This solo builder runs 24/7 local AI on his own hardware | Alex Finn

Alex Finn is an AI builder, YouTuber, and the creator of Vibe Code Academy, a community for people learning to build with AI tools. He runs one of the most ambitious local AI setups I’ve come across: three Mac Studio 512 GB machines, a DGX Spark, and a custom RTX 5090 build, all coordinated through a fleet dashboard he built himself. He’s spent five months figuring out which local models belong on which machines, how to wire them to Claude Code loops, and how to get a software factory running without babysitting it. What you’ll learn: How Alex chose between a Mac Studio (512 GB unified memory), DGX Spark, and RTX 5090, and what each is actually good forWhy Tailscale is worth installing even on a single machine, and how it lets one agent manage your entire hardware fleetHow the build loop and review loop in Claude Code workHow to allocate tasks by machine and modelWhy unlimited local inference changes the use-case math in a way a $20 cloud subscription never canWhat OpenClaw and Hermes are each best suited for, and why Alex runs five agents total with failover baked in— Brought to you by: Runway https://runwayml.com/howIAI—The creative AI platform for images, video, and more Jira Product Discovery https://atlassian.com/howiai—Prioritize with insights, build with confidence — In this episode, we cover: (00:00) Intro (02:58) Alex's hardware stack (03:48) What "ambient AI" means (04:15) Alex's red-pill moment with OpenClaw (07:04) Mac Studio vs. DGX Spark vs. RTX 5090 (13:24) How to set up local models with no technical knowledge (Tailscale + OpenClaw/Hermes) (17:16) Fleet control dashboard: assigning 24/7 tasks across machines (20:42) Local models as security scanners feeding Claude Code (22:25) How Alex allocates GLM 5.2, Qwen 3.6, and Ornith 1.0 by task (24:28) OpenClaw vs. Hermes: the honest comparison (26:55) The software factory: build loop, review loop, rocket emoji (31:55) Lightning round: favorite hardware, favorite model, prompting style (34:46) Where to find Alex — Tools referenced: • Claude Code: https://claude.ai/code • OpenClaw: https://openclaw.ai/ • Hermes: https://hermes-agent.nousresearch.com/ • Tailscale: https://tailscale.com/ • Codex (OpenAI): https://openai.com/codex • GLM 5.2 (z.ai): https://huggingface.co/zai-org/GLM-5.2 • Qwen 3.6 (Alibaba): https://huggingface.co/Qwen/Qwen3.6-35B-A3B • Ornith 1.0: https://github.com/deepreinforce-ai/Ornith-1 • Gemma 4: https://huggingface.co/collections/google/gemma-4 • Playwright (browser testing): https://playwright.dev/ • Vercel (preview deploys): https://vercel.com/ — Other references: • DGX Spark (Nvidia): https://www.nvidia.com/en-us/products/workstations/dgx-spark/ • Mac Studio (Apple): https://www.apple.com/mac-studio/ • How to design AI agent loops: schedules, goals, and subagents in Claude Code and Codex: https://www.lennysnewsletter.com/p/how-to-design-ai-agent-loops-schedules — Where to find Alex Finn: LinkedIn: https://www.linkedin.com/in/alex-finn-1848684a https://www.linkedin.com/in/alex-finn-1848684a/ YouTube: https://www.youtube.com/@AlexFinnOfficial X: https://x.com/AlexFinn — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Claire Vo?

  What is stacked around your office right now?

• 0:03Speaker 01

  I have three Mac Studio 512GB. 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. 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,

• 0:46Speaker 01

  you would be spending outrageous amounts of money.

• 0:49Claire Vo?

  What else fun are you doing with AI?

• 0:51Speaker 01

  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 kind of cracking this nut of how do you build your own software factory?

• 1:35Claire Vo?

  Welcome back to How I AI. I'm Claire Vau, 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 going to 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.

• 2:19Claire Vo?

  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. Alex, welcome to How I AI Hardware Edition, Local Model Edition.

• 3:04Claire Vo?

  I am so excited. So before we jump in, tell us, just what is stacked around your office right now?

• 3:13Speaker 01

  It feels like a sauna in this office right now, to be quite honest with you, because I got a lot going. I have three Mac Studio 512GB, which apparently puts me in the wealth class of Elon Musk having these now. I think they resell for like $30,000 each. We got a DGX Spark, as well as a computer I just built two days ago, 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 of hardware around here.

• 3:49Claire Vo?

  And you are making really good use of it.

• 3:51Speaker 01

  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 you really can't do with cloud APIs.

• 4:16Claire Vo?

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

• 4:28Speaker 01

  My big awakening was back in January when I discovered Open Claw. And so I was scrolling X one day on a Friday. I saw a blog post around Open Claw. I open it, I'm like, wow, this is really interesting. Don't know why my gut instinct is like, I gotta go buy a Mac Mini. I go to this, this is before anyone was talking about Open Claw, 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 Open Claw 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.

• 5:13Speaker 01

  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, oh, 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 Open Claw. It only just advanced the last couple of months, you know? Then they started banning frontier models like Fable. And then they, you know, the hardware prices start exploding and it just felt like everything was moving in this direction of like sovereign, own your own intelligence.

• 5:56Speaker 01

  And so I just kept investing more and more and more into it over the, the last couple of months.

• 6:00Claire Vo?

  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 tell this story, I woke up one morning and turned to my husband, because this is the kind of things I say to him. And I was like, I'm having a chat, like truly having a chat GPT 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.

• 6:46Speaker 01

  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.

• 6:57Claire Vo?

  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 cloud work. But let, let's go to, 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?

• 7:22Speaker 01

  Yeah, so I have been 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. But since then I've experimented with kind of AI focused computers like the DGX Spark. And then just recently, most recently, I've been buying kind of traditional GPUs from Nvidia, like the 5090, soon the 6000 Pro. I put together a little bit, a little chart here.

• 7:55Claire Vo?

  Yeah.

• 7:56Speaker 01

  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 kind of powerhouse, traditional Nvidia chips like the 5090, building computers around these chips. And then basically everything else, laptops, Mac minis, whatever you got sitting around your house. They all have different strengths and weaknesses, different reasons why you'd want to 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 Idiot chip, for instance, and you build it into your computer.

• 8:41Speaker 01

  You have kind of 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.

• 9:21Speaker 01

  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 4.8 level, 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 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 running on your hardware, which is amazing.

• 10:05Speaker 01

  Then you have AI computers. This is like the DGX Spark, very popular right now. It's like, I think the price just went up like, I think $4,600. A Micro Center, I think you have for $4,000. These are plug and play AI workstations. 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 kind of these midsize, good models like Qwen-3.6 pretty quickly, which is nice.

• 10:49Speaker 01

  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.

• 10:56Speaker 01

  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 kind of 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. And then you have everything else, the Mac minis, your old laptop. You can still run local models on them.

• 11:39Speaker 01

  There's options out there like Gemma four, um, like a few other really small ones. They're not going to be anything close to Frontier, but you can do small things like embedding, which is basically managing memory for your agents.

• 11:50Claire Vo?

  So you have these options and so Mac Studio is just like big beefy models, high intelligence, low kind of these AI computers, kind of like sweet middle spot. And then these chips, very, very speedy. Um, and then, you know, dusty old 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 going to 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,

• 12:34Claire Vo?

  I cannot do enough on my MacBook Pro. I'm 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.

• 12:47Speaker 01

  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.

• 13:00Claire Vo?

  Yep.

• 13:01Speaker 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, screenshot, then 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.

• 13:20Claire Vo?

  Yep.

• 13:20Speaker 01

  And then like send you screenshots of what it's doing. So it's, it's a lot better for that, too.

• 13:25Claire Vo?

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

• 13:35Speaker 01

  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 a thousand 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.

• 14:18Speaker 01

  And then so as long as you have an agent, Hermes or OpenClaw, 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 and needs, set it up, do whatever you need. Uh, I'm going to 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 can just say, hey, OpenClaw, check out the new Mac Studio I just bought. 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.

• 15:03Speaker 01

  And 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 no technical work needed.

• 15:12Claire Vo?

  And so to repeat this for folks, just so 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 little virtual private network. And then that one IT guy, OpenClaw or Hermes agent, can then use TailScale to just go into these different machines, configure and manage them.

• 15:32Speaker 01

  Yeah, exactly. So TailScale is worth getting even if you just have one computer.

• 15:37Claire Vo?

  Yeah.

• 15:37Speaker 01

  Because it creates this private network where, even if you're vibe coding on your MacBook Pro and you just have a phone, you can go on the local host on your phone that's on your computer and test your local apps out because it's all in 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.

• 16:14Claire Vo?

  This episode is brought to you by Jira Product Discovery. AI has made individual PMs incredibly productive. But multiplayer mode is where it still breaks, getting everyone aligned on what should actually get built. Decisions live in a markdown file from last week. The roadmap's a spreadsheet no one's looking at. Jira Product Discovery is where teams actually decide what to build, capture ideas, prioritize them as a team, and share a living roadmap everyone works from. It's powered by Atlassian's teamwork graph, so it can pull in customer feedback, what your team shipped, plus your goals, and suggest what to build next. And when a decision is made, you can hand it off straight to Jira,

• 16:56Claire Vo?

  so a developer or even an agent can pick it up and start building. Teams at Canva, Deliveroo, and Toast already use Jira Product Discovery. Join more than 25,000 teams at atlassian.com/howiai. Start building the right things together. Okay, so 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 effectively?

• 17:34Speaker 01

  I've been building a system over the last couple of months to coordinate all these computers and all these models. I do a lot of different things. Just to kind of set the stage here. The number one pushback I get on all this is, "Well, your computers are so expensive. 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 is in pure ROI. Not everything in your life is pure ROI dollars and cents, right? The point is the use cases it unlocks, right?

• 18:15Speaker 01

  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, which models are running on them currently,

• 18:59Speaker 01

  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. Every 30 minutes to an hour, one of these local models does a security scan. So 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 desloppifies it, right? Finds ways to optimize it, finds ways to speed it up, finds ways to make it better.

• 19:44Speaker 01

  Another local model will every 20 minutes look at Twitter, Reddit, Product Hunt, 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 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 program to solve this?"

• 20:26Speaker 01

  And this is, there's many screens here I can go through, but that is from a high level. 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.

• 20:40Claire Vo?

  And so for that, let's just talk about the coding use cases really quickly. 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?

• 20:59Speaker 01

  When getting into local models, you really want to understand what is the delineation between what you want to do with local models and what you want to 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, what the problem is, put it all in this markdown

• 21:40Speaker 01

  file that describes, like, for instance, today's has 374 findings in it of security issues.

• 21:47Speaker 01

  Every day I have a loop running in Claude Code, so slash 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 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, thousands, 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 going, okay, what can we fix? So like you don't have the closer doing all the work.

• 22:27Claire Vo?

  How do you federate work across these machines? And so we're like, do you have some that you're like, this is my coding machine? This is my market research machine. Are you like round robbing? Again, we're like using very SDR terms. I'm like, are we round robbing these leads like?

• 22:42Speaker 01

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

• 22:45Claire Vo?

  Exactly. Yeah. How do you, how do you do that allocation?

• 22:49Speaker 01

  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 signal. That's a very simple thing to find. Look for someone who has a challenge.

• 23:30Speaker 01

  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 be appropriate for those strengths?

• 23:43Claire Vo?

  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?

• 23:53Speaker 01

  So I'm going to 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, 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. Models, but I think where the connector is with everything else going on is with my Open Claw and my Hermes. They're basically the 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 my personal agent.

• 24:29Claire Vo?

  Okay, I have to ask you a question. Open Claw, Hermes. You run both? You have a favorite? Tell me.

• 24:36Speaker 01

  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 Open Claw, 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.

• 25:16Speaker 01

  I'd probably be using Open Claw because I think I've had the most wow, impressive, big bang moments with Open Claw. 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.

• 25:35Claire Vo?

  That's, I mean, that was my general takeaway as well. I was like, Open Claw doesn't, doesn't functionally work, but it works in my heart. And Hermes functionally works, but has not cracked my heart yet. The, the trick that I have, I mean, we all have like agents managing agents is now I have the lifeguard. He's an Open Claw. 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 everybody else. Cause truly I was spending like you, all my time. Trying to figure out why my agents were broken. But I just, I just love, I love my little open claws. I can't, I can't get over it. It's just too good.

• 26:15Speaker 01

  I have an emotional attachment to my open claw. I've never had an emotional attachment to my Hermes. 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. An Opus one, a Chad GPT one, and on one on a local model, and then two open claws, an Opus and a Chad GPT one. And like, I, I didn't give a 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 others. So I, I don't have that failover as well.

• 26:54Claire Vo?

  Perfect. 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?

• 27:19Speaker 01

  The most fun I've been having lately is building out my software factory. So there's been this big trend the last month, uh, 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, you know, at first it was kind of angry 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

• 27:58Speaker 01

  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 have doing, I'll try to show you enough here. Let's see what I can show you. Basically what I have doing is I have, uh,

• 28:18Speaker 01

  Two loops in Claude code going. 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 in a Claude in the morning and I go morning build and ask me a bunch of questions, what I'm thinking about, comes up with a bunch of tasks to build for my SaaS Henry Intelligent Machines. And then what it does is it goes in here and 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.

• 28:58Speaker 01

  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 had a blast building out, which I think is the future of like software development where, you know, before you, you, you, you prompt your agent all day, hey, build this, now build this, now build this, now build this.

• 29:38Speaker 01

  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 merged 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 Versale preview 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, 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?

• 30:19Claire Vo?

  I love it. I love it so much. It's given me some inspiration on some things that, that I'm going to do with my own loops. And in case folks missed it, I did do a WTF our loops episode about a week ago. Very clear. I do three loops that you can copy and paste. 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 showed a couple of them.

• 30:47Speaker 01

  Thesis, by the way, 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 either OpenAI or Clou, they talk about 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 motive 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.

• 31:29Claire Vo?

  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.

• 31:39Speaker 01

  Yes.

• 31:39Claire Vo?

  They're not like, guess what? This is my loop in the morning. It runs a cron and does this thing. And two, they probably get more eyes being vague than being specific. So I am very cynical about, about the vague posting, which is why we are a screen sharing 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?

• 32:06Speaker 01

  I'm torn between the Mac Studio and the 5090. I like the Mac Studio because I love the integration with all Apple device. 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 kind of lean the 50/90 because I can play Cyberpunk 2077 in all ultra with all the hyper realistic mods. So I think I have to lean the 50/90.

• 32:41Claire Vo?

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

• 32:50Speaker 01

  So I was on Quen, 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 reinforcement learning on Quen 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 Quen. It's faster and smarter. And so Ornith 1.0 35B has been my most used model recently, and you can run on a DGX Spark. So anyone who has that, you can load it up and it works great.

• 33:32Claire Vo?

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

• 33:45Speaker 01

  We both had the same background, working for SaaS, marketing tools, spamming people all day with email to now the nerdiest technology on planet Earth.

• 33:55Claire Vo?

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

• 34:09Speaker 01

  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. Second of all, I think I'd be taken off every single social media site. So I am a pretty nasty person to my agents. I'm not nice to them. I'll just say this, I've threatened them multiple times. The threats never seem to work, even though I've threatened to hurt their agent family. Doesn't work, they still fail. But yeah, I'm pretty mean, but I find that when, just much like Claude Code and Codex, one's stupid, I just go to the other until things calm down and they figure out what's going on and it gets fixed. And then eventually they're smart again.

• 34:46Claire Vo?

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

• 34:52Speaker 01

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

• 35:14Claire Vo?

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

• 35:19Speaker 01

  Thanks so much for having me, Claire.

• 35:22Claire Vo?

  Thanks so much for watching. If you enjoyed this 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.