I work with technical AI companies on GTM, market expansion, and partnerships. My edge is the mix of engineering background and field GTM work: understanding the product, reading the buyer, and helping it land in a new market.
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。
When buyer language is unstable, competitors are blurry, and the category is still changing names, I write down the real problem first, then choose the entry point.
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。
A partnership is not “let’s meet.” I look for why the other side should meet now, what can move after the meeting, and how distribution, trust, or capital can connect.
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。
I use AI workflows to speed up research, writing, and review. The tool is not the outcome; the outcome is seeing the important signal faster and missing fewer follow-ups.
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。
Building the first North American commercialization path: buyers, events, partnerships, materials, 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。
Turning scattered market signals into a ranked opportunity list, so judgment does not stay trapped in chat logs.
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。
Turning engineering records into contribution evidence that can be questioned, reviewed, and discussed.
让 Claude 读 commit，把工程产出读成可被反问的方法论。
My base is engineering. Electrical and computer engineering trained me to understand technical products, and to see a product’s complexity, boundaries, and real selling point instead of stopping at the demo.
The commercial work came later: GTM, market expansion, and partnerships. That work taught me to read the market and the customer more carefully: who has budget, who is only curious, and which situations are worth pursuing.
So I am not the person for “send a few more emails.” I am better suited to questions like: who should the company meet, how should it explain itself, why now, and how do we turn that judgment into meetings, materials, and shipped outcomes?
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。