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Engineer-turned operator working on strategic GTM & partnerships for technical AI — reading buyers, founders, and ecosystems across the US and Asia, then turning that judgment into commercial motion.
工程师出身的运营者，专注于技术型 AI 的战略型 GTM 与合作伙伴关系 —— 在中美之间读懂买方、创始人与生态，把这份判断转化为商业动作。
Read where demand, trust, budget, and narrative actually break — then translate into ICP, positioning, and field bets.
看清需求、信任、预算与叙事真正断裂的地方，再转化为 ICP、定位与一线打法。
Find credible resource, ecosystem, and distribution angles. Not vague BD — specific lanes with named counter-parties.
找到可信的资源、生态与分发角度。不是模糊的 BD —— 是有具体对方与真实理由的明确通路。
Use AI systems to make research and learning faster — Claude Code, Codex, custom intel pipelines. Workflow as a tool, not the identity.
用 AI 系统加速研究与学习 —— Claude Code、Codex、自建情报管线。工作流是工具，不是身份本身。
I studied electrical engineering in Nanjing, then computer engineering at Duke. Built embedded systems, wrote software, watched friends launch hardware companies. The interesting question was never the engineering — it was always: why do buyers pick this and not that?
That question is harder for technical AI products than for almost anything else. Buyer language is unsettled. Reference architectures shift quarterly. The right partnership in March can be irrelevant by August. Operators who can read that velocity — and pull a CEO into the right rooms — are the ones who keep technical companies on the rails.
That's the lane. Strategic GTM and partnerships for technical AI, with enough engineering literacy to be dangerous, and enough field time to know which signals are real.
我在南京读电子工程，后来在 Duke 读计算机工程。做过嵌入式系统，写过软件，看过身边朋友做硬件创业。真正让我兴奋的从来不是工程本身 —— 而是：买方为什么选 A，而不是 B？
对技术型 AI 产品来说，这个问题比其他任何品类都难。买方语言尚未定型，参考架构每个季度都在变，三月对的合作八月可能就没意义了。能读懂这种节奏、能把 CEO 带进正确房间的运营者，是把技术公司维持在正轨上的人。
这就是我的赛道：服务技术型 AI 的战略 GTM 与合作，既懂工程的语言，也有足够的一线时间分辨哪些信号是真的。