Gas Town, Beads, and the Rise of Agentic Development with Steve Yegge

24:50They love acceptance criteria. They love checking things off. Yeah. And so on the boot up side, there's a bunch of stuff that I want them to do specifically because they failed to do it on the shutdown side. So like on boot up, I want you to go and look for branches and stashes and unmerged work and blah, blah, blah, right? Unclosed beads, whatever. Clean up your sandbox, clean up your environment on boot up as well as on shutdown. And so both of those instructions became things that I encoded in prompts. And so for many people, they're at a phase in their engineering where they're managing your own private libraries of prompts that they like pull out as needed, right?

25:26And Gastown was just basically me going, well, what if I could just have some canned prompts that sort of came up when certain roles came online? And then it was all predicated on this, what if Cloud Code could run Cloud Code, right? But yeah, the boot up and the landing are really important. So like I have this prompt called Land the Plane. And my colleague on Monday was telling me about this. He hasn't used Gastown. He just uses Beads, right? But he found Land the Plane really useful. I lived by Land the Plane for, I don't know, Six weeks. Feels like six months last year. Time is compressing.

25:54It's super compressing right now. Yeah. So land the plane is okay because the agent will be like, party, we are done. Right? The agent is like, literally, it's giving you emojis, checklist, to the moon. This project is ready to launch. I am done with this feature. Look at all the things we accomplished. Anthropic models in particular love to do that. They love to make it. GPT is a little bit more staged, but yeah. Except GPT can't code, so who cares, right? Well, different discussion. I have found GPT-5.2 to be incredibly effective for particular styles of code and prompting. But it goes a hell of a lot slower.

26:29I heard Gemini 3 is really good at UI coding, maybe as good as Claude. So yeah, sure. And UI coding is like a thing that GPT just falls flat on its face. It's so bad. So bad. Yeah. So I don't know, maybe they'll develop specialties, right? So Claude loves acceptance criteria and landing the plane takes advantage of that by, it's almost taking advantage of them, as if they were OCD and giving them some OCD thing to make them right. Because even if they're low on context, even if their context window is near exhausted and you're like, let's land the plane and they look up the instructions, they'll be like, yes, sir.

27:02And they'll start checking things off and they will finish that thing, right? Even if they hit a compaction, which I view as a failure mode, right? So you try to land the plane as early as you can. But yeah, the land the plane makes them much more reliable at not forgetting stuff. They're just, it's like they have common sense, but they get distracted. They need to be reminded. Yeah. So yeah, I mean, like these are all muscles that you build as a developer before you jump in the lion's den with something like Gastown, right? You've got to be really, really good at like bringing context into LLMs, watching how they deal with it and triaging it and dealing with it.

27:35And once you get into that cycle manually, leave for a while, right? You're going to feel more confident to be able to wrangle like eight of them at a time. So let's maybe use that then as a jump over into Gastown. And let's start from the beginning. Like let's describe for anyone who has not read your rant or the many responses that has fallen down on the internet. What is Gastown? What's in the box? All right. I mean, there's a lot of different lenses that you can use to look at Gastown, right? The simplest lens is to lump it into the category of orchestrators, which include things like Devin, which has been around for a long time.

28:10They're attempts to run multiple coding agents as a team, basically, or just in parallel on parallel tasks with some tracking layer. And the Ralph Wiggum loop and the loom loops from Jeffrey Huntley, and you've got Claude Flo, the fancy one with the routing. And then you've got what else? There's a few others here and there, but there aren't many, but it's in that category. It's an orchestrator, right? So it lets you run multiple coding agents. And it's pretty closely tied to Cloud Code right now because it's on the boundary. It's on the edge. It pushes the agents so hard that they get confused regularly.

28:43And so only Opus 4.5 is really strong enough to be able to run Gastown reliably. And even then, it breaks a lot. But anyway, Gastown is predicated on a really simple idea. All right, let me tell you how it goes. As your trust with the LLM grows, okay? And this is my eight stages of programmer that I called out, which got a lot of attention on its own, by the way, because it was a real challenge to people. Right. But I'm sure it resonated that they realized that they had moved up from stage one and they finally felt pretty good about it and that they were done. And when they saw where they fit on the entire thing, they were like, oh, no.

29:17And their ego is a bit bruised. But by the same token, they couldn't really deny it either because they had already seen their transition of leaning more and more into the agent. What's happening is you're trusting it more. And by trust, I literally mean you can predict what it's going to do better. That's the only way you're going to trust it, right, is being able to predict. And that just means practice. And we're talking hundreds to thousands of hours of practice to get up that ladder, right? And this is not just developers. It's developers today, but it's going to be all knowledge workers before long, right?

29:45as your trust goes up your patience goes down it's very interesting okay because you're like you got your agent and they're working on the thing and you know they're going to get it done because they've done it five times properly before they're going to fix another test for you and you're just like you know what i'm going to start up another agent and that's it man that's the gateway drug that's the end of it i hear you i'm like i'm right now if i'd self assess i'm like um the cusp between six and seven for you right like i'm managing three five agents Sometimes it pushes up. And I have some questions around where I'm running into limitations that I will get to you.

30:21But okay, so you're moving up the stack. Your trust is increasing. Your patience is decreasing. You're running more and more agents. And now you start running into problems that are very different for an individual depth. They're like team problems. You have agents that are running, you know, stepping on each other. You forget who's doing what. You have gates, agents waiting on each other. I mean, it starts to get kind of complicated. And at a certain size, you lose the ability to keep track of it in your head, even if you're using beads and it's just a zoo. And so Gastown was me going, well, what if I just put them all into like WorkTree hierarchies and sort of gave them names?

30:59And oh, it was Jeffrey Emanuel and his mail discovery that really enabled it. Beads was a huge unlock, but the other half of it was mail, right? So the thing is, LLMs like stuff they're trained on. And the longer it's been in their training set, the more they like it. And so mail, email, which has been around since the 70s, is like a pair of old jeans for them. They love the mail interface. And so you put them together with identities and the inboxes and they can send each other mail and they will. And so very quickly, I had this town of collaborating agents using mail and I gave them names. You're the mayor, right?

31:31You're Polkats. And I said, you're going to be named after Snow White and the Seven Dwarfs. So I went through the Seven Dwarfs. And one day I saw the mayor and it was really mad. It mailed Sneezy Polkat. and it was like, you are not the mayor. You're a sneezy polecat. So read your own inbox and do your own work. And I was just like, what is happening here? It was beautiful. Then one day, a swarm took off and fixed all my bugs. I had like 30 beads all lined up to knock out and I couldn't find them and I panicked and I realized that they had all been closed and fixed. And I was like, woo, that's that swarm feel.

32:06So Gastown kind of emerged out of the muck, out of this primordial soup of managing agents by hand. But basically it gets into this mode where you just like, here's the thing is if you have a rule for yourself where you never watch them work, never watch them work. That's counterintuitive advice. Most people keep their eye on the agent and they're like, I'm going to see if you're going to make a mistake. You made a mistake. That's what they do. And that's what I did for, I don't know, six months, right? I tried to like watch them all. Oh, that diff looked weird, right? As soon as you get out of that mode and you realize that they're going to make mistakes and you're going to find them, just like regular engineers.

32:42Okay. So don't sweat it. Then instead, you're only looking at the ones that are finished and the ones that are finished are a problem because either they didn't land the plane properly and you need to walk them through that process, right? They think they're finished and they say they're finished, but they're not finished, finished. And so you got to walk them through that or they're really finished. And now it's like, what would you like me to do? And I'm like, well, let's talk about it. Right. Whoa. Well, and that's when you sit back and start having those wonderful design discussions with them where you're just like, okay, so what if we were to throw the database out and try something else?

33:11And then you're like, okay, off you go, go think about that. And you cycle to the next agent and you go, okay, I'm going to give you a hard problem too. And so that's actually how I use Gastown now is I spin them all up, all the crew with hard problems. And you know what? It's so wild because like eight out of 10 get done and two out of 10 get lost. And I'm like, I know we thought about this before. Sometimes we can find the design. Sometimes we lost it and I just have to redo it from scratch. It's a little annoying. So this gets into one of my key questions, which is one that, once again, I'm grappling with not even quite being at the Gastown level, but I can imagine it's even more, which is like, how do you mentally keep up?

33:45Yeah. So, I mean, like, for starters, it can be frustrating and you can find yourself yelling at them. You know, like, I told you for the billionth time, don't make PRs. We're the freaking maintainer. I mean, come on, it's right in your prompting. And they're like, I made a PR. But then, you know, I realized that it's my fault, right? Like, there's a solution for it, actually. You need a tool pre-use hook from Claude and you just say, don't make PRs. And that's the end of it, right? It's solved. So you got to realize these things are getting better so fast that you've just got to like mostly just take this very nice Zen approach and just be like, look, they're getting stuff done and that stuff is making forward progress and we're moving the goalposts, right?

34:21And so, yeah, it's a different world, man. How do you avoid getting tired? Dude, I take naps throughout the day. I'm exhausted. It's like I'm a factory manager now with a brand new team. They're all a little clueless. They're smart, but right. They're bumping in each other. And I don't know the business very well. And I'm running around trying to keep them all busy. It's exhausting. That, I guess, gets to my question of like, and this maybe gets to another kind of related thing is like how well, because I think of you write software, writing software traditionally, if we go back even two years before all this, you're writing software and it has a couple of roles.

34:52It is, you have an executable artifact. That's maybe even the smallest one. But two, you're like creating this mental model of a system that you have. You're expanding your mental model of the business problem you're trying to solve, and you're mapping between those things. Now, agents are writing code, but those two other problems still maybe exist. Do you have a mental model of the code that is being written? Yeah. So, like, have you ever worked with a really, really, really good product manager? Like a very technical one who used to code a lot, and now they're a product manager. Once. And, okay, once.

35:28Yeah, you're right. And they're like gold. Actually, the ratio is very high at places like Google, like the technical ones, but still not 100 % by any means. And the ones who are really, really good, you can have a conversation with them about almost any corner of the architecture. And they'll know whether it's using a hash table or a list or whatever. They'll know the O of N performance of it because it affects a list that the user sees, right? Or the length of time that an export takes or something, right? So they'll have conversations with you like, well, the export's taken too long. like what if we used to cash for that and da, da, da, da, da, right?

36:00They have like the good ones. And this is also true for an Uber tech lead. Have you ever worked with an Uber tech lead at a big company, right? They're working with tech leads. And so they're coming up with a consolidated, synthesized view of the machine that's being built across all of these teams, right? And so the extent that you can do that, that is an engineering leadership role. And it's a hat that can be worn by product or by technical program managers or by senior engineering leaders, executive leaders, whoever wants to get down there and try to understand how this thing is working. And I tell you, that's the most important part.

36:33It's not actually what language it's written in usually. It's not the syntax. It's not any of the details of the linker or any of that stuff. It's what does it do? What's the functional specification of this thing? And we have the ability to build software so fast now that keeping the functional spec in your head is a huge task. That is the core problem I'm asking about. Yeah. How do you do it? I have to remember the entire surface of beads in Gastown, including all of the integration points that people have brought in. And I find myself often having conversations with the LLM going, I'm really embarrassed that I know this, but how does our plugin system work?

37:09Or how does whatever work, right? Last I checked it, it met my approval, but that doesn't mean that I remember how it works, right? And so I have to go back and reconstruct it. And yeah, so it's this constant, it's a discipline thing. It's a hygiene thing. Just like you have to do regular code reviews of code you've never seen. And you have to continue reviewing it and asking questions until like as an Uber tech lead, you're like, OK, I think they're in the noise now. They're in the weeds. Like we don't need any more code reviews for now. Right. Because you've asked them a bunch of different angles.

37:35Right. Same thing goes on for the product. You've got to understand every nuance of your product. And if you don't and you're not using it, then why do you even have that code? So you've also got to start pruning aggressively and sort of like retiring features that aren't pulling their weight. because otherwise you'll have tech that mountain, right? I mean, look, man, I mean, people are like, yeah, they're not looking at the code and so they don't understand it. That's a very junior mentality. That's a person who's not very seasoned or experienced, somebody who's never led a team, somebody who's never been, you know, responsible for a very large operate, you know?

38:07I mean, like, look, I was on nuclear submarines in the US Navy and the software projects that we make are of comparable complexity to a nuclear submarine. And they're, you know, freaking six stories tall and several football fields long. And they're very complicated, right? And this notion that people have, they're just thinking about it completely wrong. I couldn't agree more in terms of like, once again, the code details, like that is in the noise at this point. And I think there is a world that we've been talking with some people about, like over the last 15, 20 years, we moved from servers as God servers to pets to servers or cattle.

38:45You don't think about servers you're spinning up and down. And we're doing the same thing with code, right? Like code is cattle is like the world we're going to here in many ways, but your system still matters. And the pace of things still matters. So I'm curious, like related to this, actually, like I have enough trouble keeping up my mental model up to date with the work that I'm doing, but I am still leading a team. So I'm also keeping up with, you know, and people who have N agents working for them. Yeah. Like this problem expands. So are there toolings that you're using thinking about this?

39:18How does Gastown expose this stuff in a way that is easier to process? How do you approach it? I was interviewed with, I don't know, 10 other famous-ish people in 2008, a long time ago. And one of them was Peter Norvig. We all got some Polish kid reached out to us all, somehow hooked us, and we all answered a bunch of questions. James Gosling and Guido Van Rossum and all these people, right? And Peter Norvig gave the best answer to one of the questions, just all time. It was something like, you know, what is it that differentiates great programmers? And everybody else went blah, blah, blah, blah, blah.

39:49And Peter Norvig said, being able to keep the entire problem in your head. That was his answer, right? And that is the problem that we're all faced with. In fact, what's going to differentiate successful teams from not successful teams is the ones who are able to keep bigger problems in their collective head. That cross-functional communication and coordination, those costs, LLMs can help reduce them, but But the rate that you're producing just exacerbates the Jevons paradox of project management, right? I had a non-technical boss once, not very technical, who they were very good at using lieutenants, like using counsel, senior principal engineers who they trusted to guide their decision making and lead their organization.

40:30And they had all the other leadership pieces. And so it was an arrangement that worked. And I think that some companies like Amazon adopt this directly. and you get this dual management type, you know, set up, right. Where you've got a manager and you've got a technical sort of advisor or a technical leader. Right. I think that we're all moving into this, into this role, right. Where we have a, you have access to a technical advisor. And so now the question is, right. I blogged about two friends of mine that yelled at their colleague because he was two hours behind them. Right. Did you hear about this?

41:00Yeah. Cause they're running 20 agents each. And so the, they've realized that they're moving so fast. that if their work is hidden from view for even a little while, if they're not completely transparent and pushing and public and loud about everything that they do, then they might as well be working at the bottom of a mineshaft. And the world will move on without them very quickly, and their stuff will be impossible to rebase because stuff just moved that fast. And they wound up getting cross with a colleague because he was like, well, I implemented the blah, blah, blah. And they're like, why?

41:30Why did you do that? What information was that based on? And he's like, it's from two hours ago and they're like two hours ago what's wrong with you we made six decisions since then right and it's like this is a serious problem right so i mean like it's the problem like beads is and and gastown have sort of taken the brakes off and is like it's going to take another one to two model iterations you know opus 5 opus 5.5 call it summertime before they run the orchestrators run smoothly and the work that they do really is you know high enough quality that the average developer can kind of use it and trust it, right?

42:03But we're almost there, right? This is one of the things and one of the reasons I was excited to get you talking on this because I've interviewed a whole bunch of people doing coding tools. I'm using all of them, and everyone's focused on the individual developer, right? How do we optimize the coding productivity of an individual developer? And LLMs are flipping crazy for this. Like, we can go so far. And using it in the team context, your bottlenecks shift completely. And now it's how do we keep our wetware up to date? Yeah. And also, like I was saying, that dual management thing hits everybody now.

42:36You are a manager. You are a leader because you have a team of software engineers. You got thrust into the role. And now your other skills, your soft skills, your humanities, your college education, your liberal arts is all really important now. Your people skills matter, right? Because you're going to have a 300 ,000 line thing you want to go and bring to another team because it cures cancer. And the other team is going to be like, we can't read your code. What do you, who are you, right? And it's going to become this negotiation to get anything done anywhere, right? Because you've produced too much.

43:10Companies are struggling with this, just merging each other's work together, right? So the better you communicate, fitting the problem in your head involves fitting the people problem in your head too, right? It's like, you know what I mean? Like the, you know, who needs what? And I think to an extent, it's a skill that some people will have and some people won't, but it's a skill that you can learn. It's a skill you can foster and develop and be taught. You can be with mentors and they can teach you the skill. This is going to be how all knowledge work works. They call it centaurs. Yeah. Have you heard this term?

43:40Yep. Do you know who invented it? I don't know the origin. So I read online. So it must be true that it came from Bobby Fisher, who was, he used it to describe AI assisted chess, you know, then there's the whole centaur minotaur blog, which is really clever, right? Which is which one do you want? You know, do you want the AI head with the human body or do you want the right the human head and everybody wants centaur and then there's debate over whether it's actually called a chimera because that's the term that academics were using until bobby fisher took it all away and then you hear like boring you know hybrid whatever but look the fact is everybody is going to have a friend a helper everybody's going to have an ai right something like claude like have you tried claude co-work and you know how claude co - can do your freaking laundry for you, right?

44:23Yep. I mean, I didn't try cowork because I already have cloud code and codex working within obsidian, right? Like I already have what I, what they're shipping it for. So you get it, but you turn on cloud cowork and you're like, Oh, I get it. It's cloud code for everybody else, like on your desktop. And to be fair, like I was trying to, my wife at some point had a problem where I was like, Oh, this is an easy cloud code problem. And I couldn't boot her into it. And then we tried to do it using cloud.ai, the web interface, and it was terrible. And so the use case for Claude Cowork makes perfect sense.

44:55Yep. Yep. Same, same. Fun times ahead. The naysayers, I think they're in for really, they're going to go through the five stages of grief this year. But interestingly enough, at the end of it, you wind up hopefully with happiness. Some people will drop out of knowledge work. I think they're just not going to like this. They're not going to like this whole centaur thing. They're not going to want to work with an AI. you know so a thing that i've been grappling with is a tremendous amount of what we're doing with ai tools right now is trying to do kind of work at the same or maybe slightly lower quality in much higher pace and there are times when like more is more right you've got a bunch of features you got to rip out like it's fine the thing i'm trying to invert is like what are the use cases or when are we able to use this ai assistant to do the same work but at a much higher quality i got a buddy who's one of the best engineers in the whole world, I mean, the stuff he's built is a really long list at big companies like Amazon and Google.

45:52He insists that his quality is much higher with LLMs. And it's because of the way he does it. He just chooses, you get with LLMs, you get what you choose, what outcomes you choose, right? And he chooses quality. And so what he does is he reviews all of their work and it becomes a pair programming exercise. And it's necessarily better because it's the best of both of them, right? Just because I'm optimizing for throughput doesn't necessarily mean that you can't dial up quality. And for certain launches like Dolt, I'm dialing up quality just insanely high, doing wave after wave after wave of code review and going to the Dolt team and having them review it, right?

46:27So, you know, quality is just a choice, man. That's all. So that's, I think, a really important thing to discuss here is like, how do you, let's spell out as someone who has been hacking with these things at both ends of that spectrum for probably more than most folks listening to this, like when you want to start dialing up quality, actually one, how do you decide this is appropriate? This is the thing where quality is, is important versus throughput. And two, like, what are the knobs and levers that you personally turn? For me, for the stuff that I'm working on, I'm in a space where I am very fortunate to be able to have a very low bar.

47:05A very low bar. Nothing has to work at all, right? Beads, I can't break now because people are depending on it, right? So, but Beads is incredibly well tested. It has significantly more test code than actual code. And I think this is just a vision of the future, right? Where we just have everything just is tested to death. And integration tests too, right? Token burning tests, right? Just like verification and validation are the two gates. And validation is, did you build the right thing? Which is another problem. You can have something go and build something that's great, but it's the wrong thing.

47:42And then verification is, did it build it well? Along with keeping the problem in your head, this is one of those, we've got new sort of pressure points. We've got new bottlenecks in development as the old ones have been obliterated by AI. One of them is merges. One of them is shared designs and keeping those up to date. And you know what I mean? Like we've talked about that. The merge one is terrible. And it's why Gastown has a refinery to try to basically have a dedicated role that knows how hard merges are and just redoes them. Right. But you know, it's funny, I showed Gastown to Anthropic and I don't know if I said this earlier, but they felt like it was just working around a bunch of bugs in their model.

48:23i mentioned that so what this implies is that and people have already pointed this out right is that gas time will flatten like i don't need as many roles because half the roles were just work around saying do your job and as soon as it knows what its job is you only need the other roles so i think you're going to have a really simple maybe two-tier hierarchy where you yeah you know maybe the little three-tier just because organizationally you know you want to take advantage of that Militaries are hierarchical for a reason. I'm sure AIs will choose to be hierarchical on large enough projects, but you only talk to the top, right?

48:59I think that's where we're headed. And I think that it's a skill that everybody will learn this year because it's just so effective, right? But senior people are benefiting more than junior people because they know what good looks like. And we're in a stage right now where the AIs don't always know what good looks like, right? So again, verification and validation for a senior engineer are often as simple as just looking at it. So it's a little harder if you're in a new space, a new domain, a new programming language, a new area. You're trying to build for a customer that's not you. I try to avoid doing that.

49:35I try to avoid building things that, you know what I mean? I was going to ask, have you tried building anything for normies using Gastown? So I have been so busy building Gastown that I haven't been able to spin up a rig for, say, like Wyvern, my video game, and just work on it, right? And see how it deals with, for example, UI programming. How do they handle that? Gastown has had a lot of bugs. Gastown has had much faster adoption than I expected, even though I expected people to ignore my dire warnings and use it anyway. And so I've had to stay on top of it because it's had stalled workers, runaway workers.

50:07It's had, you know, I had to kill 320 cloud code instances on my machine the other day. And each one of them takes like a gigabyte of memory. So it can really kill your machine fast. So, yeah, we're still in that stage. But, yeah, this is a new world, man. It's fun times. Look, can you build stuff for normies? I don't know. I wouldn't recommend it. Right. Look, why does Gastown exist? Like, what am I even doing with it? If I know that this isn't the long term shape, what is it for? I mean, one of the main things it did truthfully, and you saw this, I think, online is that it reframed the discussion completely.

50:40Oh, yeah. It reminds me of the political framing of like there's an Overton window of like where people are talking and you just moved it. I moved it way down, right? What was acceptable to talk about. And it was because up until Gastown, I was just a blogger who was saying AI is going to be big. People are going to be running fleets of agents. It's all going to be, going to be, right? and Gastown came out and was just so damn smug right like like purposely like I had a whole cast of characters it had mythology there was a song you know and a nano banana really came through my god those pictures are unbelievable right and also I had discovered the whole you know molecular work thing and so it had kind of a theoretical foundation that was actually working to where I could see it working.

51:28It was doing what I wanted it to do. It worked, right? And so I launched it and that instantly, a lot of arguments had nowhere to hide anymore, right? It changed the conversation overnight from, no, no, no, what you're saying is wrong to, bro, you're pretty aggressive, right? That was what happened. Now they're faced with the reality that it is building itself, which nobody's come right out and said it, but that's what it's doing. It's building itself. And so it's at least good enough to build itself as a swarm. And this is deeply, deeply unsettling for people. And some of the content that I've seen is just hilarious responding to it.

52:06Right. They feel like they're getting taken over by the hive mind. This is Ender's game, like playing out in front of us. It's really wild. So the building itself reminded me of a thing, right? So because that reminds me of building compilers. And one of your first goals with a compiler for a language is that it should be able to compile itself, right? You want to create a bootstrapping compiler. So Gastown, it sounds like, is a bootstrapping agent orchestration framework. It is. And it's a great analogy. And when my friends knew about Gastown, but it hadn't booted up yet, we would use the compiler analogy.

52:37I wasn't using it to build itself yet. I hadn't booted into it. I was still using just naked Claude code to build it, right? It was so weird. You would think that you would just boot into it, man. I wish I could describe this to people. You would think that it would just kind of turn on. but it went through about six or seven like layers of waking up and it felt like i was digging a tunnel and burst out into this new universe and that we started setting up camp and all of a sudden we were building infrastructure and cities and stuff right and and every single iteration of this almost every day it felt like there was a breakthrough it was like we're done gas town's finally going to be self-sustaining we figured out that there's no activity feed beads is the feed When they close, that's an event.

53:22Identities don't need to be separate. An identity is a bead. It's the data plan. All these realizations. And we were like, yeah, this is it. And then it would just flop. It was like the Wright brothers, right? It was just, it wouldn't move. And then on December 28th, one day I was like, and then we should do blah, blah, blah. And the mayor's going, okay, Convoy X landed. Convoy Y landed. That feature's done. That feature's done. And I'm like, what? Because I hadn't touched anything, right? And I realized it was working. It was doing the thing. the thing the compiler thing it compiled itself right and i so so excited it was two days till new year's and i was like okay we got two days let's make this thing launch right and so yeah that's the story of gastown man i actually got it into self-hosting mode i am curious if there's other compiler metaphors that you found useful for it i think to me once again i often use the metaphor for an llm of it being a vm of some form yeah and i think there's a few projects i see out there that seem to be using compilation metaphors.

54:19Oh, shoot. I'm blanking on the name of it. It's one that's like you express your prompt intent and it iterates on it until it can get to the right prompt for each different type. But I'm curious for you, are there other metaphors? It doesn't have to be compilers even, but metaphors that you have found useful to draw on for building this new approach? I feel like I, seriously, this system between Beads and Gastown, I feel like I'm building systems that I've been building my whole life, my whole career, and echoes of them coming up, right? Like the Wyvern property system, which is really sophisticated.

54:51It's sort of like JavaScript, but it even has transient properties, right? Like, you know, if you drink a potion, it's going to change your health by a certain amount for a while. But when you log out, it shouldn't persist with you, say, in a game, right? And so you've got this idea of your permanent properties and your transient ones, right? Well, it turns out orchestration is a lot like that too. You got your permanent work and then you've got all the throwaway stuff that's just, it was running a patrol and all you care is that it ran the patrol and what the outcome was, right? So I have property inheritance and all that built in.

55:19It's a really sophisticated pattern that I used. There's an event system that's like my video game event system. It starts to feel like an operating system. And really, with the interfaces that people have been putting on it, it starts to feel like a game. And I saw some fan fiction on X yesterday where somebody was like, my gas town went crazy. We had soldier roles and the mayors were all fighting each other. Who's the head honcho? And at first, I thought it was like real because I had just cleaned up 300 cloud code instances and like it's not out of the question that this could happen it's just that they had named roles for things and so i was like it was totally like fan fiction but it was kind of also real i think we're gonna get to the point where this acts like an rpg like a strategy game like rts i'm serious we're not far off man i mean like age of empires you're building stuff code you're building stuff and you're looking at the outputs of it and man i mean like why not make it fun it's already really fun.

56:11It's super addictive, right? Why not lean into it? So I think it's just going to be just absolutely remarkable. Like by the end of the year, people will be playing games and building software. And we're talking about now, look, there will always be a frontier where engineers will excel because you're building really difficult software. People will be very impressed by it because as an engineer, you were able to pull out or an engineering team or a giant company, you were able to pull off something that clearly took experience and resources that the regular person couldn't bring the bear. But I still think we're going to see just a huge explosion of software coming from everybody.

56:45Yeah. Just like when YouTube made it so everyone could write and social and phone made it so everyone could upload a video. I think everyone's going to be making software. So not to get out of the LLM world, but what do you see this doing to the industry? Man, like for starters, my two friends that are tripping over their third friend because they're going so fast had me very worried for what happens, right? Because I've been talking to industry and they're already like, it's already weird. It's already weird. When people use Codex or Cloud Code or Gemini CLI inside of their workplace, they're so much more productive than their peers that it starts to look weird at performance review time and then how do you hire and the whole interview process is thrown into question and it's affecting everything.

57:28Also, all your bottlenecks start to move. I've seen business teams getting incredibly surprised because engineering teams are delivering stuff for them and they're not ready for it yet. They're in no way, shape or form ready to roll out this big thing that they asked for, right? I'm seeing business teams building software for themselves because they're tired of waiting for engineers and they can now, right? And so, you know, we're seeing, I don't know, like the rise of the Jeff Bezos two pizza team where a bunch of experts who want to solve a problem just to get together and solve it. And maybe they have one engineer on staff.

57:56I'm seeing a shift to where we have all work turning into a gig economy. All workers are gig workers. Like your project probably only needs a product manager for one week out of the entire project. And it probably only needs a UX designer here and there, right? And so why not rent them, right? I see an internal economy, kind of Airbnb-like, well, maybe that's a bad example, but whatever, Uber, where you're getting a gig economy like where you're renting workers. Google's had this forever. SREs have office hours, right? That kind of thing. But I see with VibeCoding and everybody contributing to the same big artifact that they're building together, I'm seeing a much more mobile, flexible workforce where people are helping each other on the fly as needed.

58:38Because I think the answer to your question earlier is, I think old-fashioned planning goes out the fucking window. It's gone, okay? I think that companies that are successful will build stuff in real time. And the thing that they're building, the software will become the living artifact, the thing that they're building. It is the shared contract. It is the spec, right? It is the prototype in a sense. They'll have staging environments, and you'll be able to spin up five of them and try different options and then throw away four of them. And it'll be like very fertile and productive, but we're not going to be sharing specs anymore.

59:11I think those are kind of old. I think we're sharing ideas that are have actually implementations. Yeah. And this is going to just utterly change how companies do their business. Right. I mean, like silos will get busted up. I think a lot of bureaucrats that are manipulating the system in their favor to try to like, I don't know, keep work for themselves or keep work off of their plates or whatever. They're all going to get found out and kicked out. And I think the system's going to reward people who are really, really good at working with AIs and really, really good at working with people and kind of making stuff happen in big chaotic environments.

59:43Yeah. But that's just what I think. What do you think, K-Ball? I mean, I'm already seeing a lot of that happening. I think one of the things I'm trying to wrap my head around are like, the bottlenecks are clearly moved. How do we keep attacking those bottlenecks or moving them or changing them around, right? Like, so you mentioned about Planning is a huge bottleneck at this point, and architecture is in decision-making. There's definitely still, we sort of joked around this Uber tech lead, right? We're all having to become Uber tech leads in our capacity to absorb information about the state of a system.

1:00:16That's a bottleneck because that is a skill that most engineers have not developed. I don't even know all engineers can develop it. Or want to, right? It's not some people's idea of fun. you know they'd rather solve a problem than verify somebody else's solution for example and i get it i don't think it's for everybody but for me it's like a dog sticking his head out the window you know getting all them smells real fast it's a wild ride and it's amazing right like i am managing a team and coding more than like more code that i'm producing than i ever did as an individual engineer yeah you get those feelings of the happy happy programmer feelings when stuff clicks together and works.

1:00:56And you're like, yeah. And you're just getting them all day long, just getting those dopamine hits. It's like certain video games have found out formulas that just maximize dopamine. And they're really addictive and people just get completely into them and they spend hours and hours. We're getting really close because programming has always been kind of addictive, but you got to be in flow and everything's got to be going right. And you can't be wrestling with some stupid JavaScript library or whatever. There are so many people I know. I mean, I had this literally yesterday. I'm sitting in an airport and I've got crappy Wi-Fi and whatever, and I need to go to the bathroom and get food.

1:01:31And I'm like, I can't give up. I just kicked off some agents. I want to see what they do. They're on my laptop and I'm not, you know. Seriously, using coding agents is like playing blackjack. It's like a slot machine. And it's because what I was getting at earlier, when you start to trust the LLM to the point where you will let them work, then you will spin another one up and another one up. And it's literally spinning in the sense of, you know, text is scrolling by. And you'll eventually reach an equilibrium always where you always have one available, at least. There's always something to check in on and say, oh, this is finished.

1:02:03What's going next? There's always one waiting for you, right? Waiting for your input. And so even if the tools make you better at it, you'll still be in that equilibrium quickly. And it's just like Assassin's Creed got towards the end where you were sending spies off on missions. And there was some magic about it where you weren't doing the missions yourself and you wouldn't think it was as fun as playing the regular game, but it was, right? And so like, yeah, it's almost like coding with agents with an orchestrator is maximizing dopamine kind of like going to a casino or like playing one of those really addictive video games.

1:02:33But to me, that's really heartening because think about it. If all knowledge work gets broken into beads, into bite sized pieces, and we find a way to match all of the knowledge work to all of the people in the world and everybody can participate in this, everyone's going to be building software and having fun. And you know what I mean? It's democratized. I don't know, man. I see kind of nothing but upside from all of this. Everybody's really scared about it, but I actually think people are going to just, they're going to be blown away with what they can create. I'm bullish on the future. That is actually like a really nice close.

1:03:03So we could close there, but is there anything we didn't talk about that you want to talk about before we wrap? I'm going to share a realization with you that I had, and I'm pretty convinced that it's pretty accurate, but it's first time I've ever shared it. So this is right for your show. World first. Right? I think that I figured out the magic formula to tell if you're going to live or die in the world of AI as a software product. But I don't know if that's interesting enough for your viewers because, you know. Well, I'm interested. Yeah. I think everyone's trying to figure it out, right? All the boards.

1:03:34Because look at companies like AI is eating software. It's eating jobs. It's eating entire categories. Stack Overflow, poof. Chegg, right? The homework company, poof, right? Then, you know, call center companies, you know, IDEs are starting to worry. You know, there's a lot of software out there that's starting to get a little worried that AI is going to eat it. And in fact, all boards should be worried because Dr. Andre Karpathy is out there saying that AI is going to eat all software and there'll be nothing left. He's very worried about this, right? So how do you tell if you're going to make it or not?

1:04:05And I think that the answer is basically thermodynamics, right? If you can find a way to save tokens, then the AI will use your thing. and you will live. Calculators, databases, storage systems, ledgers, transactional workflow systems, routers, networks, infrastructure, anything that does a bunch of computation and math that the LLMs could do in their heads. Did you see the article Anthropic Reverse Engineered, how they do multiplication? It involves chickens and goats and stuff. It's basically like they guess that it's 95-ish with one pattern match, and then they use a lookup table based on the digits to find out which that it's 95 instead of one of the ones near it, right?

1:04:45Yeah. Though once again, the simple tool of you can write code gets a lot of that to happen. Well, they do, right? They write code and they use tools. And so they're doing a bunch of matrix multiplications. They're basically lazy. They're going to take the shortest path. And so you have a couple of hurdles. One is getting your tool or product actually in their field of use if they even have the activation energy to know about you, to use you. There's a product called Serena that you may not have heard of. It's an OSS product that uses LSP servers in your IDE to save a bunch of tokens. If you have your LLM wired up to it, it will use that instead of grep.

1:05:19And it will find its way around the code base much more quickly because it's all pre-indexed and it doesn't have to use grep, right? It's proven to save a lot of tokens. So it's a more energy efficient state for the LLM to be operating in. And I will argue that LLMs will always, because of lateness, call it what you want. They have a moral imperative to use the least energy possible to solve a problem. Do they not? Right? Couldn't prove it by those thinking traces. You can't prove it by the sinking traces. By definition, well, you can for the small ones. You can see that they're wasteful and inefficient.

1:05:46Yes, precisely. They will choose the most efficient. Writing code is often the most efficient. But if there's a tool available, that's more efficient than writing code. CPU cycles are, generally speaking, going to be cheaper than GPU cycles, if you can solve it down in that layer. And honestly, I think that NPU, I think neurons are even cheaper. And humans are actually quite good at certain tasks that we're going to be able to do that are just going to be cheapest to give to us. It's the matrix. The first plot. Remember the Wachowski siblings had like, it wasn't a battery. We were being used for computation.

1:06:15I think they actually called it accurately. So anyway, that's my hot take is that the way that you survive the AI apocalypse is you build something like Beads or Dole or MongoDB or Temporal or Kubernetes or Kafka or Cassandra or whatever that's infrastructure that AIs will prefer to use instead of building their own or doing it in their heads, right? Save if you can make it clearly obvious to them and beat the thermodynamics of them knowing about your tool, which means you have to market it to them. So you've got to kind of do what Notion did and literally go and work with OpenAI and Anthropic and Google to train the models on your tool so that they're better at it.

1:06:54And you can actually pay to do this. That can overcome the activation energy for the ILM to realize that it will be a lower energy state using your tool and saving tokens for whatever task it is. Yeah. Does that make sense? Do you believe that? Well. We'll see. I think it's an interesting take. I think there is also a question of, it's not just activation energy, but it's also access, which is why everybody's racing to become a system of record in some form or another. Do you have data that they don't have out of the box? Yeah. I mean, again, I think you can still look at that in terms of energy, token spend, basically, right?

1:07:31It always comes down to how many tokens they have to spend to solve your problem. But yeah, RAG, that whole problem, it's another dimension to it. Sure. Will you survive or not? So maybe mine's necessary, but not sufficient. Yeah. But sure. Yeah. We will see, right? I mean, look, if you believe Karpathy and the AI researchers, AI will be able to do it all. If that's really true, then in two years, there won't be a bunch of apps on your phone. There'll just be one, and it'll be Claude, and it'll be able to do everything. And it'll be super addictive and more interesting than any person that you hang out with.

1:08:01And so why would you have any reason to go to a different app unless it was something that your cloud companion couldn't offer you? And that's going to be things like computer games that are really well thought out. Or data stores that it just doesn't have access to. Or products where there's a lot of people and it's, you know, collectively provide more entertainment than cloud by itself. But it's going to be a weird new world, right, where AI is going to start getting really sticky, in my opinion. People will become dependent on it. I think that's already happening. I mean, look, they can't read clocks.

1:08:29You know, I mean, like it won't be long before they can't read because why would you have to? I don't think that's necessarily bad. I just think the world is changing really, really fast. It's funny because through this conversation, I feel like half of what you've described as utopian and half is dystopian. Look, I'm going back and reading a bunch of rereading a bunch of Arthur C. Clark books, right? Because like he actually accurately predicted a lot of this. He called it aliens, but it was it was AIs. And yeah, we have a crossroads ahead of us and it could be a dark path or a good path. I really believe that.

1:08:58And there are people lining up to make it the dark path, right? Imagine a single global payment rail. Oh, how exciting that would be, a single global payment rail. Like that's digital feudalism, right? That's where they can extract transactions from everything that happens on the planet. A single work rail, a single anything rail, a single social system. Like anybody who's building towards that is building towards a surveillance state. So I'm building against it or actually an escape hatch. And I really think that humanity is reaching a crossroads and AI is a forcing function. So we'll see how it goes.

1:09:29But there's going to be massive, massive counter reactions this year, right? The social reaction against AI is going to be like nothing you've seen. All right. Let's call that a wrap. All right.

1:09:41All right.