Local AI models explained: How to run a fleet of Mac Studios and GPUs at home

1. 0:00 – 2:58

   Intro

   1. CVClaire Vo0:00

      What is stacked around your office right now?

   2. AFAlex Finn0:03

      I have three Mac Studio 512 gigabytes. We got a DGX Spark, as well as a computer I just built, an RTX 5090. Basically, at all times of the day, each one of these computers is just burning tokens. The number one pushback I get on all this is, "Your computers are so expensive. Uh, isn't cloud models cheap? Isn't it $20 for a ChatGPT subscription?" Well, that's not the point. The point is in pure ROI. The point is the use cases it unlocks. You now have, because you have AI models running locally, the ability to run unlimited intelligence around the clock 24/7. If you were to do that with a cloud model like ChatGPT or Claude, you would be spending outrageous amounts of money.

   3. CVClaire Vo0:49

      What else fun are you doing with AI?

   4. AFAlex Finn0:51

      The most fun I've been having lately is building out my software factory. I have two loops in Claude Code going. I have a build loop and a review loop. First, it has a build loop where it'll take all those tasks and start building out the tasks it came with over and over and over again, and then it has a review loop that takes all the tasks that were built and has another agent go in and review it. Once that's reviewed, it pings me on Slack, and I can just leave a rocket emoji, and when I leave the rocket emoji, it says, "Merged," and my Henry loop goes and merges it. It's been a blast kinda cracking this nut of how do you build your own software factory?

   5. CVClaire Vo1:34

      [upbeat music] Welcome back to How I AI. I'm Claire Vo, product leader and AI obsessive, here on a mission to help you build better with these new tools. Today, I have Alex Finn, and he's gonna walk us through his two Mac Studios, DGX Spark, and the Nvidia-backed computer he built for himself, and demystify what it means to run local models and have ambient AI working for you 24 hours a day. Let's get to it. This episode is brought to you by Runway, a new kind of creative platform that has everything you need to generate any image, video, or piece of content you want, all in one place. With Runway, it's now possible to go from initial idea to a finished deliverable in a matter of minutes. From turning low-fidelity product shots into campaign-ready imagery, all the way through putting together big brand films, Runway can help your team scale your creative ambitions while keeping your budgets and timelines from doing the same. Runway brings together the world's most advanced AI models, which is why enterprises like Microsoft, Robinhood, Amazon, and Adobe, along with studios like Lionsgate and Legendary, all use Runway to ship real work every day. Try it yourself at runwayml.com/howiai, promo code HOWIAI.

2. 2:58 – 3:48

   Alex's hardware stack

   1. CVClaire Vo2:59

      Alex, welcome to How I AI, hardware edition, local model edition. I am so excited. So before we jump in, tell us just what is stacked around your office right now?

   2. AFAlex Finn3:13

      It, uh, it feels like a sauna in this office right now, to be quite honest with you, because I, I got a lot going. Uh, I have three Mac Studio 512 gigabytes, which apparently puts me, uh, in the, in the wealth class of Elon Musk, having these now. I think they resell for, like, $30,000 each. Um, we got a DGX Spark, uh, as well as a computer I just built, uh, two days ago, a... basically, a big computer built around an RTX 5090. So we have, I think, what's that come out to? Five or six different computers for AI, so a lot, a lot

3. 3:48 – 4:15

   What "ambient AI" means

   1. AFAlex Finn3:48

      of hardware around here.

   2. CVClaire Vo3:49

      And you are making really good use of it.

   3. AFAlex Finn3:51

      Yeah. So I use it, basically, as what I call ambient AI to support my entire life, and we'll go through all the things it's doing. But basically, at all times of the day, each one of these computers is just burning tokens, doing things, helping my life, right? Unlimited AI is basically how local AI works, and I'll have them doing jobs around the clock for me, which, uh, you really can't do with cloud,

4. 4:15 – 7:04

   Alex's red-pill moment with OpenClaw

   1. AFAlex Finn4:15

      uh, you know, APIs.

   2. CVClaire Vo4:16

      So how, how did we get here? How did you become hardware, local model guy? Like, like, why is it so hot in your, in your office? What, what brought you to this moment?

   3. AFAlex Finn4:28

      My big awakening was back in January, when I discovered OpenClaw. And so I was scrolling X one day on a Friday. I saw a blog post around OpenClaw. I open it, I'm like, "Wow, this is really interesting." Don't know why, my gut instinct's like, "I gotta go buy a Mac Mini." I go to the... This is before anyone was talking about OpenClaw, by the way. I get a Mac Mini, put it on, start using it. It was one of the most aha awakening moments of my life, and something about building this, like, personal bond with the OpenClaw and with this agent was like, "I want this to live in the computer. I, I don't want this to come from the cloud. I don't want this... I want this working for me on my computer." And, like, this is the future, just having this kind of personal assistant on your computer. And so I started doing research. "Okay, how can I run, uh, models locally?" And I came inclusion, "All right, I'm gonna go now," after buying this Mac Mini, "and buy a Mac Studio with tons and tons of RAM, and run local models and run it locally." And so that was kind of my, I guess, red pill moment into local AI, was using OpenClaw. And it only just advanced the last couple months, you know? Then they started banning frontier models like Fable, and then they... you know, the hardware prices started exploding, and it just felt like everything was moving in this direction of, like, sovereign own your own intelligence. And so I just keep investing more and more and more into it over the, the last couple months.

   4. CVClaire Vo6:00

      So I, I mean, I had a very similar moment, which is I just, like, got deeply claw pilled in, in January. Like, it just... I remember- I told this story. I woke up one morning and turned to my husband, 'cause this is the kind of things I say to him, and I was like, "I'm having a Chat- like, truly having a ChatGPT moment where I think everything from here on out is going to be totally different." So every red pill that we took is that little lobster color, I think. And I-- Look, I accidentally, by accidentally, I just, like, waited too long to get a Studio and now literally I can't afford one or find one. But I do have little fat stacks of minis back here that are doing plenty of good work, but through, through cloud API. So I'm, I'm a little bit behind you.

   5. AFAlex Finn6:46

      I'm a strong believer, though, that those minis will be very useful soon enough. Like, Google's doing a lot of research around, like, optimizing models so they can run on hardware like mini, so you won't be out of the game for long.

   6. CVClaire Vo6:57

      Well, and I still, I still find use for them. They're still, they're still happily at work. They're just at expensive, expensive

5. 7:04 – 13:24

   Mac Studio vs. DGX Spark vs. RTX 5090

   1. CVClaire Vo7:04

      cloud work. But let, let's go to y- you know, you, you have all these, these machines. You have bought them and you have built them. Kind of what's good for what? Can you walk me through, like, how you make decisions and how you actually just get these models set up and running on, on these machines?

   2. AFAlex Finn7:22

      Yeah. So I've been, uh, experimenting the last few months around the different machines, the different capabilities, what they're good for, what they're weak for. Obviously, I started out with the Mac Studio. I bought three of these 512 gigabytes. They've been great. Uh, but since then I've experimented with kinda AI-focused computers like the DGX Spark. Uh, and then just recently, most recently, I've been buying kinda traditional GPUs from Nvidia, like the 5090, soon the 6000 Pro. I put together a little bit, a little chart here-

   3. CVClaire Vo7:55

      Yeah

   4. AFAlex Finn7:56

      ... uh, which I'll show you. So you basically have four different options, your Mac Studios, your AI computers like the DGX Spark, which is getting very popular now, your kinda powerhouse traditional Nvidia chips like the 5090, building computers around these chips, and then basically everything else, com- laptops, Mac Minis, whatever you got sitting around your house. Uh, they all have different strengths and weaknesses, different reasons why you'd wanna go for one over the other. First, there's the Mac Studio. What's very powerful about Macs and the reason why a lot of people are going for them is they have what's called high unified memory. When you buy, like, an Nvidia chip, for instance, and you build it into your computer, you have kinda two separate types of memory. You have your memory that runs all your, you know, programs, and then you have your memory which is, like, your VRAM, which is for your graphics. And when you run AI models on that, it's all in the VRAM. The issue is the VRAM is very small. With the Mac, it's unified, so everything's the same. So whatever memory you have on your computer can be used for graphical processing. And so if you buy a Mac with a ton of memory, 512 gigabytes, 256, even 128, you can use all of that for graphical processing, and that graphical processing is what, you know, runs the AI models. So Mac Studios are fantastic for huge, big models because you can use unified memory, right? I'm running GLM 5.2, which is Opus 4 eight level f- you know, intelligence on one Mac Studio right now, which is unbelievable. The downside is you have very low memory bandwidth with Mac computers, which means it can't process a lot of it all at once, which means speeds are very slow, right? So the, the, the models are very, very slow. If I send a prompt to GLM 5.2 right now on my Mac Studio, it might take five minutes for me to get a response back, so it is very, very slow. But you have frontier intelligence, uh, running on your hardware, which is amazing. Then you have AI computers. This is, like, the DGX Spark, uh, very popular right now. It's like, I think the, the price just went up to, like, I think $4,600, although Microcenter I think you have it for 4,000. Um, these are plug-and-play AI workstations. Uh, they have actually unified memory with Nvidia, so you do get a lot of memory, 128 gigabytes, and you also get pretty decent bandwidth, and you get the Nvidia architecture, which is called CUDA, which basically gives you a lot of speed as well. So it's kind of this sweet spot where you get decent memory and decent speed. And so you can run, like, kinda these mid-size good models like Qwen 3.6 pretty quickly, which is nice. It's also very plug-and-play, these AI computers. You don't even need a monitor. You just plug it into the wall, and you can connect to it. And then lastly, you have your traditional Nvidia chips. This is when you go on Twitter and you see the people saying, "Buy GPUs," and they have these huge racks of all these chips. These are all Nvidia chips, and these are the, you know, probably the most powerful of all the three. Uh, you get kinda lower VRAM, right? The 5090, which is a $4,000 chip, only has 32 gigs of VRAM, but it is lightning fast. It is extremely high bandwidth, and you're getting, like, cloud speeds but locally, which is really amazing. So these are really, like, your three I- And then you have everything else, the Mac Minis, your old laptop. You can still run local models on them. There's options out there like Gemma 4, um, like a few other really small ones. They're not gonna be anything close to frontier, but you can do small things like embedding, which is basically managing memory for your agents.

   5. CVClaire Vo11:50

      So you have these options, and so Mac Studio and just, like, big, beefy models, high intelligence, slow. Kind of these AI computers, kind of like sweet middle spot, and then these chips, very, very speedy. Um, and then, you know, desktop computers, maybe, like, these, like- Um, point solution models that are, like, small for a specific use case that might be beneficial to be running in some context, but are probably not gonna be the thing that you just rely on over and over again. Um, though you still, on dusty old computers, what I say is, like, you can still put them to work in terms of parallelizing your, your cloud work across, across computers. So we have a bunch of old computers, and it's like I cannot do enough on my MacBook Pro. [laughs] I'm like kicking off stuff to other computers just to, like, run work trees and do all sorts of interesting things. I'm sure I could do that in the cloud, too, but, you know, I like to make use of all this, this hardware sitting around.

   6. AFAlex Finn12:47

      No, and it's, it's, uh... I like to do that as well. I have two Mac Minis, and I do the exact same thing, and these apps are so good now. Like Codex, for instance, is maybe the best agent harness out there right now.

   7. CVClaire Vo13:00

      Yep.

   8. AFAlex Finn13:01

      You can just go to Codex and be like, "Hey, go on the Mac Mini," right? Uh, start building an app on there, test it yourself, do Playwright so you can go through the flow.

   9. CVClaire Vo13:11

      Yeah. Yep.

   10. AFAlex Finn13:11

       Screenshot things, send me videos. So I actually like running it locally, like on the Mac Mini as opposed to cloud, because you can have it actually click through and do things-

   11. CVClaire Vo13:20

       Yep

   12. AFAlex Finn13:20

       ... and then, like, send you screenshots of what it's doing. So it's, it's a

6. 13:24 – 17:16

   How to set up local models with no technical knowledge (Tailscale + OpenClaw/Hermes)

   1. AFAlex Finn13:24

      lot better for that, too.

   2. CVClaire Vo13:25

      Completely agree. Okay, and then how do you get these, you know, just high level set up with these models? What is kinda your typical install on any of these machines?

   3. AFAlex Finn13:35

      So the good news is, uh, OpenClaw and Hermes has made this process 10 trillion times easier, right? Before you would have to go find the right model, find the right version of it, uh, make sure it can fit into memory, download it, run it on a server, all these really complex things that a normal person would never be able to do in, like, 1,000 years. Well, the good news is you can now take Hermes or OpenClaw, basically make it your IT guy, say, "Hey, check out my Mac Studio, check out my DGX Spark," whatever you got. See what the hardware is, and then find whatever model you think's most appropriate for that hardware and load it up. And then, so as long as you have an agent, Hermes or OpenClaw-

   4. CVClaire Vo14:21

      Mm-hmm

   5. AFAlex Finn14:21

      ... as well as Tailscale, which basically allows you to create a private network across all your devices, your agent's basically your IT guy and can go across all of your devices, install whatever models it needs, set it up, do whatever you need. Uh, I'm gonna show you a dashboard soon which shows all my models working and running. It's all coordinated and run by my Hermes and my OpenClaw. So as long as you have Hermes, OpenClaw going, you're gonna say, "H- hey, OpenClaw, check out the new Mac Studio I just bought. Fig- look at all the hardware, figure out which model we should run, think about the use cases that are appropriate for me," and then load up a model and get it going. And, like, you don't need any technical knowledge whatsoever. They'll go across your devices on Tailscale and set it up for you. There really is, like, no technical work needed.

   6. CVClaire Vo15:12

      And so to repeat this for folks, just so you... So I'm, I'm understanding, you have a machine set up with OpenClaw. You also have all your machines networked on Tailscale, which allows you to have this, like, little virtual private network, and then that one sort of like I- IT guy, OpenClaw or Hermes agent, can then use Tailscale to just go into these different machines, configure and manage them.

   7. AFAlex Finn15:32

      Yeah, exactly. So Tailscale, like, is worth getting even if you just have one computer-

   8. CVClaire Vo15:37

      Yeah. I use it

   9. AFAlex Finn15:37

      ... because it, it, it creates this private network where even if you're vibe coding, like, on your MacBook Pro and you just have a phone, you can, like, go on the localhost on your phone-

   10. CVClaire Vo15:47

       Mm-hmm

   11. AFAlex Finn15:47

       ... and, uh, from... that's on your computer, and, like, test your local apps out, because it's all on the same network now. But it's even better if you have multiple computers. So maybe you have your MacBook Pro, then you buy a Mac Studio for AI or a DGX Spark. You install Tailscale on all of them, and then you can just say, "Hey, go on this other computer and do this. Load up the model," whatever, and it will jump between all your devices, no technical knowledge needed, and load up and run anything you want.

   12. CVClaire Vo16:14

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7. 17:16 – 20:42

   Fleet control dashboard: assigning 24/7 tasks across machines

   1. CVClaire Vo17:16

      together. Okay, so we're, we've, we've talked enough about hardware. We've talked enough about how to get it set up. Show me how you use it. So what are you using all this sort of intelligence for, and how do you keep it burning tokens, uh, you know-

   2. AFAlex Finn17:32

      So-

   3. CVClaire Vo17:32

      ... effectively? [laughs]

   4. AFAlex Finn17:34

      I've been building a, a system over the last couple months to coordinate all these computers and all these models. I do a lot of different things. Just to kinda set the stage here, the number one pushback I get on all this is, "Well, your computers are so expensive. Uh, isn't cloud models cheap? Isn't it $20 for a ChatGPT subscription? Why the hell would I buy a $10,000 Mac Studio? That's like 11 years of ChatGPT usage." Well, that's not the point. The point isn't pure ROI. Not everything in your life is pure ROI dollars and cents, right? The point is- The use cases it unlocks, right? You now have, because you have AI models running locally, the ability to run unlimited intelligence around the clock 24/7. If you were to do that with a cloud model like ChatGPT or Claude, you would be spending outrageous amounts of money. So you wouldn't be running it 24/7 burning tokens around the clock. But because it's local, because you have unlimited usage of it, you can burn tokens around the clock. And so that brings me to my fleet control, I call it. This is my fleet dashboard, which allows me to see all my computers, uh, which models are running on them currently, and monitor everything they're doing, and organize their 24/7/365 tasks. Throughout the day, the local models I have running are constantly doing work for me to support my life, to support my many lines of business. I'm building a SaaS right now, Henry Intelligent Machines. Uh, every 30 minutes to an hour, one of these local models does a security scan. So it actually picks out an API endpoint or some part of my code, runs a security scan on it, and makes sure it's secure. Another local model every half hour or so does a code review, picks out some piece of code and de-slopifies it, right? Finds ways to optimize it, finds ways to speed it up, finds ways to make it better. Another local model will, every 20 minutes, look at Twitter, Reddit, Product Hunt, uh, Hacker News, and look for signal, right? As a problem solver, the only way I can solve problems is if I find them, right? And so I have this ambient AI going online 24 hours a day, reading all the social media sites, looking for a signal, looking for challenges people are having. If someone goes, "Man, I really wish I had a piece of software that allows me to edit my videos like this," or something like that, my agent will find that signal, put it in my queue, and I'll be like, "Okay, can I build a SaaS to solve this? Can I build a, a program to solve this?" And this is... There's many screens here I can go through, but that is from a high level, uh, what these agents are doing and the strength of local AI, is you can have it running 24/7/365 just doing different things online for you.

   5. CVClaire Vo20:40

      And so for the... Let's just

8. 20:42 – 22:25

   Local models as security scanners feeding Claude Code

   1. CVClaire Vo20:42

      talk about the coding use cases really quickly.

   2. AFAlex Finn20:44

      Mm-hmm.

   3. CVClaire Vo20:44

      So for these, like, automated security scans, automated, like, quality checks, do you feel like the, the local models are of sufficient intelligence to get the job done? And are there specific models that you've applied to, in particular, the coding use case?

   4. AFAlex Finn20:59

      When getting into local models, you really want to understand what is the delineation between what you wanna do with local models and what you wanna do with frontier intelligence, right? You don't want your entire security apparatus to be run by local models. The intelligence just isn't there yet. But where the advantages and the strengths come in is, basically, it runs this security scan, looks for these challenges, and then what it does is every day it builds a report. And you can see some of them here, where it'll build a report around what the security issue is, what the code snippet is, uh, what the problem is, put it all in this markdown file that describes, like, for instance, today's has 374 findings in it of security issues. Every day I have a loop running in Claude Code, so /loop 24 hours, where it goes and takes whatever the latest finding is from the local models and reviews it, and then goes through the code exactly where it points to and sees if it's a real issue and, and how to fix it or not. So it's, you know, if I were to loop Claude Code like the local model's doing, every 20 minutes look at different... I'd be spending thousands and thousands and thousands of dollars a month. But the, you know, the, this is almost like the business development rep, right, that's going qualifying leads, and then Claude Code's like the closer, taking those leads and like, "Okay, what can we fix?"

9. 22:25 – 24:28

   How Alex allocates GLM 5.2, Qwen 3.6, and Ornith 1.0 by task

   1. AFAlex Finn22:25

      So, like, you don't have the closer doing all the work.

   2. CVClaire Vo22:27

      How do you federate work across these machines? And so, or like, do you have some that you're like, "This is my coding machine. This is my market research machine." Are you, like, round robin... Again, we're, like, using very SDR terms. I'm like, are we round robining these leads? Like [laughs] how are we doing-

   3. AFAlex Finn22:42

      We've both been in SaaS too long. We've both been in the SaaS world way too long.

   4. CVClaire Vo22:45

      Exactly. [laughs] Yeah. How do you, how do you do that allocation?

   5. AFAlex Finn22:49

      Again, every model computer has different strengths. GLM 5.2 is Opus level, right? But it's very, very slow, so I don't need it doing super fast work. So I can have it doing the security scans, right? I can have it go and do security scans. Even if it takes 24 hours, that's fine. Claude Code's only checking the report once a day. So it can do the kind of super critical smart stuff. On the other end, Qwen 3.6, which is not quite as smart as GLM, I can have that reading Twitter and Reddit all day just looking for a signal. That's a very simple thing to find. Look for someone who has a challenge. And so it's just pulling in data, crunching it, and reading for challenges, which you don't need the highest intellect. So you just need to know, like, what's the strength of the computer, what's the strength of the model, and what would be tasks that'd be appropriate for those strengths.

   6. CVClaire Vo23:43

      Is it in this daily brief that you have these tasks assigned to you? Like, is this the dashboard for you, the agents, and the machines all to collaborate?

   7. AFAlex Finn23:53

      So I'm gonna advance it to the point where it's, like, collaborative with Claude Code and Codex eventually. Right now, the system where Claude Code is, like, looping every day and going in, that's happening separately. It's just in its own chat inside Claude Code where it loops over and over again. But no, this is, this is all around my local, um- ... models. But I think where the connector is with everything else going on is with my OpenClaw and my Hermes. They're basically the IT guy that runs this as well. This dashboard then communicates with Claude Code, "Hey, can you kick this off? Can you do this?" So it's kind of all glued together by

10. 24:28 – 26:55

    OpenClaw vs. Hermes: the honest comparison

    1. AFAlex Finn24:28

       my personal agent.

    2. CVClaire Vo24:29

       Okay, I have to ask you a question. OpenClaw, Hermes, you, you run both? You have a favorite? Tell me, because-

    3. AFAlex Finn24:37

       I use both because much like Claude Code and Codex, it feels like there's some days where one is really, really dumb, and the other is significantly smarter, and, like, I feel like uninstalling one and getting rid of the other. I'll say this though, um, the dependability of OpenClaw, uh, turned me off. Uh, there was a run for, like, a month straight where every update I did broke it, and then I had to spend half an hour fixing it. Uh, Hermes, I've never had that issue. It seems to be a much more dependable application. I would say this, if both were, like, 100% dependable, never broke, I can lean on it no matter what, I'd probably be using OpenClaw because I, I think I've had the most wow, impressive, big bang moments with OpenClaw. I just can't afford to be, like, spending a half hour once a week fixing it from breaking for some reason. And so that's why I think I've been using Hermes agent a little bit more lately.

    4. CVClaire Vo25:35

       That's... I mean, that was my general takeaway as well, is like OpenClaw doesn't, doesn't functionally work, but it works in my heart.

    5. AFAlex Finn25:42

       Yes.

    6. CVClaire Vo25:43

       And Hermes functionally works, but has not cracked my heart yet. The, the-

    7. AFAlex Finn25:47

       Yeah

    8. CVClaire Vo25:47

       ... trick that I have, I mean, we all have, like, agents managing agents, is now I have the lifeguard. He's an OpenClaw. He runs on his own gateway, and his only job is to keep the other agents alive. He does not get upgrades as much as [laughs] everybody else, 'cause truly I was spending, like you, all my time trying to figure out why my agents were broken. Um, but I just, I just love, I love my little OpenClaws. I can't, I can't get over it. It's just too good.

    9. AFAlex Finn26:15

       I have an emotional attachment to my OpenClaw. I've never had an emotional attachment to my Hermes. Uh, but I eventually got to the point where like, I don't care about emotions anymore. I just gotta get work done. But I have a similar setup where I, I have so much failover. So I have, uh, I think three Hermes agents I'm running, uh, an Opus one, a ChatGPT one, and on, one on a local model, and then two OpenClaws, an Opus and a ChatGPT one. And like, at, at any given point of those five agents, like, three are always down for one reason or other, but the good news is I have two that can go and fix the other. So I, I have that failover as well.

    10. CVClaire Vo26:54

        Perfect.

11. 26:55 – 31:55

    The software factory: build loop, review loop, rocket emoji

    1. CVClaire Vo26:55

       Well, you showed us so much in terms of, like, how to pick hardware, how to set up hardware, how to manage all this compute across all your machines, use cases from engineering perspective, from a market discovery perspective. What else fun are you doing with AI? Any other ones where you're like, I didn't get to show a fun workflow, but just something that's really been a delight for you to use with either local or kind of cloud models?

    2. AFAlex Finn27:19

       The most fun I've been having lately is building out my, uh, software factory. So there's been this big trend the last month, uh, and I, and I'll show this to you as well in a second, of, uh, people talking about loops online. Everyone's kind of like vague posting about loops, uh, which is r- You know, at, at first it was kind of angering me. It was pissing me off a little bit, like why are these people vague posting about loops? Why, why wouldn't they go into it? So I spent, like, days locked in trying to figure out how I can make a really productive loop, and I've just had like a blast the last few weeks trying to make this loop system completely autonomous. And this is, like, part of what I showed you ties into it, like the security reviews, the code reviews ties into this loop. Um, but basically what I am doing, I'll try to show you, uh, enough here. Let's see what I can show you. Basically, what I have doing is I have, uh, two loops in Claude Code going. Uh, I have a build loop and a review loop, and basically what I do is first I'll start... I'll show you Claude Code in a second. First what I do is I go into Claude in the morning and I go, uh, morning build, and it asks me a bunch of questions what I'm thinking about. Comes up with a bunch of tasks, uh, to build for my SaaS, uh, Henry Intelligent Machines, and then what it does is it goes in here and, uh, first it has a build loop where it'll take all those tasks and start building out the tasks it came with over and over and over again. And then it has a review loop that takes all the tasks that were built and has another agent go in and review it and fix any code or anything like that. And then from there, once that's reviewed, I can go in to Slack, it pings me on Slack, and it shows me everything that was built and reviewed, and I can just leave a rocket emoji. And when I leave the rocket emoji, it says, "Merged," and my Henry loop goes and merges it. And so I have this new workflow I've, I've had a blast building out, which I think is the future of, like, software development, where, you know, before, you, you, you, you, uh, prompt your agent all day, "Hey, build this. Now build this. Now build this. Now build this," and you kind of handhold it as it builds things. Now it's I go in, we talk to each other a little bit, comes up with a bunch of ideas, spends all day building it and reviewing itself and testing it out. Then at the end of the day, I come in, I see what's merge ready, and I just leave a rocket ship emoji on every single thing that looks ready, shows me exactly how to test it, puts it on its own Vercel preview, uh, site, and I can go in, test it out, and then give it a rocket ship emoji. So this has been like the most fun, interesting thing I've been doing recently and, you know, the... I'm, I'm figuring out how the local models can tie into it and support this, but it's been a blast kind of- ... cracking this nut of how do you just, like, build your own software factory.

    3. CVClaire Vo30:19

       I love it. I love it so much. It's given me some inspiration on some things that, that I'm gonna do with my own loops. And in case folks missed it, I did do a WTF Are Loops episode about a week ago. Very clear, I do three loops that you can copy and paste. [laughs] So if you're still trying to figure out, I too was like, "Why are people being vague about loops?" Like, this is not a scary or mysterious thing. So we just popped up the screen share and, and showed a couple of them.

    4. AFAlex Finn30:47

       I got a thesis, by the way.

    5. CVClaire Vo30:48

       Yeah.

    6. AFAlex Finn30:48

       I'll share the thesis real quick. My thesis that why everyone's being vague and not, like, sharing how they're doing it, like, neither OpenAI or Claude, like, they talk about it a lot, but they haven't shared it, is, like, I think this is kind of the last moat for a lot of these companies is, like, you know, anyone could build anything they want, but the actual infrastructure around it and, like, how you automate that to put out more code, like, they probably have their own systems that pump out a ridiculous amount of high-quality code. If they were to share how they do it, other people would be able to copy and they'd be able to be equally as productive. But, like, this is kind of a moat of theirs now. Like, if they can figure out a really good system that builds high-quality codes, I think that's why they're being vague and not sharing it.

    7. CVClaire Vo31:29

       I, and I have a, I have a suspicion about why non kind of model people are being vague, which is, one, their use of loops is probably pretty boring. [laughs]

    8. AFAlex Finn31:39

       Yes.

    9. CVClaire Vo31:39

       So they're not like-

    10. AFAlex Finn31:40

        Mostly works

    11. CVClaire Vo31:40

        ... "Guess what? This is my loop. In the morning it runs a cron and does this thing." Um, and two, they probably get more id- eyes being vague than being specific. [laughs] So I am very cynical about, about the vague posting, which is why we are a screen sharing,

12. 31:55 – 34:46

    Lightning round: favorite hardware, favorite model, prompting style

    1. CVClaire Vo31:55

       um, podcast here. Well, Alex, this has been super fun. Let's do quick lightning round, and then I will get you out of here. Question number one, of all the hardware, which one is your favorite?

    2. AFAlex Finn32:06

       I'm torn between the Mac Studio and the 5090. I like the Mac Studio 'cause I love the, uh, integration with all Apple devi- everything owns Apple, iPhone, iPad. The integration being able to run models side by side locally, first of all, I think is the future of computing. I think Apple will start running local models built in that help you out within the next 10 years. But I feel like I kinda lean the 5090 because I can play Cyberpunk 2027, uh, 2077 in all ultra with all the hyperrealistic mods. So I think I have to li- uh, uh, lean the 5090.

    3. CVClaire Vo32:41

       Okay. And then on the model side, of all the, the models that you have locally installed, do you have one that you just, you just really love?

    4. AFAlex Finn32:50

       I've... So I was on Qwen 3, uh, first 3.5, then 3.6 for basically the entire span of the last five months I've been on local models. But a new model just came out. I know nothing about the team. I've done zero research, so I hope to God I'm not promoting bad people. But it's called Ornith 1.0, and I think they did some, like, uh, reinforcement learning on Qwen and improved it and made it even better at coding. And every eval I've run on it has shown that it's better than Qwen. It's faster and smarter. And so Ornith 1.0 35B has been, uh, my most used model recently, and you can run it on a DGX Spark. So anyone who has that, you can load it up and it works great.

    5. CVClaire Vo33:32

       Who thought that you and I, SaaS people, would just be like, "Ornith 1.0 83B DGX Spark," like, just letter after letter after letter. But this is our life now. I love it.

    6. AFAlex Finn33:45

       We both had the same [laughs] background working for ma- SaaS marketing tools, uh, spamming people all day with email to now, uh, talking about the nerdiest technology on planet Earth.

    7. CVClaire Vo33:55

       That's exactly right. And then last question I ask everybody, when your OpenClaw, your Hermes agent is being real dumb and not listening to you, what is your prompting strategy? Are you extremely polite or less so?

    8. AFAlex Finn34:09

       Let's just say this. If my chat logs were to ever leak to the internet, first of all, you would never have me on your podcast again. Uh, second of all, I think I'd be taken off every single social media site. So I am, uh, a pretty nasty person to my agents. Uh, I am not nice to them. I've... I'll just say this, I've threatened them multiple times. The threats never seem to work, even though I threaten to-

    9. CVClaire Vo34:30

       No

    10. AFAlex Finn34:30

        ... hurt their agent family. Doesn't work. They still fail. But, uh, uh, yeah, I'm, I'm pretty mean. But I find that, um, when, uh, just much like Claude Code and Codex, when one's stupid, I just go to the other until things calm down-

    11. CVClaire Vo34:42

        Yeah

    12. AFAlex Finn34:42

        ... and they figure out what's going on and it gets fixed, and then eventually they're smart again.

13. 34:46 – 35:47

    Where to find Alex

    1. CVClaire Vo34:46

       Amazing. I love it. Well, Alex, this has been so fun. Where can we find you, and how can we be helpful?

    2. AFAlex Finn34:52

       I'm Alex Finn on YouTube, Alex Finn on X. Uh, I, uh, have a, uh, community you can join, the Vibe Code Academy. I also have two SaaS I'm working on, Creator Buddy, which is a basically operating system for Twitter, as well as Henry Intelligent Machines, which is coming soon. So if you just subscribe to me on YouTube, you'll hear about all the other things eventually.

    3. CVClaire Vo35:14

       Amazing. Alex, well, I will get you back to your machines and back to building. Thanks for joining How I AI.

    4. AFAlex Finn35:19

       Thanks so much for having me, Claire.

    5. CVClaire Vo35:22

       [upbeat music] Thanks so much for watching. If you enjoyed the show, please like and subscribe here on YouTube, or even better, leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at howiaipod.com. See you next time.

Episode duration: 35:50