{
  "version": 1,
  "site": {
    "title": "李金珂 · AI 产品经理",
    "logoName": "lijinke.com",
    "characterImage": "assets/character.webp"
  },
  "nav": {
    "links": [
      { "key": "about",   "zh": "关于我",     "en": "About"   },
      { "key": "work",    "zh": "工作履历",   "en": "Work"    },
      { "key": "contact", "zh": "联系方式",   "en": "Contact" }
    ],
    "cta": { "zh": "找我聊聊", "en": "Talk to me" }
  },
  "hero": {
    "pill":           { "zh": "AI 产品经理 · 制造业 AI 落地", "en": "AI Product Manager · Manufacturing AI Applications" },
    "titleLead":      { "zh": "把",                   "en": "Bringing" },
    "titleAccent":    { "zh": "AI",                   "en": "AI" },
    "titleTail":      { "zh": "用在真实业务里。",     "en": "into real business." },
    "sub":            {
      "zh": "4 年制造业产品经验 + AI 产品转型。专注 AI 在垂直行业的真实落地：质检 · RAG · 时序预测。理解 AI 能力边界，擅长 AI + 人工协同方案。",
      "en": "4 years in manufacturing product, then AI transition. Focused on real-world vertical AI deployment: QC · RAG · time-series forecasting. Pragmatic about AI's limits — believer in AI + human collaboration."
    },
    "primaryCta":     { "zh": "开始一段对话",   "en": "Start a conversation" },
    "secondaryCta":   { "zh": "查看履历",       "en": "View my work" },
    "contactLabel":   { "zh": "// 联系方式",     "en": "// Reach me" }
  },
  "stats": [
    { "num": "4+",   "label": { "zh": "年产品经验",     "en": "Years in Product" } },
    { "num": "3",    "label": { "zh": "个 AI 项目落地", "en": "AI Projects Shipped" } },
    { "num": "OPEN", "label": { "zh": "对外合作",       "en": "To Collab" } }
  ],
  "about": {
    "eyebrow": { "zh": "// 关于我",                                  "en": "// About me" },
    "title":   { "zh": "在制造业，把 AI 真正用起来",                  "en": "Making AI Actually Work in Manufacturing" },
    "lead":    {
      "zh": "4 年制造业产品经验后看到 AI 在垂直行业的落地机会，系统学习 AI 产品知识并成功转型。理解 AI 不是万能的、技术有边界 — 做的 3 个 AI 项目都是在深入理解业务痛点后，找到 AI 能真正解决问题的切入点：质检 AI+人工协同、RAG 知识库、时序预测采购建议。希望把 AI 真正用起来解决业务问题，而不是为了 AI 而 AI。",
      "en": "After 4 years in manufacturing product, I spotted real vertical AI opportunities and systematically retrained into AI product. AI has limits — all 3 of my AI projects started from a deep business pain point and found where AI genuinely solves it: AI+human QC, RAG knowledge base, time-series purchasing. I want AI to actually solve business problems, not AI for AI's sake."
    },
    "methodologyTitle": { "zh": "AI 产品落地方法论", "en": "How I Ship AI" },
    "methodology": [
      {
        "step":  "01",
        "title": { "zh": "感知 · 视觉/多模态识别", "en": "Perceive · Vision & Multimodal" },
        "desc":  {
          "zh": "视觉质检沉淀出「标注—阈值—置信度分级」范式，可迁移到目标检测、缺陷识别等感知类场景。",
          "en": "Visual QC distilled a reusable label–threshold–confidence-tiering pattern, transferable to detection and defect-recognition tasks."
        }
      },
      {
        "step":  "02",
        "title": { "zh": "决策 · 规则引擎 + 模型预测", "en": "Decide · Rules + Model Forecast" },
        "desc":  {
          "zh": "验证「先规则保下限、再模型提上限」的渐进路线，适用于预测、调度类决策场景。",
          "en": "Validated a progressive path — rules secure the floor, models raise the ceiling — for forecasting and scheduling decisions."
        }
      },
      {
        "step":  "03",
        "title": { "zh": "执行 · 人机协同补边界", "en": "Execute · Human-in-the-Loop" },
        "desc":  {
          "zh": "明确 AI 能力边界，高置信自动执行、低置信交人工接管 —— 可靠落地的核心。",
          "en": "Define AI's limits: auto-execute on high confidence, hand off to humans on low — the core of reliable deployment."
        }
      }
    ],
    "educationTitle": { "zh": "教育背景", "en": "Education" },
    "education": [
      {
        "when":  "2018 — 2022",
        "title": { "zh": "本科 · 武汉理工大学 · 工程管理", "en": "B.S. · Wuhan University of Technology · Engineering Mgmt" },
        "sub":   { "zh": "GPA 3.8/4.0 · 专业前 10%", "en": "GPA 3.8/4.0 · Top 10%" }
      }
    ],
    "trainingTitle": { "zh": "进修与认证", "en": "Continued Learning" },
    "training": [
      {
        "when":  "2024.12 — 2025.3",
        "title": { "zh": "AI 产品经理实战行动营 · 人人都是产品经理 · 起点课堂", "en": "AI PM Action Camp · Woshipm · Origin Classroom" },
        "sub":   { "zh": "大模型原理 · Prompt 工程 · RAG 知识库 · Agent 框架",   "en": "LLM Fundamentals · Prompt Engineering · RAG · Agent" },
        "desc":  {
          "zh": "完成智能客服、知识库问答等场景的产品方案设计，系统掌握 AI 产品核心能力，为转岗 AI 产品经理奠定基础。",
          "en": "Designed product solutions for smart customer service and RAG Q&A. Systematic AI product capabilities for transitioning into AI PM."
        }
      }
    ],
    "badges": [
      { "key": "4+",    "val": { "zh": "年产品经验",        "en": "Years in Product" } },
      { "key": "3",     "val": { "zh": "个 0→1 AI 项目",    "en": "0→1 AI Projects" } },
      { "key": "92%",   "val": { "zh": "RAG 回答准确率",    "en": "RAG Answer Accuracy" } },
      { "key": "100K+", "val": { "zh": "新媒体粉丝积累",    "en": "Social Followers Built" } }
    ]
  },
  "work": {
    "eyebrow": { "zh": "// 工作履历",                  "en": "// Experience" },
    "title":   { "zh": "从碳纤维制造业，到 AI 落地。", "en": "From carbon fiber, to making AI work." },
    "lead":    {
      "zh": "在欧亚瑞新材料从普通产品经理做起，4 年完成从制造业产品到 AI 产品经理的转型。专注 AI 在垂直行业的真实落地，不为 AI 而 AI。",
      "en": "Started as a product manager at Ouyarui New Materials. Over 4 years, transitioned into AI PM with a focus on real-world vertical-AI deployment — never AI for AI's sake."
    },
    "items": [
      {
        "when":  "2025.1 — 至今",
        "title": { "zh": "AI 产品经理 · 欧亚瑞新材料（碳纤维汽车、摩托车零部件）", "en": "AI Product Manager · Ouyarui New Materials (Carbon Fiber Auto/Moto Parts)" },
        "sub":   { "zh": "AI 产品线 · 深圳",                                       "en": "AI Product Line · Shenzhen" },
        "bullets": [
          {
            "zh": "主导碳纤维零部件 AI 视觉质检系统 0→1（感知）：商汤视觉 AI + 自建 1200+ 张缺陷样本，AI+人工协同（置信度 > 80% 判缺陷，50%-80% 标疑似），初筛准确率 85%，沉淀「标注—阈值—置信度分级」可复用范式。漏检率 8%→2%（↓75%），质检效率 +40%（日均 500→700 件），人工工作量 −30%。",
            "en": "Led the 0→1 of an AI visual QC system for carbon fiber parts (Perceive): SenseTime vision AI + 1,200 in-house defect samples, AI+human hybrid (confidence >80% = defect, 50–80% = review), 85% first-pass accuracy, distilling a reusable label–threshold–tiering pattern. Miss rate 8%→2% (−75%), QC efficiency +40% (500→700/day), manual workload −30%."
          },
          {
            "zh": "搭建内部销售知识库 RAG 助手（多模态交互）：整合 200+ 产品文档/报价/历史询盘，Claude API + Pinecone 多路召回与重排，定义专属切片与 Prompt 模板，50 个典型场景评测先行。新销售上手 2 周→3 天，问题响应 30 分→2 分，覆盖率 95% / 准确率 92%，销售满意度 3.2 → 4.5/5。",
            "en": "Built an internal sales RAG knowledge base (multimodal): 200+ docs / pricing / inquiry history with Claude API + Pinecone multi-route retrieval & reranking, custom chunking and prompt templates, eval-first across 50 scenarios. Onboarding 2 weeks → 3 days; response 30min → 2min; 95% coverage, 92% accuracy; sales satisfaction 3.2 → 4.5/5."
          },
          {
            "zh": "设计碳纤维原材料智能采购建议系统（时序预测 · 决策）：3 年订单（12000+ 条）+ 汇率/物流/车展档期 8 个因素，Prophet 生成 90 天需求曲线 + 规则引擎给采购建议（先规则保下限、再模型提上限）。库存周转 60 → 42 天（↑30%），缺货延期月均 8 → 2 次（↓75%），库存积压 15 → 1 天，采购决策效率 +50%；自研部署成本 < 5 万（vs 百万级 SAP）。",
            "en": "Designed a smart purchasing recommendation system (forecast · decide): 3 years of orders (12K+) + 8 factors (FX/logistics/auto-show seasonality). Prophet forecasts 90-day demand + a rule engine issues purchase advice (rules secure the floor, model raises the ceiling). Inventory turnover 60 → 42 days (+30%), stockout delays 8 → 2/month (−75%), overstock 15 → 1 day, purchasing-decision efficiency +50%; in-house cost <¥50K (vs ¥1M+ SAP)."
          }
        ]
      },
      {
        "when":  "2022.3 — 2024.12",
        "title": { "zh": "产品经理 · 欧亚瑞新材料（碳纤维汽车、摩托车零部件）", "en": "Product Manager · Ouyarui New Materials (Carbon Fiber Auto/Moto Parts)" },
        "sub":   { "zh": "碳纤维汽车 / 摩托车零部件产品线 · 深圳",            "en": "Carbon Fiber Auto/Moto Parts · Shenzhen" },
        "bullets": [
          {
            "zh": "负责碳纤维汽车 / 摩托车零部件产品线（车身覆盖件 / 整流罩 / 排气管等 200+ SKU）；主导海外品牌从 0→1 落地及销售体系搭建。",
            "en": "Owned the carbon fiber auto/moto parts product line (200+ SKUs: body kits, fairings, exhausts). Led the 0→1 launch of the overseas brand and its sales system."
          },
          {
            "zh": "管理国内外新媒体运营（小红书 / 抖音 / 公众号 / Facebook / Instagram）+ 阿里国际站产品上架；带 1 剪辑 + 1 运营助手月均产出 50+ 条内容；阿里国际站询盘转化率 +35%，新媒体粉丝累计 10 万+。",
            "en": "Ran cross-platform social media (Xiaohongshu / Douyin / WeChat / Facebook / Instagram) and Alibaba.com listings. Led a team of 1 editor + 1 ops assistant producing 50+ pieces/month. Alibaba inquiry conversion +35%, 100K+ social followers."
          }
        ]
      }
    ]
  },
  "contact": {
    "eyebrow": { "zh": "// 联系方式",        "en": "// Contact" },
    "title":   { "zh": "Open to Collab.",     "en": "Open to Collab." },
    "lead":    {
      "zh": "欢迎来聊：AI 产品共创、垂直行业 AI 落地、制造业 AI 转型经验。邮件最稳，微信最快。",
      "en": "Always open for: AI product co-creation, vertical AI deployment, manufacturing AI transition. Email is most reliable; WeChat fastest."
    },
    "methods": [
      { "type": "email",    "label": "Email",    "val": "hi@lijinke.com",      "href": "mailto:hi@lijinke.com",                    "copy": "hi@lijinke.com" },
      { "type": "wechat",   "label": "WeChat",   "val": "扫码添加",             "href": "#",                                         "copy": "" },
      { "type": "github",   "label": "GitHub",   "val": "@lijinke-design",      "href": "https://github.com/lijinke-design",        "copy": "" },
      { "type": "zhihu",    "label": "知乎",      "val": "@李金珂",              "href": "https://www.zhihu.com/people/43-33-75-5",  "copy": "" },
      { "type": "linkedin", "label": "LinkedIn", "val": "lijinke",              "href": "https://www.linkedin.com/in/lijinke/",     "copy": "" },
      { "type": "phone",    "label": "Phone",    "val": "+86 132 6837 4345",    "href": "tel:+8613268374345",                       "copy": "+86 13268374345" }
    ]
  },
  "bubble": { "zh": "Hey 👋 点我聊天", "en": "Hey 👋 Click to chat" },
  "chat": {
    "name": "李金珂",
    "status": { "zh": "AI PM · 在线", "en": "AI PM · online" },
    "greeting": [
      { "zh": "Hey 👋 我是李金珂，AI 产品经理，制造业 AI 落地方向。", "en": "Hey 👋 I'm Li Jinke, AI Product Manager focused on manufacturing AI." },
      { "zh": "点下面的话题，我们可以聊聊我在做什么 ↓",              "en": "Pick a topic below — let's talk about what I'm building ↓" }
    ],
    "topicsLabel": { "zh": "// 挑个话题", "en": "// Pick a topic" },
    "topics": [
      {
        "key": "who",
        "q": { "zh": "你是谁?", "en": "Who are you?" },
        "a": [
          { "zh": "我是李金珂，AI 产品经理。", "en": "I'm Li Jinke, AI Product Manager." },
          { "zh": "4 年制造业产品经验 + 2024 年底转型 AI 产品。专注垂直行业 AI 真实落地：质检、知识库、供应链预测。", "en": "4 years in manufacturing product + AI transition in late 2024. Focused on vertical AI deployment: QC, RAG knowledge base, supply chain forecasting." },
          { "zh": "信奉「AI 不是为了 AI 而 AI」—— 这个网站本身也是我亲手做的。",                                       "en": "I don't do AI for AI's sake. This site itself is one of my hands-on outputs." }
        ]
      },
      {
        "key": "work",
        "q": { "zh": "你最近在做什么项目?", "en": "What are you working on?" },
        "a": [
          { "zh": "在公司主导了 3 个 AI 项目 🚀",                                             "en": "Led 3 AI projects at work 🚀" },
          { "zh": "1) 碳纤维零部件 AI 质检（商汤 + 自建样本库），漏检率 8% → 2%。",            "en": "1) AI-assisted QC for carbon fiber parts (SenseTime + in-house dataset), miss rate 8% → 2%." },
          { "zh": "2) 销售内部 RAG 知识库（Claude + Pinecone），新人上手 2 周 → 3 天。",       "en": "2) Internal sales RAG knowledge base (Claude + Pinecone), onboarding 2 weeks → 3 days." },
          { "zh": "3) 时序预测采购建议（Prophet），库存周转 −30%，缺货 −75%。",               "en": "3) Time-series purchasing recommendation (Prophet), inventory turnover −30%, stockouts −75%." }
        ]
      },
      {
        "key": "collab",
        "q": { "zh": "能合作吗?", "en": "Open for collab?" },
        "a": [
          { "zh": "当然，对外标签就是 OPEN to Collab 🤝",                                "en": "Always — officially OPEN to Collab 🤝" },
          { "zh": "尤其欢迎：制造业 / B 端 AI 落地、RAG 知识库、AI + 人工协同方案。",      "en": "Especially: manufacturing/B2B AI deployment, RAG knowledge bases, AI+human hybrid systems." },
          { "zh": "发邮件最快：hi@lijinke.com，简单说清楚背景和想法就行。",                "en": "Email me: hi@lijinke.com — just share your context and idea briefly." }
        ]
      },
      {
        "key": "contact",
        "q": { "zh": "怎么联系你?", "en": "How can I reach you?" },
        "a": [
          { "zh": "右下角图标随你挑：",                                  "en": "Pick any icon below:" },
          { "zh": "· ✉️ Email · 💬 微信 (Kim-13268374345)",              "en": "· ✉️ Email · 💬 WeChat (Kim-13268374345)" },
          { "zh": "· 🐙 GitHub · 知乎 · LinkedIn",                       "en": "· 🐙 GitHub · Zhihu · LinkedIn" },
          { "zh": "邮箱最稳，微信最快。",                                "en": "Email is most reliable, WeChat is fastest." }
        ]
      },
      {
        "key": "learn",
        "q": { "zh": "怎么入门 AI 产品?", "en": "How to get into AI Product?" },
        "a": [
          { "zh": "我自己是 4 年制造业 PM 转过来的，体感：少看课，多做小项目。",                                                  "en": "I transitioned from 4 years of manufacturing PM. My take: less courseware, more small projects." },
          { "zh": "路径：先用 ChatGPT/Claude 把日常一件事自动化掉 → 再用 API 把它做成「别人也能用」的产品 → 在过程中补 LLM / Agent / Prompt / RAG 的知识。", "en": "Path: automate one daily task with ChatGPT/Claude → ship it as a product others can use via API → pick up LLM/Agent/Prompt/RAG along the way." },
          { "zh": "想细聊？发我邮件。",                                                                                          "en": "Want to go deeper? Email me." }
        ]
      }
    ]
  }
}