Aaron Wen
NO.001 个人档案 Personal File 2026

你好,我是 Hi, I am

产品经理 Product Manager

2 年互联网产品经验 2 years in internet products

aaron-wen 简历(PDF) Resume (PDF)
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01

教育背景 Education

当下正在接受的系统化训练。 The systematic training I'm currently receiving.

02

实习经历 Experience

NO.008
核心产出 Key Contributions

在 TikTok 智能客服平台(Customer Service Platform, CSP)产品基础设施团队实习,工作围绕两条主线——把 AI 客服 Bot 从刚性的固定流程升级为 Agentic 架构,以及重建”如何评估客服服务质量、机器评得准不准”这套标准。

撰写 SkillAgent 方案设计:用一个 LLM 驱动的智能体,替代 Bot 背后刚性的 JSON 图执行引擎(基于 LangGraph 的多 Agent 系统)。把原本以 JSON 图定义的 SOP 流程改写为自然语言 Skill(对 JSON 图的 Markdown 可读化替代),交给 LLM 通读完整流程、再经 ReAct 循环逐步推理与执行;配套共享的函数/工具注册表,以及基于 memory 的多轮上下文恢复。

设计 Agent 运行底座(harness):版本化的 Skill 仓库,配合 Libra 分桶 A/B 实验、按 SOP 灰度放量(以 CSAT 与转人工率衡量),接入 OpenTelemetry / Logfire 观测 token 成本、延迟与 ReAct 循环,并加入安全护栏(循环上限、重试、人工兜底)——全程对下游零改造。

主导人工标注平台的 评估新规则(New Rule)迁移:把标注标准从只给单一总分、与 CSAT 弱相关的老 GE Rate 体系,重构为 用户满意分(UES, User Experience Score)——由平台视角的 服务质量分(SQS) 6 个维度,加上用户视角的 用户预期达成(User Expectation Fulfillment) 两部分构成;新增 gating 级联判定、按 Knowledge Source(Skill / FAQ / SOP)分路打分、人工转接结果(Human Result)自动生成,并落地 A/B/C 双盲质控。看板核心指标从 GE Rate 全面切换为 SQS/UES Avg 与 QC Accuracy,人工标注吞吐从约 120 提升到 250–300 单/小时,支撑团队 CSAT(50→60%)与评测准确率(77→83%)目标;前端用规格驱动(spec-driven)AI coding 数天内完成。

Interned on the Product Infrastructure org of TikTok’s Customer Service Platform (CSP), working along two throughlines: upgrading the AI customer-service bot from a rigid fixed-flow engine to an agentic architecture, and rebuilding how the platform evaluates service quality — and whether that evaluation is itself accurate.

Authored the SkillAgent design proposal: an LLM-driven agent that replaces the rigid JSON-graph execution engine behind the bot (a LangGraph multi-agent system). SOP flows defined as JSON graphs are recast as natural-language Skills (a readable Markdown replacement for the graph) that the LLM reads end-to-end, then plans and executes step by step through a ReAct loop — backed by a shared function/tool registry and memory-based multi-turn recovery.

Designed the agent harness: a version-controlled Skill repository with Libra-bucketed A/B experiments and gradual rollout by SOP (measured on CSAT and human-transfer rate), OpenTelemetry / Logfire observability of token cost, latency, and ReAct loops, plus safety guardrails (loop caps, retry, human fallback) — all requiring zero downstream changes.

Led the New Rule migration on the Manual Annotation platform: rebuilt the scoring standard from the legacy single-score, CSAT-weakly-correlated GE Rate into a two-part User Experience Score (UES) — a platform-view Service Quality Score (SQS) across 6 dimensions plus a user-view User Expectation Fulfillment score — adding gating cascades, Knowledge-Source-based branches (Skill / FAQ / SOP), auto-generated Human Result records for bot-to-human handoffs, and an A/B/C double-blind QC workflow. Migrated the dashboard’s core metrics from GE Rate to SQS/UES averages and QC accuracy, lifted annotation throughput ~120→250–300 cases/hour, and underpinned the team’s CSAT (50→60%) and eval-accuracy (77→83%) targets; built the frontend via spec-driven AI coding in days.

SkillAgent LangGraph · ReAct SQS/UES Manual Annotation
NO.007
核心产出 Key Contributions

主导 AI Skills Hub(skillhub.tencent.com) 的端到端产品生命周期——一个面向腾讯全球工作室的 AI 能力聚合平台,独立负责产品规划、前端交互设计与内容运营策略,聚合 33,000+ 条 AI 技能及每日 AI×游戏行业洞察,显著加速各工作室在制作管线中的 AI 采用。

参与 PUBG Mobile “Bruce” AI 战斗伙伴(6 亿+ 下载)的产品定义:结合大语言模型与强化学习,起草支持”实时语音指挥 + 战术协同”的 PRD,GTM 上将其定位为手游射击品类第一个可被玩家语音指挥的可协作 AI 角色

设计并上线基于标签的 用户分群系统与内容推荐引擎:通过 NLP 自动标注用户阅读行为与知识偏好,为每位用户匹配相似同事与相关文章,实现群组级内容分发与个性化触达,显著提升平台内的知识流通效率与内容发现体验。

Led the end-to-end product lifecycle of AI Skills Hub (skillhub.tencent.com) — an AI capability platform for Tencent’s global studios. Independently owned product planning, front-end interaction design and content-ops strategy, aggregating 33,000+ AI skills plus daily AI×gaming industry insights, substantially accelerating AI adoption across production pipelines.

Contributed to the product definition of PUBG Mobile’s “Bruce” AI combat companion (600M+ downloads): combining LLM and reinforcement learning, drafted the PRD supporting real-time voice command + tactical cooperation, and positioned it in GTM as the first collaborative AI character in mobile shooter games that players can command by voice.

Designed and shipped a tag-based user segmentation system and content recommendation engine: used NLP to auto-tag user reading behavior and knowledge preferences, matching each user with similar peers and relevant articles — enabling group-level content distribution and personalized reach that measurably improved knowledge circulation and content discovery on the platform.

AI Skills Hub PUBG Mobile LLM + RL User Segmentation
NO.006
核心产出 Key Contributions

参与消费零售领域战略尽调项目,围绕中国头部餐饮连锁(星巴克中国 / 百胜中国 / 瑞幸)执行 高管薪酬对标,系统拆解各管理层级的薪酬结构与激励机制。

整合来自年报、KJRC 备案、专家访谈等碎片化数据源,构建可比薪酬模型,验证关键投资假设,识别消费零售板块的机会点。

Contributed to a strategic due-diligence project in consumer retail, conducting executive compensation benchmarking for China’s leading F&B chains (Starbucks China / Yum China / Luckin) — systematically breaking down compensation structures and incentive mechanisms across management levels.

Synthesized fragmented data from annual reports, KJRC filings, and expert interviews to build compensation models — validating critical investment hypotheses and identifying opportunities in the consumer retail sector.

Executive Compensation Benchmarking F&B Consumer Retail
NO.005
核心产出 Key Contributions

在 Vibe Coding 概念尚未流行时(2025 年 7 月)率先推动**“PM × 开发并行”协作模式**:由开发配置隔离分支并约束 Spec Coding 规范,PM 借助 Agent 直接在工程中完成原型开发并与功能逻辑并行合并上线;以此高效交付 10+ 功能模块,将 PRD 到可体验原型的周期从数周压缩至数天,受邀内部分享后被团队正式采纳。

设计游戏化系统(虚拟宠物 + 抽取玩法):定义经济模型、货币回收路径与里程碑节奏,上线后沉默用户转活跃提升 191%(由 358 人增至 2,400+ 人)、首页 DAU +35%、两月留存 +73%

独立 pitch 并落地玩家人格测试活动(腾讯游戏学堂周年庆 H5):从 PRD、前端实现到全链路数据埋点一人完成,打通 PV→Share 漏斗;20 天参与 20,000+,后被腾讯未成年人保护团队采纳为青少年心理画像工具。

参与 VALORANT Mobile 上线活动策略:以玩家行为分析驱动预约活动设计,助力预约量突破 7,000 万,沉淀为可复用的上线打法手册。

撰写多篇游戏行业深度研究,课题涵盖中小团队突围策略、米哈游海外发行模式及微信小游戏生态等;其中一篇独立报告获选为内部资讯头条,并被腾讯游戏学堂公众号转载至外部,获多位总监级领导认可。

内部 Game Jam 作为 3 人小队的 Producer & Designer 产出合作节奏游戏 OH MY BOSS,20 支队伍中拿下第 3,开幕试玩覆盖 1,300+ 员工。

Before “Vibe Coding” became a buzzword (July 2025), pioneered a “PM × Dev in parallel” workflow: engineering set up isolated branches governed by a Spec Coding convention, letting PMs use an agent to prototype directly in the codebase and merge alongside feature logic. This shipped 10+ feature modules efficiently and compressed the PRD-to-experienceable-prototype cycle from weeks to days — later formally adopted team-wide after an internal share-out.

Designed a gamification system (virtual pet + gacha): defined the economy model, sink paths and milestone pacing. Post-launch it lifted silent-to-active conversion by 191% (from 358 to 2,400+ users), homepage DAU by +35%, and 2-month retention by +73%.

Independently pitched and shipped a player personality-test H5 live-op for Tencent Game Institute’s anniversary — owned the full chain from PRD, front-end implementation to analytics instrumentation, closing the PV→Share funnel. 20,000+ participants in 20 days; later adopted by Tencent’s Minor Protection team as a psychological-profiling tool for younger users.

Contributed to VALORANT Mobile’s launch live-ops strategy: used player-behavior analysis to shape the pre-registration activity, helping reservations surpass 70M. Distilled into a reusable launch playbook.

Authored several in-depth game-industry research reports covering breakout strategies for small studios, miHoYo’s overseas publishing model, and the WeChat mini-games ecosystem — one report was selected as an internal headline story and externally reposted by Tencent Game Institute’s official account, earning recognition from multiple director-level leaders.

At an internal Game Jam, served as Producer & Designer in a 3-person team and shipped the cooperative rhythm game OH MY BOSS — placed 3rd out of 20 teams, opening-day play covered 1,300+ employees.

Gamification Live-Ops VALORANT Mobile Vibe Coding Game Jam
NO.004
核心产出 Key Contributions

完成 15+ 次利益相关方深度访谈,挖掘消费者行为变化,直接支撑内容规划与用户互动策略。

覆盖 本地选举、科技展会、文化节庆 等关键事件的多媒体报道,确保与编辑部的叙事目标对齐;将田野观察转化为战略建议,提升本地相关性与品牌共鸣。

聚焦 中国企业的海外本地化进程——从安踏在比弗利山庄的首店落子,到洛杉矶山火与社会运动等社区议题,在华语与英语读者之间搭建桥梁。

Conducted 15+ stakeholder conversations to uncover shifts in consumer behavior, directly informing content planning and engagement strategies.

Created multimedia coverage of key events (e.g., local elections, tech expos, cultural festivals), ensuring alignment with organizational messaging goals. Converted field-level observations into strategic recommendations, boosting local relevance and brand resonance.

Focused on Chinese enterprises’ overseas localization — from Anta’s debut in Beverly Hills to community issues like the LA wildfires and social movements — bridging Chinese and English-speaking readerships.

Journalism Multimedia Stakeholder Interview Content Strategy
NO.003
核心产出 Key Contributions

深度拆解 20+ 头部 GaaS 竞品游戏生态,系统追踪 3,000+ 次版本迭代(含平衡性调整、DLC 发布、运营活动等),提炼出”高频小更新 + 季度大版本”的 GaaS 生命周期模型。

利用 SQL 清洗业务数据,搭建 Tableau 仪表板实时监控 DAU、留存率、转化率等核心指标,输出结构化洞察报告并在内部会议上定期汇报,获多名组长级认可。

Deep-dove into 20+ leading GaaS competitor ecosystems, systematically tracking 3,000+ version updates (balance tweaks, DLC releases, live-ops events) to distill a “frequent small updates + quarterly major releases” GaaS lifecycle model.

Used SQL to clean business data and built a Tableau dashboard to monitor DAU, retention and conversion in real time — delivering structured insight reports and regular readouts that earned recognition from multiple team leads.

GaaS Competitive Analysis SQL Tableau
NO.002
核心产出 Key Contributions

独立主导 himentor 微信小程序 从 0 到 1 的开发——覆盖需求定义、交互设计、前端实现到后端联调;技术栈 微信小程序原生框架(WXML / WXSS / JS)+ Node.js 后端服务 + 云数据库,负责核心页面、用户体系与导师-学员匹配流程的全链路落地。

设计并产出品牌宣传内容体系:定义视觉语言、撰写 copywriting、产出物料套组,为小程序冷启动建立内容基础。

搭建并运营 小红书产品内容矩阵——多账号定位分层(产品号 / 导师号 / 学员故事号),制定选题节奏与 hashtag 策略,将小红书流量向小程序用户池沉淀,形成可复制的增长打法。

Independently led end-to-end development of the himentor WeChat Mini-Program — from requirement definition, interaction design, and front-end implementation to back-end integration. Tech stack: WeChat Mini-Program framework (WXML / WXSS / JS) + Node.js backend + cloud database — shipped core pages, the user account system, and the mentor-mentee matching flow.

Designed and produced the full brand content system: defined visual language, wrote copy, and delivered asset kits to bootstrap the mini-program’s cold start.

Built and operated a Xiaohongshu product matrix — multi-account segmentation (product / mentor / mentee-story), established content cadence and hashtag strategy, and funneled Xiaohongshu traffic into the mini-program user base, forming a repeatable growth playbook.

WeChat Mini-Program Full-Stack Xiaohongshu Growth Brand Content
NO.001
核心产出 Key Contributions

参与 2022 全球数字经济大会 的一线采访与现场报道,输出多篇稿件与现场记录,服务于新华网数字经济专题的内容池。

主持 / 协调分论坛环节并参与内容创作,配合编辑部完成主题设计、嘉宾沟通、议程把控与稿件把关,确保现场内容与传播节奏一致。

西藏自治区旅游发展厅 项目起草招标方案——从需求调研、竞品对标到预算结构撰写,独立交付可直接提报的商务文档。

担任新华网原创视频栏目 《健康解码》 的剪辑师,完成多期节目的后期制作(粗剪、精剪、字幕、音画同步、包装输出),并配合美术团队使用 Adobe Illustrator 产出节目视觉素材。

Reported on the 2022 Global Digital Economy Conference — conducted on-site interviews and produced multiple stories for Xinhuanet’s digital-economy coverage.

Moderated and coordinated breakout sessions while contributing to content creation — working with editorial on theme design, guest outreach, agenda control, and copy review to keep on-site content aligned with the broadcast cadence.

Drafted tender proposals for the Tibet Autonomous Region Tourism Bureau project — from needs analysis and competitive benchmarking to budget structuring — independently delivering submission-ready business documents.

Served as video editor for Xinhuanet’s original program “Health Decoding” — handled full post-production (rough cut, fine cut, subtitles, audio-video sync, packaging) across multiple episodes, and produced program visual assets with Adobe Illustrator alongside the art team.

Journalism Event Coverage Video Editing Adobe Illustrator
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精选项目 Selected Work

Yongge Restaurant Skill

一个把「勇哥餐饮创业说」方法论工程化为 Agent Skill 的餐饮创业决策系统:包含 9 篇方法论语料、30+ 真实案例、保本线计算器、快招识别器和街景 360° 选址打分模型。

Agent SkillPythonLLMRAGKnowledge EngineeringDecision SupportCLI Tools

LoL Match Prediction

基于早期对局数据的英雄联盟胜负预测:使用 6 种机器学习模型分析 9,879 场钻石段位比赛,以 72% 准确率预测 10 分钟后胜负走向。

Pythonscikit-learnPandasLogistic RegressionRandom ForestGradient BoostingSVMMatplotlibSeaborn

Personal Site

你正在看的这个站点。基于 Astro + Tailwind + MDX,静态部署到 GitHub Pages。

AstroTypeScriptTailwindMDX
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想法与笔记 Writing