Shawn Wang: belief

Transcript context

…It's great to be here. Yeah, we are recording this in a surprisingly hot October in San Francisco. And sometimes the studio works, but the blue angels are flying by right now, so sorry about the noise. So welcome. I've seen Phind blow up this year, mostly, I think since your launch in Feb and V2 and then your Hacker News posts. We tend to like to introduce our guests, but then obviously you can fill in the blanks with the origin story. You actually were a high school entrepreneur. You started SmartLens, which is a computer vision startup in 2017. That's right. I remember when like TensorFlow came out and people started talking about, obviously at the time after AlexNet, the deep learning revolution was already in flow. Good computer vision models were a thing. And what really made me interested in deep learning was I got invited to go to Apple's WWDC conference as a student scholar because I was really into making iOS apps at the time. So I go there and I go to this talk where they added an API that let people run computer vision models on the device using far more efficient GPU primitives. After seeing that, I was like, oh, this is cool. This is going to have a big explosion of different computer vision models running locally on the iPhone. And so I had this crazy idea where it was like, what if I could just make this model that could recognize just about anything and have it run on the device? And that was the genesis for what eventually became SmartLens. I took this data set called ImageNet 22K. So most people, when they think of ImageNet, think of ImageNet 1K. But the full ImageNet actually has, I think, 22,000 different categories. So I took that, filtered it, pre-processed it, and then did a massive fine tune on Inception V3, which was, I think, the state of the art deep convolutional computer vision model at the time. And to my surprise, it actually worked insanely well. I had no idea what would happen if I give a single model. I think it ended up being 17,000 categories approximately that I collapsed them into. It worked so well that it actually worked better than Google Lens, which released its V1 around the same time. And on top of this, the model ran on the device. So it didn't need an internet connection. A big part of the issue with Google Lens at the time was that connections were slower. 4G was around, but it wasn't nearly as fast. So there was a noticeable lag having to upload an image to a server and get it back. But just processing it locally, even on the iPhones of the day in 2017, much faster. It was a cool little project. It got some traction. TechCrunch wrote about it. There was kind of like one big spike in usage, and then over time it tapered off. But people still pay for it, which is wild.…

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