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Listen: 08:45–16:39. The team first avoided a crowded general-agent market by choosing vertical agents, then sourcing: finding creators, candidates, experts, or customers. All involve finding people and seem to share search capabilities. But that starting point answered how to avoid competitors before identifying a payer’s most important task.
Host Qu Kai used Suno to generate an opening theme, selecting one from almost a hundred candidates. This already demonstrates both the attraction and a cost of AI music: an ordinary person can produce it, while a satisfying result still requires selection.
The episode places Sora’s first demonstrations within a larger question: why can compute and model narratives advance before many mature applications exist? The guests distinguish technical progress, competitive position and application demand.
Product–market fit, or PMF, means that a group of people repeatedly finds a product worth using or paying for. These three interviews show why no single number proves it for every business. An occasional office tool can first demonstrate that individual tasks merit payment. Professional software needs to participate in real delivery. A character entertainment product must explain why people return.
The introduction uses the listings of 智谱 and MiniMax as a three-year milestone, inviting the podcast’s first guest to review AI again. Investment outcomes and market descriptions remain contemporary perspectives. A listing cannot alone validate products, returns or every early hypothesis.
Once AI writes code, can small games be consumed in a scrolling video-like feed? Zhang’s team explored that direction, then chose deeper creation tools and play among friends. Ordinary generated material may offer strangers limited value, while friendship supplies a shared reason to care.
Albert reports that five or six team members using coding agents produced dozens of small tools in two months, with no revenue from those experiments at the time. Satisfying a personal habit differs from accepting responsibility for external users, growth, operations and reliable delivery.
The introduction reports 34,000 stars within half a year of Vibe-Trading becoming open source. Wu recalls moving from specialist quantitative tools toward a multi-perspective research workstation. Stars measure attention rather than completion, live performance or verified returns.