Latent Space: The AI Engineer Podcast
00:46:05 · Shawn Wang, S1
[00:46:05] S0: Yeah. What is the Chinese developers thinking about it? [00:46:08] S1: I think it just creates a new way of thinking about using, like, LMs from a totally different angle. Yes. And then I even heard that, you know, one podcast podcaster from China, she didn't know anything about coding, but she she used Jeff to judge, you know, the clips from their podcast, whether it would go viral. And then Jeff gives, you know, gives the answer and and then he will post it on on the network. So I found out so many great use cases based on Jeff. So I just wonder what's your take with there and what probably [00:46:45] S0: Yeah. So for those who didn't listen to the podcast, we were the first podcast You are the first one. Because I forced him, because I have friends with him for four years and say, you give me the first floor, I'm gonna kill you. No, he's obviously like very, very accomplished. He was the co author on Instruct GPT, which became Chat GPT. And I think the interesting thing about Jeff is that it is the kind of the original vision of AI engineering for me, which is that instead of... Right now, AI engineering is is what we talked about, is a big harness Yeah. Around a model. And it's like, I'll do a bunch of work, and then I'll hand off to a model and say, please, god model, give me the right answer, and and you can take ten days to think about it. Jeff is like, well, no. Models are for us. We don't work for the model. [00:47:36] S1: Right.
00:47:36 · S1, Shawn Wang
[00:47:36] S1: Right. [00:47:37] S0: And so I will reduce the scope of the model to only one thing, which is to make choices or to estimate probabilities, and I will I will put it in the line on path of my critical work. So if you think about what is the failure mode of of what the LLM... Current LLM paradigm is, is that you have the big traditional SaaS, and then your LLM is this window that's a chatbot. Mhmm. Right? Mhmm. That is basically what everything is. Or you are... You look like Codex or ChatGPT or Claude Agent. And it's like chat window here Yeah. Menu here, that's it. For what Jeff represents and what system one models represent, this is why he doesn't call it decision model, he calls it system one models. Mhmm. It is that they are in the path of code. You write it as casually as you write an if statement inside of a normal programming language. And I think that is really what he means by smarter software, which I really, really endorse, which is like, your software should just be at every point smarter instead of at one point smart, and then everything else is dumb. That's really dumb. [00:48:37] S1: Mhmm. [00:48:38] S0: Yeah. I think the the speed stuff is almost secondary to the reliability, which is what he really is trying to say. That's why the company is called TypeSave. But, yeah, the speed is nice.
00:48:38 · Shawn Wang, S1
[00:48:38] S0: Yeah. I think the the speed stuff is almost secondary to the reliability, which is what he really is trying to say. That's why the company is called TypeSave. But, yeah, the speed is nice. [00:48:49] S1: I just found out that Diogo himself also a very, you know, such a character, and and he's really passionate about what he's been doing. So I just wonder, you know, you've talked to a lot of like researchers and developers or AI engineers. What do you think? Why was Diogo that made the JAF? Was it something special or different from other kinds of researchers or people that you've talked with? Because I find it quite special about Bob, about him talking to what he's building and what he's believing.
00:49:22 · Shawn Wang, S1
[00:49:22] S0: He has a nontraditional background himself. He didn't go to, Stanford PhD and, you know, the kind of... Those kind of things. So he has that sympathy with a regular developer, but he also worked on ChatGPT. Mhmm. I think that a lot of people who... Maybe they went through the traditional ML researcher path, you go to a top university and then you work on some interesting paper, and then you get hired into a lab and you work on a lab, they're in the big models cult. Everything will be solved by making our model bigger, by having more more flops in our training run, and more parameters and more, more, more, more. Right. Right? And so it takes someone who doesn't come from that mold to really say, well, actually, there's another way to do this, which is smaller stuff that is sprinkled everywhere instead of one big one. And I think that is... By the way, Diogo was not the first to do that. There's been thousands of people who've tried to say smaller model is good. Right. Faster, cheaper, better, whatever. But somehow Diogo worked. Mhmm. One, because he has the credibility of OpenAI. Two, he made it into a system one versus system two thing, which I think makes the communication very clear to the point where you can play Doom or whatever it was with... So there's some game demo that he that he showed in the launch post that was very clear, like, there's no way you will ever use LLM to do this. [00:50:46] S1: Mhmm.
00:50:46 · S1, Shawn Wang
[00:50:46] S1: Mhmm. [00:50:47] S0: And I think that showmanship that he wears the pink jacket, he... He's so passionate and he's talking. That showmanship plus the technical capability is, again, like, similar to the learning public. Like, I'm a small version of that. Right? Like, I'm technically capable and I can talk. But for him, he's like 10 x on both dimensions. And I think that really comes through when when you talk to him. [00:51:08] S1: And also, I guess my next question is about your your roles on advising other startups especially just you mentioned. I'm just very curious, you know, us an advising role, like exactly what kind of advices that you are working with those [00:51:25] S0: startups and I'm how is also curious what can you, for your listeners and for me, what do you do for other companies? [00:51:32] S1: Oh, yeah. So we are actually a community that started three years ago till we found out there are lot of Chinese based founders want to go global. And then I think they were pretty good at building or shipping products quickly, but they found a huge gap between, you know, go to market and marketing and understanding Okay. The [00:51:54] S0: You're a Chinese founder trying to break in The US. You suck at this. You are very good at your tech [00:52:01] S1: Yeah. [00:52:01] S0: And you suck at marketing [00:52:02] S1: Yeah. [00:52:02] S0: And you better talk to someone who's good because you cannot do it yourself. Yeah. That's it. Yeah. Yeah. Yeah. Right.
00:52:02 · Shawn Wang, S1
[00:52:02] S0: And you better talk to someone who's good because you cannot do it yourself. Yeah. That's it. Yeah. Yeah. Yeah. Right. [00:52:08] S1: Yeah. So that's how I truly found out that there's a huge gap between, you know, understanding the local market and, you know, and then ship the product correctly, I found your PMF. Yeah. Yeah. So that's basically, we hold out a lot of meetups and then working with some advisors here, and then try to provide different kinds of workshops and other bursaries to those founders really start to find a little bit on the right track of BMF, but still lack some talents and help from the go to market side. [00:52:40] S0: Yeah, it's interesting. We also host meetups. Diogo actually, before he launched JEV, he was actually here in this room. [00:52:49] S1: Oh,