主題演講

Hey! 👋 I'm Sebastián Ramírez (tiangolo), the creator of FastAPI, Typer, SQLModel, Asyncer, and other open source tools. I've worked with companies and teams across the world, from Latin America to the Middle East, going through Europe and the USA. Always building different types of products and custom solutions involving APIs, data processing, distributed systems, and Machine Learning. And now I've been working full time on FastAPI and friends. 🤓 ------------------------------------- Personal website: https://tiangolo.com/

Imagine you could learn the key ingredients in FastAPI, Typer, SQLModel, etc., to apply them to your product and your code. ✨ (Let me know later if the clickbait worked. 👆️) You can already learn how to use FastAPI and friends in the docs, so I won't teach you that. But you know what you would not see in other places? The history behind my open source tools, including my peculiar background, points of view, and objectives. The key ideas I consider when building things. 🤓 Maybe only philosophical principles would be boring, so I'll also give you very specific tips you can apply to your product. I'll also share some of the things you can learn from building open source projects used by lots of people, including the counterintuitive ideas you wouldn't expect. As a dessert, a random mix of extra tips I would consider. 🍪 Imagine you sat with me for 45 minutes, to give you all the random tips I could think could be useful. That's pretty much this talk. The "years of experience" in FastAPI I have, packed in a single talk, as a giant brain dump. All made of my very subjective opinions (the same as my open source projects), so take everything with a grain of salt, and a lot of coffee. ☕️ ------------------------------------- Share all the tips I could give, which are not necessarily obvious, after building FastAPI and friends, so that people can apply some ideas to their projects and products.

Dr. Tica Lin（林滿新博士）為專注於運動數據科技與人機互動領域之研究學者，致力於以人本設計思維，融合人工智慧、資料科學、視覺化與互動設計，推動虛實整合與數據驅動之使用者體驗創新發展。現任 Dolby Laboratories 資深數據視覺化科學家，並受聘為台灣國家運動科學中心科技顧問，致力於促進尖端科技於運動領域之應用與普及。 Tica擁有哈佛大學電腦科學博士、喬治亞理工學院數位媒體碩士與國立臺灣大學電機工程學士學位。她的研究建立於與運動專家的深度合作，洞察實際需求，並攜手多個國際級組織，包括NBA費城76人隊與明尼蘇達灰狼隊、美國奧運教練團隊、NCAA一級聯盟球隊與台灣國家隊，研發數據互動系統以優化訓練與比賽決策流程。她亦開創「SportsXR」創新研究方向，結合運動分析與沉浸式科技，其成果發表於 ACM CHI、IEEE VIS 等國際頂尖會議，並多次榮獲最佳論文獎。 Tica熱愛運動與設計，曾於臺大創立電機系女籃，帶領團隊勇奪全國大電盃亞軍。憑藉對籃球與數據設計的熱情，她成為NBA費城76人隊首位台灣籍數據UX設計師，在2017-18賽季協助球隊重建並重返季後賽，推動當代籃球數據分析實務。其後出版專書《我在76人的日子》，分享自身於籃球與科技交會點上的實務經驗與洞察。 2024 年，Tica 共同創辦運動科技新創 Athleo.ai，聚焦於以數據視覺化推動運動影片的分析與創作，致力於讓 AI 與互動科技普及至更多運動愛好者與教練手中。她始終相信，科技能放大運動的熱情與影響力，並透過創新研究與跨域整合，讓更多人因運動而彼此連結、拓展專業，並勇於追夢、發揮創造力。 ------------------------------------- Personal website: https://ticalin.com/ Facebook: https://www.facebook.com/SportsXR

在21世紀，數據與科技正全面重塑運動的訓練、管理與觀賽方式。本演講將帶領觀眾回顧運動數據分析的演進歷程，並深入剖析其在當前產業中的關鍵角色——從職業聯盟（如NBA、NFL）設立分析部門，到全球運動科技市場的高速成長。從選才、情蒐、戰術規劃，到選手訓練與傷害預防，資料科學與人工智慧正加速與運動產業深度融合。講者將分享自身參與NBA球隊、國家隊及運動產業合作的實務經驗，介紹當代運動數據科技如何結合電腦視覺、即時追蹤、機器學習與沉浸式互動技術，開發新一代以數據驅動的智慧決策系統。演講亦將引導聽眾掌握最新趨勢，探索跨領域研發如何整合運動科學、資料科學與人機互動設計，並展望人工智慧時代下，運動科技未來的挑戰與機會。透過科技與人本設計的結合，我們正邁向一個更即時、精準且個人化的運動新時代。 ------------------------------------- 我一直相信，科技的終點不是取代人，而是幫助我們活得更有感。 在人工智慧可以自動生成文本、撰寫程式、甚至預測比賽表現的今天，回頭看一場比賽，我們依然會因為一次突破防線的上籃、一段賽後擁抱、或一群教練為隊員量身打造的戰術而感動。運動提醒我們：人類的身體、心理與情感，依然是最有力量的演算法。 作為一位長期投入人機互動與運動科技研究的科技實踐者，我關注的不只是如何用 AI 提升數據效率，而是如何透過視覺化與互動設計，理解運動員、教練與觀眾的真實需求，讓科技成為人與人之間更深層連結的媒介。在這場分享中，我將從自己與 NBA 球隊、國家隊與一線教練合作的實務經驗出發，探討運動如何成為人機協作的重要場域。除了分享運動在AI與互動科技研發中的挑戰與機會，同時，我也想邀請大家一起思考：在 AI 時代，我們該如何以科技之力，激發那些最屬於「人」的價值與可能？

Donghee Na is a CPython Core Developer and a 2025 Python Steering Council member. He contributes to improving CPython's performance, maintenance, and internal implementations. He pursues shaping Python’s future by ensuring its efficiency, stability, and long-term growth. He is also passionate about fostering CPython activities in the APAC region, organizing local Core Sprints, and encouraging developer engagement in open-source contributions.

Let's talk about the past, present, and future of CPython. We'll review the past efforts of the Python core team and take a look at the current status of projects like Free-threading and Faster CPython. Then, we'll discuss the current state of CPython and explore what the future holds for it. ------------------------------------- Keynote for those who are interested in CPython implementation.