Machine-readable resume — fetched and edited by AI agents (Claude, Cursor, ChatGPT) through the cv-pro CLI or MCP. Also served as raw JSON at /guuumiho.json.
{ "username": "guuumiho", "header": { "name": "Tilda Pan", "tagline": "FDE / AI Engineer / RAG Engineer focused on turning real business context into working AI solutions" }, "personalInfo": { "email": "1011934252@qq.com", "phone": "18258169339", "location": "China", "birthday": "Age 26" }, "experience": [ { "company": "待补充（财务 AI 项目）", "role": "RAG 工程师", "startDate": "2025.07", "endDate": "2025.11", "bullets": [ "参与财务智能体系统技术验证，围绕合同、报表、凭证等财务文档场景探索 RAG 检索问答、文档解析、结构化转换和语音交互能力。", "参与模型选型与测试，对比 Qwen-VL、MinerU 等方案在文档解析精度、响应速度、多格式适配上的效果，并完成 Qwen3-VL-30B 私有化部署验证。", "实现多格式文件向 JSON 的结构化转换，并同步开发重复文件检测功能，支持后续检索、审核和知识库处理链路。", "定义实体与关系提取规则，完成多模态内容的实体识别、关系提取与结构化存储。", "结合财务文档特性优化 Chunk 切分策略，设计关键词检索、语义检索、多条件组合检索等检索方式。", "负责实时语音交互系统核心开发，实现流式语音转文字、声纹注册、说话人识别、回复打断和记忆系统管理等功能。", "沉淀出对财务 RAG 适用边界的判断：严格固定流程更适合 RPA，而 AI/Agent 更适合自上而下的流程重构与复杂协作场景。" ], "tags": [ "RAG", "Qwen3-VL", "MinerU", "Document AI", "JSON", "Voice AI", "Finance" ] }, { "company": "待补充（腾讯云 / FastGPT 项目）", "role": "商务经理 / 云产品解决方案沟通", "startDate": "2025.01", "endDate": "2025.06", "bullets": [ "主动拓展游戏、社交、医疗、传统行业等泛互行业云计算资源客户，了解行业动态、客户痛点和潜在商机。", "负责 TAPD / FastGPT 用户接触、拜访、产品演讲、月度线下活动邀约和需求跟进。", "通过上门拜访和线上沟通梳理企业客户业务场景、现有痛点与发展需求，协助确定需求优先级。", "基于客户业务特性提供云产品选型建议和解决方案说明，用通俗语言讲解产品功能、技术优势和服务价值。", "负责腾讯云、火山云、FastGPT 等产品的技术答疑，跟进客户试用和 POC 测试过程。", "跟进客户使用情况，收集并反馈产品建议，处理试用和使用过程中的风险、纠纷和问题反馈。", "月度完成指标多次超过老员工，成为组内 Top 1；积累客户拜访、需求理解、产品讲解、试用跟进和技术答疑经验。" ], "tags": [ "ToB", "Cloud", "FastGPT", "POC", "Solution", "Customer Discovery" ] }, { "company": "待补充（上汽大众项目）", "role": "数据运维工程师", "startDate": "2024.06", "endDate": "2024.09", "bullets": [ "负责上汽大众移动渠道端数据运维，校对多方车辆参数数据，维护车型版本、年款升级、价格、车型亮点、内饰、配置参数、选装包等数据展示。", "使用 SQL 更新数据并校验各表关联，保障前端数据展示准确。", "维护不同环境下的阿里云数据库、OSS、CDN 等资源。", "与多个数据提供商协同工作，及时反馈错误数据并对齐字段颗粒度。", "保障多源车辆参数、价格、配置、选装包等数据展示准确性，积累数据维护、环境资源和多方协同的问题定位经验。" ], "tags": [ "SQL", "Data Ops", "Aliyun", "OSS", "CDN", "Automotive" ] }, { "company": "待补充（上汽大众项目）", "role": "测试工程师", "startDate": "2024.06", "endDate": "2024.09", "bullets": [ "负责上汽大众推广传播程序的 Web 端、C 端与爬虫端功能测试、UI 测试、系统测试、Bug 提交和回归测试。", "参与需求评审，主导编写并执行 1000+ 测试用例。", "与产品经理、开发和用户沟通需求及预期效果，参与测试用例评审和问题确认。", "建立从需求评审、测试用例、Bug 提交到回归测试的质量验证经验，形成较强的验证和复盘意识。", "沉淀需求澄清、验证边界和 MVP 最小功能点判断能力，可支持 FDE 场景下的 demo 需求验证。" ], "tags": [ "QA", "Test Cases", "Regression", "Web", "UI Testing", "MVP" ] }, { "company": "待补充（宝格丽等零售品牌项目）", "role": "品牌技术支持工程师", "startDate": "2023.05", "endDate": "2024.06", "bullets": [ "以宝格丽项目为主提供二线技术支持，对接中国大陆及港澳台门店，并支持宝格丽、雅诗兰黛、LV、泸溪河等多个零售品牌。", "支持 Beanstore、SAP、CRM、AD、OA 等多个核心业务系统，保障系统稳定运行。", "日常监控 Cisco Meraki 等网络设备，配置出入栈规则，通过设备管理层实现企业网络流量管理与运维。", "维护全国门店设备运行情况，为上门工程师提供二线支持，保障设备迭代、安装、拆卸顺利进行。", "负责路由器、AP、交换机、打印机、销售仪器等软硬件设备管理与正常运转，并维护内部软硬件资产。", "累计支持过万人次，涉及跨国团队，部分沟通语言为英语；多次获得集团月度奖金、集团 AI 应用大赛奖项，并带领团队赢得年度优秀团队奖项。", "沉淀客户现场沟通、故障排查、跨团队协作和复杂问题拆解能力。" ], "tags": [ "Technical Support", "Retail IT", "SAP", "CRM", "AD", "Cisco Meraki", "Cross-functional" ] } ], "education": [], "projectsRecent": [ { "title": "本地 AI 问答与记忆实验工具", "description": "面向高频 AI 使用者，探索高密度回复、关键信息本地沉淀、多模型切换、短期上下文和中期记忆提取机制。", "url": "https://github.com/Guuumiho", "tags": [ "LLM", "Prompt", "Memory", "Multi-model" ] }, { "title": "Agent 项目源码辅助研读工具", "description": "将源码研读拆解为目录树理解、核心入口定位、执行流程追踪和模块职责总结，帮助初学者理解 Agent 项目主流程。", "url": "https://guuumiho.github.io/agent-code-learning", "tags": [ "Agent", "Source Reading", "Frontend" ] }, { "title": "专注力训练小工具", "description": "基于 Stroop 文字-颜色冲突法设计的专注训练工具，完成前端页面与部署。", "url": "https://guuumiho.github.io/Stroop-Brain-Train", "tags": [ "Frontend", "Interaction", "Cognitive Training" ] }, { "title": "任务引导工具", "description": "面向多任务推进中的思维混乱问题，探索 OS 级任务感知、上下文捕捉、偏离提醒和及时方案提供。", "url": "https://github.com/Guuumiho", "tags": [ "AI Assistant", "Tauri", "Context", "Product Prototype" ] } ], "projectsDetailed": [ { "title": "本地 AI 问答与记忆实验工具", "type": "Personal AI Tool", "startDate": "2025", "endDate": "Present", "url": "https://github.com/Guuumiho", "bullets": [ "通过系统 Prompt 设计强制简洁回复风格，迭代多个版本提示词，稳定输出高密度重点信息。", "自动整理问题与答案，将重要信息以笔记形式本地沉淀，降低连续问答后的信息流失。", "支持不同 AI 模型选择与调用，减少在多个平台之间手动切换的成本。", "设计短期上下文 + 中期记忆提取机制，通过触发机制和摘要提取器模板提升连续问答体验。", "针对临时问题设计隔离式单问模式，避免无关内容污染主线任务上下文。", "针对网络和模型波动导致的调用失败，设计原模型重试、降级模型重试、备用 API 重试、失败提示和重新发送按钮。" ], "tags": [ "LLM", "Prompt Engineering", "Memory", "API", "Fallback Strategy" ], "externalLink": { "label": "GitHub", "url": "https://github.com/Guuumiho" } }, { "title": "Agent 项目源码辅助研读工具", "type": "Learning Tool", "startDate": "2025", "endDate": "Present", "url": "https://guuumiho.github.io/agent-code-learning", "bullets": [ "面向 Agent 初学者“不知道先从哪里开始看源码、不知道某个文件作用、找不到主流程”等问题设计源码辅助研读工具。", "将源码研读流程拆解为目录树理解、核心入口定位、执行流程追踪、模块职责总结。", "帮助用户熟悉 Agent Loop、Context、Provider、Memory、Tool Calling 等 Agent 项目核心概念。", "使用 nanobot 项目作为测试样例，完成前端部署体验入口。" ], "tags": [ "Agent", "Code Reading", "Context", "Tool Calling", "Frontend" ], "externalLink": { "label": "Live Demo", "url": "https://guuumiho.github.io/agent-code-learning" } }, { "title": "专注力训练小工具", "type": "Frontend Tool", "startDate": "2025", "endDate": "Present", "url": "https://guuumiho.github.io/Stroop-Brain-Train", "bullets": [ "使用文字-颜色冲突的 Stroop 法设计专注训练工具。", "完成前端页面与部署，通过文字与颜色冲突交互，引导用户进行注意力和反应训练。" ], "tags": [ "Frontend", "Stroop", "Interaction Design" ], "externalLink": { "label": "Live Demo", "url": "https://guuumiho.github.io/Stroop-Brain-Train" } }, { "title": "任务引导工具", "type": "AI Assistant Prototype", "startDate": "2025", "endDate": "Present", "url": "https://github.com/Guuumiho", "bullets": [ "面向多任务同时推进导致的思维混乱问题，设计轻度任务引导工具。", "探索 OS 级应用形态，让 AI 感知从对话框延伸到用户正在进行的任务上下文。", "设计自动捕捉用户应用内容的能力，减少用户反复说明遇到困难时背景信息的成本。", "针对提前过度优化、任务走向偏离、反复卡住的难题等场景设计轻度提醒和方案提供机制。" ], "tags": [ "AI Assistant", "Tauri", "Context Awareness", "Product Thinking" ], "externalLink": { "label": "GitHub", "url": "https://github.com/Guuumiho" } } ], "skills": [ { "name": "AI / LLM", "items": [ "Model API 调用", "Prompt 调优", "多模型切换", "上下文记忆", "失败兜底策略", "Agent Loop", "Context", "Provider", "Memory", "Tool Calling" ] }, { "name": "RAG / Knowledge Base", "items": [ "多格式文档解析", "结构化转换", "Chunk 切分", "关键词检索", "语义检索", "多条件组合检索", "实体与关系抽取", "多模态内容处理" ] }, { "name": "Engineering", "items": [ "SQL 基础", "数据维护", "表关联校验", "阿里云数据库", "OSS", "CDN", "React", "Tauri", "Demo / Prototype Development" ] }, { "name": "Business / Field", "items": [ "ToB 客户沟通", "产品讲解", "需求梳理", "客户拜访", "POC 跟进", "技术答疑", "客户现场支持", "跨团队协作" ] }, { "name": "Quality / Operations", "items": [ "系统性问题排查", "测试用例设计", "回归测试", "Bug 提交", "企业系统支持", "网络与硬件支持", "资产与账号管理" ] } ], "contact": [ { "label": "GitHub", "url": "https://github.com/Guuumiho" }, { "label": "Agent Code Learning", "url": "https://guuumiho.github.io/agent-code-learning" }, { "label": "Stroop Brain Train", "url": "https://guuumiho.github.io/Stroop-Brain-Train" } ], "meta": { "updatedAt": "2026-07-02T22:03:39.950Z" } }