Unsupervised Learning with Jacob Effron
00:12:39 · Jacob Effron, Diogo
[00:12:39] S0: Yeah. [00:12:39] S1: Yeah. Well, really well is quite relative. I... As far as I can tell, even the best companies still require humans in the loop for all of this stuff despite superhuman results in some axes. And that still... Like like, like, people should have an answer to why that is the case. Like, if they are so unbelievably good at software, like, why do normal software engineers have anything to do? Not that I want them automated, but, like, they clearly aren't, and there's clearly something missing there. And I think thinking of, like, intelligence as this single axis that is measured by whatever the benchmark of the day is is, like, totally wrong. And there's, like, obviously nuance missing there because the people who go all in on not understanding that nuance get burned super hard. So I think that RLVR is incredible, but it is incredible at human in the loop stuff still. And I think that wisdom comes from understanding reality where it's at, knowing its weaknesses, and I... This is we are obviously use agents a lot, but we really value understanding its limitations. You know, I just wouldn't say that it's like, oh, super great, and it just solves everything, personally. May... Maybe I'm using the wrong models.
00:13:48 · Jacob Effron
[00:13:48] S0: I think a lot of our listeners are trying to figure out, you know, how... Like, a simple mental model maybe of of Jev, which I know you disagree with is is like, you know, hey. There's some set of tasks or some set of things I'm trying to do that will make sense for this. There'll be some set of things that make sense for for other things. And I'm sure 95% of our listeners right now are trying to just, adjust their own mental model of, like, okay. What am I, like, Jev use cases? When do I use Jev?
00:14:09 · Diogo
[00:14:09] S1: That's a very pragmatic take for the short term, and I'm in favor of it. This is, like, where it's about giving engineers power because, like, we shouldn't be overly opinionated about what they use it on. And if they need to do things to make it work, abso fucking lutely, hell yeah. Like, if you need to do, like, the worst dirtiest hacks to provide value in the world, like, you know, like, we are not gonna stop you. Like, that is awesome. And, also, if you have to do too many hacks, that's still our fault. You know? Like like, at a deal where you wouldn't need to do that. And the more it becomes close to, like, semantic understanding, the more beautiful the code is, the more reusable it is, the more you can plug it in into places that you wouldn't expect it to. It's kind of like the Unix philosophy of, like, doing, like, incredibly small useful things, but because people have a mental model of it, they can compose it together into big ones. I feel like this is great. And, also, like, we should not be, like, recreating regexes. We should be recreating, like, beautiful primitives. Not that I hate regexes. I just don't like reading them. You know? And, like like, you know, like, it's it's it's like it's like more... What's the word? Is it write only code type stuff? And, like, we we want to be beautiful, composable. We want to, like, build on layers. Like, LMs have failed to do so, and we want stuff to be more like software. You know? I think I have a... An okay mental model of software engineers at worst. Like, it's kind of weird that people even want to update to the latest and greatest models. I hate updating my dependencies. Like, updating dependencies is, like, the worst thing ever for production software. It's fun for personal use. Like, I used to use Arch Linux. I get it. And, also, like, why would I want to bear the burden of that? And the only reason people keep doing this treadmill of, like, maybe the next model will fix my problems is because I don't think they solve those problems so well that they can run-in the background, fuck off for literally ever, and maybe cut... You know, revisit it every, like, couple of months to see, like, is there a a cheaper or better alternative available, but it works fine right now. I don't I don't know of anything like that. And that is, that's a sad state of the world.
00:16:03 · Diogo, Jacob Effron
Like, it's very counter to how software engineering tends to work. [00:16:07] S0: I guess in your vision of the world, like, Dell, have a place in the world in, like, ten years?
00:16:07 · Jacob Effron, Diogo
[00:16:07] S0: I guess in your vision of the world, like, Dell, have a place in the world in, like, ten years? [00:16:11] S1: Super duper. Absolutely. I I I hope if that they do not have a place, then something strictly better exists. I... I'm not opposed to LMs. I'm not actually opposed to either chatbots or coding agents, completely different things. And I I really don't want to go to a world where they don't exist. The thing is I think people overuse them for a lot of tasks, and I actually hope the market for that increases too as people get better at it. And I'm I'm I'm, like, basically certain that there's tons of use cases for it at basically every single intelligence cutoff that has existed, maybe since GPT four. Like, all of these models had beautiful, awesome use cases that maybe would get Pareto dominated by another model, but, like, they should be able to run-in production. And it takes a lot of nuance and understanding, and it... It's like it's like really not built for engineers. So I love LMs. I hope that I I can continue to, like, chatbot anything that I have, like, I need, like, x... Pseudo expert level answers to that don't need to be correct. That is the use case in my mind for chatbots. And I love being able to, like, spin up, like, throw away software instantly when I want custom dashboards and stuff, because I think it's the best use case for coding agents. And that should stay for the rest of time. I don't think people should be doing that. And we should keep on building our toolbox, so to speak. Like, it's... I think it's just awesome.
00:17:31 · Jacob Effron
[00:17:31] S0: Is your intuition that the toolbox will include, like, many other shapes of models in the future with, you know, other RL acronyms? Or if we're talking ten, fifteen years from now, like, are there, you know, are there a bunch of different, like, tools in this toolbox, or is it kind of like your approach and the LLM approach cover most of the use cases?