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What 10 Years Designing Siri at Apple Taught Him About AI31:21

What 10 Years Designing Siri at Apple Taught Him About AI

Mike Chop · Co-Founder & CTO, Code Blue AI · ex-Apple, ex-Samsung Health

He came in with the Siri acquisition and spent the next decade designing how people talk to machines. Three stories: what it took to teach the world to say “Hey Siri”, what the disabled community taught Apple about its own product, and what building something for his 88-year-old mother turned into. Then twelve minutes of questions on how Apple and Samsung actually differ — and why he thinks a moment like this comes around every ten or fifteen years.

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“Your Idea Will Probably Not Work Out” — A YC Founder on What Actually Happens31:53

“Your Idea Will Probably Not Work Out” — A YC Founder on What Actually Happens

Selina Li · Co-Founder & CEO, Bubble Lab (YC W26)

Thirty minutes, almost no slides. She spent seven on her own story — Hong Kong, the national golf team, a J.P. Morgan job she was quietly miserable in — and handed the other twenty-four to whatever the room wanted to ask. Pricing when prospects vanish at the word “pricing”, landing a first client with nothing built, and why she made herself post every day for sixty days.

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Hong Kong Is Spending HK$50 Billion on AI. Here’s Where It Goes.7:11

Hong Kong Is Spending HK$50 Billion on AI. Here’s Where It Goes.

Jayne Chan · Head of StartmeupHK, InvestHK

Compute, capital, infrastructure, talent — the four buckets the government is putting money behind in AI, with real numbers and who to talk to. The Sandy Ridge cluster reaching 180,000 petaflops by 2032, 36 times today; how funded startups should approach the Hong Kong Investment Corporation; and HK$50 billion for public AI training.

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10 Vibe Coding Tips You’re Probably Not Using16:11

10 Vibe Coding Tips You’re Probably Not Using

Hanley Leung · Festival host · Taiso.AI & Lunatechs

The host’s own keynote, counting up to ten: the image and video APIs wired into your coding agent, SVG and Mermaid instead of mangled arrows, git worktrees for parallel work, and the speech engine that’s been in your browser since 2015. Ends on why an LLM returns the average by construction — and what it takes to pull anything better out of it.

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