{
  "text": "好。喂喂喂,可以聽見嗎?大家好。\n让他让他跟他说让他听一下看他们那边有没有\n欢迎大家来到哎不对那他们爱吗是不是\n但是我们是不不OKOKOK\n哎我觉得欢迎大家来到的天下直播走可以\n老林老八好大一个铁子\n他里面有反馈吗\n没有哦\n这现在应该是提供电视场的部分\n没有我开着卖呢\n请不吝点赞 订阅 转发 打赏支持明镜与点点栏目\n请不吝点赞 订阅 转发 打赏支持明镜与点点栏目\n请不吝点赞 订阅 转发 打赏支持明镜与点点栏目\n请不吝点赞 订阅 转发 打赏支持明镜与点点栏目\n请不吝点赞 订阅 转发 打赏支持明镜与点点栏目\n请不吝点赞 订阅 转发 打赏支持明镜与点点栏目\n哈喽大家欢迎来到今天的直播间\n我们等待直播间的朋友们陆陆续续进场\n哈喽各位朋友们大家晚上好\n欢迎来到今天Workbody和艾玛的直播间\n我是今天的主持人Yoyo\n今天这场直播呢\n我们聚焦两款产品的协同\n就是我们今天的Workbody与艾玛知识库\n先做一个简单的定位介绍\nWorkbody是腾讯旗下的一站式AI原生智能体工作台\n我们希望通过AI帮助每一位用户\n把复杂的工作变简单\n它不只是于回答问题\n更能理解任务\n调用资料\n自主规划步骤\n并且最终交付一个文档\nPPT\n网页等可用的成果\n而埃玛知识库是腾讯AI知识管家\n它可以帮助我们把文档\n网页\n笔记等各类资料\n汇聚成个人或团队的专属知识库\n并基于这些知识提供智能问答与创作支持\n让AI真正的读懂你\n一个是替你做事的工作台\n一个是替你记事的知识管家\n今天呢我们就来和大家聊一聊\n当这两者配合起来的时候\n能为我们的日常工作带来一些怎么样的改变\n不过在此之前啊\n我想请大家去跳出一个惯性的认知\n就是很多人其实现在还是一提到AI\n会第一反应觉得它是一个问答工具\n你问一句\n他答一句\n一个chatbot\n于是我们常常会觉得\n是不是用不好\nAI是因为我们不会提问\n不会写提示词\n但其实真正的问题有时候不在提问上\n而是在两个地方\n一个是资料每次都要重新喂\n你手里明明有现成的材料\n可是每一次展开对话\n你都得重新再和他讲一次背景\n第二个是我们产出的成果总是散落在外\nAI好不容易帮你产出了东西\n但是呢你用完就没了就散了\n就可能散落在你的电脑各个文件里面\n没有沉淀\n于是你下一次还想再做一套这样的事情的时候\n你又得从零开始跟他去讲背景\n那么这就是我们和AI之间最真实的那道坎\n你存的很行\n但是用的很少\n你达的不少\n但是你留下的却不多\n那能不能换一种方式呢\n让我们在埃马知识库里沉淀下来的知识\n不再只是静静的躺着\n而是真正的为我们去工作\n让AI产生的每一份成果都能沉淀回来\n并且用的越来越顺手\n今天这场直播\n就是想和大家探讨这两位主角的分工\nWorkbody负责让AI做的成功\n而Emma负责让AI记得住\n那这套协同具体是怎么来运作的\n接下来我们有请我们今天的嘉宾Gary老师\n来和直播间的朋友们来打个招呼吧\n大家好我是Gary\n我是Workbody的产品运营\n那今天呢我们会聚焦如何去搭一个会执行\n会沉淀会进化的AI工作流\n来讲解这本场直播\n其中设定了两个产品\n分别是我们腾讯的AI知识管家Emma\n另一个呢是我们腾讯的AI工作台Workbody\n那在正式开始直播之前呢\n大家观众都在陆续的进来\n我们先给大家看一下我们这场直播\n整体会讲哪些部分\n今天我们将从痛点切入从八个部分来进行讲解。\n最开始我们会聚焦刚刚Yoyo提到的一个痛点,叫为什么Chatbot形式在当下不够用。\n那第二个呢\n我们会提出我们workbody和Emma\n协同的底层逻辑是什么\n帮助大家更好地理解后面的内容\n然后我们会看一下两者协同\n它能够覆盖哪些场景\n用法总览是怎样的\n后续呢\n我们会进入更加实操的基础教程\n包括详细拆解这些教程\n在workbody中要怎么更详细的去跑\n最后呢\n我们会把它提炼成更详细的能力积木\n并带大家把这些能力积木\n迁移到创作者 学生 求职 职场\n这四个角色的场景中去\n大家感受一下\n他们在真实工作中的工作流\n然后在直播的最后\n我们会带着大家再回顾一遍\n我们的这个Workbody\n成Emma的工作流是怎么样的\n帮助大家能够快速的上手\n那希望今天\n大家能够通过这场直播\n来了解到我们Workbody\n成Emma\n如何形成一个资料\n到任务\n到产物\n到回环的这样一个闭环\n那今天\n那Workbody和Emma\n他刚刚我们说到了\n他不是一个\n不是两个个干个的\n而是一套配合\n那么资料从挨马进来\n任务在Workbody里面展开\n那到底我们应该怎么样去\n进行这一套闭环呢\n相信Gary老师来给大家展示\n好的 没问题\n那在开始讲这个闭环的逻辑之前呢\n我们先带大家感受一下\n我们如果不去使用这套闭环\n可能会遇见的问题\n那很多用户会给我们反馈\n包括我身边有用Workbody的朋友\n或者说用其他chatbot的朋友\n他会来跟我说\n他说感觉自己的这个AI\n总是用不顺手\n他们停留在这种\n一问一答的感觉里面\n对 而且就会觉得\n他用的不好\n是我不会写提示词\n还是说我选的模型\n不够高级\n还是说就是不太理解\n这种AI运作的逻辑是什么\n明白 实际上呢\n关于提示词和你选的模型\n这一块\n那我马上来给大家做一个解答\n那实际上这个是因为\n大多数人\n他对于我们AI使用的认知\n还停留在问一次答一次就结束\n或者说网上进一层会停留在\n我在同一个任务框里面\n去问答多次然后结束\n那这样就会导致\n下面的四个很大类的问题\n第一个叫做\n你每次需要反复的去问资料\n比如说你要重新做一个项目\n那你可能关于你自己的身份背景\n你自己的一些项目规则\n包括你的这种\n涉及到的一些相关的辅助资料\n你每次都要反复的去问给AI\n那第二个就是AI它其实始终是不记得你自己的风格审美\n你正在推进的项目\n它每次都会是第一次见你这样的感觉\n那第三个呢就是你的成果也是十分散落的\n在AI里面生成出的这种文字\n你每次都复制到别的这种文档里面\n或者复制到你的微信聊天框里\n最后下次你想起来要用的时候却找不到\n那第四个呢就是你之前所做任务的经验\n它都没有保留下来\n所以每一次呢你做完同一类事情\n之后下次做类似的事情的时候\n你还是要重新的去写你的提示词\n重新讲背景\n这个实际上才是我们使用AI时候\n遇到很多问题的原因\n那问题呢\n不是在于刚刚我们悠悠讲到的说\n模型选的不够好\n或者说你的提示词撰写的不够精致\n实际上呢\n现在大部分的模型是足够支撑大家跑完任务的\n并且你写出的提示词\n只要是人类能够读懂的\n那么在当下\n模型能力的情况下\nAI也是可以理解的\n它的真正问题\n在由于这个地方写到的\nAI它没有被组织起来\n现在的模式是\n你把东西抛给AI\nAI给你答案\n你再拿着答案\n去做进一步的处理\n用完机器\n下一次又是归零的\n而我们今天要讲的模式\n是一种全新的AI工作流的模式\n也就是你如何拿着资料\n去做任务\n形成产物\n最后回存\n回存的东西呢\n又可以赋能到\n下一次的任务里\n那么通过这样的\n一个复利的形式\n你就可以越用越懂越用越后\n那我们接下来就是来讲如何搭建\n这一个后者的工作流\n好\n那\n那这里就是回答我们悠悠刚刚的问题\n就是我们Emma和Workbody\n到底是怎么去分工的\n实际上用一句话概括\n大家方便记忆的话\n就是Emma是我们的资料底座\n而Workbody是任务引擎\n也就是我们Emma\n她可以去负责纯属我们的资料\n去沉淀知识\n形成长期的记忆供你去使用\n而Workbody这边呢\n可以调用艾玛里的资料\n然后拆分不同的任务\n然后形成各种各样类型的产物\n包括我们的PPT\n包括我们的word文档之类的\n同时还可以通过各种各样\n丰富的连接器生态\n包括专家和专家团这样的形式\n去交付各种各样的产物\n明白\n就是说其实你在艾玛里\n每攒的一份资料\n它都不是一个死的\n它就会被Workbody去调用\n并且加工\n最后变成一个可以交付的成果\n而这成果又会重新去回到艾玛\n对总结的非常正确\n这就是我下一面PP要给大家展示的\n这套工作流实际上就是在说三件事情\n分别是取用和存\n那取就是从艾玛中调用我们的相关知识\n用就是用workbody去执行\n那存就是把它再回存回body\n同样通过这样三件事情呢\n我们就可以形成一个完整的工作闭环了\n明白\n那其实很多观众朋友们也会问\n道理是这么个道理\n但是它会不会说很复杂\n门槛会不会很高\n就是具体它是操起来是怎么样的呢\n明白\n那我们今天后面直播的所有重点\n就放在如何让大家学会使用\nWorkbody成埃码\n并且学会迁移到自己真实的工作场景之中\n那现在我们先从一个\n带大家了解一下这套闭环\n可以覆盖哪些场景\n然后带大家定位一下\n看能不能覆盖到自己的生活之中\n好\n首先实际上呢\n五类可以复用大的场景\n基本上是可以覆盖大家所有需求的\n第一种呢\n是叫做把你的专业知识变成AI工具\n比如说你可以在艾玛知识库里去存储\n与你知识领域相关的一些方法论业啊\n行业上的报告啊\nSOP之类的\n然后变成一个你的专属知识户\n然后呢\n在workbody之中\n基于这个知识库去生成你的一系列\n比如说小程序啊\n比如说这种文档啊\n或者之类的PPTR之类的\n而且这个地方就是我们之前和\nWorkbody和AIMA在用户投稿中\n也有收到类似的案例\n我们有的用户就会把这个滑雪\n就是雪崩相关的非常专业的论文\n被到AIMA里面\n然后呢让Workbody基于这个生成雪崩模拟器\n那这样实际上就是非常好的一个案例\n因为如果没有AIMA约束的话\nAI它是很有可能就是在网上搜索到一些不正确的信息的\n而且可能会有一些AI幻觉\n对我们可以通过Workbody成Emma\n去这样解决AI幻觉的问题\n然后利用非常专业的知识去做底座\n那第二个是我们最常见的用法\n也就是知识库建设与提取\n你可以把所有散逻的知识放到Emma里面\n然后通过Workbody去学习和了解Emma里的东西\n然后基于这个去产出\n那么基于这个产出的更高级的用法\n就来到我们的第三个\n也就是AI辅助内容创作\n我们Workbody是可以产出各种各样的类型的产物的\n所以这个就天然适配我们在公众号啊\n小红书啊\n包括视频啊\n这样他们每个平台所需要的具体的素材是不一样的\n所以我们也可以利用Workbody成AIMA\n让AI在更了解你本身的文风的情况下去辅助你的内容创作\n那第四个就是在学习领域\n我们也可以作为学生党\n也可以把书课程论文等未经资料库\n让AI去陪你系统的学习这个领域\n那最后呢\n最高级的用法就是通过Workbody\n和挨马里面通过自动化的方式\n把你每天的重复劳作\n变成定时的任务\n那这样呢\n你就可以把你自己\n从这些重复的劳动之中解放出来\n这个也是我们今天会讲的一个重点部分\n那接下来我们就通过真实的案例\n像刚刚悠悠说的很多观众比较关心的\n我们看看怎么去使用起来吧\n好,那首先我們來到最基礎的部分,也就是你如何在這個workbody裡去用到艾瑪。我們來到workbody的真實頁面給大家操作演示一下。\n首先我们这个位置是在左侧\n点击更多\n然后点击你的爱马知识库\n好我这里是已经连接上了\n那为了给大家演示\n我们先解除绑定\n然后带大家感受一遍绑定的过程\n那点击爱马知识库之后呢\n它会出现一个立即前往授权\n大家可以点击立即前往授权之后\n这里就是可以用微信访问\n然后如果你的电脑上\n现在是登录着你的微信的话\n你就可以直接通过快捷登录的方式\n一键去登录我们的Emma\n好\n这个地方呢\n它会弹出一个授权提醒\n大家可以看一下\n它给的三个授权是\n一个是在workbody之中\n去查看资料库列表\n另一个是查看和搜索\n知识库的资料\n最后一个呢\n是你可以将知识库的文件\n添加到任务\n或者将文件保存到Emma知识库\n那么我们这个地方\n勾选同意之后\n把授权给到\n这样授权成功之后呢\n我们就可以在workbody之中\n去看到我们AIMA的知识库了\n那这样就是一个非常简单的连接的过程\n那找到AIMA\n接上AIMA之后呢\n我们现在就要调用AIMA\n也就是把AIMA之中我们储存的资料\n真正的加到workboard的任务中\n那这个地方呢\n实际上有两种主要的方式\n第一种呢\n是像我们刚刚进入的这个\nAIMA知识库的这个界面之中\n我们可以比如说鼠标悬停在某一个\n这个文档之上\n可以直接一键点击这里的\n就可以添加到我们对话框的任务之中\n那除此之外呢\n还是回到我们刚刚这个界面\n我们也可以选择多个任务\n这样点击下方添加到任务\n就可以添加到我们的对话框之中\n那除此之外呢\n如果你在比如说协同的过程中\n例如这里\n我正在打字\n那我想在这个任务过程中去添加东西的话\n我们就可以在下方点击这个加号\n选择添加文件\n有一个埃马知识库\n那么点击这个埃马知识库之后呢\n也是可以添加的\n并且这里你可以直接选择添加一个知识库\n或者添加单个知识库\n就是多个知识库这样以库为度去添加\n或者呢你也可以选择右边文件这个地方\n选择某个知识库\n里面的单个文件也是支持这样添加的\n那么这样就是我们一个调用的过程\n那最后呢\n刚刚我们也多次强调了\n我们最后有一个取用存\n存的过程\n那存这个地方呢\n也非常的便捷\n比如说现在我和Workbody\n对话之后产生了这样一个PPT\n我选择这个PPT\n在右边打开预览\n那这里有一个上传到云端的选项\n我可以选择上传到云端\n放大一点\n让观众看得更清晰一些\n好的\n看一下\n这样子\n稍微大一点\n我们点这个上传到云端\n好\n点击之后呢\n有爱马知识库\n然后我们可以去选择对应的知识库\n选择确定之后\n就可以一键保存了\n所以我们整个这个非常轻量化的流程\n大家是可以自己基于这个去探索的\n那大家可能就会有疑惑\n是不是\n就是虽然说这个\n我们刚刚能看到这个非常顺\n就这一套逻辑\n但是虽然我们学会了\n如何去接上挨码\n然后调分资料\n但具体如果在我们工作中\n它具体可以怎么样\n去进行一个实操呢\n有没有一些具体的案例\n或者场景啊\n明白\n我们这个取用团的逻辑是这样的\n但是距离我们刚刚讲到的\n覆盖的场景\n其实还有一些距离\n那这个距离\n我们就可以通过\n接下来的一个详细的拆解\n来给大家感受一下\n我们会假设\n现在我们给大家场景设定了一个任务\n叫为body造物这一个产品\n去做一场上市传播\n那这个我们接下来\n涉及到的所有的资料\n包括这个产品本身\n都是我们通过workbody去虚构的\n只是在这里给大家做教程使用\n在现实中是没有这样的案例的\n那今天呢\n我们收到的一个任务\n是我们需要交付一个完整的\n品牌上市传播的方案\n包括这个方案对应的PPT\n以及我们需要为这个活动\n做一个网页的落地页\n最后呢\n还需要给老板画一个\n这个传播矩阵的逻辑图\n那想象一下\n如果是用AI的问答的方式做\nYoyo你觉得好做吗\n就是如果只是用AI的问答方式\n它没有办法帮我去交付这个产物\n最终它可能会给我一些文字\n像给我一些markdown\n然后我还是得去复制粘贴\n回到这个可能word里面\n就会其实它\n还是会让我们的工作量非常大\n而且你像就是老板\n一下子给你提出这么多的需求\n你肯定没有办法\n在很短时间内去完成\n但是呢它的时间又很紧\n是吧\n然后你们就会\n很需要一个这样的\nAI同事来帮我们\n没错\n那今天我们的AI知识管家\n和工作台\n就会帮你解决这个问题\n首先呢我们可以\n从爱马中\n把品牌资料一次性取到位\n那其次呢\n我们可以用专家团协作\n一次性完成这些多形态的交付\n最后呢我们还可以把这些传播\n沉淀回我们的品牌知识库\n下一次活动就会直接站在这次的肩膀之上\n那我们接下来来感受一下\n那首先在正式进入我们刚刚的取用存之前\n先给大家案例一个自动化的功能\n因为实际上在你上班的时候呢\n我们很多时候会遇到一些重复性的工作\n比如说每天你可能都需要去关注这个\n我们今天的这个产品\n它的竞品是怎么样啊\n以及有没有一些新的动态啊\n可能就是你每天坐在工位上的第一件事情\n那这个时候我们是可以在Workbody中\n运用自动化这个能力去解决的\n我们来给大家演示一下\n自动化呢是在左边\n下面这一个点击它\n然后呢你可以选择从模板添加\n比如说这里有关注每天的AI新闻\n或者每天的周报\n或者经典电影推荐这样的模板\n当然呢你也可以选择自己去制作一个自动化的流程\n好我们点击这个添加自动化这个地方\n我们可以给他取一个名称\n比如说他叫资讯监控\n好\n然后可以选择一个工作空间\n当然你也可以不选\n他就会会你直接创建一个新的工作空间\n那提示的这一块\n我们就取用这里提示词\n叫扫描\n智能\n相关资讯\n然后你可以给他规定\n他需要一个什么形式去交付\n比如说\n生成一个文档\n对可以说是300字的简报啊之类的\n然后为了方便后续我们把它要存回爱妈妈\n所以可以像悠悠刚刚说的\n我们生成一个文档\n以MD文档形式\n交付\n好然后下面呢还有一些选项\n我们来告诉大家这个分别是干什么的\n那这个连接器是你可以在这个地方去勾选\n然后呢这样就是授权\n这次自动化任务可以使用这个连接器了\n当然如果你不涉及到的话\n这里可以空着\n然后下面的执行频率这里\n你可以选择按天按周\n或者按月按年\n包括你的时间也可以选\n也可以选择按间隔的方式去做\n也可以选择单次运行\n那如果是周期的话呢\n你还可以选择生肖的日期\n比如说你虽然是选择每周\n但你选的是13到16到31号\n这样一个周期\n那最后还需要重点安利的是\n我们底下有一个叫\n推送到workbody微信小程序\n那这个呢\n它实际上你在开启之后呢\n它就会把你的文件同步到云端\n你在小程序呢\n就可以收到你的数据\n那我们就把它打开\n然后保存\n这里也推荐大家\n加部后可以立马去我们腾讯\n搜索微信小程序腾讯Workbody\n去立马去体验我们这样的一个\n推送自动化\n对好\n那这个地方会提醒大家说\n我们如果要运行这个自动化任务的话\n它给了一个完全授权\n所以它可以支持去写入修改\n删除你工作空间内的文件\n包括去调用你刚刚这个地方勾选的连接器\n同时呢也可以去执行一些\n比如说从网络上搜索等这样的敏感操作\n那你可以点击我已了解风险\n然后确认创建\n那这样它就确认创建完成了\n那我们可以给大家看一下这个大概的成果\n一個自動化任務的成果大概就是這樣的。\n然後你可以在這個地方點擊MD文檔,把它上傳到雲端,保存到你對應的MR資料庫中。\n这样呢它也可以作为你后续去生成你的一些项目资料的一个参考\n那回到这里\n那如果刚刚你在自动化任务的时候勾选了推送到微信小程序\n你要在哪里查看呢\n大家可以扫码看一下我们上方workbody小程序这个二维码\n扫码之后呢就可以跟着我一起来操作一下试试哈\n我们在这个小程序进入之后\n左边点击这里\n然后呢选择连接电脑\n然后选择自动化\n找到对应的自动化任务之后\n我们就能看到我们任务详情了\n这里就跟我们刚刚\n在workbody里\n电脑端看到的内容是一模一样的\n包括呢\n我们的workbody成emma这样一个协同逻辑\n它不只是在电脑端\n在我们的小程序也可以使用\n我们在右上角扫描这个小程序之后\n大家可以试着点击一下\n这里的对话入口有一个加号\n那加号之中呢\n它就有我们的Emma知识库\n点击这个Emma知识库之后\n我们就可以在里面看到\n我们和电脑端上一模一样的\n这样的不同的知识库\n包括里面的这样的文档\n然后选择它之后呢\n你就点击添加\n它就会出现在我们的资料这里\n就可以在手机上和它写作\n那包括你如果在这个地方\n双击你的资料的话\n它是会自动的去链接\n我们的Emma知识库小程序\n所以我们就可以在\nEmma知识库小程序里去查看\n我们所有存在Emma里的内容\n非常方便\n对 非常的方便\n大家如果是在手机上\n也可以直接去操作使用\n那我们正式进入后面取用存的环节\n那首先第一步呢\n一定是就是要先有库\n然后再去调用里面的内容\n那想要把东西注入到Emma知识库里呢\n大家需要打开Emma知识库的PC端\n然后在PC端里选择知识库\n我们去新建一个知识库试试\n首先我们这个地方分为三种知识库类型\n一个是个人的知识库\n一个是共享知识库\n一个是订阅知识库\n那订阅知识库就是说\n是可以再订阅到网页\n就是我们就比如说\n我看到有一些律师\n他说他会关注一些法律的知识库\n我们是可以在这里也找到的\n对的\n这个订阅知识库\n就是大家可以看一下\n左边这个发现这个table\n我们点击去之后\n就会看到有各种各样的知识库\n然后里面就有很多\n那些权威的一点的也有\n对对对\n就比如说这样的\n我们可以看一下推荐知识库里\n比如说你刚刚提到的法律啊\nAI学习啊这些都有\n大家如果在某一些领域想\n就是获取更多的知识\n但是呢又不想自己\n去把这个东西传上去的话\n也可以考虑去发现里面\n看看有没有推荐的\n对应的知识库\n可以直接一键的订阅\n好\n那我们回到这里\n对\n我们现在呢\n先创建一个个人的知识库\n只需要点击这里的加号\n然后输入名称\n然后选择封面\n点击确认就可以了\n好\n那接下来呢\n我们就要把这个\n把这些知识库的内容\n添加到我们的Workbody里面\n然后去看一下\n我们这个多种形态的产物\n是如何产出的\n我们直接来到Workbody中\n给大家展示\n好\n首先呢\n我们需要像刚刚在前面讲的一样\n把资料库里的内容\n添加到Workbody的任务\n那以防有新的观众进来\n前面没有听到\n我们再给大家操作演示一下\n比如说现在我是在对话框里\n所以我可以直接选择加号\n然后添加文件\n选择Emma知识库\n好\n现在我去选择一个\n这个body造物的知识库\n它就会出现在这里\n那我就可以跟它说\n我说我要为body造物\n去上传这样一个新品的方案\n好\n然后你可以跟他交谈你的一些想法\n这个也是我们刚刚悠悠前面提到的提示词\n到底要怎么写的部分\n实际上就是尽量的去表达\n你心里想要的那些信息给到AI\n这样才能让AI更好地知道你要什么\n比如说这里我就跟他讲了\n我的这个传播方案里需要包含这些内容\n那还有一个主要注意的是\n就是有同学有用户给我们反馈说\n为什么我没有办法用到专家\n实际上是因为你这个地方需要去勾选一下\n给大家展示\n也是在加号这个位置\n和刚刚是一样的\n我们可以去选择专家\n去选择自己的\n比如说这样团队\n就是专家团\n或者说这个专家\n就是单个的专家\n或者说你可以去选择一些技能\n对应的技能\n好\n那我们在这个里面\n就是选择的营销战役团队\n因为我们是做了一个\n品牌营销的方向\n大家可以根据自己的需求\n自己去选择\n那就可以看到\n我们在使用这个营销战队之后呢\n它是调起了不同的专家\n比如说这里\n给我们调起了品牌分析师\n帮我去分析了\n我们这个品牌的定位\n好\n要写了活动策划时\n帮我去宣传我的活动\n包括内容创作者\n去看一下我的这个文案要怎么写\n那Team Lead\n也就是我们这个小团队的老板\n他就会把这些内容\n最后都统一总结好\n然后给我\n给了我这样一个完整的报告\n大家可以看到\n这个报告的内容\n也是非常的完善的\n基本上我刚刚上面提到的\n我需要品牌定位啊\n目标人群\n包括目标人群画像啊\n这些他都有给到\n好\n那这个地方需要注意的是呢\n这个专家团呢\n它实际上就是我们比较审视的一个方案\n因为我们会有我们的这个团队老板\n然后来分配下面的不同专家\n来帮你协同解决问题\n大家如果想以更省credits的方式去做的话呢\n我们也推荐大家可以去使用专家\n或者使用技能\n这种各种方式都可以共同去探索\n看大家的需求是什么样的\n然后呢我们接着来看\n刚刚我们提到我们运用这个专家团能力\n我们现在已经生成了一个完善的品牌传播方案了\n那后面这三个我们具体要如何去做\n我们也给大家展示一下\n比如说现在我想生成一个提案PPT\n我只需要告诉他\n说你把我这个传播方案制作成一个PPT\n那如果你有额外的要求\n你可以跟他说\n你说我想要简约的\n然后内容出出的\n他就会给你生成一个和你品牌调性一致的PPT\n我们可以看一下文件\n对\n整体的内容呢\n就是依托于我们刚刚的报告\n完全依托于报告产生的\n然后呢\n除了PPT之外呢\n比如说我这里想要一个\n清晰的传播矩阵图\n因为如果是纯文字的话\n可能它展示不是那么的清晰\n那我就跟他说\n我说\n请基于目前的产物\n去帮我画一张\nbody造物的这样一个品牌传播矩阵图\n好\n那么这个地方呢\n它有一个非常好的功能\n它就是会这样给你展示一下\n给大家看一下全貌\n非常的清晰\n对非常的清晰\n对这样就比\n纯文字的去说\n几号到几号\n这样要好很多\n而且这个地方\n还有一个非常值得安逸的点\n就是我们这个右上角\n你之后\n可以支持你下载到本地\n或者下载为图片\n也可以去复制代码\n这样基本上\n就是大家想要什么形式\n它都可以给到\n方便大家去进行二次修改\n或者说方便把你这个东西\n传播给你的同事或者老板\n好\n然后最后呢\n它还去支持了一个能力叫\n你可以运用它去制作一个预热落地页\n一个html页面的能力\n那这个也是非常的简单\n和刚刚一样\n我们只需要一句话便可以跟它说\n你说我想要为这次新品\n去做一个简单的html的预热页面\n那它就会直接给你反馈\n这样一个页面\n我们可以看一下\n对我们在浏览器里看一下\n外部\n好\n對,會直接給你反饋這樣一個頁面。我們可以看到他還有一些簡單的交互。\n對,而且這個都還只是我一句話去剷除的。\n对如果你和他交互更多轮给workbody提更多你的想法和建议的话\n他也可以做的更加完善和更加优美\n对\n然后这个地方我很想采访一下悠悠\n你知道吗就是我们最开始webcoding的时候一定会遇到一个问题\n就是你生成了这个html页面之后呢\n你把它发给你的老板或者同事发现看不完\n是不是\n我们一开始web像我们这种代码小白职场小白\n其实我们不懂那些代码\n像我们webcoding\n只是给他提了个需求\n但是呢\n他实际想要交付的时候\n他发现他需要什么\n部署云服务器\n然后需要后端\n这个时候我们就会卡在这一步上\n没有办法分享给别人\n去进行一个协作\n或者说让别人去共享你的成果\n那怎么办呢\n这一下子\n对这个问题实际上\n最开始也有个痛点\n因为最开始webcoding的时候\n我也是不太清楚说\n我这个东西要怎么弄到线上的\n那我们workbody是有一键支持\n你只用跟他说\n我说你把这个网站部署到线上吧\n他就会直接给你部署一个上线的网站\n那大家拿着这个网址\n就可以直接分享给同事们去看了\n这是一个公寓可见的网址\n包括可能有人会问\n说那这个网址我以后想改\n或者说我的这个内容想更新了\n或者我这个网站不想让别人看见了\n又怎么办呢\n我们可以点击左赛的头像这里\n哦 这里包括这里蹦出来\n一个这个body加油帐号\n给大家推荐一下\n有很多用户会反馈说\n这个积分问题\n那我们这里可以看到\n每天都可以去领取一个100的通用积分\n然后去体验我们的Workbody的产品\n包括我们也可以去通过成长计划\n去完成一些任务\n这样我们可以在更了解\nWorkbody产品的同时呢\n也就把积分赚到了\n通过这样的方式\n大家可以一边完成任务\n一边精进自己使用AI的能力\n同时还能得到一些积分\n好 回到刚刚的这个话题\n我们点击设置这一块\n会有一个数据管理的地方\n点击数据管理之后呢\n我们会看到一个我发布的应用\n我们点击这里\n就可以看到我们部署到线上的网站了\n那如果你是只想对它进行一个更新呢\n你可以选择这个三个点\n然后点重新发布\n它就会说将使用最新的本地文件去重新发布了\n发布后呢\n你的原链接是不变的\n但是里面的内容会更新为你的最新版本\n就可以支持我们去不断的迭代\n我们的网页迭代我们的产品\n对而且你在不停迭代的过程之中呢\n也不用给你的协作方不停的发不同的链接\n对只在这一个链接里让它刷新就好了\n那你如果不想要了\n你也可以直接选择删除\n就把这个从网页上下下来\n当然你的这个html文件还是在你本地的\n只不过它不是公寓可见的了\n所以整体使用起来是非常的方便\n对大家来说也都比较的友好\n那这个就是我们workbody支持的\n多种形态产物的一个演示\n对那这些产物是不是\n它最后都是可以说\n全部都同步到iMA里面去的\n对这个网页目前是暂时不可一好\n因为它是一个网页的形态\n但我们可以把这个链接\n这个链接是可以支持储存到iMA里\n我们iMA是支持上传链接的\n然后像这种\n前面我们提到的这些PPT\n这个是完全没有问题的\n非常简单\n我们再给大家演示一下\n像右上角这个上传到云端\n点击之后选择爱马知识库\n然后保存到你项目对应的知识库就可以了\n比如说这个是body造物\n我们就点击确认\n好它就会保存回body造物里面了\n包括这里我们也可以直接点击查看\n也可以在这里看到我们的PPT\n方便\n对非常方便都是打通的\n好\n那就像刚刚我们悠悠问到的问题\n就是它非常敏锐地抓到了\n就是我们最后还是需要把它保存为\n爱马知识库的\n要沉淀起来\n对因为如果你不沉淀的话\n我们实际上这一次\n它就只是一个单线程的一次性任务\n但你如果把你用的东西\n回存到了爱马知识库\n那你的这个品牌知识库\n就是越用越厚的\n那以后呢\nWorkbody再次去读取\n你的爱马知识库的时候\n里面的内容就更多\n那它实际上就是有更多的经验和素材\n可以跟我们Workbody去学习和参考\n所以下一次你在做类似的活动的时候\n你的整个积淀就更加丰富\n变成了一个老手AI这样的感觉\n那看\n你先说\n好的\n那我们先带大家回顾一下整个流程吧\n你到底做了些什么呢\n实际上呢\n从情报到手\n到我们最后制作传播方案\n并把传播方案回存\n你都可以通过Workbody\n成Emma去完成\n那比如最开始\n我们是通过自动化\n去得到了行业动态\n并且把它回存到Emma\n作为我们的资料\n那最后\n其次呢\n我们就把东西\n从在Workbody中调用MAR的资料在任务里面\n然后我们选用专家团\n或者大家可能会用专家或者技能\n去交付出一个可用的品牌传播方案\n然后我们再通过对话去拿到各种各样多份产物\n符合自己的需求\n最后再把它经验沉淀\n所以它不是一个手动的去拼凑零散信息\n并且不断的去和AI很繁杂的交汇这样一个过程\n而是说让AI充分的了解你\n充分的从你的品牌出发\n一路非常顺畅的\n走到了下次可复用的\n这样一个完整的资产\n那我有个问题\n就是刚刚这个演示非常干活\n而且步骤很详细\n但是它是不是只适用于\n刚刚那个特定的场景\n那如果说换成别的用户工作\n它可以继续复用吗\n是可以的\n那这个地方也是我们想\n给大家提出一个概念\n就是大家不要觉得\n这种工作流\n它一定是非常死板的固定的\n包括我们很多这个文章\n或者我们很多有网络上博主分享的教程\n就是不要想着说我们可能完全的去复刻他们\n因为每个人的工作内容不一样\n所以我们工作习惯和工作流程也会不一样\n究其本质我们可以发现\n我们刚刚讲到的那一些\n它实际上可以拆成六个积木\n那大家实际上就可以不是通过套模板的方式\n而是像拼积木一样去搭建自己的工作流\n我们带大家回看一下实际上有哪六块积木呢\n分别是第一个是资料积木\n也就是我们Emma里面的资料\n那第二个是理解积木\n也就是我们讲到的Workbody\n那些非常看起来很清晰的图\n包括你和Workbody去多次对话\n来帮助你理解和想清楚\n那第三个呢\n就是我们的专家技能和连接器\n这一块生态的部分\n它能够让你的AI长出更好用的手\n那第四个是交付的积木\n可以让你的AI交付多种多样的形态\n那第五个是自动化的积木\n也就是定时的去触发一些任务\n让AI自己实现动起来\n那第六个就是沉淀的积木\n也就是我们把产物回存和AMMA\n并且进行结构化的归岛\n那现在由于中途一直有观众在不停的行进来\n所以我们还是带大家看一下\n这六块积木分别是什么东西\n那这个地方的资料积木\n也就是让AI有底料\n是你给AI的上下文的来源\n也就是我们AMMA中的这样一个品牌积累\n那它就适用于AI不了解你的背景的时候\n如果你想要AI输出的东西更懂你\n你就需要这么做\n它的关键动作呢\n就是我们在iMac的PC端\n或者在iMac的小程序中\n我们去建立库\n并且上传\n然后勾选内容到Workbody里面\n流程就是\n这是iMac的PC端\n大家也可以通过小程序去感受\n如果手边现在没有电脑的话\n我们可以点击建库\n创建个人的知识库\n或者如果大家有团队共享的需求\n可以点击创建共享的知识库\n这里也支持\n选择加入方式\n可以是直接加入或者是管理员确认\n也可以去管理我们成员的权限\n那还可以像我们刚刚悠悠提到的\n就是我们可以去发现这里\n去订阅你自己需要的对应领域的知识库\n那然後呢,你就可以在workbody之中,比如說這裡,新建任務,我們就可以去上傳。\n對應知識庫的共享也可以選,然後加入也可以選。\n好\n就可以上传到我们的任务之中去使用\n这个就是我们的资料积木\n那第二个积木呢是理解积木\n也就是我们刚刚提到的就是\n适合你对于Emma中\n可能有各种各样的东西\n然后里面的内容也特别冗杂\n你这个时候如果全部去阅读的话\n可能比较难\n那你就可以去通过这样理清结构的方式\n比如说像这里提到的\n我们可以让Emma去生成这样的结构图\n然后就可以帮你很清晰的去理解\n我们这个结构是什么样的\n然后里面大概有什么内容\n通过这样的方式让自己理性结构\n同时呢它也适用于\n比如说你想让AI出一个非常复杂的报告\n或者说需要AI拆解一个非常长的任务\n那你可以先让AI去给你看一下这个股价\n以免就是AI已经跑了很多东西了\n这个时候你发现它和你想要的不太一样\n那实际上已经消耗了非常多的Credits\n也消耗了大家等待的精力\n不如在最开始的时候\n你就和AI先确定好方向\n然后再进行铲除\n那第三块积木呢\n也是我们Walkbody中非常重点的一个板块\n叫做专家技能和连接器\n我们可以带大家看一下这个部分\n在我们左边的Tab这里也是有的\n这是我们的专家\n然后呢里面有一些我们分领域\n给大家提前预制好了一些自己的专家\n比如说内容创作\n我们有内容创作的专家团\n内容创作的专家\n小红书运营的专家\n包括我们对OPC\n也有自己的这些专家\n例如电商运营啊\n数据分析啊\n我们这边都有内置好\n大家可以直接一键的去使用\n然后技能这一块也是\n有非常多各种丰富的技能\n大家可以在这个地方搜索\n自己想用的\n或者搜索关键词就可以出来\n然后连接器这里也是\n比如说Emma\n然后像QQ邮箱\n这边我们也都有\n而且我们的小程序\n也是有这些专家和技能\n以及连接器的\n大家也可以去小程序\n是体验一下\n对的没错\n我们小人事上\n现在的生态也是非常丰富\n是的\n好\n那这种时候\n你就可以让它\n去处理一些\n更加深层次\n或者需要我们这里的\n内行视角的东西\n好\n那第四块积木\n也就是我们刚刚提到\n交付积木\n在前面\n我们也有给大家演示过\n那这个的关键就在于\n你要告诉AI你要的是什么\n你如果不告诉他我要的是文档\n还是我要的是PPT\n我要的是网页\n他给你交付的结果就不一定是如你所愿的\n所以你一定要和他说的非常清楚\n因为就是只有这样的话\nAI才能知道你这个需要交付的形态是什么样的\n我们也可以给大家后进来的观众朋友们展示一下\n我们这里可以生成PPT的形式\n也可以生成这样文档的形式\n都是支持的\n那第五个呢\n就是我们刚刚提到的自动化节目\n这一块呢\n就是让AI自己去动手\n然后形成永久跑这样的逻辑\n我们可以在这里看一下\n在左边自动化\n可以自己去建设\n也可以从模板添加\n那自己建设的话\n你就可以自己去勾选技能啊\n勾选专家\n包括你也可以设置访问权限\n对你可以去用自然语言\n描述这个提示式就好\n描述你的需求\n然后去勾选一下\n你想要它生效的日期\n就可以添加一个自动化任务\n然后这个地方需要注意的是\n如果你想要跑这个自动化任务的话\n一定要是你的电脑\n就是你的电脑是开着的\n包括你电脑上的workbody是开着的\n这样它才能够成功\n因为之前大家有的用户会反馈\n报错的情况\n是因为你的电脑上的workbody没有开着\n所以还会导致这样的情况\n大家可以注意一下\n好\n那第六个就是沉淀积木\n也就是如何让AI下一次更懂你\n实际上就是我们刚刚讲到的\n我们在左边\n去把它回存到\nEmma知识库之中\n这样就能够让AI下次有更深刻的经验\n能够更加的理解你\n好\n那回看一下我们刚刚前面那个品牌策划\n它实际上用到了哪些积木呢\n就是用到了我们这里首先第一个\n自动化加沉淀\n是我们的用自动化收集行业情报的这部分\n然后第二个呢是调取资料\n我们去完成了品牌上下文的注入\n那第三个呢是资料\n加上我们的这个专家连接器\n去形成了品牌传播方案\n然后我们通过自然语言\n让它交付了\n多种各样的PPT\n落地页和矩阵图\n最后呢\n我们再把这些\n回归到我们的\n艾玛知识库里面\n完成了我们\n品牌资产库的更新\n好\n听到这里\n相信大家一下子就明白了\n其实我们\n不是为了去记住\n某一个案例怎么用\n而是我们要学会\n怎么去拼这个积木\n那我们现在\n不妨就趁热打铁\n看一下\n怎么用同一套积木\n拼出不同身份\n不同场景的工作流吧\n好的\n那这里我们给大家主要介绍四种角色和四种不同的工作流\n我们可以看一下\n你们可就是观众朋友们可能是他们之一\n但也有可能不是\n但我们可以通过这样的\n如果你是的话\n你可以等会重点看一下对应的场景\n如果不是的话呢\n你也可以学习一下我们这个积木的思维\n然后看如何应用到自己的生活之中\n那首先第一个呢是内容创作者\n第二个呢是针对我们的学生导\n那第三个是如果你有求职的需求也可以使用\n第四个呢就是我们职场人这一块\n包括我们OPC\n就是一人公司也是试用的\n我们首先来看一下内容创作者这里\n内容创作者这里呢\n用Workbody成艾玛能解决的最主要的痛点就是\n有很多时候你用AI写东西\n你就算给他为了很多的这种你的参考文档\n每次都得为\n每次都得重新告诉他\n你到底喜欢怎么样的\n他还是有可能写的不像你\n那在这个过程之中\n你可能已经很烦了\n因为你已经为了他非常多次\n告诉了他非常多次你的规则\n然后每次都要重新的改\n但是最后还是会就是和你的风格不同一\n那Workbody成Emma就可以很好解决这个问题\n你可以首先在Emma之中\n好 比如说现在我们假设有这样一个账号\n叫之下效率研究所\n我们可以去上传你参考的一些论文\n参考的一些作品\n就是你对标账号的作品\n包括你自己的个人的作品和素材\n然后呢 让Workbody基于这些\n去给你生成你的创作风格画像\n这样以后呢\n每次Workbody读到你这个\n爱马知识库的时候\n它就会读到你的创作风格画像\n会基于你这个\n去直接的生成你的\n这个后面的文章\n对\n这个有点像那种设计视觉规范\n对\n有点这种感觉\n就是你可以通过\nWorkbody成爱马的方式\n相当于帮你个人打造了一套\n你自己的这种品牌资料库的感觉\n你的自己的规范\n然后\n然后我们很多这个\n短视频或者小红书的这种博主\n他每天都会去持续的关注我们的新的热点和问题\n那这个我们就可以通过自动化去解决\n比如说我们还是可以在自动化这里去进行一个配置\n然后唯一需要改动的就是提示词的部分\n你去告诉他说我每天需要关注\n比如说AI领域的资讯\n每天为我带来十条\n好\n这里的tips就还是跟之前一样\n我们需要去做一个\n类似于规范\n比如说呃\n沉淀成MD文档的形式\n好\n然后去选择\n比如说每天早上10点钟\n推送给我\n生效日期啊\n我希望它一直生效\n那你就留空\n它就会始终生效\n然后每天我还希望在\nWorkbody的微信小程序上\n可以看到\n所以我们就勾选\n微信小程序推送\n点击保存\n好\n我已经了解风险了\n好\n确认创建\n好\n名称好\nAI资讯\n那這樣每天呢,就會給你推送你這個新鮮的選題。包括你也可以通過撰寫更複雜的,或者說更符合你需求的這個提示詞。\n然后能来匹配更符合你要求的这样一个自动化任务\n那第三个呢就是让文峰更像你\n也就是我们刚刚提到的\n你可以去用你的这些专家或者说用你的这个技能\n对\n然后去出出稿\n然后去比如说前面的世界专家\n我们刚刚提到就可以去配图\n这样就可以去生成符合各个平台调性\n或者符合各个平台要求的东西\n包括我们最后呢\n生成的这个文档\n比如说这里\n我生成的这个创作风格画像\n我也可以直接上传到这里\n点击艾玛知识库\n把它储存到我们的艾玛知识库中\n作为以后持续调用的\n这样一个基础的参考规范\n下一次就不用再让他去\n就是给他复制更多的\n你之前的作品\n然后让他像你了\n他就直接去读取这个知识库\n然后直接写出一篇\n就是你的品牌\n符合你的品牌\n符合你的调性的文章\n对\n就再也不用就是编打我们的AI说\n你一定要写的像我\n而是说你这个本身\n他一读到你的AI知识库就知道\nOK你是想要这样的风格\n我已经了解了\n好那接下来呢\n我们就可以看到我们的三种形态\n是可以一次拿的\n包括我们这里的文档啊\n包括我们这里的图片啊\n我们都是可以一次性生成的\n这里也是用到我们的专家团\n他会有一个主要的专家\n然后分派给不同的专家去执行任务\n最后我们就可以一次性的产出\n我们每个平台都可能不同的\n需要用到的这种产物\n不需要你每次都拿着重新\n再去喂AI一次\n然后告诉他这次我想要的是什么\n然后你还得拿着他最后给你的文字\n去自己的编辑图文\n现在是直接可以就是一次性产出\n这些长文啊图文啊短视频脚本\n包括如果你是我们公众号认证的\n这个作者的话\n你甚至可以直接在我们workbody里\n通过对话的方式\n让他把文章推到你的公众号后台形成草稿哦\n非常的方便\n你都不用自己复制了\n然后最后呢\n你就还是把你的这些沉淀混音的Emma之中\n下次呢\nAI就知道哪种笔法效果很好了\n包括你如果有兴趣\n你也可以把自己的数据称回Emma\n然后让Workbody调用来分析\n这样你也不用自己去做一个复盘的工作\n非常适合OPC\n对\nOPC也可以使用\n包括我们很多自媒体博主啊\n这种都可以拿这个来自己分析自己的作品\n那第二个场景呢\n就是我们的学生群体\n西梦的时候呢\n会有很多学弟学妹\n过来找我说\n我这个任务太多了\n我要学的太多了\n我实在学不明白\n我就给他推荐\n我说你可以用\n试一下我们workbody吗\n那这个呢\n就是你首先\n你不能让AI凭空答题\n比如说你现在是一个\n专业的精管类的学生\n你就不能直接跟workbody\n就这样\n我就这样\n对话我跟他说\n我说你给我讲一下\n尽管知识\n这样可能是不行的\n我们首先呢\n需要把你对应的一些\n比如说你的PPT啊\n比如说你的这个小的作业啊\n或者说你的课程笔记啊\n教材决选啊\n全部的上传到我们workabout\n埃玛之中\n然后传到埃玛之中呢\n我们就有了这样一个积淀\n好\n放进埃玛之中呢\n你就可以获得了一个\n你的基础的资料积木\n那这个地方还有一个值得推荐的是\n大家可以打开\n埃马知识库的\n这个微信小程序体验一下\n我们是支持在\n小程序中选择上传文件\n然后大家可以选择\n自己的微信文件\n那这个时候呢\n你就可以选择\n不同的群聊或者对话框\n可以直接去上传\n大家群聊内容中的\n这样的一些PDF或者PPT\n非常的方便\n而且我觉得就是\n学生群里我们\n而且还不会过期\n就是我们之前会经常\n比如说老师\n第一周就把所有的课件发出来了\n但是当时可能你忙忘了\n或者说已读\n但是没有存下来\n然后等到你最后一个星期\n想要去复习的时候\n发现它全部过期了\n对\n这个就很适合大家\n如果平时可能手边没有电脑\n或者说现在就是泛蓝了\n你就很方便可以把手机拿出来\n直接一点就存进爱马知识库里面\n对\n你就可以在手机上看\n包括它和PC端也是同步的\n而且这样的话\n也很适合大家去做小组作业这样的场景\n因为我们很多时候做小组作业\n大家的这个文件会在群里到处飞啊\n这样的方式就可以很好的\n把它都及时的存下来\n然后也方便我们workbody\n去后面做进一步的调用\n那接下来呢就是理解积木\n很多时候大家会发现\n你做不论是学生吧\n还是任何的群体\n你去学一个新的东西\n或者说一个复杂的领域\n难点在于你get不到之中的结构\n就是它的结构大量是什么样的\n它的逻辑是什么\n这也是我们之前很多博主\n会去推荐你用思维导图\n这样的形式来理逻辑\n那我们workbody\n现在也支持这样的理解积目\n比如说我们现在来到\n这样一个学习场景\n好\n我就跟他说\n我需要结合\nEmma中的这个知识库的内容\n去讲解这两个东西\n然后我是理解他们的定义的\n但是呢\n我放进案例之后\n就容易混淆了\n我需要你\n就给我说明一下它的区别\n那我们workbody\n是直接非常聪明\n它是运用这样的\n逻辑思维图的方式\n给大家看一下全貌\n很清晰\n对非常清晰\n他就不是在给你这样一大坨文字\n就是你有时候就不想看文字\n然后你看到这个图你就一眼非常的明晰\n对因为文字实际上来说\n不管他怎样的去进行这些分层啊\n或者说序号啊\n他的阅读效率也是没有那么高的\n但是我们workbody这样的svg组件的方式\n就可以去帮你非常清晰的\n就是获得这样一个\n呃我们的这个逻辑思维的图\n帮你更好的了解\n好\n哎\n那最后呢就是我们可以去交付\n首先呢我们在日常的使用过程之中\n可能大家会涉及到的这些\n你的交付的作业的形式非常多种多样的\n比如说有的课程可能是需要你提交\nword文档的形式\n有的需要你提交pdf的形式\n有的需要提交ppt的形式\n那这些都能在我们workbody中一站式的去完成\n比如说这里\n这还是笔记\n我跟他说\n我说想要一个学习笔记\n他就会出一个这样的文档\n然后我跟他说\n就是会要PPT\n他也会跟我形成这样的PPT\n或者说这样的一个\nword文档的报告\n好\n那还有一个非常重要的点\n就是大家平时\n就可能这些东西也不好存下来嘛\n我就直接\n也可以直接这样\n回存到艾玛知识库里面\n包括还有一个非常非常方便的\n就是我们这里可以有一个分享的按钮\n点击之后\n是可以直接分享到大家的微信的\n或者说你复制链接\n生成二维码这样的形式去分享\n它就不用说你先到文件夹里面\n去复制粘贴\n然后再粘贴到微信里去\n对对就不用你先点击这里\n然后在文件夹中找到它的位置\n然后再传了\n你可以直接比如这里给大家大概展示一下\n就可以直接这样\n在微信对话中去发出去\n非常的方便\n然后最后也是支持\n像我们刚刚说的\n把你的笔记回存\n好\n那对于求职者来说\n我们这里也是一个非常好的场景\n就是首先\n大家求职的时候\n很多比如说你的各种各样的项目\n你的这些简历\n包括你每次针对不同公司\n准备的不同的这种问答的模拟\n它都散过在各种各样的地方\n它不是特别好的统一起来\n而且每次给AI又要重新上传\n那你就可以在iMaj中\n去建立这样一个资料库\n比如说我们拿\nWalkbody生成了一个假的人\n他不知道他是谁\n他叫陈宇童\n是AI人\n然后呢\n这个个人经历就存在这里\n可以看到有他的项目\n有他的简历\n有他的面试稿\n然后放在这里之后呢\n然後呢,我們就可以用理解積目去讓AI幫助我們拆解我們的這個JD,就是我們的崗位描述。\n比如说这个\n我们来到求职的领域\n那现在呢\n我就是先给他上传\n这里就是选择\n添加文件\nMH知识库\n选择这个个人经历的知识库\n我们就可以把它放到这里了\n那我就是让他结合我这个知识库去判断\n我是不适合以下这个产品经理的岗位\n增长方向的产品经理\n我们workbody呢\n给了一个12-60分\n说匹配度极低\n看一下这里\n他还给了一个HTML页面\n我们可以看到\n首先他对JD做了一个\n我们的岗位做了一个竹条的拆解\n好那除此之外呢\n他还根据我的这个个人的现状\n去做了一个认知要求的主向匹配\n发现不是很合适\n好除此之外呢\n他还去做能力模型的匹配\n最后还会给你一个结论和建议\n非常的聪明\n他会告诉你说\n你可能更适合其他方向\n这个是基于你爱马知识库中资料生成的\n那你如果还是想冲击这个产品经理的岗位\n你可能需要准备这些Circle\n这样的技能\n对非常详细非常好的一个报告\n这些也很会方便我们去认识自己的能力\n对没错\n好\n那他既然有这样分析岗位和分析你的能力\n他也就可以帮你去制作一个简历\n所以我们也可以一键的去\n你看我就跟他说\n我就想找这个陈玉图同学\n就想找这个产品经理方样的工作\n就可以让他基于\nWorkbody中的这样一个简历\n去生成一个新的\n适合产品方向的中文简历\n我们来看一下他的这个结果\n对也是非常简约的\n包括我有核对哦\n这些信息也都是和我们\nWorkbody这个爱马中沉淀的\n原本的简历的信息对得上的\n就不存在瞎编的情况\n这是因为他运用了\n爱骂资料库中的内容\n而不是就是随便给他一个内容去编的\n好\n这个很方便\n对就是\n不会说从网上自己找一些\n可能跟这个擦编资料\n然后帮你写进去\n对\n整个的信息准确度也是非常高的\n好\n然后包括这里\n很多\n我们很多时候海投嘛\n会投地非常多的岗位\n所以你有时候就会忘了自己投了哪些\n然后后面被收到面试邀请\n才非常惊慌的想起来\n那我们这里就可以有一个\n制作投递跟踪表这样的一个功能\n只需要跟他说\n帮我生成一份求职进度追踪表\n他就会给你生成这样一个非常好的表格\n包括我们可以看一下这个下面\n他会有进度追踪\n面试复盘\n包括你的目标公司的清单和一些使用说明\n对\n就是非常完善的一个表格\n而且这里还给大家安利一下\n就是我们这个更多这里有一个灵感\n我们可以看到\n比如说在这里搜索招聘\n好他就会给你非常多各种各样的模板\n例如我们刚刚提到这个面试评估表啊\n然后你的这个题库和评估方案啊\n包括你的校招经历这些\n你都可以去点击他\n哎这都非常好\n就可以一键复用的\n嗯\n这都非常方便的可以完成\nOK\n点击做同管就可以这里\n好\n回到我们球这一块\n哦\n还给我们校招同学\n案例一个技能\n就是可能有一部分\n现在在观看我们直播的观众\n是今年的校招生\n或者是在看我们腾讯实习的机会\n那大家也可以去点击\n我们的这个技能这里\n可以看到搜索一下招聘\n我们也是有专门的\n腾讯校园招聘的流程引导\n岗位推荐\n简历优化与面试辅导助手的\n通过这样一个skill呢\n大家也可以更好的去\n就是帮助校招同学来了解我们腾讯\n这是更有专对性的一个skill\n大家可以下面尝试一下\n然后最后呢\n你也可以跟workbody去进行一个模拟面试\n那这个就是比较贴近和AI对话的部分了\n你可以直接跟他说\n让他你是什么什么岗位的负责人\n然后你针对我的这个经历来进行一下提问\n然后最后呢\n你也可以让AI给你生成一个复盘\n你可以把这个复盘再包装回你的Emma知识库里面\n这样就形成了一个求职的完整闭环\n那最后呢\n也可能是我们今天受众最广的一个场景\n就是我们的职场人\n那我们来看一下我们职场人\n要如何运用Workbody成M\n首先在资料积木这一块\n还是和前面一样\n相信很多职场人可能也在用了\n我们就把自己的这些经历\n比如说现在我负责这个项目\n我就导入到这个项目里面\n然后这里还推荐各位职场人\n如果你手上同时在负责多个项目的话\n我们是推荐大家把自己的不同项目\n用不同的资料库去分开的\n这样对于Workbody来说\n能更好的理解你这个资料库\n是为了一个什么事情的\n因为你如果把你自己工作的所有内容\n全部塞在一个资料库的话\n其实就相当于把一大坨东西\n全部丢给了Workbody\n这样对他来说是不利于理解的\n包括如果你有团队协作的需求\n你也可以去搭建共享的这样的一个知识库\n方便你的同事啊\n或者你的协作方和你一起查看\n好\n那大家工作中也像前面那个品牌营销一样\n会涉及到很多重复的事情\n那这样重复的事情呢\n大家就让AI自己去跑就OK\n还是我们还是来最后演示一遍\n这个自动化\n然后添加自动化\n然后大家可以去撰写名称\n撰写题词词\n勾选连接器\n然后还有执行频率\n如果有些后进来的观众呢\n这个地方没有看到的话\n可以后面关注一下\n我们Workbody的官方公众号\n我们会在视频号里有回放\n包括今天的整个直播内容\n也会变成文章\n大家可以到时候直接跟着操作和学习\n大家可以在关注Workbody的公众号\n并且回复IMA\n我们会把今天的视频资料\n以及我们的课件都会回复给大家\n对的\n好\n然后呢\n我们还有一个非常重磅的\n就是我们在Workbody的丰富的连接技术生态\n我们刚刚前面也有给大家介绍\n我们有非常多的连接器打通\n这就是我们大家之前这样讲的的MCP\n我们是直接帮大家内置好的\n好\n那通过这个我们就可以实现一些更复杂的功能\n比如说QQ邮箱\n飞书腾讯会议企业微信\n这样的功能我们都可以直接通过\n打开各种各样的连接器\n连接上之后呢\n就可以获取里面的一些信息\n那比如大家可能有时候会遇到一个场景\n例如大家可能会疑惑这个怎么用\n这比如说是腾讯会议这个连接器\n我们今天假设用户突然提了一个非常急的需求\n然后你根本来不及整理会议纪要\n就要直接工作了\n但是又涉及到的需求变更\n那这个时候你就可以打开我们腾讯会议的连接器\n它就可以直接拉取你和客户的会议纪要\n然后你就可以基于这个直接分析了\n整个的生态是非常打通的\n这个是对大家的工作非常方便的\n包括它也可以去直接读取你QQ邮箱里的内容\n这样你也不用每天去盯着看\n有没有垃圾邮箱垃圾邮件中哪些是重要的信息\n哪些是不重要的\n你可以通过我们workbody加QQ邮箱的方式直接去关注\n好\n那同时他也可以是支持我们多种方案的交付的\n例如我们这个\n好\n那直上举例哈\n比如說現在我就讓他根據我emma中的這個客戶資料和最新的婚姻記錄去分析我們的需求有哪些變化。\n他就會很好的去分析我們的需求的變化。而且他也會,比如說這個地方很亮點的。\n在下面会告诉你\n有一些事项是必须由人来解决的\n那他就会非常明确的告诉你这些东西\n告诉你来确认\n然后你确认之后呢\n他才会聚集往下推进\n包括他也会通过这样组建的方式\n但你更明确的看到\n这个排期是怎么变化的\n这个就比我们用文字描述要清晰直观很多\n而且有一些可能当PM同学也会说\n就经常拉排期改排期\n这个他就可以直接帮你生成\n非常方便\n对就是无论有多少变更\n只要你的东西维持在我们workbody加艾玛里面\n那它都是可以被处理掉的\n都是可以通过这样的方式去交付给你的\n好\n那我们还是接着就可以通过这些\n去要求它制定本次的营销方案\n比如说这里我是有勾选这个地方的专家\n给大家再演示一遍\n点击加号\n可以选择我们不同的专家\n可以根据需求去召唤更多专家\n看你自己想要什么\n比如这里我是一个营销方案\n我想要做的是增长\n我就选了一个增长超盘手\n这样一个\n然后他呢就会作为一个总监\n然后去召唤不同的\n这样的专家来帮我做事情\n你看他就组建了一个团队\n然后给我列好了计划\n然后去启动这些不同的\n然后让等他们这个回传之后呢\n最后就可以完成这样一个非常好的东西\n给我交付\n你看最后我们看一下他的成果\n文件们\n这是一个非常好的方案\n整个清单就是他会告诉你交付的一些东西\n交付了很多\n对对对\n基本上就是把我这次营销又用到的所有东西都交付全了\n包括你看这个地方我还会跟他说\n我说我想要最后再复盘一下我这个项目\n他也可以直接给到我一个复盘报告\n对就是非常完善\n你看这个结构都非常好的\n给到我这样一个项目的概述啊背景啊\n发生了什么\n然后需求变更\n他也去记录上了\n然后呢\n我可能要对外展示嘛\n所以我可能这个文档\n还需要更进一步的\n变成PPT的形式\n我就直接跟他说\n说生成复盘的PPT\n和我们这个项目\n时间线的一个回顾\n那他很快\n三分钟左右\n就可以生成好\n这个是我们的一个时间线\n可以看到非常清晰\n他分了不同的板块\n不同的日期\n然后去展示我们这个时间线\n包括也可以去形成\n我们这样一个\n可以直接使用的PPT\n对这个PPT也可以\n大家可以重新的在这里\n沉淀\n对沉淀到我们的埃玛知识库之中\n悠悠已经学会了\n我们今天提了非常多次\n一定要记得我们完整的\n对完整的取用存的这样一个过程\n不要就做到最后就断掉了\n那你这个知识复利就有一点可惜\n好\n对就是我们讲的\n悠悠已经学会了这个点\n就是我们案例的库要越用越厚\n就一定要记得回存过去\n好\n那实际上这四种角色呢\n我们都用到的是同一种股价\n也就是最开始是尽量\n把你领域的你要用的资料\n存到Emma之中\n然后第二个呢\n就是你可以去理解\n或者说你和AI去判定一个方向\n通过我们这里的这样结构图的方式去展现\n那第三个呢\n就是通过专家专家团技能等各种方式去协作\n来变形推进我们的任务\n就第四个呢\n就是我们按照需要去选择你想要的形态\n完成一个交付\n那最后呢\n就是把你的经验沉淀回埃码\n进行一个回存\n这样我们就可以形成一个完整的闭环\n也就是我们在直播最开始\n就跟大家提到的取用存这样一个概念\n那在直播的最后\n最后一个环节\n由于很多观众\n他可能会错过前面最开始的部分\n我们再大家最后回顾一次\n我们这个工作流是怎么样的\n首先明确你的目标\n你想要做什么\n然后挑出你可能最烦的那一个\n我们优先解决\n然后呢就整理资料\n把你的资料\n在我们艾玛的PC端\n或者在艾玛的小程序中\n去上传\n无论是个人的资料库\n或者共享的\n或者你直接去订阅的\n都可以去建立你的资料库\n然后在里面去上传\n这里支持非常非常多种多样的类型\n包括本地文件\n包括你的微信\n包括你其他的个人知识库\n以及一些链接\n然后还有腾讯文档\n这样的形式都可以上传\n那上传好\n这个是你的第一个\n第二\n然后接下来呢\n你就选积木\n也就是我们刚刚前面提到的六大类积木\n你可能不会全部用上\n可能只用一到两个\n可以按你的需求自己去选择\n然后呢\n你就可以拿它去跑通\n去交付你各种各样的成果\n那这个时候我们可以看到\n大家前面的演示上呢\n我们会\n因为是带给大家演示嘛\n所以我更多时候是以一句话\n去完成我们缠除结果为主\n但大家在实际的操作过程之中呢\n是可以不断地去和Workbody对话\n和叠单影的内容的\n在和workbody不断去提出你的需求\n提出你改进你的同时\n你的这个内容也会越来越好\n那在最后就是把你的产物进行一个回存\n这样形成了一个完整的闭环\n否则你的这个链路就还是断掉的\n好\n那初次掌视大家可以在直播之后\n立刻去做三件事情\n第一个呢就是建立你的第一个知识库\n无论是PC端还是说在MR\n可能整个耗时也就15分钟左右非常快\n那第二个呢\n就是选取一个任务\n你去跑一下我们的五步闭环\n按照选目标\n然后存资料\n定积木\n做交付\n然后回存去做一遍\n产出任何一个\n可以解决你实际工作痛点的成果\n那最后你可以把你的常务\n再回存给Emma\n形成一个完整的闭环\n并且把我们这套逻辑\n也可以分享给你身边的更多人\n让大家一起用起来\n对\n帮助别人也帮\n就帮助自己的同事\n也要想一下身边的朋友们\n用起来\n我们这套好用的技能\n好\n那基本上我们今天这个实操的流程\n就是这样\n好的\n非常感谢Gary\n这非常干货满满的讲解\n听完这一套逻辑\n我们最深的一个感受就是\nAI真正的价值\n其实不在于它多会回答\n而是说它能不能记住你\n并且帮你把这些事情做完\n而这些恰恰就是需要\n埃马和workbody这样的配合\n一个就是负责把它记住\n一个就是负责基于这个知识以上\n然后把它做成\n那么接下来呢\n我们来进行一个\n大家期待的Q&A环节\n大家可以在评论区\n打出自己的一些疑问\n相信屏幕前的朋友们\n此刻一定攒了不少问题\n所以我们来请Gary老师\n一一来解答一下\n好\n那在我们等待评论区的过程中\n我先来代表观众问几个问题吧\n好\n首先就是\n有些观众说\n不知道怎么找到埃玛\n我们可以再演示一遍\n在文化里面如何找到埃玛知识库\n给大家来更加清晰的\n完整的来演示一遍\n我带大家再走一遍这个流程吧\n大家可以看到我们左边\n有这一个tab\n如果有些同学找不到\n可能是因为这里被隐藏了\n就是收起的话\n你这里是看不到的\n所以你需要先把它展开\n展开你的侧边栏\n然后找到你的更多\n里面有我们的MAR知识库\n去点击它\n然后这样就可以进到我们MAR知识库里面\n那大家再看一下我们这个要怎么绑定\n如果你是第一次进入MAR知识库的话\n会出现的是这样一个界面\n你需要去点击授权\n然后授权之后呢\n它可以扫码\n然后如果你的PC上\n就是你的电脑上\n也登陆你的微信的话\n是可以直接快捷登录的\n一键登录允许\n允许之后呢\n去同意\n然后确认授权\n好\n那這樣就可以在裡面看到你的艾瑪知識庫的內容了。\n那這就是我們第一個問題。看一下還有別的問題嗎?\n还有就是\n知识库可以共建吗\n可以多人共同使用吗\n可以的\n这个地方我们也给大家重点介绍一下吧\n我们可以看到这个知识库这里有三种不同的类型\n第一种呢是个人知识库\n第二种呢是共享知识库\n第三种是订阅知识库\n那个人知识库呢就是支持你自己看的\n那共享知识库\n就是可以给分享给别人看的\n比如说这里我们有一个从\n欢迎大家订阅这个知识库哈\n从零到一用Workbody\n搭建艾玛知识库\n这里就是可以分享给别人看到\n是的\n那你说\n那大家也可以今天关注我们的\nWorkbody公众号\n回复艾玛领取这一套资料\n也可以关注艾玛的公众号\n然后回复Workbody\n领取我们今天直播的回放\n以及今天的课件资料\n以及我们的知识库\n对包括你在创建共享知识库的时候\n也可以注意一下下面的加入方式\n和成员权限\n可以选择直接加入\n或者如果说你这个\n只是想要一部分人看到\n你可以选择需要管理员去确认\n然后也可以去设置权源呈现\n这样可以看\n它是能查看不能查看\n和能否导出\n都是可以去供你自己去设置的\n包括如果你有一些基础的\n这种知识性的东西\n你不想自己往里面导入内容\n你也可以去订阅这个订阅知识库\n也就是来到我们的这个发现里面\n去选取这样的知识库\n然后你可以比如说随便点一个哈\n就点击订阅就OK了\n还有就是\n如果已经用IMA知识库来进行存资料\n并且存电的话\n那么本地的工作空间还需要存资料吗\n这个意思是指\n就是我\n是这样的\n就是你本地\n比如说这样一个工作空间里\n你存的资料是默认在你这个文件夹里面的\n那它的这个\n或者就是不太好找\n但是你的这个\n比如说这个\n它是会一直在这里的\n它不存在说你能选择\n就是这样一个情况\n那Emma的它的这个好处就是\n你是可以在云端去看到的\n比如说就像我们刚刚讲的\n你现在在PC端里存的\n你可以在你的手机小程序里去打开\n所以我理解一下这个问题\n就是首先它不是本地空间\n用不用存资料的这样一个选项问题\n它是会存在这里的\n除非你把它删掉在空间里\n但是我们还是建议你存到Emma这样一份\n因为这样你后面在workbody中\n跨工作空间\n或者跨任务的去调用是更方便的\n同时你也可以去在微信的小程序上去使用\n能够多端的去进行一个同步\n是的\n还有其他的问题吗\n观众朋友\n我这边看不太到他们的提问\n好\n我看看\n还有很多朋友在问\n回放和资料在哪里看\n我再来说一下\n就是大家可以关注workbody的公众号\n然后在后台回复\niMA会获得今天的直播回放以及资料\n也可以关注我们的iMA公众号\n然后在iMA的后台回复workbody\n也可以获得今天直播的回放以及资料\n欢迎大家来关注\n并且使用我们的workbody以及iMA小程序\n那我们再来回复最后一个问题\n就是自动化任务中的那个连接器选项\n是用来干什么的\n是这样的\n我们先给大家再看一下连接器在哪里\n我们左边这个侧边展开之后呢\n是有我们丰富的专家技能和连接器的\n连接器呢\n就是可以列成MCP这样一个东西\n那它就是我们内置好了一些和其他软件\n在AI层面上的这样一个数据的调用和交互\n内置到了我们Workbody里面\n这样你就可以直接去使用它\n那这个连接器和我们自动化这种连接器是一样的\n比如说你如果想\n假设现在你的需求是\n我现在每天定时整理邮件\n然后我的想要它每天早上\n每天给我整理QQ邮箱中的最新20条邮件\n形成报告\n形成报告以MD文档形式\n交付\n那这个地方呢\n如果你不去给他QQ邮箱的权限\n他是没有办法读到你的邮件的\n因为他没有获得你的授权\n那你就需要在这个地方去选一个连接器\n首先你可以在这个地方\n你要把它QQ邮箱的去连接上\n那由于这里这个不是很方便扫码\n我就不给大家演示了\n但你连接上之后呢\n他在自动化任务之中\n才能得到你给他的授权\n不然他就没有办法去读到你的20条邮箱\n就是你试想一下\n他随时随地能读到你的邮箱多恐怖啊\n对不对\n所以你要给它这样一个连接器的授权\n它才能够去完成你的这个自动化任务\n那这个地方选择什么样的连接器\n就取决于你是要它做一个什么样的任务\n相当于说你现在要用可可邮箱\n你就要给它安上可可邮箱这个手\n比如说你要它读取艾玛资料库的内容给你铲除\n你就需要给它安上艾玛资料库这个手\n这就是我们这里连接器的意义\n非常明白了\n那最后一个问题吧\n公众号文章可以导入艾玛吗\n是可以的\n好那这里给大家演示一下\n公众号文章导入艾玛的形式之后\n比如说我们这里点击上传文件啊\n你可以选择网页链接\n那这里你看是支持公众号\n新闻文章等公开网页链接的\n你只要把你的链接复制进去就可以了\n应该国际的小程序\n艾玛小程序它也是可以\n就是你在阅读公众号的时候\n也可以直接存到艾玛的小程序里面的\n对没有问题\n包括这个地方也可以给大家看一下\n我们有没有这个\n例如这里\n我们也是有公众号这样的\n就是我们之前拿网页链接存在的公众号\n它也会在这里显示是一个公众号的形式\n所以微信公众号文章是指示导入Emma的\n好的\n非常感谢Gary的解答\n那其实今天这一场直播聊下来呢\nGary和我都想给大家传递的\n就是一个非常朴素的转变\n别再把AI当作一个用完就走的问答工具\n而是把它当成一个能够记住你\n并且替你干活的伙伴\n如果你觉得屏幕前的观众\n如果你觉得今天的东西\n讲的东西有点多\n然后一时无从下手的话\n完全不用担心\n今晚看完直播\n大家可以只是从一个小小的行动开始\n大家可以打开挨码\n然后选一件最近你正要完成的事情\n或者你觉得很烦\n要重复完成的事情\n然后放进你的相关的一些资料\n然后让Workbody帮你去交付一个\n直接使用的成果\n去完成一个闭环\n你大概率就可以感受到那种\n就是AI终于能\n资料终于能提我开始干活的感觉了\n跨出这一步\n你就会发现\n其实你这些工作中啊\n学举中沉淀下来的资料和机面\n真的可以变成\n每一天为你去所用的生产力\n然后最后也感谢直播间\n各位关注Workbody和艾玛的朋友们\n大家可以看那个小程序的扫码\n去体验我们的小程序\n然后并且关注我们workbody\n和艾玛的公众号视频号\n来收看我们直播的回放\n然后给大家预告我们下一期的直播\n每周四晚上我们都会固定\n在workbody直播间进行直播\n也欢迎大家跟我们一起来交流\n欢迎Gary\n再次欢迎Gary来到我们的直播间\n也感谢每一位在直播间陪伴我们的伙伴们\n我们下一期直播再见\n再见\n谢谢大家\n请不吝点赞 订阅 转发 打赏支持明镜与点点栏目",
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      "text": "我是今天的主持人Yoyo",
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      "text": "我们聚焦两款产品的协同",
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      "text": "把复杂的工作变简单",
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      "text": "它不只是于回答问题",
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      "text": "调用资料",
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      "text": "并且最终交付一个文档",
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      "text": "PPT",
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      "text": "网页等可用的成果",
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      "text": "而埃玛知识库是腾讯AI知识管家",
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      "text": "笔记等各类资料",
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      "text": "让AI真正的读懂你",
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      "text": "其中设定了两个产品",
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      "text": "分别是我们腾讯的AI知识管家Emma",
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      "text": "我们先给大家看一下我们这场直播",
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      "text": "整体会讲哪些部分",
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      "text": "今天我们将从痛点切入从八个部分来进行讲解。",
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      "text": "最开始我们会聚焦刚刚Yoyo提到的一个痛点,叫为什么Chatbot形式在当下不够用。",
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      "text": "协同的底层逻辑是什么",
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    {
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      "text": "帮助大家更好地理解后面的内容",
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      "text": "然后我们会看一下两者协同",
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    {
      "start": 494.964,
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      "text": "它能够覆盖哪些场景",
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      "text": "用法总览是怎样的",
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      "text": "后续呢",
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      "text": "我们会进入更加实操的基础教程",
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      "text": "包括详细拆解这些教程",
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    {
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      "text": "在workbody中要怎么更详细的去跑",
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      "text": "最后呢",
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      "text": "我们会把它提炼成更详细的能力积木",
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      "text": "并带大家把这些能力积木",
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    {
      "start": 511.864,
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      "text": "迁移到创作者 学生 求职 职场",
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      "start": 514.784,
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      "text": "这四个角色的场景中去",
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    {
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      "text": "大家感受一下",
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      "text": "他们在真实工作中的工作流",
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      "text": "然后在直播的最后",
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    {
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      "text": "我们会带着大家再回顾一遍",
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      "text": "我们的这个Workbody",
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    {
      "start": 525.304,
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      "text": "成Emma的工作流是怎么样的",
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    {
      "start": 527.244,
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      "text": "帮助大家能够快速的上手",
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    {
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      "text": "那希望今天",
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      "text": "大家能够通过这场直播",
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      "text": "来了解到我们Workbody",
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      "text": "成Emma",
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    {
      "start": 533.764,
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      "text": "如何形成一个资料",
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    {
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      "text": "到任务",
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    {
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      "text": "到产物",
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      "start": 537.144,
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      "text": "到回环的这样一个闭环",
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      "text": "那今天",
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      "text": "那Workbody和Emma",
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      "text": "他刚刚我们说到了",
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      "text": "他不是一个",
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    {
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      "text": "不是两个个干个的",
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    {
      "start": 548.324,
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      "text": "而是一套配合",
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      "text": "那么资料从挨马进来",
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      "text": "任务在Workbody里面展开",
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      "text": "那到底我们应该怎么样去",
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    {
      "start": 555.564,
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      "text": "进行这一套闭环呢",
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    {
      "start": 557.924,
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      "text": "相信Gary老师来给大家展示",
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      "text": "好的 没问题",
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      "text": "那在开始讲这个闭环的逻辑之前呢",
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    {
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      "text": "我们先带大家感受一下",
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      "text": "我们如果不去使用这套闭环",
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    {
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      "text": "可能会遇见的问题",
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      "text": "那很多用户会给我们反馈",
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      "text": "包括我身边有用Workbody的朋友",
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    {
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      "text": "或者说用其他chatbot的朋友",
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      "text": "他会来跟我说",
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      "text": "他说感觉自己的这个AI",
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      "text": "总是用不顺手",
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      "text": "他们停留在这种",
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    {
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      "text": "一问一答的感觉里面",
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    {
      "start": 581.104,
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      "text": "对 而且就会觉得",
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      "start": 582.524,
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      "text": "他用的不好",
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    {
      "start": 583.904,
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      "text": "是我不会写提示词",
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      "text": "还是说我选的模型",
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    {
      "start": 587.384,
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      "text": "不够高级",
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    {
      "start": 588.404,
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      "text": "还是说就是不太理解",
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    {
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      "text": "这种AI运作的逻辑是什么",
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      "start": 593.364,
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      "text": "明白 实际上呢",
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      "text": "关于提示词和你选的模型",
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    {
      "start": 597.284,
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      "text": "这一块",
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    {
      "start": 597.684,
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      "text": "那我马上来给大家做一个解答",
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    {
      "start": 599.324,
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      "text": "那实际上这个是因为",
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    {
      "start": 601.104,
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      "text": "大多数人",
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    {
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      "text": "他对于我们AI使用的认知",
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      "text": "还停留在问一次答一次就结束",
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      "text": "或者说网上进一层会停留在",
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      "text": "我在同一个任务框里面",
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    {
      "start": 611.024,
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      "text": "去问答多次然后结束",
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      "text": "那这样就会导致",
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      "text": "下面的四个很大类的问题",
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      "text": "第一个叫做",
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      "text": "你每次需要反复的去问资料",
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      "text": "比如说你要重新做一个项目",
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      "text": "那你可能关于你自己的身份背景",
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      "text": "你自己的一些项目规则",
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      "text": "包括你的这种",
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    {
      "start": 627.544,
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      "text": "涉及到的一些相关的辅助资料",
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      "text": "那第二个就是AI它其实始终是不记得你自己的风格审美",
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    {
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      "text": "你正在推进的项目",
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      "start": 637.424,
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      "text": "它每次都会是第一次见你这样的感觉",
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      "text": "那第三个呢就是你的成果也是十分散落的",
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    {
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      "text": "在AI里面生成出的这种文字",
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      "text": "你每次都复制到别的这种文档里面",
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    {
      "start": 648.464,
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      "text": "或者复制到你的微信聊天框里",
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    {
      "start": 650.164,
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      "text": "最后下次你想起来要用的时候却找不到",
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      "text": "那第四个呢就是你之前所做任务的经验",
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      "start": 656.944,
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      "text": "它都没有保留下来",
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      "start": 658.204,
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      "text": "所以每一次呢你做完同一类事情",
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    {
      "start": 660.364,
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      "text": "之后下次做类似的事情的时候",
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      "text": "你还是要重新的去写你的提示词",
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    {
      "start": 664.504,
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      "text": "重新讲背景",
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    {
      "start": 665.424,
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      "text": "这个实际上才是我们使用AI时候",
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    {
      "start": 668.544,
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      "text": "遇到很多问题的原因",
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      "text": "不是在于刚刚我们悠悠讲到的说",
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      "text": "模型选的不够好",
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      "text": "或者说你的提示词撰写的不够精致",
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      "text": "现在大部分的模型是足够支撑大家跑完任务的",
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      "text": "并且你写出的提示词",
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      "text": "只要是人类能够读懂的",
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      "text": "模型能力的情况下",
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      "text": "它的真正问题",
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      "text": "在由于这个地方写到的",
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      "text": "AI它没有被组织起来",
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      "text": "现在的模式是",
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      "text": "你把东西抛给AI",
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      "text": "AI给你答案",
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      "text": "你再拿着答案",
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    {
      "start": 700.864,
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      "text": "去做进一步的处理",
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    {
      "start": 701.984,
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      "text": "用完机器",
      "chunk": 1,
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    {
      "start": 703.004,
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      "text": "下一次又是归零的",
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    {
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      "text": "而我们今天要讲的模式",
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      "text": "也就是你如何拿着资料",
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      "text": "去做任务",
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    {
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      "text": "形成产物",
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      "text": "最后回存",
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      "text": "又可以赋能到",
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      "text": "下一次的任务里",
      "chunk": 1,
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      "text": "那么通过这样的",
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      "text": "一个复利的形式",
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      "text": "你就可以越用越懂越用越后",
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      "start": 718.824,
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      "text": "这一个后者的工作流",
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      "start": 722.384,
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      "text": "好",
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      "start": 723.364,
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    {
      "start": 724.664,
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      "text": "那这里就是回答我们悠悠刚刚的问题",
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    {
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      "text": "就是我们Emma和Workbody",
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    {
      "start": 730.264,
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      "text": "到底是怎么去分工的",
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      "text": "实际上用一句话概括",
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      "text": "大家方便记忆的话",
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      "text": "就是Emma是我们的资料底座",
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      "text": "而Workbody是任务引擎",
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      "text": "也就是我们Emma",
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    {
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      "text": "她可以去负责纯属我们的资料",
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    {
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      "text": "形成长期的记忆供你去使用",
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      "text": "可以调用艾玛里的资料",
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      "start": 749.964,
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      "text": "然后拆分不同的任务",
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    {
      "start": 751.824,
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      "text": "然后形成各种各样类型的产物",
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      "text": "包括我们的PPT",
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    {
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      "text": "包括我们的word文档之类的",
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      "text": "同时还可以通过各种各样",
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      "text": "丰富的连接器生态",
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      "start": 761.244,
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      "text": "包括专家和专家团这样的形式",
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      "text": "去交付各种各样的产物",
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      "text": "明白",
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      "text": "就是说其实你在艾玛里",
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    {
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      "text": "每攒的一份资料",
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      "text": "它都不是一个死的",
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      "text": "它就会被Workbody去调用",
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      "text": "并且加工",
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      "text": "最后变成一个可以交付的成果",
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      "start": 775.784,
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      "text": "而这成果又会重新去回到艾玛",
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    {
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      "text": "对总结的非常正确",
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    {
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      "text": "这就是我下一面PP要给大家展示的",
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      "start": 782.844,
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      "text": "这套工作流实际上就是在说三件事情",
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      "text": "分别是取用和存",
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      "text": "那取就是从艾玛中调用我们的相关知识",
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      "text": "用就是用workbody去执行",
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      "text": "那存就是把它再回存回body",
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      "text": "同样通过这样三件事情呢",
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    {
      "start": 798.564,
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      "text": "我们就可以形成一个完整的工作闭环了",
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    {
      "start": 801.064,
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      "text": "明白",
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      "text": "那其实很多观众朋友们也会问",
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    {
      "start": 804.924,
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      "text": "道理是这么个道理",
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      "text": "但是它会不会说很复杂",
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    {
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      "text": "就是具体它是操起来是怎么样的呢",
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      "text": "明白",
      "chunk": 1,
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      "text": "那我们今天后面直播的所有重点",
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    {
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      "text": "就放在如何让大家学会使用",
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      "text": "Workbody成埃码",
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      "text": "并且学会迁移到自己真实的工作场景之中",
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    {
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      "text": "那现在我们先从一个",
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    {
      "start": 825.084,
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      "text": "带大家了解一下这套闭环",
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    {
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      "text": "可以覆盖哪些场景",
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    {
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      "text": "然后带大家定位一下",
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    {
      "start": 830.364,
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      "text": "看能不能覆盖到自己的生活之中",
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    {
      "start": 832.444,
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      "text": "好",
      "chunk": 1,
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      "text": "首先实际上呢",
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    {
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      "text": "五类可以复用大的场景",
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    {
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    {
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      "text": "比如说你可以在艾玛知识库里去存储",
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    {
      "start": 845.244,
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      "text": "与你知识领域相关的一些方法论业啊",
      "chunk": 1,
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      "no_speech_prob": 2.8070263127788486e-11,
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    {
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      "text": "行业上的报告啊",
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    {
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      "text": "SOP之类的",
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    {
      "start": 850.484,
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      "text": "然后变成一个你的专属知识户",
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      "no_speech_prob": 2.8070263127788486e-11,
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    {
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      "text": "然后呢",
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      "text": "在workbody之中",
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    {
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      "text": "基于这个知识库去生成你的一系列",
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      "text": "比如说小程序啊",
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    {
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      "text": "比如说这种文档啊",
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      "text": "或者之类的PPTR之类的",
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      "start": 865.544,
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      "text": "而且这个地方就是我们之前和",
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      "text": "Workbody和AIMA在用户投稿中",
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    {
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      "text": "也有收到类似的案例",
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    {
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      "text": "我们有的用户就会把这个滑雪",
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    {
      "start": 874.064,
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      "text": "就是雪崩相关的非常专业的论文",
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      "text": "被到AIMA里面",
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      "text": "然后呢让Workbody基于这个生成雪崩模拟器",
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    {
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      "text": "那这样实际上就是非常好的一个案例",
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    {
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      "text": "因为如果没有AIMA约束的话",
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      "no_speech_prob": 2.2551951625593425e-11,
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    {
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      "text": "AI它是很有可能就是在网上搜索到一些不正确的信息的",
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    {
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      "text": "而且可能会有一些AI幻觉",
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    {
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      "text": "对我们可以通过Workbody成Emma",
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    {
      "start": 893.944,
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      "text": "去这样解决AI幻觉的问题",
      "chunk": 1,
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      "avg_logprob": -0.07089714592483443,
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    },
    {
      "start": 895.924,
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      "text": "然后利用非常专业的知识去做底座",
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      "no_speech_prob": 1.593767999519269e-11,
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    {
      "start": 898.244,
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      "text": "那第二个是我们最常见的用法",
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    {
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      "text": "也就是知识库建设与提取",
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    {
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      "text": "你可以把所有散逻的知识放到Emma里面",
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    {
      "start": 904.784,
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      "text": "然后通过Workbody去学习和了解Emma里的东西",
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      "no_speech_prob": 1.593767999519269e-11,
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    {
      "start": 908.444,
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      "text": "然后基于这个去产出",
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    {
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      "text": "那么基于这个产出的更高级的用法",
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    {
      "start": 912.564,
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      "text": "就来到我们的第三个",
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    {
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      "text": "也就是AI辅助内容创作",
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    {
      "start": 915.584,
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      "text": "我们Workbody是可以产出各种各样的类型的产物的",
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    {
      "start": 919.164,
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      "text": "所以这个就天然适配我们在公众号啊",
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    {
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      "text": "小红书啊",
      "chunk": 1,
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    {
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      "text": "包括视频啊",
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    {
      "start": 924.424,
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      "text": "这样他们每个平台所需要的具体的素材是不一样的",
      "chunk": 1,
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      "avg_logprob": -0.09844939801946033,
      "no_speech_prob": 1.5878135611879785e-11,
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    {
      "start": 928.464,
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      "text": "所以我们也可以利用Workbody成AIMA",
      "chunk": 1,
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      "no_speech_prob": 1.5878135611879785e-11,
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    {
      "start": 930.884,
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      "text": "让AI在更了解你本身的文风的情况下去辅助你的内容创作",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.09844939801946033,
      "no_speech_prob": 1.5878135611879785e-11,
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    {
      "start": 936.004,
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      "text": "那第四个就是在学习领域",
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    {
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      "text": "我们也可以作为学生党",
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    {
      "start": 940.164,
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      "text": "也可以把书课程论文等未经资料库",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.09844939801946033,
      "no_speech_prob": 1.5878135611879785e-11,
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    {
      "start": 943.524,
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      "text": "让AI去陪你系统的学习这个领域",
      "chunk": 1,
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    {
      "start": 945.864,
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      "text": "那最后呢",
      "chunk": 1,
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      "no_speech_prob": 1.5878135611879785e-11,
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    {
      "start": 946.764,
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      "text": "最高级的用法就是通过Workbody",
      "chunk": 1,
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      "avg_logprob": -0.09844939801946033,
      "no_speech_prob": 1.5878135611879785e-11,
      "compression_ratio": 1.3294460641399417
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    {
      "start": 948.744,
      "end": 951.884,
      "text": "和挨马里面通过自动化的方式",
      "chunk": 1,
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      "no_speech_prob": 7.76899447552104e-11,
      "compression_ratio": 1.3053435114503817
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    {
      "start": 951.884,
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      "text": "把你每天的重复劳作",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.07812692935650166,
      "no_speech_prob": 7.76899447552104e-11,
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    {
      "start": 953.264,
      "end": 954.464,
      "text": "变成定时的任务",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.07812692935650166,
      "no_speech_prob": 7.76899447552104e-11,
      "compression_ratio": 1.3053435114503817
    },
    {
      "start": 954.464,
      "end": 955.224,
      "text": "那这样呢",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.07812692935650166,
      "no_speech_prob": 7.76899447552104e-11,
      "compression_ratio": 1.3053435114503817
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    {
      "start": 955.224,
      "end": 956.124,
      "text": "你就可以把你自己",
      "chunk": 1,
      "language": "zh",
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      "no_speech_prob": 7.76899447552104e-11,
      "compression_ratio": 1.3053435114503817
    },
    {
      "start": 956.124,
      "end": 958.424,
      "text": "从这些重复的劳动之中解放出来",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.07812692935650166,
      "no_speech_prob": 7.76899447552104e-11,
      "compression_ratio": 1.3053435114503817
    },
    {
      "start": 958.424,
      "end": 960.664,
      "text": "这个也是我们今天会讲的一个重点部分",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.07812692935650166,
      "no_speech_prob": 7.76899447552104e-11,
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    {
      "start": 960.664,
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      "text": "那接下来我们就通过真实的案例",
      "chunk": 1,
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      "no_speech_prob": 7.76899447552104e-11,
      "compression_ratio": 1.3053435114503817
    },
    {
      "start": 963.524,
      "end": 966.144,
      "text": "像刚刚悠悠说的很多观众比较关心的",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.07812692935650166,
      "no_speech_prob": 7.76899447552104e-11,
      "compression_ratio": 1.3053435114503817
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    {
      "start": 966.144,
      "end": 967.924,
      "text": "我们看看怎么去使用起来吧",
      "chunk": 1,
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      "avg_logprob": -0.07812692935650166,
      "no_speech_prob": 7.76899447552104e-11,
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    {
      "start": 969.12,
      "end": 980.12,
      "text": "好,那首先我們來到最基礎的部分,也就是你如何在這個workbody裡去用到艾瑪。我們來到workbody的真實頁面給大家操作演示一下。",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1780594140291214,
      "no_speech_prob": 1.1389410248252574e-11,
      "compression_ratio": 1.327217125382263
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    {
      "start": 980.12,
      "end": 984.22,
      "text": "首先我们这个位置是在左侧",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08462698539991058,
      "no_speech_prob": 2.4010193139245573e-11,
      "compression_ratio": 1.5201342281879195
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    {
      "start": 984.22,
      "end": 985.38,
      "text": "点击更多",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08462698539991058,
      "no_speech_prob": 2.4010193139245573e-11,
      "compression_ratio": 1.5201342281879195
    },
    {
      "start": 985.38,
      "end": 987.16,
      "text": "然后点击你的爱马知识库",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08462698539991058,
      "no_speech_prob": 2.4010193139245573e-11,
      "compression_ratio": 1.5201342281879195
    },
    {
      "start": 987.16,
      "end": 989.5,
      "text": "好我这里是已经连接上了",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08462698539991058,
      "no_speech_prob": 2.4010193139245573e-11,
      "compression_ratio": 1.5201342281879195
    },
    {
      "start": 989.5,
      "end": 990.46,
      "text": "那为了给大家演示",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08462698539991058,
      "no_speech_prob": 2.4010193139245573e-11,
      "compression_ratio": 1.5201342281879195
    },
    {
      "start": 990.46,
      "end": 991.6,
      "text": "我们先解除绑定",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08462698539991058,
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    {
      "start": 991.6,
      "end": 994.66,
      "text": "然后带大家感受一遍绑定的过程",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08462698539991058,
      "no_speech_prob": 2.4010193139245573e-11,
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    {
      "start": 994.66,
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      "text": "那点击爱马知识库之后呢",
      "chunk": 2,
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      "avg_logprob": -0.08462698539991058,
      "no_speech_prob": 2.4010193139245573e-11,
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    {
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      "text": "它会出现一个立即前往授权",
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    {
      "start": 999.06,
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      "text": "大家可以点击立即前往授权之后",
      "chunk": 2,
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    {
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      "text": "这里就是可以用微信访问",
      "chunk": 2,
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      "compression_ratio": 1.5201342281879195
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    {
      "start": 1003.86,
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      "text": "然后如果你的电脑上",
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      "compression_ratio": 1.5201342281879195
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    {
      "start": 1005.42,
      "end": 1006.98,
      "text": "现在是登录着你的微信的话",
      "chunk": 2,
      "language": "zh",
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    {
      "start": 1006.98,
      "end": 1009.58,
      "text": "你就可以直接通过快捷登录的方式",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08462698539991058,
      "no_speech_prob": 2.4010193139245573e-11,
      "compression_ratio": 1.5201342281879195
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    {
      "start": 1009.58,
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      "text": "一键去登录我们的Emma",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
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      "compression_ratio": 1.4896551724137932
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    {
      "start": 1011.7,
      "end": 1012.68,
      "text": "好",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1012.68,
      "end": 1013.72,
      "text": "这个地方呢",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1013.72,
      "end": 1015.06,
      "text": "它会弹出一个授权提醒",
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      "compression_ratio": 1.4896551724137932
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    {
      "start": 1015.06,
      "end": 1015.92,
      "text": "大家可以看一下",
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      "compression_ratio": 1.4896551724137932
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    {
      "start": 1015.92,
      "end": 1017.36,
      "text": "它给的三个授权是",
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      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1017.36,
      "end": 1018.94,
      "text": "一个是在workbody之中",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1018.94,
      "end": 1020.54,
      "text": "去查看资料库列表",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
    },
    {
      "start": 1020.54,
      "end": 1022.32,
      "text": "另一个是查看和搜索",
      "chunk": 2,
      "language": "zh",
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      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1022.32,
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      "text": "知识库的资料",
      "chunk": 2,
      "language": "zh",
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      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1023.46,
      "end": 1024.46,
      "text": "最后一个呢",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
    },
    {
      "start": 1024.46,
      "end": 1026.0,
      "text": "是你可以将知识库的文件",
      "chunk": 2,
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      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1026.0,
      "end": 1026.82,
      "text": "添加到任务",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
    },
    {
      "start": 1026.82,
      "end": 1028.68,
      "text": "或者将文件保存到Emma知识库",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1028.68,
      "end": 1029.94,
      "text": "那么我们这个地方",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1029.94,
      "end": 1031.04,
      "text": "勾选同意之后",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1031.04,
      "end": 1032.16,
      "text": "把授权给到",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
    },
    {
      "start": 1032.16,
      "end": 1037.02,
      "text": "这样授权成功之后呢",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
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    {
      "start": 1037.02,
      "end": 1038.42,
      "text": "我们就可以在workbody之中",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08665372970256399,
      "no_speech_prob": 1.497439498787667e-11,
      "compression_ratio": 1.4896551724137932
    },
    {
      "start": 1038.42,
      "end": 1040.32,
      "text": "去看到我们AIMA的知识库了",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1301363685916614,
      "no_speech_prob": 1.5092907826308455e-11,
      "compression_ratio": 1.3853820598006645
    },
    {
      "start": 1040.32,
      "end": 1043.08,
      "text": "那这样就是一个非常简单的连接的过程",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1301363685916614,
      "no_speech_prob": 1.5092907826308455e-11,
      "compression_ratio": 1.3853820598006645
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    {
      "start": 1043.08,
      "end": 1047.54,
      "text": "那找到AIMA",
      "chunk": 2,
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      "no_speech_prob": 1.5092907826308455e-11,
      "compression_ratio": 1.3853820598006645
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    {
      "start": 1047.54,
      "end": 1049.1,
      "text": "接上AIMA之后呢",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1301363685916614,
      "no_speech_prob": 1.5092907826308455e-11,
      "compression_ratio": 1.3853820598006645
    },
    {
      "start": 1049.1,
      "end": 1051.4,
      "text": "我们现在就要调用AIMA",
      "chunk": 2,
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      "compression_ratio": 1.3853820598006645
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    {
      "start": 1051.4,
      "end": 1053.96,
      "text": "也就是把AIMA之中我们储存的资料",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1301363685916614,
      "no_speech_prob": 1.5092907826308455e-11,
      "compression_ratio": 1.3853820598006645
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    {
      "start": 1053.96,
      "end": 1056.02,
      "text": "真正的加到workboard的任务中",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1301363685916614,
      "no_speech_prob": 1.5092907826308455e-11,
      "compression_ratio": 1.3853820598006645
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    {
      "start": 1056.02,
      "end": 1057.08,
      "text": "那这个地方呢",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1301363685916614,
      "no_speech_prob": 1.5092907826308455e-11,
      "compression_ratio": 1.3853820598006645
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    {
      "start": 1057.08,
      "end": 1058.7,
      "text": "实际上有两种主要的方式",
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    },
    {
      "start": 1058.7,
      "end": 1060.06,
      "text": "第一种呢",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1301363685916614,
      "no_speech_prob": 1.5092907826308455e-11,
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    {
      "start": 1060.06,
      "end": 1061.78,
      "text": "是像我们刚刚进入的这个",
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      "avg_logprob": -0.1301363685916614,
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    {
      "start": 1061.78,
      "end": 1063.56,
      "text": "AIMA知识库的这个界面之中",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1301363685916614,
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      "compression_ratio": 1.3853820598006645
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    {
      "start": 1063.56,
      "end": 1066.58,
      "text": "我们可以比如说鼠标悬停在某一个",
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      "compression_ratio": 1.3853820598006645
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    {
      "start": 1066.58,
      "end": 1067.62,
      "text": "这个文档之上",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1301363685916614,
      "no_speech_prob": 1.5092907826308455e-11,
      "compression_ratio": 1.3853820598006645
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    {
      "start": 1067.62,
      "end": 1069.74,
      "text": "可以直接一键点击这里的",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09926845602793237,
      "no_speech_prob": 1.1848182157603304e-11,
      "compression_ratio": 1.6008403361344539
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    {
      "start": 1069.74,
      "end": 1072.56,
      "text": "就可以添加到我们对话框的任务之中",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09926845602793237,
      "no_speech_prob": 1.1848182157603304e-11,
      "compression_ratio": 1.6008403361344539
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    {
      "start": 1072.56,
      "end": 1074.2,
      "text": "那除此之外呢",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09926845602793237,
      "no_speech_prob": 1.1848182157603304e-11,
      "compression_ratio": 1.6008403361344539
    },
    {
      "start": 1074.2,
      "end": 1075.98,
      "text": "还是回到我们刚刚这个界面",
      "chunk": 2,
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      "avg_logprob": -0.09926845602793237,
      "no_speech_prob": 1.1848182157603304e-11,
      "compression_ratio": 1.6008403361344539
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    {
      "start": 1075.98,
      "end": 1078.8,
      "text": "我们也可以选择多个任务",
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      "compression_ratio": 1.6008403361344539
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    {
      "start": 1078.8,
      "end": 1082.38,
      "text": "这样点击下方添加到任务",
      "chunk": 2,
      "language": "zh",
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      "no_speech_prob": 1.1848182157603304e-11,
      "compression_ratio": 1.6008403361344539
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    {
      "start": 1082.38,
      "end": 1085.26,
      "text": "就可以添加到我们的对话框之中",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09926845602793237,
      "no_speech_prob": 1.1848182157603304e-11,
      "compression_ratio": 1.6008403361344539
    },
    {
      "start": 1085.26,
      "end": 1088.4,
      "text": "那除此之外呢",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09926845602793237,
      "no_speech_prob": 1.1848182157603304e-11,
      "compression_ratio": 1.6008403361344539
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    {
      "start": 1088.4,
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      "text": "如果你在比如说协同的过程中",
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      "avg_logprob": -0.09926845602793237,
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      "compression_ratio": 1.6008403361344539
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    {
      "start": 1090.56,
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      "text": "例如这里",
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    {
      "start": 1091.6,
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      "text": "我正在打字",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09926845602793237,
      "no_speech_prob": 1.1848182157603304e-11,
      "compression_ratio": 1.6008403361344539
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    {
      "start": 1092.7,
      "end": 1095.82,
      "text": "那我想在这个任务过程中去添加东西的话",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09926845602793237,
      "no_speech_prob": 1.1848182157603304e-11,
      "compression_ratio": 1.6008403361344539
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    {
      "start": 1095.82,
      "end": 1098.82,
      "text": "我们就可以在下方点击这个加号",
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      "compression_ratio": 1.7142857142857142
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    {
      "start": 1098.82,
      "end": 1101.1,
      "text": "选择添加文件",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09218255484976419,
      "no_speech_prob": 1.3267432291685921e-11,
      "compression_ratio": 1.7142857142857142
    },
    {
      "start": 1101.1,
      "end": 1103.48,
      "text": "有一个埃马知识库",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09218255484976419,
      "no_speech_prob": 1.3267432291685921e-11,
      "compression_ratio": 1.7142857142857142
    },
    {
      "start": 1103.48,
      "end": 1105.98,
      "text": "那么点击这个埃马知识库之后呢",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09218255484976419,
      "no_speech_prob": 1.3267432291685921e-11,
      "compression_ratio": 1.7142857142857142
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    {
      "start": 1105.98,
      "end": 1107.22,
      "text": "也是可以添加的",
      "chunk": 2,
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      "avg_logprob": -0.09218255484976419,
      "no_speech_prob": 1.3267432291685921e-11,
      "compression_ratio": 1.7142857142857142
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    {
      "start": 1107.22,
      "end": 1110.4,
      "text": "并且这里你可以直接选择添加一个知识库",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09218255484976419,
      "no_speech_prob": 1.3267432291685921e-11,
      "compression_ratio": 1.7142857142857142
    },
    {
      "start": 1110.4,
      "end": 1112.42,
      "text": "或者添加单个知识库",
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      "no_speech_prob": 1.3267432291685921e-11,
      "compression_ratio": 1.7142857142857142
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    {
      "start": 1112.42,
      "end": 1115.4,
      "text": "就是多个知识库这样以库为度去添加",
      "chunk": 2,
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      "no_speech_prob": 1.3267432291685921e-11,
      "compression_ratio": 1.7142857142857142
    },
    {
      "start": 1115.4,
      "end": 1118.26,
      "text": "或者呢你也可以选择右边文件这个地方",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09218255484976419,
      "no_speech_prob": 1.3267432291685921e-11,
      "compression_ratio": 1.7142857142857142
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    {
      "start": 1118.26,
      "end": 1119.68,
      "text": "选择某个知识库",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.09218255484976419,
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      "compression_ratio": 1.7142857142857142
    },
    {
      "start": 1119.68,
      "end": 1122.64,
      "text": "里面的单个文件也是支持这样添加的",
      "chunk": 2,
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      "avg_logprob": -0.09218255484976419,
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      "text": "那么这样就是我们一个调用的过程",
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      "text": "那最后呢",
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      "text": "刚刚我们也多次强调了",
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    {
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      "text": "我们最后有一个取用存",
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      "text": "存的过程",
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      "text": "那存这个地方呢",
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      "text": "也非常的便捷",
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      "text": "比如说现在我和Workbody",
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      "text": "对话之后产生了这样一个PPT",
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      "text": "我选择这个PPT",
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    {
      "start": 1142.92,
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      "text": "在右边打开预览",
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      "start": 1144.14,
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      "text": "那这里有一个上传到云端的选项",
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      "text": "我可以选择上传到云端",
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    {
      "start": 1149.42,
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      "text": "放大一点",
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    {
      "start": 1150.22,
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      "text": "让观众看得更清晰一些",
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    {
      "start": 1153.04,
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      "text": "好的",
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    {
      "start": 1153.32,
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      "text": "看一下",
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    {
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      "text": "这样子",
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    {
      "start": 1156.86,
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      "text": "稍微大一点",
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      "text": "我们点这个上传到云端",
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    {
      "start": 1161.56,
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      "text": "好",
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    {
      "start": 1163.1,
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      "text": "点击之后呢",
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    {
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      "text": "有爱马知识库",
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      "text": "然后我们可以去选择对应的知识库",
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      "text": "选择确定之后",
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      "text": "就可以一键保存了",
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    {
      "start": 1171.12,
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      "text": "所以我们整个这个非常轻量化的流程",
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    {
      "start": 1173.94,
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      "text": "大家是可以自己基于这个去探索的",
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    {
      "start": 1176.08,
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      "text": "那大家可能就会有疑惑",
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      "text": "是不是",
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      "text": "就是虽然说这个",
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      "text": "我们刚刚能看到这个非常顺",
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      "text": "就这一套逻辑",
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      "text": "但是虽然我们学会了",
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      "text": "如何去接上挨码",
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    {
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      "text": "然后调分资料",
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      "text": "但具体如果在我们工作中",
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      "text": "它具体可以怎么样",
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      "text": "去进行一个实操呢",
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      "text": "有没有一些具体的案例",
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      "text": "或者场景啊",
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      "text": "明白",
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    {
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      "text": "我们这个取用团的逻辑是这样的",
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      "text": "但是距离我们刚刚讲到的",
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    {
      "start": 1200.32,
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      "text": "覆盖的场景",
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      "text": "其实还有一些距离",
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      "text": "我们就可以通过",
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    {
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      "text": "接下来的一个详细的拆解",
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    {
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      "text": "来给大家感受一下",
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      "text": "我们会假设",
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      "start": 1209.3,
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      "text": "现在我们给大家场景设定了一个任务",
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      "text": "叫为body造物这一个产品",
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    {
      "start": 1214.34,
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      "text": "去做一场上市传播",
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      "start": 1215.88,
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      "text": "那这个我们接下来",
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    {
      "start": 1217.66,
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      "text": "涉及到的所有的资料",
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    {
      "start": 1219.04,
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      "text": "包括这个产品本身",
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    {
      "start": 1220.58,
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      "text": "都是我们通过workbody去虚构的",
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    {
      "start": 1222.72,
      "end": 1225.14,
      "text": "只是在这里给大家做教程使用",
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    {
      "start": 1225.14,
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      "text": "在现实中是没有这样的案例的",
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      "start": 1227.12,
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      "text": "那今天呢",
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    {
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      "text": "我们收到的一个任务",
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    {
      "start": 1229.76,
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      "text": "是我们需要交付一个完整的",
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    {
      "start": 1231.96,
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      "text": "品牌上市传播的方案",
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      "text": "包括这个方案对应的PPT",
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    {
      "start": 1235.54,
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      "text": "以及我们需要为这个活动",
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      "compression_ratio": 1.3178807947019868
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    {
      "start": 1237.72,
      "end": 1239.74,
      "text": "做一个网页的落地页",
      "chunk": 2,
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      "avg_logprob": -0.10041610867369409,
      "no_speech_prob": 1.563312326813282e-11,
      "compression_ratio": 1.3178807947019868
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    {
      "start": 1239.74,
      "end": 1240.56,
      "text": "最后呢",
      "chunk": 2,
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      "compression_ratio": 1.3178807947019868
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    {
      "start": 1240.56,
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      "text": "还需要给老板画一个",
      "chunk": 2,
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      "compression_ratio": 1.3178807947019868
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    {
      "start": 1242.48,
      "end": 1244.02,
      "text": "这个传播矩阵的逻辑图",
      "chunk": 2,
      "language": "zh",
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      "no_speech_prob": 1.563312326813282e-11,
      "compression_ratio": 1.3178807947019868
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    {
      "start": 1244.02,
      "end": 1245.58,
      "text": "那想象一下",
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    {
      "start": 1245.58,
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      "text": "如果是用AI的问答的方式做",
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    {
      "start": 1247.86,
      "end": 1249.18,
      "text": "Yoyo你觉得好做吗",
      "chunk": 2,
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      "compression_ratio": 1.3178807947019868
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    {
      "start": 1249.18,
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      "text": "就是如果只是用AI的问答方式",
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    {
      "start": 1253.08,
      "end": 1255.18,
      "text": "它没有办法帮我去交付这个产物",
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    {
      "start": 1255.18,
      "end": 1258.02,
      "text": "最终它可能会给我一些文字",
      "chunk": 2,
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    {
      "start": 1258.02,
      "end": 1259.1,
      "text": "像给我一些markdown",
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      "compression_ratio": 1.3178807947019868
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    {
      "start": 1259.1,
      "end": 1261.0,
      "text": "然后我还是得去复制粘贴",
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      "compression_ratio": 1.3178807947019868
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    {
      "start": 1261.0,
      "end": 1263.68,
      "text": "回到这个可能word里面",
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    {
      "start": 1263.68,
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      "text": "就会其实它",
      "chunk": 2,
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    {
      "start": 1265.14,
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      "text": "还是会让我们的工作量非常大",
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    {
      "start": 1268.12,
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      "text": "而且你像就是老板",
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    {
      "start": 1269.34,
      "end": 1271.14,
      "text": "一下子给你提出这么多的需求",
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    {
      "start": 1271.14,
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      "text": "你肯定没有办法",
      "chunk": 2,
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    {
      "start": 1272.26,
      "end": 1273.88,
      "text": "在很短时间内去完成",
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    {
      "start": 1273.88,
      "end": 1275.78,
      "text": "但是呢它的时间又很紧",
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    {
      "start": 1275.78,
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      "text": "是吧",
      "chunk": 2,
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    {
      "start": 1276.1,
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      "text": "然后你们就会",
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    {
      "start": 1277.2,
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      "text": "很需要一个这样的",
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    {
      "start": 1279.02,
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      "text": "AI同事来帮我们",
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    {
      "start": 1281.02,
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      "text": "没错",
      "chunk": 2,
      "language": "zh",
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    },
    {
      "start": 1282.22,
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      "text": "那今天我们的AI知识管家",
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    {
      "start": 1284.14,
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      "text": "和工作台",
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    {
      "start": 1284.68,
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      "text": "就会帮你解决这个问题",
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      "start": 1285.96,
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      "text": "首先呢我们可以",
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    {
      "start": 1287.24,
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      "text": "从爱马中",
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    },
    {
      "start": 1289.04,
      "end": 1291.02,
      "text": "把品牌资料一次性取到位",
      "chunk": 2,
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      "avg_logprob": -0.14210467255873488,
      "no_speech_prob": 1.771302375608297e-11,
      "compression_ratio": 1.3666666666666667
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    {
      "start": 1291.02,
      "end": 1292.0,
      "text": "那其次呢",
      "chunk": 2,
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    {
      "start": 1292.0,
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      "text": "我们可以用专家团协作",
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    {
      "start": 1293.3,
      "end": 1295.32,
      "text": "一次性完成这些多形态的交付",
      "chunk": 2,
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      "no_speech_prob": 2.382987036864126e-11,
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    {
      "start": 1295.32,
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      "text": "最后呢我们还可以把这些传播",
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      "compression_ratio": 1.422343324250681
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    {
      "start": 1297.28,
      "end": 1298.94,
      "text": "沉淀回我们的品牌知识库",
      "chunk": 2,
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      "no_speech_prob": 2.382987036864126e-11,
      "compression_ratio": 1.422343324250681
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    {
      "start": 1298.94,
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      "text": "下一次活动就会直接站在这次的肩膀之上",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.07373798749723487,
      "no_speech_prob": 2.382987036864126e-11,
      "compression_ratio": 1.422343324250681
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    {
      "start": 1301.78,
      "end": 1303.48,
      "text": "那我们接下来来感受一下",
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    {
      "start": 1303.48,
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      "text": "那首先在正式进入我们刚刚的取用存之前",
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      "no_speech_prob": 2.382987036864126e-11,
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    {
      "start": 1307.44,
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      "text": "先给大家案例一个自动化的功能",
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      "no_speech_prob": 2.382987036864126e-11,
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    {
      "start": 1309.42,
      "end": 1311.84,
      "text": "因为实际上在你上班的时候呢",
      "chunk": 2,
      "language": "zh",
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      "no_speech_prob": 2.382987036864126e-11,
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    {
      "start": 1311.84,
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      "text": "我们很多时候会遇到一些重复性的工作",
      "chunk": 2,
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      "no_speech_prob": 2.382987036864126e-11,
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    {
      "start": 1314.54,
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      "text": "比如说每天你可能都需要去关注这个",
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      "no_speech_prob": 2.382987036864126e-11,
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    {
      "start": 1317.18,
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      "text": "我们今天的这个产品",
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    {
      "start": 1318.9,
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      "text": "它的竞品是怎么样啊",
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    {
      "start": 1320.3,
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      "text": "以及有没有一些新的动态啊",
      "chunk": 2,
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    {
      "start": 1321.96,
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      "text": "可能就是你每天坐在工位上的第一件事情",
      "chunk": 2,
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    {
      "start": 1324.2,
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      "text": "那这个时候我们是可以在Workbody中",
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      "compression_ratio": 1.4298780487804879
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    {
      "start": 1326.7,
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      "text": "运用自动化这个能力去解决的",
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      "no_speech_prob": 1.777717036077764e-11,
      "compression_ratio": 1.4298780487804879
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    {
      "start": 1328.82,
      "end": 1330.4,
      "text": "我们来给大家演示一下",
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    {
      "start": 1330.4,
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      "text": "自动化呢是在左边",
      "chunk": 2,
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    {
      "start": 1332.62,
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      "text": "下面这一个点击它",
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      "language": "zh",
      "avg_logprob": -0.08615299529093175,
      "no_speech_prob": 1.777717036077764e-11,
      "compression_ratio": 1.4298780487804879
    },
    {
      "start": 1334.8,
      "end": 1337.4,
      "text": "然后呢你可以选择从模板添加",
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      "language": "zh",
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      "no_speech_prob": 1.777717036077764e-11,
      "compression_ratio": 1.4298780487804879
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    {
      "start": 1337.4,
      "end": 1340.0,
      "text": "比如说这里有关注每天的AI新闻",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08615299529093175,
      "no_speech_prob": 1.777717036077764e-11,
      "compression_ratio": 1.4298780487804879
    },
    {
      "start": 1340.0,
      "end": 1341.26,
      "text": "或者每天的周报",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08615299529093175,
      "no_speech_prob": 1.777717036077764e-11,
      "compression_ratio": 1.4298780487804879
    },
    {
      "start": 1341.26,
      "end": 1342.96,
      "text": "或者经典电影推荐这样的模板",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08615299529093175,
      "no_speech_prob": 1.777717036077764e-11,
      "compression_ratio": 1.4298780487804879
    },
    {
      "start": 1342.96,
      "end": 1346.74,
      "text": "当然呢你也可以选择自己去制作一个自动化的流程",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08615299529093175,
      "no_speech_prob": 1.777717036077764e-11,
      "compression_ratio": 1.4298780487804879
    },
    {
      "start": 1346.74,
      "end": 1350.04,
      "text": "好我们点击这个添加自动化这个地方",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08615299529093175,
      "no_speech_prob": 1.777717036077764e-11,
      "compression_ratio": 1.4298780487804879
    },
    {
      "start": 1350.04,
      "end": 1352.7,
      "text": "我们可以给他取一个名称",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1198580448444073,
      "no_speech_prob": 1.6638222050113782e-11,
      "compression_ratio": 1.390557939914163
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    {
      "start": 1352.7,
      "end": 1354.32,
      "text": "比如说他叫资讯监控",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1198580448444073,
      "no_speech_prob": 1.6638222050113782e-11,
      "compression_ratio": 1.390557939914163
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    {
      "start": 1354.32,
      "end": 1355.68,
      "text": "好",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1198580448444073,
      "no_speech_prob": 1.6638222050113782e-11,
      "compression_ratio": 1.390557939914163
    },
    {
      "start": 1355.68,
      "end": 1357.92,
      "text": "然后可以选择一个工作空间",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1198580448444073,
      "no_speech_prob": 1.6638222050113782e-11,
      "compression_ratio": 1.390557939914163
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    {
      "start": 1357.92,
      "end": 1358.92,
      "text": "当然你也可以不选",
      "chunk": 2,
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      "no_speech_prob": 1.6638222050113782e-11,
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    },
    {
      "start": 1358.92,
      "end": 1361.72,
      "text": "他就会会你直接创建一个新的工作空间",
      "chunk": 2,
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    {
      "start": 1361.72,
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      "text": "那提示的这一块",
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    {
      "start": 1363.1,
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      "text": "我们就取用这里提示词",
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      "compression_ratio": 1.390557939914163
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    {
      "start": 1364.62,
      "end": 1366.14,
      "text": "叫扫描",
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    },
    {
      "start": 1366.14,
      "end": 1367.64,
      "text": "智能",
      "chunk": 2,
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      "compression_ratio": 1.390557939914163
    },
    {
      "start": 1367.64,
      "end": 1373.74,
      "text": "相关资讯",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1198580448444073,
      "no_speech_prob": 1.6638222050113782e-11,
      "compression_ratio": 1.390557939914163
    },
    {
      "start": 1373.74,
      "end": 1376.24,
      "text": "然后你可以给他规定",
      "chunk": 2,
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      "no_speech_prob": 1.6638222050113782e-11,
      "compression_ratio": 1.390557939914163
    },
    {
      "start": 1376.24,
      "end": 1378.0,
      "text": "他需要一个什么形式去交付",
      "chunk": 2,
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      "no_speech_prob": 1.6638222050113782e-11,
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    {
      "start": 1378.0,
      "end": 1378.56,
      "text": "比如说",
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      "compression_ratio": 1.390557939914163
    },
    {
      "start": 1378.56,
      "end": 1380.28,
      "text": "生成一个文档",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1336761573692421,
      "no_speech_prob": 2.7746529032701694e-11,
      "compression_ratio": 1.4186851211072664
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    {
      "start": 1380.28,
      "end": 1383.24,
      "text": "对可以说是300字的简报啊之类的",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1336761573692421,
      "no_speech_prob": 2.7746529032701694e-11,
      "compression_ratio": 1.4186851211072664
    },
    {
      "start": 1383.24,
      "end": 1386.02,
      "text": "然后为了方便后续我们把它要存回爱妈妈",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1336761573692421,
      "no_speech_prob": 2.7746529032701694e-11,
      "compression_ratio": 1.4186851211072664
    },
    {
      "start": 1386.02,
      "end": 1387.72,
      "text": "所以可以像悠悠刚刚说的",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1336761573692421,
      "no_speech_prob": 2.7746529032701694e-11,
      "compression_ratio": 1.4186851211072664
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    {
      "start": 1387.72,
      "end": 1388.76,
      "text": "我们生成一个文档",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.1336761573692421,
      "no_speech_prob": 2.7746529032701694e-11,
      "compression_ratio": 1.4186851211072664
    },
    {
      "start": 1388.76,
      "end": 1391.32,
      "text": "以MD文档形式",
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      "no_speech_prob": 2.7746529032701694e-11,
      "compression_ratio": 1.4186851211072664
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    {
      "start": 1391.32,
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      "text": "交付",
      "chunk": 2,
      "language": "zh",
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      "no_speech_prob": 2.7746529032701694e-11,
      "compression_ratio": 1.4186851211072664
    },
    {
      "start": 1394.12,
      "end": 1397.16,
      "text": "好然后下面呢还有一些选项",
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      "avg_logprob": -0.1336761573692421,
      "no_speech_prob": 2.7746529032701694e-11,
      "compression_ratio": 1.4186851211072664
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    {
      "start": 1397.16,
      "end": 1399.14,
      "text": "我们来告诉大家这个分别是干什么的",
      "chunk": 2,
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      "no_speech_prob": 2.7746529032701694e-11,
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    {
      "start": 1399.14,
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      "text": "那这个连接器是你可以在这个地方去勾选",
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    {
      "start": 1402.34,
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      "text": "然后呢这样就是授权",
      "chunk": 2,
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      "compression_ratio": 1.4186851211072664
    },
    {
      "start": 1404.52,
      "end": 1407.32,
      "text": "这次自动化任务可以使用这个连接器了",
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      "compression_ratio": 1.4186851211072664
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    {
      "start": 1407.32,
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      "text": "当然如果你不涉及到的话",
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    {
      "start": 1408.9,
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      "text": "这里可以空着",
      "chunk": 2,
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      "compression_ratio": 1.4412607449856734
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    {
      "start": 1409.76,
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      "text": "然后下面的执行频率这里",
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    {
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      "text": "你可以选择按天按周",
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      "compression_ratio": 1.4412607449856734
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    {
      "start": 1413.9,
      "end": 1415.12,
      "text": "或者按月按年",
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      "compression_ratio": 1.4412607449856734
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    {
      "start": 1415.12,
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      "text": "包括你的时间也可以选",
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      "no_speech_prob": 1.9606640963565347e-11,
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    {
      "start": 1416.78,
      "end": 1419.18,
      "text": "也可以选择按间隔的方式去做",
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    {
      "start": 1419.18,
      "end": 1420.82,
      "text": "也可以选择单次运行",
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      "compression_ratio": 1.4412607449856734
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    {
      "start": 1420.82,
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      "text": "那如果是周期的话呢",
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      "avg_logprob": -0.08096509646875252,
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    {
      "start": 1422.36,
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      "text": "你还可以选择生肖的日期",
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      "compression_ratio": 1.4412607449856734
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    {
      "start": 1423.84,
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      "text": "比如说你虽然是选择每周",
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      "compression_ratio": 1.4412607449856734
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    {
      "start": 1425.48,
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      "text": "但你选的是13到16到31号",
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      "compression_ratio": 1.4412607449856734
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    {
      "start": 1428.06,
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      "text": "这样一个周期",
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      "compression_ratio": 1.4412607449856734
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    {
      "start": 1428.78,
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      "text": "那最后还需要重点安利的是",
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    {
      "start": 1430.98,
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      "text": "我们底下有一个叫",
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      "compression_ratio": 1.4412607449856734
    },
    {
      "start": 1432.06,
      "end": 1434.12,
      "text": "推送到workbody微信小程序",
      "chunk": 2,
      "language": "zh",
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      "no_speech_prob": 1.9606640963565347e-11,
      "compression_ratio": 1.4412607449856734
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    {
      "start": 1434.12,
      "end": 1435.08,
      "text": "那这个呢",
      "chunk": 2,
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      "no_speech_prob": 1.9606640963565347e-11,
      "compression_ratio": 1.4412607449856734
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    {
      "start": 1435.08,
      "end": 1436.68,
      "text": "它实际上你在开启之后呢",
      "chunk": 2,
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      "no_speech_prob": 1.9606640963565347e-11,
      "compression_ratio": 1.4412607449856734
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    {
      "start": 1436.68,
      "end": 1439.24,
      "text": "它就会把你的文件同步到云端",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.10711815970284598,
      "no_speech_prob": 1.9454710412092346e-11,
      "compression_ratio": 1.403125
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    {
      "start": 1439.24,
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      "text": "你在小程序呢",
      "chunk": 2,
      "language": "zh",
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      "compression_ratio": 1.403125
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    {
      "start": 1440.5,
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      "text": "就可以收到你的数据",
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      "language": "zh",
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      "no_speech_prob": 1.9454710412092346e-11,
      "compression_ratio": 1.403125
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    {
      "start": 1441.52,
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      "text": "那我们就把它打开",
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    {
      "start": 1443.08,
      "end": 1444.0,
      "text": "然后保存",
      "chunk": 2,
      "language": "zh",
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      "compression_ratio": 1.403125
    },
    {
      "start": 1444.0,
      "end": 1446.28,
      "text": "这里也推荐大家",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.10711815970284598,
      "no_speech_prob": 1.9454710412092346e-11,
      "compression_ratio": 1.403125
    },
    {
      "start": 1446.28,
      "end": 1448.7,
      "text": "加部后可以立马去我们腾讯",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.10711815970284598,
      "no_speech_prob": 1.9454710412092346e-11,
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    },
    {
      "start": 1448.7,
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      "text": "搜索微信小程序腾讯Workbody",
      "chunk": 2,
      "language": "zh",
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      "no_speech_prob": 1.9454710412092346e-11,
      "compression_ratio": 1.403125
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    {
      "start": 1451.22,
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      "text": "去立马去体验我们这样的一个",
      "chunk": 2,
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      "compression_ratio": 1.403125
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    {
      "start": 1453.92,
      "end": 1455.16,
      "text": "推送自动化",
      "chunk": 2,
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      "no_speech_prob": 1.9454710412092346e-11,
      "compression_ratio": 1.403125
    },
    {
      "start": 1455.16,
      "end": 1456.24,
      "text": "对好",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.10711815970284598,
      "no_speech_prob": 1.9454710412092346e-11,
      "compression_ratio": 1.403125
    },
    {
      "start": 1456.24,
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      "text": "那这个地方会提醒大家说",
      "chunk": 2,
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      "compression_ratio": 1.403125
    },
    {
      "start": 1458.36,
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      "text": "我们如果要运行这个自动化任务的话",
      "chunk": 2,
      "language": "zh",
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      "no_speech_prob": 1.9454710412092346e-11,
      "compression_ratio": 1.403125
    },
    {
      "start": 1460.62,
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      "text": "它给了一个完全授权",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.10711815970284598,
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    },
    {
      "start": 1462.08,
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      "text": "所以它可以支持去写入修改",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.10711815970284598,
      "no_speech_prob": 1.9454710412092346e-11,
      "compression_ratio": 1.403125
    },
    {
      "start": 1464.54,
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      "text": "删除你工作空间内的文件",
      "chunk": 2,
      "language": "zh",
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      "compression_ratio": 1.403125
    },
    {
      "start": 1466.06,
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      "text": "包括去调用你刚刚这个地方勾选的连接器",
      "chunk": 2,
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      "no_speech_prob": 1.112702117944675e-11,
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    {
      "start": 1469.28,
      "end": 1471.08,
      "text": "同时呢也可以去执行一些",
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      "no_speech_prob": 1.112702117944675e-11,
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    {
      "start": 1471.08,
      "end": 1473.78,
      "text": "比如说从网络上搜索等这样的敏感操作",
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      "avg_logprob": -0.08915750185648601,
      "no_speech_prob": 1.112702117944675e-11,
      "compression_ratio": 1.247787610619469
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    {
      "start": 1473.78,
      "end": 1476.2,
      "text": "那你可以点击我已了解风险",
      "chunk": 2,
      "language": "zh",
      "avg_logprob": -0.08915750185648601,
      "no_speech_prob": 1.112702117944675e-11,
      "compression_ratio": 1.247787610619469
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    {
      "start": 1476.2,
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      "text": "然后确认创建",
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      "no_speech_prob": 1.112702117944675e-11,
      "compression_ratio": 1.247787610619469
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    {
      "start": 1477.18,
      "end": 1479.48,
      "text": "那这样它就确认创建完成了",
      "chunk": 2,
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      "no_speech_prob": 1.112702117944675e-11,
      "compression_ratio": 1.247787610619469
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    {
      "start": 1479.48,
      "end": 1483.48,
      "text": "那我们可以给大家看一下这个大概的成果",
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      "avg_logprob": -0.08915750185648601,
      "no_speech_prob": 1.112702117944675e-11,
      "compression_ratio": 1.247787610619469
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    {
      "start": 1485.172,
      "end": 1487.412,
      "text": "一個自動化任務的成果大概就是這樣的。",
      "chunk": 3,
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      "no_speech_prob": 1.032890092900196e-11,
      "compression_ratio": 1.341726618705036
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    {
      "start": 1487.412,
      "end": 1495.212,
      "text": "然後你可以在這個地方點擊MD文檔,把它上傳到雲端,保存到你對應的MR資料庫中。",
      "chunk": 3,
      "language": "zh",
      "avg_logprob": -0.2577650146484375,
      "no_speech_prob": 1.032890092900196e-11,
      "compression_ratio": 1.341726618705036
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    {
      "start": 1495.212,
      "end": 1501.092,
      "text": "这样呢它也可以作为你后续去生成你的一些项目资料的一个参考",
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      "text": "那如果刚刚你在自动化任务的时候勾选了推送到微信小程序",
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      "text": "你要在哪里查看呢",
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      "text": "大家可以扫码看一下我们上方workbody小程序这个二维码",
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      "text": "扫码之后呢就可以跟着我一起来操作一下试试哈",
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      "text": "我们就能看到我们任务详情了",
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      "text": "这里的对话入口有一个加号",
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      "text": "我们的Emma知识库小程序",
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      "text": "所以我们就可以在",
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      "text": "非常方便",
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      "text": "大家如果是在手机上",
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      "text": "也可以直接去操作使用",
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      "text": "那我们正式进入后面取用存的环节",
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      "text": "那首先第一步呢",
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      "text": "一定是就是要先有库",
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      "text": "然后再去调用里面的内容",
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      "text": "那想要把东西注入到Emma知识库里呢",
      "chunk": 3,
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      "text": "大家需要打开Emma知识库的PC端",
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      "text": "然后在PC端里选择知识库",
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      "text": "我们去新建一个知识库试试",
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      "text": "首先我们这个地方分为三种知识库类型",
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    {
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      "text": "一个是个人的知识库",
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    {
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      "text": "一个是共享知识库",
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      "text": "一个是订阅知识库",
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      "text": "那订阅知识库就是说",
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    {
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      "text": "是可以再订阅到网页",
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      "text": "就是我们就比如说",
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      "text": "我看到有一些律师",
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      "text": "他说他会关注一些法律的知识库",
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      "text": "我们是可以在这里也找到的",
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      "text": "对的",
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      "text": "这个订阅知识库",
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      "text": "就是大家可以看一下",
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      "text": "左边这个发现这个table",
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      "text": "我们点击去之后",
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      "text": "就会看到有各种各样的知识库",
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      "text": "然后里面就有很多",
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      "text": "那些权威的一点的也有",
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      "text": "对对对",
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      "text": "就比如说这样的",
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      "text": "我们可以看一下推荐知识库里",
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    {
      "start": 1652.312,
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      "text": "比如说你刚刚提到的法律啊",
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      "text": "AI学习啊这些都有",
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    {
      "start": 1655.512,
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      "text": "大家如果在某一些领域想",
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      "text": "就是获取更多的知识",
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      "start": 1659.452,
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      "text": "但是呢又不想自己",
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      "text": "去把这个东西传上去的话",
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      "text": "也可以考虑去发现里面",
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      "text": "看看有没有推荐的",
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      "text": "对应的知识库",
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    {
      "start": 1666.692,
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      "text": "可以直接一键的订阅",
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    {
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      "text": "好",
      "chunk": 3,
      "language": "zh",
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    {
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      "text": "那我们回到这里",
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      "text": "对",
      "chunk": 3,
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      "text": "我们现在呢",
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    {
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      "text": "先创建一个个人的知识库",
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      "text": "只需要点击这里的加号",
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      "text": "然后输入名称",
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      "text": "然后选择封面",
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      "text": "点击确认就可以了",
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    {
      "start": 1679.712,
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      "text": "好",
      "chunk": 3,
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      "avg_logprob": -0.11895522364863644,
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      "start": 1680.192,
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      "text": "那接下来呢",
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      "start": 1685.332,
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      "text": "我们就要把这个",
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    {
      "start": 1686.272,
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      "text": "把这些知识库的内容",
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      "compression_ratio": 1.3633093525179856
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    {
      "start": 1687.992,
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      "text": "添加到我们的Workbody里面",
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    {
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      "text": "然后去看一下",
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    {
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      "text": "我们这个多种形态的产物",
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      "text": "是如何产出的",
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      "text": "我们直接来到Workbody中",
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    {
      "start": 1695.032,
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      "text": "给大家展示",
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    {
      "start": 1695.832,
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      "text": "好",
      "chunk": 3,
      "language": "zh",
      "avg_logprob": -0.11895522364863644,
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    {
      "start": 1697.972,
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      "text": "首先呢",
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    {
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      "text": "我们需要像刚刚在前面讲的一样",
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    {
      "start": 1702.152,
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      "text": "把资料库里的内容",
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      "no_speech_prob": 1.7294667434275546e-11,
      "compression_ratio": 1.3712574850299402
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    {
      "start": 1703.992,
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      "text": "添加到Workbody的任务",
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    {
      "start": 1705.372,
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      "text": "那以防有新的观众进来",
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    {
      "start": 1707.612,
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      "text": "前面没有听到",
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    {
      "start": 1708.332,
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      "text": "我们再给大家操作演示一下",
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    {
      "start": 1710.012,
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      "text": "比如说现在我是在对话框里",
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    {
      "start": 1712.072,
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      "text": "所以我可以直接选择加号",
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    {
      "start": 1713.592,
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      "text": "然后添加文件",
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    {
      "start": 1714.932,
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      "text": "选择Emma知识库",
      "chunk": 3,
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    {
      "start": 1716.312,
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      "text": "好",
      "chunk": 3,
      "language": "zh",
      "avg_logprob": -0.08513192569508272,
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    {
      "start": 1717.072,
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      "text": "现在我去选择一个",
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    {
      "start": 1718.672,
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      "text": "这个body造物的知识库",
      "chunk": 3,
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    {
      "start": 1720.032,
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      "text": "它就会出现在这里",
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    {
      "start": 1721.832,
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      "text": "那我就可以跟它说",
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    {
      "start": 1724.032,
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      "text": "我说我要为body造物",
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    },
    {
      "start": 1725.332,
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      "text": "去上传这样一个新品的方案",
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    {
      "start": 1727.392,
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      "text": "好",
      "chunk": 3,
      "language": "zh",
      "avg_logprob": -0.08513192569508272,
      "no_speech_prob": 1.7294667434275546e-11,
      "compression_ratio": 1.3712574850299402
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    {
      "start": 1727.792,
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      "text": "然后你可以跟他交谈你的一些想法",
      "chunk": 3,
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      "avg_logprob": -0.07467079654182356,
      "no_speech_prob": 2.517356288700423e-11,
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    {
      "start": 1729.652,
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      "text": "这个也是我们刚刚悠悠前面提到的提示词",
      "chunk": 3,
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      "no_speech_prob": 2.517356288700423e-11,
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    {
      "start": 1731.712,
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      "text": "到底要怎么写的部分",
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    {
      "start": 1732.752,
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      "text": "实际上就是尽量的去表达",
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      "no_speech_prob": 2.517356288700423e-11,
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    {
      "start": 1735.112,
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      "text": "你心里想要的那些信息给到AI",
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      "no_speech_prob": 2.517356288700423e-11,
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    {
      "start": 1737.852,
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      "text": "这样才能让AI更好地知道你要什么",
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    {
      "start": 1740.372,
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      "text": "比如说这里我就跟他讲了",
      "chunk": 3,
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      "text": "我的这个传播方案里需要包含这些内容",
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      "text": "就是有同学有用户给我们反馈说",
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      "text": "为什么我没有办法用到专家",
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      "text": "和刚刚是一样的",
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      "text": "就是选择的营销战役团队",
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      "text": "因为我们是做了一个",
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      "text": "大家可以根据自己的需求",
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      "text": "我们在使用这个营销战队之后呢",
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    {
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      "text": "它是调起了不同的专家",
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    {
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    {
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      "text": "我们这个品牌的定位",
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      "text": "好",
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    {
      "start": 1786.312,
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      "text": "要写了活动策划时",
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    {
      "start": 1787.452,
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      "text": "帮我去宣传我的活动",
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      "text": "包括内容创作者",
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    {
      "start": 1790.412,
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      "text": "去看一下我的这个文案要怎么写",
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      "text": "也就是我们这个小团队的老板",
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      "start": 1797.652,
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      "text": "最后都统一总结好",
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    {
      "start": 1802.312,
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      "text": "给了我这样一个完整的报告",
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      "text": "大家可以看到",
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    {
      "start": 1804.972,
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      "text": "这个报告的内容",
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    {
      "start": 1805.852,
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      "text": "也是非常的完善的",
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    {
      "start": 1807.132,
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      "text": "基本上我刚刚上面提到的",
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    {
      "start": 1808.972,
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      "text": "我需要品牌定位啊",
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    {
      "start": 1810.992,
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      "text": "目标人群",
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    {
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      "text": "包括目标人群画像啊",
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    {
      "start": 1813.832,
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      "text": "这些他都有给到",
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    {
      "start": 1815.272,
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      "text": "好",
      "chunk": 3,
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      "avg_logprob": -0.07302828431129456,
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    {
      "start": 1816.812,
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      "text": "那这个地方需要注意的是呢",
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    {
      "start": 1818.672,
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      "text": "这个专家团呢",
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    {
      "start": 1820.372,
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      "text": "它实际上就是我们比较审视的一个方案",
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    {
      "start": 1822.972,
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      "text": "因为我们会有我们的这个团队老板",
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    {
      "start": 1825.732,
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      "text": "然后来分配下面的不同专家",
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    {
      "start": 1827.692,
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      "text": "来帮你协同解决问题",
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    {
      "start": 1829.052,
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      "text": "大家如果想以更省credits的方式去做的话呢",
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    {
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      "text": "我们也推荐大家可以去使用专家",
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    {
      "start": 1835.852,
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      "text": "或者使用技能",
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      "no_speech_prob": 1.7701201615594186e-11,
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    {
      "start": 1837.092,
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      "text": "这种各种方式都可以共同去探索",
      "chunk": 3,
      "language": "zh",
      "avg_logprob": -0.07302828431129456,
      "no_speech_prob": 1.7701201615594186e-11,
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    {
      "start": 1839.352,
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      "text": "看大家的需求是什么样的",
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    {
      "start": 1841.012,
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      "text": "然后呢我们接着来看",
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    {
      "start": 1843.152,
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      "text": "刚刚我们提到我们运用这个专家团能力",
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      "no_speech_prob": 2.2210896316599005e-11,
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    {
      "start": 1846.232,
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      "text": "我们现在已经生成了一个完善的品牌传播方案了",
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    {
      "start": 1848.972,
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      "text": "那后面这三个我们具体要如何去做",
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    {
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      "text": "它有一个非常好的功能",
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    {
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      "text": "这样要好很多",
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      "text": "可以支持你下载到本地",
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      "text": "就是大家想要什么形式",
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      "text": "它还去支持了一个能力叫",
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      "text": "你可以运用它去制作一个预热落地页",
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      "text": "對,會直接給你反饋這樣一個頁面。我們可以看到他還有一些簡單的交互。",
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      "text": "對,而且這個都還只是我一句話去剷除的。",
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      "start": 1967.942,
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      "text": "对如果你和他交互更多轮给workbody提更多你的想法和建议的话",
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    {
      "start": 1972.202,
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      "text": "他也可以做的更加完善和更加优美",
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      "start": 1974.402,
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      "text": "对",
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    {
      "start": 1974.922,
      "end": 1979.962,
      "text": "然后这个地方我很想采访一下悠悠",
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    {
      "start": 1979.962,
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      "text": "你知道吗就是我们最开始webcoding的时候一定会遇到一个问题",
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    {
      "start": 1984.002,
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      "text": "就是你生成了这个html页面之后呢",
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      "start": 1987.082,
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      "text": "你把它发给你的老板或者同事发现看不完",
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      "start": 1990.422,
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      "text": "是不是",
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      "start": 1991.182,
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      "text": "我们一开始web像我们这种代码小白职场小白",
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      "start": 1995.022,
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      "text": "其实我们不懂那些代码",
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    {
      "start": 1996.682,
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      "text": "像我们webcoding",
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    {
      "start": 1997.802,
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      "text": "只是给他提了个需求",
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      "start": 1999.562,
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      "text": "但是呢",
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    {
      "start": 2000.202,
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      "text": "他实际想要交付的时候",
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      "start": 2001.842,
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      "text": "他发现他需要什么",
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    {
      "start": 2003.882,
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      "text": "部署云服务器",
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    {
      "start": 2005.322,
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      "text": "然后需要后端",
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    {
      "start": 2006.662,
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      "text": "这个时候我们就会卡在这一步上",
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    {
      "start": 2008.982,
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      "text": "没有办法分享给别人",
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    {
      "start": 2010.322,
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      "text": "去进行一个协作",
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    {
      "start": 2011.482,
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      "text": "或者说让别人去共享你的成果",
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    {
      "start": 2014.682,
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      "text": "那怎么办呢",
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    {
      "start": 2015.362,
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      "text": "这一下子",
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    {
      "start": 2015.902,
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      "text": "对这个问题实际上",
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    {
      "start": 2017.642,
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      "text": "最开始也有个痛点",
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    {
      "start": 2018.782,
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      "text": "因为最开始webcoding的时候",
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    {
      "start": 2020.422,
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      "text": "我也是不太清楚说",
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    {
      "start": 2021.762,
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      "text": "我这个东西要怎么弄到线上的",
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      "text": "那我们workbody是有一键支持",
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      "text": "你只用跟他说",
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    {
      "start": 2027.662,
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      "text": "我说你把这个网站部署到线上吧",
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    {
      "start": 2029.862,
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      "text": "他就会直接给你部署一个上线的网站",
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    {
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      "text": "那大家拿着这个网址",
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    {
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      "text": "就可以直接分享给同事们去看了",
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    {
      "start": 2036.202,
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      "text": "这是一个公寓可见的网址",
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    {
      "start": 2037.642,
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      "text": "包括可能有人会问",
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    {
      "start": 2039.322,
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      "text": "说那这个网址我以后想改",
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    {
      "start": 2041.822,
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      "text": "或者说我的这个内容想更新了",
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    {
      "start": 2044.142,
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      "text": "或者我这个网站不想让别人看见了",
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    {
      "start": 2046.082,
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      "text": "又怎么办呢",
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    {
      "start": 2046.742,
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      "text": "我们可以点击左赛的头像这里",
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    {
      "start": 2049.322,
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      "text": "哦 这里包括这里蹦出来",
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    {
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      "text": "一个这个body加油帐号",
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    {
      "start": 2053.362,
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      "text": "给大家推荐一下",
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    {
      "start": 2054.342,
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      "text": "有很多用户会反馈说",
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    {
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      "text": "这个积分问题",
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    {
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      "text": "那我们这里可以看到",
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    {
      "start": 2058.282,
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      "text": "每天都可以去领取一个100的通用积分",
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    {
      "start": 2060.822,
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      "text": "然后去体验我们的Workbody的产品",
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    {
      "start": 2063.242,
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      "text": "包括我们也可以去通过成长计划",
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    {
      "start": 2065.322,
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      "text": "去完成一些任务",
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    {
      "start": 2067.062,
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      "text": "这样我们可以在更了解",
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    {
      "start": 2068.602,
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      "text": "Workbody产品的同时呢",
      "chunk": 4,
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    {
      "start": 2070.022,
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      "text": "也就把积分赚到了",
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    {
      "start": 2071.242,
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      "text": "通过这样的方式",
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    {
      "start": 2072.342,
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      "text": "大家可以一边完成任务",
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    {
      "start": 2073.582,
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      "text": "一边精进自己使用AI的能力",
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    {
      "start": 2075.542,
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      "text": "同时还能得到一些积分",
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    {
      "start": 2077.102,
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      "text": "好 回到刚刚的这个话题",
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    {
      "start": 2078.722,
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      "text": "我们点击设置这一块",
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    {
      "start": 2080.682,
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      "text": "会有一个数据管理的地方",
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    {
      "start": 2083.962,
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      "text": "点击数据管理之后呢",
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      "start": 2086.182,
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      "text": "我们会看到一个我发布的应用",
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      "text": "我们点击这里",
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    {
      "start": 2090.642,
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      "text": "就可以看到我们部署到线上的网站了",
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    {
      "start": 2093.242,
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      "text": "那如果你是只想对它进行一个更新呢",
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    {
      "start": 2095.882,
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      "text": "你可以选择这个三个点",
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    {
      "start": 2097.722,
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      "text": "然后点重新发布",
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    {
      "start": 2099.142,
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      "text": "它就会说将使用最新的本地文件去重新发布了",
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    {
      "start": 2104.862,
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      "text": "发布后呢",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.09394290508368076,
      "no_speech_prob": 1.3742832392915627e-11,
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    {
      "start": 2105.802,
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      "text": "你的原链接是不变的",
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    {
      "start": 2107.322,
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      "text": "但是里面的内容会更新为你的最新版本",
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      "no_speech_prob": 1.3742832392915627e-11,
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    {
      "start": 2109.902,
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      "text": "就可以支持我们去不断的迭代",
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    {
      "start": 2113.462,
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      "text": "我们的网页迭代我们的产品",
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    {
      "start": 2115.382,
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      "text": "对而且你在不停迭代的过程之中呢",
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      "no_speech_prob": 6.993922108122774e-11,
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    {
      "start": 2117.802,
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      "text": "也不用给你的协作方不停的发不同的链接",
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      "no_speech_prob": 6.993922108122774e-11,
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    {
      "start": 2120.762,
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      "text": "对只在这一个链接里让它刷新就好了",
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    {
      "start": 2123.702,
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      "text": "那你如果不想要了",
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    {
      "start": 2125.242,
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      "text": "你也可以直接选择删除",
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    {
      "start": 2126.862,
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      "text": "就把这个从网页上下下来",
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      "no_speech_prob": 6.993922108122774e-11,
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    {
      "start": 2128.722,
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      "text": "当然你的这个html文件还是在你本地的",
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      "no_speech_prob": 6.993922108122774e-11,
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    {
      "start": 2131.382,
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      "text": "只不过它不是公寓可见的了",
      "chunk": 4,
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      "no_speech_prob": 6.993922108122774e-11,
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    {
      "start": 2133.062,
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      "text": "所以整体使用起来是非常的方便",
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      "no_speech_prob": 6.993922108122774e-11,
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    {
      "start": 2135.482,
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      "text": "对大家来说也都比较的友好",
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    {
      "start": 2137.082,
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      "text": "那这个就是我们workbody支持的",
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    {
      "start": 2140.962,
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      "text": "多种形态产物的一个演示",
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    {
      "start": 2142.822,
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      "text": "对那这些产物是不是",
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    {
      "start": 2146.902,
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      "text": "它最后都是可以说",
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    },
    {
      "start": 2148.542,
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      "text": "全部都同步到iMA里面去的",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.1376201301995962,
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    },
    {
      "start": 2151.702,
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      "text": "对这个网页目前是暂时不可一好",
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      "no_speech_prob": 1.557214079905833e-11,
      "compression_ratio": 1.4375
    },
    {
      "start": 2153.942,
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      "text": "因为它是一个网页的形态",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.1376201301995962,
      "no_speech_prob": 1.557214079905833e-11,
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    },
    {
      "start": 2155.242,
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      "text": "但我们可以把这个链接",
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    },
    {
      "start": 2156.722,
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      "text": "这个链接是可以支持储存到iMA里",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.1376201301995962,
      "no_speech_prob": 1.557214079905833e-11,
      "compression_ratio": 1.4375
    },
    {
      "start": 2160.142,
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      "text": "我们iMA是支持上传链接的",
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      "avg_logprob": -0.1376201301995962,
      "no_speech_prob": 1.557214079905833e-11,
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    },
    {
      "start": 2162.662,
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      "text": "然后像这种",
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      "no_speech_prob": 1.557214079905833e-11,
      "compression_ratio": 1.4375
    },
    {
      "start": 2164.362,
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      "text": "前面我们提到的这些PPT",
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    },
    {
      "start": 2166.362,
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      "text": "这个是完全没有问题的",
      "chunk": 4,
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      "no_speech_prob": 1.557214079905833e-11,
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    },
    {
      "start": 2168.322,
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      "text": "非常简单",
      "chunk": 4,
      "language": "zh",
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    },
    {
      "start": 2168.962,
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      "text": "我们再给大家演示一下",
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    },
    {
      "start": 2170.122,
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      "text": "像右上角这个上传到云端",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.1376201301995962,
      "no_speech_prob": 1.557214079905833e-11,
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    {
      "start": 2172.542,
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      "text": "点击之后选择爱马知识库",
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    {
      "start": 2174.922,
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      "text": "然后保存到你项目对应的知识库就可以了",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.138656455026546,
      "no_speech_prob": 1.937601468160466e-11,
      "compression_ratio": 1.3690036900369005
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    {
      "start": 2178.022,
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      "text": "比如说这个是body造物",
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      "avg_logprob": -0.138656455026546,
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    {
      "start": 2179.322,
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      "text": "我们就点击确认",
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    {
      "start": 2180.462,
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      "text": "好它就会保存回body造物里面了",
      "chunk": 4,
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    {
      "start": 2183.562,
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      "text": "包括这里我们也可以直接点击查看",
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      "no_speech_prob": 1.937601468160466e-11,
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    {
      "start": 2186.002,
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      "text": "也可以在这里看到我们的PPT",
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    {
      "start": 2190.502,
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      "text": "方便",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.138656455026546,
      "no_speech_prob": 1.937601468160466e-11,
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    {
      "start": 2191.722,
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      "text": "对非常方便都是打通的",
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    {
      "start": 2193.722,
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      "text": "好",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.138656455026546,
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    {
      "start": 2194.002,
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      "text": "那就像刚刚我们悠悠问到的问题",
      "chunk": 4,
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      "no_speech_prob": 1.937601468160466e-11,
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    {
      "start": 2199.862,
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      "text": "就是它非常敏锐地抓到了",
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    {
      "start": 2202.202,
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      "text": "就是我们最后还是需要把它保存为",
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    {
      "start": 2204.462,
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      "text": "爱马知识库的",
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      "no_speech_prob": 3.5963166472585684e-11,
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    {
      "start": 2205.182,
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      "text": "要沉淀起来",
      "chunk": 4,
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      "no_speech_prob": 3.5963166472585684e-11,
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    {
      "start": 2206.502,
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      "text": "对因为如果你不沉淀的话",
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    {
      "start": 2208.322,
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      "text": "我们实际上这一次",
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    },
    {
      "start": 2209.582,
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      "text": "它就只是一个单线程的一次性任务",
      "chunk": 4,
      "language": "zh",
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      "no_speech_prob": 3.5963166472585684e-11,
      "compression_ratio": 1.5027472527472527
    },
    {
      "start": 2211.882,
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      "text": "但你如果把你用的东西",
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      "no_speech_prob": 3.5963166472585684e-11,
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    },
    {
      "start": 2213.722,
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      "text": "回存到了爱马知识库",
      "chunk": 4,
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      "avg_logprob": -0.07920939834029586,
      "no_speech_prob": 3.5963166472585684e-11,
      "compression_ratio": 1.5027472527472527
    },
    {
      "start": 2214.902,
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      "text": "那你的这个品牌知识库",
      "chunk": 4,
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      "avg_logprob": -0.07920939834029586,
      "no_speech_prob": 3.5963166472585684e-11,
      "compression_ratio": 1.5027472527472527
    },
    {
      "start": 2216.382,
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      "text": "就是越用越厚的",
      "chunk": 4,
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      "avg_logprob": -0.07920939834029586,
      "no_speech_prob": 3.5963166472585684e-11,
      "compression_ratio": 1.5027472527472527
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    {
      "start": 2217.562,
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      "text": "那以后呢",
      "chunk": 4,
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      "no_speech_prob": 3.5963166472585684e-11,
      "compression_ratio": 1.5027472527472527
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    {
      "start": 2218.502,
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      "text": "Workbody再次去读取",
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    {
      "start": 2220.222,
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      "text": "你的爱马知识库的时候",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.07920939834029586,
      "no_speech_prob": 3.5963166472585684e-11,
      "compression_ratio": 1.5027472527472527
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    {
      "start": 2221.362,
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      "text": "里面的内容就更多",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.07920939834029586,
      "no_speech_prob": 3.5963166472585684e-11,
      "compression_ratio": 1.5027472527472527
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    {
      "start": 2222.682,
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      "text": "那它实际上就是有更多的经验和素材",
      "chunk": 4,
      "language": "zh",
      "avg_logprob": -0.07920939834029586,
      "no_speech_prob": 3.5963166472585684e-11,
      "compression_ratio": 1.5027472527472527
    },
    {
      "start": 2225.482,
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      "text": "可以跟我们Workbody去学习和参考",
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      "text": "你的整个积淀就更加丰富",
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      "text": "变成了一个老手AI这样的感觉",
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      "text": "那看",
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      "text": "你先说",
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      "text": "好的",
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      "text": "那我们先带大家回顾一下整个流程吧",
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      "text": "你到底做了些什么呢",
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      "text": "实际上呢",
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      "text": "从情报到手",
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      "text": "到我们最后制作传播方案",
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      "text": "并把传播方案回存",
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      "text": "你都可以通过Workbody",
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      "text": "成Emma去完成",
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      "text": "那比如最开始",
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      "text": "我们是通过自动化",
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      "text": "去得到了行业动态",
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      "text": "并且把它回存到Emma",
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      "text": "作为我们的资料",
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      "text": "那最后",
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      "text": "其次呢",
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      "text": "我们就把东西",
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    {
      "start": 2260.542,
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      "text": "从在Workbody中调用MAR的资料在任务里面",
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      "text": "然后我们选用专家团",
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      "text": "或者大家可能会用专家或者技能",
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    {
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      "text": "去交付出一个可用的品牌传播方案",
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    {
      "start": 2271.322,
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      "text": "然后我们再通过对话去拿到各种各样多份产物",
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      "start": 2274.782,
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      "text": "符合自己的需求",
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      "text": "最后再把它经验沉淀",
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      "start": 2277.142,
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      "text": "所以它不是一个手动的去拼凑零散信息",
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      "start": 2280.582,
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      "text": "并且不断的去和AI很繁杂的交汇这样一个过程",
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      "text": "而是说让AI充分的了解你",
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      "text": "充分的从你的品牌出发",
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      "text": "一路非常顺畅的",
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    {
      "start": 2289.802,
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      "text": "走到了下次可复用的",
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    {
      "start": 2291.342,
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      "text": "这样一个完整的资产",
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      "text": "那我有个问题",
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      "text": "就是刚刚这个演示非常干活",
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      "text": "而且步骤很详细",
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      "text": "但是它是不是只适用于",
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      "text": "刚刚那个特定的场景",
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      "text": "那如果说换成别的用户工作",
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      "text": "它可以继续复用吗",
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      "text": "是可以的",
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      "text": "那这个地方也是我们想",
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    {
      "start": 2309.902,
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      "text": "给大家提出一个概念",
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    {
      "start": 2311.142,
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      "text": "就是大家不要觉得",
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    {
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      "text": "这种工作流",
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      "start": 2313.742,
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      "text": "它一定是非常死板的固定的",
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      "start": 2315.922,
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      "text": "包括我们很多这个文章",
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    {
      "start": 2318.142,
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      "text": "或者我们很多有网络上博主分享的教程",
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      "start": 2321.442,
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      "text": "就是不要想着说我们可能完全的去复刻他们",
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      "start": 2324.982,
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      "text": "因为每个人的工作内容不一样",
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    {
      "start": 2326.962,
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      "text": "所以我们工作习惯和工作流程也会不一样",
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      "start": 2329.582,
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      "text": "究其本质我们可以发现",
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      "text": "我们刚刚讲到的那一些",
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    {
      "start": 2333.222,
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      "text": "它实际上可以拆成六个积木",
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    {
      "start": 2335.482,
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      "text": "那大家实际上就可以不是通过套模板的方式",
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    {
      "start": 2339.022,
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      "text": "而是像拼积木一样去搭建自己的工作流",
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    {
      "start": 2342.202,
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      "text": "我们带大家回看一下实际上有哪六块积木呢",
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      "start": 2345.262,
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      "text": "分别是第一个是资料积木",
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      "text": "也就是我们Emma里面的资料",
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      "text": "那第二个是理解积木",
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      "text": "也就是我们讲到的Workbody",
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      "text": "那些非常看起来很清晰的图",
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      "text": "包括你和Workbody去多次对话",
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    {
      "start": 2358.582,
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      "text": "来帮助你理解和想清楚",
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      "start": 2360.342,
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      "text": "那第三个呢",
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    {
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      "text": "这一块生态的部分",
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      "text": "它能够让你的AI长出更好用的手",
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      "text": "那第四个是交付的积木",
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      "text": "那第五个是自动化的积木",
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      "text": "也就是定时的去触发一些任务",
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      "text": "让AI自己实现动起来",
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      "text": "那第六个就是沉淀的积木",
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      "text": "也就是我们把产物回存和AMMA",
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    {
      "start": 2382.262,
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      "text": "并且进行结构化的归岛",
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      "start": 2383.962,
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      "text": "那现在由于中途一直有观众在不停的行进来",
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      "text": "所以我们还是带大家看一下",
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      "text": "这六块积木分别是什么东西",
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      "text": "那这个地方的资料积木",
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    {
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      "text": "也就是让AI有底料",
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    {
      "start": 2397.122,
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      "text": "是你给AI的上下文的来源",
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    {
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      "text": "也就是我们AMMA中的这样一个品牌积累",
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      "text": "那它就适用于AI不了解你的背景的时候",
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      "text": "如果你想要AI输出的东西更懂你",
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      "text": "你就需要这么做",
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    {
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      "text": "它的关键动作呢",
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    {
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      "text": "就是我们在iMac的PC端",
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    {
      "start": 2411.822,
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      "text": "或者在iMac的小程序中",
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      "text": "我们去建立库",
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      "text": "并且上传",
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    {
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      "text": "然后勾选内容到Workbody里面",
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    {
      "start": 2418.662,
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      "text": "流程就是",
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    {
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      "text": "这是iMac的PC端",
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      "text": "大家也可以通过小程序去感受",
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    {
      "start": 2422.862,
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      "text": "如果手边现在没有电脑的话",
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      "text": "我们可以点击建库",
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    {
      "start": 2426.342,
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      "text": "创建个人的知识库",
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    {
      "start": 2428.282,
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      "text": "或者如果大家有团队共享的需求",
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    {
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      "text": "可以点击创建共享的知识库",
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      "start": 2432.722,
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      "text": "这里也支持",
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    {
      "start": 2433.922,
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      "text": "选择加入方式",
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    {
      "start": 2435.142,
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      "text": "可以是直接加入或者是管理员确认",
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    {
      "start": 2437.242,
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      "text": "也可以去管理我们成员的权限",
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      "text": "那还可以像我们刚刚悠悠提到的",
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      "text": "就是我们可以去发现这里",
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    {
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      "text": "去订阅你自己需要的对应领域的知识库",
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      "text": "那然後呢,你就可以在workbody之中,比如說這裡,新建任務,我們就可以去上傳。",
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      "text": "對應知識庫的共享也可以選,然後加入也可以選。",
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      "start": 2461.233,
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      "text": "好",
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    {
      "start": 2461.453,
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      "text": "就可以上传到我们的任务之中去使用",
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    {
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      "text": "这个就是我们的资料积木",
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      "text": "那第二个积木呢是理解积木",
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      "text": "也就是我们刚刚提到的就是",
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      "text": "适合你对于Emma中",
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      "text": "可能有各种各样的东西",
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      "text": "然后里面的内容也特别冗杂",
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      "start": 2478.913,
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      "text": "你这个时候如果全部去阅读的话",
      "chunk": 5,
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      "avg_logprob": -0.10815342322929755,
      "no_speech_prob": 2.5145129034509495e-11,
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    {
      "start": 2481.093,
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      "text": "可能比较难",
      "chunk": 5,
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    {
      "start": 2481.813,
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      "text": "那你就可以去通过这样理清结构的方式",
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    {
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      "text": "比如说像这里提到的",
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    {
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      "text": "我们可以让Emma去生成这样的结构图",
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    {
      "start": 2491.433,
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      "text": "然后就可以帮你很清晰的去理解",
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      "text": "我们这个结构是什么样的",
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      "text": "然后里面大概有什么内容",
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    {
      "start": 2498.413,
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      "text": "通过这样的方式让自己理性结构",
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    {
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      "text": "同时呢它也适用于",
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    {
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      "text": "比如说你想让AI出一个非常复杂的报告",
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    {
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      "text": "或者说需要AI拆解一个非常长的任务",
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      "text": "那你可以先让AI去给你看一下这个股价",
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    {
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      "text": "以免就是AI已经跑了很多东西了",
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    {
      "start": 2514.493,
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      "text": "这个时候你发现它和你想要的不太一样",
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      "text": "那实际上已经消耗了非常多的Credits",
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    {
      "start": 2519.573,
      "end": 2521.113,
      "text": "也消耗了大家等待的精力",
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    {
      "start": 2521.113,
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      "text": "不如在最开始的时候",
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      "text": "你就和AI先确定好方向",
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    {
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      "text": "然后再进行铲除",
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      "text": "那第三块积木呢",
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      "text": "也是我们Walkbody中非常重点的一个板块",
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    {
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      "text": "叫做专家技能和连接器",
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      "text": "我们可以带大家看一下这个部分",
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      "text": "在我们左边的Tab这里也是有的",
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    {
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      "text": "给大家提前预制好了一些自己的专家",
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    {
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      "text": "小红书运营的专家",
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      "text": "包括我们对OPC",
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    {
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      "text": "例如电商运营啊",
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      "text": "数据分析啊",
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      "text": "我们这边都有内置好",
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    {
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      "text": "大家可以直接一键的去使用",
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      "text": "然后技能这一块也是",
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      "text": "有非常多各种丰富的技能",
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      "text": "大家可以在这个地方搜索",
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      "text": "自己想用的",
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      "text": "或者搜索关键词就可以出来",
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      "text": "然后连接器这里也是",
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      "text": "比如说Emma",
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    {
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      "text": "然后像QQ邮箱",
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      "text": "这边我们也都有",
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      "text": "而且我们的小程序",
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      "text": "也是有这些专家和技能",
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      "text": "以及连接器的",
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      "text": "大家也可以去小程序",
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      "text": "是体验一下",
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      "text": "对的没错",
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    {
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      "text": "现在的生态也是非常丰富",
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      "text": "是的",
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      "text": "好",
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      "text": "那这种时候",
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      "text": "你就可以让它",
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      "text": "去处理一些",
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    {
      "start": 2590.613,
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      "text": "更加深层次",
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    {
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      "text": "或者需要我们这里的",
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      "text": "内行视角的东西",
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      "text": "好",
      "chunk": 5,
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      "avg_logprob": -0.13074078402676426,
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    {
      "start": 2594.913,
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      "text": "那第四块积木",
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    {
      "start": 2597.073,
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      "text": "也就是我们刚刚提到",
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    {
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      "text": "交付积木",
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    {
      "start": 2598.793,
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      "text": "在前面",
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    {
      "start": 2599.413,
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      "text": "我们也有给大家演示过",
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    {
      "start": 2600.773,
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      "text": "那这个的关键就在于",
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    {
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      "text": "你要告诉AI你要的是什么",
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      "text": "你如果不告诉他我要的是文档",
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      "avg_logprob": -0.09358407355643608,
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      "text": "还是我要的是PPT",
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    {
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      "text": "我要的是网页",
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    {
      "start": 2608.473,
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      "text": "他给你交付的结果就不一定是如你所愿的",
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    {
      "start": 2611.613,
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      "text": "所以你一定要和他说的非常清楚",
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    {
      "start": 2613.613,
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      "text": "因为就是只有这样的话",
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    {
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      "text": "AI才能知道你这个需要交付的形态是什么样的",
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    {
      "start": 2618.793,
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      "text": "我们也可以给大家后进来的观众朋友们展示一下",
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      "text": "我们这里可以生成PPT的形式",
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    {
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      "text": "也可以生成这样文档的形式",
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      "text": "都是支持的",
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      "text": "那第五个呢",
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    {
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      "text": "就是我们刚刚提到的自动化节目",
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      "start": 2633.033,
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      "text": "这一块呢",
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    {
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      "text": "就是让AI自己去动手",
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    {
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      "text": "然后形成永久跑这样的逻辑",
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    {
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      "text": "我们可以在这里看一下",
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    {
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      "text": "在左边自动化",
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    {
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      "text": "可以自己去建设",
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    {
      "start": 2643.333,
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      "text": "也可以从模板添加",
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    {
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      "text": "那自己建设的话",
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      "text": "你就可以自己去勾选技能啊",
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    {
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      "text": "勾选专家",
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      "text": "包括你也可以设置访问权限",
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    {
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      "text": "对你可以去用自然语言",
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    {
      "start": 2654.093,
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      "text": "描述这个提示式就好",
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    {
      "start": 2655.373,
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      "text": "描述你的需求",
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    {
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      "text": "然后去勾选一下",
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      "text": "你想要它生效的日期",
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    {
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      "text": "就可以添加一个自动化任务",
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    {
      "start": 2660.593,
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      "text": "然后这个地方需要注意的是",
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    {
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      "text": "如果你想要跑这个自动化任务的话",
      "chunk": 5,
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    {
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      "text": "一定要是你的电脑",
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    {
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      "text": "就是你的电脑是开着的",
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    {
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      "text": "包括你电脑上的workbody是开着的",
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    {
      "start": 2669.653,
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      "text": "这样它才能够成功",
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    {
      "start": 2670.693,
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      "text": "因为之前大家有的用户会反馈",
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    {
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      "text": "报错的情况",
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      "text": "是因为你的电脑上的workbody没有开着",
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    {
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      "text": "所以还会导致这样的情况",
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      "text": "大家可以注意一下",
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    {
      "start": 2678.773,
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      "text": "好",
      "chunk": 5,
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      "text": "那第六个就是沉淀积木",
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    {
      "start": 2681.573,
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      "text": "也就是如何让AI下一次更懂你",
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    {
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      "text": "实际上就是我们刚刚讲到的",
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      "text": "我们在左边",
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    {
      "start": 2686.433,
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      "text": "去把它回存到",
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    {
      "start": 2690.553,
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      "text": "Emma知识库之中",
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    {
      "start": 2691.573,
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      "text": "这样就能够让AI下次有更深刻的经验",
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    {
      "start": 2694.193,
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      "text": "能够更加的理解你",
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      "text": "好",
      "chunk": 5,
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      "avg_logprob": -0.09769303160286992,
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    {
      "start": 2695.953,
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      "text": "那回看一下我们刚刚前面那个品牌策划",
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    {
      "start": 2702.033,
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      "text": "它实际上用到了哪些积木呢",
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      "text": "就是用到了我们这里首先第一个",
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    {
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      "text": "自动化加沉淀",
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    {
      "start": 2707.293,
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      "text": "是我们的用自动化收集行业情报的这部分",
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    {
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      "text": "然后第二个呢是调取资料",
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    {
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      "text": "我们去完成了品牌上下文的注入",
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      "text": "那第三个呢是资料",
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    {
      "start": 2716.213,
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      "text": "加上我们的这个专家连接器",
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    {
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      "text": "去形成了品牌传播方案",
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      "avg_logprob": -0.09769303160286992,
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    {
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      "text": "然后我们通过自然语言",
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      "text": "让它交付了",
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      "text": "多种各样的PPT",
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    {
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      "text": "落地页和矩阵图",
      "chunk": 5,
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    {
      "start": 2725.613,
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      "text": "最后呢",
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    {
      "start": 2726.353,
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      "text": "我们再把这些",
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    {
      "start": 2727.153,
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      "text": "回归到我们的",
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      "compression_ratio": 1.3660714285714286
    },
    {
      "start": 2728.273,
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      "text": "艾玛知识库里面",
      "chunk": 5,
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    {
      "start": 2729.253,
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      "text": "完成了我们",
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      "compression_ratio": 1.3660714285714286
    },
    {
      "start": 2729.953,
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      "text": "品牌资产库的更新",
      "chunk": 5,
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      "avg_logprob": -0.1156819912013162,
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      "compression_ratio": 1.3660714285714286
    },
    {
      "start": 2731.133,
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      "text": "好",
      "chunk": 5,
      "language": "zh",
      "avg_logprob": -0.1156819912013162,
      "no_speech_prob": 1.6578533684752372e-11,
      "compression_ratio": 1.3660714285714286
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    {
      "start": 2731.893,
      "end": 2732.733,
      "text": "听到这里",
      "chunk": 5,
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    {
      "start": 2732.733,
      "end": 2734.813,
      "text": "相信大家一下子就明白了",
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    {
      "start": 2734.813,
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      "text": "其实我们",
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      "compression_ratio": 1.3660714285714286
    },
    {
      "start": 2735.473,
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      "text": "不是为了去记住",
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      "compression_ratio": 1.3660714285714286
    },
    {
      "start": 2737.113,
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      "text": "某一个案例怎么用",
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      "compression_ratio": 1.3660714285714286
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    {
      "start": 2738.373,
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      "text": "而是我们要学会",
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    },
    {
      "start": 2739.593,
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      "text": "怎么去拼这个积木",
      "chunk": 5,
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      "compression_ratio": 1.3660714285714286
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    {
      "start": 2741.473,
      "end": 2742.393,
      "text": "那我们现在",
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      "compression_ratio": 1.3660714285714286
    },
    {
      "start": 2742.393,
      "end": 2743.453,
      "text": "不妨就趁热打铁",
      "chunk": 5,
      "language": "zh",
      "avg_logprob": -0.1156819912013162,
      "no_speech_prob": 1.6578533684752372e-11,
      "compression_ratio": 1.3660714285714286
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    {
      "start": 2743.453,
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      "text": "看一下",
      "chunk": 5,
      "language": "zh",
      "avg_logprob": -0.1156819912013162,
      "no_speech_prob": 1.6578533684752372e-11,
      "compression_ratio": 1.3660714285714286
    },
    {
      "start": 2743.933,
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      "text": "怎么用同一套积木",
      "chunk": 5,
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    },
    {
      "start": 2745.713,
      "end": 2747.033,
      "text": "拼出不同身份",
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    },
    {
      "start": 2747.033,
      "end": 2748.693,
      "text": "不同场景的工作流吧",
      "chunk": 5,
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    },
    {
      "start": 2748.693,
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      "text": "好的",
      "chunk": 5,
      "language": "zh",
      "avg_logprob": -0.1156819912013162,
      "no_speech_prob": 1.6578533684752372e-11,
      "compression_ratio": 1.3660714285714286
    },
    {
      "start": 2749.373,
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      "text": "那这里我们给大家主要介绍四种角色和四种不同的工作流",
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      "no_speech_prob": 2.2910555397270826e-11,
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    {
      "start": 2753.813,
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      "text": "我们可以看一下",
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    {
      "start": 2754.713,
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      "text": "你们可就是观众朋友们可能是他们之一",
      "chunk": 5,
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      "avg_logprob": -0.08044977621598677,
      "no_speech_prob": 2.2910555397270826e-11,
      "compression_ratio": 1.5948275862068966
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    {
      "start": 2757.773,
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      "text": "但也有可能不是",
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    {
      "start": 2758.873,
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      "text": "但我们可以通过这样的",
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    {
      "start": 2760.753,
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      "text": "如果你是的话",
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      "compression_ratio": 1.5948275862068966
    },
    {
      "start": 2761.473,
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      "text": "你可以等会重点看一下对应的场景",
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      "avg_logprob": -0.08044977621598677,
      "no_speech_prob": 2.2910555397270826e-11,
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    {
      "start": 2763.773,
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      "text": "如果不是的话呢",
      "chunk": 5,
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      "compression_ratio": 1.5948275862068966
    },
    {
      "start": 2764.873,
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      "text": "你也可以学习一下我们这个积木的思维",
      "chunk": 5,
      "language": "zh",
      "avg_logprob": -0.08044977621598677,
      "no_speech_prob": 2.2910555397270826e-11,
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    {
      "start": 2767.153,
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      "text": "然后看如何应用到自己的生活之中",
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      "text": "那首先第一个呢是内容创作者",
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    {
      "start": 2771.733,
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      "text": "第二个呢是针对我们的学生导",
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    {
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      "text": "那第三个是如果你有求职的需求也可以使用",
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    {
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      "text": "第四个呢就是我们职场人这一块",
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    {
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      "text": "包括我们OPC",
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    {
      "start": 2780.933,
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      "text": "就是一人公司也是试用的",
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    {
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      "text": "我们首先来看一下内容创作者这里",
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      "text": "内容创作者这里呢",
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    {
      "start": 2788.213,
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      "text": "用Workbody成艾玛能解决的最主要的痛点就是",
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    {
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      "text": "有很多时候你用AI写东西",
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    {
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      "text": "你就算给他为了很多的这种你的参考文档",
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    {
      "start": 2797.393,
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      "text": "每次都得为",
      "chunk": 5,
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    {
      "start": 2798.513,
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      "text": "每次都得重新告诉他",
      "chunk": 5,
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    {
      "start": 2799.833,
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      "text": "你到底喜欢怎么样的",
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    {
      "start": 2800.973,
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      "text": "他还是有可能写的不像你",
      "chunk": 5,
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      "compression_ratio": 1.4147058823529413
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    {
      "start": 2802.713,
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      "text": "那在这个过程之中",
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    {
      "start": 2804.013,
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      "text": "你可能已经很烦了",
      "chunk": 5,
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    },
    {
      "start": 2805.153,
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      "text": "因为你已经为了他非常多次",
      "chunk": 5,
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    {
      "start": 2806.693,
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      "text": "告诉了他非常多次你的规则",
      "chunk": 5,
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      "compression_ratio": 1.4147058823529413
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    {
      "start": 2808.213,
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      "text": "然后每次都要重新的改",
      "chunk": 5,
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    {
      "start": 2809.513,
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      "text": "但是最后还是会就是和你的风格不同一",
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    {
      "start": 2812.813,
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      "text": "那Workbody成Emma就可以很好解决这个问题",
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    {
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      "text": "你可以首先在Emma之中",
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      "compression_ratio": 1.3556231003039514
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    {
      "start": 2817.773,
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      "text": "好 比如说现在我们假设有这样一个账号",
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      "avg_logprob": -0.11969268321990967,
      "no_speech_prob": 2.31895058711018e-11,
      "compression_ratio": 1.3556231003039514
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    {
      "start": 2821.293,
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      "text": "叫之下效率研究所",
      "chunk": 5,
      "language": "zh",
      "avg_logprob": -0.11969268321990967,
      "no_speech_prob": 2.31895058711018e-11,
      "compression_ratio": 1.3556231003039514
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    {
      "start": 2822.713,
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      "text": "我们可以去上传你参考的一些论文",
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      "avg_logprob": -0.11969268321990967,
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    {
      "start": 2826.893,
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      "text": "参考的一些作品",
      "chunk": 5,
      "language": "zh",
      "avg_logprob": -0.11969268321990967,
      "no_speech_prob": 2.31895058711018e-11,
      "compression_ratio": 1.3556231003039514
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    {
      "start": 2828.473,
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      "text": "就是你对标账号的作品",
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      "language": "zh",
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      "compression_ratio": 1.3556231003039514
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    {
      "start": 2829.793,
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      "text": "包括你自己的个人的作品和素材",
      "chunk": 5,
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      "avg_logprob": -0.11969268321990967,
      "no_speech_prob": 2.31895058711018e-11,
      "compression_ratio": 1.3556231003039514
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    {
      "start": 2832.433,
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      "text": "然后呢 让Workbody基于这些",
      "chunk": 5,
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      "no_speech_prob": 2.31895058711018e-11,
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    {
      "start": 2835.193,
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      "text": "去给你生成你的创作风格画像",
      "chunk": 5,
      "language": "zh",
      "avg_logprob": -0.11969268321990967,
      "no_speech_prob": 2.31895058711018e-11,
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    {
      "start": 2837.273,
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      "text": "这样以后呢",
      "chunk": 5,
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    {
      "start": 2838.833,
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      "text": "每次Workbody读到你这个",
      "chunk": 5,
      "language": "zh",
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      "text": "它就会读到你的创作风格画像",
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      "text": "会基于你这个",
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      "text": "去直接的生成你的",
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    {
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      "text": "这个后面的文章",
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      "text": "对",
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      "text": "这个有点像那种设计视觉规范",
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    {
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      "text": "对",
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      "text": "有点这种感觉",
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      "text": "就是你可以通过",
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      "text": "Workbody成爱马的方式",
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      "text": "相当于帮你个人打造了一套",
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      "text": "你自己的这种品牌资料库的感觉",
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      "text": "你的自己的规范",
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      "text": "然后我们很多这个",
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      "text": "短视频或者小红书的这种博主",
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      "text": "他每天都会去持续的关注我们的新的热点和问题",
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      "text": "然后唯一需要改动的就是提示词的部分",
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      "text": "你去告诉他说我每天需要关注",
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      "text": "每天为我带来十条",
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      "text": "这里的tips就还是跟之前一样",
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      "text": "类似于规范",
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      "text": "比如说呃",
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    {
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      "text": "沉淀成MD文档的形式",
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    {
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      "text": "好",
      "chunk": 5,
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      "avg_logprob": -0.1451957466897,
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      "text": "然后去选择",
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      "text": "比如说每天早上10点钟",
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      "text": "推送给我",
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    {
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      "text": "生效日期啊",
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      "text": "我希望它一直生效",
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      "text": "那你就留空",
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      "text": "它就会始终生效",
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      "text": "然后每天我还希望在",
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      "text": "Workbody的微信小程序上",
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      "text": "可以看到",
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      "text": "所以我们就勾选",
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      "text": "微信小程序推送",
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      "start": 2917.153,
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      "text": "点击保存",
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    {
      "start": 2918.173,
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      "text": "好",
      "chunk": 5,
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      "avg_logprob": -0.1451957466897,
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    {
      "start": 2919.573,
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      "text": "我已经了解风险了",
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    {
      "start": 2921.313,
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      "text": "好",
      "chunk": 5,
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      "start": 2921.553,
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      "text": "确认创建",
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    {
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      "text": "好",
      "chunk": 5,
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    {
      "start": 2923.353,
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      "text": "名称好",
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      "text": "AI资讯",
      "chunk": 5,
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      "start": 2929.75,
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      "text": "那這樣每天呢,就會給你推送你這個新鮮的選題。包括你也可以通過撰寫更複雜的,或者說更符合你需求的這個提示詞。",
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      "text": "然后能来匹配更符合你要求的这样一个自动化任务",
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      "text": "那第三个呢就是让文峰更像你",
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      "text": "也就是我们刚刚提到的",
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      "start": 2947.21,
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      "text": "你可以去用你的这些专家或者说用你的这个技能",
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      "start": 2952.25,
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      "text": "对",
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      "start": 2955.29,
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      "text": "然后去出出稿",
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      "text": "然后去比如说前面的世界专家",
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      "text": "我们刚刚提到就可以去配图",
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      "text": "这样就可以去生成符合各个平台调性",
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    {
      "start": 2965.71,
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      "text": "或者符合各个平台要求的东西",
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      "start": 2967.27,
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      "text": "包括我们最后呢",
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      "text": "生成的这个文档",
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      "text": "比如说这里",
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    {
      "start": 2969.71,
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      "text": "我生成的这个创作风格画像",
      "chunk": 6,
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      "avg_logprob": -0.08614357905601387,
      "no_speech_prob": 2.40239616394744e-11,
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    {
      "start": 2971.27,
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      "text": "我也可以直接上传到这里",
      "chunk": 6,
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      "avg_logprob": -0.08614357905601387,
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    {
      "start": 2973.63,
      "end": 2974.83,
      "text": "点击艾玛知识库",
      "chunk": 6,
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      "no_speech_prob": 2.40239616394744e-11,
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    {
      "start": 2974.83,
      "end": 2976.99,
      "text": "把它储存到我们的艾玛知识库中",
      "chunk": 6,
      "language": "zh",
      "avg_logprob": -0.08614357905601387,
      "no_speech_prob": 2.40239616394744e-11,
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    {
      "start": 2976.99,
      "end": 2978.69,
      "text": "作为以后持续调用的",
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      "avg_logprob": -0.08614357905601387,
      "no_speech_prob": 2.40239616394744e-11,
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    },
    {
      "start": 2978.69,
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      "text": "这样一个基础的参考规范",
      "chunk": 6,
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      "no_speech_prob": 2.40239616394744e-11,
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    },
    {
      "start": 2980.71,
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      "text": "下一次就不用再让他去",
      "chunk": 6,
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    {
      "start": 2983.09,
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      "text": "就是给他复制更多的",
      "chunk": 6,
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    {
      "start": 2985.15,
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      "text": "你之前的作品",
      "chunk": 6,
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      "avg_logprob": -0.08614357905601387,
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    {
      "start": 2985.91,
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      "text": "然后让他像你了",
      "chunk": 6,
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    {
      "start": 2986.99,
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      "text": "他就直接去读取这个知识库",
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    {
      "start": 2988.95,
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      "text": "然后直接写出一篇",
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    {
      "start": 2990.59,
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      "text": "就是你的品牌",
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    {
      "start": 2992.47,
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      "text": "符合你的品牌",
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    {
      "start": 2993.59,
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      "text": "符合你的调性的文章",
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    {
      "start": 2994.91,
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      "text": "对",
      "chunk": 6,
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      "avg_logprob": -0.08614357905601387,
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    {
      "start": 2995.29,
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      "text": "就再也不用就是编打我们的AI说",
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      "text": "你一定要写的像我",
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      "text": "而是说你这个本身",
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    {
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      "text": "他一读到你的AI知识库就知道",
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    {
      "start": 3003.35,
      "end": 3005.25,
      "text": "OK你是想要这样的风格",
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    {
      "start": 3005.25,
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      "text": "我已经了解了",
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    {
      "start": 3006.35,
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      "text": "好那接下来呢",
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    {
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      "text": "我们就可以看到我们的三种形态",
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    {
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      "text": "是可以一次拿的",
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      "text": "包括我们这里的文档啊",
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    {
      "start": 3013.79,
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      "text": "包括我们这里的图片啊",
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      "text": "我们都是可以一次性生成的",
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      "text": "这里也是用到我们的专家团",
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      "text": "他会有一个主要的专家",
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    {
      "start": 3021.03,
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      "text": "然后分派给不同的专家去执行任务",
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      "start": 3023.53,
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      "text": "最后我们就可以一次性的产出",
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      "start": 3026.03,
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      "text": "我们每个平台都可能不同的",
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    {
      "start": 3027.79,
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      "text": "需要用到的这种产物",
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    {
      "start": 3029.19,
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      "text": "不需要你每次都拿着重新",
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    {
      "start": 3031.49,
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      "text": "再去喂AI一次",
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    {
      "start": 3033.23,
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      "text": "然后告诉他这次我想要的是什么",
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      "start": 3035.15,
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      "text": "然后你还得拿着他最后给你的文字",
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    {
      "start": 3037.23,
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      "text": "去自己的编辑图文",
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      "start": 3039.07,
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      "text": "现在是直接可以就是一次性产出",
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    {
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      "text": "这些长文啊图文啊短视频脚本",
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    {
      "start": 3044.95,
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      "text": "包括如果你是我们公众号认证的",
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    {
      "start": 3047.43,
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      "text": "这个作者的话",
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      "start": 3048.17,
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      "text": "你甚至可以直接在我们workbody里",
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    {
      "start": 3050.51,
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      "text": "通过对话的方式",
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    {
      "start": 3051.59,
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      "text": "让他把文章推到你的公众号后台形成草稿哦",
      "chunk": 6,
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    {
      "start": 3054.63,
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      "text": "非常的方便",
      "chunk": 6,
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    {
      "start": 3055.53,
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      "text": "你都不用自己复制了",
      "chunk": 6,
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    {
      "start": 3056.89,
      "end": 3058.85,
      "text": "然后最后呢",
      "chunk": 6,
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    {
      "start": 3058.85,
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      "text": "你就还是把你的这些沉淀混音的Emma之中",
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    {
      "start": 3061.75,
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      "text": "下次呢",
      "chunk": 6,
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    {
      "start": 3062.49,
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      "text": "AI就知道哪种笔法效果很好了",
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    {
      "start": 3064.53,
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      "text": "包括你如果有兴趣",
      "chunk": 6,
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    {
      "start": 3066.01,
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      "text": "你也可以把自己的数据称回Emma",
      "chunk": 6,
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    {
      "start": 3068.31,
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      "text": "然后让Workbody调用来分析",
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    {
      "start": 3070.23,
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      "text": "这样你也不用自己去做一个复盘的工作",
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    {
      "start": 3072.79,
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      "text": "非常适合OPC",
      "chunk": 6,
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      "avg_logprob": -0.11989713101773648,
      "no_speech_prob": 2.2205674798936315e-11,
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    {
      "start": 3074.17,
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      "text": "对",
      "chunk": 6,
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    {
      "start": 3074.59,
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      "text": "OPC也可以使用",
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    {
      "start": 3075.73,
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      "text": "包括我们很多自媒体博主啊",
      "chunk": 6,
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    {
      "start": 3077.55,
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      "text": "这种都可以拿这个来自己分析自己的作品",
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      "no_speech_prob": 2.2205674798936315e-11,
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    {
      "start": 3080.09,
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      "text": "那第二个场景呢",
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    {
      "start": 3082.57,
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      "text": "就是我们的学生群体",
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    {
      "start": 3083.75,
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      "text": "西梦的时候呢",
      "chunk": 6,
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    {
      "start": 3085.31,
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      "text": "会有很多学弟学妹",
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    {
      "start": 3086.57,
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      "text": "过来找我说",
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    {
      "start": 3087.79,
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      "text": "我这个任务太多了",
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    {
      "start": 3089.55,
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      "text": "我要学的太多了",
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    {
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      "text": "我实在学不明白",
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    {
      "start": 3091.35,
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      "text": "我就给他推荐",
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      "text": "我说你可以用",
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    {
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      "text": "试一下我们workbody吗",
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      "start": 3094.63,
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      "text": "那这个呢",
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    {
      "start": 3096.33,
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      "text": "就是你首先",
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    {
      "start": 3097.21,
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      "text": "你不能让AI凭空答题",
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    {
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      "text": "比如说你现在是一个",
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    {
      "start": 3100.55,
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      "text": "专业的精管类的学生",
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    {
      "start": 3102.01,
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      "text": "你就不能直接跟workbody",
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    {
      "start": 3104.03,
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      "text": "就这样",
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    {
      "start": 3104.99,
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      "text": "我就这样",
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    {
      "start": 3105.93,
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      "text": "对话我跟他说",
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    {
      "start": 3107.05,
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      "text": "我说你给我讲一下",
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    {
      "start": 3108.77,
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      "text": "尽管知识",
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    {
      "start": 3111.03,
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      "text": "这样可能是不行的",
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    {
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      "text": "我们首先呢",
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    {
      "start": 3115.65,
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      "text": "需要把你对应的一些",
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    {
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      "text": "比如说你的PPT啊",
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    {
      "start": 3120.67,
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      "text": "比如说你的这个小的作业啊",
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    {
      "start": 3122.87,
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      "text": "或者说你的课程笔记啊",
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    {
      "start": 3124.71,
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      "text": "教材决选啊",
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    {
      "start": 3125.37,
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      "text": "全部的上传到我们workabout",
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    {
      "start": 3127.15,
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      "text": "埃玛之中",
      "chunk": 6,
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    {
      "start": 3128.03,
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      "text": "然后传到埃玛之中呢",
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    {
      "start": 3129.55,
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      "text": "我们就有了这样一个积淀",
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    {
      "start": 3130.75,
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      "text": "好",
      "chunk": 6,
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    {
      "start": 3131.61,
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      "text": "放进埃玛之中呢",
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    {
      "start": 3133.29,
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      "text": "你就可以获得了一个",
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    {
      "start": 3134.91,
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      "text": "你的基础的资料积木",
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    {
      "start": 3136.41,
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      "text": "那这个地方还有一个值得推荐的是",
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    {
      "start": 3138.55,
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      "text": "大家可以打开",
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    {
      "start": 3139.63,
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      "text": "埃马知识库的",
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    {
      "start": 3141.45,
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      "text": "这个微信小程序体验一下",
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    {
      "start": 3142.89,
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      "text": "我们是支持在",
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    {
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      "text": "小程序中选择上传文件",
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    {
      "start": 3147.29,
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      "text": "然后大家可以选择",
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    {
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      "text": "自己的微信文件",
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    {
      "start": 3150.43,
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      "text": "那这个时候呢",
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    {
      "start": 3151.73,
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      "text": "你就可以选择",
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    {
      "start": 3152.47,
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      "text": "不同的群聊或者对话框",
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    {
      "start": 3154.01,
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      "text": "可以直接去上传",
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    {
      "start": 3155.57,
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      "text": "大家群聊内容中的",
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    {
      "start": 3157.11,
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      "text": "这样的一些PDF或者PPT",
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    {
      "start": 3159.37,
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      "text": "非常的方便",
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    {
      "start": 3161.25,
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      "text": "而且我觉得就是",
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    {
      "start": 3162.55,
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      "text": "学生群里我们",
      "chunk": 6,
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    {
      "start": 3163.53,
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      "text": "而且还不会过期",
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    {
      "start": 3164.81,
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      "text": "就是我们之前会经常",
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    {
      "start": 3166.59,
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      "text": "比如说老师",
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    {
      "start": 3167.51,
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      "text": "第一周就把所有的课件发出来了",
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    {
      "start": 3170.17,
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      "text": "但是当时可能你忙忘了",
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    {
      "start": 3171.73,
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      "text": "或者说已读",
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    {
      "start": 3172.43,
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      "text": "但是没有存下来",
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    {
      "start": 3174.05,
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      "text": "然后等到你最后一个星期",
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    {
      "start": 3176.23,
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      "text": "想要去复习的时候",
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    {
      "start": 3177.11,
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      "text": "发现它全部过期了",
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    {
      "start": 3178.49,
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      "text": "对",
      "chunk": 6,
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    {
      "start": 3179.33,
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      "text": "这个就很适合大家",
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    {
      "start": 3180.85,
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      "text": "如果平时可能手边没有电脑",
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    {
      "start": 3182.69,
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      "text": "或者说现在就是泛蓝了",
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    {
      "start": 3184.33,
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      "text": "你就很方便可以把手机拿出来",
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    {
      "start": 3186.29,
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      "text": "直接一点就存进爱马知识库里面",
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      "start": 3188.85,
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      "text": "对",
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      "start": 3189.15,
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      "text": "你就可以在手机上看",
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      "text": "包括它和PC端也是同步的",
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      "text": "而且这样的话",
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      "start": 3193.17,
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      "text": "也很适合大家去做小组作业这样的场景",
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      "start": 3195.71,
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      "text": "因为我们很多时候做小组作业",
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      "start": 3197.37,
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      "text": "大家的这个文件会在群里到处飞啊",
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      "start": 3200.11,
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      "text": "这样的方式就可以很好的",
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      "start": 3201.61,
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      "text": "把它都及时的存下来",
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      "text": "然后也方便我们workbody",
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      "start": 3204.47,
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      "text": "去后面做进一步的调用",
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      "start": 3206.03,
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      "text": "那接下来呢就是理解积木",
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      "text": "很多时候大家会发现",
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      "text": "你做不论是学生吧",
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      "text": "还是任何的群体",
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      "text": "你去学一个新的东西",
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      "start": 3214.11,
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      "text": "或者说一个复杂的领域",
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    {
      "start": 3215.47,
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      "text": "难点在于你get不到之中的结构",
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      "text": "就是它的结构大量是什么样的",
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      "text": "它的逻辑是什么",
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      "start": 3221.65,
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      "text": "这也是我们之前很多博主",
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    {
      "start": 3224.21,
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      "text": "会去推荐你用思维导图",
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    {
      "start": 3225.95,
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      "text": "这样的形式来理逻辑",
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      "text": "那我们workbody",
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    {
      "start": 3228.25,
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      "text": "现在也支持这样的理解积目",
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      "text": "比如说我们现在来到",
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      "text": "这样一个学习场景",
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      "start": 3232.67,
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      "text": "好",
      "chunk": 6,
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      "text": "我就跟他说",
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      "text": "我需要结合",
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    {
      "start": 3235.55,
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      "text": "Emma中的这个知识库的内容",
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    {
      "start": 3237.57,
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      "text": "去讲解这两个东西",
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      "text": "然后我是理解他们的定义的",
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      "start": 3243.19,
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      "text": "但是呢",
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      "start": 3244.03,
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      "text": "我放进案例之后",
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      "start": 3244.93,
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      "text": "就容易混淆了",
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      "text": "我需要你",
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    {
      "start": 3246.49,
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      "text": "就给我说明一下它的区别",
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      "text": "那我们workbody",
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    {
      "start": 3249.73,
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      "text": "是直接非常聪明",
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    {
      "start": 3251.03,
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      "text": "它是运用这样的",
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    {
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      "text": "逻辑思维图的方式",
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    {
      "start": 3253.67,
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      "text": "给大家看一下全貌",
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    {
      "start": 3254.81,
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      "text": "很清晰",
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    {
      "start": 3256.43,
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      "text": "对非常清晰",
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    {
      "start": 3257.43,
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      "text": "他就不是在给你这样一大坨文字",
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      "text": "就是你有时候就不想看文字",
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      "start": 3261.79,
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      "text": "然后你看到这个图你就一眼非常的明晰",
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      "start": 3264.59,
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      "text": "对因为文字实际上来说",
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      "start": 3266.63,
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      "text": "不管他怎样的去进行这些分层啊",
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      "start": 3268.79,
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      "text": "或者说序号啊",
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      "start": 3269.95,
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      "text": "他的阅读效率也是没有那么高的",
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      "text": "但是我们workbody这样的svg组件的方式",
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    {
      "start": 3275.15,
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      "text": "就可以去帮你非常清晰的",
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    {
      "start": 3277.03,
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      "text": "就是获得这样一个",
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    {
      "start": 3278.75,
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      "text": "呃我们的这个逻辑思维的图",
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    {
      "start": 3280.55,
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      "text": "帮你更好的了解",
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      "start": 3281.95,
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      "text": "好",
      "chunk": 6,
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    {
      "start": 3284.31,
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      "text": "哎",
      "chunk": 6,
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    {
      "start": 3286.43,
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      "text": "那最后呢就是我们可以去交付",
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      "start": 3291.31,
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      "text": "首先呢我们在日常的使用过程之中",
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    {
      "start": 3294.73,
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      "text": "可能大家会涉及到的这些",
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      "start": 3296.91,
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      "text": "你的交付的作业的形式非常多种多样的",
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      "start": 3299.99,
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      "text": "比如说有的课程可能是需要你提交",
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      "text": "word文档的形式",
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    {
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      "text": "有的需要你提交pdf的形式",
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    {
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      "text": "有的需要提交ppt的形式",
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    {
      "start": 3307.55,
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      "text": "那这些都能在我们workbody中一站式的去完成",
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    {
      "start": 3310.77,
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      "text": "比如说这里",
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    {
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      "text": "这还是笔记",
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    {
      "start": 3314.03,
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      "text": "我跟他说",
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    {
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      "text": "我说想要一个学习笔记",
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    {
      "start": 3319.81,
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      "text": "他就会出一个这样的文档",
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      "text": "然后我跟他说",
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      "text": "就是会要PPT",
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      "text": "他也会跟我形成这样的PPT",
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      "text": "或者说这样的一个",
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    {
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      "text": "word文档的报告",
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      "text": "好",
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      "start": 3331.09,
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      "text": "那还有一个非常重要的点",
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      "start": 3333.27,
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      "text": "就是大家平时",
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    {
      "start": 3335.07,
      "end": 3337.17,
      "text": "就可能这些东西也不好存下来嘛",
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    {
      "start": 3337.17,
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      "text": "我就直接",
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    {
      "start": 3338.43,
      "end": 3340.07,
      "text": "也可以直接这样",
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    {
      "start": 3340.07,
      "end": 3341.67,
      "text": "回存到艾玛知识库里面",
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    {
      "start": 3341.67,
      "end": 3344.01,
      "text": "包括还有一个非常非常方便的",
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    {
      "start": 3344.01,
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      "text": "就是我们这里可以有一个分享的按钮",
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    {
      "start": 3346.11,
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      "text": "点击之后",
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    {
      "start": 3347.91,
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      "text": "是可以直接分享到大家的微信的",
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    {
      "start": 3350.17,
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      "text": "或者说你复制链接",
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    {
      "start": 3351.95,
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      "text": "生成二维码这样的形式去分享",
      "chunk": 6,
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      "no_speech_prob": 4.627720082139852e-11,
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    {
      "start": 3353.93,
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      "text": "它就不用说你先到文件夹里面",
      "chunk": 6,
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    {
      "start": 3357.43,
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      "text": "去复制粘贴",
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    {
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      "text": "然后再粘贴到微信里去",
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    {
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      "text": "对对就不用你先点击这里",
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    {
      "start": 3362.93,
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      "text": "然后在文件夹中找到它的位置",
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    {
      "start": 3364.53,
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      "text": "然后再传了",
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    {
      "start": 3365.33,
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      "text": "你可以直接比如这里给大家大概展示一下",
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    {
      "start": 3367.77,
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      "text": "就可以直接这样",
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    {
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      "text": "在微信对话中去发出去",
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    {
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      "text": "非常的方便",
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    {
      "start": 3372.39,
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      "text": "然后最后也是支持",
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    {
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      "text": "像我们刚刚说的",
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    {
      "start": 3374.77,
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      "text": "把你的笔记回存",
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    {
      "start": 3375.79,
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      "text": "好",
      "chunk": 6,
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      "avg_logprob": -0.11575423712017892,
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    {
      "start": 3378.79,
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      "text": "那对于求职者来说",
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    {
      "start": 3380.49,
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      "text": "我们这里也是一个非常好的场景",
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    {
      "start": 3382.29,
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      "text": "就是首先",
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    {
      "start": 3382.97,
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      "text": "大家求职的时候",
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    {
      "start": 3384.45,
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      "text": "很多比如说你的各种各样的项目",
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    {
      "start": 3386.81,
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      "text": "你的这些简历",
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    {
      "start": 3388.43,
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      "text": "包括你每次针对不同公司",
      "chunk": 6,
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    {
      "start": 3390.53,
      "end": 3392.67,
      "text": "准备的不同的这种问答的模拟",
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    {
      "start": 3392.67,
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      "text": "它都散过在各种各样的地方",
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    {
      "start": 3394.75,
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      "text": "它不是特别好的统一起来",
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    {
      "start": 3396.27,
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      "text": "而且每次给AI又要重新上传",
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    {
      "start": 3398.09,
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      "text": "那你就可以在iMaj中",
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      "compression_ratio": 1.3701492537313433
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    {
      "start": 3399.77,
      "end": 3401.05,
      "text": "去建立这样一个资料库",
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    {
      "start": 3401.05,
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      "text": "比如说我们拿",
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      "compression_ratio": 1.17989417989418
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    {
      "start": 3401.93,
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      "text": "Walkbody生成了一个假的人",
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    {
      "start": 3404.25,
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      "text": "他不知道他是谁",
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    {
      "start": 3405.33,
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      "text": "他叫陈宇童",
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    {
      "start": 3406.09,
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      "text": "是AI人",
      "chunk": 6,
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    {
      "start": 3406.93,
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      "text": "然后呢",
      "chunk": 6,
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    {
      "start": 3407.71,
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      "text": "这个个人经历就存在这里",
      "chunk": 6,
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      "compression_ratio": 1.17989417989418
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    {
      "start": 3409.27,
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      "text": "可以看到有他的项目",
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    {
      "start": 3410.47,
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      "text": "有他的简历",
      "chunk": 6,
      "language": "zh",
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    {
      "start": 3411.15,
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      "text": "有他的面试稿",
      "chunk": 6,
      "language": "zh",
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    {
      "start": 3411.97,
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      "text": "然后放在这里之后呢",
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    {
      "start": 3420.257,
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      "text": "然後呢,我們就可以用理解積目去讓AI幫助我們拆解我們的這個JD,就是我們的崗位描述。",
      "chunk": 7,
      "language": "zh",
      "avg_logprob": -0.24404347149622924,
      "no_speech_prob": 1.0443523650038866e-11,
      "compression_ratio": 1.543103448275862
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    {
      "start": 3426.757,
      "end": 3427.877,
      "text": "比如说这个",
      "chunk": 7,
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      "avg_logprob": -0.1335973041813548,
      "no_speech_prob": 2.3093794238038257e-11,
      "compression_ratio": 1.3191489361702127
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    {
      "start": 3427.877,
      "end": 3429.917,
      "text": "我们来到求职的领域",
      "chunk": 7,
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      "avg_logprob": -0.1335973041813548,
      "no_speech_prob": 2.3093794238038257e-11,
      "compression_ratio": 1.3191489361702127
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    {
      "start": 3429.917,
      "end": 3432.757,
      "text": "那现在呢",
      "chunk": 7,
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      "avg_logprob": -0.1335973041813548,
      "no_speech_prob": 2.3093794238038257e-11,
      "compression_ratio": 1.3191489361702127
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    {
      "start": 3432.757,
      "end": 3434.137,
      "text": "我就是先给他上传",
      "chunk": 7,
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      "avg_logprob": -0.1335973041813548,
      "no_speech_prob": 2.3093794238038257e-11,
      "compression_ratio": 1.3191489361702127
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    {
      "start": 3434.137,
      "end": 3436.157,
      "text": "这里就是选择",
      "chunk": 7,
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      "avg_logprob": -0.1335973041813548,
      "no_speech_prob": 2.3093794238038257e-11,
      "compression_ratio": 1.3191489361702127
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    {
      "start": 3436.157,
      "end": 3437.457,
      "text": "添加文件",
      "chunk": 7,
      "language": "zh",
      "avg_logprob": -0.1335973041813548,
      "no_speech_prob": 2.3093794238038257e-11,
      "compression_ratio": 1.3191489361702127
    },
    {
      "start": 3437.457,
      "end": 3438.317,
      "text": "MH知识库",
      "chunk": 7,
      "language": "zh",
      "avg_logprob": -0.1335973041813548,
      "no_speech_prob": 2.3093794238038257e-11,
      "compression_ratio": 1.3191489361702127
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    {
      "start": 3438.317,
      "end": 3440.737,
      "text": "选择这个个人经历的知识库",
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      "no_speech_prob": 2.3093794238038257e-11,
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    {
      "start": 3440.737,
      "end": 3442.797,
      "text": "我们就可以把它放到这里了",
      "chunk": 7,
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      "avg_logprob": -0.1335973041813548,
      "no_speech_prob": 2.3093794238038257e-11,
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    {
      "start": 3442.797,
      "end": 3446.117,
      "text": "那我就是让他结合我这个知识库去判断",
      "chunk": 7,
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      "no_speech_prob": 2.3093794238038257e-11,
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    {
      "start": 3446.117,
      "end": 3448.557,
      "text": "我是不适合以下这个产品经理的岗位",
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      "no_speech_prob": 2.3093794238038257e-11,
      "compression_ratio": 1.3191489361702127
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    {
      "start": 3448.557,
      "end": 3451.717,
      "text": "增长方向的产品经理",
      "chunk": 7,
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    {
      "start": 3451.717,
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      "text": "我们workbody呢",
      "chunk": 7,
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    {
      "start": 3453.297,
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      "text": "给了一个12-60分",
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      "compression_ratio": 1.3191489361702127
    },
    {
      "start": 3455.097,
      "end": 3456.177,
      "text": "说匹配度极低",
      "chunk": 7,
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      "avg_logprob": -0.1335973041813548,
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    {
      "start": 3456.177,
      "end": 3458.337,
      "text": "看一下这里",
      "chunk": 7,
      "language": "zh",
      "avg_logprob": -0.1477771076729627,
      "no_speech_prob": 1.130485722394825e-11,
      "compression_ratio": 1.3526785714285714
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    {
      "start": 3458.337,
      "end": 3462.117,
      "text": "他还给了一个HTML页面",
      "chunk": 7,
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      "no_speech_prob": 1.130485722394825e-11,
      "compression_ratio": 1.3526785714285714
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    {
      "start": 3462.117,
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      "text": "我们可以看到",
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    {
      "start": 3463.057,
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      "text": "首先他对JD做了一个",
      "chunk": 7,
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      "avg_logprob": -0.1477771076729627,
      "no_speech_prob": 1.130485722394825e-11,
      "compression_ratio": 1.3526785714285714
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    {
      "start": 3465.197,
      "end": 3467.217,
      "text": "我们的岗位做了一个竹条的拆解",
      "chunk": 7,
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      "avg_logprob": -0.1477771076729627,
      "no_speech_prob": 1.130485722394825e-11,
      "compression_ratio": 1.3526785714285714
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    {
      "start": 3467.217,
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      "text": "好那除此之外呢",
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    {
      "start": 3473.377,
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      "text": "他还根据我的这个个人的现状",
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      "compression_ratio": 1.3526785714285714
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    {
      "start": 3475.857,
      "end": 3478.557,
      "text": "去做了一个认知要求的主向匹配",
      "chunk": 7,
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      "no_speech_prob": 1.130485722394825e-11,
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    {
      "start": 3478.557,
      "end": 3480.897,
      "text": "发现不是很合适",
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    {
      "start": 3480.897,
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      "text": "好除此之外呢",
      "chunk": 7,
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      "avg_logprob": -0.1477771076729627,
      "no_speech_prob": 1.130485722394825e-11,
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    {
      "start": 3482.897,
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      "text": "他还去做能力模型的匹配",
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    {
      "start": 3484.997,
      "end": 3487.397,
      "text": "最后还会给你一个结论和建议",
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    {
      "start": 3487.397,
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      "text": "非常的聪明",
      "chunk": 7,
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    {
      "start": 3488.317,
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      "text": "他会告诉你说",
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      "avg_logprob": -0.1032414378890072,
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    {
      "start": 3489.477,
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      "text": "你可能更适合其他方向",
      "chunk": 7,
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    {
      "start": 3491.337,
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      "text": "这个是基于你爱马知识库中资料生成的",
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    {
      "start": 3493.837,
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      "text": "那你如果还是想冲击这个产品经理的岗位",
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    {
      "start": 3496.777,
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      "text": "你可能需要准备这些Circle",
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    {
      "start": 3498.957,
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      "text": "这样的技能",
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    {
      "start": 3499.957,
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      "text": "对非常详细非常好的一个报告",
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    {
      "start": 3502.637,
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      "text": "这些也很会方便我们去认识自己的能力",
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    {
      "start": 3506.517,
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      "text": "对没错",
      "chunk": 7,
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    {
      "start": 3507.597,
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      "text": "好",
      "chunk": 7,
      "language": "zh",
      "avg_logprob": -0.1032414378890072,
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      "compression_ratio": 1.3300970873786409
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    {
      "start": 3508.157,
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      "text": "那他既然有这样分析岗位和分析你的能力",
      "chunk": 7,
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    {
      "start": 3514.377,
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      "text": "他也就可以帮你去制作一个简历",
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    {
      "start": 3516.457,
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      "text": "所以我们也可以一键的去",
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    {
      "start": 3519.177,
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      "text": "你看我就跟他说",
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    {
      "start": 3520.777,
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      "text": "我就想找这个陈玉图同学",
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      "no_speech_prob": 2.203457555305377e-11,
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    {
      "start": 3522.437,
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      "text": "就想找这个产品经理方样的工作",
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    {
      "start": 3524.237,
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      "text": "就可以让他基于",
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    {
      "start": 3525.777,
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      "text": "Workbody中的这样一个简历",
      "chunk": 7,
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      "compression_ratio": 1.4810495626822158
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    {
      "start": 3527.037,
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      "text": "去生成一个新的",
      "chunk": 7,
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      "avg_logprob": -0.11513774578387921,
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    {
      "start": 3528.697,
      "end": 3530.237,
      "text": "适合产品方向的中文简历",
      "chunk": 7,
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      "no_speech_prob": 2.203457555305377e-11,
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    {
      "start": 3530.237,
      "end": 3531.717,
      "text": "我们来看一下他的这个结果",
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    {
      "start": 3531.717,
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      "text": "对也是非常简约的",
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    {
      "start": 3533.537,
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      "text": "包括我有核对哦",
      "chunk": 7,
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      "no_speech_prob": 2.203457555305377e-11,
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    {
      "start": 3534.797,
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      "text": "这些信息也都是和我们",
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    {
      "start": 3536.277,
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      "text": "Workbody这个爱马中沉淀的",
      "chunk": 7,
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      "avg_logprob": -0.11513774578387921,
      "no_speech_prob": 2.203457555305377e-11,
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    {
      "start": 3538.217,
      "end": 3540.117,
      "text": "原本的简历的信息对得上的",
      "chunk": 7,
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    },
    {
      "start": 3540.117,
      "end": 3541.757,
      "text": "就不存在瞎编的情况",
      "chunk": 7,
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    {
      "start": 3541.757,
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      "text": "这是因为他运用了",
      "chunk": 7,
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    {
      "start": 3543.357,
      "end": 3544.737,
      "text": "爱骂资料库中的内容",
      "chunk": 7,
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      "avg_logprob": -0.1549865858895438,
      "no_speech_prob": 2.439955008870509e-11,
      "compression_ratio": 1.4087837837837838
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    {
      "start": 3544.737,
      "end": 3547.697,
      "text": "而不是就是随便给他一个内容去编的",
      "chunk": 7,
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      "no_speech_prob": 2.439955008870509e-11,
      "compression_ratio": 1.4087837837837838
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    {
      "start": 3547.697,
      "end": 3548.217,
      "text": "好",
      "chunk": 7,
      "language": "zh",
      "avg_logprob": -0.1549865858895438,
      "no_speech_prob": 2.439955008870509e-11,
      "compression_ratio": 1.4087837837837838
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    {
      "start": 3548.217,
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      "text": "这个很方便",
      "chunk": 7,
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      "avg_logprob": -0.1549865858895438,
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    {
      "start": 3549.157,
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      "text": "对就是",
      "chunk": 7,
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    {
      "start": 3549.837,
      "end": 3552.097,
      "text": "不会说从网上自己找一些",
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      "no_speech_prob": 2.439955008870509e-11,
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    {
      "start": 3552.097,
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      "text": "可能跟这个擦编资料",
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      "text": "很多",
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      "text": "我们很多时候海投嘛",
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      "text": "会投地非常多的岗位",
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      "text": "所以你有时候就会忘了自己投了哪些",
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      "text": "然后后面被收到面试邀请",
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      "text": "才非常惊慌的想起来",
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      "text": "那我们这里就可以有一个",
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      "text": "他就会给你生成这样一个非常好的表格",
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      "text": "包括你的目标公司的清单和一些使用说明",
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      "text": "就是非常完善的一个表格",
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      "text": "好他就会给你非常多各种各样的模板",
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      "text": "例如我们刚刚提到这个面试评估表啊",
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      "text": "然后你的这个题库和评估方案啊",
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      "text": "包括你的校招经历这些",
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      "text": "你都可以去点击他",
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      "text": "哎这都非常好",
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      "text": "这都非常方便的可以完成",
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      "text": "点击做同管就可以这里",
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      "text": "回到我们球这一块",
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      "text": "岗位推荐",
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      "text": "那这个就是比较贴近和AI对话的部分了",
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      "text": "让他你是什么什么岗位的负责人",
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      "text": "然后你针对我的这个经历来进行一下提问",
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      "text": "然后最后呢",
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      "text": "你也可以让AI给你生成一个复盘",
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      "text": "你可以把这个复盘再包装回你的Emma知识库里面",
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      "no_speech_prob": 2.1699248300977025e-11,
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    {
      "start": 3684.397,
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      "text": "这样就形成了一个求职的完整闭环",
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    {
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      "text": "那最后呢",
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    {
      "start": 3687.577,
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      "text": "也可能是我们今天受众最广的一个场景",
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    {
      "start": 3689.737,
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      "text": "就是我们的职场人",
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      "text": "那我们来看一下我们职场人",
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    {
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      "text": "要如何运用Workbody成M",
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    {
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      "text": "首先在资料积木这一块",
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    {
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      "text": "还是和前面一样",
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    {
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      "text": "相信很多职场人可能也在用了",
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    {
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      "text": "我们就把自己的这些经历",
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    {
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      "text": "比如说现在我负责这个项目",
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    {
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      "text": "我就导入到这个项目里面",
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      "text": "然后这里还推荐各位职场人",
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    {
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      "text": "如果你手上同时在负责多个项目的话",
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    {
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      "text": "我们是推荐大家把自己的不同项目",
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    {
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      "text": "用不同的资料库去分开的",
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      "text": "这样对于Workbody来说",
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    {
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      "text": "能更好的理解你这个资料库",
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    {
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      "text": "是为了一个什么事情的",
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    {
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      "text": "因为你如果把你自己工作的所有内容",
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    {
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      "text": "全部塞在一个资料库的话",
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    {
      "start": 3725.797,
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      "text": "其实就相当于把一大坨东西",
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    {
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      "text": "全部丢给了Workbody",
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    {
      "start": 3729.397,
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      "text": "这样对他来说是不利于理解的",
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    {
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      "text": "包括如果你有团队协作的需求",
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    {
      "start": 3733.477,
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      "text": "你也可以去搭建共享的这样的一个知识库",
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    {
      "start": 3735.917,
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      "text": "方便你的同事啊",
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    {
      "start": 3737.257,
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      "text": "或者你的协作方和你一起查看",
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    {
      "start": 3739.037,
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      "text": "好",
      "chunk": 7,
      "language": "zh",
      "avg_logprob": -0.07646091905220476,
      "no_speech_prob": 1.856487186202571e-11,
      "compression_ratio": 1.4357541899441342
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    {
      "start": 3740.777,
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      "text": "那大家工作中也像前面那个品牌营销一样",
      "chunk": 7,
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      "no_speech_prob": 1.856487186202571e-11,
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    {
      "start": 3744.297,
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      "text": "会涉及到很多重复的事情",
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    {
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      "text": "那这样重复的事情呢",
      "chunk": 7,
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    {
      "start": 3747.257,
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      "text": "大家就让AI自己去跑就OK",
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    {
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      "text": "还是我们还是来最后演示一遍",
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    {
      "start": 3752.397,
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      "text": "这个自动化",
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    {
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      "text": "然后添加自动化",
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    {
      "start": 3755.617,
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      "text": "然后大家可以去撰写名称",
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    {
      "start": 3758.037,
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      "text": "撰写题词词",
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    {
      "start": 3759.257,
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      "text": "勾选连接器",
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    {
      "start": 3760.437,
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      "text": "然后还有执行频率",
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    {
      "start": 3761.817,
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      "text": "如果有些后进来的观众呢",
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    {
      "start": 3763.857,
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      "text": "这个地方没有看到的话",
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    {
      "start": 3765.377,
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      "text": "可以后面关注一下",
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    {
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      "text": "我们Workbody的官方公众号",
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    {
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      "text": "我们会在视频号里有回放",
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    {
      "start": 3769.977,
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      "text": "包括今天的整个直播内容",
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    {
      "start": 3771.657,
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      "text": "也会变成文章",
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    {
      "start": 3772.697,
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      "text": "大家可以到时候直接跟着操作和学习",
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    {
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      "text": "大家可以在关注Workbody的公众号",
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    {
      "start": 3778.617,
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      "text": "并且回复IMA",
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      "avg_logprob": -0.1163556368454643,
      "no_speech_prob": 1.7644458810694985e-11,
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    {
      "start": 3779.997,
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      "text": "我们会把今天的视频资料",
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    {
      "start": 3782.317,
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      "text": "以及我们的课件都会回复给大家",
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    {
      "start": 3784.897,
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      "text": "对的",
      "chunk": 7,
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    {
      "start": 3785.537,
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      "text": "好",
      "chunk": 7,
      "language": "zh",
      "avg_logprob": -0.1163556368454643,
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    {
      "start": 3785.937,
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      "text": "然后呢",
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    {
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      "text": "我们还有一个非常重磅的",
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      "compression_ratio": 1.4721407624633431
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    {
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      "text": "就是我们在Workbody的丰富的连接技术生态",
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    {
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      "text": "我们刚刚前面也有给大家介绍",
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    {
      "start": 3792.877,
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      "text": "我们有非常多的连接器打通",
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    {
      "start": 3795.077,
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      "text": "这就是我们大家之前这样讲的的MCP",
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    {
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      "text": "好",
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      "text": "那通过这个我们就可以实现一些更复杂的功能",
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      "text": "比如说QQ邮箱",
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      "text": "飞书腾讯会议企业微信",
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      "text": "这样的功能我们都可以直接通过",
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      "text": "打开各种各样的连接器",
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      "text": "连接上之后呢",
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      "text": "就可以获取里面的一些信息",
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      "text": "那比如大家可能有时候会遇到一个场景",
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      "text": "例如大家可能会疑惑这个怎么用",
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      "text": "这比如说是腾讯会议这个连接器",
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      "text": "我们今天假设用户突然提了一个非常急的需求",
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      "text": "然后你根本来不及整理会议纪要",
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      "text": "就要直接工作了",
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      "text": "但是又涉及到的需求变更",
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      "text": "那这个时候你就可以打开我们腾讯会议的连接器",
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      "text": "它就可以直接拉取你和客户的会议纪要",
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      "text": "然后你就可以基于这个直接分析了",
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    {
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      "text": "整个的生态是非常打通的",
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    {
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      "text": "这个是对大家的工作非常方便的",
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      "text": "包括它也可以去直接读取你QQ邮箱里的内容",
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    {
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      "text": "这样你也不用每天去盯着看",
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      "text": "有没有垃圾邮箱垃圾邮件中哪些是重要的信息",
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      "text": "哪些是不重要的",
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      "text": "你可以通过我们workbody加QQ邮箱的方式直接去关注",
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      "text": "好",
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    {
      "start": 3861.897,
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      "text": "那同时他也可以是支持我们多种方案的交付的",
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      "text": "例如我们这个",
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      "text": "那直上举例哈",
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      "text": "比如說現在我就讓他根據我emma中的這個客戶資料和最新的婚姻記錄去分析我們的需求有哪些變化。",
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      "text": "他就會很好的去分析我們的需求的變化。而且他也會,比如說這個地方很亮點的。",
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      "start": 3883.404,
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      "text": "在下面会告诉你",
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      "text": "有一些事项是必须由人来解决的",
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      "text": "那他就会非常明确的告诉你这些东西",
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      "text": "他才会聚集往下推进",
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      "text": "包括他也会通过这样组建的方式",
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      "text": "但你更明确的看到",
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      "text": "这个就比我们用文字描述要清晰直观很多",
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      "text": "这个他就可以直接帮你生成",
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      "text": "非常方便",
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      "text": "对就是无论有多少变更",
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    {
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      "text": "只要你的东西维持在我们workbody加艾玛里面",
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      "text": "那它都是可以被处理掉的",
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    {
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      "text": "都是可以通过这样的方式去交付给你的",
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      "text": "好",
      "chunk": 8,
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      "avg_logprob": -0.0924657448050902,
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    {
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      "text": "那我们还是接着就可以通过这些",
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    {
      "start": 3921.604,
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      "text": "去要求它制定本次的营销方案",
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    {
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      "text": "比如说这里我是有勾选这个地方的专家",
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    {
      "start": 3927.004,
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      "text": "给大家再演示一遍",
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    {
      "start": 3928.064,
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      "text": "点击加号",
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    {
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      "text": "可以选择我们不同的专家",
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    {
      "start": 3931.984,
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      "text": "可以根据需求去召唤更多专家",
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    {
      "start": 3934.284,
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      "text": "看你自己想要什么",
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    {
      "start": 3935.644,
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      "text": "比如这里我是一个营销方案",
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    {
      "start": 3937.244,
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      "text": "我想要做的是增长",
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    {
      "start": 3938.424,
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      "text": "我就选了一个增长超盘手",
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      "text": "这样一个",
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      "text": "然后他呢就会作为一个总监",
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      "text": "然后去召唤不同的",
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      "start": 3944.344,
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      "text": "这样的专家来帮我做事情",
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      "text": "你看他就组建了一个团队",
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      "start": 3949.184,
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      "text": "然后给我列好了计划",
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      "text": "然后去启动这些不同的",
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      "start": 3955.104,
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      "text": "然后让等他们这个回传之后呢",
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      "start": 3958.064,
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      "text": "最后就可以完成这样一个非常好的东西",
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      "text": "给我交付",
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      "start": 3962.384,
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      "text": "你看最后我们看一下他的成果",
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      "start": 3964.384,
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      "text": "文件们",
      "chunk": 8,
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      "start": 3965.884,
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      "text": "这是一个非常好的方案",
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      "text": "整个清单就是他会告诉你交付的一些东西",
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      "text": "交付了很多",
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      "start": 3976.504,
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      "text": "对对对",
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      "text": "基本上就是把我这次营销又用到的所有东西都交付全了",
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      "text": "包括你看这个地方我还会跟他说",
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    {
      "start": 3982.864,
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      "text": "我说我想要最后再复盘一下我这个项目",
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      "start": 3985.044,
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      "text": "他也可以直接给到我一个复盘报告",
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    {
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      "text": "对就是非常完善",
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      "text": "你看这个结构都非常好的",
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      "text": "给到我这样一个项目的概述啊背景啊",
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      "text": "发生了什么",
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      "text": "然后需求变更",
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      "text": "他也去记录上了",
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      "text": "然后呢",
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    {
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      "text": "我可能要对外展示嘛",
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    {
      "start": 4001.124,
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      "text": "所以我可能这个文档",
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      "text": "还需要更进一步的",
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    {
      "start": 4003.284,
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      "text": "变成PPT的形式",
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      "text": "我就直接跟他说",
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      "text": "说生成复盘的PPT",
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    {
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      "text": "和我们这个项目",
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    {
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      "text": "时间线的一个回顾",
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      "text": "三分钟左右",
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      "text": "就可以生成好",
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      "text": "这个是我们的一个时间线",
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      "text": "可以看到非常清晰",
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    {
      "start": 4015.644,
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      "text": "他分了不同的板块",
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    {
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      "text": "不同的日期",
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    {
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      "text": "然后去展示我们这个时间线",
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      "text": "包括也可以去形成",
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      "text": "我们这样一个",
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      "text": "可以直接使用的PPT",
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      "text": "对这个PPT也可以",
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      "start": 4026.764,
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      "text": "大家可以重新的在这里",
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    {
      "start": 4029.564,
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      "text": "沉淀",
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    {
      "start": 4029.964,
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      "text": "对沉淀到我们的埃玛知识库之中",
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      "text": "悠悠已经学会了",
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    {
      "start": 4033.524,
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      "text": "我们今天提了非常多次",
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      "text": "一定要记得我们完整的",
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      "text": "对完整的取用存的这样一个过程",
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      "text": "不要就做到最后就断掉了",
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      "text": "那你这个知识复利就有一点可惜",
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    {
      "start": 4044.784,
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      "text": "好",
      "chunk": 8,
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    {
      "start": 4045.324,
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      "text": "对就是我们讲的",
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    {
      "start": 4047.184,
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      "text": "悠悠已经学会了这个点",
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    {
      "start": 4048.644,
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      "text": "就是我们案例的库要越用越厚",
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    {
      "start": 4050.684,
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      "text": "就一定要记得回存过去",
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      "text": "好",
      "chunk": 8,
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      "start": 4052.984,
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      "text": "那实际上这四种角色呢",
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      "start": 4055.204,
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      "text": "我们都用到的是同一种股价",
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      "start": 4056.824,
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      "text": "也就是最开始是尽量",
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    {
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      "text": "把你领域的你要用的资料",
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    {
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      "text": "存到Emma之中",
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      "text": "然后第二个呢",
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    {
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      "text": "就是你可以去理解",
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      "text": "或者说你和AI去判定一个方向",
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      "start": 4066.544,
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      "text": "通过我们这里的这样结构图的方式去展现",
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      "text": "那第三个呢",
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      "text": "就是通过专家专家团技能等各种方式去协作",
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    {
      "start": 4074.244,
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      "text": "来变形推进我们的任务",
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      "text": "就第四个呢",
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      "text": "就是我们按照需要去选择你想要的形态",
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    {
      "start": 4080.104,
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      "text": "完成一个交付",
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      "text": "那最后呢",
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      "text": "就是把你的经验沉淀回埃码",
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      "text": "进行一个回存",
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      "text": "这样我们就可以形成一个完整的闭环",
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      "text": "也就是我们在直播最开始",
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      "text": "就跟大家提到的取用存这样一个概念",
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      "start": 4092.824,
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      "text": "最后一个环节",
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    {
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      "text": "由于很多观众",
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    {
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      "text": "他可能会错过前面最开始的部分",
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      "text": "我们再大家最后回顾一次",
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      "text": "我们这个工作流是怎么样的",
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      "text": "首先明确你的目标",
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      "text": "你想要做什么",
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      "text": "然后挑出你可能最烦的那一个",
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      "text": "我们优先解决",
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      "text": "然后呢就整理资料",
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      "text": "在我们艾玛的PC端",
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      "text": "或者在艾玛的小程序中",
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      "text": "去上传",
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      "text": "无论是个人的资料库",
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      "text": "这里支持非常非常多种多样的类型",
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      "text": "这样的形式都可以上传",
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      "text": "那上传好",
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      "text": "这个是你的第一个",
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      "text": "然后接下来呢",
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    {
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      "text": "你就选积木",
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    {
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      "text": "也就是我们刚刚前面提到的六大类积木",
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      "text": "你可能不会全部用上",
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      "text": "可以按你的需求自己去选择",
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      "text": "然后呢",
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      "text": "你就可以拿它去跑通",
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      "text": "去交付你各种各样的成果",
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      "text": "那这个时候我们可以看到",
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    {
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      "text": "大家前面的演示上呢",
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    {
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      "text": "我们会",
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    {
      "start": 4157.024,
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      "text": "因为是带给大家演示嘛",
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    {
      "start": 4158.524,
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      "text": "所以我更多时候是以一句话",
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    {
      "start": 4160.324,
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      "text": "去完成我们缠除结果为主",
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    {
      "start": 4162.384,
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      "text": "但大家在实际的操作过程之中呢",
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      "text": "和叠单影的内容的",
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      "start": 4168.624,
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      "text": "在和workbody不断去提出你的需求",
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      "text": "提出你改进你的同时",
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      "text": "你的这个内容也会越来越好",
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      "text": "那在最后就是把你的产物进行一个回存",
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      "text": "这样形成了一个完整的闭环",
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      "text": "否则你的这个链路就还是断掉的",
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      "text": "好",
      "chunk": 8,
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      "text": "那初次掌视大家可以在直播之后",
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      "start": 4188.104,
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      "text": "立刻去做三件事情",
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      "text": "第一个呢就是建立你的第一个知识库",
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      "text": "无论是PC端还是说在MR",
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      "start": 4194.644,
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      "text": "可能整个耗时也就15分钟左右非常快",
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      "text": "那第二个呢",
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      "text": "就是选取一个任务",
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      "text": "你去跑一下我们的五步闭环",
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      "text": "按照选目标",
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      "text": "然后存资料",
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      "start": 4205.204,
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      "text": "定积木",
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      "text": "做交付",
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      "text": "然后回存去做一遍",
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    {
      "start": 4209.724,
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      "text": "可以解决你实际工作痛点的成果",
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      "text": "再回存给Emma",
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      "text": "形成一个完整的闭环",
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      "text": "并且把我们这套逻辑",
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      "text": "也可以分享给你身边的更多人",
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      "start": 4220.384,
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      "text": "让大家一起用起来",
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      "text": "对",
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      "text": "帮助别人也帮",
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      "start": 4226.764,
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      "text": "我们这套好用的技能",
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      "text": "好",
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      "text": "那基本上我们今天这个实操的流程",
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      "text": "好的",
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      "start": 4232.964,
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      "text": "非常感谢Gary",
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      "text": "这非常干货满满的讲解",
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      "text": "听完这一套逻辑",
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      "text": "我们最深的一个感受就是",
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      "text": "AI真正的价值",
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      "start": 4242.244,
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      "text": "其实不在于它多会回答",
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      "text": "而是说它能不能记住你",
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      "start": 4245.824,
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      "text": "并且帮你把这些事情做完",
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      "text": "而这些恰恰就是需要",
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      "text": "埃马和workbody这样的配合",
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    {
      "start": 4251.364,
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      "text": "一个就是负责把它记住",
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    {
      "start": 4253.144,
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      "text": "一个就是负责基于这个知识以上",
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      "start": 4256.224,
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      "text": "然后把它做成",
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    {
      "start": 4257.184,
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      "text": "那么接下来呢",
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    {
      "start": 4258.404,
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      "text": "我们来进行一个",
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    {
      "start": 4259.544,
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      "text": "大家期待的Q&A环节",
      "chunk": 8,
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    {
      "start": 4261.384,
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      "text": "大家可以在评论区",
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    {
      "start": 4263.984,
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      "text": "打出自己的一些疑问",
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    {
      "start": 4265.404,
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      "text": "相信屏幕前的朋友们",
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    {
      "start": 4266.984,
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      "text": "此刻一定攒了不少问题",
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    {
      "start": 4268.884,
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      "text": "所以我们来请Gary老师",
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    {
      "start": 4271.004,
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      "text": "一一来解答一下",
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    {
      "start": 4272.364,
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      "text": "好",
      "chunk": 8,
      "language": "zh",
      "avg_logprob": -0.15480765174416936,
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      "compression_ratio": 1.255639097744361
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    {
      "start": 4273.964,
      "end": 4277.124,
      "text": "那在我们等待评论区的过程中",
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    {
      "start": 4277.124,
      "end": 4281.184,
      "text": "我先来代表观众问几个问题吧",
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    {
      "start": 4281.184,
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      "text": "好",
      "chunk": 8,
      "language": "zh",
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    {
      "start": 4281.604,
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      "text": "首先就是",
      "chunk": 8,
      "language": "zh",
      "avg_logprob": -0.15480765174416936,
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      "start": 4285.144,
      "end": 4287.724,
      "text": "有些观众说",
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    {
      "start": 4287.724,
      "end": 4291.324,
      "text": "不知道怎么找到埃玛",
      "chunk": 8,
      "language": "zh",
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      "no_speech_prob": 2.1787601237055476e-11,
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    {
      "start": 4291.324,
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      "text": "我们可以再演示一遍",
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    {
      "start": 4292.804,
      "end": 4295.964,
      "text": "在文化里面如何找到埃玛知识库",
      "chunk": 8,
      "language": "zh",
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      "no_speech_prob": 2.1787601237055476e-11,
      "compression_ratio": 1.4053156146179402
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    {
      "start": 4295.964,
      "end": 4298.144,
      "text": "给大家来更加清晰的",
      "chunk": 8,
      "language": "zh",
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    {
      "start": 4298.144,
      "end": 4300.124,
      "text": "完整的来演示一遍",
      "chunk": 8,
      "language": "zh",
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      "start": 4300.124,
      "end": 4302.244,
      "text": "我带大家再走一遍这个流程吧",
      "chunk": 8,
      "language": "zh",
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      "no_speech_prob": 2.1787601237055476e-11,
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    {
      "start": 4302.244,
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      "text": "大家可以看到我们左边",
      "chunk": 8,
      "language": "zh",
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      "no_speech_prob": 2.1787601237055476e-11,
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    {
      "start": 4304.064,
      "end": 4305.004,
      "text": "有这一个tab",
      "chunk": 8,
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    {
      "start": 4305.004,
      "end": 4306.364,
      "text": "如果有些同学找不到",
      "chunk": 8,
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    {
      "start": 4306.364,
      "end": 4307.864,
      "text": "可能是因为这里被隐藏了",
      "chunk": 8,
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    {
      "start": 4307.864,
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      "text": "就是收起的话",
      "chunk": 8,
      "language": "zh",
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      "compression_ratio": 1.4053156146179402
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      "start": 4308.884,
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      "text": "你这里是看不到的",
      "chunk": 8,
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      "start": 4309.904,
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      "text": "所以你需要先把它展开",
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      "start": 4311.804,
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      "text": "展开你的侧边栏",
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    {
      "start": 4313.044,
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      "text": "然后找到你的更多",
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    {
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      "text": "里面有我们的MAR知识库",
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      "text": "去点击它",
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      "no_speech_prob": 1.683337150226727e-11,
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      "start": 4317.924,
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      "text": "然后这样就可以进到我们MAR知识库里面",
      "chunk": 8,
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      "compression_ratio": 1.5224913494809689
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      "start": 4320.804,
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      "text": "那大家再看一下我们这个要怎么绑定",
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      "text": "如果你是第一次进入MAR知识库的话",
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    {
      "start": 4327.004,
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      "text": "会出现的是这样一个界面",
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      "text": "你需要去点击授权",
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      "text": "然后授权之后呢",
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      "start": 4332.144,
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      "text": "它可以扫码",
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      "text": "然后如果你的PC上",
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    {
      "start": 4334.524,
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      "text": "就是你的电脑上",
      "chunk": 8,
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    {
      "start": 4335.364,
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      "text": "也登陆你的微信的话",
      "chunk": 8,
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      "start": 4336.464,
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      "text": "是可以直接快捷登录的",
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    {
      "start": 4337.964,
      "end": 4339.644,
      "text": "一键登录允许",
      "chunk": 8,
      "language": "zh",
      "avg_logprob": -0.13251668377652195,
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    {
      "start": 4339.644,
      "end": 4342.644,
      "text": "允许之后呢",
      "chunk": 8,
      "language": "zh",
      "avg_logprob": -0.13251668377652195,
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      "compression_ratio": 1.5224913494809689
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    {
      "start": 4342.644,
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      "text": "去同意",
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      "language": "zh",
      "avg_logprob": -0.13251668377652195,
      "no_speech_prob": 1.683337150226727e-11,
      "compression_ratio": 1.5224913494809689
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    {
      "start": 4343.404,
      "end": 4344.984,
      "text": "然后确认授权",
      "chunk": 8,
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      "compression_ratio": 1.5224913494809689
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      "start": 4344.984,
      "end": 4345.984,
      "text": "好",
      "chunk": 8,
      "language": "zh",
      "avg_logprob": -1.5405287742614746,
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    {
      "start": 4346.581,
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      "text": "那這樣就可以在裡面看到你的艾瑪知識庫的內容了。",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.4759488717103616,
      "no_speech_prob": 1.1990960308017051e-11,
      "compression_ratio": 1.1865284974093264
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    {
      "start": 4350.581,
      "end": 4353.581,
      "text": "那這就是我們第一個問題。看一下還有別的問題嗎?",
      "chunk": 9,
      "language": "zh",
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      "no_speech_prob": 1.1990960308017051e-11,
      "compression_ratio": 1.1865284974093264
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    {
      "start": 4353.581,
      "end": 4355.901,
      "text": "还有就是",
      "chunk": 9,
      "language": "zh",
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      "compression_ratio": 1.7076923076923076
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    {
      "start": 4355.901,
      "end": 4359.941,
      "text": "知识库可以共建吗",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.1160393325534798,
      "no_speech_prob": 2.0789982582702926e-11,
      "compression_ratio": 1.7076923076923076
    },
    {
      "start": 4359.941,
      "end": 4361.821,
      "text": "可以多人共同使用吗",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.1160393325534798,
      "no_speech_prob": 2.0789982582702926e-11,
      "compression_ratio": 1.7076923076923076
    },
    {
      "start": 4361.821,
      "end": 4362.441,
      "text": "可以的",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.1160393325534798,
      "no_speech_prob": 2.0789982582702926e-11,
      "compression_ratio": 1.7076923076923076
    },
    {
      "start": 4362.441,
      "end": 4364.361,
      "text": "这个地方我们也给大家重点介绍一下吧",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.1160393325534798,
      "no_speech_prob": 2.0789982582702926e-11,
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    },
    {
      "start": 4364.361,
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      "text": "我们可以看到这个知识库这里有三种不同的类型",
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    },
    {
      "start": 4367.761,
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      "text": "第一种呢是个人知识库",
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      "compression_ratio": 1.7076923076923076
    },
    {
      "start": 4369.601,
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      "text": "第二种呢是共享知识库",
      "chunk": 9,
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    {
      "start": 4371.261,
      "end": 4372.601,
      "text": "第三种是订阅知识库",
      "chunk": 9,
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      "no_speech_prob": 2.0789982582702926e-11,
      "compression_ratio": 1.7076923076923076
    },
    {
      "start": 4372.601,
      "end": 4375.641,
      "text": "那个人知识库呢就是支持你自己看的",
      "chunk": 9,
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      "avg_logprob": -0.1160393325534798,
      "no_speech_prob": 2.0789982582702926e-11,
      "compression_ratio": 1.7076923076923076
    },
    {
      "start": 4375.641,
      "end": 4376.881,
      "text": "那共享知识库",
      "chunk": 9,
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      "no_speech_prob": 2.0789982582702926e-11,
      "compression_ratio": 1.7076923076923076
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    {
      "start": 4376.881,
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      "text": "就是可以给分享给别人看的",
      "chunk": 9,
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      "start": 4379.821,
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      "text": "比如说这里我们有一个从",
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      "compression_ratio": 1.7076923076923076
    },
    {
      "start": 4381.481,
      "end": 4383.181,
      "text": "欢迎大家订阅这个知识库哈",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.1160393325534798,
      "no_speech_prob": 2.0789982582702926e-11,
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    },
    {
      "start": 4383.181,
      "end": 4384.621,
      "text": "从零到一用Workbody",
      "chunk": 9,
      "language": "zh",
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      "compression_ratio": 1.490566037735849
    },
    {
      "start": 4384.621,
      "end": 4385.801,
      "text": "搭建艾玛知识库",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.11717250927074535,
      "no_speech_prob": 2.1115045475972316e-11,
      "compression_ratio": 1.490566037735849
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    {
      "start": 4385.801,
      "end": 4387.621,
      "text": "这里就是可以分享给别人看到",
      "chunk": 9,
      "language": "zh",
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    {
      "start": 4387.621,
      "end": 4387.941,
      "text": "是的",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.11717250927074535,
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    {
      "start": 4387.941,
      "end": 4389.701,
      "text": "那你说",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.11717250927074535,
      "no_speech_prob": 2.1115045475972316e-11,
      "compression_ratio": 1.490566037735849
    },
    {
      "start": 4389.701,
      "end": 4392.721,
      "text": "那大家也可以今天关注我们的",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.11717250927074535,
      "no_speech_prob": 2.1115045475972316e-11,
      "compression_ratio": 1.490566037735849
    },
    {
      "start": 4392.721,
      "end": 4394.541,
      "text": "Workbody公众号",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.11717250927074535,
      "no_speech_prob": 2.1115045475972316e-11,
      "compression_ratio": 1.490566037735849
    },
    {
      "start": 4394.541,
      "end": 4396.661,
      "text": "回复艾玛领取这一套资料",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.11717250927074535,
      "no_speech_prob": 2.1115045475972316e-11,
      "compression_ratio": 1.490566037735849
    },
    {
      "start": 4396.661,
      "end": 4398.481,
      "text": "也可以关注艾玛的公众号",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.11717250927074535,
      "no_speech_prob": 2.1115045475972316e-11,
      "compression_ratio": 1.490566037735849
    },
    {
      "start": 4398.481,
      "end": 4399.641,
      "text": "然后回复Workbody",
      "chunk": 9,
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      "no_speech_prob": 2.1115045475972316e-11,
      "compression_ratio": 1.490566037735849
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    {
      "start": 4399.641,
      "end": 4401.901,
      "text": "领取我们今天直播的回放",
      "chunk": 9,
      "language": "zh",
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      "no_speech_prob": 2.1115045475972316e-11,
      "compression_ratio": 1.490566037735849
    },
    {
      "start": 4401.901,
      "end": 4403.341,
      "text": "以及今天的课件资料",
      "chunk": 9,
      "language": "zh",
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      "text": "以及我们的知识库",
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      "text": "对包括你在创建共享知识库的时候",
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      "text": "也可以注意一下下面的加入方式",
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    {
      "start": 4408.741,
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      "text": "和成员权限",
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      "text": "可以选择直接加入",
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      "text": "或者如果说你这个",
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    {
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      "text": "只是想要一部分人看到",
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      "text": "你可以选择需要管理员去确认",
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      "text": "然后也可以去设置权源呈现",
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      "text": "这样可以看",
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      "text": "它是能查看不能查看",
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      "text": "和能否导出",
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      "text": "都是可以去供你自己去设置的",
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      "text": "包括如果你有一些基础的",
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      "text": "这种知识性的东西",
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      "text": "你不想自己往里面导入内容",
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      "text": "你也可以去订阅这个订阅知识库",
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    {
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      "text": "也就是来到我们的这个发现里面",
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      "text": "去选取这样的知识库",
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      "text": "然后你可以比如说随便点一个哈",
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      "text": "就点击订阅就OK了",
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      "text": "还有就是",
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      "text": "如果已经用IMA知识库来进行存资料",
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      "text": "并且存电的话",
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      "text": "那么本地的工作空间还需要存资料吗",
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      "text": "这个意思是指",
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      "text": "就是我",
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      "text": "是这样的",
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      "text": "就是你本地",
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      "text": "比如说这样一个工作空间里",
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      "text": "你存的资料是默认在你这个文件夹里面的",
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      "text": "那它的这个",
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      "text": "或者就是不太好找",
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      "text": "但是你的这个",
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      "text": "比如说这个",
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      "text": "它是会一直在这里的",
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      "start": 4471.061,
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      "text": "它不存在说你能选择",
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      "text": "就是这样一个情况",
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      "text": "那Emma的它的这个好处就是",
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      "text": "你是可以在云端去看到的",
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    {
      "start": 4479.781,
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      "text": "比如说就像我们刚刚讲的",
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      "text": "你现在在PC端里存的",
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      "start": 4482.461,
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      "text": "你可以在你的手机小程序里去打开",
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    {
      "start": 4485.101,
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      "text": "所以我理解一下这个问题",
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      "text": "就是首先它不是本地空间",
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    {
      "start": 4489.221,
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      "text": "用不用存资料的这样一个选项问题",
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    {
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      "text": "它是会存在这里的",
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    {
      "start": 4493.561,
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      "text": "除非你把它删掉在空间里",
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    {
      "start": 4495.901,
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      "text": "但是我们还是建议你存到Emma这样一份",
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    {
      "start": 4498.781,
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      "text": "因为这样你后面在workbody中",
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    {
      "start": 4500.841,
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      "text": "跨工作空间",
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    {
      "start": 4501.961,
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      "text": "或者跨任务的去调用是更方便的",
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    {
      "start": 4504.421,
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      "text": "同时你也可以去在微信的小程序上去使用",
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    {
      "start": 4507.681,
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      "text": "能够多端的去进行一个同步",
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    {
      "start": 4510.321,
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      "text": "是的",
      "chunk": 9,
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    {
      "start": 4510.641,
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      "text": "还有其他的问题吗",
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    {
      "start": 4513.801,
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      "text": "观众朋友",
      "chunk": 9,
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    {
      "start": 4514.281,
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      "text": "我这边看不太到他们的提问",
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    {
      "start": 4516.121,
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      "text": "好",
      "chunk": 9,
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    {
      "start": 4516.641,
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      "text": "我看看",
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    {
      "start": 4517.201,
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      "text": "还有很多朋友在问",
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    {
      "start": 4519.161,
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      "text": "回放和资料在哪里看",
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    {
      "start": 4520.581,
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      "text": "我再来说一下",
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    {
      "start": 4521.741,
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      "text": "就是大家可以关注workbody的公众号",
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    {
      "start": 4524.721,
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      "text": "然后在后台回复",
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      "text": "也可以关注我们的iMA公众号",
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      "text": "然后在iMA的后台回复workbody",
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    {
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      "text": "也可以获得今天直播的回放以及资料",
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      "start": 4537.121,
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      "text": "欢迎大家来关注",
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      "start": 4538.821,
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      "text": "并且使用我们的workbody以及iMA小程序",
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      "start": 4542.801,
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      "text": "那我们再来回复最后一个问题",
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      "start": 4546.221,
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      "text": "就是自动化任务中的那个连接器选项",
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      "text": "是用来干什么的",
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      "text": "是这样的",
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      "text": "我们先给大家再看一下连接器在哪里",
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      "text": "我们左边这个侧边展开之后呢",
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      "text": "是有我们丰富的专家技能和连接器的",
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      "text": "连接器呢",
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      "text": "就是可以列成MCP这样一个东西",
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      "text": "那它就是我们内置好了一些和其他软件",
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    {
      "start": 4568.041,
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      "text": "在AI层面上的这样一个数据的调用和交互",
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      "text": "内置到了我们Workbody里面",
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    {
      "start": 4573.201,
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      "text": "这样你就可以直接去使用它",
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      "text": "那这个连接器和我们自动化这种连接器是一样的",
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      "text": "比如说你如果想",
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      "text": "假设现在你的需求是",
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      "text": "我现在每天定时整理邮件",
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      "text": "然后我的想要它每天早上",
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    {
      "start": 4593.701,
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      "text": "每天给我整理QQ邮箱中的最新20条邮件",
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    {
      "start": 4601.861,
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      "text": "形成报告",
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    {
      "start": 4604.741,
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      "text": "形成报告以MD文档形式",
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      "text": "交付",
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      "text": "那这个地方呢",
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      "text": "如果你不去给他QQ邮箱的权限",
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    {
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      "text": "他是没有办法读到你的邮件的",
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    {
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      "text": "因为他没有获得你的授权",
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      "text": "那你就需要在这个地方去选一个连接器",
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      "text": "首先你可以在这个地方",
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      "text": "你要把它QQ邮箱的去连接上",
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    {
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      "text": "那由于这里这个不是很方便扫码",
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    {
      "start": 4630.401,
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      "text": "我就不给大家演示了",
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      "text": "但你连接上之后呢",
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    {
      "start": 4632.781,
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      "text": "他在自动化任务之中",
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    {
      "start": 4634.481,
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      "text": "才能得到你给他的授权",
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    {
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      "text": "不然他就没有办法去读到你的20条邮箱",
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    {
      "start": 4639.361,
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      "text": "就是你试想一下",
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    {
      "start": 4640.341,
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      "text": "他随时随地能读到你的邮箱多恐怖啊",
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    {
      "start": 4642.681,
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      "text": "对不对",
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    {
      "start": 4643.001,
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      "text": "所以你要给它这样一个连接器的授权",
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    {
      "start": 4645.561,
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      "text": "它才能够去完成你的这个自动化任务",
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    {
      "start": 4647.641,
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      "text": "那这个地方选择什么样的连接器",
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    {
      "start": 4650.201,
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      "text": "就取决于你是要它做一个什么样的任务",
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    {
      "start": 4652.521,
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      "text": "相当于说你现在要用可可邮箱",
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      "text": "你就要给它安上可可邮箱这个手",
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    {
      "start": 4656.761,
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      "text": "比如说你要它读取艾玛资料库的内容给你铲除",
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      "start": 4659.921,
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      "text": "你就需要给它安上艾玛资料库这个手",
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    {
      "start": 4662.061,
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      "text": "这就是我们这里连接器的意义",
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    {
      "start": 4664.301,
      "end": 4666.221,
      "text": "非常明白了",
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    {
      "start": 4666.221,
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      "text": "那最后一个问题吧",
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    {
      "start": 4668.061,
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      "text": "公众号文章可以导入艾玛吗",
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    {
      "start": 4670.461,
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      "text": "是可以的",
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    {
      "start": 4671.301,
      "end": 4673.201,
      "text": "好那这里给大家演示一下",
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      "compression_ratio": 1.5249169435215948
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    {
      "start": 4673.201,
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      "text": "公众号文章导入艾玛的形式之后",
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      "start": 4677.541,
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      "text": "比如说我们这里点击上传文件啊",
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      "start": 4679.441,
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      "text": "你可以选择网页链接",
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      "start": 4681.081,
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      "text": "那这里你看是支持公众号",
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      "text": "新闻文章等公开网页链接的",
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    {
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      "text": "应该国际的小程序",
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      "text": "就是你在阅读公众号的时候",
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      "text": "对没有问题",
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      "text": "包括这个地方也可以给大家看一下",
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      "text": "我们有没有这个",
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      "text": "我们也是有公众号这样的",
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      "text": "就是我们之前拿网页链接存在的公众号",
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      "text": "它也会在这里显示是一个公众号的形式",
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    {
      "start": 4711.881,
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      "text": "所以微信公众号文章是指示导入Emma的",
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      "text": "好的",
      "chunk": 9,
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    {
      "start": 4715.661,
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      "text": "非常感谢Gary的解答",
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      "text": "那其实今天这一场直播聊下来呢",
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      "text": "别再把AI当作一个用完就走的问答工具",
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    {
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      "text": "如果你觉得今天的东西",
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      "text": "讲的东西有点多",
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      "text": "然后一时无从下手的话",
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    {
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      "text": "完全不用担心",
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      "text": "今晚看完直播",
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    {
      "start": 4744.881,
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      "text": "大家可以打开挨码",
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    {
      "start": 4746.661,
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      "text": "然后选一件最近你正要完成的事情",
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    {
      "start": 4749.501,
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      "text": "或者你觉得很烦",
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    {
      "start": 4750.661,
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      "text": "要重复完成的事情",
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    {
      "start": 4752.021,
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      "text": "然后放进你的相关的一些资料",
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      "text": "然后让Workbody帮你去交付一个",
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    {
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      "text": "直接使用的成果",
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    {
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      "text": "去完成一个闭环",
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    {
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      "text": "你大概率就可以感受到那种",
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      "text": "就是AI终于能",
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    {
      "start": 4763.861,
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      "text": "资料终于能提我开始干活的感觉了",
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    {
      "start": 4766.281,
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      "text": "跨出这一步",
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    {
      "start": 4767.201,
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      "text": "你就会发现",
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      "text": "其实你这些工作中啊",
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    {
      "start": 4769.901,
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      "text": "学举中沉淀下来的资料和机面",
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    {
      "start": 4771.981,
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      "text": "真的可以变成",
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    {
      "start": 4773.201,
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      "text": "每一天为你去所用的生产力",
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    {
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      "text": "然后最后也感谢直播间",
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    {
      "start": 4778.341,
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      "text": "各位关注Workbody和艾玛的朋友们",
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    {
      "start": 4780.801,
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      "text": "大家可以看那个小程序的扫码",
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      "compression_ratio": 1.256965944272446
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    {
      "start": 4784.901,
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      "text": "去体验我们的小程序",
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    {
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      "text": "然后并且关注我们workbody",
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    {
      "start": 4788.981,
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      "text": "和艾玛的公众号视频号",
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      "no_speech_prob": 1.690235104656601e-11,
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    {
      "start": 4791.761,
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      "text": "来收看我们直播的回放",
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      "no_speech_prob": 1.690235104656601e-11,
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    {
      "start": 4793.681,
      "end": 4797.841,
      "text": "然后给大家预告我们下一期的直播",
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      "no_speech_prob": 1.690235104656601e-11,
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    {
      "start": 4797.841,
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      "text": "每周四晚上我们都会固定",
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    {
      "start": 4799.841,
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      "text": "在workbody直播间进行直播",
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      "compression_ratio": 1.4828897338403042
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    {
      "start": 4801.701,
      "end": 4803.821,
      "text": "也欢迎大家跟我们一起来交流",
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      "no_speech_prob": 1.690235104656601e-11,
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    {
      "start": 4803.821,
      "end": 4804.641,
      "text": "欢迎Gary",
      "chunk": 9,
      "language": "zh",
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      "no_speech_prob": 1.690235104656601e-11,
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    {
      "start": 4804.641,
      "end": 4807.101,
      "text": "再次欢迎Gary来到我们的直播间",
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    {
      "start": 4807.101,
      "end": 4810.261,
      "text": "也感谢每一位在直播间陪伴我们的伙伴们",
      "chunk": 9,
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      "avg_logprob": -0.12051378356085883,
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      "compression_ratio": 1.4828897338403042
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    {
      "start": 4810.261,
      "end": 4811.981,
      "text": "我们下一期直播再见",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.12051378356085883,
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      "compression_ratio": 1.4828897338403042
    },
    {
      "start": 4811.981,
      "end": 4812.681,
      "text": "再见",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.12051378356085883,
      "no_speech_prob": 1.690235104656601e-11,
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    {
      "start": 4812.681,
      "end": 4813.561,
      "text": "谢谢大家",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.12051378356085883,
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      "compression_ratio": 1.4828897338403042
    },
    {
      "start": 4816.481,
      "end": 4846.461,
      "text": "请不吝点赞 订阅 转发 打赏支持明镜与点点栏目",
      "chunk": 9,
      "language": "zh",
      "avg_logprob": -0.12484338704277487,
      "no_speech_prob": 1.3341956706103275e-10,
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