Podcasts about queries

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

To be “born with a veil” (or caul) was once regarded as far more than an unusual circumstance of birth. Being born with a caul—a portion of the amniotic membrane adhering to a newborn's head or face—is exceedingly rare, but across centuries of European folklore it has been interpreted as a visible mark of destiny. Those born with one might be exceptionally lucky, possess healing powers or second sight, or enjoy supernatural protection. In darker traditions, however, the same sign could mark a future witch, vampire, or uncanny wanderer between worlds. We  begin with a quoted passage from Stephen King’s The Shining, in which it’s revealed that the psychically gifted boy, Danny, is born with a veil and then go on to explore the colorful vocabulary once used to describe the caul. Across Europe, the membrane was imagined as a cap, hood, helmet, veil, cloak, or even a “shirt.” Such expressions as the Scottish “sely hoo,” or blessed hood, and various European equivalents of “lucky cap” and “victor's cloak” reveal how closely the caul became associated with good fortune. Next, we explore the earliest written references to caul superstition. A fourth-century biography of the short-lived Roman emperor Diadumenian claims that he was born with a strange membrane resembling a diadem. The same source remarks that midwives sold preserved cauls to lawyers who believed they would bring success in court. Medieval clerical writings likewise condemn officials who carried cauls as charms to silence their opponents. From there, the episode turns to the long practice of preserving cauls as family treasures. Surviving examples include a fragment enclosed in a locket made for John Monson in 1597 and the caul of King Frederik III, kept in a silver casket in the Danish royal collections. Seventeenth-century wills describe cauls mounted in jeweled gold settings and passed down as heirlooms believed to ensure a family's continuing prosperity. We then address the caul's most famous power: protection from drowning. By the eighteenth century, cauls were carefully dried on paper and sold to sailors. Newspaper advertisements addressed “gentlemen of the Navy” and offered them as proven safeguards for long voyages. Dickens satirized the trade in the opening of David Copperfield, whose caul attracts only one bidder—an attorney offering cash and sherry, but refusing to pay enough to guarantee himself against drowning. Caul kept as sailor’s charm displayed at Pitt Rivers Museum, Oxford. Mrs. Karswell then shares some amusing 19th-century anecdotes found in the long-running Oxford University publication, Notes and Queries. One report has a caul-bearing child who hild floated like a cork whenever his mother tried to bathe him. Another describes a prognosticating caul that physically transformed in sympathy to events unfolding in the life of the person consulting it. We then travel to Iceland, where fetal membranes were associated with the fylgja, a guardian spirit or external portion of the soul. The afterbirth could not safely be discarded, exposed to animals, or burned, lest the child lose this spiritual companion. Yet the caul's magical potential also made it dangerous: witches were said to use it in love potions, and a seventeenth-century French royal midwife warned her colleagues never to preserve one because sorcerers might obtain it. The episode then moves eastward, where caul folklore grows considerably darker. In Romania and Poland, those born beneath the membrane might be destined to become strigoi or other vampiric beings after death. In Slovenia, Croatia, and Serbia, however, caul-born men could become supernatural defenders known as Krsniki or Zduhaći. Their spirits left their bodies at night to battle dragons, witches, night-hags, and rival spirit warriors threatening the community's health, weather, and harvest. Next, we encounter their Italian counterparts, the Friulian Benandanti, or “good walkers.” Sixteenth- and seventeenth-century inquisitorial records describe men and women who claimed that everyone born with a caul was summoned in adulthood to fight witches during nocturnal spirit journeys. Their preserved “little shirts” were worn around the neck, blessed with prayers and Masses, and believed to protect soldiers or help lawyers win cases. Finally, we return to the sea with the curious modern discovery of a preserved caul purportedly belonging to a survivor of the the sinking of the Titanic. Its provenance may be uncertain, but the topic at least provides an excuse to close out with a snippet of the uniquely grim yet lighthearted folk song, “When the Great Ship Went Down.”

Send us Fan MailRomans 10:14–21: Hearing, Preaching, and Israel's Response Ken Corkins and pastor Rocky Ellison continue “Pondering the Bible” in Romans 10:14–21 (RSV), aiming to finish chapter 10. They unpack Paul's chain of questions about calling, believing, hearing, preaching, and being sent, suggesting it reads more clearly in reverse: God sends a preacher, people hear, believe, then call on Jesus. They discuss why this passage is controversial, since Paul quotes Isaiah, Psalms, and Deuteronomy throughout and sometimes not word-for-word, raising debate about context and accuracy. Paul argues Israel has heard and had both general revelation (creation) and specific revelation (Scripture), yet many still reject the gospel, while Gentiles accept it. Using William Barclay's outline, they frame the section as four questions with four answers emphasizing Israel's responsibility for unbelief. 00:00 Welcome and Setup 00:35 Reading Romans 10 02:14 Why So Confusing 02:26 Chain of Questions 09:34 Isaiah and Faith 12:43 Have They Heard 16:30 Israel and Gentiles 19:52 Hands Held Out 22:03 Barclay Simplifies 25:27 Final Thoughts 26:16 Closing Sign OffNEW!: Rate us at Podchaser  Find us at www.pondergmc.org. Feedback is welcome: PonderMethodist@gmail.com Music performed by the Ponder GMC worship team.Cover Art: Joe WagnerRecorded, edited and mixed by Snikrock 

Watch as a full video interview on YouTubeCorissa Hollenbeck is an associate agent with Janklow & Nesbit. She grew up in Massachusetts and attended Phillips Academy Andover, King's College London, the University of Freiburg, and the Columbia Publishing Course. She has worked at VICE, Marie Claire, and The Guardian. Corissa has worked with a broad range of prize-winning and bestselling authors whilst assisting literary agent and Managing Director Will Francis, and is now building her own list of authors.We had a really interesting chat with Corissa, hearing about how she first got into agenting, and how every part of the query package is important when submitting to agents. She also tells us about how she sometimes works with authors BEFORE she signs them, and talks about the impact of AI on the amount of queries and what that means for the future.Links:Query Corissa nowSupport us on Patreon and get the podcast early and ad-free, along with other great benefits, including a bonus episodes: https://www.patreon.com/ukpageonePage One - The Writer's Podcast is brought to you by Write Gear, creators of Page One - the Writer's Notebook. Learn more and order yours now: https://www.writegear.co.uk/page-oneFollow us on FacebookFollow us on InstagramFollow us on BlueskyFollow us on ThreadsPage One - The Writer's Podcast is part of STET Podcasts - the one stop shop for all your writing and publishing podcast needs! Follow STET Podcasts on Instagram and Bluesky Hosted on Acast. See acast.com/privacy for more information.

T-SQL Tuesday #200 was in July, hosted by Brent Ozar, and it was a great topic: How do you recognize a bad query? In the age of AI, when lots of people will get queries written by others (people or AIs), how can you easily and quickly review code? Review is already a challenge in the software world, and I am sure it's going to be even more challenging as people let machines author more database code. Lots of you might hope that an AI agent will write better code than your average developer, but I don't know if I'd count on that. There is a ton of poor query examples on the Internet and that's where AI models are trained. You need some sort of feedback loop, good testing, and strong guidance if you want better query code. I think it's as likely as not that AIs will produce poor queries just like humans. Just faster. Read the rest of Finding Bad Queries

Our guest this week is Derik Pridmore, CEO and co-founder of OSARO. OSARO develops intelligent AI robotics for real-world warehouse automation, delivering scalable fulfillment solutions that optimize throughput, uptime, and overall performance. In this conversation, Pridmore breaks down how warehouse robotics has evolved from limited perception systems to adaptable AI-driven automation. He shares why hardware-agnostic design, continuous learning, and real-world monitoring matter more than flashy demos — and why the biggest breakthroughs in robotics still depend on balancing specificity, reliability, and safety. Learn more: https://www.osaro.com Also this week, cohosts Steve Crowe, Mike Oitzman, and Gene Demaitre discuss the recent news about the FCC announcement to ban foreign legged and mobile robots from import to the U.S. – SPONSORS – This episode is brought to you by Tiger Data Every growing Postgres database eventually hits a wall. Queries slow down, dashboards lag, and teams consider adding a second database. Tiger Data, creators of TimescaleDB, extends Postgres with time-series primitives, columnar storage, and automatic partitioning so your queries stay fast on live data. No pipelines, no migration, no second system. Just Postgres, built for the workload you actually have. Try it free at https://www.tigerdata.com/go/trial?utm_source=content-syndication&utm_medium=referral&utm_campaign=robotics-ads

The countdown is on to the MLB trade deadline. Arden and Ben discuss some Blue Jays trade chips (5:33) before looking at a couple potential targets (33:42) and making some predictions (58:34). This podcast is produced and sound engineered by Cristian Ceniti and hosted by Ben Nicholson-Smith and Arden Zwelling. Contact us: attheletters@sportsnet.ca The views and opinions expressed in this podcast are those of the hosts and guests and do not necessarily reflect the position of Rogers Media Inc. or any affiliate. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

When we look at the performance of software, we use Pnn notation to indicate the latency of an issue. A P95 problem is one that exceeds the time that 95% of the other queries take. In other words, this is the 5% slowest things happening, which can include database slowdowns that impact your application. For many years in software development, we have tended to work on the P95 or P90 issues, the slowest items. This is primarily because those can make a big difference to the system's performance. If I fix the slowest things, then the system feels faster. Certainly, I know lots of DBAs and developers will apply this logic to database queries. They focus on the slowest queries and tune them to improve the system. If most things are quicker, especially the things most users notice, then the system feels faster. Read the rest of Fixing P1 Queries

In this episode, Doug Pagnutti, Developer Advocate at Tiger Data, discusses how time series databases like TimescaleDB are transforming industrial automation, robotics, and AI applications. He shares insights on integrating these databases with various sensors, managing data at scale, and optimizing performance both on the cloud and on the edge. Key Topics: - The role of time series data in robotics and industrial automation - How TimescaleDB extends PostgreSQL for high-performance time series workloads - Differences between open source and managed cloud versions - Strategies for integrating various industrial controllers and messaging pipelines - Techniques for managing intermittent connectivity with edge devices - Advanced tools like continuous aggregates and data compression for big data - Enabling multimodal data queries with hybrid search stacks - Future applications of time series data in AI-driven environments and energy systems - Best practices for storing telemetry, spatial, and metadata efficiently – SPONSORS – This episode is brought to you by Tiger Data Every growing Postgres database eventually hits a wall. Queries slow down, dashboards lag, and teams consider adding a second database. Tiger Data, creators of TimescaleDB, extends Postgres with time-series primitives, columnar storage, and automatic partitioning so your queries stay fast on live data. No pipelines, no migration, no second system. Just Postgres, built for the workload you actually have. Try it free at https://www.tigerdata.com/go/trial?utm_source=content-syndication&utm_medium=referral&utm_campaign=robotics-ads

Sponsor Link:NordVPN - Secure your online presence with our exclusive offer for Space Nuts listeners. Check out www.nordvpn.com/spacenuts for details.In this engaging Q&A episode of Space Nuts, Andrew Dunkley and Professor Fred Watson dive into listener inquiries that span the cosmos. From the intriguing concept of black hole evaporation to the mysteries of Jupiter's atmosphere and the latest on the Artemis 2 mission, this episode is packed with fascinating insights and scientific discussions.In this episode:- The mechanics of black hole evaporation and how cosmic microwave background radiation affects their lifespan.- An exploration of Jupiter's thin atmosphere and how it compares to the dense atmospheres of moons like Titan and planets like Venus.- Insights into the Artemis 2 mission and the implications of visual observations of meteorite impacts on the moon's far side.- A look ahead at upcoming solar missions and the cutting-edge technology being deployed to study our sun.- The significance of cosmic rays and their impact on human perception in space.Resources & Links:- [Parker Solar Probe](https://www.nasa.gov/content/parker-solar-probe) - NASA's mission to study the sun's outer atmosphere.- [Artemis Program](https://www.nasa.gov/specials/artemis/) - NASA's initiative to return humans to the moon.- [The Cosmic Microwave Background](https://map.gsfc.nasa.gov/universe/uni_cmb.html) - Understanding the remnants of the Big Bang.- [Titan and Its Atmosphere](https://solarsystem.nasa.gov/planets/titan/overview/) - NASA's insights into Saturn's largest moon.Join Andrew and Fred Watson as they unravel the complexities of space science and encourage curiosity about the universe. Don't forget to send in your questions for future episodes!Become a supporter of this podcast: https://www.spreaker.com/podcast/space-nuts-astronomy-insights-cosmic-discoveries--2631155/support.(00:00) Andrew Dunkley takes audience questions on this week's Space Nuts(02:53) If black holes are colder than cmb, how much does this lengthen(09:12) Fred: Does Jordy have an unusually thin atmosphere for a rocky planet(10:48) Titan has a much higher atmospheric pressure than our own planet Jordy(17:16) Michael from Switzerland claims Artemis 2 astronauts saw meteorite flashes on moon(24:55) Houston has had a main B undervolt problem(25:07) Final question today comes from somebody who forgot to tell us their name(34:04) Andrew Dunkley: Thanks for your company. Bye. You're listening to the Space Nuts podcast

On the show this week, Deise Yumi Asami shares how her AI-enabled robotics startup, Maximo, is transforming solar panel installation and making solar panel installation faster, safer, and more efficient. In this conversation, you'll hear how robotics, AI vision, and smart field deployment are helping reshape the future of renewable energy infrastructure. Deise also shares the experience of starting a new company from the ground up while being incubated within a larger parent company. Learn how innovation is encouraged and celebrated inside a large organization, and how AES organizes its innovation group. Learn more: https://maxrobotics.ai/ – SPONSORS – This episode is brought to you by Tiger Data Every growing Postgres database eventually hits a wall. Queries slow down, dashboards lag, and teams consider adding a second database. Tiger Data, creators of TimescaleDB, extends Postgres with time-series primitives, columnar storage, and automatic partitioning so your queries stay fast on live data. No pipelines, no migration, no second system. Just Postgres, built for the workload you actually have. Try it free at https://www.tigerdata.com/go/trial?utm_source=content-syndication&utm_medium=referral&utm_campaign=robotics-ads

Andy Lonsberry is redefining what welding robots can do, and in this conversation, he reveals how AI is pushing manufacturing beyond the limits of traditional automation. In this episode, Mike Oitzman and Gene Demaitre sit down with Andy Lonsberry, CEO of Path Robotics, to explore how his team is using AI, reinforcement learning, and real-time visual feedback to make welding robots smarter, more adaptive, and capable of handling complex real-world conditions. From shipbuilding to infrastructure and other large-scale industrial applications, Andy explains why the future of welding depends on robots that can think, react, and move to the work—not just wait for the work to come to them. They discuss: - The founding story of Path Robotics - Why traditional welding automation plateaued - How AI and reinforcement learning improve weld quality - The role of real-world sensor data in adaptive welding - Why mobile, legged robots open new industrial possibilities - The business model behind Robotics as a Service - What's next for AI-powered manufacturing If you're interested in the future of industrial automation, physical AI, and the next generation of robotics, this conversation is a must-listen. Submit your robotic startup for the Startup Showcase: https://www.therobotreport.com/calling-all-robotics-startups-apply-to-robobusiness-startup-alley/ – SPONSORS – This episode is brought to you by Tiger Data Every growing Postgres database eventually hits a wall. Queries slow down, dashboards lag, and teams consider adding a second database. Tiger Data, creators of TimescaleDB, extends Postgres with time-series primitives, columnar storage, and automatic partitioning so your queries stay fast on live data. No pipelines, no migration, no second system. Just Postgres, built for the workload you actually have. Try it free at https://www.tigerdata.com/go/trial?utm_source=content-syndication&utm_medium=referral&utm_campaign=robotics-ads

In Episode 251 of The Robot Report Podcast, hosts Steve Crowe and Mike Oitzman are joined by special guest Sarah Wynn, Senior Editor for Packaging OEM, to recap their firsthand experiences on the show floor at Automate in Chicago. The conversation traces the industry's shift away from early-stage humanoid hype toward the practical, real-world deployment of Physical AI and edge computing. It then transitions into how software orchestration, digital twins, and advanced kinematics are solving labor shortages and preserving vital manufacturing knowledge. The following innovators, executives, and organizations were highlighted or featured via interview vignettes during the episode: Boston Dynamics & Agility Robotics: Discussed regarding the static floor displays of their respective industrial humanoids, Atlas and Digit. ABB: Featured in a vignette with Craig McDonnell (Managing Director, Business Line Industries, ABB Robotics) discussing Physical AI, AI-powered palletizing, and collaborations with Nvidia. FANUC: Highlighted for real-time motion tracking in assembly, protein processing automation, and natural language robot programming. Sereact: Featured in an interview clip with Mason Coleman (Director of Sales, North America) addressing zero-shot picking, e-grocery trends, and workforce reallocation. Schneider Electric: Discussed for their views on cloud latency limitations and their push for hardware-agnostic, open automation systems. Rockwell Automation: Featured in an audio clip with Ara Surenian (Business Manager, Production Logistics) introducing FactoryTalk Orchestration following their acquisition of OTTO Motors. SEW-EURODRIVE, Festo, & CODI Manufacturing: Noted for their compact, gantry-style robotic cells and packaging line solutions. Vention: Recognized for their prominent and accessible automation platform demos. Kassow Robots: Featured in an interview clip with founder Kristian Kassow, who explained the strategic advantages of 7-axis cobots over traditional 6-axis configurations for mobile manipulators and confined spaces. – SPONSORS – This episode is brought to you by Tiger Data Every growing Postgres database eventually hits a wall. Queries slow down, dashboards lag, and teams consider adding a second database. Tiger Data, creators of TimescaleDB, extends Postgres with time-series primitives, columnar storage, and automatic partitioning so your queries stay fast on live data. No pipelines, no migration, no second system. Just Postgres, built for the workload you actually have. Try it free at https://www.tigerdata.com/go/trial?utm_source=content-syndication&utm_medium=referral&utm_campaign=robotics-ads

In the small Somerset village of Sidcot, George Beacham was remembered as a cattle doctor, a conjurer, and a man with knowledge of the old ways. When his final burial wish was ignored, the story says his widow's cottage erupted with moving furniture, falling household goods, and the sound of the dead man's boots coming slowly down the stairs.The BOOKBY US A COFFEEJoin Sarah's new FACEBOOK GROUPSubscribe to our PATREONEMAIL us your storiesJoin us on INSTAGRAMJoin us on TWITTERJoin us on FACEBOOKVisit our WEBSITEResearch Links:https://archive.org/stream/historyofsidcots00knig/historyofsidcots00knig_djvu.txthttps://www.sdnq.org.uk/wp-content/uploads/2020/05/Somerset-County-Herald-Notes-and-Queries-1919-transcript-by-Paul-Mansfield.pdfhttps://www.themodernantiquarian.com/site/5615/banwell-forthttps://hauntedhosts.com/haunted-places/avon/location/1235-banwell-blown-cross-hauntinghttps://www.themodernantiquarian.com/site/2092/wimblestonehttps://www.paranormaldatabase.com/somerset/somedata.phpThanks so much for listening, and we'll catch up with you again on tomorrow.Sarah and Tobie xx"Spacial Winds," Kevin MacLeod (incompetech.com)Licenced under Creative Commons: By Attribution 4.0 Licencehttp://creativecommons.org/licenses/by/4.0/SURVEY Hosted on Acast. See acast.com/privacy for more information.

The June13, 2026, Dirt Doctor Podcast with Howard Garrett answers questions sent in by podcast listeners and web site readers to the Dirt Doctor site's "Ask Howard" feature. Queries this week on many subjects including what to do about caterpillars and worms, is wood mulch safe, fireblight, grass burs, photinia diseases, and how to use corn meal. Show Notes:SPCA dogsEFI Newsletter Cheat Sheet  ComfreyFireblight   Worms (caterpillars)SpinosadPipevine Swallowtail caterpillars  Other worms (planarian)BioWash Purely GreenTomato diseasesFleasBeneficial nematodesOrangesMosquito program trapsSpraying for chiggersLiquid CedarWood mulch disease spread?New Red oak - watering Beech tree grow in North Texas?Ornamental tree - Flamethrower RedbudCorn Gluten mealGrass burs Poor unhealthy soilJapanese beetles Photinia and Sick tree treatmentTools - Hori Hori knife   Advertisers:Summit Chemical mosquito solutions. https://summitchemical.com/mosquito-solutions/Dramm Corporation Consumer Products https://www.dramm.com/html/main.isxCrazy Water is the only mineral water bottled in Texas. Rich with Mother Nature-infused minerals, which are more readily absorbed by your body. https://drinkcrazywater.com/ Dr. Ohhira probiotics have been part of the Garrett family health regimen for years. https://drohhiraprobiotics.com/dr-ohhira-probiotics/https://www.dirtdoctor.com

Because the canvas roof had been waterproofed with gasoline, the small flame that touched it on July 6, 1944 swept across the Hartford circus big top in seconds, and most of the 167 people it killed were children.EPISODE BLOG PAGE (includes sources): https://weirddarkness.com/HartfordCircusFireREAD or DOWNLOAD the full transcript of this episode: https://weirddarkness.tiny.us/39d8nfwhFEATURED STORIES IN THIS EPISODE: Three boys fishing in the middle of the night hear a blood-curdling scream. But it wasn't a human making all that noise – it was an extraterrestrial. And thus began a series of meetings with alien beings! (What Do You Say When Meeting An Extraterrestrial?) *** A day of hilarity turns into a day of horror as an uncontrollable fire breaks out at the Ringling Bros Barnum & Bailey Circus – resulting in the most deadly circus disaster in history. (The Day The Clowns Cried) *** Most ghosts and specters do a great job of scaring the pants off you – and some can get creative with how they do it, with stacking chairs, making toys talk, slamming doors, etc. But apparently not all spooks are worried about their reputation – and when it comes to haunting, they just phone it in, doing the bare minimum. (Lazy Phantasms)CHAPTERS & TIME STAMPS (All Times Approximate)…00:00:00.000 = Show Open00:01:39.923 = Lazy Phantasms00:12:35.047 = What Do You Say When Meeting An Extraterrestrial? ***00:42:41.671 = The Day The Clowns Cried ***00:52:38.951 = Show Close*** = Begins immediately after inserted ad breakLISTEN ON PODCAST APPS: Look for this podcast on Apple Podcasts, Spotify, iHeart Radio, Amazon Music, Pandora, TuneIn Radio, and other podcast apps. Get a list of free listening apps here: https://weirddarkness.com/wdapps*No AI Voices Are Used In The Narration Of This Podcast*SOURCES and RESOURCES:“What Do You Say When Meeting An Extraterrestrial?” from Anomalien.com: https://weirddarkness.tiny.us/44h5ykk9“Lazy Phantasms” posted at Esoterx.com: https://weirddarkness.tiny.us/2y69m7hu“The Day The Clowns Cried” by Rachel Souerby for Weird History: https://weirddarkness.tiny.us/4ek5rsup(Over time links may become invalid, disappear, or have different content. I always make sure to give authors credit for the material I use whenever possible. If I somehow overlooked doing so for a story, or if a credit is incorrect, please let me know and I will rectify it in these show notes immediately. Some links included above may benefit me financially through qualifying purchases.)WeirdDarkness® is a registered trademark. Copyright ©2026, Weird Darkness.Originally aired: November, 2021Weird Darkness ranges from a shapeless apparition that appeared inside the Tower of London in 1817, to a string of close-range UFO and humanoid encounters reported across North America, to the Hartford circus fire of 1944 that killed 167 people in under ten minutes.It opens inside the Tower of London in October 1817, where a cylinder of dense, white and pale-azure fluid about the thickness of a man's arm materialized over the supper table of Edmund Lenthal Swifte, the Keeper of the Crown Jewels. Swifte was holding a glass of wine and water to his wife's lips in the Jewel House, with her sister and his young son present, when the shape hovered for roughly two minutes, drifted around the room, and settled over his wife's right shoulder, at which she cried out that it had seized her. He struck at the wood paneling behind her with his chair, but the figure left no mark, and a scientific friend who afterward examined the sealed, curtained, candle-lit room could account for none of it. The thing wore no period costume and delivered no message, and forty-three years later Swifte set the encounter down in the journal Notes and Queries, insisting at eighty-three that he had neither amplified nor abridged a word of it.From there it moves to a wave of close-range encounters, beginning on a cold January night in 1972 when sixteen-year-old John Yeries and three companions, fishing near Battle Creek Bridge east of Anderson, California, saw a seven-foot, greenish-brown humanoid with a large teardrop-shaped ear on one side of its head and heard it loose a scream that sent them sprinting for their car. Darrell Rich's father Dean returned to the bridge with a pistol, only to back away when a deep growl rose from the brush, and a police search of the area turned up nothing. The following year, on October 4, 1973, insurance agent Gary Chase pulled over at the Santa Susana Pass near Simi Valley, California and watched an elliptical craft roughly seventy feet long, marked with a nested V insignia, hover above a creek while a figure in a wetsuit-like suit crawled across its hull toward a protruding hose. Other witnesses report the same intrusions: patrolman Lonnie Zamora saw two small, white-clad figures beside a landed craft in New Mexico in 1964, and Mrs. Wallace Bowers found fifteen-inch footprints in the snow and watched an orange disk hover over the power lines outside her home in Vader, Washington. Bernice Niblett spent the winter of 1967 alone on Keats Island in British Columbia, where she watched lights maneuver over the water night after night and became convinced that the two stiff, oddly formal Hydro men who appeared at her cabin were not the utility workers they claimed to be — a year-long ordeal documented by Canadian UFO researcher John Magor that eventually drove her off the island.The episode closes with the Hartford circus fire of July 6, 1944, when the canvas big top of the Ringling Brothers and Barnum & Bailey circus, waterproofed with a mixture of white gasoline and paraffin wax, caught at the edge and was consumed in under ten minutes, killing 167 of the roughly 7,000 people inside, most of them children. As the flames climbed the roof, the bandleader struck up 'Stars and Stripes Forever,' the circus's coded signal for an emergency, while the Great Wallendas scrambled down from their high wire unhurt. Ringmaster Fred Bradna called for a calm exit, but the crowd ignored him as burning canvas and hot wax fell from above. Two of the exits were blocked by the steel chutes used to move animals in and out, so many of the dead were trampled there rather than burned, and a photograph of the clown Emmett Kelly carrying a single bucket of water toward the blaze fixed the catastrophe in memory as the day the clowns cried. Investigators never settled the cause, though the state fire marshal leaned toward a carelessly dropped cigarette. A fifteen-year-old circus hand named Robert Dale Segee confessed to setting the fire years later and then recanted. And one young victim, her face barely touched by the flames, was never claimed — buried under the name Little Miss 1565 and identified only decades afterward, and only disputably, as Eleanor Cook.

All links and images can be found on CISO Series This week's episode is hosted by me, David Spark, producer of CISO Series and Andy Ellis, principal of Duha. Joining us is Dmitriy Sokolovskiy, senior vice president, information security, Semrush. This episode was recorded in front of a live audience at the offices of Aqueduct Technologies in Canton, MA. See photos from the event. In this episode: A clock on everything The oversight loop Not a better tool, a different one It's not the alerts A huge thanks to our sponsor, Strike48 It's no secret that AI is only as good as the data available to it. Strike48 unifies agentic AI with unmatched log visibility while avoiding the typical hefty price tag. Build and deploy agents for phishing detection, alert triage, threat correlation and more. Queries existing logs where they currently live, so you can keep the technology you already have. Learn more at Strike48.com.   A huge thanks to our sponsor, Dropzone AI Dropzone AI delivers a team of AI agents that investigate alerts, hunt threats, and respond to attacks across your full security stack. No playbooks required. No hidden humans in the critical path. Your analysts stay in control, directing strategy while AI agents handle the investigation workload at machine speed. Learn more at dropzone.ai.

Send us Fan MailJoel Brigham runs Brigham Editorial (developmental edits, manuscript critiques, query help), teaches high school English in Illinois, and is an editor for RevPit — a community that gives away free developmental edits each spring.REVPIT•    Free developmental editing contest — editors (not authors) mentor selected writers through full drafts•    Applications open April, winners announced early May; ~14–15 editors participate•    Year-round mini-event: 10 Queries — public feedback on first 5 pages + query lettersTHE FIRST DRAFT•    One goal: words on the page. Momentum beats perfection — always•    Psychology backs this up: Goal Gradient Effect, Zeigarnik Effect, and Commitment Principle all support just keeping going•    Comparing your messy first draft to your last polished book is a trap — every published book started the same way5 DRAFTING HINDRANCES•    Starting slow — avoid waking-up scenes, mirror descriptions, dream openers. Try dropping into the middle of something (*in medias res*)•    Perfectionism — editing as you go wastes time on scenes that may not survive. Grammar is the last step•    Weak character foundation — know their goal, fear, flaw, and wound as early as possible•    No tension — even “everyday life” chapters before the inciting incident need friction, stakes, or a ticking clock•    Info dumping — no backstory or flashbacks in chapters 1–2. Backstory is a breadcrumb, not a full loafFOR DISCOVERY / PANTSER WRITERS•    Check in every 15–20k words — assess without forcing rigid plot beats•    By 20k: your character should have a clear want and be on the book's core journey•    Made a change mid-draft? Drop a note and keep writing forward as if it's always been that way — don't stop to rewriteLINKSJoel's services: brighameditorial.com  •  RevPit: reviseresub.com  •  Show notes: writerswithwrinkles.net Support the show Visit the WebsiteFind Full Episodes on YouTube!Writers with Wrinkles Link Tree for socials and more!

We're incredibly grateful to have a fresh Isaac Smith on the show - after he said nary a word on Footy Classified on Tuesday night. He might also have a manspreading issue. St KIlda's Mitch Owens reports in from ground level as he recovers from a hamstring injury, and K-Mac is worried about Jay Z in the wake of Scott Pendlebury's 433rd game. The team reflects on what Neale Daniher has meant to footy, and Australia. Then Jay Z and the team dive deep into Essendon's search for its new coach - where all roads currently seem to lead to one man. K-Mac has rebranded The Queen's Queries into the Royal Commission - and she ponders on what big events could be held at The Lodge in Canberra. Isaac has several names for both his Penthouse and Outhouse, then the team finishes with a round of Unpopular Opinions.See omnystudio.com/listener for privacy information.

In Chapter 15, a familiar face from the earlier books makes an appearance in Voldemort's secret lab... but they're not happy about what's going on.

The team assembles ahead of footy's biggest milestone, and there's nobody more excited about Scott Pendlebury's 433rd game than Jay Z Clark. But first, we need to find out if Isaac has any more modelling plans. Jay Z takes his Chief's Agenda to Essendon and Tasmania, then we look at Isaac playing a game of local footy in Cairns, and the latest at the Devils. K-Mac has a list of the world's best bromances in her Queen's Queries, we get a round of Unpopular Opinions, Adelaide assistant coach Scott Burns joins the show, and Isaac has plenty of nominations for his Penthouse, and one for the Outhouse.See omnystudio.com/listener for privacy information.

On this episode of Remote Ruby, Chris, Andrew, and David kick things off with dentist trauma, gold star stickers, and fiber internet. The conversation centers on Rails direct routes, why they can be more powerful than helpers, the upcoming Rails World CFP and ticket rush, how AI is becoming more practical inside real engineering teams, a reminder to fill out the Rails survey, and Chris's continued work expanding the Rails Getting Started Guide into a more realistic e-commerce tutorial with wishlists, reviews, ratings, and product images. Hit download now to hear more!LinksJudoscale- Remote Ruby listener giftHow to use Direct Routes in Rails (GoRails)On Rails Podcast- Brian Scanlan: Building AI-First at Intercom2026 Ruby on Rails Community Survey (Planet Argon)Rails World -Sept 23-24, 2026, Austin, TXHoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.JudoscaleMake your deployments bulletproof with autoscaling that just works.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Chris Oliver X/TwitterAndrew Mason X/TwitterJason Charnes X/Twitter

It's a supersized edition of the show - Isaac has made it here from Warrnambool, but Jay Z is on special assignment so we welcome Theo Doropoulos to the team - as we look at another silent appearance from Isaac on Footy Classified. Ben McKay has been dropped to the VFL this weekend, so the team shares their views on how he can get back into form. Plus, more pressure builds up at the Blues. The team chats about the tactics ahead of tonight's game, before speaking to Fremantle Assistant Coach Jade Rawlings, then K-Mac has a double dose of the Queen's Queries. Isaac has plenty of room in both his Penthouse and Outhouse this week, then the team chats about Scott Pendlebury's upcoming AFL Games Record, and Isaac cops some harsh feedback for his list of the Best Midfielders of the 21st Century. Hawthorn Assistant Coach David Hale joins the team from Perth, as does West Coast Norm Smith Medallist Andrew Embley to take us behind the scenes of the Eagles 2006 Premiership reunion.See omnystudio.com/listener for privacy information.

Backyard Gardening for Birds with guest Laura Erickson, Award-Winning Author, 100 Plants to Feed the Birds Native birds require native habitat, and the Spring season arrives with many opportunities to shop locally for native plant species that will support birds, pollinators and other important insect life... but how does one decide where to look and what to plant? Birding expert Laura Erickson combines indepth research with her own unique manner of storytelling to answer these questions. Erickson has received many national, state, local, and organization awards for her conservation and education work, as well as for her writing. She has written thirteen books about birds and was a columnist and contributing editor for BirdWatching magazine. She is the recipient of the American Horticultural Society's Book Award (in 2023) for 100 Plants to Feed the Birds. Since 1986 Erickson has been producing the long-running “For the Birds” radio program, played on many public and community radio stations. Her website provides access to her books, articles, photos and podcasts, as well as her bird search app. She has been a scientist, teacher, writer, licensed wildlife rehabilitator, blogger, public speaker, photographer, Science Editor at the Cornell Lab of Ornithology and contributor to the popular Journey North educational website. This podcast will inspire both birders and gardeners. INFORMATION RESOURCES Read Wendy's Substack Article, Counting Robins - https://storywalkerwendy.substack.com/p/counting-robins-and-eco-art-materials Explore 110 Ways to Help Birds - https://www.lauraerickson.com/ways-to-help/ Order 100 Plants to Feed the Birds - https://www.lauraerickson.com/book/100-plants-feed-birds/ Find Laura Erickson's Books and Articles - https://www.lauraerickson.com/writing/ Listen to the “For the Birds” Podcasts - https://www.lauraerickson.com/radio/ Subscribe to Laura Erickson's blog on Substack - https://lauraerickson.substack.com/ Search for Birds - https://www.lauraerickson.com/birds/ Learn more about the Story Walking Radio Hour at https://storywalking.com Reach Out to Wendy with Comments and Queries - email storywalkerwendy@gmail.com or text 401 529-6830. Subscribe to Story Walking - https://storywalking.com/the-listening-grove/ Follow Story Walking on Facebook - https://www.facebook.com/StoryWalkingRadioHour/ or instagram - https://www.instagram.com/storywalkerwendy/   RELATED EPISODES Bird Building Collision Monitoring: Migratory Bird Conservation - https://dreamvisions7radio.com/bird-building-collision-monitoring/ Nature as Teacher: Stories and Reflections from Nature Journaling - https://dreamvisions7radio.com/nature-as-teacher/ Nature-Inspired Creative Expression - https://dreamvisions7radio.com/nature-inspired-creative-expression/ Subscribe to Wendy's substack to receive notifications of new podcast and product releases -https://storywalkerwendy.substack.com/ Purchase Wendy's book, The Angel Heart - https://www.amazon.com/Angel-Heart-Wendy-Nadherny-Fachon/dp/1967270279/ref=sr_1_1 Read about DIPG: Eternal Hope Versus Terminal Corruption by Dean Fachon begin to uncover the truth about cancer - https://dipgbook.com/ Learn more at https://netwalkri.com email storywalkerwendy@gmail.com or call 401 529-6830. Connect with Wendy to order copies of Fiddlesticks, The Angel Heart or Storywalker Wild Plant Magic Cards. Subscribe to Wendy's blog Writing with Wendy at www.wendyfachon.blog. Join Wendy on facebook at www.facebook.com/groups/StoryWalkingRadio

Google just posted a monster quarter — search ad revenue up 19% year on year, cloud breaking $20 billion for the first time, and products built on Gemini growing over 800%. But behind the headline numbers lies a more nuanced story for brands and marketing leaders trying to figure out where to place their bets.The "doomish narrative" that ChatGPT, Perplexity, and AI Overviews are gutting Google Search simply isn't supported by the data. Google Search is still growing. Queries are at an all-time high. And AI Overviews are actively driving that growth, not cannibalising it.But that doesn't mean everything is rosy for the brands appearing in those results.In this episode, Charlie Marchant (CEO of Exposure Ninja) and Dale Davies (Head of Marketing at Exposure Ninja) break down:• Why Google's 19% ad revenue surge may actually be fuelled by brands panicking about organic traffic losses — and whether that panic is justified• How AI Overviews are reshaping click behaviour, with impressions staying stable but clickthrough rates shifting in ways that don't always hurt conversions• The rise of "personal intelligence" in search — where two users searching the same query could see entirely different results — and what that means for keyword rank trackers• Why Google's long-term vision looks far more like AI Mode than traditional blue links, and how quickly that transition could happen• The emerging "agentic commerce" trend where Google becomes a marketplace that completes transactions without users ever visiting your website• How AI chatbots like Claude are creating entirely new brand ranking systems — with only three recommendation slots instead of ten organic positions• What the earnings call doesn't tell you: the DOJ antitrust case, competitive pressure from ChatGPT and Claude, and whether Gemini is an underdog in its own rightCharlie shares her framework for how marketing leaders should allocate budget in this shifting landscape — the 80/20 rule that prioritises what's already working while leaving room for strategic experimentation.As Charlie explains in the episode:"If people are still scared in 2026, it's because they haven't yet shifted their SEO strategy to understand how AI Overviews and other AI platforms are part of that search journey."Whether you're weighing up organic versus paid, trying to figure out how agentic search affects your eCommerce strategy, or simply trying to make sense of what Google's numbers actually mean for your business — this episode gives you a clear-eyed breakdown of where search is heading and what to do about it.Get the podcast show notes:https://exposureninja.com/podcast/dojo-74/Listen on your favourite podcast player instead:Apple: https://podcasts.apple.com/gb/podcast/googles-revenue-is-exploding-but-are-brands-winning-too/id1161818237?i=1000765508727Spotify: https://open.spotify.com/episode/1KIX7OCYzx9DCe7qUg4pBm?si=uxQkVJ20QR-7mnghSFAY6wListen to these episodes next:ChatGPT Sends 21% of Its Traffic to Google. Here's Why That Matters.https://exposureninja.com/podcast/dojo-73/What Does AI Really Think of Your Brand?https://exposureninja.com/podcast/dojo-71/Do Rankings Still Matter with AI Search?https://exposureninja.com/podcast/dojo-66/

The whole team is back together this week - but is Isaac pushing for a new gig on the catwalk? Collingwood's Billy Frampton joins the team before tonight's game, and Jay Z thinks tanking could be back on the agenda after the AFL's draft changes. Isaac names his top 10 midfielders of the 21st century, The Queen's Queries includes which teams' ladder positions are the most fraudulent, and we wrap it up with a round of Unpopular Opinions. Triple M Footy's Thursday Rub is Jack Heverin, Isaac Smith, Kate McCarthy, and Jay Z Clark.See omnystudio.com/listener for privacy information.

After a gunman stormed Donald Trump's dinner with the press, questions are being revived about the president's security. Germany's top general explains the country's new defence strategy. And listeners respond to our Weekend Intelligence episode on the passport bros who go abroad to find “a good woman”.An earlier version of our lead story stated that the gunman shot a Secret Service agent. Subsequent reports indicate it is unclear whose shot struck the agent.We have now edited the start of the segment.Guests and host:John Prideaux, host of “Checks and Balance” and US editorTom Nuttall, Berlin bureau chiefCarla Subirana, reporterRosie Blau, co-host of “The Intelligence”Jason Palmer, co-hosts of “The intelligence”Topics covered: Donald Trump, assassination attempt, White House dinner, Cole Tomas AllenCarsten Breuer, Bundeswehr, NATO, UkrainePassport bros, tradwife, misogyny Listen to what matters most, from global politics and business to science and technology—Subscribe to Economist Podcasts+For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

After a gunman stormed Donald Trump's dinner with the press, questions are being revived about the president's security. Germany's top general explains the country's new defence strategy. And listeners respond to our Weekend Intelligence episode on the passport bros who go abroad to find “a good woman”.Guests and host:John Prideaux, host of “Checks and Balance” and US editorTom Nuttall, Berlin bureau chiefCarla Subirana, reporterRosie Blau, co-host of “The Intelligence”Jason Palmer, co-hosts of “The intelligence”Topics covered: Donald Trump, assassination attempt, White House dinner, Cole Tomas AllenCarsten Breuer, Bundeswehr, NATO, UkrainePassport bros, tradwife, misogyny Listen to what matters most, from global politics and business to science and technology—Subscribe to Economist Podcasts+For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

Why are law firms losing visibility even when they rank number one on Google?AI search is changing how people find and choose attorneys. With over 80% of legal searches now triggering AI overviews, traditional rankings are getting pushed down the page, and click-through rates are dropping fast. David Arato breaks down what is actually working right now in AI search, and why leads coming from tools like ChatGPT and Claude are converting at a much higher rate.The conversation goes deeper into how the customer journey is changing. Instead of short keyword searches, users are asking layered questions and having longer conversations. By the time they reach a law firm's website, they are often already decided.You will also hear how David was able to get content ranking in AI results in just hours, what role schema markup plays, and why most law firms are wasting time chasing high-volume keywords instead of focusing on how people actually search today.What You'll LearnWhy AI overviews are replacing traditional search visibilityHow conversational search is changing client behaviorWhat “conversational content” actually means in practiceHow schema markup can accelerate AI visibilityWhy most SEO content is no longer effectiveHow smaller firms can compete without massive budgetsIf your firm is still relying on traditional SEO rankings, this episode breaks down what is changing and how to adapt your content for AI-driven searchLearn more from Lexicon Legal Contenthttps://lexiconlegalcontent.com/Connect with David AratoLinkedIn: https://www.linkedin.com/in/davidaratoEmail: David@lexiconlegalcontent.comResources:● Connect with Ian● Download a Tackle Box!● Supercharge your marketing and grow your business with video case stories today!● Subscribe to the YouTube Channel Hosted on Acast. See acast.com/privacy for more information.

Hev, Izzy, K-Mac and Jay Z are back from Gather Round, and it seems Isaac was lost for words for the first time ever when he appeared on Footy Classified during the week. Lachie Neale has spoken out about his future, then Collingwood's Lachie Schultz joins the team boundary-side before their massive game against Carlton. The Zak Butters tribunal hearing cops a beating, with calls for complete reform - as Jay Z reveals why Jason Johnson couldn't make the hearing in person. K-Mac has her Queen's Queries, Isaac puts plenty of people in his Penthouse and Outhouse, then the team whip through some Unpopular Opinions - as Hev launches from the top rope on Gather Round criticism.See omnystudio.com/listener for privacy information.

Jay Z starts the show by explaining what really happened with an alleged confrontation between him and fellow footy reporter Tom Morris. Isaac and K-Mac both had golf days this week, and we get access to Jay Z's first articles from The Geelong Advertiser. The Queen's Queries asks which team will go winless and undefeated for the longest this season, and the team discusses Lance Collard's tribunal hearing for an alleged homophobic slur. Isaac has plenty of things to put in both his Penthouse and Outhouse, and the team whips around the room for some Unpopular Opinions. Triple M Footy's Thursday Rub is Jack Heverin, Isaac Smith, Kate McCarthy, and Jay Z Clark.See omnystudio.com/listener for privacy information.

Patrick explores faith’s practical questions through honest listener interactions, offering ways to handle suffering and confession, even as he gently challenges assumptions about spiritual maturity and everyday life. He addresses marriage struggles, the pain of regret, and the hope found in sacramental practices, always weaving in wisdom from Catholic tradition and lived experience. Queries about family, intention in prayer, and different Catholic rites receive thoughtful, sometimes surprising responses that linger well beyond the broadcast. Breanna (email) - What is the proper way to offer suffering up? (00:44) Chris (email) – Protestants go to hell? I must have misheard or misunderstood what you said. Thank you for clarifying it. (13:29) Stephanie (email) - The Church keeps drilling it into my head - go to Confession, go to Confession, go to Confession. Even if I don't really have anything to confess? (18:50) Rosie - My husband let me know he is leaving and I am pregnant with my 4th child. He said the only way he will stay is if we use contraceptives. What can I do? (28:41) Dustin - You were talking about liturgical rites yesterday. How many rites are there? (39:02 Lisa - Did you have any recommendations on Catholic homeschooling program? (43:27) Sylvia (email) – My husband is very verbally and physically abusive, but I stay with him because I’m Catholic. (44:52)

The team is ready and assembled - and joined by K-Mac's Mum! Attention quickly turns to the Herald Sun's Top 50 influential AFL Figures - and where Jay Z placed in that list. In the Chief's Agenda, Bobby Hill is back at Collingwood, pressure is ramping up at the Saints, and Carlton is under fire for not flying the flag. Isaac's segment Penthouse or Outhouse is charging up the order as he puts several things in the Outhouse, before Brian Taylor invades the studio to take Jay Z to task. The Queen's Queries looks at Fremantle's start to 2026 among several topics, then Hawthorn President Andy Gowers is in the box to provide clarity on Dylan Moore and Connor MacDonald's arrest in the USA, as well as their position on playing in Tasmania. Finally, it's Jay Z's Unpopular Opinions. Triple M Footy's Thursday Rub is Jack Heverin, Isaac Smith, Kate McCarthy, and Jay Z Clark.See omnystudio.com/listener for privacy information.

Since the start of the Iran war, Beit Issie Shapiro has been operating its emergency hotline to provide support in the area of disabilities to both professionals and families. Queries can be submitted in Hebrew, Arabic and English, by sending a WhatsApp message to 0523692864. KAN's Naomi Segal heard more from Ahmir Lerner, CEO of Beit Issie Shapiro. (Photo: Beit Issie Shapiro)See omnystudio.com/listener for privacy information.

On this week's edition of the podcast, Peter and Ronan reflect on a Players Championship that simmered for a long time before boiling over in dramatic fashion as Cameron Young held off Matt Fitzpatrick after both players found themselves in a heavyweight battle down the stretch as Ludvig Åberg fell away on the back nine.Rory McIlroy played all four rounds at TPC Sawgrass as did Séamus Power while Shane Lowry went home on Friday. McIlroy was quick to ease fears over a back twinge which prevented him from playing a practice round and he is open to the idea of adding one more event to his pre-Masters diary.

K-Mac is back after her cricket sojourn, but she's not stoked with one member of the Triple M Family. Plus, we've done some digging on Jay Z's Noosa trip, and found someone he shared the 10-person dorm with. The Chief's Agenda features chat about Kysaiah Pickett's personal leave, plus Dylan Moore and Connor MacDonald's scissor lift incident. The Queen's Queries is back, then Carlton CEO Graham Wright is in the box to talk about the fallout from Sam Docherty's comments, and their round 0 loss to Sydney. Finally, Isaac is back with Penthouse or Outhouse, and Jay Z has Unpopular Opinions. Triple M Footy's Thursday Rub is Jack Heverin, Isaac Smith, Kate McCarthy, and Jay Z Clark.See omnystudio.com/listener for privacy information.

What if you could deploy ML features and real-time data pipelines without building complex infrastructure from scratch? In this episode, host Benjamin sits down with Nikhil Simha, CTO at Zipline AI and co-author of Chronon AI, to explore how Chronon, an open-source system that generates data infrastructure from simple queries, is transforming feature engineering at companies like OpenAI and Airbnb. Learn why iteration speed matters for fraud detection, how to serve thousands of signals at a massive scale, and what the future of analytical databases looks like in an AI-first world. Whether you're scaling real-time ML systems or building customer-facing analytics, this conversation is packed with practical insights on bridging the gap between data scientists and ML engineers.

In this insightful episode of the We Don't PLAY!™️ Podcast, host Favour Obasi-ike, MBA, MS demystifies the critical evolution of search in the age of artificial intelligence, focusing on a concept she terms "AI SEO 101." The central theme revolves around the distinction and strategic importance of closing both prompt gaps and keyword gaps to secure valuable brand citations. While traditional SEO has focused on optimizing for short, fragmented keywords (e.g., "best travel deals"), the rise of conversational AI assistants like ChatGPT and Perplexity has given birth to the prompt—a longer, full-sentence query (e.g., "What are the best international travel deals for a family of four in summer 2026?").Favour argues that many businesses are unprepared for this shift, leaving a "prompt gap" in their content strategy. While they may have content targeting keywords, they lack the in-depth, conversational, and authoritative answers that AI models seek when responding to user prompts. The ultimate goal for any brand is to become a direct brand citation in an AI-generated answer, a feat achieved only by providing comprehensive, well-supported information. As Favour compellingly states, the answers provided by AI are sourced directly from the content available on the web: "Where are those responses AI is getting coming from in the first place? They're coming from your website."The core of the strategy lies in recognizing that your website is the foundational pillar of your digital presence. To bridge the prompt gap, Favour advocates for a robust pillar-cluster content model. This involves creating a main "pillar" page that exhaustively answers a primary customer question, supported by numerous "cluster" pages that explore related sub-topics in detail. This creates a dense, interconnected web of expertise that signals authority to search engines and AI alike. The episode emphasizes a shift from merely creating blogs to creating comprehensive resource hubs, complete with FAQs, multimedia content, and evidence-backed claims, much like a digital research paper.Favour provides a clear action plan: identify the core questions your audience is asking, build out content that answers them conversationally and in-depth, and structure this content logically on your website. She also touches on the technical side, noting that URLs should remain concise and keyword-focused, while the content on the page should be rich and prompt-focused. Ultimately, the episode is a powerful call to action for businesses to stop dwelling on information and start implementing a forward-thinking content strategy. By treating your website as a definitive library of answers, you can close the gaps in your SEO strategy and ensure your brand not only survives but thrives in the new era of AI-powered search.Key TakeawaysPrompts vs. Keywords: A prompt is a conversational, full-sentence question (10-25 words) posed to an AI, whereas a keyword is a short, fragmented search query (2-5 words) used in traditional search engines.The Goal is Brand Citation: In the new landscape of AI search, the primary objective is to have your brand and website cited directly as the authoritative source in an AI-generated answer.Your Website is the Foundation: All digital roads lead back to your website. It is the most critical asset for building authority and providing the in-depth answers that AI models are looking for.Close the Prompt Gap with Conversational Content: To appear in AI search results, you must create content that directly and comprehensively answers the full questions your audience is asking, not just targets keywords.Adopt a Pillar-Cluster Model: For each major question your audience has, create one main "pillar" page with a complete answer and support it with multiple "cluster" pages that cover related sub-topics. This builds a powerful web of expertise.Content as a Resource Hub: Think of your content less like a series of blog posts and more like a library of research. Support your answers with data, evidence, multimedia, and links to other authoritative sources to build trust and credibility.Action Over Acronyms: While understanding terms like AEO (Answer Engine Optimization) is useful, the focus should be on the practical implementation of creating high-quality, question-answering content.Memorable Quotes"A prompt is keywords in confirmation of the context that has been started by conversation.""You can't say a brand without connecting a website.""Where are those responses AI is getting coming from in the first place? They're coming from your website.""Don't be in a place where you're dwelling on information and not taking action on implementation.""15% of new searches every day out of at least 8.5 billion searches a day are new, including yours.""You don't put a prompt in your URL, you put a keyword in your URL.""You're not just creating blogs, you're creating calls to action."Frequently Asked Questions (FAQs)What is the main difference between a prompt and a keyword? A prompt is a long, conversational question asked to an AI, while a keyword is a short, fragmented phrase used in a traditional search bar. Your content strategy needs to address both.Why are brand citations important in AI SEO? A brand citation is when an AI search tool names your website as the source of its information. It positions your brand as a trusted authority, driving traffic and credibility.Is blogging still relevant in 2026? Yes, absolutely. However, the format has evolved. Modern blogging should focus on creating in-depth, conversational articles that function as answers to user prompts, effectively turning your blog into a resource hub or "audio blog."How do I start closing the prompt gap on my website? Begin by identifying the most common and important questions your customers ask. Then, create comprehensive content (like a detailed FAQ page or a pillar article) that answers these questions thoroughly and links to supporting cluster pages.What is the pillar-cluster model? It's a content strategy where you create one major "pillar" page that covers a broad topic in-depth. You then create multiple "cluster" pages that address specific sub-topics related to the pillar, with all cluster pages linking back to the main pillar page. This structure organizes your content and signals deep expertise to search engines.Timestamps[00:00] Introduction: AI SEO 101 - Prompt Gaps vs. Keyword Gaps.[01:35] Defining a "Prompt": A conversational query of 10-25 words.[02:48] Defining a "Keyword": Traditional short, medium, and long-tail search terms.[05:52] The Central Role of Your Website in Brand Citations.[06:34] How Search Engines Match Pages to Queries.[07:17] Core Concept: A prompt is "keywords in conversation."[08:03] The Solution: Closing the gap with conversational, FAQ-style content.[09:43] Strategy Deep Dive: The Pillar-Cluster content model (1 Pillar + 9 Clusters).[15:02] Where AI Gets Its Answers: Your website is the source for LLMs.[16:02] Building Authority: Go beyond facts and support claims with experience and evidence.[19:00] Your website is the common thread in all customer interactions.[20:44] Technical SEO Tip: Use keywords in your URLs, not long prompts.[22:36] Market Opportunity: 15% of the 8.5 billion daily searches are entirely new.[23:38] Content with Purpose: Your content should create calls to action, not just exist as a blog.[24:14] Closing Remarks & How to Connect.[25:12] Podcast Outro.Book SEO Services | Quick Links for Social Business>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book SEO Services with Favour Obasi-ike⁠>> Visit Work and PLAY Entertainment website to learn about our digital marketing services>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the We Don't PLAY Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Purchase Flaev Beatz Beats Online>> Favour Obasi-ike Quick LinksSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

(SPOILER) Your Daily Roundup covers today's Patreon live re-watch, Taylor Frankie Paul at the NBA Celebrity Game, Love is Blind couples and filming queries & an awful high school janitor story.   Music written by Jimmer Podrasky (B'Jingo Songs/Machia Music/Bug Music BMI) Ads: Factor Meals - 50% off your first box PLUS free breakfast for a year at https://factormeals.com/realitysteve50off Promo Code: realitysteve50off Square⁠ - $200 off Square hardware at https://square.com/go/realitysteve Learn more about your ad choices. Visit megaphone.fm/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

(SPOILER) Your Daily Roundup covers today's Patreon live re-watch, Taylor Frankie Paul at the NBA Celebrity Game, Love is Blind couples and filming queries & an awful high school janitor story.   Music written by Jimmer Podrasky (B'Jingo Songs/Machia Music/Bug Music BMI) Ads: Factor Meals - 50% off your first box PLUS free breakfast for a year at https://factormeals.com/realitysteve50off Promo Code: realitysteve50off Square⁠ - $200 off Square hardware at https://square.com/go/realitysteve Learn more about your ad choices. Visit megaphone.fm/adchoices

The internet isn’t being searched the same way anymore—and most businesses haven’t caught up. In this episode of the BOB Podcast, Jaryd Krause welcomes back Rad Paluszak to explore the transition from SEO to GEO (Generative Engine Optimization) and what it means for founders, operators, and investors navigating an AI-driven world. Instead of ranking pages, AI answer engines are ranking entities, brands, and trust. Rad explains why traditional SEO playbooks are breaking down, how AI engines source and rotate answers, and why brand mentions across the web now matter more than links ever did. You’ll learn: Why AI engines rarely repeat the same sources—and how that changes traffic forever How brand authority is built through mentions, associations, and personal brands Why being listed alongside established brands can instantly elevate visibility The growing role of Reddit, social platforms, and communities in AI discovery How to structure content for AI without sacrificing real users What businesses should stop obsessing over—and what actually moves the needle Whether you run content-heavy sites, ecommerce brands, SaaS, or are evaluating businesses to buy, this episode offers a practical framework for adapting to AI without panic—or guesswork. AI is coming either way.The question is whether your brand will be visible when it does.

Substack admits data breach Russian attacks target Winter Olympics GitHub Codespaces enable RCE Get the show notes here: Huge thanks to our sponsor, Strike48 It's no secret that AI is only as good as the data available to it. Strike48 unifies agentic AI with unmatched log visibility while avoiding the typical hefty price tag. Build and deploy agents for phishing detection, alert triage, threat correlation and more. Queries existing logs where they currently live, so you can keep the technology you already have. Learn more at Strike48.com.

OpenClaw targets ClawHub users Notepad++ update delivers malware APT28 attackers abuse Microsoft Office zero-day Get the show notes here: https://cisoseries.com/cybersecurity-news-openclaw-targets-clawhub-users-notepad-update-delivers-malware-apt28-attackers-abuse-microsoft-office-zero-day/ Huge thanks to our sponsor, Strike48 It's no secret that AI is only as good as the data available to it. Strike48 unifies agentic AI with unmatched log visibility while avoiding the typical hefty price tag. Build and deploy agents for phishing detection, alert triage, threat correlation and more. Queries existing logs where they currently live, so you can keep the technology you already have. Learn more at Strike48.com.  

This episode shows how to pick the best blog posts to rewrite for stronger SEO and AI search visibility using Google Search Console. You'll use impressions to spot what Google already surfaces, avoid keyword cannibalization by consolidating overlapping posts, and turn recurring topics into content hubs instead of guessing what to write next.Timestamps[0:00] Introduction[0:28] The goal: choose blogs to rewrite for AI + SEO visibility[1:02] “Follow the breadcrumbs” using Google Search Console[1:20] Why “blogging is good for SEO” is too vague to execute[2:17] Why revisiting old posts is a core blogging skill[3:58] Impressions explained (visibility beyond page one)[4:45] Filter Search Console to view only blog page URLs[5:01] Prioritize the posts with the most impressions[5:46] Spotting keyword cannibalization in the Queries tab[6:38] Consolidate posts + delete the weaker one + 301 redirect -- CONTACTLeave Feedback or Request Topics:https://forms.gle/bqxbwDWBySoiUYxL7

James Gray (The i Paper), Calvin Betton (grand slam-winning ATP coach) and George Bellshaw (tennis writer and broadcaster) answer your questions on all things tennis over the course of an hour. Topics include... - The Big 3/Big 4 era and was it good for tennis? - The Sincaraz dominance - Andy Roddick's scheduling ideas - What happens when doubles pairs break up - The Winter County Cup - Dream coaching jobs - Top 10 predictions for 2026 - Emma Raducanu's pre-season plans And much, much more! Learn more about your ad choices. Visit podcastchoices.com/adchoices

Google Search Console (GSC) New! Branded and Non-Branded Queries + Annotation Filters | Marketing Talk with Favour Obasi-Ike | Sign up for exclusive SEO insights.This episode focuses on Search Engine Optimization (SEO) and the new features within Google Search Console (GSC).Favour discuss the recently introduced brand queries and annotations features in GSC, highlighting their importance for understanding both branded and non-branded search behavior.The conversation also emphasizes the broader strategic use of GSC data, comparing it to a car's dashboard for website performance, and explores how this data can be leveraged to create valuable content, such as FAQ-based blog posts and multimedia assets, often with the aid of Artificial Intelligence (AI) tools. A key theme is the shift from traditional keyword ranking to ranking for user experience and the interconnectedness of various digital tools in modern marketing strategy.--------------------------------------------------------------------------------Next Steps for Digital Marketing + SEO Services:>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Need SEO Services? Book a Complimentary SEO Discovery Call with Favour Obasi-Ike⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Visit our Work and PLAY Entertainment website to learn about our digital marketing services.>> Visit our Official website for the best digital marketing, SEO, and AI strategies today!>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Need SEO Services? Book a Complimentary SEO Discovery Call with Favour Obasi-Ike⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the We Don't PLAY Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠--------------------------------------------------------------------------------As a content strategist, you live with a fundamental uncertainty. You create content you believe your audience needs, but a nagging question always remains: are you hitting the mark? It often feels like you're operating with a blind spot, focusing on concepts while, as the experts say, "you don't even know the intention behind why they're asking or searching."What if you could close that gap? What if your audience could tell you, explicitly, what they need you to create next?That's the paradigm shift happening right now inside Google Search Console (GSC). Long seen as a technical tool, recent updates are transforming GSC into a strategic command center. It's no longer just for SEO specialists; it's the dashboard for your entire content operation. These new developments are a game-changer, revealing direct intelligence from your audience that will change how you plan, create, and deliver content.Here are the five truths these new GSC features reveal—and how they give you a powerful competitive edge.1. Stop Driving Your Website Blind: The Dashboard AnalogyManaging a website without GSC is like driving a car without a dashboard. You're moving, but you have no idea how fast you're going or if you're about to run out of fuel. GSC is that free, indispensable dashboard providing direct intelligence straight from Google. But the analogy runs deeper. As one strategist put it, driving isn't passive: "when you're driving, you got to hit the gas, you got to... hit the brakes... when do you stop, when do you go, what do you tweak? Do you go to a pit stop?"You wouldn't drive your car without looking at the dashboard. So you shouldn't have a website and drive traffic and do all the things we do without looking at GSC, right?Your content strategy requires the same active management—knowing when to accelerate, when to pivot, and when to optimize. The new features make this "dashboard" more intuitive than ever, giving you the controls you need to navigate with precision.2. The Goldmine in Your Search Queries: Branded vs. Non-BrandedThe first game-changing update is the new "brand queries" filter. For the first time, GSC allows you to easily separate searches for your specific brand name (branded) from searches for the topics and solutions you offer (non-branded). This is the first step in a powerful new workflow: Discovery.Think of your non-branded queries as raw, unfiltered intelligence from your potential audience. These aren't just keywords; they're direct expressions of need. Instead of an abstract concept, you see tangible examples like:• “best practices for washing dishes”• “best pet shampoo”• “best Thanksgiving turkey meal”When you see more non-branded than branded queries, it's a powerful signal. It means you have access to a goldmine of raw material you can build content on to attract a wider audience that doesn't know your brand… yet. This isn't just data; it's a direct trigger for your next move.3. From Keyword to "Keynote": Creating Content with ContextOnce you've discovered this raw material, the next step is Development. This is where you transform an unstructured keyword into a strategic asset by adding structure and meaning. It's a progression: a raw keyword becomes a more defined keyphrase, which can be built into a keystone concept, and ultimately refined into a keynote.What's a keynote? Think about its real-world meaning: "when somebody sends you a note, it has context, right? It's supposed to mean something and it's supposed to say something specific." A keynote isn't just a search term; it's that term fully developed into a structured piece of content that delivers a specific, meaningful answer.This strategic asset can take many forms:• Blogs• Podcast episodes• Articles• Newsletters• Videos/Reels• eBooks4. The Most Underrated SEO Tactic: Your New Secret WeaponYou've discovered the query and developed it into a keynote. Now it's time for Execution. The single most effective format for executing on this strategy is one of the most powerful, yet underrated, SEO tactics in history: creating content around Frequently Asked Questions (FAQs).The rise of Large Language Models (LLMs) has fundamentally changed search behavior. People are asking full, conversational questions, and search engines are prioritizing direct, authoritative answers. A "one blog per FAQ" strategy is the perfect response. It's a secret weapon that's almost shockingly effective.FAQ is the new awesome the most awesome ever. I I said that on purpose.How awesome? By creating a single, targeted blog post for the long-tail question, "full roof replacement cost [city]," one site ranked number one on Google for that exact phrase in just 30 minutes. That's the power of directly answering a question your audience is already asking.5. It's Not About New Features, It's About New ActionsThe real purpose of these GSC updates isn't to give you more charts to observe; it's to prompt decisive action. Every non-branded query is a signal for what content to create next, feeding a powerful strategic loop that builds your authority over time.This is where it all comes together in a professional content framework. As the source material notes, "That's why you have content pillars and you have content clusters." Your non-branded queries show you what clusters your audience needs, and your FAQ-style "keynotes" become the assets that build out those clusters around your core content pillars.This data-driven approach empowers you to:• Recreate outdated content with new, relevant insights.• Repurpose core ideas into different formats to reach wider audiences.• Re-evaluate which topics are truly resonating.• Reemphasize your most valuable messages with fresh content.Conclusion: What Does Your Dashboard Say?Google Search Console is no longer just a reporting tool. It has evolved into an essential strategic partner that closes the gap between the content you produce and the value your audience is searching for. It's your direct line to understanding intent, allowing you to move from guessing what people want to knowing what they need.Now that you know how to read your website's dashboard, what's the first turn you're going to make?See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Kicking off your work week with lots of intriguing topics to discuss and possibly rant about. Jamie Steyer Johnson and Brian “BMac” Mackay take you through the wild weekend of women's college basketball action.HerHoopStats.com: Unlock better insight about the women's game.The Her Hoop Stats Newsletter: https://herhoopstats.substack.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This is a presentation where D.J. Paris talks about effectively using AI tools like ChatGPT to solve business challenges. DJ provides strategies for crafting better AI prompts, discusses various real estate industry problems, and leads an interactive session where participants share their professional challenges. The presentation covers topics like prospecting, client communication, time management, and local marketing strategies, with practical advice on leveraging AI to improve business processes. If you'd prefer to watch this interview, click here to view on YouTube! This episode is brought to you by Real Geeks and Courted.io. 

If a regular web search isn't doing it for you, and even a generative artificial intelligence chatbot response leaves you wanting more — you could try “deep research.” Our personal tech columnist tried it out and breaks down how it works. But those queries come at an environmental cost. In a new report Google is detailing how much energy a single query uses. Julie Chang hosts. Sign up for the WSJ's free Technology newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices