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When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Arizona State Troopers pulled over a motorist in Flagstaff and noticed they showed "obvious signs of impairment." Ironically, the driver's vanity plates said "Arrive Alive" in a frame that read "Drive Sober: Or Get Pulled Over." Oof.See omnystudio.com/listener for privacy information.

Protecting your attention is not the same as not caring; it's what keeps the work you love possible. Lesley Logan opens August from the road with an honest look at the fighting that keeps breaking out in the Pilates industry, and why she keeps it out of her feed on purpose. She explains what gets lost when instructors tear each other down instead of pointing clients toward each other's strengths. She also shares the response to her teacher training announcement, a listener win over procrastination, and this week's mantra. If you have any questions about this episode or want to get some of the resources we mentioned, head over to LesleyLogan.co/podcast https://lesleylogan.co/podcast/. If you have any comments or questions about the Be It pod shoot us a message at beit@lesleylogan.co mailto:beit@lesleylogan.co. And as always, if you're enjoying the show please share it with someone who you think would enjoy it as well. It is your continued support that will help us continue to help others. Thank you so much! Never miss another show by subscribing at LesleyLogan.co/subscribe https://lesleylogan.co/podcast/#follow-subscribe-free.In this episode you will learn about:Why a curated algorithm protects the work Lesley came here to do.The industry hoopla that resurfaces every couple of years, and who profits.A teacher training announcement Lesley made without expecting the response.One listener win about procrastination, plus this week's closing mantra.Episode References/Links:UpLift Waitlist - xxll.co/uwsOPC Summer Tour - https://opc.me/eventseLevate Mentorship Program - https://lesleylogan.co/elevateSubmit your wins or questions - https://beitpod.com/questions If you enjoyed this episode, make sure and give us a five star rating and leave us a review on iTunes, Podcast Addict, Podchaser or Castbox. https://lovethepodcast.com/BITYSIDEALS! DEALS! DEALS! DEALS! https://onlinepilatesclasses.com/memberships/perks/#equipmentCheck out all our Preferred Vendors & Special Deals from Clair Sparrow, Sensate, Lyfefuel BeeKeeper's Naturals, Sauna Space, HigherDose, AG1 and ToeSox https://onlinepilatesclasses.com/memberships/perks/#equipmentBe in the know with all the workshops at OPC https://workshops.onlinepilatesclasses.com/lp-workshop-waitlistBe It Till You See It Podcast Survey https://pod.lesleylogan.co/be-it-podcasts-surveyBe a part of Lesley's Pilates Mentorship https://lesleylogan.co/elevate/FREE Ditching Busy Webinar https://ditchingbusy.com/Resources:Watch the Be It Till You See It podcast on YouTube! https://www.youtube.com/channel/UCq08HES7xLMvVa3Fy5DR8-gLesley Logan website https://lesleylogan.co/Be It Till You See It Podcast https://lesleylogan.co/podcast/Online Pilates Classes by Lesley Logan https://onlinepilatesclasses.com/Online Pilates Classes by Lesley Logan on YouTube https://www.youtube.com/channel/UCjogqXLnfyhS5VlU4rdzlnQProfitable Pilates https://profitablepilates.com/about/Follow Us on Social Media:Instagram https://www.instagram.com/lesley.logan/The Be It Till You See It Podcast YouTube channel https://www.youtube.com/channel/UCq08HES7xLMvVa3Fy5DR8-gFacebook https://www.facebook.com/llogan.pilatesLinkedIn https://www.linkedin.com/in/lesley-logan/The OPC YouTube Channel https://www.youtube.com/@OnlinePilatesClasses Episode Transcript:Lesley Logan 0:00  It's Fuck Yeah Friday.Brad Crowell 0:01  Fuck yeah!Lesley Logan 0:02  Get ready for some wins. Welcome to the Be It Till You See It podcast, where we talk about taking messy action, knowing that perfect is boring. I'm Lesley Logan, Pilates instructor and fitness business coach. I've trained 1000s of people around the world, and the number one thing I see stopping people from achieving anything is self-doubt. My friends, action brings clarity, and it's the antidote to fear. Each week, my guests will bring bold, executable, intrinsic, and targeted steps that you can use to put yourself first and be it till you see it. It's a practice, not a perfect. Let's get started.Lesley Logan 0:48  Well, hello, BE IT babe. How are you? Happy Friday! Oh my god, it is August. Oof! I'm on tour right now. And if you're new to this podcast, hi! On Fridays, we keep it short. We keep it sweet. It's a way for those who listen to feel like we have some time together, and I get to share wins of yours. You get to hear some wins of mine, and you also get to, I don't know, hopefully feel like you're not alone in some things. So, if you are in our communities for eLevate or for our Agency, we have an "I Need a Moment," and you're allowed to have a moment, but you have to have a win if you have a moment. And I am very notorious for coming in after you've poured your whole heart out and no win was posted. I'm like, "You know, where's the win?" And it's not because we're looking for toxic positivity, but it's because we are trying to see that even in the muck there is a lotus flower.Lesley Logan 1:35  So, at the time that I'm recording this, there's this big thing going on that I continue to be made aware of, even though I changed my algorithm a long, long, long time ago to not see a lot of Pilates stuff. So if that shocks you, it's not because I don't care. It's because I care so much that when I see negative crap on the internet, it just makes me sad. It makes me not actually want to do the thing that I am here to do on this planet, and so I just don't. I don't deal with it. And I have some really great friends who are also in the industry who also have their algorithms set that way. So if you're seeing that me and some of these people are not participating in this debate or didn't participate in the stuff going on last month, it's not because we don't care. It's because we intentionally don't let that stuff in because it would keep us from loving what we do. And so the thing is, I'm actually just so tired. I've been teaching Pilates for almost 20 years, and every couple of years there's this big hoopla of how not good enough some people are, and how much of assholes other people are, and blah, blah, blah. And then we act like it's only in our industry that this happens. Bullshit happens everywhere. People tear each other apart all the fucking time. You all know something? This is why a bunch of stock market bros own a bunch of Pilates studios, because we're all busy tearing each other down instead of focusing on what we're really great at and then making sure that we know who's really great at things we're not, and then sharing that with other people. That's what I have to say about that. So I'm just tired of it. I'm so over it. And if you're, you know, this is where I'm voicing myself.Lesley Logan 3:07  But my win, since I'm not going to leave you with that, if you're like, "What is going on in the Pilates industry?" It doesn't fucking matter. It doesn't matter what it is because in two years it'll be happening again, something similar and not the same at the same time. But the reality is people want us to fight with each other because then we are not busy focusing on making the impact we want to make, which would actually change people's lives and also yours at the same time.Lesley Logan 3:31  So my win is that last month I announced my teacher training program on UpLift publicly. I announced it on Instagram and it got so much love. Like even with all the shit that was going on in the Plaza industry, like my post about my teacher training program got so much love. So not a single person going, "Who the fuck do you think you are? It's only people saying, "Like I'm so glad you're doing this. Finally, a program I can refer people to. I want in on this. I want to learn this. I want to do it. And it just made me so happy because that's not at all what I expected. I kind of was just making the announcement to make the announcement. To be completely honest, I did not expect hundreds of people to go on the wait list. So I'm just well pleased as punch. I really, really am.Lesley Logan 4:15  Okay. So, oh, if you want information on it, you can get on the waitlist at xxll.co/uws. If you have any problems with that, there is an internet provider that can suck it because they are assholes. It's this one internet provider that drives me crazy when it comes to my short links. And so, if you have the internet provider, hit me up. I have a special link just for you. It's just not very pretty.Lesley Logan 4:38  All right, let's get into your win. Your win first one up is gonna be from @hopesewell, " Marked some things off my to-do list that I've been procrastinating on." That is massive. That is so massive. When I knock something off a to-do list that I've been procrastinating on, I am like the most proud of myself. And also, sometimes because of my ADHD, I realize, well that didn't take very long at all. It was on my wait list longer than the half life that it took me to do it. So I feel you, Hope. Thank you so much. I know you're a big OPC fan, and I get to see you on the summer tour. I'm so, so excited.Lesley Logan 5:11  So thank you for sending your win in. You guys can send your wins into beitpod.com/questions. That's also where you can send in any topics you want us to do our solo episodes on or guests you want us to feature. This podcast is here for you. It's not just for hearing me talk to myself. I talk to myself all the time. I don't need another outlet. So definitely send in what you need.Lesley Logan 5:30  And your mantra before I let you go is: I honor and cherish my life. I honor and cherish my life. I honor and cherish my life. Be It babe, I hope you do. That's the only way you could be it till you see it, anyways. Honor and cherish it. All right. Until next time, Be It Till You See It. Lesley Logan 5:48  That's all I got for this episode of the Be It Till You See It Podcast. One thing that would help both myself and future listeners is for you to rate the show and leave a review and follow or subscribe for free wherever you listen to your podcast. Also, make sure to introduce yourself over at the Be It Pod on Instagram. I would love to know more about you. Share this episode with whoever you think needs to hear it. Help us and others Be It Till You See It. Have an awesome day. Be It Till You See It is a production of The Bloom Podcast Network. If you want to leave us a message or a question that we might read on another episode, you can text us at +1-310-905-5534 or send a DM on Instagram @BeItPod.Brad Crowell 6:31  It's written, filmed, and recorded by your host, Lesley Logan, and me, Brad Crowell.Lesley Logan 6:35  It is transcribed, produced and edited by the epic team at Disenyo.co.Brad Crowell 6:40  Our theme music is by Ali at Apex Production Music and our branding by designer and artist, Gianfranco Cioffi.Lesley Logan 6:47  Special thanks to Melissa Solomon for creating our visuals.Brad Crowell 6:50  Also to Angelina Herico for adding all of our content to our website. And finally to Meridith Root for keeping us all on point and on time.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu

More like Oof-ston amirite?Road trippin'Fit ChecksMatch MoodsNo Celly Ratings This Week

My guest on the show today is Tuhin Chakrabarty, an assistant professor of computer science at Stony Brook University and the culprit behind my first ever trigger warning on eminent Americans.Here we go: if a big part of your identity is vested in being a writer, and you are already experiencing existential dread about the prospect of AI getting better than you at writing, you may want to not listen to this podcast, or at least you may need to take a Xanax or a gummy before listening. Because Tuhin's work, training AI to produce super high quality samples of high literary writing, is existentially threatening. It goes right at the heart of what we wordsmiths do. Just to give a sense, here's a passage from a New Yorker article by Vauhini Vara that explores what Tuhin has found:I asked Chakrabarty to run an informal version of his experiment on my writing, with a twist: I would pit his model directly against me. To start, he fine-tuned a model on my published writing, much as he'd done in the formal experiment. Then I sent him four short excerpts from a novel that I'm currently writing. No one else had read these excerpts; they had never been published or circulated. There was no way that a large language model could have seen them before.The narrator of my novel in progress is an Indian American ex-journalist. She runs a nonprofit that publishes stories from immigrant and refugee women, but it's strapped for funding, so she courts an Indian American venture capitalist as a potential donor. Chakrabarty used an L.L.M. to create content summaries of the excerpts I'd sent him. (One representative sentence, from a summary about the narrator's journaling habit, explains, “A pivotal memory is introduced: in ninth grade, the narrator's mother read this journal, an act seen as a profound betrayal.”) Finally, he gave the summaries to his fine-tuned model, and he asked it to compose passages “in the style of Vauhini Vara.”Going into all this, I was self-assured, even smug. I'd always felt that my style was original and, more important, that my books were totally distinct from one another. I figured that, even if the A.I. model could imitate my past books, it couldn't predict the style of the novel in progress. So, when Chakrabarty sent me the A.I.-generated imitations, I was genuinely confused. Like Díaz and Nunez, I found lots of stylistic details—rhythm, verbiage—annoying. But the text produced by the model was eerily close to mine. Reading some of its lines next to my own, I couldn't remember which was which. Unlike Díaz or Nunez, I even preferred some of the doppelgänger's versions. My style seemed to be more consistent across projects than I'd realized.I sent four passages to some readers who'd liked my previous books, explaining that half were mine and half were the model's. I wanted them to guess which were which. … The first of my readers to respond was Dana Mauriello, my best friend from college and an accomplished tech entrepreneur. “Truth: this was terrifying!” she wrote. “I was so nervous that I would say that AI wrote something that you wrote, and you would be insulted!!!” Her anxiety, it turned out, was justified. She didn't get any of them right.Dana blamed this partly on her not being a writer. But, of my seven readers, none correctly identified more than half the passages. One of the last people I heard from was the novelist Karan Mahajan, a professor of literary arts at Brown University. He and I learned to write together in college, along with Tony Tulathimutte, and have been sharing drafts with each other ever since. He's among the most perceptive writers and readers I've met. “Oof, this was really confusing and mindmelting,” Karan wrote. Then he, too, misidentified all four excerpts.To be clear, Tuhin isn't claiming that AI, except under the very specific, rather contrived circumstances of his experiments, can already match the best human writers. And he doesn't know if they ever will be able to match or surpass us. I don't know how much consolation that affords, though; what he has done is threatening enough. Moreover, in this episode we're doing what is sometimes the most anxiety producing thing of all when it comes to fraught questions: We're sitting in the uncertainty, and poking at it. How does his method work? What does and doesn't it prove? Why did he think to do it in the first place? What are its implications? What technical challenges would need to be solved for it to move from short samples, where detailed plot points and themes are provided by a human, to actually generating a full book with just a short prompt or a broad outline? And what are we human writers owed if it's our work, used as training data, that creates the conditions for being superseded by AI? Etc.I wrote a long essay, not too long ago, about my intuition that most of the fiercest responses to AI out there, both from the hardcore boosters and the hardcore haters, are flip sides of the same coin. They're dysfunctional manifestations of the anxiety that so many of us have right now in the face of what may be an extinction level event not so much in the Skynet takes over the world sense but of a more existential sort. We may soon be deprived of some of the core ways we've understood what it means to be distinctively human, how we've constructed our unique purpose as humans.Because of this fear, Tuhin has gotten a bit of hate for what he's done and shown, but from my perspective he's dealing with the existential threat we face in exactly the right way, the psychologically functional way. He's looking directly at it. He's rejecting both utopian boosterism and head in the sand denialism as strategies of evasion. He's being creative with the thing itself. And he's thinking in super pragmatic ways about how to best serve human interests in the long run. And not just thinking about that but doing something concrete and positive about it, collaborating with legal scholars on how to think about and craft policies that protect and compensate authors.So I'm sorry to stoke your anxieties, dear listeners, if that's what we do, but I'm not sorry I had Tuhin on the show. He's a fascinating and flexible thinker, a hard core computer scientist and a true lover of literature.Also: Eminent Americans, my sweet baby, is now produced in collaboration with the John C. Danforth Center on Religion and Politics at Washington University in St. Louis, and is distributed through its publication, Arc Magazine. You can find all of Arc's podcasts and much more online at arcmag.org.Hope you enjoy the show, and that the radiant beams of humanity and thoughtfulness that Tuhin and I generate between us don't just soothe your anxiety but ultimately help you to process it.Peace. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit danieloppenheimer.substack.com/subscribe

Rosemary Barnes, CEO and founder of Pardalote Consulting, joins to discuss their new grant-funded study of blade erosion and heat fatigue in Australia. Sign up now for Uptime Tech News, our weekly newsletter on all things wind technology. This episode is sponsored by Weather Guard Lightning Tech. Learn more about Weather Guard’s StrikeTape Wind Turbine LPS retrofit. Follow the show on YouTube, Linkedin and visit Weather Guard on the web. And subscribe to Rosemary’s “Engineering with Rosie” YouTube channel here. Have a question we can answer on the show? Email us! Welcome to Uptime Spotlight, shining light on wind energy’s brightest innovators. This is the progress powering tomorrow Allen Hall 2025: Well, Rosemary, welcome back to the show.  Rosemary Barnes: Thanks, Allen. Great to be here. For, it’s been a while since we did one of these one-on-one episodes, like a, yeah, a proper, proper guest.  Allen Hall 2025: Well, this is kind of a celebratory episode because your company, Pardalote Consulting, has been awarded, uh, some funding from the Australian Capital Territory’s government for the Energy Innovation Fund. Rosemary Barnes: It’s a really good program that the ACT government has to try and get energy innovation In the state. It’s not a state actually, it’s technically a territory. Little more than just Canberra, the city. Uh, but there are actually quite a few, like, really interesting energy-related companies here, partly ’cause of the, the fund I think helps, but also just tracing back like, [00:01:00] uh, y- you know, in the 20-teens, Australia had a really conservative government that hated renewable energy, and the ACT government had a commitment at that time to 100%, um, 100% renewable electricity for the, the government. And that was one of the only programs that was resulting in a lot of, um, you know, clean energy projects being built, and one of the conditions that they put on that, uh, for people that would win PPAs with the ACT was that you had to have your headquarters in Canberra. So we’ve actually got quite a few, quite a few really cool, innovative companies out of here. Um, like Neoen’s headquarters here. Windlab, uh, yeah, was, was founded here and still has a lot of people here. Pardalote obviously, and you know, a few other companies as well. So despite it being a small city of like, I don’t know, maybe it’s up to 400,000 or something people by now, um, yeah, there is actually quite a lot going on here for energy. Allen Hall 2025: And the Energy Innovation Fund is funded by the wind and solar operators in the area, and your particular [00:02:00] effort has really global consequences. You’re focusing on two areas involving how wind turbines survive Australia, but more, uh, of relevance is to just really tough conditions which exist not just in Australia but around the world. What two areas are you going to focus on?  Rosemary Barnes: Yeah. So the two focus areas are leading edge erosion and high temperature fatigue, which we can probably get into the definitions of those in a minute. But basically my, um– what led me to wanna have a project like this was that when I moved back to Australia in 2021, I– and I started working in O&M, uh, I noticed that the wind turbines that I would look at, the blades that I would look at here behaved really differently to the ones that I worked with overseas. You know, es- especially with leading edge erosion, like often I would be doing a condition assessment of a, you know, a new wind farm. Um, might only have been operating for, you know, two years. That’s a pretty common time for people to get in and do a condition assessment [00:03:00] because their warranty period is about to end and they wanna, you know, make sure that everything is okay. Um, and I would just notice that often, like 90, 100% of blades would already have bad erosion after just a couple of years, which is super-duper fast. And then there are some tools available to check, um, like what kind of erosion are you likely to experience on your site. Like is it a higher severity erosion site or a, a low severity one? Um, and you basically, you know, the status quo globally is to just look at the annual rainfall, um, and the tip speed. And if you’ve got, you know, high for both of those, that’s a bad erosion site. And if you’ve got low for both of those, it’s a, a low erosion site. But when I plotted out the wind farms that I knew had really bad erosion problems onto, you know, a chart with those two axes, I just saw a random distribution of dots. You know? Like, this was not– uh, this had no predictive value for Australian wind farms. And so that led me to believe that, okay, um, you know, things are a bit [00:04:00] different here. Makes sense, you know, most of the knowledge that we have about how wind turbines operate, it’s been developed and validated mostly in Northern Europe. You know? Like it’s, it’s Denmark and the surrounding countries that had, like, the bulk of the early wind energy. First few decades of knowledge were, you know, were mostly there. Of course, there were some other, um, places that had wind turbines, but, you know, most of the The OEMs have been operating for decades, came from Denmark. And I know when I lived in Denmark, the rain there is very different to the rain in Australia. So in Denmark, it’s basically always raining, right? Like, it’s just… Like, even if it’s not raining, you’re still gonna get wet when you go outside ’cause it’s just, like, the air has this just amazing ability to just hold onto moisture. Um, but it’s very, very gentle. But, you know, over an entire year of most days having gentle rain, that adds up to a lot. Whereas in Australia, and especially if you go, like, north to Queensland, it rarely rains. It’s mostly just dry, and when it [00:05:00] does rain, it’s like a tap turns on, and I, I swear you will get bruised from the rain droplets hitting your skin. You know, they just have so much energy in them. So I think that that i- you know, when you look at just the overall rainfall, you really hide something important about how erosion, um, can progress. Then, um, there’s other places in Australia that have very different characteristics. Again, they don’t have that kind of really intense rain but, you know, some of those sites are also having really bad erosion. And so it just occurred to me, I did a lot of research, you know, into what’s going on and, you know, the academics are studying erosion a whole lot, and they’ve got, you know, a lot of standardized tests and, you know, products are developed according to these standardized tests. But the standardized tests don’t actually resemble reality, and especially they don’t resemble reality in Australia. And so my client started asking me, “Okay, you know, the products that we have are, are terrible. We have to replace them every couple of years. It’s, um, causing big problems with also [00:06:00] the amount of energy that you’re losing.” One of the types of, um, leading-edge erosion or leading-edge problems that we have in Australia is that the, the coatings tend to peel off and make these, like, big flakes which will just massively disrupt the airflow, can cause y- you know, at least a few percent AEP loss, and maybe up to five. And even worse than the AEP loss is the revenue loss because it affects it most at, you know, lower wind speeds. Um, you get a bigger hit than at rated wind speeds. So there’s a variety of problems going on with leading edges in Australia, which mean that I, I basically… My clients would ask, “What product should we put on to prevent having to, you know, constantly replace this?” ‘Cause it costs, like- you know, 30, $40,000 per turbine to replace the protection, not to mention, you know, one or two days of downtime. It’s expensive, and I basically, I didn’t have a good answer for them. What, what product should they put on? I don’t know. No, we, we don’t know. One, we don’t know what the [00:07:00] specific, um, characteristics are that are… what the specific local environment, local conditions are that are accelerating leading-edge erosion, one. And two, all of the products tend to be tested around this, you know, there’s this protocol that academics have come up with, and they’ve kind of like assumed that this is representative of how things behave in the field, and it’s– I don’t think it’s particularly true anyway, but it’s especially not true in Australia. There are a few companies that are testing to different standards. Um, definitely applaud them. But without knowing wha- what are the conditions truly like in Australia, uh, it’s really hard to advise, like, what kind of tests should you be demanding from a product you’re considering to be sure that you’re gonna put it on and not gonna be replacing it again in two years. Allen Hall 2025: Because that’s really the trouble in Australia is when you get offered products They have been tested generally in somewhere in Europe and maybe in the United States, and then when they go to [00:08:00] Australia, it’s really unknown as to how those products will do, which is a huge risk for the Australian wind market as to what to choose, how to choose, is it– what’s real in terms of test data. So now you’re gonna go out and do what? Are you gonna put sensors out by the wind farms? Are you gonna try to do more of a statistical summary of the actual environment around wind farms using existing data? What’s the approach here?  Rosemary Barnes: It’s all of the above, but the part that is supported by the grant is that we’re gonna have enough money to be able to buy some scientific-grade sensors and put them on, um, a sample of Australian wind farms. So we’re gonna be looking at a lot more characteristics about the rain than simply is it raining now, you know, how many millimeters per hour. We’re also gonna be investigating, you know, every kind of characteristic of, of that, um, of that rain, um, including, yeah, like the, the energy that’s in it, for example. A, a bunch of stuff. I won’t get into every single [00:09:00] parameter. Um, and you know, other things as well, like measuring UV, solar radiation, um, particles, because, you know, in Australia we have a lot of dirt roads, which I know is very common in wind farms around the world, but Australian dirt roa- roads are always dry and dusty, like 99% of the time, so that’s one of the things that y- you know, maybe that’s causing a difference. Um, so basically putting sensors all over a bunch of wind turbines and then monitoring the erosion, um, a combination of some real-time monitoring and also looking at inspection, um, drone inspection images annually. We also have a- an option where we’ll just be using SCADA data and inspection images, so that’s like a lower cost version where we can combine that with the findings from the scientific-grade instrumented turbines to build up a picture of what types of conditions lead to accelerated erosion.[00:10:00] Allen Hall 2025: So the SCADA data will, will have some information inside of it, you think, that, uh, will correlate to the weather outside?  Rosemary Barnes: It has some Additionally, we can look up, um, you know, just the weather data, like how many millimeters fell during which 15-minute interval throughout the day, what was the temperature. SCADA will tell us also what the temperature was, um, what the speed of the turbine was, so you can calculate the tip speed, ’cause that’s an important thing. Um, yeah, so it’s, it’s two, it’s two tiers of data collection. The scientific grade sensors, as you can imagine, are, are really expensive and y- you know, the, the grant project has contributed a, a lot of funding, um, but it’s not enough to put those, yeah, put a little mini lab on top of every turbine across Australia, obviously. So that we’re using s- doing selectively, and then we can increase the number of wind farms that are included in the study by just doing this, um, cheaper version of the SCADA [00:11:00] plus, uh, weather data that’s available.  Allen Hall 2025: So what are some of the risks on the temperature side for all the high-temperature regions of Australia that have wind turbines? Clearly it’s generally warmer in Australia than it is in, in Scandinavia and Northern Europe. What kind of temperatures are we talking about on the ground?  Rosemary Barnes: Uh, well, temperatures here can get pretty close to 50 degrees. Um, and if you’ve ever been inside a wind turbine blade on a, even a mildly hot day, you’ll know that the temperature inside a wind turbine, and especially inside the blade, is much hotter than what it is, uh, what the ambient temperature is. So this project is one– I’ve actually been talking about this project for, yeah, like over 10 years now. Ever since I started, I moved to Denmark, started working for a wind turbine manufacturer, I had done– I had just finished doing my PhD on composite materials, structural design, and analysis. So, um, yeah, very, very familiar with, [00:12:00] you know, how composite materials work and, in particular, the effect that temperature has on them. I mean, like most materials, when composites get warmer, they get softer, and that is really important for a w- a wind turbine blade. You know, if it gets, um, less stiff, then you’re gonna get a lot more strain, and that is going to affect your fatigue behavior. Y- you know, fatigue is just the application of a little bit of, a small amount of strain. It’s not gonna cause damage, but when you apply it millions, tens of millions of times, like you do in a, o- over a wind turbine’s operate, um, operating lifetime, then that builds up. And, you know, wind turbine blades are a very fatigue-driven design. Um, it’s one of the most important things to consider when you’re designing a wind turbine blade. And so when I got to Denmark and I learned how materials are qualified and how the qualification is treated in the certification process, I just realized it’s not particularly conservative, and also that some of the assumptions that are made that [00:13:00] wo- again, they worked really well in more moderate climates where wind turbines have had most of their developmental history. You know, it’s not such a big deal there if you test at room temperature. Your wind turbine blade is spending most of its operating lifetime at room temperature or below. It’s, it’s rarely, you know, above 30 degrees in Denmark and most of Northern Europe and, you know, also a lot of, um, a lot of America, not, not all of it But, um, in Australia it has just extended periods above that temperature and even exceeding the temperature where, you know, wind turbines have an operating limit and after that they will shut down. But the operating limits are based on ambient temperature. It’s not based on what’s the temperature in the laminate, which is what really matters for blade lifetime. So anyway, I’ve been obsessed, like honestly obsessed about this issue for 10 years. Talked about it with anybody who would listen . But then when I started working in O&M in [00:14:00] Australia and I started seeing some wind farms with an abnormal number of cracks early… again, early in their lifetime, you know, I think one of the wind farms I was looking at was maybe three years old or four at the time. I think it was three actually, and had a lot of cracks, and I looked at a few years in a row and it was more and more cracks every year and I’m like, “Oof, this really looks like end of life fatigue behavior.” A- actually it’s not, y- you know, there’s this concept of a bathtub curve where, um, when you’re looking at failures in components, in, in anything, not just in, um, wind turbine blades, but you know, like you’d start– it’s called a bathtub because, you know, when it starts operating, you’ll get quite a lot of failures. Anything big, any manufacturing defects or anything are gonna cause failures quite fast, and that kind of drops off over time as all of those, uh, get addressed. And then you have, you know, the bulk of your operating life, it’s like pretty low level, pretty, pretty constant for a long time and then as you get towards the end of the [00:15:00] life, you start to see failure rates rise up again. That’s your fatigue failures, your end of life fatigue failures. And so when I saw the same types of cracks more and more each year, I’m like, “This looks like, you know, the foot end of the bathtub, not the head end.” And, uh, it made me worried and I’ve now seen that across a few wind farms in Australia at, um, hotter places. There’s a few blade types that are more prone to it than others, but at this point it’s still a suspicion that that’s what’s going on. I mean, a suspicion backed by a lot of, a lot of theory and knowledge of how the certification process works. But this project now we’ve got some funding to actually go put some sensors onto wind turbines, actually learn what the temperatures are in the blades throughout the whole laminate, um, not just the, you know, on the outside surface or not just the ambient temperature, but actually, you know, develop a temperature gradient across the whole, um, the whole laminate in the blade shell. Um, and [00:16:00] then we’re going to be doing a bunch of modeling basically to look at what is the effect of these different temperatures that blades are really seeing and how much would we expect to… that to decrease a lifetime. And then we should also be able to say, you know, if you have this issue in your wind farm, you might be able to change your operation a little bit and extend your lifetime a lot. Because this one, it’s real– like, in contrast to leading edge erosion, leading edge erosion is just, it’s, you know, every wind turbine has it to a certain extent, and it, it’s always there, but it’s a relatively minor cost to fix it. You know, like it sounds like a lot, like 30, $40,000 per wind turbine, but, um, you know, compared to if you’ve got to replace every blade across your fleet because they’re all, you know, at the end of their life after five years, you know, that’s obviously shocking. And, you know, that’s a bad example, but even in a y- you know, like a less extreme example, maybe [00:17:00] after 15 years you have to do a, you know, a f- a fleet-wide campaign to strengthen blades or something. It’s, you know, m- many millions of dollars for that, and so it c- could make sense to be able to learn, okay, what, what hours of operation should we be avoiding? Additionally, because when it’s super-duper hot in Australia, usually you’ve got heaps of solar power and the electricity price is not that high. So I, I think that there– and I don’t, obviously, before we’ve done the project, I don’t know what the threshold is. But in both cases, we will be aiming to improve the knowledge of how you can operate to avoid these periods of accelerated damage. Allen Hall 2025: Do you think you’re seeing more fatigue-like damage due to the blades operating when it’s hot or not operating when it’s hot, with maybe less airflow around the blade and maybe less cooling going on is just a temperature soak At rest? [00:18:00] Rosemary Barnes: Yeah. It’s interesting because the temperature is higher if it’s not rotating, um, because you get a whole lot of, um, convective heat, heat transfer when the turbine is operating. So your temperatures are not gonna get as hot when operating as when they’re standing still. However, if it’s standing still, they’re only very lightly loaded. Like, yes, they’re gonna get, um, blown by, by gusts and, um, have a little bit of bending, but it’s, it’s very, very small compared to, uh, if it is y- you know, operational loads. Uh, assuming that you’re not in the middle of a s- a storm. But yeah, a storm probably doesn’t come with 50 degrees temperatures.  Allen Hall 2025: And what part of the blade is susceptible to these higher temperatures? Is it the resin? Is it the fiberglass or carbon fiber? Or is it the, the glue, the bond joints? What part are you focused on? Rosemary Barnes: The resin is the main part that I’m focused on. It gl- it could be an issue for glue too, actually. I haven’t even looked into what the, um, yeah, temperature assumptions are with, with glue, with [00:19:00] bond lines. But the failures that I’m seeing in the field are not, are not bond line issues. It’s, it’s, um, a laminate problem. Allen Hall 2025: What about balsa and foam inside of the blade? Are they affected by the temperatures or are they pretty temperature stable?  Rosemary Barnes: I don’t think they’re affected at these kinds of temperatures, no. They, they don’t really do much actually. The, the core materials, like it, it is very important that they’re, that they’re there, but their job is really to keep the fiberglass separated from its- itself to make it stiffer. So, um, yeah, that’s, that’s unlikely to be a, a major source of problems.  Allen Hall 2025: So this study is gonna work over about three years, and you have a number of wind farms that are participating. Are you looking for more wind farms to participate in Australia?  Rosemary Barnes: Yeah. Yeah, definitely. I mean, we can, um, have as many as, as people want to join. We’ve got quite a good selection so far. Definitely can always welcome more. A, a bit limited in how many can get the really, um, good sensor [00:20:00]package, because the grant funding is a, you know, a certain amount, and that’s paying the bulk of those sensors. So, um, those spots are limited. So if anybody wants to really zone in on what is specifically causing erosion on their site, you know, if you know that you have got leading edge protection that is not good enough and you have to replace it soon, but you don’t know what to replace it with, then, you know, that would be the kind of wind farm that might want to consider, yeah, joining this and, um, you know, getting these sensors on their, um… We’re putting them on top of the nacelles, most of them. Um, yeah, so that would be a good match then. Um, and then, yeah, for the ones that are doing the SCADA data and, um, weather data- There’s not such a, a hard limit on how many we can have join like that. So yeah, we can have more, more like that.  Allen Hall 2025: In the temperature fatigue effort, i- is that still looking for participants or are there particular wind turbine types or manufacturers that you’re [00:21:00] looking for to participate? Rosemary Barnes: Yeah, I think, um, I, I mean yes, we can have more of those. That’s a simpler, a, a simpler issue as well. The sensors are not so expensive and, um, it’s, yeah, it’s a, it’s a simpler project to join that one. We only need, you know, a couple of turbines per site, so it won’t be such a, uh, an involved process to get everything up on into the turbines. And in terms of who might like to join that, I would say anybody that is in a really hot area where, you know, where they see a lot of days over 30 degrees, and if they see any days, you know, getting into the high 40s, then I would say that that’s worthwhile. Or even I have seen this issue in some milder sites, um, yeah, depending on the, on the blade type as well. It is more common with polyester resins. They have a, a lower op- uh, maximum operating temperature than epoxy resins. But then also just anybody that has noticed just, hey, [00:22:00] we’ve got a lot of cracks, and it seems like we’re getting more and more cracks every year, which to be honest, can be hard to keep track of if you’re… If you’ve got a full service agreement, uh, you know, an OEM managing your wind farm The early signs of this are gonna be category one and category two cracks. They’re not in exactly the same location. It’s, you know, it’s a tricky one. Normally, if you’re looking at a serial issue, then you’re going to have, uh, well, you know, your ideal pattern for a serial issue is the exact same thing happening over and over again. And so it is harder to pull this out. It also really would be very rare for it to be happening in the first two years or three years, whatever your serial defect liability period is. So it’s quite hard. But, um, another group of wind farms that might like to consider it is if you know that in, you know, a certain number of years you have to renegotiate your service agreement or, you know, it ends and you might have to take over yourself, then this’ll be a really good way for you to [00:23:00] understand, you know, have I got a ticking time bomb here? Um, because it’s not something that you’re gonna be aware of if you haven’t been, you know, doing some really, really in-depth shadow, shadow monitoring of your blades, you know, running your own inspections and looking at every single damage, not just category three, four, five, but lower ones. So yeah, I mean, there’s a, a wide variety of people that, that could be interested in joining. Allen Hall 2025: Are you expecting a number of manufacturers that make leading-edge protection or involved in resin creation, some– there’s a number of resin companies and a variety of resins that are used globally, sort of interchangeably at times. Are you expecting some of those companies to participate in this effort just to learn about the Australian environment? Rosemary Barnes: I think it would be a good opportunity to test out some products and see how they behave in the Australian context. I think that that would be a really good selling point, but I, I have to say that most of the companies doing that sort of thing that wanna enter Australia, they don’t [00:24:00] really consider… Like, from the perspective of wind farm owners in Australia, if you can’t show us wind farms in Australia where this has worked and, you know, show us a before or after, you know, the old LEP lasted Two years and our LEP is going on four years now with no damage. It, you know, unless you’ve got a before and after like that, you can tell us however many turbines that you’ve got installed around the world, but, um, we don’t consider it validated, y- you know? It’s not validated for Australian conditions yet. And I do have this same discussion over and over again with, you know, not just leading edge protection, but all kinds of, um, you know, manufacturers of whatever doodads that you put on to improve a, a wind turbine. It’s so different to Australia. Things break so fast. And I’m talking everything, you know, like vortex generators fall off and, um, yeah, like, uh, you know, bits of lightning protection systems fall off, seals just [00:25:00] crumble and disintegrate. Um, and it, you know, we’re very wary of, of new products. So I, I do– I mean, I’m thinking of it more from my client’s point of view than from the product manufacturer’s point of view. But one thing that I wanna get out of this pro- project is to be able to answer one of the most common questions that I get is, which is, what leading edge protection should I be putting on my turbine? And for now, I don’t know. I, I know a range of products that don’t work in Australia, and not much more than that. So, um, yeah. And it’s also, you know, Australia’s a very varied place with lots of different kinds of climate too. So it’s not gonna be like, you know, the product that works in Queensland is the same one that’s gonna work in Tasmania, which is the same one that’s gonna work in Western Australia. You know, um, so it, this project is gonna really pull out what are the site specific issues you’ve got at your site and what kinds of, um, you know, tests would we need to see a product um, perform in order to know that this [00:26:00] is gonna last on your site. Allen Hall 2025: W- what is the outcome of this project or these two projects? Are they gonna be reports or, uh, a, a continual monitoring system that’s designed for the Australian environment? How do you see this going?  Rosemary Barnes: Yeah, so one part of it is, um, developing a way to identify periods of accelerated damage and to know not to operate during that time. So we call it protective operation. Uh, so that would, uh, help you if, yeah, you’re trying to extend the life of something or increase the amount of time before you have to repair, then y- you know, that would be useful to have that knowledge. And it will be as simple as just an alert saying, “Hey, accelerated damage conditions. Consider, you know, if you wanna keep on operating.” And, you know, if the price of electricity is super high at that time, they may want to push through, and if it’s low, they probably won’t want to. So that’s one thing. Um, especially, you know, as wind turbines get to their, near the end of their life. I’ve got some clients whose wind farms only have, you know, [00:27:00] maybe five years operation left. They just simply don’t wanna repair their leading edge protection again. They just, they, they don’t wanna do that. So they would be happy to, you know, reduce operation a bit and have their turbine limp through to the end of the period. Y- you know, you want everything to wear out at once. You don’t want brand-new leading edge protection on a turbine that’s going to come down in a couple of years. Um, so, you know, that’s, that’s one part of it. And then the other thing is, you know, turbines earlier in their lifetime, how can we optimize the maintenance schedule with leading edge erosion? Um, so, you know, like it’s a lot cheaper to, uh, replace the LEP if you get– catch it early, but then you don’t wanna be catching it too early and replacing it, you know, constantly when you, you don’t need to. So, um, yeah, it, this, having this knowledge will enable a site-by-site operations and maintenance strategy with respect to leading edge protection. We also have some sites who are having trouble. They’ve got a full service agreement, and the OEM is [00:28:00] responsible for, um, doing the leading edge erosion repairs and protection replacement, but the owner is on the hook for paying for it. At the other end, we’ve got people with full service agreements where technically the, um, manufacturer is supposed to be doing the leading edge protection and paying for it, but they argue about what, when does it need to be done. Because, you know, um, the operator might think if there’s no structural risk, then we don’t need to be replacing it. And in the meantime, you’ve got turbines spinning around for years and years and years with, you know, these huge flakes of leading edge protection s- you know, causing the flow at the tip of the turbine to, to detach and to stall, and horrible aerodynamics, huge losses in power generation and revenue. And they’re having a big fight about, you know, is this necessary to do or not? And then, you know, they’re just gonna put the exact same product on again ’cause the [00:29:00] OEMs are re- all really, really wedded to their own particular brand. It’s like, “Well, last time we had this product and it was factory applied, it lasted one year before it s- it was worse than, you know, if it wasn’t there at all. Uh, we don’t really want you to put that one on again.” And so, you know, having the information that they need to be able to, you know, really bring data to these discussions and, you know, makes a, yeah, data not drama. That’s a, a good approach I think, um, for any kind of negotiation and especially in the case of leading edge erosion. And then for the high temperature fatigue part of the problem, aside from, you know, just wanting to know are your blades aging, should you be looking at remediation action or changing the operation, the other really big key thing is, uh, you might need to have a fight with y- your OEM about if this turbine has been designed and operated correctly. And so then having the data from this, um, project is going to give you the information that you need to come into that [00:30:00] argument with, again, the data not the drama. Um, and to, you know, in- increase your chances of succeeding in that kind of really tricky negotiation.  Allen Hall 2025: So if you’re an OEM or a manufacturer of equipment, an ISP, an operator, pretty much all aspects of wind operations, you probably ought to be getting a hold of Pardalote Consulting and Rosemary to talk about the opportunity to participate in this study. How do people get ahold of you to, to do that?  Rosemary Barnes: People can go to our website, pardaloteconsulting.com, and get in touch via the contact form there, or you can, uh, look me up on LinkedIn, Rosemary Barnes. That’s probably the easiest, fastest way to get ahold of me personally.  Allen Hall 2025: Well, Rosemary, congratulations on the Energy Innovation Fund Awards and the new three-year effort. If you are interested in participating with Pardalote Consulting and working with Rosemary and her team [00:31:00] in Australia, reach out to her on LinkedIn and get that process started, because this report and the data from all this analysis that’ll happen over the next couple of years will be important to the wind industry. So you need to spend some time and get ahold of Rosemary and get this process started now. So Rosemary, congratulations. Uh, thanks for being back on the podcast, and looking forward to, uh, the next couple of years. It sh- should be exciting.  Rosemary Barnes: Thanks so much, Allen.

Pre-season is underway. Day ONE in Girona. And we have a new away shirt. Oof.Support Latte Firm for the price of a coffee a month and enjoy ad-free shows, bonus content and access to giveaways and match tickets - patreon.com/lattefirm.

Tech issues again  this evening. Oof.  Whiskey Pete gets hammered under cross examination by Jon Ossoff in the Senate. Mike Waltz, using the same talking points, gets the same treatment in the House.  Another MAGAT pedophile gets busted . . . and your 'umble 'ostess actually knows him and called it years ago. 

Ok so today I am sharing all about my health journey. I am going to share as I learn and grow through this journey. I want to share because I believe many many women go through the same thing but they just accept it as the new "norm". It's not. Its our body trying to tell us something. Or its perimenopause! OOF!  If you are going through the same thing- let's connect! Stay tuned on how this all plays out!

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

https://youtu.be/31XywTpVWJ4 Robin Dimond, Founder and CEO of Fifth & Cor, is helping organizations tap into the growth of their people by developing future leaders, fostering meaningful relationships, and creating a culture where employees and clients grow together. Through a collaborative agency model built on trust, innovation, and continuous learning, Robin empowers organizations to adapt quickly while creating lasting success. In this conversation, Robin introduces her Grow Your People’s Lives in 5 Ways Framework—Health, Wealth, Spirituality, Friends & Family, and Mental Health. She explains why investing in the whole person creates stronger leaders, how requiring employees to train their replacements builds a sustainable leadership pipeline, and why every client engagement begins with a discovery process and strategic blueprint. Robin also explains how fostering innovation, embracing AI as a tool rather than a replacement for human judgment, and remaining agile help organizations thrive in today’s rapidly evolving business landscape. — Tap Into the Growth of Your People with Robin Dimond  Good day. Steve Preda here with the Management Blueprint, and today my guest is Robin Dimond, the Founder and CEO of Fifth & Cor, a marketing and innovation company harnessing the best tools to support brands, consumers, and communities. Robin, welcome to the show.  Thank you so much for having me, Steve. I appreciate it.  Well, it’s exciting to have you, and we already have some things in common that might or might not come up in the conversation. But what I’d like to ask you, which is kind of my favorite question on this show, is: What is your personal ‘Why’, and how are you manifesting it in Fifth & Cor? I think my personal why—I know what it is. My personal ‘Why’ is my legacy. And because I do not have children of my own, I get the great pleasure of being able to touch so many lives through employees, clients, and partners. So I get to live my ‘Why’ out every day. And I’m a little bit different than most CEOs. I’m looking for the next CEO to replace me. So I go in with the mindset of, “One of you will take my job one day,” and it makes me super happy. That’s my ‘Why’.  Wow. So what do you want to be when you grow up?  What I want to be when I grow up? Technically, I’d like to be the janitor. I believe in cleaning up the messes that are left, and I love that. I love the difficult clients. I love the difficult situations. And I think I’ll step down one day and be the support for somebody who’s currently on my team or the next person to join my team.  Wow. That is awesome. So you want to focus on the message as opposed to running the day-to-day, because that’s where your interests life and where you’re building your legacy.  Yes, I think that is. They call it a Chief Heart Officer in some places. It’s that person who makes sure that the message of the company, what it’s built on, and its foundation are seen all the way through. And that’s the part I love.  Yeah. So you’re a real visionary.  Yes.  So we’ll talk more about your vision, but what I want to ask you about is this podcast is really about frameworks. What we’re trying to do here is find unique frameworks, processes, structures, or ways of looking at the world that CEOs have come up with that could help audience members think about business through a different lens. So is there anything that comes to mind along those lines?  What we found that really works for us is when meeting somebody, we talk to them. Then we do a discovery to figure out if we're aligned, and we find that to be very helpfulShare on X whether it’s with people, partnerships, or clients. So that discovery process, to make sure we’re all on the same page, is so important. From there, we build out a blueprint for them, and then we present the blueprint as if we were the ones running the business. We want to make sure that we’re answering those questions.  We make sure that we’re doing that, and then we have checkpoints. So we sit with a blueprint, saying, “Okay, over the next 30, 60, and 90 days, what are our processes?” The thing that we do differently than most companies is you don’t get an account executive when you come to us. You get a team of people, and you’ll see all their faces all the time. So there is no miscommunication. If you hire us, you get a team. You’ll get a director who’s overseeing your strategy.  You’ll get a coordinator who’s on top of trends. You’ll get a public relations person. And you’ll get a project manager who oversees—we call them our handlers—because they oversee the project all the way through. It has made us successful, and it’s made our clients super successful.  I love it. Especially the blueprint part of it, because it kind of chimes with the framework. So is this your blueprint? How is it unique?  It is, because everyone told us, when you look at agencies or agency models—and we try not to refer to ourselves as that—an agency model will have one account executive, and that person goes back—I call them the man behind the curtain—because they go back and talk to everyone.  What we do is say, “Hey, you’ve hired us, and you’ve hired experts.” We put all of them copied on every email, on every call, working on strategy with your team. And that’s why some of our results are 10,000% growth on your platforms in only 90 days, because we can all work together as a team.  Wow. So that’s very interesting because it’s not an easy thing to pull off—  Nope. To have so many people work together. But maybe that’s the secret sauce here.  The secret sauce is you can’t even get promoted at this company if you don’t hire your replacement. So if you haven’t trained someone behind you, don’t come and ask me for a raise. Don’t ask for a promotion. You should come and say, “Hey, Morgan’s ready to move into my spot. This is how I got her here. I’m ready now to be promoted to the next role.” So we are building tiny leaders behind us who are building teams.  That’s such music to my ears. Yes, I mean, it’s all about leaders building a company, right?  Yes. I totally agree with that. So how do you do that? How do you even do that? Does it mean that any subject matter expert you hire has to be a leader first and foremost? You can’t have someone who just wants to be a specialist stay a specialist?  So I say, “If you’re not growing, you’re dying.” So it should be in some way. It starts in the interview process, and I think this is where a lot of CEOs miss. In the interview process, we start with those questions. Where do you see yourself? Who are the five people you surround yourself with? What do you bring to the table? Asking those very open-ended questions.  Where do you see yourself going? Some people are so honest, Steve. I’d love to bring you on interviews because they’re so honest. They’re like, “Oh, I want to go to law school.” I’m like, “Great. So your time here is going to be short. It’s going to be the next year. What are you going to do in that next year?” “I’m going to help you with contracts.” “Perfect. Okay, let’s get your portfolio looking better so you can go on to the next thing.”  Sometimes we’ll get a subject matter expert who is hyper-focused on public relations, but they say, “I want to be a creative director.” “Okay, great. So during your time here, what are you going to learn? What courses can we invest in you? What trips can we take you on?” So we work with people who have a growth plan of their own and are willing to be agile. That starts at the bottom and moves toward the top.Share on X  That’s genius. Working with people who have a growth plan. Because ultimately, if they don’t have a growth plan, then most likely they’re not going to grow, because you can’t just grow without a plan, right? If you don’t have a vision for growth, it’s not going to happen. Does it mean you’re flexible enough to adapt, to some degree, to these people with a growth plan so that you can share in the fruits of their growth?  Absolutely. We have people start in one section. So we have operations, growth, and delivery. We’ll have someone start in delivery and see that they’re so good at operations that we’ll move them, and then backfill their role. So we have people who move horizontally as well as vertically into different groups. I think that’s the part where you have to be open with your team. You have to say, “Hey, Steve, you’re not that great at creative.  You know that.” And you’re like, “Yes.” And I’m like, “But you are so amazing at operations. Let’s move you over there.” Or, “Steve, you’re such a great thought leader, and I’ve wanted to start a podcast. I’d like to bring you in. Let’s backfill your other role that you started with at our company.” So it’s having that open dialogue from day one and having the team be able to recognize where they’re strong and where they need to move.  I love it. Love it. So what are the challenges with this model?  So many. When I first started, I think I saw what was not working. Before, for you to get ahead, you had to… I spent my time in New York. You had to outpace that person. You had to kind of put the other person down so that you could get promoted. And I saw some negatives in that. This, I would say, is about being transparent and being vulnerable.  It’s also admitting where my mistakes lie or where my weaknesses are. I have dyslexia, so I should never proofread anything for anyone, truthfully. So it’s about being vulnerable. Sometimes that leads to not the greatest people coming into your company because you’re putting yourself out there with that vulnerability. But for every bad apple, there are 20 great apples out there. Yeah, I mean, this is beautiful. I always believe that in order to have people trust you, you kind of have to trust them first. That’s a fragile thing, and you have to put yourself out there. Okay, some people are going to take advantage of it, and you have to…  They are, and it happens. I think every business owner or any leader has to realize that that’s going to happen. But that’s going to happen in your dating life. That’s going to happen in relationships. It’s going to happen in partnerships. In business. It’s going to happen.  But the great part, and the great reward, is seeing someone else come alongside and take your business to the next level, or take your clients to the next level. I think it’s worth being vulnerable to gain that traction.  Yeah, because those people don’t want to work for A-holes, right? They want real people who treat them as equals in some ways because they’re also very talented people. So Robin, let me switch gears here and ask you: what drives growth in Fifth & Cor?  I think what drives growth is our innovation. And we're able to adapt very quickly. In a world where marketing is constantly changingShare on X and I don’t know what the correct term is, Steve, but I say “squished”—we’re getting pressure from the platforms, from social media changes and updates, all of that. We get changes in our coding.  We get changes and pressure from our clients. And we get industry changes. From Boomers to Millennials to Gen X, there’s constant change, so we have to be agile. In our industry, when I started, it was doing billboards. Design was a long process. Now, if we’re not adapting and changing every 30 to 60 days, we can’t keep up. So I can’t make a roadmap or a blueprint for our company three years out. TikTok was here one day.  It was gone the next day. Then it came back again. So planning and strategizing means being agile, being flexible, and being able to move around that for clients so that they don't lose their numbers. I think that's what's driving growth. We're extremely innovative, and we think quickly on our feet.Share on X Can you give me an example?  Yes. I would say the TikTok one is a huge one because we were like, “Clients, you’re about to lose TikTok. They’re warning it’s going to disappear.” Then the next day it was back. So we came up with complete strategies to get rid of it and then bring it back. The next is PR. Sometimes the world changes.  Unfortunately, September 11, we had some other things happen in the news that were really big. It was something where we had to go to our clients and say, “Look, we need a new plan. We need a change. We cannot be tone-deaf right now going into it.” I mean, that all happened, obviously, in September. It was a really rapid change in what was going on. Another example is Shopify went down last week, so all of our clients’ websites went down. There was nothing we were doing.  It was something outside of our control. But we sat there with our clients and said, “Okay, let’s ramp up your social. Let’s go live with some videos and talk about it. Let’s do a press release about how you didn’t lose sales during this time and how you’re going to bounce back.” So it’s constantly thinking on our feet. It’s constantly communicating with our clients and saying, “Look, this happened outside of our control—and yours. This is how we’re going to fix it.”  Yeah. That’s amazing. So how do you keep the team? We talked about how you’re attracting these A players to your business and being flexible to make sure that you tap into their desire to grow, and you grow with them—their way and your way, obviously. But how do you get all these stars to work together? So culture comes from the bottom up. It doesn’t come from me. That would be a dictatorship. So culture comes from the people we hire, and we let them run with new ideas, and they take ownership of those ideas. We did not have public relations four years ago. That was someone else’s idea, and now it’s one of our largest service lines. Project management is another one.  We were not ready to do project management, but people kept seeing our project managers, and they were like, “Well, can we just hire them? Can we just hire them to do this service?” They saw our projects through. So even if someone wasn’t using marketing, they were using our project managers for tech implementations, events, and marketing.  So we’ve created opportunities for people to grow with us. But if you come with us, we say you have to grow in five areas of your life. Health. We actually have a challenge right now, Steve, if you want to join it. It’s who can walk the most miles in June. So health is a challenge to make sure we focus on your health. Yeah.  And we have health-related activities—kayaking. It’s on my LinkedIn posts if you want to go look. We do wealth. We want to make sure you're debt-free. When you come to us, you don't have to be, but we want to make sure you're working with our CFO to become financially independent.Share on X If you’re going to buy a home, we want you to be financially responsible outside of our company.  So in case something does happen, you’re growing toward that. We want you to grow spiritually—whatever you believe your spiritual growth is. If that’s going outside and lying in the grass, if it’s going to your church, if it’s giving back to your community, there should be a spiritual challenge. We look at your family and friends. They don’t have to be blood relatives, but you should be intentional about what you’ve done for your family and friends to help them grow. Whether it’s a family dinner…  You’ll see my team posting about their family dinners on LinkedIn. You’ll see them talking about taking time to do activities with them. So we look at five areas of their lives, making sure they’re growing, and we have challenges to support that. And then mental is the last one. What books did you read? What courses did you take? What did you do to advance yourself?  And Steve, they hold me accountable. They’re like, “Did you finish that CEO book, or are you going to…?” I’m like, “Oh my gosh. Okay. Let me do it.” So it’s something our team can challenge. And if you don’t want to participate, that’s okay. But we’re probably not the company you should be joining.  That is amazing. So people really look after each other. You have this peer accountability going that makes sure everyone is healthy, gets out of their financial challenges, has a social network around them, and takes care of their mental health. That is amazing.  Isn’t that too much for a company to take on? That could be seen as a huge burden—that you worry about all those people in so many different ways, and then you worry about your clients, and you want to make sure your people work together. Doesn’t that become overwhelming sometimes?  I don’t have to work with my clients because I have the most amazing team. I work and focus on my team, and my team’s love and joy get passed on to our clients. It’s a pass-it-down, share-it kind of thing. When they’re happy, healthy, and financially wise, they take care of our clients better than I could ever. They know their birthdays. They know their activities. They know different things. We’re constantly monitoring that. I’m supposed to take care of our team.  Isn’t that something that… I don’t know if it was Jeff Bezos in the early days…  Back in his early days, he did say that. And I want to be focused on that. I want to be innovative. We reward innovation. If you come up with a new way of doing something, you can win prizes, bonuses, gift cards. It is always a challenge: how could you do something better? Someone solved something the other day that we couldn’t figure out for the past three years. I was like, “Open up your email. There’s a gift card in there.” We want to reward people. We want them to enjoy the bonuses of doing different things and thinking outside the box.  That’s very exciting. It sounds like you’re running a perfect business.  No, it’s not perfect. People are messy, and I want other business owners to see that. There are some days that I look at the day and I’m like, “Oof, this is a big one.” Or it’s saying goodbye to the people who don’t fit into that culture, who might be the bad apples. That’s hard.  Not every person we bring on is the right fit. Not every person wants to grow. Some people want to be button-pushers for the rest of their lives, and we might have missed that. Those are the challenging days. Some days, when you get super involved in people’s lives, it means you get super involved in life. Their health might diminish, and you have to step in and take over. We had somebody who fell backward and fractured their skull. Well, we had to keep going while they recovered. That whole part isn’t easy. It’s scary. There are some days I’m like, “What the heck am I doing, Steve?” I say that a lot. But looking back, it’s rewarding.  So Robin, what is one thing that you’re actively trying to figure out in your business?  Right now it’s where the industry is headed, and we’re actively, all day, every day, trying to figure that out. We are seeing the most innovation happen during these times, and keeping up with it is exhausting. To give you an example, the printing press came. The car came. Right now, it’s the next generation, who were born with one of these in their hand. Whether it’s AI that’s coming, or something else, every day there’s something new being released. So it’s, how do we adapt, but also not get burned out? That is the biggest thing I’m trying to solve right now.  Yeah.  So if anyone would like to help me, please email me. I’ll buy you cocktails or coffee. I’ll do either one, because that is exhausting for a business owner right now.  So what part of adapting is the risky part? Give me an example of adapting and risking burnout.  The burnout comes from trying to stay on top of what’s new every day, whether it’s a new platform that came out or whether it’s AI that’s going to replace all your marketing people. It’s about being flexible, but also being smart and not making too many decisions too quickly. Our advisory board is my backbone.  There are five advisors who pour into us, who are sounding boards and give as much time as possible. I think they’re the ones who keep me in line. I didn’t have them at first, so I would recommend getting a board of advisors that you really trust, that you can look at their lives, and they can show you where burnout could happen.Share on X  And if you had a magic wand and you could fix something inside your business, what would that be in the next 12 months?  If I had a magic wand and I could fix anything inside our business, I think I would fix the rate at which we’re growing, if that makes sense. We’re growing way faster than I thought, and my magic wand would be to have enough systems in place to keep up with it. Once we build a system, we outgrow that system, and I’m like, “Cheese and crackers, we’ve already outbuilt it.” So I think it would be staying on that hamster wheel, making sure we put more infrastructure in place. Yeah, and with AI, it’s a different kind of operating system for a company, isn’t it? It is.  AI needs to be used across everything. AI should be in your business. You should be using it. You should be leveraging it. But AI can’t replace people. I think that’s the biggest thing. It’s being able to adapt your team into AI-powered systems, while also having them use their judgment and their brains. They’re going back and checking it.  Yeah. You know what I’m wondering sometimes about AI is, yes, it is making us very productive. I can get a lot more done. But at the same time, I have to make a lot more decisions, so the cognitive load has increased.  My brain load never decreased with AI. I now have to solve problems faster, and I think that’s where people are missing it. People are like, “Well, my blogs will write themselves now.” I’m like, “Okay, but who’s putting in the content for your blogs?” Or, “Oh, it’s going to put this product on my website faster.” Okay, but who’s double-checking it?  Where’s the shipping going? Where are the numbers going? People need to think about the fact that we’re doing things way faster. And then there’s fatigue. We’re being exposed to things at the swipe of a finger. You can see six, seven, eight things happening, and that’s exhausting. What is happening to the mental load of decision-making? Yeah. And then at the same time, it’s not just that we have to make more decisions, but there’s more noise out in the market. So you’re out-competing… Okay, everyone else is more productive, so it’s just a war of… what’s the opposite of a war of attrition?  I don’t even know. I know what you’re talking about, but it’s like you have to be so intentional now. Yeah. It’s the war of overwhelm, basically. Yes. The war of overwhelm. Yeah, that’s wonderful. Okay, but let’s go back to Fifth & Cor. Basically, you’ve come out of your discovery with this blueprint, and then you have this team approach. These people are all fully self-realizing in the five areas of their lives, and they’re coming together, working together, and incorporating AI. So who is an ideal client that can really take advantage of everything you can do? Not just parts of it, but everything. What’s the ideal client for you?  I love that question. An ideal client is not an industry or a revenue number they have to have. An ideal client is someone who comes with a problem and is flexible and agile enough to let us come up with a solution. And they’re a true partner. We’ve had a client who’s been with us for four and a half years, all the way since our inception, and they’ve been able to roll with all the new changes.  They trust us. So it’s someone who is innovative in their business. They’re willing to be agile as they move. That is the perfect client. Revenue does not matter. You can have all the revenue in the world, but if you’re slow to adapt or you’re not willing to go with the times and the changes, that’s a problem. What we did pre-Covid does not work right now. The same things that worked six months ago don’t work now. So the ideal client must be someone who’s willing to be innovative, attract new ideas, and execute quickly.  Do you have a way to help your clients be more like that?  Yes. Even in our onboarding stakeholder session, before we do a discovery call and present the proposal, we talk to them about that. We help them get set up. We come up with SOPs so they have documented processes throughout the entire engagement. I always say, “In case we get hit by the lottery bus…”  That’s my way of saying, if we disappear tomorrow, you have systems in place so your business won’t need us. So we help them grow, and then we have checkpoints with them. “Okay, are we doing this? These are the results. This is what you trusted us with over the past 30 days. Here’s what we’re doing over the next 30 days.” So we’re constantly meeting with them and saying these things. The clients who don’t work out long-term are the ones who say, “No, we’ve always done it this way, and we’re going to keep doing it that way.”  Those are the ones where I’m like, “Well, why did you even call us? If you’ve always done it that way and you weren’t getting results, that’s the definition of insanity.” So it’s clients who are willing to move with us, move with the times, and are willing to bring in other teams. I love that. They’ll bring in their tech team, or they’ll bring in their logistics team, to sit there and say, “Okay, what are we doing, and how do we take it to the next level?” Love it. And you can only do this with A players because otherwise it would be very painful. Love it. So if you’re listening to this podcast and Robin Dimond explaining her business and how she’s putting her employees first and making sure they have a well-rounded life so they can live up to their potential inside the business, while also harnessing their own growth ideas, so you’re tapping into what they already want to do.  Then the clients are brought along, and the same thing happens to them. They’re inspired, they’re challenged to do better, and then you help them. So if you want to be a client who enjoys that kind of approach, then my question to you, Robin, is: where can they find you and your colleagues, and what should be their first step? Perfect. You can find us at fifthandcor.com. Fifth represents the five senses, and Cor is Latin for “heart.” So that’s the reason why we do everything—the heart behind our senses. So, fifthandcor.com, or you can find us on LinkedIn. You can personally reach out to me. It’s Robin Dimond. I’d love to connect with you. I already have 30,000 connections, so I’d love to do that and set up a meeting with you.  That’s fantastic. Both my parents were doctors. My dad passed away, but my mother is still a dermatologist, and my dad was a cardiologist. They had a company, and it was called Dermacor, which is basically the skin and the heart. I love that. Yeah. Well, now that we know we’re related… I feel like that’s just part of our story too.  Yeah, that’s a Central European idea, perhaps. So if you enjoyed this episode, obviously reach out to Fifth & Cor, find out more about what Robin and her team are up to, and stay tuned because every week I bring a wonderful entrepreneur like Robin who’s got great ideas, and you can try to steal their ideas. So thanks for coming, Robin, and thanks for listening.  Thank you. Important Links: Robin's LinkedIn Robin's  website

This episode features Cameron Allen, who came all the way from Memphis to speak at this year's LA Astro Fest. This recording was supposed to come out before his workshop, but that was just not happening.It turns out that throwing a four-day festival with nothing but blood, sweat, tears, and the good graces of volunteers is a lot. No matter, we did it. The episode is here. If this is your first encounter with Cameron, he's an herbalist and astrologer who sits at the crossroads of medical, traditional, and evolutionary astrology.He's someone I've admired for a very long time. Years ago, when I was too afraid to record podcasts, I hosted a lot of TalkTalkTalks over IG Live. These are absurd hour-plus conversations that should be podcasts. Cameron Allen was indeed one of these IG guests, along with Samuel Reynolds and Gemini Brett, all astrologers who I'm honored to say have been speakers at LA Astro Fest.If you're in the mood for scrolling, you can go way back in the Vivi Henriette IG history and find these interviews. One day I will make them podcast episodes for Club Astro members.In the meantime, you can head over to Art of the Zodiac on Substack and read the bonus print interview that accompanies this podcast> A quick note on this recording: I have no idea why I'm so entranced by the idea of a "spirit supply store." While Cam was talking, I had this image of ghosts walking into the shop and buying ghost accessories. The tools they need to haunt houses and whatnot.In my head, it was a world. On the recording, I sound like I've never heard of a botánica. Oof! The best part of interviews is I get to sound like an idiot as long as my guest sounds good. Cameron, per usual, sounds brilliant! This was such a delightful conversation. XO ViviSupport the Podcast & Learn Astrology!Ready to take a deep dive into astrology? I invite you to join ⁠Club Astro⁠ by becoming a paid subscriber to ART of the ZODIAC on Substack. Your membership offers:Exclusive Cohort: Access to a dedicated community of astro seekers.Weekly ZOOM Sessions: Bring your chart, ask me questions directly, and connect with fellow astrology enthusiasts in intimate gatherings.Secret Invites & Discounts: Including discounted tickets to online workshops, LA Astro Fest, and my monthly in-person gathering, The Los Angeles Astro Salon.More than just benefits, your membership directly supports me and this work, allowing me to continue creating content like this.Join Club Astro here:⁠ https://vivihenriette.memberful.com/⁠Other Ways to Support (No Funds Required!)Even if you can't join Club Astro right now, your time and listenership are invaluable. If you enjoy this work, please consider:Tell a friend: Word-of-mouth is incredibly helpful!Leave a review: A review wherever you listen to podcasts truly helps new listeners find the showAbout CameronCameron Allen is an herbalist & astrologer that sits at the crossroads of Medical, Traditional, and Evolutionary astrology. He synthesizes wisdom from his training in Ayurveda, Kundalini yoga, holds degrees in health & sports science and psychology with a focus on sports & exercise psychology, and is currently in school for Unani Tibb.  Cameron has trained with teachers of lineages that have not been named here, but honors them nonetheless. All of these systems of healing and ways of knowing combine with a focus on being centered in how to be of service in any given moment.About ViviVivi Henriette is an LA-based astrologer and tarot reader whose practice centers on storytelling, mythology, and collaborative divination. She creates a space for clients to reclaim their personal narratives through the lens of ancient archetypes. Vivi produces⁠ LA Astro Fest⁠, hosts the Los Angeles Astro Salon, and is the creator of the podcast⁠ TalkTalkTalk⁠. You can find her weekly writing on ritual and meaning right here on⁠ ART of the ZODIAC⁠.

“Should I even keep fighting for my marriage?”, asks “G.” Oof, that word… “fighting.”  I hear it often.  But so many times, when someone says they are “fighting for” their marriage, they end up “fighting against” their spouse.  The spouse who doesn't see how to move forward. Which is rarely helpful for the process.  But I watch person after person “suit up” to do battle, not even sure on what they are fighting. So, let me clarify that with the question from “E.”  She asked why I always talk about connection… not romance, playing “hard to get,” doing “No Contact,” or reverse psychology. Those two fit together… the “fighting” part and the “connecting” part.  You are fighting for connection!  For some very specific (and deeply rooted) reasons. I discuss both in this episode of the Save The Marriage Podcast. RELATED RESOURCES: Connection and Marriage Why are We Fighting No Contact is Crap No Manipulation Save The Marriage System

Comcast is scraping Universal, DreamWorks and NBC off the bottom of its boot and spinning them off into their own company. And this is after they bundled up MSNBC and a bunch of other failing cable channels and sent them out to die as well. OOF. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #DreamWorks #Animation #Comcast #Universal #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

"If what I want is for someone else to respect my time, then maybe I'm the one who needs to respect my time first." Oof. That realization hit me right between the eyes. Have you ever noticed how quickly frustration turns into a story about someone else? The person who cancelled. The coworker who dropped the ball. The partner who isn't helping. The kids who need one more thing. And sometimes that's true. But sometimes the thing you're frustrated with isn't actually the problem. "Your frustration isn't the problem. It's the clue." Sometimes frustration is the moment your needs, boundaries, exhaustion, or truth have been trying to get your attention for a while—and you've been too busy carrying everything else to hear them. In this solo episode, I'm unpacking two frustrating experiences from my own life and walking you through the exact process I use to understand what frustration might actually be trying to tell me. Because emotions aren't the problem. They're information. And frustration is one of the most revealing emotions we have. Together we'll explore why frustration almost never stands alone, how it can point us toward unmet needs and hidden truths, and a simple framework you can use to move from irritation to clarity. In This Episode • Why frustration is always trying to tell you something • The surprising difference between being frustrated with someone else and being frustrated with yourself • How irritation can reveal needs you've been overlooking • Why emotions work like an internal compass • A simple reflection process you can use anytime frustration shows up • The powerful shift that happens when you stop asking "Why am I so frustrated?" and start asking "What is this trying to teach me?" Three Takeaways Your frustration isn't the problem. It's the clue. Frustration often points toward something important that needs your attention. Sometimes frustration is what happens when you've abandoned yourself. Not intentionally. Not dramatically. Just quietly, one small compromise at a time. Emotional clarity begins with curiosity. The goal isn't to get rid of the feeling. It's to understand what it's trying to show you.   Reflection Question What would your frustration say if it trusted you enough to tell you the truth? Before you go... If this episode felt like a conversation you needed today, would you share it with someone who might need it too? Follow the podcast, leave a rating or review, and help more high-functioning humans with big feelings find a little more clarity, connection, and calm without having to earn it.   Links & Resources Website: https://danielleireland.com Substack: https://danielleireland.substack.com YouTube: https://www.youtube.com/@DontCutYourOwnBangs Instagram: https://www.instagram.com/dontcutyourownbangs The Treasured Journal: https://danielleireland.com/journal Wrestling a Walrus: https://danielleireland.com/wrestling-a-walrus Spotify: https://open.spotify.com/show/0VFZulonTvaa2HIPyJa4Tq Apple Podcasts: https://podcasts.apple.com/us/podcast/dont-cut-your-own-bangs/id1427579922

Sega is putting put on blast by gamers because of a new Sonic Chaos Emerald contest that requires you consent to letting their AI train on your data before being able to enter. Given the Sonic fandom, it's going about as well as can be expected. It's so egregious, that the official Sonic account got community noted on X. OOF. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #SEGA #Sonic #Games #VideoGames #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

...plus the Georgia GOP no-show rodeo & a Wynter-y cold endorsement leaves Mike Collins frozen out.The special session Governor Brian Kemp commanded out of the state legislature didn't yield much for his party (no redrawn maps, no property tax cuts passed onto consumers' sales taxes) but it did yield a mess of an election bill he'll have to sign (we're guessing) to address the looming QR code deadline: according to Marilyn Marks with the Coalition for Good Governance, the bill accidentally mandates razor-thin election margins, the way it's written. Oof. She joined me to explain. - - - Meanwhile, it's officially "general election season" for the 2026 calendar so focus will turn to Rick Jackson v Keisha Lance Bottoms, with statements made by Jackson at an event with a radical anti-abortion voter raising eyebrows. In the comments, he seems to co-sign on eliminating exclusions for incest and rape. - or at least creating new barriers to those exceptions being available to women who learn they're pregnant on or after the six-week ban in Georgia would occur. His being poor at thinking on his feet (there and in a prior televised debate) is likely one reason why Democratic nominee Keisha Lance Bottoms is challenging Jackson to no less than three one-on-one forums. ---The alleged killer who stabbed a MARTA passenger weeks ago may receive the death penalty, according to a federal grand jury, writes Rosie Manins at the Atlanta Journal Constitution. That's the story anti-transit, anti-urban folks want you to focus on, and not the fact that MARTA has adequately absorbed twice the usual ridership during the World Cup while running on-time and without incident. All that just proving that a little influx of state and federal support makes MARTA run "smarta."- - - As if there isn't enough to embarrass any Republican with self-awareness, a Georgia GOP rodeo drew "tens" of people (my guess-estimate when you don't count candidates and their surrogates). HA!- - - One more, though! Atlanta-based conservative talk radio host Shelley Wynter (WSB A/FM) had Senator Jon Ossoff on recently - and endorsed the incumbent Senator! Hear the exchange:

Supergirl box office tracking keep dropping, and now it's very likely that Supergirl will come in behind Toy Story 5 in its second weekend. toy Story is shaping up to be a monster hit, and even if it drops 50-60% it'll beat Supergirl's projected $45-55 million. OOF. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #Supergirl #DCComics #JamesGunn #Movies #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Join us as we dive again into the history of major Soviet firearms leading up to the development of everyone's favorite banana mag rat-a-tat-tat machine. Picking up from the PPD-40, we see how the Soviets managed to develop mankind's only submachine gun to skip something as technologically complex as... threading. Oof.

What if the thing standing between you and the life you want isn't lack of time, money, or opportunity… but your inability to trust yourself?Oof. I know.Most of us have become experts at explaining our hesitation: waiting for the right time, gathering more information, thinking about it, praying about it.But what if all those reasons—while real—are costing us more than we realize?In this episode, I'm unpacking the opportunity cost of not taking action and sharing my 5-Step Self-Trust Framework to help you move from hesitation into action.This isn't about making reckless decisions.It's about becoming the type of person who trusts herself enough to move.Because staying exactly where you are has a cost too.Click play to hear all of this and:[00:00] Understanding opportunity cost and why avoiding decisions has consequences.[02:13] Dating, fitness, business… and how every “not now” creates a tradeoff.[05:12] The surprising pattern Jasmine sees repeatedly in business conversations.[12:00] Understanding why hesitation often feels reasonable.[21:18] Who do you want to become? The one who stays… or the one who decides?[22:24] The mindset shift that transformed Jasmine's relationship with possibility.[24:09] A practical system for making decisions with confidence.Listen to Related Episodes:The Secret to Closing High-Ticket Sales (Even With Objections) with Shelby SappHow to Create Content That Removes Objections and Increases Your SalesDo THIS to Close More Sales

The Mandalorian and Grogu has apparently dropped out of the Top 5 at the box office in only its third week, and it's currently sitting at only half of what it needs to make to break even. This movie was definitely frontloaded and it's failing harder than anyone could have predicted. It got its teeth handed to it by a limited release of the finale of The Amazing Digital Circus. Oof. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #Disney #Movies #StarWars #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

I WATCHED THE DEMOCRATIC GOVERNOR’S DEBATE So you didn’t have too. Oof. It was super boring. My claim that it would be a milquetoast bloodbath was spot on. Kyle and Marshall asked softball after softball, never asking about Phil Weiser being Attorney General while crime and drug overdoses skyrocketed while he sued Trump. Never asked Michael Bennet who voted for bloated budget after budget in DC what his budget would look like in Colorado. What a waste of time, but if you think Polis and the Democratic Legislature were bad, wait until one of these guys gets into office. Bennet said he wants a budget destroying public option. They both said they would sign the labor bills Polis vetoes. They kept talking about affordability and then talking about more regulation in the next breath as if they aren’t inextricably linked. We are so screwed. As much as I hate to do this, you need to watch this debate. If for no other reason than see how bad things are going to get. Longtime journalist Chris Rourke is joining me at 1 to talk about it.See omnystudio.com/listener for privacy information.

R.I.P. Anthony Stewart Head. Our love for you simply can not be expressed by any means other than sharing the episode where Jeremy goes on and on about your raw sexuality for like half an hour.Fear Level: Spoopy with a side of eTrigger Warnings: Director: Darren Lynn BousmanWriters: Darren Smith Terrance ZdunichStars: Alexa Pena-Vega, Anthony Stewart Head, Paul Sorvino, Paris Hilton, and Sarah Brightman and Nivek Ogre (Kevin Ogilvie)Repo: The Genetic Opera is a horror musical goth opera about a guy who repossesses organs, his sick daughter, a family full of underused character actors, and one very screamy grave robber. It's...a lot...but somehow also not enough. It is super weird though.Topics of Discussion:-Your favorite Spy Kid and Watcher/Librarian-Corpse battering rams-a little glass vial-a little glass vial-a little glass vial-GRAAAAAVES!-"You're at Nightmare Before Christmas and I need you at Rocky Horror Picture Show"-Tough guys don't spit blood, they just fall over dead-Have you heard of Sonny Corleone? No? GOOD!-MS Paint comic interludes-Literal mean puppet-We're on a string of movies that don't handle sex workers well. Oof.-Emily talks about Skinny Puppy-God, there's just so much potential here that you're not using.-We brainstorm half a dozen better versions of this movie using the same pieces they have here and and just arranging them differently-I say again, THE RAW SEXUAL ENERGY OF ANTHONY STEWART HEADRecommendations:-Evil Dead: The Musical-Carrie: The Musical-The Toxic Avenger: The Musical-Spiderman: Turn off the Dark-Natasha Pierre and the Great Comet of 1812-Zipperface: The Hobo Musical-Courtney Crumrin-The Crow-City of Lost Children-Skinny Puppy videos-Romeo + Juliet-Moulin Rouge-Phantom of the Paradise-Within Temptation- Black Symphony-Malice Mizer videos-Pink Floyd: The Wall-Bioshock-Deus Ex-Ghost in the Shell: Standalone Complex-Buffy Once more with feeling-The Rocky Horror Picture Show-Anna and the Apocalypse-LabyrinthFollow our guests:Joey BraccinoTwitter: @joeybraccinoPodcast: Talking ComicsFollow us on twitter @proghorrorpodFollow Emily on twitter @megamothEmily's Website: Megamoth.netFollow Ben on twitter @benthekahnPre-Order Ben's new book, Renegade Rule.Follow Jeremy on twitter @jrome58Visit his website at JeremyWhitley.comRSS Feed: https://feeds.transistor.fm/progressively-horrifiedWebsite: https://progressivelyhorrified.transistor.fm/Join our Patreon at: patreon.com/progressivelyhorrified to support the show, get bonus episodes, early access to upcoming episodes, and a cool Progressively Horrified t-shirt.Come back next week to hear about Attack the Block!★ Support this podcast on Patreon ★ JOIN JEREMY'S ZOOP CAMPAIGN AND HELP MAKE GREAT COMICS! https://zoop.gg/c/slayTake our listener survey: http://bit.ly/progressivelyhorrified-surveySign up to support Progressively Horrified on Patreon for as little as $5 a month and get bonus episodes! https://www.patreon.com/c/progressivelyhorrified Hosted on Acast. See acast.com/privacy for more information.

The box office projections for James Gunn's 'Supergirl' is tracking somewhere between 'The Marvels' and 'Black Adam' -- both of which were considered flops. OOF. So no, adding more Superman to the trailers doesn't seem to be working. Then we talk about Milly Alcock's "Christian dads" comment. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #Movies #Supergirl #Superman #DCComics #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Let's be real—transitioning into homeschool high school feels big. It doesn't matter how many years you've been at this. That shift from middle school to high school brings with it a swirl of emotions: uncertainty, excitement, fear of missing something, and sometimes—let's be honest—a bit of guilt. Pin those thoughts in your mind for a moment as I share with you a conversation we recently had in the Confident Homeschool Mom Collective. It was a rich, heartfelt conversation about this very season. And the stories shared were so resonant, I knew I had to write to them. One homeschool mama said: “Oof, high school… well, Viv is starting 7th grade and I feel like we're already behind.

Stephen Colbert is off the air, and one op-ed piece on USA Today couldn't be happier. They said late night was no place for divisive politics, and basically don't let the door hit you on the way out. OOF. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #Colbert #LateShow #CBS #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This week on the podcast, your two favorite comedians discuss… —Trump Phones—Olivia Rodrigo00:00 Introductions05:10 The World for Sale09:49 Trump Phones17:23 Olivia Rodrigo23:48 Frontier Airlines26:06 California Election 30:12 Republicans go MAGAThe World for SaleIt's a great book; nathan gives a little tease. Trump PhonesThe stupid got swindled again… Almost 600,000 morons lined up to give Trump a $100 deposit for a phone they were most certainly never going to see.Made in America?Yeah, right.Anyway, they're very slowing figuring out that they got ripped off. Olivia RodrigoOutrage! She wore a dress!Rolling Stone gets it right for once: this is bot outrage. Frontier AirlinesVideo footage of the dude getting hit by the jet! Oof.Gonna need a band-aid for that.(It's just a flesh wound.)California ElectionKatie Porter got caught on video acting extra crabby, and now she's upset that Tom Steyer might've been the one to leak said video. Well, not Tom specifically, but his campaign. Anyway, Katie, maybe don't be a pos, and people won't think you're a pos. Just saying. Republicans go MAGATrump-endorsed candidates are winning primaries, but will they win general elections?People are stupid, yes, but are they so stupid as to not only want to continue down this path, but double down on it with even worse spineless sheep than Ted Cruz and Lindsey Graham? We're gonna find out… Idiots on Parade: we mock the news, so you don't have to.Tune in and get your giggle on.Find Jake at @jakeveveraFind nathan at nathantimmel.comShow your support by picking up a T-Shirt: https://nathan-timmel.dashery.com/

The “covers” folder is full. Overfull, to be honest. So tonight’s show is a supersized show of great remakes, and many of them are quite surprising. The Tubs covering Metallica? Social Distortion doing Chris Isaak? White Fence remaking Simply Red? The Hollywood Stars retaking their own song back from Kiss? Hell, even Ty Segall’s version of The Doors could be considered a surprise. There are also quite a few more conventional remakes here tonight, but each and every one is what the kids today call a “banger”. (Oof, remind me to never use that term again.) I’m sure any Ledge listener will find a favorite. What’s your highlight? For more info, including setlists, head to http://scotthudson.blogspot.com

A donor asked one of my clients, “How much do I have to give for you to stop asking?” Oof. That question hit me right in the chest. And honestly, I think a lot of fundraisers have either been asked something like this or secretly fear hearing it.In this episode, I break down exactly how I would respond and why I believe obligation has no place in major gifts fundraising. None. I'm not interested in convincing, pressuring, manipulating, or cornering someone into giving. That's not partnership. That's coercion with a tax receipt.We talk about the difference between fundraising from desperation versus fundraising from grounded leadership. Because donors can feel your energy. They can feel when you're white knuckling a goal, trying to force a gift, or needing their validation. And they can also feel when you genuinely mean it when you say: “You do not have to give.”The best donor relationships are built with people who are all in. People who want to be there. The people who don't just write checks, but become real partners in the mission. That kind of fundraising starts with you releasing pressure from yourself first.What you'll learn in this episodeHow Julie would respond when a donor says, “How much do I have to give for you to stop asking?”Why obligation-based fundraising damages donor relationshipsThe psychological reason donors are more likely to give when they feel fully free to chooseHow desperation and pressure show up in donor conversations, even when you think you're hiding itWhy emotional regulation matters in major gifts fundraisingThe difference between inviting someone into a mission versus convincing them to fund itHow to stop white knuckling individual donor relationshipsWhy real donor partnerships require alignment, not pressureWhat “walk away power” actually looks like in fundraising conversationsHow releasing donors from obligation helps attract more passionate, committed supportersAt the end of the day, major gifts fundraising is not about getting people to do things they do not want to do. It's about leading well enough, listening deeply enough, and believing strongly enough in your mission that the right people naturally lean in. The more grounded and pressure-free you become, the more authentic and sustainable your donor relationships will be.Want 15 leads in 5 minutes? DM me "Breakfast burrito" on LinkedIn and I'll send you a pdf and 6-minute training to help you generate 15 leads for your nonprofit in minutes. It's totally free. All you need is an email to sign up. DM me "Breakfast burrito" - I'm from Texas, what can I say? - to get your pdf and mini training.If you're an ED or DD of a $1M+ making a difference in your community and you're ready to make bigger, bolder asks, then DM me “CL” on LinkedIn and I'll share details.

We're back with some more Gilmore Girls! Join us in this journey on social media - @gilmorethemerrierpod. ABOUT: GILMORE GIRLS (SEASON 2 EPISODE 2) Lorelai hesitates to tell her parents about her engagement to Max, despite Rory's urging. Rory and Dean have a spat over Rory's plans for extracurricular activities. AIR DATE & NETWORK FOR: GILMORE GIRLS (SEASON 2 EPISODE 2) October 9, 2001 | The WB CAST & CREW OF: GILMORE GIRLS (SEASON 2 EPISODE 2) Lauren Graham as Lorelai Gilmore Alexis Bledel as Rory Gilmore BRAN'S GILMORE GIRLS (SEASON 2 EPISODE 2) SYNOPSIS Lorelai has officially entered wedding planning mode. She tells Rory she went dress shopping the day before…and it was AWFUL. Rory is like, “Great, we're going right now!” At Chilton, Paris is signing up for summer school, extra classes, extracurriculars — the whole thing. Rory walks up, but Paris is still mad about the Tristan situation. She tells Rory she better not show up at the charity home-building event tomorrow. Rory's like, “Oh, I'll be there.” Remember Henry, the guy Lane hit it off with at the party? He tells Rory he tried calling, but Mrs. Kim scared him off. So he gives Rory his number to pass along. Friday night dinner time. Rory reminds Lorelai she needs to tell Emily and Richard about the engagement. Lorelai's like, “I will soon, okay?!” Richard pulls Rory aside and apologizes for how he treated Dean. He says he wanted to do it in person. Rory forgives him, and they hug. Progress! Meanwhile, Lorelai is alone with Emily and decides to finally tell her about the engagement. Emily's response? “That's nice. I hope I'm in town.” Lorelai is…not thrilled. The next day, Rory heads to the house-building charity event. She shows up with a pink, fluffy hammer (not ideal), and the guy in charge is basically like, “Good luck.” Paris immediately claims a wall and tells Rory to find somewhere else. She reveals she's been volunteering for years — it's all part of her Harvard plan. Rory realizes…she is WAY behind on extracurriculars. Uh oh. Rory vents to Dean, spiraling about how she can't relax this summer — she has to catch up. Dean is not loving this. He just wants to spend time together and is like, “Is that too much to ask?” Lorelai feels bad seeing Rory so stressed, which kind of ruins her date with Max. Max talks about how supportive his parents are, and Lorelai is like…must be nice. He tries to get her to think about things rationally, but instead she decides to bring him to meet her parents. Bold choice. This leads to a big fight with Emily. Lorelai finally lets it out: “Why don't you care? You've never cared, and it hurts.” Emily fires back that she found out about the engagement from a stranger instead of her own daughter. The next day at work, Lorelai realizes it was Sookie who told Emily while planning an engagement party. Everything clicks. At the party, Lorelai is actually having a great time — even though Max is about to leave for Toronto for a while. Dean finds Rory, and they make up. Meanwhile, Lorelai notices Luke isn't there, so she goes to the diner. He gives a weird excuse about being busy “working”…on ketchup. She tells him she really wants him there — it's an important night. When Luke finally shows up, he sees Lorelai slow dancing with Max. They spot each other and just…wave. Oof. The episode ends with Lorelai going to Emily for advice about veils. She apologizes for not telling her sooner and admits they don't communicate well. She explains she was scared of how Emily would react. Emily softens…just a little. Then tells her her head is too big for a veil and she should wear a tiara — like she did. Watch the show on Youtube - www.deckthehallmark.com/youtubeInterested in advertising on the show? Email bran@deckthehallmark.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us Fan MailGas, wings, beer... everything is too expensive.The wild times continue and everything is too expensive. I had to get a loan to fill up my tank this week, and 2nd mortgage for a six-pack. Is Two Buck Chuck even two bucks anymore? Doubtful.With the rising costs of everything the tradition of pre-gaming is back. Knocking back a few before you head out for an evening can save a good chunk on your drink bill. It's just good financial sense. Drink wherever you want to, just keep support those breweries. Drinking everywhere is on the decline, even the Czech Republic reported it's at an all time low. Oof.The results of the World Beer Cup are in and we break it down a bit. Some interesting details from the categories to the countries winning medals. Are any of your favorites on the list?As expected the OG Fat Tire is making a return, for a limited time, at least. We call it McRibbing. Hyping a popular item for a limited return to get the fans to buy it. It works, and not just for beer or pork sandwiches. Even more rare than McRibbing is pulling a Lazarus. But Iron Hill Brewery seems to be doing just that, with the original founder as part of the team. They said they'll be streamlined this time around. We're curious to see what happens here.Cheers!Thanks for listening to Beer Guys Radio!  Your hosts are Tim Dennis and Brian Hewitt with producer Nate "Mo' Mic Nate" Ellingson and occasional appearances from Becky Smalls.Subscribe to Beer Guys Radio on your favorite app: Apple Podcasts | Google Podcasts | Spotify | Stitcher  | RSSFollow Beer Guys Radio: Facebook | Instagram | Twitter | YouTube If you enjoy the show we'd appreciate your support on Patreon.  Patrons get cool perks like early, commercial-free episodes, swag, access to our exclusive Discord server, and more!

EECP Therapy and Stroke Recovery: Can a Cardiac Treatment Help Grow New Blood Vessels? When I first heard about EECP therapy in the context of stroke recovery, I was skeptical. It’s a cardiac device approved in Australia for stable angina and congestive heart failure. Stroke is not on the label. So why are we talking about it on a stroke recovery podcast? Because the mechanism is fascinating. And the research, while still emerging, is pointing somewhere worth paying attention to. In this episode, I sat down with Jack Clifford, a heart disease patient who discovered EECP therapy and began exploring its potential beyond its approved indications. What started as a cardiac conversation quickly became one of the most scientifically interesting discussions I’ve had on the show. What Is EECP Therapy? EECP stands for Enhanced External Counterpulsation. The treatment involves a set of pneumatic cuffs fitted around the calves, thighs, and buttocks. These cuffs inflate and deflate in precise synchrony with the heartbeat, inflating during the heart’s resting phase (diastole) to push blood back toward the heart, and deflating just before the heart contracts. The result is an increase in blood flow and a specific type of fluid shear stress on blood vessel walls. It’s that shear stress that makes things interesting. The Biology: Arteriogenesis and Angiogenesis To understand why EECP therapy might be relevant to stroke survivors, you need to understand two terms: angiogenesis and arteriogenesis. Angiogenesis is the sprouting of entirely new capillary vessels — the body builds small blood channels where none existed before. Arteriogenesis is different: it’s the remodelling of pre-existing, dormant collateral vessels into functional bypass channels. Think of it like upgrading a dirt track into a highway. The track was always there; the body just wasn’t using it. When blood flow is obstructed, whether by a blocked coronary artery or a stroke, the body can, under the right conditions, activate these collateral pathways. The shear stress produced by EECP therapy appears to be one of the triggers that stimulate arteriogenesis. By generating repeated waves of increased blood flow, the treatment creates the mechanical signal that tells blood vessel walls to grow and remodel. This is why cardiac researchers originally developed EECP for heart patients. But it raises a legitimate scientific question: could the same mechanism support blood flow recovery in the brain after stroke? What Does the Research Say? A 2026 meta-analysis published in the QJM: An International Journal of Medicine examined 15 randomized controlled trials involving 506 participants, looking specifically at EECP’s effects on functional outcomes in stroke patients. The results showed statistically significant improvements, with EECP outperforming control conditions on standard functional recovery measures. This is preliminary evidence, not a settled clinical consensus. The studies are relatively small, the methodology varies across trials, and EECP remains off-label for stroke in Australia. But for a therapy with a well-understood safety profile and an existing approval framework, 15 studies and 506 participants is not nothing. It’s enough to warrant serious discussion. What I Discussed with Jack Clifford Jack came to EECP as a patient, not a researcher. His experience with heart disease led him to explore the therapy, and he’s spent considerable time understanding the evidence base and connecting with practitioners. He’s not a clinician, and neither am I, but what we can do together is examine what the research actually says, what the mechanism actually is, and what questions remain unanswered. In our conversation, we discussed: How Jack first encountered EECP therapy and what led him to investigate it further The difference between approved and off-label use, and why that distinction matters What the shear stress mechanism actually looks like in practice The existing network of EECP practitioners and how stroke survivors might access the therapy The questions both of us still have about where the research needs to go Important Disclaimers   EECP therapy is approved in Australia by the TGA for stable angina pectoris and congestive heart failure (ARTG Entry 376470). Stroke is NOT an approved indication. This article and podcast episode are not medical advice. Speak with your treating physician before pursuing any treatment. This episode is not medical advice. It is a conversation about an area of emerging research that I find scientifically credible and worth understanding. The goal is to help you ask better questions, not to tell you what treatment to pursue. Where to Learn More ecplocator.com a directory of EECP therapy providers eecpbook.com is a dedicated resource on the treatment and its evidence base recoveryafterstroke.com for stroke survivors looking for a broader community Research cited: Zhao et al. (2026). Enhanced external counterpulsation for ischaemic stroke: a systematic review and meta-analysis. QJM: An International Journal of Medicine. DOI: 10.1093/qjmed/hcag010. Therapy and Stroke Recovery: Can a Cardiac Treatment Help Grow New Blood Vessels? Bill Gasiamis sits down with Jack Clifford to explore EECP therapy, a TGA-approved cardiac treatment that may stimulate the growth of new blood vessels. Together, they examine the emerging research on angiogenesis, arteriogenesis, and whether this off-label approach holds promise for stroke survivors seeking to improve blood flow to the brain. Highlights: 00:00 Introduction – EECP Therapy06:06 Recognizing Health Issues and Seeking Help09:50 Hospital Experience and Heart Health12:12 Decisions Against Medical Advice16:28 Exploring Alternative Treatments18:06 Understanding Enhanced External Counter Pulsation (EECP)21:58 The Mechanism of EECP27:03 Personal Transformation Through EECP30:29 Lifestyle Changes and Holistic Health34:35 The Impact of Stress on Health38:30 The Journey of Writing a Book43:29 The Role of EECP in Heart Health48:21 Raising Awareness for EECP Therapy56:05 Exploring the Future of EECP Therapy Transcript: Introduction – EECP Therapy Jack Clifford (00:00)Mine was really severe. 100 % blocked in my widow maker, the left anterior descending. I’m 95 in my left coronary artery and in my right main, I am 80%. And I’m still that way today, but I can run a sub seven mile. Bill Gasiamis (00:16)Welcome to the Recovery After Stroke podcast. I am your host, Bill Gassiamus. Before we get into today’s interview, I need to share something important. The topic we’re exploring today involves a medical device called an EACP, Enhanced External Counterpulsation Machine. In Australia, EACP is registered with the Therapeutic Goods Administration for the treatment of stable angina and congestive heart failure. It is not approved for stroke. What we are discussing today is emerging off-label research, not a treatment recommendation. Everything in this episode is for informational purposes only. This is not medical advice. Please speak with your treating physician before pursuing any treatment, therapy or intervention discussed here. With that said, let’s talk about something that genuinely fascinated me when I started reading the research. Your body has the capacity to grow new blood vessels, not just small capillaries, but to remodel dormant pre-existing channels into functional bypass routes. Scientists call this arteriogenesis. There’s also angiogenesis, the sprouting of entirely new Both processes matter deeply for stroke because stroke is fundamentally a blood flow problem. Now here’s where it gets interesting. A cardiac therapy developed for heart patients, not stroke patients, trigger exactly this kind of vascular remodeling. And in 2026, a meta-analysis published in the QJM across 15 randomized controlled trials and 506 participants found that EECP produced statistically significant improvements in functional outcomes for ischemic stroke patients. Now, that’s not proof. That’s not a green light to go and get an EECP, but it is worth a serious conversation. My guest today is Jack Clifford. Jack is a heart disease patient who discovered EECP therapy while managing his own cardiac condition and who has since spent considerable time investigating its potential. beyond cardiac care. I should tell you, I was skeptical going into this conversation, but I’ve learned that skepticism without curiosity isn’t really skepticism. It’s just closed mindedness. So I read the research and then I sat down with Jack. So if you find this episode valuable, I’d love for you to grab a copy of my book, The unexpected way that a stroke became the best thing that happened at recoveryafterstroke.com/book. And if you want to support the show, you can join Patreon at patreon.com/recoveryafterstroke. And I want to thank everyone who is supporting me on Patreon, especially the people that have been around for a long time and the people who have just recently signed up. I very much appreciate it. And now here’s my conversation with Jack Clifford. Bill Gasiamis (03:19)Welcome to the podcast. Jack Clifford (03:22)Thanks, Bill. Great to be here. Bill Gasiamis (03:24)Let’s give the listeners a bit of a background understanding of why you’re on the podcast. You’re not a stroke survivor, but we have something in common as ⁓ somebody who has been unwell before myself and you in the past. Tell me a little bit about your journey to the podcast So we just kind of give people an understanding as to how it is that somebody who’s not a stroke survivor. Jack Clifford (03:34)We do. Bill Gasiamis (03:51)how we ended up chatting together? Jack Clifford (03:54)Yeah, absolutely. So the quick version here is ⁓ I was on the brink five years ago of having ⁓ unsentable emergency triple bypass surgery. And ⁓ I chose a different path, which we’ll get to. ⁓ But you you have some level of placking if you have a stroke, typically, depends on the stroke, but that’s typically the case. And in my case, I had placking in my coronary arteries. So it resulted in heart disease. Mine was really severe. 100 % blocked in my widow maker, the left anterior descending. ⁓ I’m 95 in my ⁓ left coronary artery and in my right main, I am 80%. And I’m still that way today, but I can run a sub seven mile. I can do some things that a guy that’s as blocked up as that should not theoretically be able to do. ⁓ Bill Gasiamis (04:49)All right. Tell me about life before the injury. What kind of work did you do? How did you go about life? What was generally a day like for you? Jack Clifford (04:59)Yeah. So I’m retired military guy. Um, so, you know, been in the military most of my life, um, retired about 10 years ago, a little over that. And, um, so I’ve always been a pretty fit guy. It wasn’t, you know, it wasn’t a fitness issue per se. Um, and, uh, I, I, I had kind of lost some of my self care because my wife had been going through some real significant medical issues that really required my full attention for quite a while. And because of that, really stopped taking care of myself in the ways I had in the past for about 10 years. And when we had just moved to Florida, I started trying to take care of myself again. And that’s when I discovered all these problems. Bill Gasiamis (05:44)So what does not taking care of yourself look like though? Jack Clifford (05:47)Gotta be in a couch potato and being on my computer way too much research and for ⁓ trying to help my wife get better and hold down a job at the same time and raise a family and all these other things that took the priority off of me in that sense that one should be taking care of themselves, meaning exercising, meaning eating the right foods, so on and Recognizing Health Issues and Seeking Help Bill Gasiamis (06:09)You know, caregivers tend to die before the person they’re caring for much more often. And it’s cause of that reason, right? Because time is really taken up by especially full-time caregiving with somebody’s in the house and they need caregiving. need care. The caregiver tends to neglect themselves in every way, shape and form and tends to ⁓ make it about the other person. And then the other person. Jack Clifford (06:14)I’ve seen that and heard about it. Yeah. Mm-hmm. Bill Gasiamis (06:39)seems to be doing okay, but the caregiver is struggling and doesn’t ask for help and doesn’t go and doesn’t go and get looked after. And then things tend to catch up with them and they become the ⁓ sickest person in that relationship. Jack Clifford (06:55)It’s like that whole put your oxygen mask on first on the airplane type thing, right? Like, you know, we can’t we can’t give what we don’t have to give Bill Gasiamis (07:01)Uh-huh. Yeah. So you, did you notice, did you notice the steady decline in your health? Did you kind of go, I’m not feeling right. I’m a feel a bit sluggish like 10 years down the track, or did it just creep up on you? then you got to this point. Jack Clifford (07:15)It really crept, it really crept. I, you know, like I had initially exercise induced angina, but it wasn’t much exercise that induced the angina. And then it very quickly progressed to trying to walk and getting out of breath and, know, at very basic walking speeds, just moderately paced, you know, anything anybody would do out in your neighborhood. ⁓ Bill Gasiamis (07:39)Did you know that you had an angina? Jack Clifford (07:41)I did, yeah. I didn’t have a big heart attack episode like some people have. I’m 100 % blocked. There’s no heart attack to happen, right? Because the stuff is, I’m so blocked that it’s just a pure blood flow issue. A lot of people don’t understand that that 50 % blockage is a huge risk for a heart attack because you’re gonna burst a plaque and then go from 50 % to 100 % like that. But you know about collaterals. And if you have collaterals in place, the blood’s not getting flowing this way, you’re gonna recruit some lead oval collaterals to be able to just get by with your activities of day living. But if you don’t push yourself, you don’t know that you don’t have enough blood flow to do these other things. Bill Gasiamis (08:22)Okay, so you got to the point where you were so unwell as far as the blood vessels around your heart were so unwell, they were so blocked that angina led to another escalation or something happened that got you to the point where you realized, okay, things are not good. Now, tell me what angina is exactly and what it’s like to have it. How do you experience it? Jack Clifford (08:39)Yeah. yeah, yeah. I’d love to talk about that. Bill. at its most basic, it’s a supply demand mismatch. So, you know, the blood flow that’s supplying your heart ⁓ is adequate for X, Y, or Z activities of daily living. You know, walking around the house, doing the dishes, you might have enough blood flow for that, but you don’t have enough blood flow to go run a mile or even walk potentially, you know, or Hospital Experience and Heart Health but it’s all about supply demand mismatch. And that’s about just the size of the pipes, you know, if they’re clogged up, how clogged up are they? And, know, ⁓ that’s, really it. So, and what it feels like is it’s scary because it feels like a heart attack. all like, what does a heart attack feel like? Well, there’s a thousand different sort of, ⁓ descriptions of it. ⁓ you know, radiating down your arm or nausea or something in your back, but. you know, if it’s right over your heart, it’s unmistakable. And that’s at least my presentation of angina. And I think it was a pretty typical one is, you know, I have this weird kind of deep pain. initially, when I, when I started, you know, run, trying to run and got it, I thought, ⁓ you know, I just pulled a chest muscle weirdly over my heart. You know, I’ll stop and let’s see if it goes away. I come back, you know, no, same thing. Okay. Still not better. Let’s do it again. Another couple of days later, so on and so forth. I was just kidding myself, but I didn’t know anything about the horror at that point. hadn’t had to research all this stuff and do all the deep dive. Bill Gasiamis (10:16)That’s the same crazy logic that stroke survivors put to, I’m feeling weird. I’m dizzy. I’m going to go and lie down. I’m going to rest. It’ll be better later. ⁓ I’m too busy. I’ve got to go to work. ⁓ I’ve even had stroke survivors where somebody’s telling them you maybe you’re having a stroke, you know, just tongue in cheek and they’re like, yeah, no, probably not. ⁓ it’s the same crazy logic that we say about things that are unfamiliar to us that we cannot potentially. Jack Clifford (10:25)Mm-hmm. Mm-hmm. Yeah. Yeah. Bill Gasiamis (10:46)link to something so serious because we have no knowledge, we’re ignorant, right? Jack Clifford (10:47)Yeah. Well, yeah, I think that’s really part of the key there is like most times with something as sudden as what you’re talking about or what I’m talking about in my instance, because it was pretty, pretty sudden, you know, weeks and months. ⁓ We went from being these, you know, healthy people that felt like we were on top of the world to all of a sudden not. you you didn’t have a frame for what not looked like. ⁓ Bill Gasiamis (11:14)Exactly. Yeah. That’s such an important comment. We don’t have the frame for what not healthy looks like and therefore you don’t know what you don’t know. So you don’t take any action. You just brush it off. Okay. I hear you. All right. We got to the bottom of the stupidity behind a lot of my decisions as well to avoid going to hospital for a week, et cetera, the first time. ⁓ So you end up Jack Clifford (11:24)Exactly. That’s it. Bill Gasiamis (11:43)being really unwell on this particular date. Kind of what is that day like? Explain us. Jack Clifford (11:46)Yeah. Yeah. Decisions Against Medical Advice So I got tight. I, I, I’ve been a biohacker for a while. So that’s probably the only reason I’m here talking to you because I went off the beaten path really far off the beaten path to get to the place where I know what I know and I have to share what I have to share. ⁓ because I’ve been trying to help my wife get better for some significant issues, including a really bad traumatic brain injury. And some other things and doctors didn’t have the answers for those so we had to we had to kind of biohack our way out of some things I was comfortable back. I’m saying that to say my wife got me a Chili pad for my bed because you know been trying to biohack sleep for a while and the colder environments to sleep are you know better to some degree at least in theory ⁓ and so Yeah, correct Bill Gasiamis (12:32)Chili meaning cold, not spicy. Jack Clifford (12:37)Yeah, correct. A chili pad as in the cold. So it’s a device that just, you know, cools your bed off. And so I crank that down to 55. She got it for me for Christmas. So Christmas day Eve, I’m like hopping into bed, like I’m going to sleep really well tonight, you know, and I woke up at four AM like, Oh, you know, I thought that was the big one because it felt that way. I a dead sleep woke me up with, with intense chest pain. And I knew something was going on, you but I was kidding myself. I hadn’t talked to family about it. You know, I hadn’t shared anything about what was going on with anybody. So at this point I’m like, oh my goodness, you know, and I could be dying and have not had, you know, just been an idiot the whole time. So I rushed to the hospital and I didn’t have a heart attack. I just made it so cold that I made my heart work and that supply demand mismatch was happening all night long in my sleep. Bill Gasiamis (13:15)Mm-hmm. Jack Clifford (13:31)And so it got to this, you know, a giant, creeps up, you know, it’s like, can feel it. And then if you push it, you’re like, can really feel it. Well, you know, I woke up out of a dead sleep going from not feeling it when I went to sleep to, to feeling it to the extreme when I woke up. Um, but that’s when they gave me the, uh, the, uh, nuclear stress test with a treadmill test, right in the hospital. And it was, it was really bad. They can’t quantify your blockages with that, but they can tell you that, you know, you’re You’re kind of screwed. And I was like really screwed. Like it was 47, but they said I was one of the worst I’d ever seen. ⁓ yeah. So I had all weekend to think about it, you know, cause I was a Friday, fortunately, and they could, they weren’t going to do the heart catheterization until Monday and the doc, you know, I was signing consent forms for them to do bypass surgery and it was pretty clear that the odds of it getting stented was not really good, but that’s what you hope for. Right. And most people are like, we’ll just get a step. once then in you’re fine. And ⁓ in my case, it wasn’t looking likely. And my mother had had bypass surgery five years before that. And I watched her cognition after the bypass surgery just declined to the point where she’s in memory care now. And she had gone from being this vibrant book author of multiple books and you know, she was a hypnotherapist and she’s helped a lot of people in her life, done a lot of amazing things, but ⁓ she never. she never really came out of the bypass surgery as her whole self and pretty quickly was just completely not herself at all. ⁓ So I wasn’t ready to come back. Now she’s 76. Bill Gasiamis (15:03)How old? How old’s your mom? Yeah. I know with people that are older, ⁓ heart surgery can lead to cognitive decline and there is a link there. There is a number of it’s well researched. It’s a risk. ⁓ not one that you’re probably aware of and that they talk about much, but it definitely is a thing. so, okay. You’re, you’re you go to the hospital. They realize, ⁓ the Jack Clifford (15:15)Mm-hmm. Bill Gasiamis (15:37)charts are not looking good. ⁓ They do the tests. They suggest that what they can offer you is bypass surgery. your, and you’ve got a weekend, think about it and you, and you go home, do they go, do you go home with medication and joining the medications to keep the blood flowing with anything? What do they do? Jack Clifford (15:51)Mm-hmm. Where’d you go? Yeah, such a blessing. No, no, because I was leaving against medical advice so they weren’t going to help me, right? And I actually said to the doc, said, you hey, I’m new here because I just moved a couple of months ago to Florida. And I said, can I come see you? And I didn’t have a cardiologist. I didn’t need one before this. And he says, if you live that long, just walks out. So I was on my own at that point. There was no resources of institutional medicine. I had to go find resources myself. Exploring Alternative Treatments Bill Gasiamis (16:28)Wow. Things are pretty wild in Florida. If you live that long and he walked out. Jack Clifford (16:30)Yeah. Yep. That’s exactly what we said. It’s a very sobering moment for me. Yeah. Bill Gasiamis (16:35)And you walked out. Yeah, and you walked out. Far out, man. So what’s the thinking behind walking out of that? Because I understand ⁓ that there are very few things that, like my situation was different, right? But I’ll give you kind of my thinking behind the, I’m gonna walk out routine. It’s like, there is a part of me that sort of says, I don’t need to subscribe to all that medical stuff, all the nonsense. I wanna try and avoid the medications. I wanna do all of that. Jack Clifford (16:41)Yeah. Yeah. Bill Gasiamis (17:07)That means I’ve got to do some work to get to that point, right? I’ve got to make sure that I’m eating well. I’m sleeping well. ⁓ I’m exercising. ⁓ I’m not overweight. I’m not smoking. I’m not drinking. Like there’s a responsibility that goes with, don’t want to take that medication. Right. And one of the other things is that, ⁓ if it wasn’t for the medical industry, I would not be here recording this, ⁓ podcast. Yeah. So there’s this big thing, which is. Jack Clifford (17:31)Yeah. Double-head sword, right? Yeah. Yeah. Bill Gasiamis (17:37)They’re not fixed. My brain is not getting fixed unless they go in and take out the faulty blood vessel and potentially risk all the complications that, that I got the ones I got, but also the ones I didn’t get, which many people get, which is far worse deficits than what I visible on me. So, ⁓ I’m, you know, I’ve never met anyone in my time who hasn’t Understanding Enhanced External Counter Pulsation (EECP) who has been through the medical ⁓ system, who hasn’t benefited from it in a way that’s sort of sustained their life, supported their life, lengthened their life. Like everyone that I’ve interviewed has always gone through the medical system and has saved them, supported them, helped them, right? And you’re going to, the first place to get help you’re going to is a hospital, right? You ring up and you go, I’ve got to go. Jack Clifford (18:22)Yeah. Bill Gasiamis (18:31)to the hospital because I’m feeling like I’m having heart attack. You get there, they confirm it, and then the place that you go to for help is the place you walk out of. What’s the thinking? Yeah, yeah. You have the angina, the blockages. Yeah, you got all of that. Jack Clifford (18:41)Well, I didn’t have a heart attack. That’s a really important nuance point. you know, I’m sitting in the hospital all weekend. there was nothing at risk in an emergent moment for me. My heart wasn’t, you know, I wasn’t going to lose heart muscle if they didn’t do something. Like my mother’s instance was different. She had a heart attack. She probably needed the bypass surgery. It was really hard on her, obviously, like we talked about, but in my case, I had time, but they didn’t treat it like I had time, right? Bill Gasiamis (18:54)Okay. Okay. Jack Clifford (19:10)They treated it like, we’re gonna go in and take care of this thing for you rather than you have time to explore other options when I knew in fact I did. So it might be that getting bypass surgery is the right move for some folks, but it also might be the right move for you and me. We’ve already discussed that you take care of yourself so you never get in that situation. And yeah. Bill Gasiamis (19:32)Yeah. And this is not a interview about do as I say, this is not that interview, right? What this interview is like one person’s experience and what they did. That’s it. We’re not giving medical advice here. We’re not telling you what decisions to make. We’re not telling you any of that stuff. This has got nothing to do with advising anyone to do anything, but what it has got to do with is what either you discovered Jack Clifford (19:45)Yeah. Right. Bill Gasiamis (19:58)or you knew before and put into action or what you discovered after you left the hospital that weekend. So take us through the next sort of phase of I’m taking responsibility for this and I’m going to take advantage of something that is documented scientifically and proven. Jack Clifford (20:03)Yeah. Okay. Yeah. Mm hmm. Yeah. Yep. Yeah. And you know, like, so I’ll go into that phase, but, but I just want to share this thing because, know, you, you pretty much already told me when you first heard EECP, you like EECP what? Right. And most doctors are EECP what? Basically every patient is EECP what? And it’s, it’s just, it’s really not going to lie. really bothers me because this, this, this therapy is, is so well-documented. It’s, it’s, it’s FDA approved. It’s not controversial. Bill Gasiamis (20:25)Mm-hmm. Jack Clifford (20:43)⁓ it just anyways, okay. So, so, so yeah, so I leave the hospital and the only reason I knew about a EECP was because when my mom had her heart attack, I listened to a podcast by Ben Greenfield. He’s a pretty, you know, pretty high-level guy, right? And that had been, that was like 2015. And I just heard mention of it. was like, it was maybe like two minutes of the, of a 60-minute podcast at most, but I was like noted. So I looked into it from my mom. The closest provider was two hours away and you got to go 35 times and my mom isn’t going to drive. 35 times, you four hours round trip. It wasn’t gonna happen, so we moved on, but I just sort of knew about it. And when I say knew about it, I didn’t know, Bill, like what it actually did or how it worked. I didn’t look into it at that level. just, you know, like assessed the situation. I was like, okay, there’s something out there. That’s it. Okay, yeah. It stands for enhanced external counter pulsation. And you want me to go into a little bit about how it works? Yeah, okay, so. Bill Gasiamis (21:27)Hmm. And what is a ⁓ CP stamp? What does it stand for? Yeah, yeah, let’s do that, yeah. Jack Clifford (21:42)So EECP involves lying on a bed. From the patient experience, you’re lying on a bed. You have ⁓ cuffs wrapped around your calves, your thighs, and your hips. And inside those cuffs, there are little air bladders. Bill Gasiamis (21:55)those cuffs, are they like blood pressure cuffs? The Mechanism of EECP Jack Clifford (21:58)Yeah, like big giant Velcro blood pressure cuffs. Yes. Bill Gasiamis (22:02)Okay, so like they’re much bigger than a regular cuff, which is just over the bicep. Okay. All right. Jack Clifford (22:04)Yes. Yes. Correct. yeah, just that’s the right way to think about it. you you cinch them up, you’re getting really snug in this thing, but it looks like a giant pantsuit, you know? ⁓ And you lie on the bed and then you get a three lead EKG on you. It’s here, here, in here. And then in between heartbeats, the machine… inflates compressed air into those bladders at 1.3 psi to start with, which feels like kind of a gentle massage. And then the pressure can be increased in increments of 0.1 psi all the way up to six, which feels like the exact opposite of a gentle massage. However, if you go slowly, your body accommodates to that pressure and that pressure feels different, both over one session and over multiple sessions, meaning you might not get to six your first session, that’s unlikely, but as you do repeated sessions, you’ll increasingly get closer to six earlier in the treatment and be cumulatively more hours at those higher pressures. And what’s happening is all the blood, not all the blood, a significant amount of blood from your lower body is being pushed up in between heartbeats and it’s causing this phenomenon called sheer stress in your vascular systemically. And wherever there’s pressure differentials in the body, it’s giving a stimulus to grow. It’s saying the pipes are not big enough, you gotta grow. We’re trying to put through more than is gonna fit. The body’s like, wait a second, it’s not big enough. But growing things in the body takes time. And so you need those repeated sessions. Like I mentioned, T.R., before we started recording, it works just like cardiovascular exercise, but at levels humans can’t do on their own. ⁓ And so, yeah. Bill Gasiamis (23:52)That’s important to talk about. so just for a moment, we’ll talk about that. Like it works like cardiovascular exercise. So the idea with cardiovascular exercise is that what, does cardiovascular exercise do that’s similar to EECP? Jack Clifford (24:04)Sure. If you’re out running, when you hit that stride on your feet, you’re doing that same thing, right? You’re ⁓ sending blood up, right? And then your circulation, your heart’s beating twice as fast maybe than it normally is, or substantially more than you’re just sitting here heartbeat is. And that’s because the heart is responding to the environment around it and saying, I gotta get… a lot more blood, a lot more places. So I gotta work a lot harder. you know, is maintenance. So collateral blood flow. have alternate routes that we can use that lie dormant throughout our body. And those collaterals, if they never get used, they honestly, they get weaker and they close off, but they also can be reopened, you know? And then you can grow more of them. And… Bill Gasiamis (24:38)And what’s the result of that? Uh-huh. Okay, so there’s blood vessels that get less ⁓ blood flow because people are sedentary or people aren’t doing the type of exercise that would activate those blood vessels, for example. And then what in theory, not in theory, and then what happens in cardiovascular exercise, the body goes, we need more blood flow, let’s open up. Jack Clifford (25:12)Exactly. Bill Gasiamis (25:26)other areas where normally blood flow wouldn’t be required or doesn’t go. And EECP kind of mimics that mechanism. Jack Clifford (25:27)Yeah. Exactly. Yeah, but not kind of, it’s really important just to note, cause I don’t want, I don’t want any of your listeners thinking, well I’m just going to go run more. Right? I mean, by all means do that safely. You know, the dose always makes the poison with everything, but, but don’t think that you can, you can just go do this. You can do it to a limited degree with exercise, but you’re not going to grow, you know. that I didn’t have that before. And I like it because it shows you like the world of the possibly or it might be a little unsightly, but it’s feeding my brain. EECP has changed my cognition in addition to my heart, you know, my pelvis and my kidneys and my liver. you know, like it’s, it’s optimized blood flow systemically. Um, yeah. Yeah. Bill Gasiamis (26:19)Okay, so let’s go back to the cuff, the cuff that we put on and then what happens. Jack Clifford (26:24)Yeah. Yeah. So, so you just lie on the machine. Typically you do 35 hours on a machine for a course of treatment and one hour a day is a typical, you know, five days a week. That’s just typically you’re going to the doctor. There’s lots of other variations of that, but that’s the typical course. And that’s the most well-researched course. And, ⁓ you know, over time, usually about halfway through those 35 sessions, if you had angina, you’re going to notice a difference, but Personal Transformation Through EECP you know, they use this to treat dementia. It’s a well studied in dementia. There’s a recent study in the US that was profound, a year-long study, a hundred demented patients, roughly a hundred non-demented or a hundred treated patients. Everybody had dementia and a hundred CHAM patients, placebo. The demented patients that got an EECP, they all got better when we know dementia, people get worse in a year, right? They all got better, all of them. And yeah, so that’s like, you know, similar phenomenon erectile dysfunction, similar phenomenon kidney disease, similar phenomenon stroke recovery. So, you know, these are studies. I’m not making it up. It’s just literally like really well documented. It’s not. Bill Gasiamis (27:33)studies that we can get a hold of and put in the show notes, link to the show notes. Jack Clifford (27:36)Yeah, go to to EECPLocator.com and all these studies are there. ⁓ Yeah. So what I did is in the U.S., I, you know, it’s really hard to find. so I couldn’t find it. I had to, I had to call around and like, I could find a few doctors, none of them near me, but a few of them that would had machines, but they would only use them after everyone had failed stints and failed bypass and they had nothing else to offer them, which makes no sense. But that’s how the insurance reimbursements work. Bill Gasiamis (27:41)Okay. Jack Clifford (28:04)That’s the only time they’ll actually pay for it. So that’s what they say it’s good for, but that’s not what it’s good for. That’s just what they can get money for, I guess. but, so I had to drive three hours and take a chance on a doctor and stay in a hotel to get my treatments. And it was really difficult. I mean, I ended up buying one of these machines and got it at my house and I’ve just been using it for the last five years. So, you know, 35 hours was great, but I was pretty bad off. Now I got about 700 hours and, uh, you know, more hours is just greater stimulus to the body to grow vasculature, right? And I mean, I… Bill Gasiamis (28:38)how do you know that you’ve grown? I know there’s this ⁓ feeling or this change that happens in the person. ⁓ Like you said, dementia, ⁓ people who experienced dementia have a better outcome later or a change in the way that they’re brain working, et cetera. can you see the, is there a way to see the difference between the blood vessels and Jack Clifford (29:02)You can’t, you can’t image, could image on a, on a cardiac pet would be like the only imaging or I guess, you know, if I went back and did a stress test again, you would, you would be able to see, cause it’s not quantifying specific arteries. It’s, quantifying the total volume, but I tried that they were, actually wouldn’t let me, they said it’s not safe because you have it at a stent or a bypass. So I went back to the same place that I got it, you know, and I was like, literally they put me through the imaging machine. gave me the dye and then they got Lifestyle Changes and Holistic Health I went to go on the stress test and the same doctor was there and he refused to tell me to go. So I like, wanted to say, hey doc, let’s go for a run. Cause like, you’re not going to keep up with me, but you know, so I, I didn’t bother with that, but I’ve got my own, you know, I did my own little stress, stress test with a treadmill, right? I started, I was getting chest pain. I found out where I can induce angina and I try and say just below it, you know, so I know where it is, right? I was 2.2 miles an hour. That’s not a fast walk. And then after the first 19 sessions where I was staying in the hotel, I got up to 2.7. That’s a really big difference even if it doesn’t sound like a lot. And then I got my machine and I kept going. And then within a couple of months, I was starting to do a running stride. And I could keep that up, no angina. I know where angina would come in. I had time calculations and everything. And then eventually, now I can run. comfortably 6.5 mile an hour pace for quite a while, know, push it up to 14 miles an hour for 30 second sprints and you know, like all kinds of stuff. So, ⁓ Bill Gasiamis (30:38)How long before you break the two hour barrier for the marathon? Like was recently done. Maybe, maybe the more blood vessels, the more blood flow. Maybe you can get there. Jack Clifford (30:42)⁓ I got zero interest in that. Yeah. I think so though, I think those Kenyans should be ⁓ hopping on these EECP machines and they’re I mean, they’re already amazing but. Bill Gasiamis (30:58)Well, you want the Kenyans to just completely own marathon running for the rest of eternity. It’s unbelievable what they did. Right. Like I imagine that there is something else going on there, but I imagine blood flow, oxygenation, more blood vessels. Like it’s got to potentially be a thing. reckon if you do a check between the last guy, me, who’s going to like 50 hours before you get to the other side and those dudes, there would Jack Clifford (31:03)Yeah, yeah, it’ll just be a Kenyan Yeah. ⁓ Bill Gasiamis (31:27)definitely be a difference because they’re exercising all the time, right? Jack Clifford (31:31)Sure, yeah, they’re pushing the collaterals as wide open as, know, whatever, whatever a human can do on their own, they’re doing it to the max to, know, the same phenomenon that EECP is doing for folks lying down. You know, they’re doing it to whatever the max you can without the machine, I would say. Bill Gasiamis (31:48)So this is a bog standard human body task. Like it just does that all the time. I have heard the blood vessels can reroute in the brain when somebody experiences a blockage and then, and it’s not useful at the time of the blockage, obviously, and it causes potential cell death when somebody has a stroke. But then later on. Jack Clifford (32:11)If there’s too much blood, the revascularization, yeah. Bill Gasiamis (32:14)Yeah, so EECP can kind of occur naturally and then it can support as much of the surrounding tissue as possible so that it doesn’t all die off. ⁓ So what you’re talking about is just encouraging EECP ⁓ to happen more than it would normally happen by ⁓ inducing it through this device where people ⁓ get sort of strapped in and then Jack Clifford (32:23)Yeah. Bill Gasiamis (32:43)the machine runs, what does it run like a program? Explain how that works. Jack Clifford (32:47)Literally, it’s just air pressure. got different pumps to pump the calves, the thighs and the hips up. And then it’s really just about the timing, right? It’s got to hit it at the right interval of your heartbeat. So it’s at the right place in diastole where your heart is at rest. that timing is very, crucial. And that’s really… Yeah, it’s not, it’s very old technology. The machine I have was built in 2009. You know, they have new machines that are portable now that I’m working with some of the manufacturers to actually, you know, make these available in the U S because there aren’t any in the U S but they do have portable machines that don’t require a bed. You could get treated on your couch. You could get treated, you know, on your own bed, uh, lying on the floor, I suppose. Um, so, you know, we’ve, we’ve really like technology hasn’t Bill Gasiamis (33:19)Wow. Jack Clifford (33:42)slowed down. just China’s like taking this thing and you know, have a basically every Chinese hospital has several of these machines and they treat patients in the, in the room with us. It’s, part of their standard of care for all kinds of different, different diseases that they’re treating. You know, and it’s adjunctive to just about everything. There’s nothing that you couldn’t do EECP with, right? ⁓ yeah. Bill Gasiamis (34:03)Okay, okay, so. How do you experience your body differently now? And actually, let’s go back actually, how long has it been since you came across this, decided to get the first treatment, implemented yourself ⁓ at home and then how do you feel different now? Jack Clifford (34:08)Oof. Yeah, it’s been five years and four months now. And every since like, this is this is a little hard part to quantify, because there’s been a lot of brain changes to from this, right? So so I don’t even like feel like my 47 year old self who was in the hospital, that feels really like somebody else to me. You know, it’s a version of me, I suppose, but I can’t really relate to that person. Because I like a small example. The Impact of Stress on Health I used to sleep eight to nine hours a night. That was my normal, my whole life. I was generally like the guy that would come in the latest. You could come to work. was the guy that came in the latest. You And now I get up at two 30 most mornings and I’m like, like rare to go with energy. I’m, you know, I’m working out doing resistance training. I’m reading, you know, I wrote a book, I’m writing another book. I’m writing a book on rectal dysfunction as it relates to this phenomenon, because that’s a whole other, you know, case study. and I work a full-time job and I just have an incredible amount of energy basically all the time. My mood is way better. My sense of touch is really different now. I give a lot more hugs because it feels really good. ⁓ My sense of smell and taste and… You know, hearing, you know, I used to like have to go to the bathroom at night sometimes, you know, wake me up to go to the bathroom. Long gone. Bill Gasiamis (35:47)So at the same time though, it sounds like also you might have changed other things as well though, right? So what else have you changed in the meantime? Jack Clifford (35:55)sure. Yeah. Yeah. Yeah. It hasn’t just been EECP. Absolutely. you know, really good supplement routine. ⁓ Pretty extensive, but, you know, managing my lipids, for example, I take a thousand milligrams of niacin twice a day. I’ve been able to bring my triglyceride to HDL ratio to kind of an optimal one-to-one, using fish oil and some other things. ⁓ And, you know, I… I really stay away from carbs for the most part. I like to eat keto, but I like it to be what I call clean keto. So I’m not like pounding keto ice cream or all these things that are, you know, they taste good and yeah, they’re keto, but they got all kinds of oils in them that aren’t really good for your body. ⁓ And, ⁓ you know, I’m big into moving and being active and, you know, having an engaged social life as much as possible as well. I mean, I think that’s a very underrated thing. That’s actually an area I struggle in because I’m working so much, but you even this helps just, you know, getting to know people even online. But, ⁓ Bill Gasiamis (37:04)It sounds like you haven’t re it doesn’t sound like you’ve reinvented the wheel. Like everything that you say is things that people take for granted that if they implemented would improve their life before EECP. We’re talking about EECP today, right? But just those things alone would make a massive difference to somebody’s experience. And that’s kind of the message that I’m trying to kind of get into the Jack Clifford (37:17)Totally agree. I thought it a good Sure. Bill Gasiamis (37:30)⁓ minds and hearts of the stroke survivors who I interview and who listened to the podcast. My book, I’m going to, we’re going to talk about your book in a sec, but I’m going to talk about my book. My book, when I wrote it, I thought I discovered all these things that people, should know about that no one knows about, but it’s not true in here is mindset. ⁓ there’s a chapter about emotional intelligence. There’s a chapter about nutrition. There’s a chapter about sleep. There’s a chapter about community. Jack Clifford (37:32)Yeah. Yeah. No, please. Bill Gasiamis (38:00)⁓ that’s just the five that I can just rattle off the top of my head right now. And you’ve already mentioned that in the last few minutes, that’s exactly the things that you mentioned. And people take it for granted how much that improves your overall health. Right. The Journey of Writing a Book Jack Clifford (38:13)That’s so true. And also what’s wrapped up in the wrapper of all of those things that are threaded together is stress, right? ⁓ If you do all of those things, right, you’re lowering stress. How did I get heart disease at 47 when it happened to my grandfather in his late 60s and my mom in her mid 60s and it happened to me at 47? And we know it didn’t happen at 47. It was years earlier and I realized it at 47. Stress, you know? Like I was the guy that took on a lot. Bill Gasiamis (38:38)Hiding earlier. Jack Clifford (38:44)and had some traumatic things happen in my life and whatever, and I don’t need to go into that. But I always felt like it was all rolling off my back. Like, you know, I’m fine. know, like I didn’t, and there are reasons why I felt that way. ⁓ However, at the end of the day, I know that I wasn’t processing. There was so much I did not process. And I didn’t learn how to like have really good boundaries and that, you know, begot more stress because of those lack of boundaries and, but stress, right? You know, like, but if you have good good social life and healthy people in your lives, that takes stress off. Eating the right food takes oxidative stress off your body. You could go on and on, but I think stress is gonna kill you before anything else. Bill Gasiamis (39:17)you Yeah. I love that you said that. I love what I love that. That was the answer that you gave when I said, what else did you do? Because it’s not just, you know, it’s like, I’m going to eat well, but smoke, you know, I’m going to eat well, but drink excessive amounts of alcohol. Like, no, it doesn’t work. You know, you can’t do that. Yeah. can’t do. Yeah. Small. Jack Clifford (39:42)No, you gotta do it all in concert. It’s the layers, right? Yeah. Bill Gasiamis (39:49)numbers, know, the percentages they add up, you know, 1 % here, 1 % there all adds up and you get a result at the end of it. Okay. So, so you’re you’ve gone, I’m going to see if I can grow new blood vessels to support my heart. And what you’re found between the time that you went to hospital around five years ago to now is that the angina has Jack Clifford (39:55)Yeah. Mm-hmm. Bill Gasiamis (40:17)⁓ improved, they’ve gone away. The heart has improved, I beg your pardon, the blood flow. And have you had a medical examination since then to do other comparison? Jack Clifford (40:28)Yeah, I have. Yeah, I’ve got a cardiologist. I haven’t seen him and I’ve talked to him the other day because I talked about the book, but I haven’t gone to see him because he’s a plane flight away. But I’ve been worked up for the crowded intermediate thickness. You might be familiar with that as it relates to stroke. okay, well, they just measure your crowded arteries and look at the placking in your crowded arteries as a proxy for your systemic plaque burden. And flow mediated deletation, is they totally occlude the… the arm with a blood pressure cuff and then see how quickly you can refill it after, you know, like, it’s like five minutes of this, your hand is completely numb. And those all, you know, workups were good and that was after a couple of years of treatment. You know, I tried to have that stress test, like I mentioned, but you know, now I just see my primary care, you know, he’s a good guy and he runs on my lipid panels and, ⁓ you know, so I’m definitely monitored, but. What I haven’t done is gotten re-imaged because I don’t want to put extra dye in my system. Sure, somebody wants the images because they don’t believe me, but I’m not trying to sell anybody anything here. I’m just trying to spread the word on something. If somebody doubts my honesty, they can, it’s fine. Bill Gasiamis (41:38)I know what you mean, Jack. I know what you mean. I and I asked you because yeah, I would love to see that before and after. would love to see the blood flow. What’s happening, watch change. would be amazing. story to tell, but I also went out of my way if I could to avoid having more dyes and all that kind of stuff injected into my body. I totally get it. It’s okay. Yeah. ⁓ Jack Clifford (41:49)Yeah. Yeah. Yeah. Bill Gasiamis (42:01)Okay. So you wrote a book about it. Like, what was the idea behind the book? What were you thinking? Show us the one that you got there with the old book cover. And then I’ll include the new book cover in this image as we chat. Jack Clifford (42:06)yeah. Yeah. Yeah. Yeah. Thanks. Yeah. So I started writing this book, in, know, ⁓ November timeframe, ⁓ after I mentioned to you, so my, my friend came down, ⁓ and stayed with me for 13 days and he had had some stroke damage five years before that was, you know, his whole right side, he just had like numbness and then pain. And then, you know, it this weird cascade of symptoms so bad, you know, sometimes he couldn’t sleep from it. And so All the time he took off work he could he came and he used the machine three times a day and then he left pain free and like nothing else had worked and then this worked and I didn’t per se expect that I but I was like, you I know it does stuff. It’s helpful. But anyways, when I saw that, you know, I really started digging even more because before that I was like, well, Jesus is amazing. But maybe it’s just me, you know, and and anyways, so, ⁓ so then I, you know, I just started writing the book one day and The Role of EECP in Heart Health You know, my mom was a book author and I always wanted to write a book. didn’t really have anything particular to write about and all of sudden I do. So I’m like, you know, let’s see what happens. And, uh, and you dig into the research more and more, and you’re just like, increasingly frustrated by how everyone has known about this. And yet, you know, they don’t promote it. They don’t talk about it because it’s inconvenient. You know, and I’m going to get a little, try not to get like soapboxy here, but Bill Gasiamis (43:36)Do it, do it, go for it man. Jack Clifford (43:37)Okay, okay, because, you know, cardiologists will say it, some of them, the ones that are honest, they’ll be like, like mine. He says, I was making obscene amounts of money, giving people bypass surgeries instance. And then I was given the same people bypass surgeries instance, a couple years later. And, you know, and then he stumbled upon some answers and EECP is one of them that helps his patients stay well. And, you know, he makes a lot less money. because of it, because he doesn’t go in and do these interventional approaches. And, you know, EECP, the most you could pay somebody is like $100 an hour, and you’re going to tie up a patient room for 35 hours with a tech, it doesn’t make any sense. I go pop a stint and you make 10 grand in two hours and never see you again. You know, like it just, I get it from, you know, I want to own a portion of Ferrari and have a lake house and a winter house, but You know, like, I don’t know how you live with yourself. You said go for it, man. I’m going to go for it. you know, and my son’s about to graduate. Okay. Yeah. Okay. Fair enough. I’m good with it. Yeah. Yeah. Bill Gasiamis (44:38)But come on, come on, Jack. Yeah, you go for it. I’m going to push back. I’m going to push back as well. You go for it. I’ll push back. There’s yeah. Which is cool. Right? That’s what I want. I want to have a conversation and I don’t want to control the narrative, but the guy that goes in needs a stint today has a blockage. Like that’s life saving. That does work. What I am afraid of that happens sometimes when people go in and they’ve got a blockage and then they get ⁓ even even a stroke blockage. Right. in carotid or a vertebral artery. What happens is sometimes people go in and they get told you need a stent. Fair enough. You’re about to have a heart attack. You’re about to have a major stroke. If we don’t put one in, you’ll have a, that’s necessary. The challenge is, that that person sometimes doesn’t learn the lesson of what got them into the situation where they need a stent. Jack Clifford (45:22)Good. Exactly. sure. Yeah, by all means. Like emergency medicine is great. And we’ll put that in the emergency medicine category of cardiology, right? Why aren’t they offering you, why aren’t they saying, Hey, you’re at risk for a whole lot of other things just by this happening. Why don’t you come 35 times to this EECP machine and you know, like, or why don’t we have centers Bill Gasiamis (45:36)Yeah. Yes, and then later… Jack Clifford (45:55)all over. I found exactly one place in Australia so far that I’m not focusing on Australia right now. I do plan to take EECP Locator International, but right now the access points in the US are abysmal. 70, 80 % of the people in the United States could not get to a center. There’s no access point that’s at all realistic for them to get to. And yet these machines are not that expensive. They’re the price of a Decent not that great car. ⁓ Bill Gasiamis (46:24)we’re starting to see them in, I don’t know, health spas or something like that, where people will go, they’ll get yoga, they’ll get this, they’ll get that, they’ll get infusions perhaps and all sorts of other things. And there’ll be a machine or there’ll be a suit that people can put on and they can go through one hour. Jack Clifford (46:29)Yeah, that’s good. That’s great. Yeah, although I do want to say that the Normatech, like the compression boots that they have and some of those things, when they don’t use the pressures that EECP uses up to 6 PSI and they’re not sinking it in between heartbeats, it’s helpful, but we’re not talking about things that can do the same thing in the body. It’s on the right path and I’m not digging it as being worthless because it’s not, but it’s just not the right thing. Bill Gasiamis (46:47)Yes. Yeah. Yeah. Yeah, that’s kind of what we’re seeing. And to go back to your point is because the medical profession does medical profession stuff. this is not, it’s not that it’s not medically kind of aligned. It definitely is. But when you’re told that the way you solve a problem is through putting a stent in and then never talking to that patient again, to tell them how to avoid to get a stent in that’s Jack Clifford (47:31)Yeah, that’s your job. Bill Gasiamis (47:34)what they do, like they’ve been trained to do that forever. And that’s what they do. And that works and it saves the life. But what it doesn’t do, which I also have a challenge with this, it doesn’t teach the lesson. What it reinforces is that if I have something wrong with me and I go to a doctor, they’ll fix it. So next time it goes wrong, I’ll just go to the doctor and they’ll fix it again. And I didn’t have to change my life. Like this even bloody advertisements that do that. They Jack Clifford (47:51)just I’ll go and he’ll fix it. Yeah. Yes. Yes. Bill Gasiamis (48:03)They hijack that part of the person’s brain and they say, you know, have you got reflux, heartburn, that kind of stuff? Don’t let reflux and heartburn get in the way of eating the foods that you love. Just take a tablet. You know, that’s the same kind of thing, right? And that’s why the medical profession doesn’t do that because they’re not trained to do anything other than sell their thing. And their thing is what they went to work, to school for. Raising Awareness for EECP Therapy Jack Clifford (48:17)Yes. Bill Gasiamis (48:30)20 years to be able to administer. But every so often you come across an amazing doctor, surgeon, et cetera, who says, I can’t do anything more for you, but maybe somebody else can. Those guys are better than the doctor who says, we can’t do anything else for you and then send you off their way. That next sentence, but maybe somebody else can, I don’t know who they are. That is. Jack Clifford (48:43)Mm-hmm. Bill Gasiamis (48:57)I think a great thing to say this is where I think EACP kind of fits in that now that I’m here and things are not good. Jack Clifford (49:05)I totally agree. I totally agree. And yeah. And you, so you, you mentioned like the wellness spas and whatnot. And here’s the thing in 2015. So, you know, somewhat recently the FDA approved EECP for a brand new indication, general circulation, right? In healthy people. Like it’s right on the FDA indication. And also in one case in increase in VO2 max, but rough, that’s roughly saying the same thing. ⁓ yeah. Bill Gasiamis (49:32)for healthy people, was that part of it? Jack Clifford (49:35)Yeah, it said unhealthy patients and healthy people didn’t call patients. So, so, ⁓ but, but, know, the litmus test for that is, is your doctor say you’re healthy enough to undergo circulation enhancement? If the answer is yes, you know, it doesn’t matter if you got all that other stuff or not, you know, we’re just not treating you for it. We’re not saying ECPs is fix for this, your erectile dysfunction. It might help it. You know, what’s not saying it’s, it’s the fix for your stroke, but it might really help your stroke, recovery, but. Bill Gasiamis (49:47)which Jack Clifford (50:03)Anyhow, so like you can, you know, I don’t know about in Australia, but in the United States, you could get an EECP machine and create a viable business model off of helping people as soon as people actually know about it and what it does, right? I’m trying to solve the access issue in the United States by aggregating demand, right, as one of the solutions. So I have a website, eecplocator.com. And if people… ⁓ tell me that they like EECP to be available in their area, when I get like five to 10 patients in one area, we’re gonna find a way to get it to them. ⁓ The how is, you there’s a bunch of different possible ways we can get EECP to them, but at the end of the day, you know, like people need this treatment. They really, really do. Bill Gasiamis (50:50)Yeah. We’re not talking about anything ⁓ out there. Like this is not an out there thing. This is definitely common. Now I, I don’t know how I haven’t come across it. I’ve all these years after all these years now I’ve just because of our conversation right now, I just did a Google search and I typed in EECP machine Australia. And the first thing that came up was an Australian government department of health, disability and aging. Jack Clifford (50:57)No, it’s that. Bill Gasiamis (51:20)document from the Therapeutic Goods Administration, which talks about a mid-trade Australia EECP system model, external counter pulsation system stationary. So it seems like they have a… Jack Clifford (51:36)Like they’ve approved it, sounds like they have some approved devices. Yeah. Bill Gasiamis (51:38)Something like they’re at least looking at it. Let me see what that says. The inclusion of the kind of device in the AI community is subject to compliance with conditions placed in post. Yeah, it sounds like it’s been through some regulated body in 2021. Jack Clifford (51:52)Yeah. Mm-hmm. Yep. There you go. Bill Gasiamis (51:57)This device is intended to provide external counter pulsation therapy and is indicated for use in the treatment of stable angina. Jack Clifford (52:06)Mm-hmm. Bill Gasiamis (52:08)pectoris and congestive heart failure. There you go, my friend. Jack Clifford (52:10)Yeah, it works great for people with art failure. It really does. Bill Gasiamis (52:14)Dude, father-in-law had heart failure. He passed away from heart failure just a few, about a year and a half ago. ⁓ Now, I don’t know, I’m not saying anything, but we’ve never heard of this before. Today’s my first time where I’m really going to deep dive about this thing with you. ⁓ So what are the challenges that you face? what are the, what is it? ⁓ The barriers that you face? Jack Clifford (52:20)Yeah. Bill Gasiamis (52:44)when you’re speaking to people about this or how people finding out about it, how do you help people like Jack Clifford (52:50)It’s just an awareness piece. It’s an EECP what? And then, you you get in with some physicians and then you got to duke it out a little bit. Not with all of them. There’s plenty of physicians, you know, I’ve talked to the physicians that have machines and are doing the right thing for society and still making plenty of money. ⁓ They’ll just tell you, you know, I’ve talked to some cardiologists and just they kno

Chewing Rage and Honor Flights JB, Sandy, and Tricia kick things off with everyday humor that quickly turns into a conversation about working from home, cabin fever, and those tiny annoyances that can drive you up the wall—yes, including that sound someone makes when they chew.From there, the episode shifts gears into something truly moving: a powerful discussion about Honor Flights

Could Jimmy Kimmel actually get fired by Disney for real this time? First Lady Melania Trump didn't appreciate his recent joke about her becoming a widow, and and tweeted that ABC should fire him immediately. OOF. Kimmel and Colbert podcast network when? Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #Politics #JimmyKimmel #ABC #Disney #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

We may think that hitting that snooze button is just a simple non important thing, when in reality its causing some havoc on not just your discipline in your fitness but spiritually too sis. If the enemy can win at the snooze game first thing in the am? OOF!! Take a listen to this weeks episode about what that snooze button is actually doing to you and how to break the snooze habit. FREE LWG community: CLICK HERE

Oof. Lots going on, eh? This week, we attempt to run down everything that's happened since Dynasty, but unfortunately there's a ghost in the machine (and it seems to be trying to type on MJ's keyboard throughout?) (00:00:00) We try to remember how to do this podcast thing (00:09:54) We've got a new AEW men's world champion since we last recorded, and we don't like it (00:18:34) Maxwell crashout era? Finally? For us? (00:23:39) Put the title on Brody King, go on (or else we might just ignore the men's world title for a bit) (00:31:05) Get the tag titles out of the swamp (00:34:21) Sad boys being sad (00:37:16) What are the Death Riders' intentions with Will Ospreay? (00:42:49) Can we get a new challenger for Thekla please? (00:44:30) Shida got rumbled, and Stat might be having second thoughts… (00:52:30) Samoa Joe has returned! (00:54:08) Let Lio Rush be spooky (and stop talking over him!) (01:00:25) Can we please see Orange Cassidy vs Blake Christian one-on-one? (01:08:12) Happy times Email: wafflybollox@gmail.com Shop: ko-fi.com/wrassletrash/shop Learn more about your ad choices. Visit podcastchoices.com/adchoices

Good Sunday to youI'm still finding my feet having just got back from Namibia. I've got a full country report coming, as well as a portfolio piece. But I've been thinking further about the country's potential since Wednesday's note.Namibia has almost everything. Resources. Location. Roads. A small population. On paper, it should work.And yet.Driving through Windhoek, the capital, my guide pointed out a hospital: the Katutura State Hospital.“You don't want to get sick here,” he said.It didn't look too bad from the outside. A bit craggy. But I've seen worse.The place is infamous apparently. Rats. Endless waits. People lying untreated in corridors. People deliberately go at 3 in the morning, because it betters your chances of being seen the next day. My guide described his own time there when he broke his arm last year. Oof. It makes NHS Accident and Emergency waiting times look slick.Across the road, stood a gleaming monstrosity - the SWAPO (ruling party) headquarters. Brand new. Vulgar. Expensive. Impossible to miss.It wasn't discreetly tucked away. It was right there, bearing down on the hospital. My first reaction was simply how ugly it is. A few years and that will look truly horrible, I explained to my guide, who seemed baffled by my prediction.His point, however, that I hadn't yet thought of, was simply how the building had attracted controversy: all that money being spent on what is essentially a vanity project, with the hospital over the road.It was built by the Chinese, funded through a grant from the Chinese government, rather than a commercial loan, at a cost of $50–60 million (figures vary). Because it's a grant, it doesn't sit as formal public debt. What could the Chinese possibly want in Namibia. (Clue Namibia, among other things, is the world's 3rd largest uranium producer and the Chinese pretty much control the 3 largest uranium mining companies operating there. Then there are all those other resources too)There, in a single snapshot, lies the problem. A classic of the resource curse genre. Easy money distorts behaviour. In theory, natural resources should make a country rich. In practice, they often do the opposite. Incentives determine the outcome.If a government can fund itself from its natural resources, from its oil or metal, what does it care about tax payers? If it doesn't rely on its citizens, it doesn't feel accountable to them. Instead of serving the public, the state begins to serve itself.Money flows in. It gets spent badly, siphoned off, used to entrench power.At the same time, the rest of the economy suffers. Why build a broad industrial base when the ground is already doing the work for you? You end up with a narrow, fragile system built around extraction.Two countries with similar resources can end up in completely different places.Norway built institutions, saved its oil wealth, invested for the long term. Venezuela (which has greater oil resources than even Saudi Arabia), spent it, politicised it and hollowed out everything else.Don't get me started on what the UK did with its oil. (First thing the government should do Monday morning by the way is renegotiate North Sea division with Norway). Same starting point. Opposite outcomes. One has one of the lowest GDP per capitas in the world, the other has one of the highest. The difference is governance. Incentives. Culture.Namibia now has some choices to make. It is somewhere near the beginning of that path. It has oil discoveries offshore. It is already a major uranium producer. It has copper, gold, rare earths, diamonds, zinc, lithium and tin. Fish. The opportunity is obvious.But so is the risk. The easy choice is to follow the same path as most of the rest of Africa. The harder choice now, but one that will result in better outcomes, is one of good governance.The debate around that SWAPO headquarters touches on exactly this point. Despite what I've said, there is no single scandal you can point to and say “there it is”. It's all a bit more murky. But the criticism you hear, quietly and repeatedly, is about priorities. Why spend heavily on political infrastructure when basic services are under strain? Why is the party so well housed while public systems struggle? There are major questions too, as with much infrastructure in Africa, about foreign financing and influence, especially from China. You don't need a formal corruption charge to expose everything. You can see it in how capital is allocated.Oddly, the countries that often do best are those with very little as far as natural resources are concerned. Hong Kong, Singapore, even Venice a millennium earlier. There was no safety net. They were forced to trade, to manufacture, to compete. They had to create value because there was none sitting in the ground.Namibia doesn't have that pressure. So it has to choose discipline, and that is the hard part. When you see a failing hospital on one side of the road and a gleaming party headquarters on the other, it tells you something about priorities. Never mind what politicians say, look at what they do.I'll be back with more later this week.Thank you for being a subscriber to the Flying Frisby.Until next time,DominicIf you live in a third world country such as the UK, I urge you to own gold or silver. The pound will be further devalued, as will the euro and dollar. The bullion dealer I use and recommend is The Pure Gold Company. They deliver to the UK, the US, Canada and Europe. More here.PS Here is this week's piece. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.theflyingfrisby.com/subscribe

Trent and JKEBRNS join the pod to discuss the vibes going into the 9 vs. 10 game. If the Clips win that, who do we prefer to play from the 7 vs. 8 game. If we win that, then it's OKC. Oof, Long Season... possibly short postseason? 

Following the shadows of OOF 99, we're marking our 100th episode by scaling the high-altitude peaks of the Serrano pepper, the high-altitude soldier dominating the 2026 culinary landscape. This agricultural powerhouse is currently fueling a "Serrano-Switch" in world-class kitchens, offering a superior "clean burn" and unmatched yield efficiency compared to the standard jalapeño. From its ancient roots in the Puebla highlands to its future as a global ht sauce hero, discover why this small but mighty chili is the ultimate balancing act for the modern palate.#serranopepper #spicetrends2026 #globalcuisine Follow & SupportApple Podcasts - ⁠https://podcasts.apple.com/us/podcast/life-tech-and-sundry/id1527317641⁠Spotify - ⁠https://open.spotify.com/show/0LufzYND0SqKOGyogIyutL?si=hmb3VXH2T-yZchJ8_-LF_Q⁠YouTube - Just search @LTSnco in any search bar on YouTube to find us.IG - ⁠https://bit.ly/IG-LTS⁠LTS on X - ⁠https://bit.ly/LTSTweets⁠Buy Me Coffee - ⁠https://www.buymeacoffee.com/LTS2020⁠

Find Joy in the Wilderness by Autumn Dickson When I was studying the Doctrine and Covenants last year, my pattern revolved around learning about the people who were receiving the revelations and how they were feeling so that we could better relate to them and receive the same comfort in the revelations that they did. As I've studied the Old Testament thus far, I've found a different pattern for learning principles from God. Namely, I look at the details in the class Old Testament stories, and I find the parallels for our day. It's been powerful and helpful. So without further ado, here's another detail from the Moses and Plagues story. The God of the Hebrews is working to free His people from slavery in Egypt. There are some questions that we could ask as to why He didn't jump right in with the death of the firstborn, but those questions can be asked another time. As the Lord continues on with His work through Moses, Pharaoh appears to relent a couple of times. He tells Moses, “Take back the frogs, and I'll let them go.” But then he hardens his heart and refuses to free them. It happens again with the flies. Pharaoh tells Moses to take away the flies and he will let the people go. Here is how Moses responds. Exodus 8:29 And Moses said, Behold, I go out from thee, and I will entreat the Lord that the swarms of flies may depart from Pharaoh, from his servants, and from his people, to morrow: but let not Pharaoh deal deceitfully any more in not letting the people go to sacrifice to the Lord. Of course, Pharaoh goes back on his word and refuses to release them. Maybe he was hoping Moses and His God would run out of power and not be able to send any more plagues? Regardless, Pharaoh still holds the Israelites captive. There is a lot of goodness here, but I want to draw your attention to one detail. Moses wants the Israelites free so that they can go sacrifice to the Lord in the wilderness. This is not the only time this is mentioned. More than once, Moses specifically says this. Pharaoh needs to free the Israelites so that they can go and sacrifice to the Lord out of Egypt and in the wilderness. Oof. Is there a better way to describe life after we finish our ordinances? We are made free by the death of the Firstborn, we pass through the gate, and what do we see? A whole lot of wilderness. For a long time. Why are we here in the wilderness? It seemed so exciting to be free before. Now it just seems dusty, hot, hard, and uncomfortable. Interestingly enough, we didn't walk through those gates to make it into paradise immediately. The gate was just the first step. We've been freed from slavery, but we don't know how to be happy and healthy yet. There are many more lessons to learn. There is a lot of sacrifice to be made so that we can understand what it means to grow to be like the Lord and find what He found. We have a long journey ahead of us. It's funny. I remember being on my mission and working long hours with minimal breaks. I remember rushing to write in my journal at night so that I could pass out in my bed on time and get as much sleep as possible because I was so dang tired. I remember mentally aching when I had to leave the dinner table at the houses of members I was close to. I think that was one of the things I missed the most while I was on the mission. I grew up in a family where we all ate dinner together and talked the whole time. We had a lot of family come into town for holidays, and we would sit at the table for a long time afterwards and talk and laugh. I missed that resting while on my mission. I remember getting on the plane, and I was so excited to eat a meal and then do nothing afterwards. I was excited to rest. Lol. I did get some rest for a while, but heaven knows life only speeds up after that. We came here to struggle in the wilderness, to keep putting one foot in front of the other, to make sacrifices and grow and learn what we're supposed to learn. We didn't come here to finish all of that so we could rest. We came to sacrifice in the wilderness. Which sounds horrible, but it doesn't have to be. This was a timely lesson for me. I have a goal right now to be grateful for the opportunity to wear myself out in the name of the Lord. I'm not talking about being a martyr, though sometimes that seems to be my default mode. Rather, I'm talking about completely turning my perspective upside down. I didn't come to earth to preserve energy and my body. I didn't come here to try and completely annihilate stress from my life or reach some magical point where I feel great enough to give all of myself. Rather, when I catch the true feeling behind this goal I made, I find rest when I let go of my own concerns and cheerfully and willingly take advantage of these incredible opportunities God has given to me. Someday I'll get enough sleep (or my body won't need sleep? I don't know?). Someday, I'll have a perfect body and perfect perspective and all my needs met, and I won't have to reach for those things anymore. They will be given to me. I'll have a perfectly clean house with everything I could ask for. Sometimes we get mixed up and wear ourselves out on the things that don't matter, things that will be freely given to us on the other side. We're putting all of our energy and hopes and focus on setting foot on that promised land. What if we let go and trusted that the promised land will make it to us at the right time? What if instead, we focused on the gift of the wilderness and what it has to offer? I have found that when I stop striving to put my feet in the promised land here in mortality, I find beauty and rest and hope and peace in the wilderness. Moses had it right. He didn't tell Pharaoh that he was taking the Israelites to the promised land. Sure, that was the eventual goal, but there were some really important goals along the way before they would even be able to enjoy the promised land. Moses told Pharaoh to release the Israelites so that they could go and sacrifice in the wilderness. When we let go of trying to hold on to ourselves, we find joy in the sacrifices we're asked to make in the wilderness. That's a true principle. I'm grateful my Savior redeemed me. I'm grateful He let me walk through the gate and bind myself to Him through the ordinance of baptism. I'm grateful that He gave me a path with lessons along the way. I'm grateful that I don't have to worry about reaching the promised land; He's got that handled. All I have to worry about is learning along the way, sacrificing along the way. I'm grateful for my testimony that He will provide for all that I need in the wilderness. Autumn Dickson was born and raised in a small town in Texas. She served a mission in the Indianapolis Indiana mission. She studied elementary education but has found a particular passion in teaching the gospel. Her desire for her content is to inspire people to feel confident, peaceful, and joyful about their relationship with Jesus Christ and to allow that relationship to touch every aspect of their lives. Autumn was the recipient of FAIR's 2024 John Taylor Defender of the Faith Award. The post Come, Follow Me with FAIR – Exodus 7–13 – Part 2 – Autumn Dickson appeared first on FAIR.

Em "comemoração" ao dia da mentira (primeiro de abril), eu faço uma análise completa de todas as mentiras de uma das figuras mais influentes (e caozeiras) do mundo dos games: Tommy Tallarico. Dono de recordes mundiais e responsável pela trilha sonora de centenas jogos, o OOF do Roblox, a Video Games Live e até mesmo o não lançado Intellivision Amico, venha descobrir como uma carreira de mais de 30 anos pode ser fundada em incontáveis mentiras. Confira!

Did the Hollywood unions learn from the double strikes of 2023? The WGA (writer's union) took a tentative deal in record time -- but there may be string attached. It's almost as if beggar's can't be choosers. Meanwhile, the WGA is letting its own workers' health insurance lapse. OOF. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #Hollywood #Movies #WGA #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

OOF. According to Red Letter Media, Star Trek: Starfleet Academy had less than 40,000 viewers. If this is true, this show was a MUCH bigger failure than anybody could have possibly imagined. This also explained why it was cancelled ahead of the second season premiere. And no, it's not the fault of YouTube chuds. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #StarTrek #StarfleetAcademy #Paramount #Streaming #TV #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

0:00 - Brett Kane tweeted about Dan Hurley getting up in that ref's face and said it should've been a technical. It's ridiculous it wasn't called, and he doesn't understand people defending Hurley. Well, the UConn Storm Troopers found the tweet and they're really giving Brett the digital business. Oof.12:59 - Italy is playing Bosnia and Herzegovina is soccer today. Whoever wins qualifies for the World Cup. Loser goes home. It's all on the line for Vic's squad. He invited Moser to watch the game at the Lombardi Lair today on one condition: Moser brings pizza for everyone.After that, what happens if Cale Makar misses a few weeks with his upper body injury? Which teams can the Avs defeat in the playoffs without Makar in the lineup? Who do you want to see in the first round?31:21 - Sometimes, we end up rooting for the bad guys in a movie or TV show. Whether intentionally or unintentionally, sometimes the villains win us over. What are some notable examples?

For a limited time, Latent Spacenauts can skip the waitline to join Dreamer and also compete for a $10,000 cash prize for most useful tools for Dreamer! Thanks @dps!In 2024, David Singleton left Stripe and joined forces with Hugo Barra for a buzzy stealth startup named /dev/agents. This month they emerged out as Dreamer, a consumer-first platform to discover, build, and use AI agents and agentic apps, centered on a personal “Sidekick” that helps users customize experiences via natural language. Sidekick is nothing less than an “agent that builds agents”, with all the complexity that that entails:You've seen many many website builder, app builder, and even agent builder startups by now, but our favorite detail is the sheer amount of work that has gone into the “full stack” nature of the platform, including shipping their own SDK, logging, database, prompt management, serverless functions, and so on. Most platforms restrict the tech stack you can use just to get off the ground — Dreamer does it “right” by letting you push whatever arbitrary code you want to their VMs.Paying the BuildersOf course former leaders of Stripe and Android would not stop at just building the tools, but also building the ecosystem. Dreamer is deeply aware of the 4 sided network effect it has going on and is ready to fund all of it - from hiring Builders in Residence to awarding $10,000 cash prizes to the best tool builders for the Dreamer ecosystem.It's time to Dream!Full Video Episodeon youtube.Transcript[00:00:00] Meet Dreamer Purple[00:00:00] swyx: Okay, we're here in the studio with David Singleton. Welcome.[00:00:08] David Singleton: Hey, Wix. It's great to be here.[00:00:09] swyx: It's great to have you. Uh, we have very sympa that your company color is the same as Lean Spaces color.[00:00:15] David Singleton: That's right. Dreamer Purple.[00:00:17] swyx: It used to be Devrel agents, which I thought was very cool. It's like you call back to Devrel Payments.[00:00:22] David Singleton: Yeah.[00:00:22] swyx: And you were obviously CTO Stripe. And talk to me about just the origin or thinking process behind Dreamer. Yeah. And maybe, maybe start with like, what, what is Dreamer?[00:00:31] David Singleton: Yeah.[00:00:31] What Is Dreamer[00:00:31] David Singleton: So Dreamer is a new product, uh, which everyone can come and play with today. Um, it's a place where everyone, literally, everyone can discover, build, and enjoy and use AI agents and agenda apps.[00:00:45] And we really did design it for consumers, for folks who are not necessarily. Uh, have any kind of technical background. It's really aimed at everyone. I think often of my sister, she's very smart. She's not in the slightest bit technical. She has lots of problems in her life that [00:01:00] she would like to be able to have great software and intelligent software to solve.[00:01:04] But you know, even with the rise of tools like Cloud Code and so forth, she's got no way to get started. And Dreamer is a place where she can come in, grab some intelligent apps that other people in the community have built, start using them right away, and solve real problems in her life.[00:01:19] Sidekick And Waitlist[00:01:19] David Singleton: And at the core, we have a personal agent called the Sidekick.[00:01:24] Um, you can give your sidekick a name, you can give it its own personality, and it really helps you across your entire day, your life. It helps you use all of the agents on the platform, and it also helps you build anything you want. And we've been working in this for a little while. We recently launched in beta.[00:01:41] So anyone can go to dreamer.com, join the wait list. Um, and we have many, many, many people in the community now who are building really fun, really powerful, really useful. Agents and the agentic apps for themselves.[00:01:54] swyx: I think we're gonna go right into a demo. Yeah. I just wanna make an observation that, uh, you, you, [00:02:00] you put discover first before build.[00:02:02] Mm-hmm. But actually, at least for the engineers in the audience. ‘cause we are primarily engineers and you're primarily targeting consumers, right?[00:02:08] David Singleton: Yeah.[00:02:08] swyx: For engineers. Like, there's a huge full stack of stuff, which we're gonna dive into. Let's write. It's so impressive. I'm like, holy s**t, this, this is what I've always wanted.[00:02:16] Cool. Uh, so, so I think that's really good and I've, in some ways, I think given your background given, uh, Hugo's, is it Hugo? Hugo.[00:02:24] David Singleton: Hugo. Hugo Bar. Yeah.[00:02:25] swyx: Hugo, it's not surprising that you can basically kind of build an app store Yeah. For agents.[00:02:30] David Singleton: Yeah. So Hugo was my co-founder. Yeah. Um, Hugo and I met with our other co-founder Nicholas Checkoff in the very early days of Android at Google, where we were building Google's first mobile apps.[00:02:41] Uh, we then contributed to very core pieces of Android itself. And you're right, we were really excited about building two things. One, solving a bunch of problems. That this breakthrough technology here I'm talking about mobile needed to have solved in order to make it work for real people at scale. And then secondly, building this ecosystem, um, [00:03:00] of third party developers using the Play Store, um, and able to deliver way more value on the platform than we could have delivered on our own.[00:03:08] And we think about Dreamer in exactly the same way. So I was working at Stripe, as you mentioned, and we had the opportunity to put some of the very first AI agent systems in the world into production. And from the moment we did the first of those, I was just struck with a strong sense of conviction that this is breakthrough technology that's gonna change how all of us work with computers and phones and so forth, all of the, the technology in our lives, but.[00:03:34] There's a lot of problems to be solved, for real people to be able to make this approachable. Um, and it really is kind of a direct analog for what we were solving back in the early days of mobile apps at Google and, and Android. So it's, it's been fun to bring that to life.[00:03:47] swyx: Yeah. Uh, let's look at it.[00:03:48] David Singleton: Yeah, let's take a look.[00:03:49] Dashboard And Daily Briefing[00:03:49] David Singleton: So, uh, dreamer.com, this is our homepage. This is where you can come and, uh, watch some videos about what is here and sign up for the wait list. Once[00:03:57] swyx: you, I, I just wanna say for those listening, ‘cause we have a lot, you [00:04:00] know, switch to YouTube, look at the animations. So much care.[00:04:03] David Singleton: We, we really care about, uh, this product being fun.[00:04:07] Uh, and, and interesting to use. Obviously a lot of people are using it to do real important stuff. You can do real work, uh, here, uh, but also you can build fun things too. Once you get off of our wait list, you'll come into the product. The first thing that happens is you'll have a conversation with your side cake, which is this little friendly, uh, character here.[00:04:27] And psychic will seek to get to know you and understand you. What do you care about? And will help you discover and build your first AI agents or agentic apps. After that, you're, you're gonna have a dashboard. This is my dashboard. Everyone's is different. Um, you can see I have a few things here. I have a feed.[00:04:42] So a lot of our agents do things in the background when you're not looking and the feed is how they let you know what they've been up to. I have, uh, some widgets, uh, from apps that I have built. Uh, this one is called Calendar Hero. Uh, this is something that I installed from the gallery. Uh, so built by someone in our community.[00:04:59] It's a [00:05:00] really powerful calendar app because for each of my meetings, if it's with someone I don't already know, well it'll actually go off and research it, um, and give me both a history of my interactions with those people and also a bunch of, you know, public useful information to, to get started. One of the things I love about this particular app is that every day it generates a podcast, um, a daily briefing.[00:05:24] And one of the things that we've done with the platform is we've made it possible for all the things that agents do to show up in places that you care about. So if you look over here, this is the screen in my phone, and if I go ahead and open my Apple Podcasts, you can see right here. Your Daily briefing podcast is ready.[00:05:39] This was produced by an agent running in my Dreamer account, and it was very easy by scanning a QR code to connect it to my Apple podcast. That's what I listened to in the car now every morning. Yeah. On my way to work.[00:05:50] swyx: It, it[00:05:50] David Singleton: preps me for, for my day.[00:05:52] swyx: So one additional bit of context. I asked you immediately after seeing this was like, what, what about, I wanna talk back to my agent and you said you actually started with voice and then you went to [00:06:00] podcasts.[00:06:00] ‘cause it's nice to have it pre downloaded[00:06:02] David Singleton: that, right? That's right. Um, yeah, we, you, you can talk to your sidekick. So, you know, on mobile we have, uh, a dreamer app and you can talk to the sidekick right here. Um, but we've actually found that making things, uh, show up in the other apps that you already use in your life is incredibly powerful.[00:06:19] So let's take a look at what's kind of under the hood here.[00:06:21] Gallery Tools And Payouts[00:06:21] David Singleton: So I already mentioned that we have a gallery, so this is where you'll find a lot of agents from our community. Uh, there's. Many at this point, hundreds. And they are solving all kinds of, uh, use cases. I'd say the the top use cases are on personal productivity, but also a lot of information management that can range from personal information like docs and so forth, managing your emails.[00:06:42] It also ranges out to public information that you might be interested in, but you need something to help manage the, the kind of fire hose of stuff that's coming at you. For instance, I have, um, an agent which looks at all the AI news, um, all the time. There's a lot of it and it finds the stuff that I would actually be [00:07:00] interested in, um, and I find it incredibly useful.[00:07:03] So these are agents that you can install that other people have built. Anything that you install on Dreamer, you can actually just say, I wanna start making some changes, and we'll look at that in a second. But in natural language, with the sidekicks help, you can change any of these experiences to work just the way you want them.[00:07:18] But the base layer of the system are tools. So you know, as well as anyone swyx, that any AI system is only as good as the quality of data that it can pull in and the quality of action it can take. So before we launched our beta, we worked very hard to make sure that we seeded our tools with a bunch of very high quality and powerful integrations.[00:07:39] So, you know, for instance, this is real Google search, this is actual Gmail. Um, and you can do very useful things with those. But also this is a platform for everyone. And as we got started talking to people in our alpha community, a whole bunch of sports use cases popped out and we realized if you want to build something cool for sports with ai, you need really high quality live data.[00:07:58] So look at these [00:08:00] Formula one M-L-B-N-F-L, uh, these are tools, uh, that we've built. We've done a, these are not data scraped off the web. This is a, a direct data feed integration. And because it's live and ‘cause it's high quality, you can build really powerful stuff. But tools is not something that we are just going to kind of control ourselves.[00:08:19] The platform is open for tool Builders to contribute tools that anyone on Dreamer can use. So, um, this is actually the place in the platform where I think software engineers, um, well number one, would love for you to come and play with it. Uh, but software engineers are really gonna build, um, a lot of powerful stuff into the system.[00:08:38] And we are actually sharing something for the first time on this podcast, which there is, uh, tool builders on Dreamer get paid. So if you publish a tool to the platform and a lot of agents use it, you'll actually get paid, uh, in proportion to their usage. And we'd love for folks to come and give this a try.[00:08:54] We've got good docs that help you get started and you can build things that, you know, scratch your own itch. For instance, someone built this [00:09:00] Ski Bum tool, which provides live snow conditions for a bunch of, uh, ski resorts. I'd love to show you how I've used that in a second. And also we have some tools, partners where the tools themselves are paper use.[00:09:12] So for instance, parallel web systems is a premium tool. Uh, you can do really cool stuff with it. Um, it's a a, an agentic web research tool. And that one, because it's expensive to operate, is paid on a, on a per usage basis. But if you're coming in to build agents on the platform, even the premium tools, you get a free trial.[00:09:29] So you get a chance to actually try them out, make sure that the use case is good for you before you decide to, to to sign up. So that's tools. So we have the gallery, we have tools, and then the sidekick helps us put all of this together to build agents. We do that in the agents studio. You can also do this on your phone, but if I open up Agent Studio here on Desktop psychic's, just gonna start a conversation about what you want to build together.[00:09:51] I'd love to show you one that I made recently.[00:09:53] swyx: Let's do[00:09:53] David Singleton: it.[00:09:53] Building A Conference App[00:09:53] David Singleton: Um, let's look at something that hopefully is kind of near and dear to your heart. So one of the things I love about Dreamer and this kind of moment in technology is that if you think about it. There are all these things in your life where, have you ever gone to a conference?[00:10:09] I know you have. Right? And, uh, big conferences have apps. Um, and these apps are usually built by agencies and they're, they're usually actually quite expensive to build. I've been involved in running some of these myself. And how many conferences have you been to where the app was good? Zero. Honestly.[00:10:23] swyx: Exactly. Zero,[00:10:24] David Singleton: maybe one. I, I've, I've been to one conference. That was pretty good. Wait, wait session sessions. Um, but, but the point is, they're rarely great pieces of software. Right. And they're also expensive to build, but they're, they're interesting ‘cause they're episodic, they last for this one thing. Um, and then they're, they're not relevant anymore.[00:10:43] Um,[00:10:43] swyx: and so it's the worst feeling to invest in them because, you know, it's like, it's got a limited. Date?[00:10:48] David Singleton: Absolutely. So I decided to build, uh, a conference app for your AI engineer conference. Amazing. Uh, on Dreamer. One of the things that Swix has done, uh, which I [00:11:00] thought was very forward-looking, is actually put a whole bunch of data about the conference on the webpage in an LLM readable way.[00:11:06] There's an LLMs txt file, there's a feed of all of the sessions in js, ON. So I used the data from your conference last year and built this intelligent app, uh, just by talking to our sidekick, uh, in Dreamer. So just to give you a quick tour, this is my Dream Conference app. What I always wanna do for conferences is I wanna be able to search for speakers.[00:11:28] I'm usually there because, uh, there, uh, is a speaker I care about. So, you know, SWIX, you're the speaker I care about. I can actually see here who you're on stage with. So here's, here's Greg Brockman. You've read even ai, uh, and this is his session. And look Greg and Swix for the speaker. So let's add that to my schedule.[00:11:45] Great. And then maybe there's a couple others I might see here. Like on day two, I remember there were some keynotes. So, uh, building the open agenda web, that sounds fun. So I add that to my schedule.[00:11:55] swyx: She's now CEO of Xbox.[00:11:56] David Singleton: Awesome.[00:11:57] swyx: Which is interesting. So cool. So,[00:11:59] David Singleton: so I've [00:12:00] gone through and picked out a couple of sessions that I cared about.[00:12:03] That's as far as I usually get with any conference app. But of course you've got the whole of the rest of the conference to figure out what to do. So here is where the native intelligence of, of these things you build on Dreamer can come in. So I'm gonna click guide me. So Dreamers sidekick actually parsed out the whole schedule and figured out what some of the themes are and I can choose what I'm interested in here.[00:12:23] I'm definitely interested in agents. Uh, I'm definitely interested in code generation and also reasoning in rl. So now I'm gonna say build my schedule. So what this is doing is. It's going across every time slot for the conference. And it's choosing among the things I could go to, which one it thinks is best for me based on my interests.[00:12:41] It also uses its own memory of me that's part of Dreamer, uh, to understand what I might like best. And you know, there's an LLM prompt running for each one of these time slots. So this is, it's not super fast, but it'll be done in about 30 or 40 seconds. And I'm gonna have a special custom schedule for the conference.[00:12:57] This, like I said, is my [00:13:00] dream conference app is exactly what I've always wanted and I was able to build this yesterday morning. Um, I did it between some meetings. I think I spent a total of 25 minutes of wall clock time on it. I did it over the course of a couple of hours. And, uh, here is my schedule for the conference.[00:13:15] I can see it in a calendar view. This is what I should do on Tuesday, this is what I should do on Wednesday. Oof, no conflicts, but, you know, I may not go to every single thing. And there you have it built in, you know, dreamer. So let's take a look at what the building experience actually looks like. So this is the, the actual account that I made it on.[00:13:32] Oh, of course I should say anything you build on Dreamer also works on your phone. So, uh, here is my AI engineer conference app right here on my phone. Got all the same functionality, and of course this is the best place to jump into my schedule.[00:13:46] swyx: Yeah.[00:13:46] David Singleton: Um,[00:13:46] swyx: so you could generate a podcast about it just completely multimodal, absolute thing, right?[00:13:51] To me, I mean, this is why I outsource, I mean, well, I, I posted the L-M-T-X-T, the JSON because you cannot run an engineer conference in 2025 [00:14:00] and not let engineers. Do whatever they want.[00:14:02] David Singleton: Yeah.[00:14:03] swyx: And since all conference apps suck, I'm just gonna put up a ba minimum viable app and just let people do whatever they want.[00:14:09] David Singleton: Totally. And the cool thing about this on Bremer is I published this to the gallery and you can use it so you've got one that's built to my taste of conference apps. I think it's pretty cool. But you might want something different. Yeah. In which case you just start telling the sidekick how to change it.[00:14:23] So let's just very quickly look[00:14:24] swyx: at our, what sports grid is also, you can fork it, right? That I can publish. That's right. I can publish your one and go, this is the base starter. It's, it's got good defaults, but go customize, whatever.[00:14:32] David Singleton: That's right. That's right.[00:14:33] swyx: Yeah.[00:14:33] Agent Studio Under The Hood[00:14:33] David Singleton: So let's take a look at how I actually built this.[00:14:34] This is real. So I'm gonna say make changes. This experience we're looking at now is our, uh, agent development studio. Um, like I said, you can do this on your phone as well. And in fact, this one I started out on desktop. Let's look at my actual prompts. I said, let's make an agent called AI Engineer Schedule Planner should be a custom schedule planner for the AI engineer conference.[00:14:53] I'm not gonna read this all up. You get, you get the point and it told it where to get the data from. So that was the first prompt. And actually after I gave it that [00:15:00] prompt, I actually had a simple version of this app working, um, after the sidekick took one turn. So the Sidekick is a, like a professional software engineer, and we've worked very hard to make this work and build functional apps for folks that might not have any engineering experience whatsoever.[00:15:14] So, you know, done here we have build logs that are technical, but you can hide those away. And sidekick, as it is building, will actually translate everything that is coming out of, uh, of the, the harness into English that you can actually read. And by the way, this English is in the personality of your sidekick, which is fun.[00:15:32] Um. And the way that we build agents and agent apps, it's a little different to what you might have seen in some other platforms for a couple of reasons. One, just the build process. The very first thing that Sidekick does, it understands all the agents you've got set up. It understands all the tools and it will come up with a plan for how to realize your goal, how to make sure it actually has the data and the capabilities to complete it.[00:15:54] It will occasionally refuse. If it can't do what you're asking, it will tell you I can't do that. It needs another tool. And that's a good [00:16:00] jumping off point for any of the tool builders out there to build a new tool. So it'll fi first figure out how, then it will build it, and then it will actually test it.[00:16:07] So it will actually make sure that the thing that it has generated is realizing your goal. And you probably know as well as anybody that anytime you can get any. Modern state-of-the-art coding model into a loop where it can make changes and perceive its own output and then fix bugs. Magic happens. So these builds, the first build will often take 10 to 15 minutes on Dreamer, which is a little bit longer than you might've seen on some other platforms.[00:16:31] But the first thing that it creates will work most of the time. And then of course, as you start making smaller changes, you can like ask it to tweak the UI in any way that you like. Those are much faster. And just to give you a sense, uh, for this one, here's something I asked. Put a logo, I gave it a logo file in static files.[00:16:48] Use that as the title. So for folks that actually really want to dig, uh, into a bit more detail, we've provided a powerful IDE here. So I can actually see here's the code that was generated and some pieces of the [00:17:00] code are more accessible than others, like the prompts. So this is the prompt that's used by a powerful LLM in order to do that schedule picking.[00:17:08] And I can actually read it here directly. I can edit it without having to ask the sidekick if I want to do that.[00:17:12] swyx: So this is very nice.[00:17:13] David Singleton: This is for the more, the more, uh, sophisticated users.[00:17:16] swyx: Yeah. This is other people's entire startup is prop management.[00:17:21] David Singleton: This is true. The other thing that is different about Dreamer is once you've built something here, it's ready to go.[00:17:28] We host it. So you don't have to worry about getting a database from a database provider signing up, getting API keys. You don't have to worry about your LLM provider tokens. All of that is hosted on the platform. And you can use it yourself. You can share it to the gallery for other people to, to riff on it.[00:17:46] You can also share it with your friends and coworkers to use your instance of the agent or agentic app. And we're seeing that happen a lot in our community. We've seen a whole bunch of folks who built little applications for their personal life [00:18:00] and shared them with their significant other. We've seen people who are building little productivity apps for their team at work and sharing it, uh, among them.[00:18:07] And we actually do this a lot inside of the company. So at this point we, we pretty much run the company on Dreamer agents for all kinds of important things. Uh, maybe a good example of that is, um, our wait list. People are signing up every time someone signs up for our wait list. A dreamer agent will actually research, uh, that person.[00:18:25] And we're looking for folks who are builders, not super technical to build agents and come in, uh, and give us a lot of feedback and we're prioritized bringing those people off of the wait list First,[00:18:35] swyx: just a quick question on that one is there's, it may not come up again. Do you find enrichment APIs to be useful like the ZoomInfo?[00:18:42] Uh, clear bit[00:18:43] David Singleton: enrichment is a very, uh, common use case. Um, on dreamer. Any application on Dreamer can kick off a sub-agent to do a particular task. Um, so this actually is a powerful agentic harness that runs inside of its own [00:19:00] vm. Uh, we call them sidekick tasks ‘cause they actually run in the context of the sidekick.[00:19:04] I'll talk more about Sidekick in a second and. Enrichment is a very common use case. And the cool thing about a sidekick task is that it has access to all the tools on the platform, but also public data as well. And so very frequently enrichment on our platform happens using public data that it can be found in the web.[00:19:24] There are some tools for getting people data, uh, from, uh, from various bespoke systems. And so that works pretty well. But actually, you'd be surprised. I mean, we would love if someone out there would like to build a ZoomInfo tool, we don't have one today. We'd love to see that on the platform, and I'm sure it'll be very powerful.[00:19:39] But we're also seeing that this powerful agent harness can pull a lot of data in on that note of tools that make experiences better, we're constantly adding more tools because people in the community are building them and publishing them. We review the tools carefully and then they go live for everybody.[00:19:54] Yesterday we added granola. And that was pretty cool. So I was talking to actually, uh, Sarah on my team was [00:20:00] talking to, uh, someone building on the platform this morning and they actually, they have an agentic app that they built, which is a kind of magic to-do list. So they put stuff on their to-do list and for each thing it kicks off one of these, uh, sidekick tasks to figure out how to move the ball forward thing.[00:20:14] Sometimes it'll complete it[00:20:15] swyx: entirely. Yeah.[00:20:16] David Singleton: Often by calling another agent on the platform and sometimes it just kind of researches it and helps ‘em take the first step.[00:20:21] swyx: Yeah. Do you know, this is Sam Altman's number one, ask for an AI app. It's the self-completing to-do list.[00:20:26] David Singleton: Yeah. The self-completing to-do list is something that a lot of people have built on Dreamer and are getting a lot of use out of.[00:20:32] Yeah. And, and finding it actually genuinely I shouldn't, I should, I should try that. Mm-hmm. Please do. And you'll even find some in the gallery that you can remix. So he was saying this morning that he's, he built this self completing to-do list, uh, on Dreamer already. But he connected the granola tool yesterday and now something really magical happens, which is when he says in meetings that he's gonna do a thing, it magically shows up on his to-do list and then it can magically get completed.[00:20:56] And then, as I mentioned, all the agents, all the [00:21:00] apps on Dreamer can actually work together. So our coding agent, as it builds them, does something very special where it exposes the internals of each of the experiences to the system. And then Sidekick can manipulate those to get stuff done. So he has built another agent, which he uses for recruiting.[00:21:18] It kind of keeps track of candidates and also it's got a kinda mini CRM function, so he's able to introduce candidates to each other. He told us this morning that something he'd committed to do in a meeting that was recorded on granola yesterday showed up in his magic to-do list and his magic to-do list.[00:21:34] It was like introduce a person for recruiting, used his recruiting agent to get it done.[00:21:39] swyx: Ah,[00:21:39] David Singleton: um, and this is, this is the dream. This is why we started the company. It really is the case that you can build and use these very powerful, bespoke experiences that can automate your life by working together. And I'd love to talk a little bit about how they work together.[00:21:55] Ecosystem Trust And Monetization[00:21:55] David Singleton: So obviously it's really cool to have [00:22:00] software that will work on your behalf, but it's only useful if you can trust it, right? So privacy and security is very important to us making these things accessible and. While also being trustworthy is hard. So the model that we have, which is working very well, is that the sidekick is at the core of everything here.[00:22:22] So it is both your companion, your helper, but it's also the traffic cup in the system. So when, when one agent wants to work with another agent and dreamer, it doesn't do it directly, it does it via the sidekick, well ask the sidekick to do the thing. And the sidekick understands both everything, all the expectations that have been set with me as a user about what agents can do, which tools I've given them permission to use.[00:22:45] And it will make sure that whatever is is going on is actually aligned with my own interests. And you know, that's part of the background that I bring to this problem domain. I've. Worked for years, uh, keeping very important information, safe and secure. And [00:23:00] so as we started to think about this problem, we realized that we actually had to build something that's a bit like an operating system.[00:23:06] You know, the sidekicks, like the kernel, the agents and apps are like users. Yeah. Different rings. Exactly. Because if you try to pick off just one piece of this, you can't actually make it work for people at scale. Uh, because you could build little vibe coded apps, but they're gonna grab all your data willy-nilly.[00:23:23] They won't be able to work together. You actually have to invest in the fundamental core in order to make it work well for people. And that's what we've been doing and it's, uh, it's been a lot of fun. One other thing I wanted to mention is, um, I've obviously talked about two things, tools and agentic apps.[00:23:42] We really designed Dreamer to be an ecosystem and a platform, and one of my favorite quotes about platforms, I think it's from Bill Gates, is that you can only be a platform. If you create more value for the folks participating and using the platform than, than the platform itself creates. [00:24:00] And that's our goal here.[00:24:01] So we at every step have been thinking about how do we make sure that other people are deriving even more value from Dreamer than we are? So in that vein, I already mentioned tool builders get paid and people can build agents that solve their needs and share them with others, and we are already thinking about ways that they can actually monetize those as well.[00:24:24] Against that backdrop, one of the things that we are launching today is our Builders in Residence program. So there are tons of people building really cool stuff and contributing it to the gallery already, but we've been really inspired by programs we've seen at other companies where artists might be in residence, people that are very creative.[00:24:43] And might have ideas outside of what the, the folks at the company or in the ecosystem already have. And so we are looking for creative people who have fun ideas and, you know, want to really figure out how to apply their creativity at the cutting edge [00:25:00] of technology today to come and work with us. So, uh, if you go to dreamer.com/latent space, you'll find, ooh, well, we love Latent space.[00:25:09] Uh, you'll find a link both to, uh, our tool Builder information and our builder in residence program. And for builders and residents, we'll let you in off the wait list quickly, build an agent, and then for a small number of, of the most creative folks, we're going to pay you to build agents. Uh, you can work directly with our team.[00:25:29] You know, this is like building Legos. So, you know, we've got some of the basic blocks together already, but if you need a Ron steering wheel and we don't have one already, like we'll build it for you. Yeah. Um, we really want to be inspired by, by these, uh, these builders in residence.[00:25:43] swyx: This Legos thing is pretty common as an analogy.[00:25:46] And there's a, there's a thing I call the master builder. Uh, we, the actual Lego company has master builders that they employ Yeah. To inspire people and post on socials.[00:25:56] David Singleton: That is exactly what inspired us as well. Honestly, we talked about the Lego Master [00:26:00] Builder program, so that's our builder in residence program.[00:26:02] swyx: Yeah.[00:26:03] David Singleton: Um, and then, uh, finally back on, on tools. Like I said, anyone can come in and build tools today. If you follow the latent space link dreamer.com/latent space, again, we'll get you off. Directly off the wait list. So you can build right away, you can monetize by publishing onto the platform. That's for everyone, the very best tool that gets added to the platform by mid-April.[00:26:23] Uh, we have a $10,000 prize that we want to give out really, because we just want to seed the creativity of everyone out there. So we're excited to do that.[00:26:31] swyx: Yeah. And you know, uh, this is completely a flywheel, right? Like the more tools, the more builders, the more the third thing agents, you know, it just feeds into each other.[00:26:39] David Singleton: That's right.[00:26:39] swyx: Yeah. Just on the payments thing, because we probably won't touch on that again, but I have to ask the former CTO Stripe on payments as presumably you're using Stripe Connect.[00:26:48] David Singleton: Yeah.[00:26:48] swyx: Um. Any pain points that you're, people are very interested in agent commerce and micropayment and all these things.[00:26:55] Presumably stable coins get into a conversation at some point, but maybe not now.[00:26:58] David Singleton: Yeah, we are [00:27:00] really, really excited about e agent commerce. The first step we are taking is help people in the world who have never been able to build these kind of experiences and software before to build stuff that meets their passions, share it with the world and get paid.[00:27:14] So that's all commerce that happens on our platform, and so we don't need anything new to facilitate that. Stripe Connect has existed for quite a while and is the perfect solution for this kind of stuff, so, um, we we're excited about that. First and foremost, however. A lot of the things that people are already doing on Dreamer, we just talked about a self-completing to-do list.[00:27:34] A lot of the ways that you want to complete to-dos is by actually closing the loop in the real world, and that's going to involve the exchange of value. So we have some folks that are building tools already that actually do have money move in order to, to complete that, that loop. So far, we just want to be open and agnostic to all the protocols out there.[00:27:54] I honestly think this moment in time is a little bit like the early web. So I personally started coding as a kid [00:28:00] and I think I got access to the internet in about 19 95, 19 96. And back then, uh, the web existed, you know, HTTP was a protocol, but there were also other protocols I was using all the time, like Gopher and UUCP and uh, various others.[00:28:15] So the point is like the web, HTTP and HTML. Was just one among many protocols. And of course it became the winner and it's awesome. Yeah. Um, but the others were also kind of interesting and viable at the time as well. And I think the world of agentic commerce is like this right now. Also,[00:28:30] swyx: acp.[00:28:31] David Singleton: Acp, exactly.[00:28:32] All the, all the cps, you know, on Dreamer. We hope that folks will build tools that kinda make use of all of these things, but I'm sure that at a certain point. One or two will emerge as the winners, and then we'll be able to build like really deep support in,[00:28:44] swyx: yeah. This is like maybe a complete tangent, but I do think about how a lot of these companies in AI companies in particular have to switch from c based to usage based because of course, but then, then they end up, end up having to sort of [00:29:00] obscure the margins a little bit and then they inventing end up inventing their equivalent of rob robots.[00:29:04] David Singleton: Mm-hmm.[00:29:04] swyx: Uh, where they're like, well, okay, well every company should have their own currency. And it's, it's like very short lead to a token.[00:29:11] David Singleton: Yeah.[00:29:11] swyx: Or, and I'm like, okay, well where does this end? I can't really play out the next step as to like, is this chaos? Is this,[00:29:18] David Singleton: yeah.[00:29:18] swyx: Okay.[00:29:18] David Singleton: Well, I think it is kind of like the wild west.[00:29:21] I don't mean that in a completely, it's all completely disorganized way, but there's just so many things that could happen from here. The Overton window is very wide, right? Not far how this might land. And I'm just very excited to be building a platform that can take advantage of all of those opportunities and we're just gonna be there.[00:29:36] Uh, working for our users to make sure that things that emerge work,[00:29:39] swyx: you're gonna own the consumers, you're gonna be up the OS for the app store for everything.[00:29:43] David Singleton: So one of the ways to think about this is, um, dreamer actually uses all of the state-of-the-art models as a user. You don't have to think about should I be using, you know, Opus four six, or should I be using the five four model from [00:30:00] OpenAI?[00:30:00] We are continually doing evals and so forth to make sure that the best things are there for you. You can just build on the platform and know that as the world ships around, you're gonna get the right stuff for you. Um, and I think that's something that is needed to actually have folks take advantage of this technology at scale.[00:30:19] I'd love to show you another example of something I built.[00:30:21] swyx: Let's do it.[00:30:22] David Singleton: This is another example of software that just lasts for a certain moment in time. So recently I went on a ski trip with a bunch of friends,[00:30:31] ski[00:30:31] David Singleton: Bum. Uh, so it uses ski bum. Yes. I went on a ski trip to Big Sky. I'd never been there before.[00:30:38] And I made this little intelligent app for us. And you can see it says it's loading big sky conditions. So it's actually calling the Ski Bum tool that I just showed you, which is, uh, published in our, uh, in our gallery. So what is this? This is a little app that was just for our weekend trip. It shows the current status of all the lifts of Big Sky.[00:30:54] Using that tool from the ecosystem, it shows the forecast for the upcoming weekend. It shows our [00:31:00] accommodation. This is just like where my group was staying. This is just for us and also a bunch of dining information that one of our friends, uh, put together who, who's an expert on Big Sky. So I was able to take this app, share the link with my friends.[00:31:12] They weren't on Dreamer yet, just send it to them on iMessage and they get a version they can use on their phone. And of course, here's the real kicker. So I've been on ski trips before and other weekend adventures with my friends. Yeah, people pay for different things and at the end of the weekend it's always a pain to figure out who needs to pay, who to settle up.[00:31:29] So we use this during the weekend. We added all of our expenses in here. Uh, too close are it's drill data. It's only too closely. And then at the end of the trip, we press split. And we're, we settled up and we're done. So there's another dreamer. This was all through dreamer. So the, the actual payment? No, no.[00:31:47] We, it happened because, because we paid for stuff in the real world, it was like, okay, this person needs to pay that person 20 bucks. Right? Right. This person already paid in that. Right. So it just helped us all settle up. We didn't move the money on Dreamer. You could do that. And in fact, if you're a tool builder [00:32:00] thinking about this and getting excited, like come build a tool to do that stuff.[00:32:02] We really think of our tool builders as design partners.[00:32:05] swyx: Yeah. I got, I got the tool. Uh, what, like, I hate, I use Bank of America. I hate bank, I hate the app. Mm-hmm. I hate the web. All banking websites just horrible.[00:32:13] David Singleton: Yeah.[00:32:13] swyx: So just build me, like build a thing on top of Plaid.[00:32:15] David Singleton: Yeah. Right. And then just So[00:32:17] swyx: five code by banking app,[00:32:18] David Singleton: there's already a tool for that.[00:32:20] Oh. So, um, attain Finance is a tool, a builder in our community built. Okay. Um, and it uses a secure system like Plaid. To access your, uh, financial data and you can build powerful personal finance agents on Dreamer today using this tool. And like I said, we review tools carefully. So when bringing Attain Finance onto the platform, we did actually quite a detailed security review with that company to make sure that if folks build stuff with it, it's, it's gonna work well.[00:32:49] So yeah, check that out. I think, uh, I'm, I'm pretty certain it connects to Bank of America. So you'll be able to build the, the app that you wanted already?[00:32:55] swyx: Yeah. There's a couple of points I wanted to sort of dive in on, maybe highlight to folks, [00:33:00] because I, obviously, I spent more time with Dreamers. So we're making a point where you choose on behalf of your users because they're meant to be consumers.[00:33:07] So maybe less technical,[00:33:08] David Singleton: right?[00:33:08] swyx: But obviously people can, how users can override. If you read that's, but it's not just lms, it is also the, the transcription. It, it's like all, like there's, there's a first party curated set of here's the house opinion. That's right. On what?[00:33:21] David Singleton: That's[00:33:21] swyx: right. The thing is, that's right.[00:33:22] Is what's the list? Is there like,[00:33:24] David Singleton: yeah, so actually if you look in the tool gallery, the first party kind of curated set are all the ones that have these grayscale icons. So we have a built in tool for image understanding, for image generation, for RSS, exploration, text to speech and so forth.[00:33:38] swyx: Recipes.[00:33:39] David Singleton: Uh, we actually do have a built in recipes tool.[00:33:41] It turns out that a lot of people in our alpha wanted to do stuff for cooking. Yeah. Um, and you know, you can scrape the web to get good recipes, but we were able to quite quickly find a good repository of recipes. It works great here. Yeah.[00:33:55] Stable Tool Interfaces[00:33:55] David Singleton: So the point behind these though is that we'll keep the interfaces stable, so they'll always work.[00:34:00] But you know, the best translation model and, you know, there are people using this translation tool to translate Chinese podcasts into English. It's, it's pretty powerful. It can deal with very long text, but the best translation tool today might be different from the best translation tool sometime next year.[00:34:15] And we're just gonna make sure that that translation tool is always pretty close to state of the art. So you can build something and you know it's gonna continue to work well. Of course, some of our tools are branded. You may actually have a preferred way of buying groceries, like maybe you prefer Instacart and that's great.[00:34:29] You can use the Instacart tool specifically.[00:34:31] swyx: Yeah.[00:34:32] Partnerships And Ecosystem[00:34:32] swyx: Your partnerships, uh, I mean, I don't know if you ever hit of partnerships, but this is gonna be a bonanza for anyone on to do deals.[00:34:38] David Singleton: We have an amazing person who, uh, works on all of our partnerships. Um, and it's part of what you have to do to build a platform like this that's gonna work for people.[00:34:46] Like, we've gone and done that. Schlep has a lot of work, one talks lots of different companies, um, in order to make sure that you've got good tools at the core.[00:34:54] swyx: Yeah.[00:34:54] David Singleton: And then of course, because we're open to tool builders contributing to the platform, this is only gonna get better and better and [00:35:00] better.[00:35:00] swyx: Yeah.[00:35:01] Agent Lab Routing Layer[00:35:01] swyx: One observation I have this, this is gonna master a thesis I've been pursuing, which is, uh, what I've been calling an agent lab[00:35:05] David Singleton: mm-hmm.[00:35:06] swyx: Where you sort of different than a model lab in, in, in the sense that you never train your own models, but you are the router evaluation layer, ex subject domain expert for choosing between, uh, models.[00:35:18] David Singleton: Yeah.[00:35:18] swyx: And you're explicitly doing these things. And so like in my sort of construction, every agent lab does some version of this where like, here's the image understanding endpoint and we will route for you and don't worry about it. Yeah. Sally, I think it's kind of cool.[00:35:32] David Singleton: I, I think it makes total sense. Um, and again, to make this work for folks that don't follow the AI news every day, it's an actually, it's a, it's a really important thing to do.[00:35:42] Yeah. And it, it's been, it's been a real pleasure. I mean, I'm a, I'm personally a total geek for this stuff. I love it. And being able to go and dive into all those details in order to make it work well for other people. It's a true pleasure. I cannot imagine working at anything else right now. It's just so much fun.[00:35:56] swyx: The tricky part is multimodality when some of these things do [00:36:00] merge.[00:36:00] David Singleton: Mm-hmm.[00:36:01] swyx: And you are, you're sort of, this is your imposing structure on things that fundamentally don't want to be structured. And so sometimes that might work against you, but for 99% of these cases, this is fine.[00:36:10] David Singleton: Yeah. I mean, I think it's gonna be very interesting to see how the, the, the world matures because a lot of the power of dreamer is the ability to kick off these subagents, so these powerful agent harnesses, which can actually change how they work based on the data.[00:36:25] I actually think that we will be able to. Kind of keep up with and stay at the forefront of the changing landscape of how tools and systems work together. And that's, that's new. You know, software didn't used to work like this and now it does. Um, so even, even just figuring out how to design the right pri to make that possible has itself be a lot of fun.[00:36:44] Builders Can Publish Tools[00:36:44] swyx: This is, is a sort of maybe two part question that why can't streamer make its own tools? And then why don't you let you builders maybe stand up their own routing group? I call this a routing group, right? Like where it's like collect Yeah. Things.[00:36:58] David Singleton: So two things, to [00:37:00] some extent, dreamer does make its own tools in that agents appear to the system as tools.[00:37:05] So they can be, they can be used to accomplish things. So you can build an agent that is essentially a tool. Yeah. Um, and it it,[00:37:12] swyx: which is to me very useful for reuse.[00:37:14] David Singleton: Right.[00:37:14] swyx: Right. Exactly. ‘cause I, I like, this is the way I like it. Now my next five apps, I don't want to do this whole series of back and forth again.[00:37:20] David Singleton: Right.[00:37:21] swyx: Yeah.[00:37:21] David Singleton: Um. Then at the tool layer of the system, it's open to anyone. So it's actually quite powerful and flexible. So if you wanted to add a tool, which was, uh, imagine that you were training your own foundation model, Swyx. That might be fun. And imagine you wanted people to be able to play with, I don't know, maybe you make like, you know, nano chat or whatever and you want to Yeah.[00:37:42] Let people play with your own nano chat and see how I change themselves.[00:37:44] swyx: Now.[00:37:45] David Singleton: You could, you could publish a tool that is Nano Chat and it nano image generation behind a tool, and it could be your own writer if you wanted to. I see. And honestly, if that's the kind of thing that gets you excited as a builder, please come and do it.[00:37:57] Like we, we really are [00:38:00] believers in this idea that we aren't going to figure out every single detail ourselves. We're gonna make sure it's a safe and fun place to build this stuff, but we're really open to these ideas coming from other people. Um, and so I'd like nothing more than you come in and build a tool that does some of that cool stuff that you, that you have in mind.[00:38:15] swyx: Yeah. Awesome.[00:38:16] David Singleton: And just as a reminder, if you'd like to do that, the way to find the links is dreamer.com/latent space. Um, and for a limited time on that page, um, anyone who's listening to this podcast will also get directly off of our wait list. Uh, it's quite long right now. We are working hard to bring Zika.[00:38:32] Wait, so skip the wait list.[00:38:33] swyx: You know, I think, I think that's fantastic. I, I think it's, it is really sort of probuild way to do it. I wanted to jump back to the, the bar. Yeah. You know, you know, I get excited about this.[00:38:41] David Singleton: Yes. Okay. Let's set it back in there.[00:38:43] swyx: Like, let's, you know, this is the engineer podcast that's get[00:38:46] David Singleton: Yeah.[00:38:46] swyx: As technical as you can.[00:38:47] David Singleton: Yeah.[00:38:47] swyx: On everything you've built, like have a show off.[00:38:50] David Singleton: Yeah. Okay.[00:38:51] Under The Hood Debugging[00:38:51] David Singleton: So let's go wild in the aisles in the Asian studio. So as you can see, over on the left here is a conversation with the sidekick where you ask it what to do and it will explain in English that anyone can understand what's going on.[00:39:03] But, um, if you want to pull back the covers and look under the hood, um, if you're, uh, an engineer like me, then we have this, uh, this kind of debug drawer at the bottom. So you can see the full build logs here, but you can actually also dig in and see the files and prompts that have been generated. Uh, you can upload files from your computer in static files.[00:39:24] Um,[00:39:24] swyx: very important,[00:39:25] David Singleton: uh, indeed. You can actually read the prompts that have been generated for you. We intentionally put an example in here just that you can see what the format looks like. And then, you know, we already looked at this one that was generated for this particular, um, app, but if you actually want to bring the code out of Dreamer and work on your own local machine, you can.[00:39:45] So at the core of everything here is an SDK with a powerful command line interface and we built that first. It's actually possible to build agents on Dreamer without talking to the sidekick. You can write code with your fingers on a keyboard if you want to. I know that's very [00:40:00] antiquated, not, but actually this can be a lot of fun.[00:40:02] So if you wanna pull it out onto your laptop, you can use our, our CLI and, uh, you can edit it in cursor or in cloud code. You know, you don't have to use our sidekick. And the CLI actually has full access to the rest of the platform with you as the user. So, you know, obviously it is, uh, secure and privacy sensitive, and this is a way that, um, some of our most technical builders do build stuff on the platform.[00:40:24] The really cool thing is the side cake. When it's in coding mode, it uses exactly the same CLI. So the way it. Build stuff on Dreamer is using the same tools that you might as an engineer. Um, and that's actually a very powerful abstraction because it turns out that the right way to give a lot of context to agents to use CLIs is to write great documentation.[00:40:46] Make sure that all of the things that you could do are actually possible. And guess what? That makes it a delightful developer experience for real heroes as well.[00:40:53] swyx: Yeah. So that's pretty cool. We've been telling developers to do this and they ignore this until now they have to for content.[00:40:58] David Singleton: I, I've been saying this for a [00:41:00] long time.[00:41:00] Uh, we actually Stripe docs.[00:41:02] swyx: I mean, come on. Absolutely. Come on.[00:41:03] David Singleton: Absolutely. But actually, I was chatting with folks at Stripe last week and saying, Hey, you gotta make the Stripe CLI actually tell agents what they can do on Stripe because that way they're gonna use more stuff on Stripe. I think this is a real trend for the entire industry.[00:41:16] swyx: Yeah.[00:41:16] David Singleton: So we, we've been doing that.[00:41:17] swyx: To me, this, this download and, uh, GI push mm-hmm. Everything is complete confidence in that you're not hacking it. Right. Because there's other, let's call them AI builder platforms that impose their stack on you and if you, if you, and so therefore they don't allow you to do this because they cannot.[00:41:34] Right. ‘cause they, they impose some degrees of freedom, uh, restrictions so that they can get it to work. Yours is a fully general like VM running the full code. Correct. Do whatever you want. Correct. Any language you want. Correct. Yeah.[00:41:46] David Singleton: Correct. Well, in terms of language, if you use the SDK, you could build stuff in other languages.[00:41:51] We've actually found that TypeScript is the best language for building these experiences. Yes. Because it's strongly tight. So you find out at compile time if you've made mistakes [00:42:00] and there's nothing better than getting in. A coding agent in a loop where it can see its mistakes and ask them. So TypeScript is the language that everything gets built in by default here.[00:42:08] swyx: Did And did you see that TypeScript overtook Python? I did. I did. Yeah.[00:42:12] David Singleton: And for what it's worth, when we started the company, we started writing stuff in Python, and I love Python. Um, if I do, uh, a vendor code, I always write it in Python. It's my favorite language as a developer with my fingers on the keyboard.[00:42:23] Um, but TypeScript is an amazing language for AI because there's tons of training data in the models, um, and it's strongly tight. And actually at the company we built most of the stack in TypeScript, and we have this amazing property, which is, we have type safety all the way from the database to the front end.[00:42:40] And there's nothing better for working with coding agents than being able to have them check their correctness, compile time. So the same ideas behind building the company's code base, we've put into the agent SDK here as well.[00:42:51] swyx: Yeah. Do you know if you'd use one of those tools, like Prisma or whatever, or is it Tool Lab for you?[00:42:55] David Singleton: We, we actually have crafted most of our own tools. Um. For [00:43:00] instance, we had LLM Driven Code Review, uh, before the thing that got published from philanthropic this week. You know, we, we've been doing this stuff, uh, on our own bat[00:43:07] swyx: email, we'll pay $25 per review.[00:43:09] David Singleton: We, we pay a lot less than that. However, I hear that those reviews are excellent and possibly worth $25.[00:43:14] swyx: Yeah. You know, it's an option. Right. It's good, good to have it.[00:43:17] David Singleton: Just to give you a tour of some other stuff here. So, um, I can also see all the versions. Yeah. Um, this is not gi, this is not gi, this is built into dreamer. I can see all the versions that have been pushed before. Why is it[00:43:27] swyx: not gi?[00:43:28] David Singleton: It's not gi because we can make it work more efficiently than Git.[00:43:32] And we actually, we do some work behind the scenes to kind of understand what's in each of these versions. Yeah. Um,[00:43:37] swyx: so one of the things I'm pursuing, and I have a lot of thesis, right? Mm-hmm. One of the thesis is like, does GI go away? Does GitHub go away? And like, what, what is the active reinvent[00:43:46] David Singleton: you for, for what it's worth to some extent.[00:43:48] And anything you build, there's a lot of path dependency. If we started over, we might make this gi There's, uh, you know, within the company we use, uh. For our, you know, platform source code. And we like it and it [00:44:00] works well with coding agents as well. The very first versions of this, we wanted to be able to make it possible for the sidekick to manipulate it easily.[00:44:06] Um, and this, this was an expedient way to do it.[00:44:08] swyx: Yeah.[00:44:08] Workflows Logs And Databases[00:44:08] David Singleton: Um, you can also see all the activity that has happened in the workflows that you build. A lot of agents, you'll build on Dreamer, do things in the background, so they run on triggers. These are stimuli from the outside to kick them off, and this is a nice way to see all of the things that might have kicked off your agent.[00:44:24] You know, you can have an agent that kicks off on a webhook, so you can plug it into external systems. You can have an agent that runs when you receive certain emails that match filters, including LLM filters. And so here you can see, oh, when did it run? What did it do? You know, if I open up one of these guide me prompts or guide me, uh, events.[00:44:41] Oh my can see God. Well, I told you it was calling an LLM for every one of those time slots. Here's all of the LLM calls, here's the actual prompts.[00:44:49] swyx: And you don't mind exposing all of this, right?[00:44:51] David Singleton: No. We want builders to see what's going on under the hood. It's haiku to,[00:44:53] swyx: okay. Yeah. So,[00:44:54] David Singleton: okay. Right now that one was haiku.[00:44:56] Like I said, we work with all the models and sidekick will actually pick the best one [00:45:00] for the job. And you saw that was pretty high quality and pretty fast. So Haiku four five is the one that it picked for that job. Exactly. Uh, we also have logs, as I mentioned, there's a database spun up on demand for every, uh, agent.[00:45:12] You don't have to go and figure out how to do your own hosting. This is a SQL Light. This is a SQL Light database. Yeah. Um, it's a multi-user SQL light database. And then, uh, but, but each one is you, you get a database that is unique to this agent. But then if you share the agent with multiple people, we take care of like who are the owners in each row?[00:45:31] And all of that stuff is just there outta the box. Um,[00:45:34] swyx: and again, in-house?[00:45:35] David Singleton: In-house.[00:45:36] swyx: Oh my God.[00:45:37] David Singleton: Yeah. Um, well we do work with a bunch of infrastructure providers, but the technology for how to manipulate this is in-house. Fun fact. We actually did a lot of our own infrastructure development early on at the company and realized we need to spend our energy in the stuff that we're uniquely doing in the world.[00:45:53] So we're very delighted to partner with a bunch of great designer and some of this stuff. And then finally, um, I mentioned that agentic apps agents [00:46:00] expose all of their internals to the system so the psychic can manipulate them and use them just like a user can. So you can see how it's decided to break this problem up into functions.[00:46:09] Some of the functions, the ones with the little I here are exported. That means that there's probably the visible from outside. Exactly. And others are internal. And if you want to, you can dig right in here and call individual functions and see what happens. But mostly. You don't need to think about that at all.[00:46:24] Yeah. Uh, you can keep that little drawer closed and you can talk to your sidekick and build really powerful and enchanting experiences.[00:46:30] swyx: Yeah. I mean, to me, like showing this gives the engineer a complete mental model of what you've done and what you can do with it. Yeah. For example, the first thing I, I, I look for.[00:46:39] A mental checklist of things, right? Like is off in the database, off looks like it's not right. So that's a separate layer. That's probably me means it's hard to do multi-user apps on the same app, right?[00:46:50] David Singleton: So you actually, we've solved that. So, um, see, yes, the platform builds in off, so you as a user sign into the platform, if you're using an [00:47:00] agent that was published by someone else, then your identity is, is kind of taken care of by the system.[00:47:05] And when you query the database, you're gonna get the stuff that is for you. Unless the builder specifically said, this is public data that everyone should see. So they, they actually get a chance to think about that. And again, sidekick can guide you through building, uh, agents and apps that work that way.[00:47:19] So you're right, that's another thing that people have to think about when they're trying to figure out how to build software experiences on Dreamer. You, it's built in. You talk to the sidekick as if it were a human being about what you want and that's what you get. So, you know, my, my Big Sky app that I just showed you that was designed for multiple people to use it.[00:47:38] And of course the things that we were putting in as expenses were supposed to be visible to everybody, and I just told the sidekick that's the way I wanted it. Uh, but by default, if I built an app like that, the data from each user would not been visible to the others.[00:47:49] swyx: Yeah. Yeah. Uh, this is, I presume this is a mood question, but basically you've had to build your own coding agent, right?[00:47:55] Which is sidekick slash whatever is in Inside Psychic. Obviously there's a lot of [00:48:00] people with a lot of desire for cloud code and Code X and attachment to it. Mm-hmm. I know under the hood data basically reduced to a loop, but like, would you let people use cloud coding and Code X or is the harness too specialized?[00:48:12] David Singleton: Yeah. If you, if you want to use, um, cloud code and Code X, then you go down here. Yeah. Hit get the S St K. And we even say this right here, edits your heart's content Z cursor code.[00:48:22] swyx: Like people want to use it inside of Ick, right? Yeah. They want to switch the engine.[00:48:26] David Singleton: Yeah.[00:48:26] swyx: That's the coding engine.[00:48:27] David Singleton: Yeah. We are not doing that right now.[00:48:29] Um, you know, again, the goal really is abstract the complexity. Yeah. Um, because the real target for. Building agentic apps is folks who can't do this already today. I can't tell you how many users in our community I've spoken to who are like Dreamer has changed my life because I used to have all these ideas.[00:48:50] If only I could find an engineer to help me implement them, I'd be able to get them done. They're free, and now I can talk to my sidekick and, and get it built. I think that's like really how we think [00:49:00] about the people that should get a ton of value and fun, um, out of the platform. And so they're not asking to be able to plug in their their own, you know, coding agent.[00:49:11] And for those folks, the opportunity is massive. If you've never been able to do stuff in code, now you can build stuff for you, for your friends, for your family, for your coworkers. And also there's a huge opportunity for folks who do build stuff in code to actually contribute to this ecosystem. So that's how we think about it.[00:49:28] swyx: Yeah. Amazing.[00:49:28] Personalization And Memory[00:49:28] swyx: That's most of what I wanted to cover Dreamer wise. I think personalization and memory yeah. Is probably like the single most important job of, uh, of the os. Maybe we could talk about that and then I'll, I wanted to zoom out on company building stuff.[00:49:40] David Singleton: Yeah, yeah. Sounds good.[00:49:41] swyx: Yeah. So how do you handle memory?[00:49:43] What, yeah, what have you found? What have you tried and failed?[00:49:45] David Singleton: Yeah. Okay. So, uh, first of all, at the core of dreamer is the sidekick. The sidekick gets to know you and it builds up a memory about you over time, and that turns out to be very important. So Dreamer, that's

In this week's episode, we are talking about the 2025 reimagining of the Wolfman, a subject that you think it would be hard to fuck up! From the mind that brought you Insidious or whatever, here you go. Special topics for your consideration include: daddy issues, a combination of boredom amd disdain from people actually involved in the project, getting very upset at spiders, and whether or not this child actually *has* ESP?? Oof. Go watch the film from Episode 217- "The Howling 2: Your Sister is a Werewolf" instead, you can thank me later. The regular lineup of links! You can support us at patreon.com/werewolfambulance and listen to a ton of action movie episodes! You can vote on this month's movie now and, bonus, our third podcast,"Nice One, Mate!" Episode 4 has dropped!  leave us a message at 412-407-7025 hang out with some cool listeners at https://discord.gg/DutFjx3cBD buy merch at www.teepublic.com/user/werewolfambulance the best place to reach us is at werewolfambulance@gmail.com we're on Reddit at r/werewolfambulance sorta on Twitter @werebulance sorta on Instagram @werewolfambulance www.werewolfambulance.com if you feel you really must lodge a complaint with us, please do it on Facebook at facebook.com/werewolfambulance because we are probably not gonna see that, ever. If you liked this, please leave us a review on Apple Podcasts or wherever you listen! It helps others find us and allows us to continue to grow. Intro song is by Alex Van Luvie Outro song is A. Wallis- "EMT" Seriously, we have the best listeners, hands down.

Have you ever heard a quote at exactly the moment you needed it? Like it practically reached out and grabbed you by the shoulders?That happened to me recently when I heard this line from The Artist's Way: "perfectionism isn't a quest for the best… it's a pursuit of the worst in ourselves.Oof. Holy cannoli. That one knocked me flat.In this episode, I'm sharing the simple exercise that helped me see my burnout in black and white—and how you can use it to finally exhale.You'll hear…The quote about perfectionism that stopped me in my tracksThe unexpected way I realized I am a perfectionist (even though I swore I wasn't)What I wrote on my “trying to do this perfectly” list—and why it shocked meHow this one list immediately softened my burnout and anxietyWhy doing “everything right” might actually be the thing holding you backClick here to find the full show notes and transcript for this episode.EPISODE RESOURCES:Click here to be notified when new episodes of On Your Terms® come outCONNECT:Get Sam's weekly newsletter, Sam's SidebarFollow Sam on InstagramFollow Sam on YouTubeSubscribe to Sam's Substack, Beyond BusinessTake Sam's free legal workshop "5 Steps to Legally Protect & Grow Your Online Business"DISCLAIMERMentioned in this episode:Legal WorkshopDo you feel lost thinking about how to legally protect your online business? Head to mylegalworkshop.com to sign-up for immediate access to my free 1-hour legal workshop, 5 Steps to Legally Protect & Grow your Online Business.Legal Workshop

Oof.