{
  "text": "本集節目由VOAI絕好生創技術支援。VOAI提供富有情緒的高擬真台灣口音AI聲音,並提供上傳圖片與音檔一鍵生成虛擬人的功能,是最具台灣味的AI平台。\n使用優惠碼 AI-LRB26。\n吉祥VOAI網站以及API方案95折優惠。\n大家聽到的這集Podcast是透過VOAI API生成的哦。\n更多資訊請下資訊欄。\n每天都有一堆新的AI工具冒出来\n是不是常常不知道该从哪里开始\n别担心\n这里是AI懒人报\n每天帮你精选五个最多人讨论的AI工具影片\n每天十分钟\n让AI走进你的生活中\n让你不知不觉成为效率大师\n今天这个工具最有趣的地方是\n它可能让你再也不用半夜起来\n修坏掉的Jason\n这对我们这些每天跟AI流程打交道的人来说\n简直是福音\n好,那我们刚开始吧\n先从一个很多人都有过的痛苦经验说起\n你花了好几个小时设计一个自动化流程\n测试的时候跑得很完美\n你觉得这次终于搞定了\n然后你把它排成\n让它每天早上自动跑\n第一天没问题\n第二天没问题\n第三天早上你打开电脑\n一堆错误\n为什么?\n因为AI今天心情好\n把一个应该是数字的栏位输出成字串了\n或者他觉得自己很聪明\n在Jason里多加了一个他觉得有用的栏位\n结果你的后端完全吃不下去\n所以你不是去改流程\n你是去写验证逻辑\n写从事机制\n写各种备缘处理\n花了整个周末\n结果整个系统变得比手动作还复杂\n说真的\n我觉得这是用AI做自动化\n最让人崩溃的地方\n你明明是为了省时间\n结果花更多时间在管理AI的输出\n这个问题的根源在哪\n在于现在主流的大型原模型\n包括Chattgpt Cloud\n还有最近很红的Astra跟Fable\n他們的設計邏輯都是預測下一個Token\n就算你在Prompt里寫得再清楚\n請只輸出JSON\n他還是有一定機率會偷偷加一個Markdown的反引號\n或者換行多一個空格\n或者把Tru寫成加引號的Tru\n這並不是模型笨\n這其實是他的架構\n本質上就是機率性的\n沒辦法百分之百保證格式\n就像你請一個很會寫文章的朋友幫你填表格\n他可能會寫得很精彩\n但格式就不一定每次都對了\nJerby就是衝著這個問題來的\nJerby是Type Safe AI這家公司開發的\n他們把它歸類為System 1 Model\n跟我們熟悉的大型原模型是完全不同的東西\n他不是用來聊天的\n也不是用來寫文章或解釋概念的\n他只做一件事看你給他的資料\n對照你定義的schema\n輸出一個結構完全正確的決策結果\n你可以把它想像成一個非常嚴謹的表格填寫圓\n你給他什麼格式\n他就保證給你什麼格式\n这里最关键的技术概念叫做\nConstraint Decoding\n也就是在推理层面就做好限制\n一般的模型\n在生成每一个Token的时候\n理论上可以选任何字\n但Jav的做法是\n你传给他一个Schema\n你可以先把它想成资料格式规则\n他在生成每个Token的时候\n会直接把所有金会违反这个规则的选项\n从词汇表里遮掉\n如果这个栏位的形别是不灵质\n那他在那个位置能选的词就只有\n2跟false\n其他字根本不在选项里\n这并不是说他很努力不出错\n这是他在架构上就不可能出错\n这两件事的差别非常大\n前面那种你还是需要验证\n后面这种你可以直接信任输出\n就像你相信一个计算机算出来的结果一样\nTypeSafe AI把这个特性叫做schema safe\n意思就是输出永远符合你定义的行别结构\n即对开发者来说\n这意味着你可以把原本那些验证成\n从事逻辑\n得适修正的程式码\n大部分都砍掉\n你定义好行别\n交给Jeff\n就会拿到一个结构完全正确的结果\n每次都是\n这就像你请了一个永远不会犯错的秘书\n他交给你的文件永远都是格式正确的\n你完全不用再检查一遍\nJeff提供三个主要的功能界面\n第一个叫Choice\n你给他一个输入\n再给他一个选项清单\n他直接回传最符合的那个选项\n最多可以有255个选项\n这是像一个超级精准的选择题机器\n你问他问题\n他马上给你正确答案\n第二个叫score\n他会对输入打一个分数\n同时附上confidence\n也就是信心分数\n让你知道他对这个分数有多确定\n这就像一个很专业的评论员\n不只给分数\n还会告诉你\n他打这个分数的把握有多大\n第三个叫no-o\n这个比较特别\n他会输出一个0到1之间的几率值\n代表某件事发生的可能性\n而且這個機率是經過較準的\n不是隨便說說的\n意思是如果NO說退款機率是0.8\n就代表10封類似的信裡\n大概有8封真的會是退款需求\n這個數字是可靠的\n這三個功能合在一起\n你就可以做很多事了\n比如說你有一個客服系統\n每天收到幾千封信\n你想要自動分類\n判斷優先順序\n偵測有沒有退款需求\n用傳統的大型語言模型\n你要寫很長的prompt\n還要解析它輸出的文字\n然後還要擔心它今天心情不好\n输出格式跑掉\n用GF\n你就把信的内容传进去\n定义好schema\n他直接给你一个Json\n类别是什么\n优先顺序\n几分\n是不是退款请求\n信心分数多少\n全部结构完整\n直接可以用\n这就像你把一大堆文件\n丢给一个超级有效率的助理\n他马上就能把所有资讯整理好\n分门别类\n让你一眼就能看懂\n社群里有人做了一个测试\n一次处理1000封信\n平均回应时间大约是200毫秒\n整批跑完费用是多少\n非常少\n因为JF的定价非常低\n输入是每百万Token四分美金\n输出是免费的\n这个价格跟主流的大型原模型\n比起来差距非常大\n简直是佛星价\n说到速度\nTypeSafe AI官方的说法是\nJF可以比传统Transformer模型\n快20到200倍\n便宜40到400倍\n我知道这种数字看起来像行销化数\n所以要说清楚\n这个速度优势\n是在特定的结构化决策任务上\n而不是在所有任务上\n你不能拿Jeff来写一篇文章\n他根本不会\n但如果你的任务是\n看这段资料\n告诉我他属于哪个类别\n那这个速度差距是真实存在的\n这就像你请一个\n短跑选手去跑马拉松\n他可能跑不完\n但如果你让他去跑100公尺\n他觉得是世界级的\n为什么Jeff可以这么快\n因为他根本不需要\n做一般语言模型做的那一事\n他不需要生成自然语言\n不需要维护复杂的上下文\n也不需要做什么深度推理\n他的工作就是看schema\n看输入输出\n一个符合schema的决策\n整个推理路径短很多\n所以快很多\n这就像一个专注于单一任务的机器人\n他只做他被设计来做的事\n所以效率极高\n这个速度差距在某些使用情境上\n会让整件事从可以用变成真正好用\n最明显的例子是浏览器自动化\n现在有很多工具\n可以让AI控制你的浏览器\n你给他一个目标\n他自己去点页面\n填资料\n跳到下一页\n但如果你真的试过用\nAstra或Fable做这件事\n你会发现他慢得很痛苦\nAgent\n结一张网页的截图\n传给模型\n然后你等2秒\n3秒\n有时候5秒\n模型才决定要点哪里\n这段时间里\n网页可能已经更新了\n或者跳出一个弹窗\n然后Agent的动作就失效了\n这就像你请一个动作很慢的人\n帮你抢限量商品\n等到他反应过来\n商品早就被抢光了\n这就是为什么浏览器自动化到现在\n还是很难真正稳定部署的原因之一\n延迟太高\n反映跟不上动态网页的节奏\n但如果你把决策层换成JF\n延迟从秒级将到毫秒级\nAgent看到DOM结构\n马上对照Schema\n马上输出下一个动作\n他不需要思考\n他就是执行\n这样的速度才能让浏览器自动化\n从一个很酷的示范\n变成一个可以真正跑在生产环境的东西\n这就像你给机器人装上超快速反应晶片\n它能瞬间判断并执行任务\n让整个流程顺畅无比\n好,現在來到我覺得最有意思的部分,也就是第二個重點。\nJeff的定位很特別,他並不是要取代Astra或Fable,他其實是要跟他們一起用。\n你可以把它想像成一個團隊裡的兩個角色,一個負責策略,一個負責執行。\n我把這個架構叫做雙腦架構\n概念很直觀\n你用大型語言模型做需要深度推理的事\n用Jav做需要快速執行的事\n舉個具體的例子\n假設你在建一個自動化的軟體開發agent\n他可以看需求\n寫程式\n跑測試\n修bug\n當一個新的功能需求進來的時候\n你先把它交給Fable或Astra\n這些模型有深度的推理能力\n它們可以分析整個專案程式碼\n理解依賴關係\n想清楚要怎麼實作\n然后产出一份详细的执行计划\n就是慢的贵的但值得\n因为这个阶段真的需要深度思考\n这就像一个公司的策略长\n负责制定整体方向和计划\n计划出来之后\nFable退场\nJeff接手\nJeff比较适合负责\n判断下一步要做什么\n输出固定格式的指令\n如果测试失败了\nJeff不需要重新思考整个架构\n他只需要看错误讯息\n对照行别定义\n修掉那个行别不符的地方\n再跑一次几毫秒就完成\n真正写大段程式码还是交给Fable或Extra\n这就像公司的执行长\n负责把策略长的计划\n分解成一个个具体任务\n并快速高效的执行\n你把昂贵的推理集中在规划阶段\n把执行交给一个高速低成本\n格式保证正确的引擎\n这样的组合才能让多A-系统真正规模化\n以前这种架构的问题是成本和延迟\n如果一个复杂的任务需要50个为决策\n每个决策要2秒花3分美金\n整个流程就要花1分多钟\n1块多美金单次执行\n这样的单位经济学根本没办法做自动化\n因为跑几次就亏了\n但Jeff把每个为决策的成本和延迟\n都压到几乎可以忽略的程度\n这个架构才真正变得可行\n这就像以前你需要请好几个顾问来做决策\n现在你只需要请一个策略顾问\n然后把执行交给一个超便宜又超快的机器人\n那实际上怎么把JAB接近你现有的流程\n有几个很具体的切入点\n最直接的方式是找出你现有AI流程里最容易坏掉的节点\n通常就是那些让LLM从非结构化的资料里抽取资讯\n然后格式化成某个固定格式的步骤\n比如说从一封信里抓出客户名称\n订单编号\n问题类别\n然后写进CIM\n这种步骤犯成Jav是最直接的\n你定义好schema让Jav做抽取\n格式问题直接消失\n这就像你把一个经常出错的环节\n换成一个永远不会出错的自动化机器人\n瞬间解决了所有问题\n另一个很适合的使用情境是\nvibe coding\n就是你用AI辅助快速开发的那种模式\n现在的vibe coding流程通常是\n让AI生成程式码\n你去跑\n跑出错误\n把错误贴回去给AI\n等它修\n再跑\n这个回圈很慢而且很烦\n因为Jeff速度过快\n对程式语法的理解是原生的\n你可以在IDE里设定一个背景监听器\n每次有编译错误或测试失败\n自动把错误丢给Jeff\n让他在背景直接修掉\n你甚至不需要手动介入\n他已经修好了\n这就像你身边有一个超级程式码修正助手\n在你写程式的时候\n他随时帮你检查并修复错误\n让你专心在创作上\n这个部分说完\n我要讲一个很重要\n但很多人在兴奋的时候会忘掉的是\n速度越快\n出错的代价越高\n当你有一个模型\n可以在毫秒内生成并执行程式码\n你根本来不及在他出错之前阻止他\n如果Jeff在自动化流程里\n有权限存取你的档案系统\n执行终端机指令\n更新资料库\n而他以200倍的速度在跑\n那出了问题的时候\n你看到的已经是结果\n不是过程\n这就像你开一台超级跑车\n速度很快\n但如果失控\n造成的损害也会非常巨大\n所以安全边界必须跟着调整\n几个原则要记住\n第一\n就算是高准确定的模型\nAI生成的程式码\n预设要当成不信任的来处理\n不要因为它很少出错\n就给它最高权限\n第二\n如果你要在本机跑Jab做自动化\nDocker容器把执行环境隔离起来\n给它最小必要的权限\n不要给root\n第三\n如果Jab在做浏览器自动化\n不要用你平常已经登录的浏览器设定档\n用独立的一次性的工作阶段\n这样就算它误判了某个DOM元素\n也不会动到你的生产环境\n这就像你使用任何强大的工具一样\n必须先做好安全防护\n才能避免不必要的风险\nJeff帮你省下了写程式码的时间\n但这些省下来的时间\n你要拿去建更完善的自动化测试和安全验证\n你的角色从写程式的人\n变成设计系统架构和做安全审计的人\n这就像以前你需要自己造车\n现在你只需要设计好车子的功能和安全系统\n然后让机器人去制造\n最后来说说我对Jeff这件事的整体看法\n过去一年多AI产业的主旋律\n是把模型做得越来越大\n越来越聪明\n越来越贵\n每个人都在追AGI\n都在比谁的模型推理能力更强\nExtra跟Fable在这条路上做得很好\n他们真的很厉害\n但真正在用AI做产品的人\n工程师\n独立开发者\n小团队\n我们不是每天都在解历史难题\n我们大部分时间需要的是\n这个资料是不是不领值\n这个请求应该要分派给哪个服务\n这封信的优先顺序\n是高还是低\n这些问题不需要深度推理\n需要的是快\n准\n格式正确\n这就像我们日常生活\n大部分时候我们需要的不是哲学家\n而是一个能快速精准回答问题的工具人\nJeff代表的是AI执行层的工业化\n他承认一件事\n未来的自动化不会是一个超级大脑\n慢慢把所有事情都做完\n而是一堆高度专门化的引擎分工合作\n各自在自己最擅长的部分做到最好\n这就像一个现代化的工厂\n每个环节都有专门的机器人负责\n协同合作\n达到最高的效率\n对那些花了无数个夜晚\n在除错Jason格式问题的人来说\n这个东西出现的时机真的很刚好\n如果你觉得今天的内容让你有点收获\n那就帮我到Apple Podcast按赞追踪\n留个五星好评吧\nFB IG Flats\n收AI懒人报就找到我了\n你的支持是我持续优化\n内容的最大动力\n最近的AI发展依然快的让我害怕\n不想出过明天再继续\n收听AI懒人报啰\n我是汤懒懒\n我们明天见\n拜拜",
  "language": "zh",
  "duration": 918.000938,
  "segments": [
    {
      "start": 0.0,
      "end": 12.2,
      "text": "本集節目由VOAI絕好生創技術支援。VOAI提供富有情緒的高擬真台灣口音AI聲音,並提供上傳圖片與音檔一鍵生成虛擬人的功能,是最具台灣味的AI平台。",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.20544819954114082,
      "no_speech_prob": 1.1222250559383973e-11,
      "compression_ratio": 1.1596091205211727
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    {
      "start": 12.8,
      "end": 15.0,
      "text": "使用優惠碼 AI-LRB26。",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.20544819954114082,
      "no_speech_prob": 1.1222250559383973e-11,
      "compression_ratio": 1.1596091205211727
    },
    {
      "start": 15.0,
      "end": 19.2,
      "text": "吉祥VOAI網站以及API方案95折優惠。",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.20544819954114082,
      "no_speech_prob": 1.1222250559383973e-11,
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    {
      "start": 19.2,
      "end": 23.8,
      "text": "大家聽到的這集Podcast是透過VOAI API生成的哦。",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.20544819954114082,
      "no_speech_prob": 1.1222250559383973e-11,
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    },
    {
      "start": 23.8,
      "end": 25.8,
      "text": "更多資訊請下資訊欄。",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.20544819954114082,
      "no_speech_prob": 1.1222250559383973e-11,
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    },
    {
      "start": 25.8,
      "end": 29.98,
      "text": "每天都有一堆新的AI工具冒出来",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.1089296280962866,
      "no_speech_prob": 2.388018949250892e-11,
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    {
      "start": 29.98,
      "end": 32.6,
      "text": "是不是常常不知道该从哪里开始",
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      "no_speech_prob": 2.388018949250892e-11,
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    {
      "start": 32.6,
      "end": 33.56,
      "text": "别担心",
      "chunk": 0,
      "language": "zh",
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    {
      "start": 33.56,
      "end": 35.3,
      "text": "这里是AI懒人报",
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    {
      "start": 35.3,
      "end": 39.22,
      "text": "每天帮你精选五个最多人讨论的AI工具影片",
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      "start": 39.22,
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      "text": "每天十分钟",
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      "text": "让AI走进你的生活中",
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      "start": 42.68,
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      "text": "让你不知不觉成为效率大师",
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      "text": "今天这个工具最有趣的地方是",
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      "start": 47.28,
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      "text": "它可能让你再也不用半夜起来",
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      "start": 50.26,
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      "text": "修坏掉的Jason",
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      "start": 51.3,
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      "text": "这对我们这些每天跟AI流程打交道的人来说",
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      "text": "简直是福音",
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      "start": 55.48,
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      "text": "好,那我们刚开始吧",
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      "start": 56.84,
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      "text": "先从一个很多人都有过的痛苦经验说起",
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      "start": 60.18,
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      "text": "你花了好几个小时设计一个自动化流程",
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    {
      "start": 63.14,
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      "text": "测试的时候跑得很完美",
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      "start": 64.7,
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      "text": "你觉得这次终于搞定了",
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      "start": 66.44,
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      "text": "然后你把它排成",
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    {
      "start": 67.44,
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      "text": "让它每天早上自动跑",
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      "start": 69.28,
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      "text": "第一天没问题",
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      "start": 70.64,
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      "text": "第二天没问题",
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    {
      "start": 71.4,
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      "text": "第三天早上你打开电脑",
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    {
      "start": 72.9,
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      "text": "一堆错误",
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      "start": 73.92,
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      "text": "为什么?",
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    {
      "start": 74.64,
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      "text": "因为AI今天心情好",
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    {
      "start": 75.78,
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      "text": "把一个应该是数字的栏位输出成字串了",
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      "start": 79.3,
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      "text": "或者他觉得自己很聪明",
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      "start": 81.12,
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      "text": "在Jason里多加了一个他觉得有用的栏位",
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    {
      "start": 83.74,
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      "text": "结果你的后端完全吃不下去",
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      "start": 86.08,
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      "text": "所以你不是去改流程",
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      "start": 87.98,
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      "text": "你是去写验证逻辑",
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      "start": 89.36,
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      "text": "写从事机制",
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      "start": 90.38,
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      "text": "写各种备缘处理",
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    {
      "start": 91.6,
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      "text": "花了整个周末",
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    {
      "start": 92.82,
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      "text": "结果整个系统变得比手动作还复杂",
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      "start": 95.56,
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      "text": "说真的",
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      "text": "我觉得这是用AI做自动化",
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    {
      "start": 98.12,
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      "text": "最让人崩溃的地方",
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    {
      "start": 99.58,
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      "text": "你明明是为了省时间",
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      "start": 100.96,
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      "text": "结果花更多时间在管理AI的输出",
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      "start": 103.5,
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      "text": "这个问题的根源在哪",
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      "start": 105.3,
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      "text": "在于现在主流的大型原模型",
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      "text": "包括Chattgpt Cloud",
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      "start": 110.08,
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      "text": "还有最近很红的Astra跟Fable",
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      "start": 112.94,
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      "text": "他們的設計邏輯都是預測下一個Token",
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      "text": "就算你在Prompt里寫得再清楚",
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      "start": 118.9,
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      "text": "請只輸出JSON",
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      "text": "他還是有一定機率會偷偷加一個Markdown的反引號",
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      "start": 124.04,
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      "text": "或者換行多一個空格",
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      "start": 126.08,
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      "text": "或者把Tru寫成加引號的Tru",
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      "text": "這並不是模型笨",
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      "text": "這其實是他的架構",
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      "start": 131.74,
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      "text": "本質上就是機率性的",
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      "start": 133.36,
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      "text": "沒辦法百分之百保證格式",
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      "start": 135.16,
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      "text": "就像你請一個很會寫文章的朋友幫你填表格",
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      "start": 138.64,
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      "text": "他可能會寫得很精彩",
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      "start": 140.2,
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      "text": "但格式就不一定每次都對了",
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      "start": 142.3,
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      "text": "Jerby就是衝著這個問題來的",
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      "start": 147.64,
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      "text": "他們把它歸類為System 1 Model",
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      "start": 150.24,
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      "text": "跟我們熟悉的大型原模型是完全不同的東西",
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      "start": 153.44,
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      "text": "他不是用來聊天的",
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      "start": 155.24,
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      "text": "也不是用來寫文章或解釋概念的",
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      "start": 158.14,
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      "text": "他只做一件事看你給他的資料",
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      "start": 160.36,
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      "text": "對照你定義的schema",
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      "start": 162.16,
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      "text": "輸出一個結構完全正確的決策結果",
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      "text": "你可以把它想像成一個非常嚴謹的表格填寫圓",
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      "text": "你給他什麼格式",
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      "text": "这里最关键的技术概念叫做",
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      "text": "Constraint Decoding",
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      "text": "也就是在推理层面就做好限制",
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      "text": "一般的模型",
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      "start": 179.2,
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      "text": "在生成每一个Token的时候",
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      "text": "理论上可以选任何字",
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      "text": "但Jav的做法是",
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      "text": "你可以先把它想成资料格式规则",
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      "text": "会直接把所有金会违反这个规则的选项",
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      "text": "从词汇表里遮掉",
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      "text": "如果这个栏位的形别是不灵质",
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      "text": "那他在那个位置能选的词就只有",
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      "text": "2跟false",
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      "text": "其他字根本不在选项里",
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      "text": "这是他在架构上就不可能出错",
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      "text": "这两件事的差别非常大",
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      "text": "前面那种你还是需要验证",
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      "start": 217.8,
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      "text": "就像你相信一个计算机算出来的结果一样",
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      "text": "TypeSafe AI把这个特性叫做schema safe",
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      "text": "意思就是输出永远符合你定义的行别结构",
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      "text": "即对开发者来说",
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      "text": "这意味着你可以把原本那些验证成",
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      "text": "从事逻辑",
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    {
      "start": 232.68,
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      "text": "得适修正的程式码",
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      "text": "大部分都砍掉",
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      "text": "你定义好行别",
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      "text": "交给Jeff",
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      "text": "就会拿到一个结构完全正确的结果",
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      "text": "这就像你请了一个永远不会犯错的秘书",
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      "text": "他交给你的文件永远都是格式正确的",
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      "text": "你完全不用再检查一遍",
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      "text": "Jeff提供三个主要的功能界面",
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      "text": "第一个叫Choice",
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      "text": "你给他一个输入",
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      "text": "这是像一个超级精准的选择题机器",
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      "text": "他马上给你正确答案",
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      "text": "第二个叫score",
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      "text": "他会对输入打一个分数",
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      "text": "同时附上confidence",
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      "text": "也就是信心分数",
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      "text": "让你知道他对这个分数有多确定",
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      "text": "这就像一个很专业的评论员",
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      "text": "不只给分数",
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      "text": "还会告诉你",
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      "text": "他打这个分数的把握有多大",
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      "text": "第三个叫no-o",
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      "text": "这个比较特别",
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      "text": "他会输出一个0到1之间的几率值",
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      "text": "代表某件事发生的可能性",
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      "text": "而且這個機率是經過較準的",
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      "text": "不是隨便說說的",
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      "text": "意思是如果NO說退款機率是0.8",
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      "text": "就代表10封類似的信裡",
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      "text": "大概有8封真的會是退款需求",
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      "text": "這個數字是可靠的",
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    {
      "start": 298.52,
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      "text": "這三個功能合在一起",
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      "start": 299.98,
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      "text": "你就可以做很多事了",
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      "start": 301.58,
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      "text": "比如說你有一個客服系統",
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    {
      "start": 303.32,
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      "text": "每天收到幾千封信",
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      "text": "你想要自動分類",
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    {
      "start": 305.98,
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      "text": "判斷優先順序",
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    {
      "start": 307.16,
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      "text": "偵測有沒有退款需求",
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      "start": 308.72,
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      "text": "用傳統的大型語言模型",
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      "start": 310.52,
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      "text": "你要寫很長的prompt",
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      "start": 311.82,
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      "text": "還要解析它輸出的文字",
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    {
      "start": 313.68,
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      "text": "然後還要擔心它今天心情不好",
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    {
      "start": 315.48,
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      "text": "输出格式跑掉",
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      "start": 316.54,
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      "text": "用GF",
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      "start": 317.44,
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      "text": "你就把信的内容传进去",
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    {
      "start": 319.2,
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      "text": "定义好schema",
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    {
      "start": 320.18,
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      "text": "他直接给你一个Json",
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      "start": 321.54,
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      "text": "类别是什么",
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      "start": 322.6,
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      "text": "优先顺序",
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    {
      "start": 323.74,
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      "text": "几分",
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      "start": 324.36,
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      "text": "是不是退款请求",
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    {
      "start": 325.84,
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      "text": "信心分数多少",
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    {
      "start": 327.1,
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      "text": "全部结构完整",
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    {
      "start": 328.36,
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      "text": "直接可以用",
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    {
      "start": 329.16,
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      "text": "这就像你把一大堆文件",
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    {
      "start": 331.3,
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      "text": "丢给一个超级有效率的助理",
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    {
      "start": 333.06,
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      "text": "他马上就能把所有资讯整理好",
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    {
      "start": 335.26,
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      "text": "分门别类",
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    {
      "start": 335.9,
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      "text": "让你一眼就能看懂",
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    {
      "start": 337.02,
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      "text": "社群里有人做了一个测试",
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    {
      "start": 338.66,
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      "text": "一次处理1000封信",
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    {
      "start": 340.0,
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      "text": "平均回应时间大约是200毫秒",
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    {
      "start": 342.58,
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      "text": "整批跑完费用是多少",
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    {
      "start": 344.18,
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      "text": "非常少",
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    {
      "start": 345.46,
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      "text": "因为JF的定价非常低",
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    {
      "start": 347.16,
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      "text": "输入是每百万Token四分美金",
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    {
      "start": 349.58,
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      "text": "输出是免费的",
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    {
      "start": 350.74,
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      "text": "这个价格跟主流的大型原模型",
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    {
      "start": 353.24,
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      "text": "比起来差距非常大",
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    {
      "start": 354.48,
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      "text": "简直是佛星价",
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    {
      "start": 355.8,
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      "text": "说到速度",
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    {
      "start": 357.38,
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      "text": "TypeSafe AI官方的说法是",
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      "text": "JF可以比传统Transformer模型",
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    {
      "start": 362.5,
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      "text": "快20到200倍",
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      "start": 364.22,
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      "text": "便宜40到400倍",
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      "start": 366.3,
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      "text": "我知道这种数字看起来像行销化数",
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    {
      "start": 369.0,
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      "text": "所以要说清楚",
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      "start": 370.0,
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      "text": "这个速度优势",
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    {
      "start": 370.98,
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      "text": "是在特定的结构化决策任务上",
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      "start": 373.46,
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      "text": "而不是在所有任务上",
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    {
      "start": 374.96,
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      "text": "你不能拿Jeff来写一篇文章",
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      "start": 376.9,
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      "text": "他根本不会",
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      "text": "但如果你的任务是",
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      "start": 379.48,
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      "text": "看这段资料",
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    {
      "start": 380.56,
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      "text": "告诉我他属于哪个类别",
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      "start": 382.5,
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      "text": "那这个速度差距是真实存在的",
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      "start": 385.2,
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      "text": "这就像你请一个",
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    {
      "start": 386.36,
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      "text": "短跑选手去跑马拉松",
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      "start": 388.1,
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      "text": "他可能跑不完",
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    {
      "start": 389.32,
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      "text": "但如果你让他去跑100公尺",
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    {
      "start": 392.44,
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      "text": "他觉得是世界级的",
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    {
      "start": 393.78,
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      "text": "为什么Jeff可以这么快",
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    {
      "start": 395.26,
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      "text": "因为他根本不需要",
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    {
      "start": 396.62,
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      "text": "做一般语言模型做的那一事",
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    {
      "start": 398.32,
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      "text": "他不需要生成自然语言",
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    {
      "start": 400.04,
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      "text": "不需要维护复杂的上下文",
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      "text": "他的工作就是看schema",
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      "text": "看输入输出",
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      "start": 407.56,
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      "text": "一个符合schema的决策",
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      "text": "整个推理路径短很多",
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      "text": "所以快很多",
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      "text": "这就像一个专注于单一任务的机器人",
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      "text": "所以效率极高",
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      "start": 418.86,
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      "text": "这个速度差距在某些使用情境上",
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      "text": "会让整件事从可以用变成真正好用",
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      "text": "现在有很多工具",
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      "text": "可以让AI控制你的浏览器",
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      "text": "你给他一个目标",
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      "text": "他自己去点页面",
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      "start": 433.68,
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      "text": "填资料",
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      "start": 434.38,
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      "text": "跳到下一页",
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      "text": "但如果你真的试过用",
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      "text": "Astra或Fable做这件事",
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      "start": 438.5,
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      "text": "你会发现他慢得很痛苦",
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      "text": "Agent",
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    {
      "start": 440.54,
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      "text": "结一张网页的截图",
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      "text": "传给模型",
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      "text": "然后你等2秒",
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      "text": "3秒",
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      "text": "有时候5秒",
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      "text": "模型才决定要点哪里",
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      "start": 447.42,
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      "text": "这段时间里",
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      "text": "网页可能已经更新了",
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    {
      "start": 450.2,
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      "text": "或者跳出一个弹窗",
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      "text": "然后Agent的动作就失效了",
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      "text": "这就像你请一个动作很慢的人",
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      "text": "帮你抢限量商品",
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      "start": 457.24,
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      "text": "等到他反应过来",
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      "text": "商品早就被抢光了",
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      "text": "这就是为什么浏览器自动化到现在",
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      "text": "还是很难真正稳定部署的原因之一",
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      "start": 466.62,
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      "text": "反映跟不上动态网页的节奏",
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      "text": "但如果你把决策层换成JF",
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      "text": "延迟从秒级将到毫秒级",
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      "text": "Agent看到DOM结构",
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      "text": "马上对照Schema",
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      "text": "马上输出下一个动作",
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      "start": 479.82,
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      "text": "他不需要思考",
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      "text": "他就是执行",
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      "text": "从一个很酷的示范",
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    {
      "start": 485.7,
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      "text": "变成一个可以真正跑在生产环境的东西",
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      "text": "这就像你给机器人装上超快速反应晶片",
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      "text": "它能瞬间判断并执行任务",
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      "start": 495.36,
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      "text": "让整个流程顺畅无比",
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      "text": "好,現在來到我覺得最有意思的部分,也就是第二個重點。",
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      "text": "Jeff的定位很特別,他並不是要取代Astra或Fable,他其實是要跟他們一起用。",
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      "text": "你可以把它想像成一個團隊裡的兩個角色,一個負責策略,一個負責執行。",
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      "text": "我把這個架構叫做雙腦架構",
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      "text": "概念很直觀",
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      "text": "你用大型語言模型做需要深度推理的事",
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      "text": "用Jav做需要快速執行的事",
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    {
      "start": 520.434,
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      "text": "舉個具體的例子",
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      "start": 522.054,
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      "text": "假設你在建一個自動化的軟體開發agent",
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      "text": "他可以看需求",
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      "text": "寫程式",
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      "text": "跑測試",
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      "text": "修bug",
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      "text": "當一個新的功能需求進來的時候",
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      "text": "你先把它交給Fable或Astra",
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      "text": "這些模型有深度的推理能力",
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      "text": "它們可以分析整個專案程式碼",
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      "start": 538.454,
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      "text": "理解依賴關係",
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      "start": 539.874,
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      "text": "想清楚要怎麼實作",
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      "start": 541.434,
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      "text": "然后产出一份详细的执行计划",
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      "text": "就是慢的贵的但值得",
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      "text": "因为这个阶段真的需要深度思考",
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      "text": "这就像一个公司的策略长",
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      "text": "负责制定整体方向和计划",
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      "text": "计划出来之后",
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      "text": "Fable退场",
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      "text": "Jeff接手",
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      "text": "Jeff比较适合负责",
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      "text": "判断下一步要做什么",
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    {
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      "text": "输出固定格式的指令",
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      "text": "如果测试失败了",
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      "text": "Jeff不需要重新思考整个架构",
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      "text": "他只需要看错误讯息",
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      "text": "对照行别定义",
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      "text": "修掉那个行别不符的地方",
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      "text": "再跑一次几毫秒就完成",
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      "text": "真正写大段程式码还是交给Fable或Extra",
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      "text": "这就像公司的执行长",
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      "text": "负责把策略长的计划",
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      "text": "分解成一个个具体任务",
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      "text": "并快速高效的执行",
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      "text": "你把昂贵的推理集中在规划阶段",
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      "text": "把执行交给一个高速低成本",
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      "text": "格式保证正确的引擎",
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      "text": "这样的组合才能让多A-系统真正规模化",
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      "text": "以前这种架构的问题是成本和延迟",
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      "text": "如果一个复杂的任务需要50个为决策",
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      "text": "每个决策要2秒花3分美金",
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      "text": "整个流程就要花1分多钟",
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      "text": "1块多美金单次执行",
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      "text": "这样的单位经济学根本没办法做自动化",
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      "text": "因为跑几次就亏了",
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      "text": "但Jeff把每个为决策的成本和延迟",
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      "text": "都压到几乎可以忽略的程度",
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      "text": "这个架构才真正变得可行",
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      "text": "这就像以前你需要请好几个顾问来做决策",
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      "text": "现在你只需要请一个策略顾问",
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      "text": "然后把执行交给一个超便宜又超快的机器人",
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      "text": "那实际上怎么把JAB接近你现有的流程",
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      "text": "有几个很具体的切入点",
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      "text": "最直接的方式是找出你现有AI流程里最容易坏掉的节点",
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      "text": "通常就是那些让LLM从非结构化的资料里抽取资讯",
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      "text": "然后格式化成某个固定格式的步骤",
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      "text": "比如说从一封信里抓出客户名称",
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      "text": "订单编号",
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      "text": "问题类别",
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    {
      "start": 654.554,
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      "text": "然后写进CIM",
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      "text": "这种步骤犯成Jav是最直接的",
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      "text": "你定义好schema让Jav做抽取",
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      "no_speech_prob": 2.569794377293988e-11,
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    {
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      "text": "格式问题直接消失",
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    {
      "start": 663.034,
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      "text": "这就像你把一个经常出错的环节",
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      "compression_ratio": 1.2680722891566265
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    {
      "start": 665.854,
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      "text": "换成一个永远不会出错的自动化机器人",
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      "no_speech_prob": 2.569794377293988e-11,
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    {
      "start": 668.794,
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      "text": "瞬间解决了所有问题",
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    {
      "start": 670.374,
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      "text": "另一个很适合的使用情境是",
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      "text": "vibe coding",
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      "text": "就是你用AI辅助快速开发的那种模式",
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      "text": "现在的vibe coding流程通常是",
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      "text": "让AI生成程式码",
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      "text": "你去跑",
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      "text": "跑出错误",
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      "text": "把错误贴回去给AI",
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      "text": "等它修",
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      "text": "再跑",
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      "text": "这个回圈很慢而且很烦",
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      "text": "因为Jeff速度过快",
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      "text": "对程式语法的理解是原生的",
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      "text": "你可以在IDE里设定一个背景监听器",
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      "text": "自动把错误丢给Jeff",
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      "text": "让他在背景直接修掉",
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      "text": "你甚至不需要手动介入",
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      "text": "他已经修好了",
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      "text": "这就像你身边有一个超级程式码修正助手",
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      "text": "在你写程式的时候",
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      "start": 707.514,
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      "text": "他随时帮你检查并修复错误",
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      "text": "让你专心在创作上",
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    {
      "start": 711.634,
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      "text": "这个部分说完",
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      "text": "我要讲一个很重要",
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      "text": "但很多人在兴奋的时候会忘掉的是",
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      "text": "速度越快",
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      "text": "出错的代价越高",
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      "text": "当你有一个模型",
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      "text": "可以在毫秒内生成并执行程式码",
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      "text": "你根本来不及在他出错之前阻止他",
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      "text": "如果Jeff在自动化流程里",
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      "text": "有权限存取你的档案系统",
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      "text": "执行终端机指令",
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    {
      "start": 732.394,
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      "text": "更新资料库",
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      "text": "而他以200倍的速度在跑",
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      "text": "那出了问题的时候",
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      "text": "你看到的已经是结果",
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      "text": "不是过程",
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      "text": "这就像你开一台超级跑车",
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      "text": "速度很快",
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      "text": "但如果失控",
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      "text": "造成的损害也会非常巨大",
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      "text": "所以安全边界必须跟着调整",
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      "start": 747.834,
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      "text": "几个原则要记住",
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      "text": "第一",
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    {
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      "text": "就算是高准确定的模型",
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      "text": "AI生成的程式码",
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      "start": 752.934,
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      "text": "预设要当成不信任的来处理",
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      "text": "不要因为它很少出错",
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      "text": "就给它最高权限",
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      "text": "第二",
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      "text": "如果你要在本机跑Jab做自动化",
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      "no_speech_prob": 1.5182874055219564e-11,
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    {
      "start": 762.034,
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      "text": "Docker容器把执行环境隔离起来",
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    {
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      "text": "给它最小必要的权限",
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      "text": "不要给root",
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      "text": "第三",
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      "text": "如果Jab在做浏览器自动化",
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    {
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      "text": "不要用你平常已经登录的浏览器设定档",
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    {
      "start": 774.674,
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      "text": "用独立的一次性的工作阶段",
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    {
      "start": 777.614,
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      "text": "这样就算它误判了某个DOM元素",
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    {
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      "text": "也不会动到你的生产环境",
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    {
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      "text": "这就像你使用任何强大的工具一样",
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      "text": "必须先做好安全防护",
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      "text": "才能避免不必要的风险",
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      "text": "Jeff帮你省下了写程式码的时间",
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    "global_repeat_text": "本集節目由VOAI絕好生創技術支援。VOAI提供富有情緒的高擬真台灣口音AI聲音,並提供上傳圖片與音檔一鍵生成虛擬人的功能,是最具台灣味的AI平台。",
    "tail_repeat_run": 1,
    "tail_repeat_text": "格式正确",
    "hallucination_region_count": 0,
    "hallucination_regions": [],
    "qc_ok": true,
    "qc_errors": []
  }
}