I work on strategic GTM, market development, and partnerships for technical AI products — turning buyer signal, ecosystem context, and product depth into positioning, sales conversations, and partner routes.
工程师出身，转做运营。专注技术型 AI 的战略、GTM 与合作 —— 在中美两边读买方、读创始人、读生态，再把判断做成实际的业务。
Understand where demand, trust, budget, and language break down, then turn that into ICP, positioning, and field priorities.
看清需求、信任、预算、叙事真正断的地方 —— 再写成 ICP、定位与一线打法。
Map the ecosystem partners, investors, events, and operators that can move a buyer conversation forward.
找可信的资源、生态、渠道入口。不是模糊的 BD —— 要有具体的合作方，要有真实的理由。
Use AI systems to make research, synthesis, and review faster while keeping the judgment explicit.
用 AI 系统加快研究和学习 —— Claude Code、Codex、自己搭的情报管线。工作流是工具，不是身份。
How I built early North America GTM around buyers, events, partnerships, and revenue signal.
为一家 Series A AI 平台在北美做出第一条 GTM 路径，从零跑到 $3M ARR。
A market research engine for turning scattered signals into ranked, auditable opportunities.
把散落的市场信号变成可审计、可排序的机会 —— 一套 venture-studio 引擎。
An AI-assisted contribution review system: Claude reads commits, the methodology stays auditable.
让 Claude 读 commit，把工程产出读成可被反问的方法论。
I studied electrical engineering in Nanjing, then computer engineering at Duke. I built embedded systems, wrote software, and spent enough time around technical founders to know that product quality alone rarely explains market pull.
The harder question is usually commercial: who feels the pain, what language do they use, which partner or event creates trust, and what proof gets a buyer to keep moving?
That is the lane: GTM and partnerships for technical AI, with enough engineering literacy to understand the product and enough field experience to know which signals matter.
我在南京读电子工程，后来去 Duke 读计算机工程。搭过嵌入式系统，写过软件，看着身边几个朋友做硬件创业。真正让我上心的从来不是工程本身 —— 而是：买方为什么选这家，不选那家？
对技术型 AI 产品来说，这件事尤其难。买方的语言还没定下来，参考架构每季都在变，三月对的合作到八月可能就没意义了。能跟上这种节奏、能在关键时刻把 CEO 带到该见的人面前的运营者，才是把一家技术公司稳住的人。
我做的就是这件事 —— 技术型 AI 的战略 GTM 与合作。既懂工程的语言，也有足够的一线时间，能分辨哪些信号是真的。