關於About
我原本學的是教育與華語文教學。後來一路走進 AI、新創、內容溯源與區塊鏈，才發現自己沒有真的離開原本的事：理解人怎麼學、怎麼相信、怎麼在陌生系統裡找到自己的位置。I started in education and Chinese-language teaching. Then I moved through AI, startups, content provenance, and blockchain - and realized I had not really left the same question: how people learn, trust, and find their place inside unfamiliar systems.
現在我在 Numbers Protocol 做共同創辦人與成長策略長，參與建構內容來源與數位信任基礎建設。外面看起來是技術與市場；我實際上每天面對的是翻譯，把工程語言翻成使用者能採取行動的語言，也把人的猶豫翻回產品與策略。也因為這樣，我把自己定位成一種新型態的「AI 管理師」：同時帶人類同事，也帶一整排 AI 同事。Today I am co-founder and Chief Growth Officer at Numbers Protocol, working on content provenance and digital trust infrastructure. From the outside it looks like technology and market building. Day to day, it is translation: turning engineering language into something people can act on, and turning human hesitation back into product and strategy. That's also why I think of myself as a new kind of "AI manager": leading human teammates and a whole row of AI coworkers at the same time.
工作之外，我在台北抱石、測試新的 AI 工具、獨旅，也收藏一些看似不務正業的線索。那些路徑最後常常又繞回同一件事：人如何和不熟悉的世界建立關係。Outside work, I boulder in Taipei, test new AI tools, travel alone, and keep a trail of links that look unrelated until they do not. They usually circle back to the same thing: how people build a relationship with an unfamiliar world.
Through-lineThrough-line
教育與語言Education & language
政大華語文教學碩士、臺北教育大學教育學士的背景，讓我很早就習慣把複雜的系統拆成人能進入的節奏。With an MA in Teaching Chinese as a Second Language from NCCU and a BA in Education from NTUE, I learned early to turn complex systems into rhythms people can enter.
新創現場Startup field work
從 AI / deep learning 新創、IP 估價，到 Numbers Protocol 的成長與國際合作，我看見同一個問題反覆出現：技術要被採用，得先被真正理解。Across AI and deep learning startups, IP valuation, and global growth at Numbers Protocol, the same question kept returning: technology needs to be understood before it can be adopted.
現在的題目Current thesis
我現在寫 AI coworker methodology，也談內容溯源與 C2PA，因為這些題目最後都回到信任：我們怎麼知道自己在和什麼合作、引用什麼、相信什麼。I now write about AI coworker methodology, content provenance, and C2PA because they return to trust: how we know what we are working with, citing, and believing.
PlaygroundPlayground
一些比較不像履歷、但很像我的入口。A few entrances that are less resume, more me
從鮪魚到 Twitter 討論串——來自這對貓咪的聲音。From tuna to Twitter threads — hear it from the duo.
從 AI 倫理到 zine 設計From AI ethics to zine design
台北岩館 + 我最新的路線Taipei gyms + my latest sends
我獨自探索過的 70+ 個城市——還在增加中70+ cities I've solo-explored — and counting
延伸了解Go deeper
我做過的專案、演講、媒體報導與 Podcast，都整理在這一頁。The projects, talks, media coverage, and podcasts I've been part of are all gathered in one place.
看完整經歷See full experience想為團隊或活動找人談企業 AI 導入、AI 協作與內容溯源——這裡有主題與聯絡方式。Looking for someone to talk with your team or event about enterprise AI adoption, AI collaboration, and content provenance — topics and contact are here.
看演講邀約See speaking pageNumbers Protocol 為 AI agentic economy 建構內容溯源基礎建設；團隊為 C2PA 會員，曾共同設計 ERC-7053 / ERC-7517， 也出現在 SXSW 2023 Pitch、UNICRI、Google News Initiative 等公開場域。 Numbers Protocol builds content provenance infrastructure for the AI agentic economy; the team is a C2PA member, co-designed ERC-7053 / ERC-7517, and has appeared in public venues including SXSW 2023 Pitch, UNICRI, and Google News Initiative.
已公開可查證來源：Publicly verifiable sources: LinkedIn · Crunchbase · TheOrg · Numbers Protocol
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