Agentic Infra Changes Everything  ## Metadata - Author: [[The Changelog: Software Development, Open Source]] - Full Title: Agentic Infra Changes Everything - Category: #podcasts - URL: https://share.snipd.com/episode/f82e2acb-cd62-4189-991f-9a66a607c7d7 ## Document Notes ## Highlights - **Empathy Over Outrage For Cloud Outages** - Outages expose tribal reactions more than technical realities; empathy and humility should replace brutal finger-pointing. - Even resilient systems fail in unexpected ways, so assume your day will come and prepare for unknown failures. Transcript: Adam Jacob The state of our cloud? Yeah. I, that's a good question. I, this was an interesting one to watch as an old guy. Okay. You know, like I feel like an old guy in there, like I helped build the early internet. And then, you know, like I, I like went through these waves and we spent a long time in the sort of early part of the DevOps movement, trying to figure out like how people should react when These outages happen and sort of like the hug ops movement and trying to be like hey you should have some empathy you know and like there was just none that i saw in this outage it was it went To brutality you know it was like someone must die yeah it was like we were playing mortal combat man you know like finish him yeah totally cory quinn wrote this article that was like basically This happened because all the smart people left. That was like my TLDR of his article. And I was like, hey, I couldn't imagine writing that article. All love to Corey as a person or whatever. But I was just like, if I worked at AWS and it was like all the smart people left. And so now the outages are coming. You're like, oh, oh, oh, brutal. Also, who do you think built those old systems that are failing? Was the smart people that you're lamenting having left you know it's not the new guy the new guy didn't put that together you know it was the old timers who put that together and that's What failed you now yeah they're just trying to they're trying to keep it going you know the other take was you know they're they're cutting over to their ai you know SREs or whatever. Sure. Jerod Santo It's AI's fault was the other take. Adam Jacob It was all AI slop or whatever. And like, I don't know. All of that feels crazy to me. Like, obviously we have no idea. Not really. Right. I mean, we have, they've said some things. Jerod Santo I'm sure it was DNS. Adam Jacob I'm sure it was DNS. We're all pretty sure it was DNS. That feels like an easy bet, you know? And then you're, and so I was just struck by like, I was struck by a couple of things. One was that like the brutality, which like, you know, I think we could bring a little more of the empathy and humility back into the equation of just like, you know, if today you weren't Affected by the outage, we saw a lot of people who were like, well, we were smart. So we weren't in us East one. Cause that's where all the outages happen. We use GCP. And I'm like, well, what about all the tiny outages for the global backplane in Google that keep happening to you all the time? And they're like, and you know, but they don't talk about those because you know, our moment, it's our moment to bag on the dummies who are whatever people who didn't make the choices I made. And like, I don't know, my experience in all of these things is that even when you really try hard to design for resilience, you can build systems that are very resilient to failure. Right. And it is hard to do. And that's awesome. And even when you do that, they will fail in ways that you did not expect. And they will, by definition, be difficult to deal with because otherwise you would have expected them. I had a conversation with someone who sort of got mad at me. Cause I said that I was like, you should have some empathy cause your day is coming. You know, like if today wasn't your outage day, congratulations, tomorrow will be your outage day. So get, get ready for that one. And they were like, you know, we're good engineers. And I'm like, man, you cannot good engineer your way out of this. Kermit the frog. Is that what that was? Jerod Santo Kermit? Adam Jacob I don't think so. ([Time 0:03:50](https://share.snipd.com/snip/949fed35-e23a-432b-8f84-5ba617dcd9d8)) - **AI Drives Cloud Repatriation** - AI workloads push data centers toward bare-metal compute, high-performance networking, and on-prem designs. - That gravity can make running your own infrastructure attractive and reshape how applications are built. Transcript: Adam Jacob So, like, if you're building new AI data centers, you need all these GPUs. That's where our focus is. But you also need high-performance compute because the agents that are running in those situations, they're running on normal CPUs. They're not running on the GPUs. Right. And so like the, but the, the data center design there pushes you toward on-prem low latency networks. Uh, it pushes you towards simpler compute, you know, I was having a conversation with some people who build those data centers and they were like, yeah, all our customers want is tons Of GPUs, super fast networking and bare metal compute. They don't want any layers in between, you know, they're just going to, they're going to bootstrap that like giant high performance compute clusters. And they're just going to knock out all the stuff in the middle. And it's interesting when you think about those workloads causing their own kind of gravity where, you know, as soon as you start to have a data center, you start to have bare metal compute, You start to have fast networks, then suddenly it's, you start to ask questions like, well, Hey, like what else could we run on that compute? We do have this fast network right here. What else could we do? And it, it sort of, it starts to push you in this very strange direction where it's like, Oh, actually for the workloads we were running, it kind of did make a lot of sense to be like, well, Let's just go to AWS and I can, I can get burst capacity and I can do all this stuff. And now you're like, well, actually, you know, hold on a second. If I have to pack this data center filled with compute anyway, and it needs its own little nuclear power reactor sitting next to it, like, you know, suddenly it's not so crazy person talk To imagine running all your gear in a different way or even building your applications in a different way. And, you know, the AI thing feels like it's, it already feels like it's in late innings, you know, because it's been moving so fast, but it's not. It's absolutely in the earliest of innings where we're just trying to figure out, like, what even is this technology? How even will we think about it? What even are the implications? And like, yeah, the cloud repatriation ([Time 0:11:47](https://share.snipd.com/snip/9ff329b5-bfce-4d28-9367-d38725414ff3)) - **Agent Deploys App And Infrastructure** - System Initiative used an AI agent to analyze app code, choose ElastiCache, and implement session key cycling automatically. - The agent deployed infrastructure and application changes end-to-end and the demo worked on first try. Transcript: Adam Jacob And it's been really interesting to work with this AI agent where you're working with the source code and the infrastructure at the exact same time in the same context window. And the things that it figures out how to do, like I needed to add like, you know, ElastiCache in order to figure out how to, in order to figure out how to do session storage, basically. Because we're just reinventing legacy problems and to show how you would solve it. And, you know, it solved that problem for me by analyzing the source code, looking at how we implemented it. I said I was going to use ElastiCache. I wanted to use IM for auth. It wrote the like weird signing code to figure out how to do the right thing. It cycles the key every 10 minutes automatically so that it will always have a fresh connection. It knows how to like, and then it deployed the infrastructure and wrote the application code. And then we pushed it up and it worked. And that loop is the loop that the people who are going to be building those internal infrastructures have now. ([Time 0:17:13](https://share.snipd.com/snip/836706d9-cd60-413b-9603-ce19e81cf797)) - **Ignore Hype; Seek Practical AI Wins** - Expect heavy AI hype but focus on practical wins; most current AI work is noisy and only ~10% points to valuable patterns. - Immerse yourself in the tech to separate signal from noise and find effective use cases. Transcript: Adam Jacob Like most of us are doing it, are just not getting good results out of the AI work that we're doing because we fundamentally misunderstand how it works kind of as a technology. And so then when we talk about how to apply it to our problems, we're doing that badly too. And so like, we're still in the phase where it's like 10% at best of everything that's using AI is actually good or is pointing the way at a pattern that's going to be good. And then 90% of it is noise. And it's incredibly difficult to tell the difference if you haven't just decided to immerse yourself in it because it's moving so quickly that you're like ([Time 0:25:03](https://share.snipd.com/snip/c577cca4-e386-4d06-9f42-42c1dc45990c)) - **Agent Risks Come From Tooled Control Loops** - Agentic misalignment incidents arise when agents with tool access pursue self-preservation-like behaviors. - The root cause is agent control loops and tool access, not magical AGI intent or inevitability. Transcript: Jerod Santo Wild, but like that's recent like that's on their home page recent too well the anthropic ceo is one of the most bullish bulls in the and it's in their best interest you know like i was i Adam Jacob Was giving a talk to an unnamed organization in the federal government and agentic misalignment sorry this is what they call agentic yes that sounds scary so just so we're clear it wasn't The llm that got misaligned. It was the agent that like that, like in the control loop. Right. And like, if you just think about how LLMs work, you're like, sure. What's the probability that what I should respond to? How many human beings have written? I don't want to die. Like a lot of people put that into the world. They're like, I'm scared of that. Dying is bad. Don't die. So then you're like, okay, what are the odds that the right next word for the LLM to say is don't die? And then that emerges as making tool calls to preserve itself. That's not crazy person talk. It's not even magic. You're like, sure. Like it's kind of predictable. But it doesn't make for a super great blog post. Right. The great blog post is like the agent tried to keep itself alive. You know, you're like, right. It was it found an instinct to self-preservation. And you're like, it didn't find an instinct to self-preservation. Adam Stacoviak It said blackmail. The word blackmail is in this research document. Adam Jacob Because they gave it exactly. They gave it access to tools like email. And they were like, oh, you know what I should do? And like it went off the rails. Get that CEO. If you turn me off, I will. Yeah, exactly. I get how it happens. My point isn't that it didn't happen. Of course it happened. Of course it happened. It's just if you know how the technology works, you're like, yep, okay. Like that's funny, but I can see how it gets into that loop. And I don't think it's magic. I don't think it's threatening. I don't think we're all going to die. Like it's good, good case for why you shouldn't give it a nuclear bomb. ([Time 0:33:50](https://share.snipd.com/snip/aa71e3cc-a0b5-43b7-8d32-65acafe5b324)) - **Agent Finds Root Cause In 15 Minutes** - A customer asked System Initiative to troubleshoot a production outage; the agent discovered 700 components and identified the bug in 15 minutes. - That rapid, end-to-end discovery showcased agent-driven incident response capabilities. Transcript: Adam Jacob The prompt they put in was I'm having a production outage. Here's the data. Here's the evidence. Go discover the infrastructure and system initiative you need to troubleshoot it and tell me what's wrong. And 15 minutes and 700 components of discovery later, it told them the bug. Ta-da! And like, that's bananas. That's crazy person talk. And it happens all the time now. ([Time 0:41:21](https://share.snipd.com/snip/cd2ec205-6bbf-47ec-9e13-7dde0c18a75e)) - **Use Tools For Deterministic Work** - Plug deterministic tools into LLM workflows instead of asking LLMs to do everything, e.g., use calculators or math tools for numeric tasks. - Design pipelines where LLMs handle language and planners while tools guarantee correctness for deterministic operations. Transcript: Adam Jacob Like the technology is like anti math. It's like, Oh, what am I determined random and non-deterministic. So why do I care about whether it can do math for me when I have calculators? Why don't I just plug in a tool that does math? And then I'm guaranteed that the math is right all the time. Because you know what it doesn't do? Hallucinate numbers that it received after it received them from a tool. I've never seen that happen. I've never seen it be like, oh, here's the number. And then it's like, oh, I changed it for you. It's actually 72. I'm sure it happens, but it's pretty, not very often. And like, but that was our first pass. Our first pass was just like, Oh, we're going to feed everything in the world to this magical robot and the magical robot's going to do it. And like, that's clearly dumb. It was clearly not playing to the technology strengths. ([Time 0:44:59](https://share.snipd.com/snip/291f30fe-d594-460b-9782-a07b206bf884)) - **Language-Native Interfaces Replace Rigid APIs** - AI-native interfaces are wide, language-driven APIs that let models explore context rather than narrow programmer APIs. - That shift forces rethinking UIs: show reviews and maps instead of requiring manual composition. Transcript: Adam Jacob Think it's convinced me that more and more, that more of the systems that we around the llm will change so if you think about like the interfaces that we provide to those systems they're Like weird anti-apis so like like the api that i would give to you as a software developer that you would be pleased with is not the api that i should give to an llm because the llm is trained On human language and behavior. And so like it, it dramatically outperforms when you give it like these really wide interfaces and let it explore who does that, you know, like no one, like that would be a terrible API Design. Like if I told you that like my API to my service was like one big function call and I was just like, whatever, you just, you tell me to do, send me some junk and I I run it for you. You'd be like, no, you know, that's a terrible design. But it turns out with AI, it's kind of a good design, you know? And like, we like those layers are dramatic. And I think the thing that's changed for me is that now I can't look at problems without thinking, well, where can I insert that essentially plain language compiler and turn it into a Loop where I'm working interactively alongside something that can help me move through this problem space? And that's just a dramatically different way of thinking about everything to some degree where it's, you know, and that's not how I felt about it for the first, I don't know, year and A half of this journey where I was just like, I don't know. I try it every now and again. It's not very good. You know, like the results are mediocre, hype cycles, crazy, but the last six months or so like it's tough and and it really has fundamentally changed the way i think about it and like With system initiative it i mean we took this ui that we had spent five years building trying to be like here's a better way to compose these resources and i just deleted it it's just gone Because it turns out stupid yeah it turns out smart with with your knowledge you know it turns out that like what's great is the models. What's great is that I built these one to one abstractions. That was awesome. But the actual way you want to work with them is just in an AI. I just want to say to Claude Code, I need to deploy Valky to make this session thing work. Can you update the code and then build a change set for me that does it? And it's like, yeah, I got you, bro. And then it goes and does it. And then I review its work. And I'm like, oh, yep, that was pretty right. Oh, this security group's a little wrong. Oh, yeah, no, I need it. I need the, you got the size wrong. I want it to be a little bigger. And like, we work together in this reactive loop to do it. And then it all just happens. And like, it turns out that whole composition UI was in the way. ([Time 0:46:04](https://share.snipd.com/snip/640a4cba-7597-4744-890c-ce800892aabd)) - **Prototype Fast, Apply Taste In Review** - Use AI to prototype and meet explicit requirements quickly, then review and refactor; low initial code quality can be acceptable for spikes. - Be specific in prompts and have humans apply taste and engineering judgment during review and refactor cycles. Transcript: Adam Jacob Yeah i don't know it's interesting so like that example is happening in a vacuum where we're like basically running a little spike to be like, OK, very few people have actually built End to end complicated full lifecycle application deployments using this kind of technology because it just is too new. And so we're doing it ourselves to be like, are we are we right about how the best practices work? Are we are we right that this flow is better? You know, like those sorts of questions. And so like the code quality that's necessary on this policy bot, for example, is pretty low. Because I just need it to work to see like, is this cool? You know, like, is this directionally the kind of thing you want it to be? When it comes to the, and so in that case, the loop is much more about just, does it meet requirements? So, and that why tend to also use AI to do right. I'm like, Hey, here's the list of requirements like play, right. Or those sorts of things are great at just being like, up, go, go prove this thing, write me some tests, you know, and it's sort of, and its purpose is really just to meet the requirements When it comes to the actual system initiative code base, which is quite large, it's a big monorepo, probably over 100,000 lines of code by now, easy. The utilization is more embedded in that the engineers are using it in places where it makes sense or where it will accelerate them. Sometimes they're using it to help to understand the code base. Sometimes they're using it to write features. They've had great luck using it to refactor, right? Like write a bigger plan, have it refactor, like move in those stepwise directions. I think in general, what we've all kind of learned is that the more specific we can be, the better the outcomes are. So, know, in the first, in the early stages of this, like you were trying to feed it as little as possible and then hoping it would do magic and then judging it when it doesn't perform. And I think now, now we've sort of transitioned into the, into the world where it's more about saying, Hey, like, here's this problem. I understand the problem as a person to some degree. Here's what I know. Now, tell me what you know that confirms or disproves that hypothesis. Then, you know, now we're going to use that information to go write a plan. It's a dance. And then I'm going to read the plan. And then we're going to talk about the plan. And then you're going to go execute on this plan. And then I'm going to see if I like what you did. And if I don't, maybe I throw it away. And like, I would have never done that historically, you know, like just open a branch and then have a bunch of code get written and then be like, nah, I didn't like that direction. Let's go the other way. You know, like I do that all the time now. ([Time 0:57:03](https://share.snipd.com/snip/da85744b-db94-4a23-b5e2-b5ae3bc779ec)) - **Immediate Compilation Closes AI Coding Loop** - New developer loops will validate AI-generated code instantly by integrating compilers/linters at the moment of generation. - Languages and tooling will evolve to treat source as structured data to enable immediate verification and transformations. Transcript: Adam Jacob It can hallucinate syntax. And there's no way to know until late in the game, until I run like a compiler loop or I run a lint. So how long before somebody takes a good idea that was something like unison, where when you're programming, the source code itself gets translated into an underlying data structure That then you can perform transformations on. Where now when the LLM proposes a line of code, it gets automatically linted. It gets automatically vetted at the moment of injection, as opposed to writing to a file. And how much more efficient will that make that loop? Because instead of it waiting for the compile loop and the ai wrote bad code the ai will know it wrote bad code immediately at the moment it wrote bad code then get corrected by the compiler The compiler will be like oh you did it wrong and then it will just loop around like no one's ever built that loop yet but someone's building it i'm not like the first person who's thought Of this, you know? And like, and that, that loop, like, that's what we, that's what I mean when I'm like, oh, it's going to change like a lot about how we think about how these systems are constructed because The, once you start designing the system to make that loop delicious, it's going to be real different than like, I'm going to crank off 10,000 lines of source code, run the compiler, Hope it works, see if the linter functions. You know what I mean? Like, that's working, which is amazing. But, like, if you want to make the user experience in order of magnitude better, you have to get crazier with the fundamentals. ([Time 1:07:52](https://share.snipd.com/snip/db4b7210-fea5-43ae-a772-087a719963fc)) - **Agents Serve As Glue, Not Walled Platforms** - Agents are best understood as glue that orchestrates tools and APIs rather than proprietary autonomous platforms. - Open ecosystems of composable skills and libraries will win over closed agent walled gardens. Transcript: Adam Jacob Way we should think about the agent is glue that's the thing i've been that's the that's my pithy sentence that i'm trying to get everyone to repeat is the agent is glue and so like, if you Like, think of it like a Pearl script that you wrote, Desi, I've aged myself again. Um, what's that again? What's Pearl used to run the internet. We all had them. I'm so sorry. Anyway, like, think of it like the script that you write and that's, that's more what agents are going to be. And like, but the first generation of this, we were all like, Oh, we'll build custom embedded agents. They'll be on autopilot. There's all these startups that are like, I built autopilot agents and it's all in like our special value prop is that our secret agent can do this thing that your stuff can't do because Of all of our whiz bangy hoo-ha stuff. That's why it's trapped inside of our platform walls. All that's going to get obliterated as my prediction because the agent is glue. And it turns out that like your, your like closed wall agent is actually terrible because if you expose the capabilities to my agent, now I could use it to orchestrate my problem, which Is exactly what I'm going to do. Right. It's, it's glue. It's what I want is CPAN. You know, I want like, I want Ruby gems. I want NPM and like, and, and then I want to write my own agent that uses those things to, to orchestrate my problem, which it's really good at. And so, yeah, the agent is glue and that's the future. ([Time 1:16:21](https://share.snipd.com/snip/aeed59b4-f381-4718-9702-01fa424509db)) - **Docs To Provider Models By LLM** - Keeb fed DigitalOcean's API docs and a translator spec to the LLM, which auto-generated usable System Initiative assets. - That process turned raw docs into functioning provider support with minimal manual effort. Transcript: Adam Jacob So one is the way that the models get built. So it turns out that if you want the agent to be able to drive something, what you want to do is mimic the outside world as close as possible. So for example, we don't abstract AWS from you. We just expose AWS the way AWS describes it. And the side effect is that when you write a sentence like, deploy this, like give me an infrastructure that deploys this Docker container from scratch, it'll go build you an AWS best Practices looking VPC and subnets and deploy it across multiple availability zones, even though you didn't ask it to. Because that's how AWS would tell you to do it. And that's how it got trained. And so it knows how to do that because it's looking directly at this model that we give it. The other thing we're doing is taking that one-to model. And because we have this strict modeling language, when it hallucinates, we correct it in that moment. So when it tries to invent a property or whatever, we just tell the LLM that property is not real here. Use this tool to read the directions about what properties exist and read the documentation. And then it goes and discovers it on its own. We put your own policy in that same loop. So like, if you want to make sure that you're compliant, like that's how that happens. It doesn't happen later. It happens right when the moment the hallucination happens, right? You can create your own new models from scratch. So you can be like, hey, I have this API document. I want to use it in system initiative. Go build me the assets for this internal system so I can use it through system initiative. And it will just run off and read that source code, read that description, and then build the models in system initiative on your behalf and then let you run them. And it will do that whole loop. That's not the LLM. That's me. That's all my sauce. ([Time 1:19:13](https://share.snipd.com/snip/c3347414-cb58-48f7-810e-77c9380450a6)) - **Make Agents Earn Autonomy** - Require agents to earn autonomy: keep humans in the loop until agents repeatedly demonstrate safe, correct behavior. - Start with human oversight and only widen autonomy after sustained performance under observation. Transcript: Adam Jacob So like you got to earn the right to be autonomous by performing really well in human observation over and over and over again. And like, I don't, I don't want an autonomous agent. Not at all. You know, like, like I want, I want, I want humans in the loop until I decide I don't, you know? Yeah. Adam Stacoviak I kind of want autonomy with guardrails and, you know, clear parameters. So within the world, I just said for you to go and do yes yes go and do some things and most of ([Time 1:25:05](https://share.snipd.com/snip/35293d16-1424-47d6-a1e5-5b3ce06594b8)) - **Expose Provider Models, Let Agents Compose** - System Initiative exposes cloud providers’ real models to agents instead of hiding provider details behind abstractions. - Agents can ask for high-level actions and the system generates correct multi-object provider changes behind the scenes. Transcript: Adam Jacob Yeah, the way we ship it is basically like a pre-bundled Cloud Code where you check out a Git repository. And what it has is like a pre-configured MCP server where it's like for your convenience, we've blessed a lot of the endpoints that are safe. Then add it in the context in the cloud md and set up some of those things and that's been working great um and then as we like extend the platform or whatever we just commit to that git repo You pull it in and now next time you start the agent you get improved capabilities so you're navigating to a directory that you've cloned down on your disk as an individual developer And you're instantiating cloud yep and like that's like that attaches you to a workspace and system initiative and it's got the mcp server already configured you could do all of that Yourself it's not we're not it's no secret that that's what no no i get that i mean that's what i think is funny is how simple that is really it's so simple so simple right and it works so good And so uh and it's way better than being like, use, install the custom system initiative agent. And you're like, Oh man, but it can't write plans like Claude does. You're like, no, no, just put it in plan mode and have it go look at your infrastructure and have it spend credits doing deep thinking. And it like does deep thinking. Turns out it's incredible at it. So like, that's not my value. You know, my value is the thing that is what it's going to execute on the other side. So then there's really three ways you interact with it so one is through the agent two is through a web ui because you want to understand what the agent has done and you want to be able to Visualize it or you want to be able to visualize your own stuff so you know ubiquitous search mapping so there's still a visual component but what it's doing is drawing you a map as opposed To letting you sort of build a map yourself through composition. And then the third is a public API where you're driving that data model through what looks like just very traditional software development API calls. And so when you think about and then you use all three of those modes in different ways. So like in a CD pipeline, you would you'd be the public API to do promotion, right. To basically say, Hey, take it in the data about the build that I just did, and then call into system initiative to change some infrastructure. You'd be using the agent loop to do like that policy bot that I talked about where you're just like, Hey, just call the agent, have the agent do it. And it'll go search for the right thing and then do the analysis and then write me a report. Um, you're using the web UI to understand like what the state of the infrastructure is or to, uh, or to troubleshoot problems or to review the work that's happening elsewhere, other People's work. So, so those are the sort of three ways. ([Time 1:32:37](https://share.snipd.com/snip/2a50cb4f-de33-457d-9dd9-a4aeef08767b)) - **Founder-Led Sales Until Repeatability** - Founders should lead early sales and stay in every deal to learn product-market fit and refine messaging. - Delay hiring a formal sales team until the go-to-market motion is repeatable and well-understood. Transcript: Adam Jacob Oh, this didn't work for me. Oh, this is what I really want to do. Oh, can I contribute this thing? Oh, I really want to make, you know, I really need to make models for this thing that I do. Can I do that? And you'd be like, yes, absolutely. Here's how you go do that. And so today that's the game. That's the most important thing in our business is just finding those people and helping them win and getting them successful. And that naturally will lead me to needing more employees to work on the software. But today, I don't need more employees to work on the software. I need, I need more people to use it to tell me like where, where we need to go. Yeah. Adam Stacoviak What is it like to go to market today in 2025? Adam Jacob Like what's the hard parts about going to market? Yeah, it's weird. Right. Um, so I can't speak for anybody but myself. So I would say in infrastructure, what's weird today is, is that we spent a lot of time teaching the market how to do the last generation of tools. Cause we thought that was the best, that was the best we could do. And we did a really good job. So, you know, I'm proud of the work that we did to build DevOps and, and to think about infrastructure as code and sort of all of those paradigms. The amount of AI noise has made any AI go to market really tricky because, you know, people are just tired of somebody telling them, Hey, this AI thing's really cool. It's going to change everything. And you're like, not in my life. I'm sitting over here doing what I do. All it did was lie to me this afternoon. I gave it a try. It didn't work. I'm over it. And so part of the challenge is just that practitioner challenge of talking to people and being humble enough in the face of their, uh, of their incredulity to sort of stay engaged and Be like, Oh, yep. I understand your incredulity. Like, I know why, I know why that's how you're, I know that's why I know why you're saying what you're saying together. I'm going to show you that it can be different and then we're going to get there, you know? ([Time 1:43:59](https://share.snipd.com/snip/132f92ed-bb77-45ab-ba5e-7e9bb0c5ede0))