00:43:09 · Matt Stockton
[00:43:09] S1: Yeah, yeah. Admittedly, I never got into the OpenCLAR or Hermes thing. Like my tools that I use on a day to day basis are basically ClawdCode and Codecs. I think that tools like Codecs, have made incredible progress in the last six months to be much more accessible to a wider and more general audience, folks that aren't doing code. Mean, it's unfortunate that Claude Code is named Claude Code because people are like, Okay, I don't code, I don't need that. But for the longest time, before co work came out, I was trying to get people that are technical enough to use it, but are not coders to use the thing, right? Because I think the big breakthrough there is like, you got to use the tools that have access to your file system and can use the operating system tools. Like, that's clear to me. You shouldn't be using ChatGPT in the browser. And I think Codex and CoWork, they're going to continue to make that user experience work for a wide audience. So I think they will kind of consume a lot of the workflows and kind of like that's where you operate, right? That's where you do your work. It's it's amazing how many data sources and tools kind of plug into that ecosystem now. So my gut is that will continue to happen. It is still feels very early, though, because, I mean, you know, we're obviously in the rabbit hole. Like, I don't know how many what percent of people doing work out there is actually using Codecs? Probably less than 1%, maybe. I don't know. But I do think eventually, my view is that that category of tool is going to kind of subsume a lot, a lot of work. People will be spending a lot of time in that type of tool.
00:45:08 · Khe Hy
[00:45:08] S2: I'll jump in as someone who probably burned like thirty hours on OpenCLaw. I think the, the, and then I stopped once they took away the subsidy. And once I had to actually pay for my Opus calls, I was like, woah, this is not worth $50 a day. But one thing that I have observed particularly on Codec. So, so why did I like OpenClaw? It removed two key frictions for me. One was it's just nice to be able to text from your phone. Like, I think that's really until you actually do it, you're like, well, could just open app and chat. Like, no, no, no. Like, we live inside our messaging app, so there's just something so powerful about the lack of friction, which I really miss. I use it a lot less because I got rid of the texting thing. The second is cron jobs and for non non nerds, which is just scheduled tasks, which this is for another podcast, but like for something that seems so simple is like deceptively hard to do. They break, you don't, there's no way to track them and so on. And OpenClaw made cron jobs really easy. And, but now, with Codecs, the Codecs, don't if either of you have used Codecs on your mobile phone. It is an amazing experience. It literally maps to what you would see on, on coda, on your desktop. So, so you can run full codex from your desktop, unlike Claude CoWork, which has dispatch, which is kind of just weird. It just, I don't get it. It doesn't work for me. So that'd be one. And then the automations are really, really good as well. So again, you can have an interface to like, show me all my scheduled tasks is actually quite easy to the point that I didn't buy a Mac mini, I did set up an old Mac as, like, an always on server for OpenCLaw. And I'm actually in the process of converting that into an always on codex so that when I'm traveling, I can still talk to my codex instance because my machine will be on 20 fourseven. So it's effectively gonna become like a local server serving codex that I can talk through through iPhone gateway. That, like I'm this close this weekend. I should get it done and it's going to be awesome. Yeah. So a very simple one. Again, I start with a lot of personal agent things. So I have an agent, it's still rough, that basically can schedule calendar meetings for me. And so it has delegated access to my calendar.
00:47:44 · Khe Hy
And so I'll just go into Codex on my phone and say, schedule a meeting with Brett this Friday at 01:30 p. M. And then if I want, I can have it send you the invite as well. And so, like, but I need to be it needs to be on my Codex, right, because the permissions are local to my machine. They're not, like, in the cloud. So that would be one. Another one I have, this is super cool. I call it the CRM tickler file. And so I have a CSV file that has all the people in my network and a frequency in which I would like to just, like, stay connected with them. And so then I have a skill that actually, well, first I have that hooked up on Codex to iMessage because like 70% of my work, like, I don't think I've ever emailed Matt. It's like always been through to you as well. So it's like, what's the point of a CRM if it can't read your iMessage today in this day and age? It's kind of pointless. So I have it hooked up to my iMessage and my Latour Gmail. And every Friday it runs, it basically looks through both sources and sees like who falls outside of the window of like, you know, who do you need to get in touch with? And then it sends me an email of a list of the people that are like past their window. And so I could... But let's say it runs every week, but let's say I wanted to run that on like a Wednesday night and I'm on the road. I could just say, run the CRM tickle skill.
00:49:19 · Matt Stockton, Brett Caughran
[00:49:19] S1: That's cool. Yeah, I've never, I have not gotten into the use the AI from the mobile thing yet. And I'm kind of resisting it because I'm already I'm already spending too much time on this stuff as it is. Right? And so it's interesting, though. I mean, think that's a big part. You're not the first person that said the tech... The texting aspect was really important to them to be able to kind of manage and run run these things. But, yeah, it's it's interesting. [00:49:48] S0: Matt, what are your thoughts on the I've heard a lot about the Chinese models. There's been this sort of wall of like token, you know, token burn, token maxing, token efficiency. Uber ran through their tokens for the year and four months. [00:50:02] S1: But it [00:50:02] S0: seems like people are pointing to sort of the fast follow of the Chinese models as a sort of antidote to that, with sort of routers on top of that. What are your thoughts on that space?
00:50:18 · Matt Stockton
[00:50:18] S1: Yeah, it's interesting. You know, I I have dabbled with running local models on my machine just just as a way to just see how it works, right? Like download some weights and run them. And I don't have a super beefy machine, right? Like, the models aren't great. But the smaller models and some of the open source models, they're getting really good. I mean, if you look at the benchmarks, and you kind of have to take the benchmarks with a grain of salt sometimes, but the open weight models are very effective. And my belief is that most people's work right now, like that they're actually doing, probably doesn't require like the frontier model anyways, right? Probably a lot of the tasks that people want to use is very serviceable by these smaller models. So I think it has legs. And I think, you know, these these smaller models will continue to get better. I think, you know, companies, if they want to cost optimize, this is going to be a route that they go, right? Like, you can already see, like Shopify put out an interesting post a couple months ago. They took a workflow that they were running at scale on a frontier model and kind of, I don't know if they fine tuned a model, but they did something to run it much more cheaply, right? And like, they got a 100x, I think, cost improvement there. So I think that will happen. I think it's tricky because it's much easier, I think, to just like use the frontier model and run after it. But I think as companies have things that they need to do at scale, and it's it's repeatable and they've proven it it provides value like that cost optimization, angle will become more important. What are your thoughts there?
00:52:16 · Brett Caughran, Matt Stockton
[00:52:16] S0: I don't have very detailed thoughts. I just hear people talking about it more. So it seems to make sense to me. I've been a fan of, you know, tools like Perplexity Computer, which is sort of like an abstracted OpenCLO that has access to 18 models. And I find it just to be more consistent than I do a Claude, which is really important for the workflows that I do. And I think people are trying to struggle to figure out Excel as well too. And this common response I get is that, shouldn't try and update a model with Frontier Intelligence. A lot of that should be done in a coding environment. So how do you start to think about actually deploying more deterministic use cases at potentially local low cost models and use frontier intelligence for a stack of things when you really want think through a debate or apply judgment and sort of think deeply with the strongest intelligence. But to deploy frontier intelligence across a full stack of the sort of agentic investor process doesn't feel like, one, it doesn't feel like the most effective methodology. I don't want a stochastic nondeterministic tool in the quantitative realms of my investment process. So I think that's part of why I've been more excited with agents. Like you see a little bit of glimpse of that of like the LLM as the orchestrator of these various tools, calling sub agents, spinning up coding environments, etcetera. [00:53:45] S1: But