How I AI - How I run autonomous coding agents from my phone with OpenAI Symphony + Linear | Alessio Fanelli (Kernel Labs)

Episode Date: July 6, 2026

Alessio Fanelli, founder of Kernel Labs and co-host of Latent Space podcast, walks us through two very different AI workflows: (1) a fully autonomous coding setup using OpenAI Symphony + Linear, where Linear acts as a state machine and Symphony manages agents through the whole dev lifecycle with zero babysitting; (2) Codex with browser access searching eBay for underpriced Pokémon cards—autonomously browsing, extracting PSA certificate numbers, and flagging deals on $10K–$20K cards for his San Carlos card shop, Merlin Games.What you’ll learn:Why “agent manager” is a better mental model than “agent prompter”Why local Mac Minis don’t scale, and what a cloud VPS unlocksHow to wire Symphony and Linear together as an agent state machineHow to track token costs per task (and what 221 million tokens buys you)What Glimpse does, and why better agent senses extend autonomous runsWhy your CLAUDE.md probably needs a full purge, not more instructionsHow Codex scouts underpriced $10K Pokémon cards on eBay at scaleThe new category of small business that AI just made possible—Brought to you by:Firecrawl—Power AI agents with clean web dataJira Product Discovery—Prioritize with insights, build with confidence—In this episode, we cover:(00:00) Intro(02:24) Prompter vs. agent manager(04:31) Live demo: Symphony + Linear(09:31) Setting up Symphony(14:15) Purging your skills files(18:06) The benefits of this system(19:10) Demo: Using Codex to hunt for Pokémon cards(24:17) The benefit of AI for small businesses(28:23) Lightning round—Tools referenced:• OpenAI Codex: https://openai.com/codex• OpenAI Symphony (open-source framework): https://github.com/openai/symphony• Linear (project management/agent state machine): https://linear.app• PSA (Professional Sports Authenticator) grading: https://www.psacard.com• TCGplayer (card pricing): https://www.tcgplayer.com• eBay (used for card price scouting): https://www.ebay.com—Other references:• Meta Ray-Ban glasses: https://www.ray-ban.com/usa/ray-ban-meta-smart-glasses• The Monk and the Riddle by Randy Komisar: https://www.amazon.com/Monk-Riddle-Creating-Making-Living/dp/1578516447/ref=sr_1_1• The Divine Comedy by Dante Alighieri: https://www.amazon.com/dp/0451208633• AS Roma (football club Alessio and Claire are both fans of): https://www.asroma.com/en—Where to find Alessio Fanelli:X: https://x.com/FanaHOVALatent Space podcast: https://www.latent.space/—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 [e-mail].

This is my favorite positive outcome of AI, which is small business creation. Just the ability to, like, intersect the human world in a way that has been historically very inefficient, has been a quality of life improvement for me. You know, my dad, their business, they deliver fish to restaurants. They got like this freezer with the pros and stuff. And like somebody's going out there with like the pen and paper every morning, kind of like writing down what's there. Sometimes they're like, oh my God, we're missing like three tuna. So like we're missing a box of shrimp.

All of that work now can easily be automated. even with just with the Madaglases. And you have another use case, which is the use case that my nine-year-old wants to see. So let's do our Pokemon card by AI use case. So I use codex for two things. The first one is like getting the PSA certificates to keep track of a specific number for each grade.

Then the next thing I'm working on is when you go to like all these trade shows, people are coming to you, they're selling you cars and you got to price them in real time. That whole process is super inefficient because people are like searching each car manually, like on eBay or like DCG player, getting the number, you can actually use AI to save clock time for real people by doing these things autonomously. Welcome back to How IAI. I'm ClaraVo, product leader and AI obsessive, here on a mission to help you build better with these new tools. Today I'm speaking with Alessio Finnelli, founder of Colonel Labs and co-host of the Leighton Space podcast. He's going to show us how

he uses Open AI's Symphony plus linear to automate all his engineering tasks, and and how he has Codex goal shopping for very expensive Pokemon cards. Let's get to it. Quick word from today's sponsor, Firecrawl. If you're building with AI agents, you've probably hit the same wall. Your agent needs data from the web, but the right pages are difficult to find, buried in JavaScript or blocked behind logins.

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many agents autonomously across a project, but we actually haven't seen many people do it. I still see a lot of prompting, even if you're prompting into a loop or a goal or something that spawn subagents, people are really still human in the loop. And so I'd love for you to tell us how you came to this point of doing more autonomous management of your agentic tasks. I started this podcast called Laydance Space three and a half years ago. And my co-host, he had built this thing called Small Engineer at the time, which was kind of like the first autonomous coding thing. Over time, it's always been a cool demo, but I feel like the models were not quite as good to really do longer running tests. They definitely change, you know, end up last

year, and I think everybody kind of feels the same. And what really clicked for me was like starting to move away from being an agent prompter to kind of be an agent manager. And that is kind of taken a lot of different ways. So the first thing that everybody tried was kind of like the Camben board. you would kind of put all these things in there move them back and forth. What I found is that it was hard to get two, three, four turns through that. It was easy to get to

the task and kick it off, but then it was hard to intervene on it. And also like having it local just didn't quite work. So the big thing for me was moving away from kind of like local runtime to having it in a VPS in the cloud and then having different channels to

talk it to. So you can kind of like text the agents. You can use linear to talk to them. You can prompt them directly in the shell. And this is also like something, I guess like in the last month, you know, Codex also added Codex mobile. Cloud is also adding kind of like the mobile management. But I'll kind of run you through what I do. And then maybe people get some inspiration from it. Great. And I will be able to benefit from this because I'm staring at my four Mac minis over here. So I'm still running locally. And I just come downstairs and like kick them alive every now and then.

Right. So maybe you'll convince me to move all this to the cloud. Let's see. Yeah. So I try to use some more fun examples for it in like the matter one. So I own a car store in San Carlos called Marlonian Games. And so one of my interests is trading cards. So my setup is I have this thing called Zuo.

Zoh is basically like an agent plus a PPS. So this machine, for example, is like, you know, 32 giga RAM per course. I have all my coding agents pre-logged in here. And you can also use some of the open source models if you want. And what I have here is kind of like the sourcing thing where you can basically use it as your own server. On this, I have the Open AI Symphony setup. So symphony is basically, I mean, you can kind of look at it up for a better description, but it's kind of like a loop for turning issues into coding runtime and then having kind of like linear

as a source of truth for it. So what I have on my linear, I basically have all these different projects. So this one is Power Buyer, for example. And I work on it sometimes through symphony, sometimes I work on it through codex directly or clock code directly. And if you go into any of these things, basically what you see is you have the original task.

So this is what I've wrote as the initial spec, which is pretty simple. Then I'll basically move it from here to do. So this still symphony needs to work on it. What symphony does, it creates a codex workpad. So the agent kind of makes a plan on how to implement it. And as a plan, it has a acceptance criteria, different validations. And symphony has a file called workflow.md, where you basically explain what it should do for these.

This will kind of go to work and then eventually move it to human review. So what you can do here is review it on GitHub and you can add a me, in the PR and show you. You can add all these different comments, you know, I guess now the bin resolved. This is like too long, blah, blah, blah. And then you just move it to rework. So once you move it to rework and then we'll do, we'll do, we'll kick one off while we record this. It creates a rework checklist.

So it goes through all the comments and it's like, okay, um, these are all the things that went wrong, kind of addresses them. It tells you how it addressed them line by line, um, moves it back to rework. took from rework to done once it gets merged. And that's kind of like the flow. I don't really look at the traces one by one. I just kind of direct the agent to work on it. So whenever, even if you're outside, right, like I might be on my phone and I'm looking at something here.

Let's say here we're like, hey, this is kind of like noisy. What I can do is like I can create a new task as like clean up, bring me stable. Let's remove the spread column. It's too noisy. Let's also make the set name clickable. So I look at other cards. And here, I'll simply put it in to do, create a shoe, and then each of these symphonies has its own dashboard on it.

So this one is TZGVar or buyer. These are previous tasks that run. So one of the things I'm also trying to do is try and figure out, out how much is software going to cost to build? So I think people understand the idea of like the agents fried it, but sometimes it's hard ahead of time to know how many tokens it's going to take. And so it's hard to price and understand what it's actually worth doing. So as you can see, most of these tasks are kind of like, you know, 15, 30, 60.

But then this one is like 221 million tokens. And so you can kind of go back here and be like, okay, this task was how to make it deployable in Versailles. So, you know, this old thing was just not working. It was originally built as kind of like a local thing. So it had to like, like, you know, regard the storage, kind of like, you know, change other requests or end all, blah, blah, blah.

So this is like quite a big task. So it kind of makes sense that it costs a lot of tokens. And so from here, you can kind of start to think about how in the future can I make these more efficient by either adding more checks or adding better descriptions or better tooling. So the task we just created is he just kind of went live. So here you can kind of see, you know, obviously there's usually like,

you know, four or five of these in different projects that are running. So I don't really want to see the whole thing, but I just want to glance. And I'm sure I can make this UI a little prettier. Maybe once they give us FableFi back, that will be good enough. But so this is working, right? And so in a little bit, this is going to go from in progress to like Human Review. And once it goes to Human Review, then we can kind of look at the Versal Preview. And we can make comments on the code and on the front end and kind of move it back. But I could be doing this here. I could be doing this on my phone.

I could be doing this anywhere, really. And to kind of put it to the extreme ahead, let me see if I can find it. I created this project called PiQ, which was basically like putting your repo plus the Pi agent in a VPS. And then anybody on the internet, because some do I think I put it in a defense. project because send you like a coding task to your product. So it's almost like in the future, you know, and I think some people now have this idea like a request for prompt instead of like request for for requests. Everything is just how do you transfer context between people?

So before we move on to maybe another workflow, what you just showed me was, look, you can just create a linear project for any of one of your code bases. You can integrate that with Zoe and with symphony and then all you're doing is really tasking linear as sort of like your state machine for all the work that needs to happen in your code base you can manage that on linear from your phone you can manage that from your desktop from the web and you don't really have to worry about the framework of how that task gets you know broken down how it gets implemented even how your comments get reviewed that's all set up and i just wanted to share for people symphony um is something that open AI open sourced as sort of a framework for autonomous runs. So it's just a very opinionated way

to do this work. And it basically does what you just showed. It monitors a linear board, spends up agents when it gets assigned something. And then, you know, you can can land it in a PR and it gets marked as done. How simple was it for you to like actually set up symphony? Because I think people look at these things and are like, okay, that makes sense. But what do I actually do with this GitHub repository. And I know they have these two options here, which is like basically tell your coding agent to build it for you or there's this reference implementation. How did you actually implement symphony? Yeah, I took the Elexier implementation that really like the core things to change are like the workflow.md and the main in the main folder that kind of like explains

how to do it and then build the UI. So the symphony itself doesn't have a visual UI for it. And I also don't think it has the same, it doesn't have by default the ledger for token usage. So it's only like a state monitor. It doesn't actually look at like, you know, how much have you spent per task and kind of like all these different things. But yeah, I think like overall the hardness, you know, it's pretty straightforward. I think like the reality is like how do you build tools for it to be more effective? And that's kind of like one of the main things. Also, Colonel Labs we've been working on.

So we've built this other thing called Glimps, which is kind of like a playwright extension that coding agents can use to take screenshots to do visual dips between screenshots, take videos. And so it's almost like, yeah, how do you let these runs kind of go longer and longer? So it's less about the orchestration itself

and like the tools you give it to keep going versus coming back to you with the human review. And I think that's also why so important to like keep track of how many tokens and not much time it takes because it's usually like directionally it explains you how many issues it ran into you know so if you expect something you should start to have at some point some idea of like how many tokens you think this will take you know is this like a 10 million token task is this like a hundred million token task and if the reality is very far away from your expectations there's probably something in the tooling layer

that you can do to improve. So that's really where most of the kind of like value comes from for people. Yeah. One of the lessons I want people to take away from this is I get asked all the time, like, Claire, how do we build our own agent orchestration platform? And like they send me these like giant very complicated documents and workflow diagrams. And, you know, pointing them to just like symphony spec MD, which just describes how the system is supposed to work.

It's in natural language. and it just is very prescriptive about what the primitives are of this workflow and what to store and record and how to move things forward in sort of the software development lifecycle. It's very long. It's very detailed, but it's literally just a markdown file. And I think people kind of over-engineer at first what these things can be. And ultimately, the power of LLMs, especially these newer models, is you could just get. them a spec for how they will work and they will, you know, they will lock to that spec when executing whatever you hand it. Yeah, I think everybody just wants to have the magic skills file that does everything for

them on like the magic MD, the self-serve business problems. I think the reality is like now more than ever, like small sentences of like very a lot weight, you know? Like, for example, I build this work three manager before the coding agents themselves at it. And in every agent's MD, I was like, you have to use the word tree manager. And now sometimes I forget I had in some projects and then I start a task and it's like reinstall. I'm like, you don't need it anymore. But like, because I had deadline, every time it's not doing that. And I think a lot of folks have been talking about purging your markdown files now every few months. I think that's something that obviously makes sense.

I think the models themselves also have this tendency to like add rather than remove. So if you're like, hey, you don't need to always use the work through manager. Instead of removing that line, it's going to add a line to say that you don't have to use it all the time though, you know? And now you're kind of getting more and more confused. So for example, the skill that MD is not super descriptive on what to do, but it's like, hey, this is where you put the symphony. This is how you should architect it.

So every symphony instance is kind of like its own name. It's got the repo that you're working from. It's got the work spaces for each task. It's got logs, which include the token usage. And it's got the state of this run. And then these are the things that you need. So if you don't have them asked me, otherwise don't ask for stuff. These are the exact commands.

These are the flags. but I'm not really telling it what to do and what to use. I'm just saying these are like things that you got to keep in mind and then you kind of let the model work. See, this is like a good example. I think it just added this today when I was adding a new project. It's like this one is a ready to register ear and it's like this should not be in this file. Like it should look up every time.

It should just search every time what's already there. You know, those are like all examples of like if you just let the model kind of do their own things. that you just end up with this like very weird things. Okay, so red diff your skills and red diff your markdown files and get some stuff. Yeah, they're not there. I feel like the create skill thing that the codex app, for example, at it. I think it's like a great idea, but I think for a lot of people it just puts them in a lot of trouble. Because it's like they're not very descriptive in the skill itself.

And then the model is like very focused on following the skill. And so they're actually doing themselves for the service a lot of time. 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.

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Join more than 25,000 teams at Atlassian.com slash how I-a-I. Start building the right things together. Do you feel like you're getting a lot more done on this orchestrator? Is it just like better ergonomics and that you can manage it on the go? What's the benefit you feel of using something like symphony? I think it's definitely, I don't know about the getting more done. I think it, you know, in the limit you get more done, but in practice it's like you can really like, stay on top of like 100 new things that they

the thing that's really helpful is like having the full S-Zero 1 task in one place. So because it has the original spec, it has the first work pad, it has the rework work pad. Every time you're like, how did things go wrong? You can kind of pinpoint where that was.

And then you can use that to inform, you know, your agent's MD in the future, like the symphony workflow workflow. That MP. Versus if you're just using Kodaks, it's kind of, it's really hard to search through conversations, you know. And so everything is like, how do you shape the context, you know, like symphony is just a way to shape the context. It's not, it's not giving you any new capability that you wouldn't have by

using the coding agents directly. It's just helping you wield it. I love that. So, okay, you, you have shown us how you're doing sort of this orchestration of agents, or at least like workflow management of agents. Again, like, let's make this much more accessible for people. This is just a workflow to manage your agents, especially round coding. And you have another use case, which this one is the use case that I wanted to see. This other one is the use case that my nine-year-old wants to see. So let's do our Pokemon card by AI use case. People think there are a lot of startups.

There are way more Pokemon cars than they're ever going to be startups to follow. So one thing, the reason why I build this power buyer thing is for us to kind of keep track of, you know, inventory we want to buy and things like that. Yep. So I use codex for two things. The first one is, like, getting the PSA certificates to keep track of, like, you know, basically Pokemon cars, you can get them graded by PSA. And then there's kind of like a specific number for each grade.

And that's available to run API. But you need one certificate number here to start from. There's obviously no way to just download that. So what I have is, like, you know, I have codex, pursue. Google fill out the certificate number for every card that costs more than $1,000. So I give you browser access, for example, here. And it's just, you know, going on on the internet and it's looking at things and it's like downloading the images.

It's extracting the number from it. And then what you can then do is like, you know, use the eBay PSA premium want. Let's find some underpriced cards from our. Okay, so is that a skill? Yes. The skill is basically telling you how to figure out which cars are we're looking for. And then it just says how to batch them, you know? So just do five per batch.

You don't want to get like, you know, captured by eBay or stuff like that. And then there's also things, for example, there's different grading companies. So you might have, you know, a PSA 10 is the same as, like a PSA 9 is the same as like a, a BGS or CGCE 10. And so all these different rules you might want to have in there. And so here, just, you know, through the APIs, looking up from our power by your software, the cars that we want to look at.

And then it's using the in-app browser for eBay. Let's see if I can open it here. So then I was going here and it's like looking at just searching some of the, and these are all like, you know, $10, $20, $50,000. cards. You don't really do this for $5. And so now it kind of goes in here and, you know, you'll see it then. Tell us, you know, this card is underpriced or this card.

This card is, you know, we're buying right now. And yeah, this is like, you know, a good example of like you can build anything, but then at some point you've got to have some business outcome or like some way to make money with the software that you built. And trading cards is actually a good. great example of like the more money you can put to work, the more money you can make because it's like an inventory-based business. So you can only make as much as you can tell. And so having this to help us automate looking for some of these like higher value cards, for example,

it's like super useful. And then the next thing, you know, I'm working on is, and this is, I guess, getting the weeds of the business, but when you go to like all these trade shows, people might see them on it kind of like Instagram breels or whatnot, where people are coming to you, they're selling you cars and you got to price them in real time. The whole process is like super inefficient because people are like searching each car manually like on eBay or like TZG player, getting the number, blah, blah, blah. And so you're actually losing a lot of money. So when people talk about, you know, AI response time, I think for these long running tasks, it's actually not that useful, but you can actually use AI to save clock time for real people by doing these things autonomously.

So yeah, it's just been fun to try and apply it outside of like, hey, I'm building the tool for you to build the tool to build the tool so that hopefully somebody down the line does something that is worth for someone to use. So yeah, for example, we got this ampilene on right here. Let's see, I guess it broke the link. See? Even it depends on the best. of them. This is something actually, it's funny. People talk about software automation, but code access is kind of like the dollar sign. It's like tight skills. So sometimes when you're like linking with the dollar on the thing, it pre-fills it to a skill. So right now, right now it's looking for a skill. Instead of a URL. You see like TZG power by here. HGPS. So yeah, that's a good.

That's a good example of like, you know, even these small things in software are sometimes broken. Yeah. But yeah, just this is overall one trend where it's like there are a lot of businesses that are based on kind of like highly adornogeneous data that have been impossible to scale with software. Because before you have kind of like something as malleable as an LM that can go through these things,

it's really hard to use even like text or image classification for these things. And so yeah, I think you're going to see a lot of money. more of these businesses. Like, I think the same, I forget who it was, but the same thing is happening with like vintage clothing, for example. Some people are doing something similar for that stuff. Because again, you see it all the time, right? Oh my God, I went to Goodwill and I saw this, I don't know, Prada back that was like in the

Goodwill tank. And this has always been kind of like a mismatch between like human bandwidth and like the information that is coming from them. This is my favorite one in that. I think it enables what a very positive outcome of AI, which is small business creation. And I think, you know, this business of clearly trading cards are a huge business. But no, what you're able to create this bigger business because you have the leverage of AI. And this is something that a human would have to manually do.

And just the limits of time space and human cognitive capacity means you're probably unable to capture as much of this business as you are today. I also think I love this use case because it shows where AI helps you intersect the physical world in a really effective way. And an example, maybe I'll do a podcast on it, is a couple weeks ago I had a rage out about how much is in my house. And most of it is books. Most of what's in my house is piles of books. And so I placed a bet with my children and my husband. I said, how many books do you think we have in this house? and I went around with a camera and I took pictures of every, like every pile of books.

There's books everywhere. And I had Gemini go through, because seeing Gemini is like particularly good at this. And I had, we have 600 books in this house. It's like more, it's a hundred books per person in this house. But I was able to catalog all these books, put them in categories, mark where they physically are, find all the duplicates because, you know, I buy a book and my husband. buy a book. And just the ability to like intersect the human world in a way that has been historically very inefficient has been a quality of life improvement for me with AI. And that's on a personal level. But I also think as a small business owner, it's, it's really important.

Yeah. I think that's like the thing about AI that most people don't want to look at just because every previous technology was like so like the economies of scale will help you get more leverage out of it versus even the software factories, right? At some point, even if you're like Salesforce, it's not that you can release 5,000 features a week. There's some limit to which you can get leverage out of these models in specific tasks versus, you know, my dad, their business back in, I grew up in Rome.

They deliver fish to restaurants. And they got like this freezer with the frozen stuff and they have the fresh fish. And it's kind of like somebody's going out there with like the pen and paper every morning, kind of like writing down what's there. Sometimes they're like, oh my God, we're missing like three tuna. So like we're missing a box of shrimp. And all of that work now can easily be automated, you know, even with just with the meta glasses or something else.

And so you're kind of helping actually, even at a small scale, you can get a lot of leverage out of it. And yeah, I was, you know, I went for the first time to Japan last fall. And I think that's a good example of like most things that are kind of like smaller businesses that two or three people run. And they're very happy to do it. And hopefully we see a lot more of that in the U.S. too.

Yep. Well, that's the life that I live. I'm very happy to be a small business of one, one and a half people and a bunch of agents. Well, this has been awesome. I really appreciate you showing us the range of, you know, coding all the way to sort of more like physical or inventory-based AI. We'll do a quick lightning round question and then we will get you out of here. You know, my first question is, what are you excited? about that you think most people aren't doing with AI that you are either starting to do

or you think people will start to do in the next couple of months. For me, recently has been personal finances. I mean, Chagipiti just added the connectors for all your accounts. We just sold their house. And so I was like, what should I do with the money? And it's actually pretty good because it keeps you on track. I think for me in the past, the thing has been like, I don't want to like re-figure out what am I doing right now where I have invested my money, should invest somewhere else. It's the

SpaceX thing, real, blah, blah, blah. I think having AI is kind of like an offloading thing. Because in the past, sometimes people are like, I'm looking to make sure I'm not fucking it up. You know, it's not like I'm actually adding a lot to it. I'm just kind of stressed I'm going to mess it up. And so if you can have AI be the safety net, it kind of frees you from a lot of things. I do the same. Like, I was using this thing called Waifer, which is like a weekly unlimited tokens on the open source models. They just shut it down sadly.

But I was having to read my Gmail every five minutes. And I was like, just read, is there any email I should actually respond or look at? Before I was always like, oh, maybe I'm missing something. I should like check my inbox, blah, blah, blah. Now I know 100% that if something important comes in, I'll know about it. And that kind of removes a lot of stress from it. So it's kind of being this kind of like context offloading. I think people should do more of that.

I completely, completely agree. Okay, my second question, because we were talking about books, and I'm going to make you laugh, which is I'm going to turn off portrait mode. We have, we have this book. Look at us. Yeah. Yeah, yeah.

I mean, we have, I think we have a lot of the same books. You know, we're very typical. I'm sticker or a farming, you know, with my page markers. Love that. So what's a book that you always recommend to people? Oh, what's a book? I think it depends. I feel like in different times of your life, you need different books.

One thing I actually always recommend is called The Monk and the Riddell, which is kind of like based on startups and venture capital, but it's basically this VC meeting with this founder, and the founder is like, I'm going to do this startup for like funeral homes. And I don't really like funeral homes, but I think it's a big market. And then once I sell the company, I'll be able to do what I'd like. And the VC is kind of like, well, why don't you just do what you like now? And it's like, well, I don't know.

It's kind of risky. It's like, I'd rather just do this thing that is like big and like then I'll do what I like. And I think I see it a lot in founders where it's like I should do the thing that people want me to do versus like doing the thing you're passionate about. I think that's one that I always recommend to people. It's very short. People like it. Outside of that, yeah. I mean, Divine Comedy by Dante.

I grew up in Italy, and in Italy for three years, you have to study the Divine Comedy. One year, you know, infernal, purgatory heaven. It's just a reminder of like how great the human mind can be. Like, you know, in the middle of the 1200s. Okay, we're going to do behind the scenes, Howie AI. My son, Henry, is over here in the corner listening to the podcast. Henry, just say really loudly. Are we making you learn about Dante's Inferno as well?

Dante's Inferno, I told you, about going to H-E-Double L. Hell. You don't remember? Yeah, yeah. Yeah, I'm teaching you about that. Yeah. And the seven days. You know, I also, quick aside, are you an AS Roma fan?

Of course. We're a Roma family. I only do this that I can buy one in the few. My six-year-old is like three days out of the week wearing a Roma kit. He's got like three different ones. So my husband and my boys are Italian citizens. I am not, but they are. Nice.

No, I literally, we're God. I just reached out to like the president of Roma. Like last week, I was like, hey, I can do anything I can do to help you use AI and be a better club. I'll do it. I'll be, I'll come out there. Put us into. My husband and I. We'll sign up for the ASRoma AI transformation.

Perfect. I love it. Okay. My last lightning round question, which is when you're prompting and AI is going off the rails, what's your strategy? What do you do? Oh, man. It's hard.

So after I swear it a couple times, I usually, so one, like, you know, I have this absorption to all the providers. So maybe I'll just try another one. Yeah, restarting conversations. I think that's obviously like a great example, kind of like tweaking the prompt to put things in there. Try and break down the problem in smaller pieces maybe. That's been another one.

You know, sometimes you're being too ambitious. I think that's great. I think in general, if you're not getting enough failures, you're probably not trying hard enough. You know, you're not being ambitious enough on what you're doing. Yeah. And then, yeah, remember to be, that's why sometimes I use,

I usually like start and I type something. And then once I get frustrated, I go back and I just like use speech to text to longer prompt. Yep. I'm like, all right. I can't, I can't like keep typing these things. Yeah. It helps sometimes because like once you start talking, you maybe just add a few more details that help.

Yep. But I think it really depends on the task sometimes. Yeah. My friend Hillary came on the podcast. She's actually been on twice, very popular guest. She calls it the Yappers API, which is she just goes. You just go like this until you've gotten an all out and you press enter. you don't even look at it and that is usually the most effective thing.

Well, this has been super fun. Thank you so much for joining. Where can we find you and how can we be helpful? I'm on Twitter at Fana H-H-O-V-A. And then, yeah, you can subscribe to the latest space podcast. What else? Well, I also run a space NSF called Kernel. So if you want to come work from here, we have an open coworking.

We do like 15, 20 events every month. We just did a Matt Palladis class yesterday with some. somebody from Open AI as the teacher. So, yeah, just come and say hi. Yeah, and if you're building anything interesting in this kind of like, you know, software factory space, I'm always happy to chat. And remind us where the store is.

San Carlos called Marlion Games. Okay, we got it all. Thank you so much for joining Hawaii AI. Thank you. 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 how IAIIPod.com. See you next time.