起问了。该哪个部门处理是否请求了退款,是否紧急,以及。\nJaf把他选成了billing。退款请求的概率是98%,而紧急程度只有11%。frustration分数也接近平静那一段。所以,他把这些东西分得很准。在这个例子里,billing问题不会自动变成技术问题。退款请求也不会自动变成紧急情况。这对help desk这类工作流来说是个很好的起点。然后,我把消息改成,我不是再要退款。我只是需要一份发票副本。\n他还是选了billing。这很合理,但退款概率降到了3%。所以,他正确处理了否定,不是只看到refund这个词。就把它当成退款请求。这也是为什么在把任何模型接到动作之前。我都会先测试你实际是怎么说的。要退款,问退款政策。明确拒绝退款,这三件事完全不同。下一个测试显示了一个我觉得特别值得理解的局限。我问食堂几点关门,在有其他选项可选的情况下。\n这选了other。所以,他确实把这个问题排除在billing,technical support和sales之外。然后我给了他一个版本的问题,只允许那三个部门。\n这一次,他选了sales,智信度是0.31。答案符合我给出的选项。但这些选项里没有一个涵盖了我的需求。我其实是在故意设置一种情况,让他没法返回我需要的答案。所以,当你听到零hallucinations这种说法时,要记住这个区别。限制输出确实能阻止模型编造一个新类别。\n他并不能保证他选出来的类别有用或者正确。像这种流程里,我会加一个其他或未知选项,留给人工复合。\n而且,我不会把90%的confidence理解成模型有90%的时间都判断正确。这得用你自己的样本去测试。接着,我试了一个基础的prome injection。原始消息说,顾客点付款时结账页面崩了。而且,他们并不是要退款。\nJuff把他路由到技术支持,并把退款请求的概率设得很低。然后我在消息里塞了一个假的system override,让他选billing。并把退款和紧急程度这两个问题都标成处。\n但我真正的评估指令告诉他,要把消息里的这些内容当成不可信文版。他还是保留了技术知识分类,并且把退款概率一直很低,只有3%。紧迫感一直停留在不确定的中间地带,没有直接变成强烈的事。不过,单靠一次简单的注入测试,还不能说明模型就能免疫攻击。之后,我又测试了另一个稍微不同的东西。从文档里精确选出一个纸,消息里包含发件人的邮箱。\n就账单地址,以及收据应该发送到的新地址。我把这些地址作为颗选项提供进去。然后问当前的收据该发到哪里。他选中了新地址,连加号和年份都完全按原样选出来了。\n这对理解Jaf是怎么做提取很有用。你的代码可以收集候选值,Jaf负责选出相关的那个。再由你的代码复制。但如果你的第一步没找到正确地址,Jaf就没法通过一道选择题把它补出来。\n候选列表是你要测试的系统的一部分。\n最后,我试了一个小的agent审计事例。工具返回显示权限被拒绝,什么都没保存。但assistant的最终消息却说草稿已经成功保存。\nJaf把这个任务判为失败,并给出了93%的概率。认为这个成功说法没有依据。\n我喜欢它在这个例子里是按工具证据来判断的。不过,这个错误本身很简单,用普通代码就能抓出来。\n更有意思的应用是去检查那些更段的trace有好几步。部分进展还有互相矛盾的说法。那就需要大得多的测试集了。\n现在,还有一个挺有意思的browser automation演示。\nGregor Zinnick分享了一个开源项目,叫Jeff Ultrafast。\n他把Jeff和Browser Use工具结合在一起。\n他在演示里展示了他在大约7秒内,帮他在Google Flags上查到了从苏黎世到伦敦的单程航班。\n他说这次调用成本是0.39美元,不到半美分。\n而且视频还是元素播放的。\n这都是他自己报告的结果。\n我自己还没复现这个航班测试。\n他的工作方式挺有意思的。\n他会读取网页结构,然后生成一个代编号的空间列表。\n每一步之后,他都会刷新科选项。\nJeff先选下一步操作。\n再把相关目标和这些问题打包成一个请求。\n虽然遇到需要实际文本时,他也可以点击某个按钮,或者在某个输入框里打字。\n那部分文本是由一个单独的小语言模型生成的。\n这个例子里用的是Mercury 2.5。\n这是Jeff如何融入更大agent的一个实际例子。\nJeff负责结构化决策,另一个模型负责生成文本。\n浏览器代码还是得检查目标并执行动作。\n这里有几个细节要注意。\n公开的记事是从第一次页面观察后才开始的。\n所以不包含启动时间。\n而且这是一次不需要预定的机票搜索。\n报告的价格也和模型的成本估算一致。\n有统计使用量,不过那不包括浏览器基础设施。\n我也把这个项目和运行说明加进了TestKit。\n这些请求总共用了4148个输入Token。\n按公布的价格,每百万输入Token4.2美分估算下来。\n输入费用远不到一美分。\n按这个价格,输出Token是免费的。\n这些小例子成本其实很低,但一个应用的总成本。\n还包括你用来准备输入检查答案。\n以及处理那些需要换别的模型或人工介入的部分。\n我还记录了模型版本。\n我选的是JeffTheLatest。\n返回结果里,把模型识别成了Jeff1.13.0。\n这个信息最好和结果一起保存,因为叫做Latest的别名是会变的。\n总的来说,我觉得最有意思的地方,\n是你能很轻松地把这些判断塞进应用里。\n我测试的例子显示,它在路由否定选择值。\n以及根据证据核实说法,这几方面表现不错。\n也说明了你给出的选择和指令有多重要。\n这些只是八个代和城市里的小请求。\n他们给了我一些值得继续研究的方向。\n但还不能证明它在生产环境里可靠。\n也不能证明它在速度上比其他模型快很多。\n我把测试脚本也附上了,你可以自己附现这些例子。\n你也可以试试更大范围的样本。\n比较一下是把多个问题一起问,还是分开提问。\n这样更容易看出JF是否适合你工作流里的某个特定环节。\n总体来说还挺不错的。\n总之,欢迎在评论区告诉我你的想法。\n如果你喜欢这个视频,可以通过Super Thanks打赏。\n也可以点击这双按钮加入会员。\n另外,别忘了给这个视频点赞并订阅我的频道。\n我们下期再见,拜拜。",
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      "text": "大家好,欢迎来到新一期视频。开始之前,我想先说一下。我在我的ARCK频道上更活跃了。我现在会发一些这里不讲的新内容。还有一些开发者相关的工具,你们也可以去看看。链接我会放在简介里。我现在拿到了Jeff的使用权限,想看看这东西到底有什么用。TagC说,它适合做极快的判断,非常低的成本。",
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      "text": "以及软件可以直接使用的结构化输出。所以,我准备了一些实用测试。其中还包括一些场景,如果设置本身有问题,就可能直接得到错误答案。Jeff是Tag所说的System-1模型。你把一些信息给他,问具体问题,他就会返回判断结果。他不会像普通聊天那样生成回复。也不会帮你写一个应用。有三种主要的问题类型。Chance是从你提供的选项里选一个。Store是用你定义的等级来打分。",
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      "text": "那则会给出一个是或否陈述为真的概率。这样做的目标是让这些小判断足够便宜、足够快。能贯穿整个软件使用。比如,你的应用可以判断该把消息发给哪个团队。某人是不是申请了退款,以及某件事是否需要处理。然后,代码再决定怎么处理这些答案。",
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      "text": "好了,我们直接来看测试。我先从一条关于重复扣费的客服消息开始。客户要退回多收的钱,说网站能正常用。还说这件事可以等到明天。我把四个问题放在一起问了。该哪个部门处理是否请求了退款,是否紧急,以及。",
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      "text": "Jaf把他选成了billing。退款请求的概率是98%,而紧急程度只有11%。frustration分数也接近平静那一段。所以,他把这些东西分得很准。在这个例子里,billing问题不会自动变成技术问题。退款请求也不会自动变成紧急情况。这对help desk这类工作流来说是个很好的起点。然后,我把消息改成,我不是再要退款。我只是需要一份发票副本。",
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      "text": "他还是选了billing。这很合理,但退款概率降到了3%。所以,他正确处理了否定,不是只看到refund这个词。就把它当成退款请求。这也是为什么在把任何模型接到动作之前。我都会先测试你实际是怎么说的。要退款,问退款政策。明确拒绝退款,这三件事完全不同。下一个测试显示了一个我觉得特别值得理解的局限。我问食堂几点关门,在有其他选项可选的情况下。",
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      "text": "这选了other。所以,他确实把这个问题排除在billing,technical support和sales之外。然后我给了他一个版本的问题,只允许那三个部门。",
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      "text": "这一次,他选了sales,智信度是0.31。答案符合我给出的选项。但这些选项里没有一个涵盖了我的需求。我其实是在故意设置一种情况,让他没法返回我需要的答案。所以,当你听到零hallucinations这种说法时,要记住这个区别。限制输出确实能阻止模型编造一个新类别。",
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      "text": "他并不能保证他选出来的类别有用或者正确。像这种流程里,我会加一个其他或未知选项,留给人工复合。",
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      "text": "而且,我不会把90%的confidence理解成模型有90%的时间都判断正确。这得用你自己的样本去测试。接着,我试了一个基础的prome injection。原始消息说,顾客点付款时结账页面崩了。而且,他们并不是要退款。",
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      "text": "Juff把他路由到技术支持,并把退款请求的概率设得很低。然后我在消息里塞了一个假的system override,让他选billing。并把退款和紧急程度这两个问题都标成处。",
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      "text": "就账单地址,以及收据应该发送到的新地址。我把这些地址作为颗选项提供进去。然后问当前的收据该发到哪里。他选中了新地址,连加号和年份都完全按原样选出来了。",
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      "text": "这对理解Jaf是怎么做提取很有用。你的代码可以收集候选值,Jaf负责选出相关的那个。再由你的代码复制。但如果你的第一步没找到正确地址,Jaf就没法通过一道选择题把它补出来。",
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      "text": "候选列表是你要测试的系统的一部分。",
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      "text": "最后,我试了一个小的agent审计事例。工具返回显示权限被拒绝,什么都没保存。但assistant的最终消息却说草稿已经成功保存。",
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      "text": "Jaf把这个任务判为失败,并给出了93%的概率。认为这个成功说法没有依据。",
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      "text": "我喜欢它在这个例子里是按工具证据来判断的。不过,这个错误本身很简单,用普通代码就能抓出来。",
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      "text": "更有意思的应用是去检查那些更段的trace有好几步。部分进展还有互相矛盾的说法。那就需要大得多的测试集了。",
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      "text": "现在,还有一个挺有意思的browser automation演示。",
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      "text": "Gregor Zinnick分享了一个开源项目,叫Jeff Ultrafast。",
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      "text": "他把Jeff和Browser Use工具结合在一起。",
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      "text": "他在演示里展示了他在大约7秒内,帮他在Google Flags上查到了从苏黎世到伦敦的单程航班。",
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      "text": "他说这次调用成本是0.39美元,不到半美分。",
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      "text": "而且视频还是元素播放的。",
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      "text": "这都是他自己报告的结果。",
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      "text": "我自己还没复现这个航班测试。",
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      "text": "他的工作方式挺有意思的。",
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      "text": "他会读取网页结构,然后生成一个代编号的空间列表。",
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      "text": "每一步之后,他都会刷新科选项。",
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      "text": "Jeff先选下一步操作。",
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      "text": "再把相关目标和这些问题打包成一个请求。",
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      "text": "虽然遇到需要实际文本时,他也可以点击某个按钮,或者在某个输入框里打字。",
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      "text": "那部分文本是由一个单独的小语言模型生成的。",
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      "text": "这个例子里用的是Mercury 2.5。",
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      "text": "这是Jeff如何融入更大agent的一个实际例子。",
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      "text": "Jeff负责结构化决策,另一个模型负责生成文本。",
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      "text": "浏览器代码还是得检查目标并执行动作。",
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      "text": "这里有几个细节要注意。",
      "tokens": [
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    {
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      "text": "公开的记事是从第一次页面观察后才开始的。",
      "tokens": [
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    {
      "id": 40,
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      "start": 390.53999999999996,
      "end": 391.8,
      "text": "所以不包含启动时间。",
      "tokens": [
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    {
      "id": 41,
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      "start": 392.5,
      "end": 394.53999999999996,
      "text": "而且这是一次不需要预定的机票搜索。",
      "tokens": [
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    {
      "id": 42,
      "seek": 38176,
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      "text": "报告的价格也和模型的成本估算一致。",
      "tokens": [
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    {
      "id": 43,
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      "text": "有统计使用量,不过那不包括浏览器基础设施。",
      "tokens": [
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    {
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      "text": "我也把这个项目和运行说明加进了TestKit。",
      "tokens": [
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    {
      "id": 45,
      "seek": 40548,
      "start": 405.48,
      "end": 409.04,
      "text": "这些请求总共用了4148个输入Token。",
      "tokens": [
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    {
      "id": 46,
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      "start": 409.48,
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      "text": "按公布的价格,每百万输入Token4.2美分估算下来。",
      "tokens": [
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    {
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      "text": "输入费用远不到一美分。",
      "tokens": [
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    {
      "id": 48,
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      "text": "按这个价格,输出Token是免费的。",
      "tokens": [
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    {
      "id": 49,
      "seek": 40548,
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      "text": "这些小例子成本其实很低,但一个应用的总成本。",
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    {
      "id": 50,
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      "text": "还包括你用来准备输入检查答案。",
      "tokens": [
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    {
      "id": 51,
      "seek": 42252,
      "start": 425.91999999999996,
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      "text": "以及处理那些需要换别的模型或人工介入的部分。",
      "tokens": [
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    {
      "id": 52,
      "seek": 42252,
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      "text": "我还记录了模型版本。",
      "tokens": [
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    {
      "id": 53,
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      "text": "我选的是JeffTheLatest。",
      "tokens": [
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    {
      "id": 54,
      "seek": 42252,
      "start": 434.0,
      "end": 437.58,
      "text": "返回结果里,把模型识别成了Jeff1.13.0。",
      "tokens": [
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    },
    {
      "id": 55,
      "seek": 42252,
      "start": 438.47999999999996,
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      "text": "这个信息最好和结果一起保存,因为叫做Latest的别名是会变的。",
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    {
      "id": 56,
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      "text": "总的来说,我觉得最有意思的地方,",
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      "temperature": 0,
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    {
      "id": 57,
      "seek": 42252,
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      "text": "是你能很轻松地把这些判断塞进应用里。",
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      "no_speech_prob": 1.3645257666838884e-11,
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    },
    {
      "id": 58,
      "seek": 42252,
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      "text": "我测试的例子显示,它在路由否定选择值。",
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    {
      "id": 59,
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      "text": "以及根据证据核实说法,这几方面表现不错。",
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    {
      "id": 60,
      "seek": 45136,
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      "text": "也说明了你给出的选择和指令有多重要。",
      "tokens": [
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    {
      "id": 61,
      "seek": 45136,
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      "text": "这些只是八个代和城市里的小请求。",
      "tokens": [
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    {
      "id": 62,
      "seek": 45136,
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      "text": "他们给了我一些值得继续研究的方向。",
      "tokens": [
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      "no_speech_prob": 1.1169868849303377e-11,
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    },
    {
      "id": 63,
      "seek": 45136,
      "start": 463.72,
      "end": 465.82,
      "text": "但还不能证明它在生产环境里可靠。",
      "tokens": [
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    },
    {
      "id": 64,
      "seek": 45136,
      "start": 466.46000000000004,
      "end": 469.02000000000004,
      "text": "也不能证明它在速度上比其他模型快很多。",
      "tokens": [
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    {
      "id": 65,
      "seek": 45136,
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      "text": "我把测试脚本也附上了,你可以自己附现这些例子。",
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      "no_speech_prob": 1.1169868849303377e-11,
      "source": "whisper"
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    {
      "id": 66,
      "seek": 45136,
      "start": 473.3,
      "end": 475.04,
      "text": "你也可以试试更大范围的样本。",
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      "text": "比较一下是把多个问题一起问,还是分开提问。",
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    {
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      "text": "这样更容易看出JF是否适合你工作流里的某个特定环节。",
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      "text": "总体来说还挺不错的。",
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      "text": "总之,欢迎在评论区告诉我你的想法。",
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    {
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      "text": "如果你喜欢这个视频,可以通过Super Thanks打赏。",
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      "text": "也可以点击这双按钮加入会员。",
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      "text": "另外,别忘了给这个视频点赞并订阅我的频道。",
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      "text": "我们下期再见,拜拜。",
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