云启资本

云启资本

@yunqipartners

科技常新，寻找未来开创者

534 articles18 episodes

Articles

A Deep Dive into 70 Years of Optical Computing: What New Breakthroughs Await Beyond Moore's Law? | Yunqi Science --- In 1965, Gordon Moore, then director of the R&D laboratory at Fairchild Semiconductor, published an article in *Electronics* magazine titled "Cramming More Components onto Integrated Circuits." In it, he made a bold prediction: the number of transistors on an integrated circuit would double approximately every year, with computing power growing exponentially while costs declined. This observation, later refined to a doubling every 18–24 months, became the famous "Moore's Law" that has driven the semiconductor industry for nearly six decades. However, as transistor sizes approach the atomic scale, the physical limits of silicon-based electronic computing are becoming increasingly apparent. Heat dissipation, quantum tunneling effects, and manufacturing costs are all pushing traditional computing toward a wall. In this context, optical computing — using photons instead of electrons as information carriers — has emerged as one of the most promising paths beyond Moore's Law. This article traces 70 years of optical computing development, examining its technical principles, key breakthroughs, and the new frontiers it may open in the post-Moore era. ## The Physical Advantages of Photons Why light? The answer lies

A Deep Dive into 70 Years of Optical Computing: What New Breakthroughs Await Beyond Moore's Law? | Yunqi Science --- In 1965, Gordon Moore, then director of the R&D laboratory at Fairchild Semiconductor, published an article in *Electronics* magazine titled "Cramming More Components onto Integrated Circuits." In it, he made a bold prediction: the number of transistors on an integrated circuit would double approximately every year, with computing power growing exponentially while costs declined. This observation, later refined to a doubling every 18–24 months, became the famous "Moore's Law" that has driven the semiconductor industry for nearly six decades. However, as transistor sizes approach the atomic scale, the physical limits of silicon-based electronic computing are becoming increasingly apparent. Heat dissipation, quantum tunneling effects, and manufacturing costs are all pushing traditional computing toward a wall. In this context, optical computing — using photons instead of electrons as information carriers — has emerged as one of the most promising paths beyond Moore's Law. This article traces 70 years of optical computing development, examining its technical principles, key breakthroughs, and the new frontiers it may open in the post-Moore era. ## The Physical Advantages of Photons Why light? The answer lies

"The Future Computer" will have a computing architecture composed as follows —

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Podcasts

Vol.17 48-Hour Xiaohongshu Hackathon Hit: How an AI-Native Product That Broke the "Retention Curse" Was Built --- Two weekends ago, I participated in a 48-hour hackathon hosted by Xiaohongshu. Our team of four built an AI-native product from scratch — no code, no design background between us — and ended up winning the "Most Popular" award. The product? A voice diary app called **"Echo"** that uses AI to turn fragmented daily moments into serialized, episodic "life podcasts." Think *This American Life*, but starring you. What surprised me wasn't that we won. It was that people kept using it *after* the demo. Here's the dirty secret of AI hackathons: most projects die the moment judges stop clapping. The "retention curse" is real — users try your GPT wrapper once, say "neat," and never return. We broke that pattern. Our daily active user rate among beta testers hit 34% in week one, which for a hackathon product is basically unheard of. How? Three deliberate choices we made against hackathon orthodoxy. **First, we refused to build a chatbot.** The default AI product in 2024 is still "talk to a large language model." We explicitly rejected this. Chat interfaces create *performance anxiety* — users feel pressure to ask the "right
Vol.17 48-Hour Xiaohongshu Hackathon Hit: How an AI-Native Product That Broke the "Retention Curse" Was Built --- Two weekends ago, I participated in a 48-hour hackathon hosted by Xiaohongshu. Our team of four built an AI-native product from scratch — no code, no design background between us — and ended up winning the "Most Popular" award. The product? A voice diary app called **"Echo"** that uses AI to turn fragmented daily moments into serialized, episodic "life podcasts." Think *This American Life*, but starring you. What surprised me wasn't that we won. It was that people kept using it *after* the demo. Here's the dirty secret of AI hackathons: most projects die the moment judges stop clapping. The "retention curse" is real — users try your GPT wrapper once, say "neat," and never return. We broke that pattern. Our daily active user rate among beta testers hit 34% in week one, which for a hackathon product is basically unheard of. How? Three deliberate choices we made against hackathon orthodoxy. **First, we refused to build a chatbot.** The default AI product in 2024 is still "talk to a large language model." We explicitly rejected this. Chat interfaces create *performance anxiety* — users feel pressure to ask the "right

Vol.17 48-Hour Xiaohongshu Hackathon Hit: How an AI-Native Product That Broke the "Retention Curse" Was Built --- Two weekends ago, I participated in a 48-hour hackathon hosted by Xiaohongshu. Our team of four built an AI-native product from scratch — no code, no design background between us — and ended up winning the "Most Popular" award. The product? A voice diary app called **"Echo"** that uses AI to turn fragmented daily moments into serialized, episodic "life podcasts." Think *This American Life*, but starring you. What surprised me wasn't that we won. It was that people kept using it *after* the demo. Here's the dirty secret of AI hackathons: most projects die the moment judges stop clapping. The "retention curse" is real — users try your GPT wrapper once, say "neat," and never return. We broke that pattern. Our daily active user rate among beta testers hit 34% in week one, which for a hackathon product is basically unheard of. How? Three deliberate choices we made against hackathon orthodoxy. **First, we refused to build a chatbot.** The default AI product in 2024 is still "talk to a large language model." We explicitly rejected this. Chat interfaces create *performance anxiety* — users feel pressure to ask the "right

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