EP12: Taxi Driver by Night, AI Startup Founder by Day (Cantonese) (Jacky Man)

EP14: Floppy Disks, Language Models & the I-Ching (Cantonese) (Dr. Bruce Cheung)

Dr. Bruce Cheung (張維) has been doing AI in Hong Kong since before most people had a computer. One of the first Computer Science graduates from HKU in 1985, he built statistical language models on floppy disks using LISP and Prolog — decades before anyone said 'LLM.' Now Head of the College of Life Sciences and Technology at HKU SPACE, he bridges AI with law and education, arguing that Hong Kong's universities have been quietly world-class in AI research since the 1980s while the public only noticed when ChatGPT arrived. Plus: why Manus costs too much, whether Gemini will ever serve Hong Kong, ADHD in the AI age, and Aug's theory that the I-Ching is the 'bones' for structured AI thinking — which Bruce agrees with.

EP12: Taxi Driver by Night, AI Startup Founder by Day (Cantonese) (Jacky Man)

Jacky Man (文子軒) is a 22-year-old HKU double-degree student who co-founded Mastermock — an AI-powered exam question generator that helps teachers save four hours a week on paper-setting. He drives a taxi at night to fund his zero-revenue startup, pitched his way through dozens of rejections to land a government grant, scaled from ten employees down to two (and got better results), and still finds time to hunt for a girlfriend. A raw, honest look at what it's like to be young, broke, and building in Hong Kong's AI scene.

EP09: Expert Systems, Agent Risk & the Fixer Economy (Ronald Yu)

Ronald's tech career predates AI being a boardroom imperative by decades — IBM (on the same GM manufacturing team as pre-Craigslist Craig Newmark), Wang Labs, HP, FedEx Singapore (where the client-server system later used for Apple/Foxconn supply chain was first architected), then computer forensics, then law. He co-founded MakeBell with Kenneth and teaches AI Law at CityU and CUHK. We dig into the symbolic-to-generative-to-neurosymbolic arc, why vibe-coded agents will break at enterprise scale (and the coming "fixer economy" for cleanup specialists), and one oral-exam trick for assessing students in the GPT era.

EP08: Legal LLMs, Pre-Training & Custom AI Hardware (Jeremy Chow)

Jeremy is a private M&A lawyer who runs a legal AI startup — and runs his own LLM inference hardware out of his living room because he's been building custom PCs since age 14 (for Call of Duty), then mined Dogecoin on graphics cards, and discovered all that knowledge transfers perfectly to training and serving models. We go deep on why lawyers can't use ChatGPT, Claude, or Gemini with client data, why the entire private equity domain is essentially absent from LLM training corpora, why Qwen punches way above its weight, and why the pre-training ceiling is real.

EP07: Why Search Is Broken & What Comes After RAG (Michelangiolo Mazzeschi)

Michelangiolo quit quantitative finance during COVID to teach himself computer science from his home in Italy — now he builds some of the most interesting search infrastructure in Hong Kong. We dig into why vanilla RAG is broken, his "covariate search" approach that applies variable weights across multiple semantic dimensions at once, and an algorithmic tagging method he's publishing that aims to outperform zero-shot LLM labelling at scale. Then the fun part: Michelangiolo wrote and illustrated a 460-page Lovecraftian comic called "The Crimson Duke" using Midjourney — discovering a probabilistic consistency trick for character generation along the way.