Podcasts about Simile

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

This talk was given by Gil Fronsdal on 2026.08.20 at the Sati Center in Redwood City, CA. ******* One of the most remarkable and original teachings of the Buddha is found in 7th discourse in the Middle Length Discourses. Couched in a short, fun story, this text describes insight and the unfolding of the path in naturalistic terms that are as relevant today as in the time of the Buddha. We will slowly read and discuss this short text so that it comes alive for our modern times. No familiarity with the Buddha's discourse or teachings necessary. A translation and Gil's study guide on the discourse will be sent out a week before the class. ******* Video of this talk is available at: https://www.youtube.com/watch?v=jaTo1Hb5xa8. ******* A machine generated transcript of this talk is available. It has not been edited by a human, so errors will exist. Download Transcript: https://www.audiodharma.org/transcripts/24825/download ******* For more talks like this, visit AudioDharma.org ******* If you have enjoyed this talk, please consider supporting AudioDharma with a donation at https://www.audiodharma.org/donate/. ******* This talk is licensed by a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License

This talk was given by Gil Fronsdal on 2026.08.20 at the Sati Center in Redwood City, CA. ******* One of the most remarkable and original teachings of the Buddha is found in 7th discourse in the Middle Length Discourses. Couched in a short, fun story, this text describes insight and the unfolding of the path in naturalistic terms that are as relevant today as in the time of the Buddha. We will slowly read and discuss this short text so that it comes alive for our modern times. No familiarity with the Buddha's discourse or teachings necessary. A translation and Gil's study guide on the discourse will be sent out a week before the class. ******* Video of this talk is available at: https://www.youtube.com/watch?v=jaTo1Hb5xa8. ******* A machine generated transcript of this talk is available. It has not been edited by a human, so errors will exist. Download Transcript: https://www.audiodharma.org/transcripts/24825/download ******* For more talks like this, visit AudioDharma.org ******* If you have enjoyed this talk, please consider supporting AudioDharma with a donation at https://www.audiodharma.org/donate/. ******* This talk is licensed by a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License

Joon Sung Park is the Founder and CEO of Simile, the AI simulation company building foundation models of human behaviour; allowing companies to test how real people may think, decide and act before making a decision in the real world. Simile has now raised $300 million in total, including a $200 million Series B announced last week at a $2 billion valuation, led by Greenoaks and Index Ventures. AGENDA: 00:00 We Will Pay $100M for a Single Query on Some Models 10:00 Why Stock Markets May Not Exist in 5 Years Time 15:00 The Best Companies All Have Unique Data Acquisition Strategies 19:00 The Best AI Companies Have Clear and Fast Reward Functions 24:00 How We Sign Fortune 500 Companies for $10M Contracts in Weeks 32:00 Does Similie Kill Kalshi and Polymarket? Prediction vs Changing the Future 42:00 Inside Similie's $300M Raise; What Every Founder Needs to Know    

44 The kingdom of heaven is like unto a treasure hidden in a field. Which a man having found, hid it, and for joy thereof goeth, and selleth all that he hath, and buyeth that field.Simile est regnum caelorum thesauro abscondito in agro : quem qui invenit homo, abscondit, et prae gaudio illius vadit, et vendit universa quae habet, et emit agrum illum. 45 Again the kingdom of heaven is like to a merchant seeking good pearls.Iterum simile est regnum caelorum homini negotiatori, quaerenti bonas margaritas. 46 Who when he had found one pearl of great price, went his way, and sold all that he had, and bought it.Inventa autem una pretiosa margarita, abiit, et vendidit omnia quae habuit, et emit eam. 47 Again the kingdom of heaven is like to a net cast into the sea, and gathering together of all kind of fishes.Iterum simile est regnum caelorum sagenae missae in mare, et ex omni genere piscium congreganti. 48 Which, when it was filled, they drew out, and sitting by the shore, they chose out the good into vessels, but the bad they cast forth.Quam, cum impleta esset, educentes, et secus littus sedentes, elegerunt bonis in vasa, malos autem foras miserunt. 49 So shall it be at the end of the world. The angels shall go out, and shall separate the wicked from among the just.Sic erit in consummatione saeculi : exibunt angeli, et separabunt malos de medio justorum, 50 And shall cast them into the furnace of fire: there shall be weeping and gnashing of teeth.et mittent eos in caminum ignis : ibi erit fletus, et stridor dentium. 51 Have ye understood all these things? They say to him: Yes.Intellexistis haec omnia? Dicunt ei : Etiam. 52 He said unto them: Therefore every scribe instructed in the kingdom of heaven, is like to a man that is a householder, who bringeth forth out of his treasure new things and old.Ait illis : Ideo omnis scriba doctus in regno caelorum, similis est homini patrifamilias, qui profert de thesauro suo nova et vetera.Laurence of Brindisi, a Capuchin friar who for some years ruled his whole Order, acquired great fame for learning and eloquence, and labored with remarkable success in most parts of Europe preaching to Catholics, to Protestants, and to Jews. When 80,000 Turks invaged Hungary in 1605, he it was who inspired the united Christian armies of 18,000 men to the attack and himself led them to complete victory riding before them bearing a large cross. He died in Lisbon in 1611. 1. The Treasure Hidden in the Field (vv. 44)A man discovers a treasure so valuable that he joyfully sells everything to buy the field.Notice two things:He doesn't give up everything reluctantly.He gives it up because he has found something infinitely better.Jesus isn't saying Christians should despise earthly goods. He's saying that once you've discovered the Kingdom, everything else finds its proper place.2. The Pearl of Great Price (vv. 45–46)This parable is slightly different.The first man stumbles upon the treasure.The merchant has been searching all along.Some people are converted unexpectedly. Others spend years searching for truth before they find Christ.Again, Jesus emphasizes the worth of the Kingdom.3. The Dragnet (vv. 47–50)Jesus is describing the visible kingdom on earth.The Church contains:saintssinnerspeople growing in holinesspeople resisting graceThe final separation belongs to God.Sometimes we wonder why evil seems to remain in the world—and even within the visible Church. Jesus says the final judgment comes at the end of the age.4. The Householder (vv. 51–52)Then He says:"Every scribe instructed in the kingdom of heaven is like a householder who brings forth from his treasure things new and old." A faithful disciple doesn't discard the old. He doesn't cling only to the new. He treasures both.As Catholics, we naturally think of:the Old Testament fulfilled in Christthe continuity of apostolic teachingthe Church preserving ancient truth while continually proclaiming it anew.

Bongani Bingwa speaks to Simile Shange, former MasterChef South Africa contestant, about his emotional exit in this week's high-pressure pie challenge and the lessons he takes from the competition. 702 Breakfast with Bongani Bingwa is broadcast on 702, a Johannesburg based talk radio station. Bongani makes sense of the news, interviews the key newsmakers of the day, and holds those in power to account on your behalf. The team bring you all you need to know to start your day Thank you for listening to a podcast from 702 Breakfast with Bongani Bingwa Listen live on Primedia+ weekdays from 06:00 and 09:00 (SA Time) to Breakfast with Bongani Bingwa broadcast on 702: https://buff.ly/gk3y0Kj For more from the show go to https://buff.ly/36edSLV or find all the catch-up podcasts here https://buff.ly/zEcM35T Subscribe to the 702 Daily and Weekly Newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio7See omnystudio.com/listener for privacy information.

MN 7 Mar 29, 2026 Vatthupama Sutta The Simile of the Cloth The many different kinds of impurities that defile the mind are compared to a dirty cloth. When the mind is clean we find joy, which leads to states of higher consciousness. Finally, the Buddha rejects the Brahmanical notion that purity comes from bathing in sacred rivers.

 44 The kingdom of heaven is like unto a treasure hidden in a field. Which a man having found, hid it, and for joy thereof goeth, and selleth all that he hath, and buyeth that field.Simile est regnum caelorum thesauro abscondito in agro : quem qui invenit homo, abscondit, et prae gaudio illius vadit, et vendit universa quae habet, et emit agrum illum. 45 Again the kingdom of heaven is like to a merchant seeking good pearls.Iterum simile est regnum caelorum homini negotiatori, quaerenti bonas margaritas. 46 Who when he had found one pearl of great price, went his way, and sold all that he had, and bought it.Inventa autem una pretiosa margarita, abiit, et vendidit omnia quae habuit, et emit eam. 47 Again the kingdom of heaven is like to a net cast into the sea, and gathering together of all kind of fishes.Iterum simile est regnum caelorum sagenae missae in mare, et ex omni genere piscium congreganti. 48 Which, when it was filled, they drew out, and sitting by the shore, they chose out the good into vessels, but the bad they cast forth.Quam, cum impleta esset, educentes, et secus littus sedentes, elegerunt bonis in vasa, malos autem foras miserunt. 49 So shall it be at the end of the world. The angels shall go out, and shall separate the wicked from among the just.Sic erit in consummatione saeculi : exibunt angeli, et separabunt malos de medio justorum, 50 And shall cast them into the furnace of fire: there shall be weeping and gnashing of teeth.et mittent eos in caminum ignis : ibi erit fletus, et stridor dentium. 51 Have ye understood all these things? They say to him: Yes.Intellexistis haec omnia? Dicunt ei : Etiam. 52 He said unto them: Therefore every scribe instructed in the kingdom of heaven, is like to a man that is a householder, who bringeth forth out of his treasure new things and old.Ait illis : Ideo omnis scriba doctus in regno caelorum, similis est homini patrifamilias, qui profert de thesauro suo nova et vetera.

 44 The kingdom of heaven is like unto a treasure hidden in a field. Which a man having found, hid it, and for joy thereof goeth, and selleth all that he hath, and buyeth that field.Simile est regnum caelorum thesauro abscondito in agro : quem qui invenit homo, abscondit, et prae gaudio illius vadit, et vendit universa quae habet, et emit agrum illum. 45 Again the kingdom of heaven is like to a merchant seeking good pearls.Iterum simile est regnum caelorum homini negotiatori, quaerenti bonas margaritas. 46 Who when he had found one pearl of great price, went his way, and sold all that he had, and bought it.Inventa autem una pretiosa margarita, abiit, et vendidit omnia quae habuit, et emit eam. 47 Again the kingdom of heaven is like to a net cast into the sea, and gathering together of all kind of fishes.Iterum simile est regnum caelorum sagenae missae in mare, et ex omni genere piscium congreganti. 48 Which, when it was filled, they drew out, and sitting by the shore, they chose out the good into vessels, but the bad they cast forth.Quam, cum impleta esset, educentes, et secus littus sedentes, elegerunt bonis in vasa, malos autem foras miserunt. 49 So shall it be at the end of the world. The angels shall go out, and shall separate the wicked from among the just.Sic erit in consummatione saeculi : exibunt angeli, et separabunt malos de medio justorum, 50 And shall cast them into the furnace of fire: there shall be weeping and gnashing of teeth.et mittent eos in caminum ignis : ibi erit fletus, et stridor dentium. 51 Have ye understood all these things? They say to him: Yes.Intellexistis haec omnia? Dicunt ei : Etiam. 52 He said unto them: Therefore every scribe instructed in the kingdom of heaven, is like to a man that is a householder, who bringeth forth out of his treasure new things and old.Ait illis : Ideo omnis scriba doctus in regno caelorum, similis est homini patrifamilias, qui profert de thesauro suo nova et vetera.The daughter of the King of Aragon and the grandniece of St Elizabeth of Hungary, St Elizabeth married Denis, King of Portugal. Becoming a widow, she entered the order of the Poor Clares and died A.D. 1336.

The race to build superintelligence is producing models that keep getting better at objective problems, but not at behaving like actual people. Joon Sung Park, founder and CEO of Simile and creator of Stanford's "Smallville" generative agents study, argues that simulating human society requires a fundamentally different kind of model. He frames today's frontier models as the "CPU of intelligence"—rational, superhuman at problems with right answers—and Simile as creating the "GPU of intelligence," built to encode the diversity of people's values, preferences, and tastes. It simulated 1,000 Americans and predicted their behavior 85% as accurately as people reproduce their own answers. CVS uses it for concept testing; some customers simulate their own earnings calls. Joon's larger bet: a "CERN of human society" that could one day model bank runs, climate cooperation, or the early signals of a collapsing democracy. Hosted by Sonya Huang, Sequoia Capital

(Insight Santa Cruz)

(Insight Santa Cruz)

 44 The kingdom of heaven is like unto a treasure hidden in a field. Which a man having found, hid it, and for joy thereof goeth, and selleth all that he hath, and buyeth that field.Simile est regnum caelorum thesauro abscondito in agro : quem qui invenit homo, abscondit, et prae gaudio illius vadit, et vendit universa quae habet, et emit agrum illum. 45 Again the kingdom of heaven is like to a merchant seeking good pearls.Iterum simile est regnum caelorum homini negotiatori, quaerenti bonas margaritas. 46 Who when he had found one pearl of great price, went his way, and sold all that he had, and bought it.Inventa autem una pretiosa margarita, abiit, et vendidit omnia quae habuit, et emit eam. 47 Again the kingdom of heaven is like to a net cast into the sea, and gathering together of all kind of fishes.Iterum simile est regnum caelorum sagenae missae in mare, et ex omni genere piscium congreganti. 48 Which, when it was filled, they drew out, and sitting by the shore, they chose out the good into vessels, but the bad they cast forth.Quam, cum impleta esset, educentes, et secus littus sedentes, elegerunt bonis in vasa, malos autem foras miserunt. 49 So shall it be at the end of the world. The angels shall go out, and shall separate the wicked from among the just.Sic erit in consummatione saeculi : exibunt angeli, et separabunt malos de medio justorum, 50 And shall cast them into the furnace of fire: there shall be weeping and gnashing of teeth.et mittent eos in caminum ignis : ibi erit fletus, et stridor dentium. 51 Have ye understood all these things? They say to him: Yes.Intellexistis haec omnia? Dicunt ei : Etiam. 52 He said unto them: Therefore every scribe instructed in the kingdom of heaven, is like to a man that is a householder, who bringeth forth out of his treasure new things and old.Ait illis : Ideo omnis scriba doctus in regno caelorum, similis est homini patrifamilias, qui profert de thesauro suo nova et vetera.Margaret was born in Hungary, though the Saxon royal stock and was married to Malcom III, King of Scotland. Her long reign of thirty years was illustrious for her inexhaustible charity to the poor. She died A.D. 1093 and is honoured among the Patrons of Scotland.

(Insight Santa Cruz)

(Insight Santa Cruz)

(Insight Meditation Community of Richmond) In the Simile of the Cloth sutta, the Buddha describes liberation as going beyond mere "purity" to an inner freedom from mental limitation.

(Insight Meditation Community of Richmond) In the Simile of the Cloth sutta, the Buddha describes liberation as going beyond mere "purity" to an inner freedom from mental limitation.

(Insight Meditation Community of Richmond) We often hope something outside us will do the purifying for us. The Buddha gently redirects: we need to do the inner work. This talk is based in the Simile of the Cloth sutta (MN7) and explores what that inner purification actually requires, and how wisdom makes it possible.

(Insight Meditation Community of Richmond) We often hope something outside us will do the purifying for us. The Buddha gently redirects: we need to do the inner work. This talk is based in the Simile of the Cloth sutta (MN7) and explores what that inner purification actually requires, and how wisdom makes it possible.

Quante volte al giorno si dice «va bene»? Per accordarsi, per accettare, per riempire il silenzio — sembra la soluzione per tutto. Il problema è che usare sempre la stessa espressione suona ripetitivo e artificioso. In italiano esistono alternative molto più naturali e precise, ognuna adatta a un contesto diverso. Smettila di Dire Sempre "Va Bene" Alternative per Esprimere Accordo e Conferma Quando qualcuno dice qualcosa e si è d'accordo, invece del solito «va bene» si può ricorrere a queste espressioni. 1. Certo! / Certamente! Si usa per confermare qualcosa in modo deciso e leggermente formale. È perfetto quando si vuole trasmettere sicurezza e professionalità. «Possiamo vederci alle tre?» — «Certo! Ti aspetto al bar.» «Hai capito le istruzioni?» — «Certamente, nessun problema.» 2. Esatto! / Esattamente! Perfetto quando qualcuno ha capito o detto qualcosa nel modo giusto. È come dire: «hai centrato il punto». «Quindi dobbiamo prendere il treno delle otto?» — «Esatto! Quello è il più veloce.» «La riunione è stata spostata a venerdì?» — «Esattamente, hai capito bene.» 3. Giusto! / Già! Molto informale, tipico del parlato quotidiano. «Già» in particolare esprime un accordo quasi pensieroso, come se si stesse elaborando ciò che si è appena sentito. «Roma è la capitale d'Italia.» — «Giusto! Me lo ero dimenticato.» «Domani è lunedì.» — «Già... e ho ancora mille cose da fare.» 4. Senza Dubbio / Indubbiamente Più formale, usato per esprimere un accordo forte e convinto. In una conversazione tra amici può suonare eccessivamente serio — meglio riservarlo a contesti professionali o quando si vuole dare particolare peso alle proprie parole. «La pizza napoletana è la migliore del mondo.» — «Senza dubbio! Non si discute.» «Questo progetto richiede molto lavoro.» — «Indubbiamente, ma ne vale la pena.» Alternative per Accettare o Approvare Qualcosa Quando qualcuno propone qualcosa e si accetta, queste espressioni sono molto più efficaci del generico «va bene». 5. D'Accordo! La sostituzione più diretta e naturale di «va bene». Funziona in quasi tutti i contesti — formali e informali — ed è l'espressione che i madrelingua usano più spesso al suo posto. «Ci vediamo sabato per studiare insieme?» — «D'accordo! A che ora?» «Ti va di mangiare la pizza stasera?» — «D'accordo, ma la pago io questa volta!» 6. Perfetto! Quando non si accetta soltanto, ma si è anche soddisfatti della proposta. È come dire: «non si sarebbe potuto organizzare meglio». «Ho prenotato il ristorante per le otto.» — «Perfetto! Non vedo l'ora.» «Il documento è pronto, te lo mando adesso.» — «Perfetto, grazie mille.» 7. Ottimo! Simile a «perfetto», ma con un tono leggermente più formale o professionale. Si sente spesso in ufficio o in contesti accademici. «Ho finito il rapporto in anticipo.» — «Ottimo lavoro! Puoi mandarmelo?» «Ho trovato una soluzione al problema.» — «Ottimo! Spiegami tutto.» 8. Benissimo! Un po' più caloroso ed emotivo rispetto a «ottimo». È il superlativo di «bene» — trasmette calore e partecipazione genuina. «Ho superato l'esame d'italiano!» — «Benissimo! Lo sapevo che ce la facevi!» «Posso portare il dolce alla cena?» — «Benissimo, tutti ti adoreranno!» Alternative Entusiaste Quando «va bene» sarebbe troppo poco e si vuole esprimere vera soddisfazione o gioia. 9. Fantastico! / Magnifico! / Meraviglioso! Tre espressioni potentissime per trasmettere entusiasmo autentico. Attenzione però: usarle troppo spesso le svuota di significato. Meglio tenerle per i momenti davvero speciali. «Ti regalo un viaggio a Napoli per il tuo compleanno.» — «Fantastico! Quando si parte?» «Ho trovato i biglietti per il concerto.» — «Meraviglioso! Pensavo fossero esauriti.» 10. Che Bello! / Che Notizia! Più colloquiale, usato soprattutto quando si riceve una buona notizia inaspettata. È spontaneo e genuino — tipicamente italiano. «Mia sorella aspetta un bambino!» — «Che bello! Quando nasce?» «Ho trovato lavoro finalmente!» — «Che notizia! Sono così contenta per te!» 11. Evviva! / Finalmente! «Evviva» è una pura esclamazione di gioia — quasi un piccolo festeggiamento con le parole. «Finalmente» si usa invece quando si aspettava qualcosa da lungo tempo: si sente tutta la pazienza accumulata. «Domani non lavoro!» — «Evviva! Andiamo al mare?» «Hanno riaperto quel ristorante che amavamo.» — «Finalmente! Ci prenoto subito un tavolo.» Alternative con Rassegnazione o Pazienza Quando si accetta qualcosa senza esserne entusiasti, l'italiano offre espressioni molto efficaci — e a volte persino teatrali — per comunicarlo. 12. Se Non C'è Alternativa... / Se Non Si Può Fare Altrimenti... Per accettare qualcosa con rassegnazione elegante. Il tono comunica chiaramente: «non è quello che si voleva, ma cosa si può fare?» «Dobbiamo alzarci alle cinque di mattina per prendere il treno.» — «Se non c'è alternativa... metterò tre sveglie.» «La riunione è spostata a sabato mattina.» — «Se non si può fare altrimenti, ci sarò.» 13. Pazienza! / Poco Male! «Pazienza» si usa quando qualcosa non è come si vorrebbe, ma lo si accetta con serenità — quasi con filosofia. «Poco male» si usa invece quando il problema non è grave: è come dire «non è la fine del mondo». «Non riesco a venire alla tua festa, mi dispiace.» — «Pazienza! Ci vedremo un'altra volta.» «Il treno ha dieci minuti di ritardo.» — «Poco male, ho il mio libro.» 14. Dai, Va Bene lo Stesso / In Fondo Va Bene Per accettare qualcosa con un tono rilassato e senza drammi. «Dai» in apertura di frase ammorbidisce tutto — è una piccola concessione informale che segnala disponibilità senza entusiasmo. «Il caffè è finito, c'è solo il tè.» — «Dai, va bene lo stesso. Prendo il tè.» «Il ristorante era pieno, andiamo in pizzeria?» — «In fondo va bene — la pizza mi piace anche di più!» Riepilogo: Quando Usare Quale Espressione SituazioneEspressioni consigliateAccordo deciso o formaleCerto, Certamente, Senza dubbio, IndubbiamenteConfermare che qualcuno ha capito beneEsatto, Esattamente, GiustoAccordo informale e pensierosoGià, GiustoAccettare una propostaD'accordo, Perfetto, Ottimo, BenissimoEsprimere entusiasmo per una buona notiziaFantastico, Meraviglioso, Che bello, EvvivaSollievo dopo una lunga attesaFinalmenteAccettazione con rassegnazionePazienza, Poco male, Se non c'è alternativaAccettazione rilassata e informaleDai va bene lo stesso, In fondo va bene Domande Frequenti Qual È la Differenza tra "Perfetto" e "Ottimo"? Entrambe esprimono approvazione, ma «perfetto» ha un tono più personale e soddisfatto — si usa quando si è contenti di come stanno le cose. «Ottimo» è leggermente più formale e si sente spesso in contesti professionali o accademici, come risposta a un risultato o a una prestazione. Si Può Ancora Usare "Va Bene"? Sì, «va bene» è corretto e naturale — il problema nasce quando viene usato in modo automatico per qualsiasi situazione. Avere a disposizione alternative più precise permette di scegliere l'espressione più adatta al contesto, rendendo il proprio italiano più ricco e credibile. Quando Si Usa "Pazienza" e Quando "Poco Male"? «Pazienza» si usa quando si accetta qualcosa di spiacevole con serenità, spesso con una sfumatura di rassegnazione consapevole. «Poco male» si usa invece quando il problema è oggettivamente lieve — è come dire che non c'è motivo di preoccuparsi. Queste Espressioni Funzionano Sia nel Parlato che nello Scritto? La maggior parte funziona in entrambi i contesti. Alcune — come «già», «dai va bene lo stesso» o «evviva» — sono tipiche del parlato informale e suonerebbero fuori luogo in un testo scritto formale. Altre — come «certamente», «indubbiamente» o «d'accordo» — si adattano bene anche alla comunicazione scritta professionale. Se stai imparando a variare il tuo italiano, non fermarti qui — continua con l'articolo dedicato alle alternative a 'sono in ritardo'. Accedi a siti e contenuti italiani da qualsiasi parte del mondo e in totale sicurezza con NordVPN! Usa il codice coupon LEARNAMO per ricevere uno sconto speciale! { "@context": "https://schema.org", "@type": "Quiz", "name": "Quiz: Alternative a 'Va Bene' in Italiano", "description": "Quiz interattivo sulle alternative all'espressione 'va bene' in italiano. 10 domande su accordo, approvazione, entusiasmo e rassegnazione nel parlato e nello scritto.", "educationalLevel": "Intermedio B1-B2", "learningResourceType": "Quiz", "inLanguage": "it", "hasPart": [ { "@type": "Question", "name": "Quale espressione si usa per confermare che qualcuno ha capito o detto qualcosa nel modo giusto?", "acceptedAnswer": { "@type": "Answer", "text": "Esatto" } }, { "@type": "Question", "name": "In italiano, 'Già' può esprimere accordo pensieroso.", "acceptedAnswer": { "@type": "Answer", "text": "Vero" } }, { "@type": "Question", "name": "Quale espressione è più adatta in un contesto professionale per approvare un risultato?", "acceptedAnswer": { "@type": "Answer", "text": "Ottimo" } }, { "@type": "Question", "name": "Quale espressione si usa quando si accetta qualcosa con rassegnazione perché non esiste altra scelta?", "acceptedAnswer": { "@type": "Answer", "text": "Se non c'è alternativa" } }, { "@type": "Question", "name": "'Benissimo' è il superlativo di quale parola?", "acceptedAnswer": { "@type": "Answer", "text": "bene" } }, { "@type": "Question", "name": "Quale espressione trasmette che una situazione negativa non è grave e non merita preoccupazione?", "acceptedAnswer": { "@type": "Answer", "text": "Poco male" } }, { "@type": "Question", "name": "'Già' è adatta per un testo scritto formale.", "acceptedAnswer": { "@type": "Answer",...

(Insight Meditation Community of Richmond) Drawing on the Buddha's Simile of the Cloth (MN7), this talk explores how greed, aversion, and delusion stain the mind and obscure clear seeing. We look at how these defilements manifest in meditation and everyday experience, and how to meet them with awareness rather than resistance.

(Insight Meditation Community of Richmond) Drawing on the Buddha's Simile of the Cloth (MN7), this talk explores how greed, aversion, and delusion stain the mind and obscure clear seeing. We look at how these defilements manifest in meditation and everyday experience, and how to meet them with awareness rather than resistance.

(Insight Meditation Community of Richmond) An introduction to the retreat theme of Awakening the Pure Heart.

The Simile of the Driverless Bus read out by a lay disciple. Extract from "Emptiness and Stillness - A Tribute to Ajahn Brahm". Support us on https://ko-fi.com/thebuddhistsocietyofwa BSWA teachings are available: BSWA Teachings BSWA Podcast Channel BSWA DeeperDhamma Podbean Channel BSWA YouTube

(Insight Meditation Community of Richmond) An introduction to the retreat theme of Awakening the Pure Heart.

I Led Zeppelin plagiatori seriali? Ecco alcune curiosità a riguardo del grande gruppo rock. Se un brano è un successo mondiale, state certi che qualcuno di diverso dall'autore che l'ha portato in vetta ne rivendicherà la paternità.I casi eclatantiFu così per My Sweet Lord di George Harrison (1970) contro He's So Fine delle Chiffons (1962). Per Ghostbusters di Ray Parker Jr. (1984) contro I Want a New Drug di Huey Lewis and the News (1984). Per Ice Ice Baby di Vanilla Ice (1989) contro Under Pressure, di Queen e David Bowie (1981). Per Surfin' U.S.A. dei Beach Boys (1963) contro Sweet Little Sixteen di Chuck Berry (1958).

Can you believe it! Six years ago I started this podcast at the height of COVID on a complete whim. Never thought I'd be doing this for more than a few weeks let alone six freaking years! And what a way to celebrate by bringing back my first-ever guest, Staunton, Va.-based singer-songwriter Scott Miller. Scott cut his teeth in the mid-90s during the burgeoning alt.country or no-depression or insurgent country scene that never truly got off the ground. Signed to country legend Steve Earle's E-Squared Label, Scott's band the V-Roys released two dynamite albums before the band split and Scott started his prolific solo career. In this episode, Scott catches us up on what he's been up to since he last joined awhile ago. We talk about his latest single the ingenious Like a Simile from his forthcoming new record. Scott also touches on a new vinyl-only compilation album and dishes on how he balances his music career with his life on his Shenandoah Valley farm. So sit back and welcome Scott Miller back to For Songs!

AI Twins and the Future of Research The Experience Strategy Podcast Episode Overview Two Wall Street Journal articles are making waves in the market research world — one asking whether AI can replace human research participants, and another profiling a teenage-founded startup called Aura that's already attracted McDonald's and EY. Dave, Joe, and Aransas bring their combined decades of consumer research experience to the question everyone in insights is quietly asking: is this the end of primary research, or the beginning of something more powerful? What We Cover The two WSJ articles at the center of this conversation The first covers Simile, a startup building agentic AI twins modeled on real people for polling and market research. The second profiles Aura, a company founded by people younger than Aransas's high schooler, betting that AI bots can predict human behavior better than humans themselves. Dave's evolving reaction — Worry, skepticism, and then possibility His first instinct was worry. Stone Mantel has built its practice on deep consumer research, and the promise of AI twins that can answer with 0.5% accuracy at first felt wrong. But the more he sat with it, the more he saw a useful analogy: flight simulators. Simulators serve a real purpose as long as everyone is clear they are not the same as flying the actual plane. The critical flaw in current AI twin models Both Dave and Joe land on the same problem independently: AI twins are built on static preferences and demographic profiles. They treat people as if behavior is fixed — "this is how soccer moms respond" — when the entire premise of situational research is that behavior shifts with context. What mode is the person in? What situation are they navigating? Those questions are not being asked. Joe puts it plainly: they didn't ask anything about modes. Where AI twins might actually work well Trend prediction and aggregate market analysis are reasonable use cases. If you want to know whether fruit-flavored tea is about to have a moment, AI models scanning historical purchasing data and cultural signals can probably get you there. The harder problem — and the more valuable one — is understanding what a specific person cares about in a specific moment, and that requires something current AI twins are not equipped to provide. What AI twins could become with better design Dave raises an intriguing possibility: after completing primary research with a real consumer, could that data become the seed for ongoing simulation and modeling? Not as a replacement for the research, but as a way to extend its value across time and decisions. He also flags the bias risk — every feedback loop that improves AI accuracy may also drift it further from the original human signal. Joe's Wall-E scenario The Terminator isn't Joe's fear. Wall-E is. Personal language models hanging out in your Alexa, learning everything you say and do, eventually making purchasing decisions on your behalf — and research shifting to focus on the PLM rather than the person. The result: consumers with no agency, led entirely by AI intermediaries and the consumer goods companies they serve. The consent problem CBS claimed 400,000 people opted in to being replicated as AI twins. Aransas is skeptical — and direct. That was some very fine print. Companies building AI twin programs need to be serious about how they are collecting this data, not just technically compliant. Key Idea If AI can actually predict behavior change, it is no longer a tool — it is strategy. That quote, attributed to a Coca-Cola executive in the second article, captures what is at stake. Dave frames it through the lens of superpowers: AI gives companies the ability to do things they could not do otherwise. The question is whether the thing they are doing actually reflects how real humans behave. Continue the Conversation Join Dave, Joe, and Aransas on The Experience Strategist Substack to go deeper on this episode's themes.

March 29, 2026 MN7 Vatthupama Sutta -The Simile of the Cloth The many different kinds of impurities that defile the mind are compared to a dirty cloth. When the mind is clean we find joy, which leads to states of higher consciousness. Finally, the Buddha rejects the Brahmanical notion that purity comes from bathing in sacred rivers.

DAY 128: The Simile of Light Welcome to the Gospel in a Year on the Catholic Sprouts Podcast.  In this episode we are reading Luke 11:29-54   To get the most out of this journey through the Gospels, we suggest you PRINT THE GOSPEL IN A YEAR NOTEBOOK. It's free and ready for you right here --> http://catholicsprouts.com/the-gospels-in-a-year-on-the-catholic-sprouts-podcast   Thank you for joining us! Come Lord Jesus!

Ajahn Brahm describes why he bows to Buddha statues (and sometimes Jesus' statues) with a series of stories about the qualities he admires in the Buddha: virtue, stillness, and kindness. He then answers audience questions about choosing a meaningful life and finding meaning in the life we have. This teaching was given during Ajahn Brahm's UK visit to Anukampa, at Thames Buddhist Vihara on the 17th of June 2025. Sutta reference: MN 27: The Shorter Discourse on the Simile of the Elephant's Footprint (Cūḷahatthipadopama Sutta) https://suttacentral.net/mn27/en/bodh... Teaching retrieved from Anukampa Bhikkhuni Project:  https://www.youtube.com/watch?v=ZaNtcbIPQBU&t=2s Ajahn Brahm is the Spiritual Adviser of Anukampa Bhikkhuni Project. Donations to Anukampa are welcome, please visit https://anukampaproject.org/donate/ Support us on https://ko-fi.com/thebuddhistsocietyofwa BSWA teachings are available from: BSWA Teachings BSWA Podcast Channel BSWA DeeperDhamma Podbean Channel BSWA YouTube  

In a class that combines suttas and personal stories, Ajahn Brahm discusses a simile comparing steps along the path to Enlightenment to seven types of people who have fallen in water. He then references a second sutta about the “jhanagami” shortcut and tells stories of two famous non-returners, one of whose stories dispels the myth that the Buddha was perfect even in previous lives. This is part of a series of teachings for the "Living in Harmony with the World" weekend retreat led by Ajahn Brahm & Ajahn Canda in the UK, Oxford, November 2025. Sutta references: AN 7.15: A Simile with Water https://suttacentral.net/an7.15/en/su... AN 3.94: Autumn https://suttacentral.net/an3.94/en/su... SN 6.1: The Appeal of the Divinity (Brahmā Sahampati) https://suttacentral.net/sn6.1/en/suj... MN 81: Ghaṭīkāra Sutta https://suttacentral.net/mn81/en/suja... Teaching retrieved from Anukampa Bhikkhuni Project: https://www.youtube.com/watch?v=gESTtqPmF78&t=1s Ajahn Brahm is the Spiritual Adviser of Anukampa Bhikkhuni Project. Donations to Anukampa are welcome, please visit https://anukampaproject.org/donate/ Support us on https://ko-fi.com/thebuddhistsocietyofwa BSWA teachings are available from: BSWA Teachings BSWA Podcast Channel BSWA DeeperDhamma Podbean Channel BSWA YouTube

Hai mai sentito un italiano arrabbiarsi e pensato: "Ma cosa sta dicendo?!" In questa guida scoprirai le parolacce italiane più comuni, capirai quando vengono usate e imparerai a sostituirle con alternative educate perfette per contesti formali, lavorativi o semplicemente quando vuoi essere più gentile. 5 Espressioni Volgari Italiane Comuni e i loro Eufemismi 1. Cazzo - La Più Versatile Partiamo dalla più versatile di tutte: "cazzo". Questa parola si riferisce letteralmente all'organo sessuale maschile, ma nella pratica quotidiana ha perso quasi completamente questo significato e viene usata come esclamazione universale in moltissime situazioni diverse. Quando Si Usa? Sorpresa: "Cazzo! Non ci credo!" — Esprime stupore di fronte a qualcosa di inaspettato. Dolore: Quando ti fai male al piede → "Cazzo!" — Una reazione istintiva al dolore fisico. Frustrazione: "Ma che cazzo sta succedendo?" — Esprime confusione e irritazione. Ammirazione: "Cazzo, che bella macchina!" — Paradossalmente, può esprimere entusiasmo positivo. Rabbia: "Ma che cazzo vuoi?!" — Usata per respingere qualcuno con irritazione. Come vedi, è un vero e proprio "coltellino svizzero" delle parolacce: si adatta praticamente a ogni emozione! Le Alternative Cortesi AlternativaLivello di FormalitàNoteCavolo!UniversaleLa più comune e sicura in ogni contestoAccidenti!Formale/InformalePerfetta in ogni situazioneAccipicchia!InformaleUn po' antiquata, ma simpaticaCapperi!InformaleUsata soprattutto al Centro-NordPerbacco!FormaleMolto elegante, quasi letterariaMannaggia!InformaleTipica del Sud Italia, molto espressivaCribbio!RegionaleUsata in Emilia-RomagnaCacchio!InformaleVicinissima all'originale, ma meno volgareDiamine!FormaleRaffinata e appropriataUrca!InformaleInformale ma innocuaPorca miseria!UniversaleMolto comune e socialmente accettata Consiglio: "Cavolo" è la tua scelta più sicura. Funziona sempre, ovunque e con chiunque! 2. Vaffanculo - L'Espressione Italiana Più Famosa al Mondo La famosa espressione che tutto il mondo conosce grazie ai film italiani: "Vaffanculo" è una contrazione di "va' a fare in culo", un invito molto esplicito e volgare. Nonostante la sua volgarità, è diventata quasi un simbolo dell'italiano arrabbiato nella cultura popolare internazionale. Quando Si Usa? Per mandare via qualcuno in modo aggressivo: "Vaffanculo, lasciami in pace!" — Esprime il desiderio di allontanare qualcuno in modo deciso. Come risposta a un'offesa: Qualcuno ti insulta → "Ma vaffanculo!" — Una reazione difensiva immediata. Per esprimere totale disprezzo: "Lui e le sue idee possono andare a fanculo" — Manifesta rifiuto totale. Tra amici, scherzosamente: "Ahah, ma vaffanculo!" — Sì, tra amici intimi può essere sorprendentemente affettuoso! Le Alternative Cortesi AlternativaSignificato/UsoNoteVai a quel paese!Allontanati!Il classico sostituto, capito da tuttiVai a farti benedire!Vai via!Ironicamente religiosoVai a quel posto!Allontanati!Versione abbreviata e discretaLevati dai piedi!Togliti di mezzo!Focus sull'allontanamento fisicoSparisci!Vai via!Diretto ma non volgareVai a stendere!Vai a fare altro!Come se dovesse stendere i panniVai a fare un giro!Allontanati!Apparentemente innocuoMa vai via!Lasciami stare!Semplice ed efficaceE togliti di mezzo!Non disturbare!Quando qualcuno dà fastidioVai a dar via i ciclisti!Vai a fare altro!Espressione toscana, colorita ma non volgareVai a farti friggere!Vai via!Divertente e innocua Curiosità: In alcune regioni d'Italia, "vai a quel paese" viene abbreviato in "vattene a..." e basta. Il gesto della mano che indica "via" completa il messaggio! 3. Stronzo/Stronza - L'Insulto Personale "Stronzo" significa letteralmente "escremento", ma viene usato quasi esclusivamente per descrivere una persona cattiva, meschina o che si comporta male. È uno degli insulti personali più comuni in italiano. Quando Si Usa? Per descrivere una persona cattiva o meschina: "Il mio ex è uno stronzo" — Giudizio negativo su una persona. Per qualcuno che si comporta male: "Non fare lo stronzo!" — Un rimprovero per un comportamento scorretto. Come insulto diretto: "Sei proprio uno stronzo!" — Attacco frontale alla persona. Per descrivere un'azione scorretta: "È stata una mossa da stronzo" — Critica a un'azione specifica. Le Alternative Cortesi AlternativaSfumaturaNoteMascalzonePersona disonestaClassico, quasi da film d'avventuraFarabuttoPersona ingannatriceElegante nella sua cattiveriaCafonePersona rozzaSottolinea la mancanza di educazioneMaleducatoSenza maniereNeutro e descrittivoVillanoPersona rozzaUn po' antiquato ma efficaceScreanzatoSenza creanzaPersona senza educazioneZoticoRozzo e sgradevoleForte ma appropriatoPoco di buonoPersona inaffidabileGenerico ma chiaroFetentePersona spregevoleUsato molto al SudCarognaPersona cattivaForte, ma non una parolacciaPersona spregevoleIndividuo deprecabileQuando vuoi essere formale ma durissimo Nota importante: "Stronzo/a" ha una versione femminile regolarmente usata. Anche le alternative seguono lo stesso schema: mascalzone → mascalzona, cafone → cafona, ecc. 4. Merda - L'Esclamazione di Frustrazione "Merda" significa letteralmente "escremento" o "feci", ma il suo uso nella lingua parlata va ben oltre il significato letterale. È una delle esclamazioni più versatili per esprimere frustrazione, disappunto o, sorprendentemente, anche buona fortuna. Quando Si Usa? Quando qualcosa va male: "Che merda!" / "Merda, ho perso il treno!" — Esprime frustrazione immediata. Per descrivere qualcosa di pessima qualità: "Questo film è una merda" — Giudizio molto negativo. Come esclamazione di frustrazione: Rovesci il caffè → "Merda!" — Reazione istintiva a un incidente. Per situazioni sfortunate: "Sono nella merda" (= nei guai) — Descrive una situazione difficile. Per augurare buona fortuna (!): "Merda!" — Soprattutto nel mondo del teatro! Curiosità Teatrale Nel mondo dello spettacolo italiano, dire "merda" prima di uno show porta fortuna! Questa tradizione deriva dalla Francia: più carrozze c'erano fuori dal teatro (e quindi più escrementi di cavallo per strada), più pubblico c'era dentro. Quindi "merda" = tanto pubblico = successo! Le Alternative Cortesi AlternativaContestoNoteAccidenti!UniversaleSempre appropriatoMannaggia!InformaleEsprime frustrazione in modo coloritoPorca miseria!UniversaleMolto comuneChe sfortuna!FormaleNeutro e descrittivoChe disastro!UniversalePer situazioni catastroficheChe rabbia!InformaleEsprime il sentimento direttamenteMa dai!InformaleEsclamazione di incredulitàNon ci posso credere!UniversalePer sorpresa negativaChe disdetta!FormaleUn po' formale ma efficaceMaledizione!DrammaticoDrammatico ma non volgarePer tutti i diavoli!TeatraleTeatrale e divertente Alternative per Descrivere Qualcosa di Pessima Qualità Invece di dire "Questo è una merda", puoi usare: "Questo film è una schifezza" — Esprime disgusto per la qualità. "Questa pizza è orribile" — Giudizio negativo diretto. "Che porcheria!" — Espressione di disapprovazione forte. "È fatto proprio con i piedi" — Espressione idiomatica che significa "fatto malissimo". 5. Coglione - L'Insulto per la Stupidità "Coglione" si riferisce letteralmente ai testicoli, ma viene usato quasi esclusivamente per descrivere una persona stupida o che ha fatto qualcosa di particolarmente sciocco. È uno degli insulti più comuni per criticare l'intelligenza o il giudizio di qualcuno. Quando Si Usa? Per definire qualcuno stupido: "Quello è proprio un coglione" — Giudizio sulla mancanza di intelligenza. Per descrivere un'azione stupida: "Ho fatto una coglionata" — Ammissione di un errore sciocco. Autoironico, per sé stessi: "Sono stato un coglione a fidarmi" — Autocritica per una decisione sbagliata. Per prendere in giro qualcuno: "Non fare il coglione!" — Invito a comportarsi seriamente. Per descrivere qualcuno che si fa ingannare: "Mi hanno preso per il culo come un coglione" — Descrive l'essere stati ingannati. Le Alternative Cortesi AlternativaSfumaturaNoteScioccoPoco intelligenteClassico e innocuoStupidoPoco intelligenteDiretto ma non volgareTontoLento di comprendonioSimpatico, quasi affettuosoFessoIngenuo/stupidoMolto usato, specialmente al SudBabbeoScioccoSuona quasi comicoCitrulloStupidottoBuffo e non offensivoGrulloScioccoTipico toscanoIngenuoTroppo fiduciosoQuando la "stupidità" è più innocenzaGonzoCredulonePersona che si fa facilmente ingannareAlloccoPoco sveglioCome l'uccello notturnoPolloIngenuo/creduloneMolto comune: "Non fare il pollo!" Alternative per "Coglionata" (Azione Stupida) Invece di dire "Ho fatto una coglionata", puoi usare: "Ho fatto una sciocchezza" — Ammissione neutra di un errore. "Ho fatto una stupidaggine" — Simile, leggermente più forte. "Ho fatto una fesseria" — Versione informale ma accettabile. "Ho combinato un pasticcio" — Enfatizza il disordine causato dall'errore. Tabella Riassuntiva: Parolacce e Alternative ParolacciaUso PrincipaleAlternativa MiglioreAlternativa FormaleCazzo!Esclamazione universaleCavolo!Accidenti! / Perbacco!Vaffanculo!Mandare via qualcunoVai a quel paese!Vai a farti benedire!Stronzo/aPersona cattivaCafone/aPersona spregevoleMerda!FrustrazioneMannaggia!Che disdetta!CoglionePersona stupidaFessoSciocco / Ingenuo Domande Frequenti È Importante Conoscere le Parolacce Italiane Anche Se Non le Uso? Assolutamente sì! Conoscere le parolacce è fondamentale per comprendere il vero italiano parlato. Nei film, nelle serie TV, nelle conversazioni quotidiane e persino nei libri contemporanei, queste espressioni sono molto comuni. Capirle ti permetterà di interpretare correttamente il tono e le emozioni di chi parla, anche se scegli di non usarle tu stesso.

What makes someone truly a philosopher? How does knowledge differ from opinion? Does evil really exist? Find out as we discuss all this and more, breaking down Plato's definition of what a philosopher is, and whether it is possible to put philosopher-kings in place. Follow us on X! Give us your opinions here!

 44 The kingdom of heaven is like unto a treasure hidden in a field. Which a man having found, hid it, and for joy thereof goeth, and selleth all that he hath, and buyeth that field.Simile est regnum caelorum thesauro abscondito in agro : quem qui invenit homo, abscondit, et prae gaudio illius vadit, et vendit universa quae habet, et emit agrum illum. 45 Again the kingdom of heaven is like to a merchant seeking good pearls.Iterum simile est regnum caelorum homini negotiatori, quaerenti bonas margaritas. 46 Who when he had found one pearl of great price, went his way, and sold all that he had, and bought it.Inventa autem una pretiosa margarita, abiit, et vendidit omnia quae habuit, et emit eam. 47 Again the kingdom of heaven is like to a net cast into the sea, and gathering together of all kind of fishes.Iterum simile est regnum caelorum sagenae missae in mare, et ex omni genere piscium congreganti. 48 Which, when it was filled, they drew out, and sitting by the shore, they chose out the good into vessels, but the bad they cast forth.Quam, cum impleta esset, educentes, et secus littus sedentes, elegerunt bonis in vasa, malos autem foras miserunt. 49 So shall it be at the end of the world. The angels shall go out, and shall separate the wicked from among the just.Sic erit in consummatione saeculi : exibunt angeli, et separabunt malos de medio justorum, 50 And shall cast them into the furnace of fire: there shall be weeping and gnashing of teeth.et mittent eos in caminum ignis : ibi erit fletus, et stridor dentium. 51 Have ye understood all these things? They say to him: Yes.Intellexistis haec omnia? Dicunt ei : Etiam. 52 He said unto them: Therefore every scribe instructed in the kingdom of heaven, is like to a man that is a householder, who bringeth forth out of his treasure new things and old.Ait illis : Ideo omnis scriba doctus in regno caelorum, similis est homini patrifamilias, qui profert de thesauro suo nova et vetera.Born at Syracuse in Sicily of noble parents, St Lucy gave herself to Jesus and chose death rather than to lose the incorruptible treasure of her virginity, A.D. 303. Her name occurs in the Canon of the Mass.

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44 The kingdom of heaven is like unto a treasure hidden in a field. Which a man having found, hid it, and for joy thereof goeth, and selleth all that he hath, and buyeth that field.Simile est regnum caelorum thesauro abscondito in agro : quem qui invenit homo, abscondit, et prae gaudio illius vadit, et vendit universa quae habet, et emit agrum illum. 45 Again the kingdom of heaven is like to a merchant seeking good pearls.Iterum simile est regnum caelorum homini negotiatori, quaerenti bonas margaritas. 46 Who when he had found one pearl of great price, went his way, and sold all that he had, and bought it.Inventa autem una pretiosa margarita, abiit, et vendidit omnia quae habuit, et emit eam. 47 Again the kingdom of heaven is like to a net cast into the sea, and gathering together of all kind of fishes.Iterum simile est regnum caelorum sagenae missae in mare, et ex omni genere piscium congreganti. 48 Which, when it was filled, they drew out, and sitting by the shore, they chose out the good into vessels, but the bad they cast forth.Quam, cum impleta esset, educentes, et secus littus sedentes, elegerunt bonis in vasa, malos autem foras miserunt. 49 So shall it be at the end of the world. The angels shall go out, and shall separate the wicked from among the just.Sic erit in consummatione saeculi : exibunt angeli, et separabunt malos de medio justorum, 50 And shall cast them into the furnace of fire: there shall be weeping and gnashing of teeth.et mittent eos in caminum ignis : ibi erit fletus, et stridor dentium. 51 Have ye understood all these things? They say to him: Yes.Intellexistis haec omnia? Dicunt ei : Etiam. 52 He said unto them: Therefore every scribe instructed in the kingdom of heaven, is like to a man that is a householder, who bringeth forth out of his treasure new things and old.Ait illis : Ideo omnis scriba doctus in regno caelorum, similis est homini patrifamilias, qui profert de thesauro suo nova et vetera.St Bibiana was murdered at Rome under Julian the Apostate A.D. 363.

44 The kingdom of heaven is like unto a treasure hidden in a field. Which a man having found, hid it, and for joy thereof goeth, and selleth all that he hath, and buyeth that field.Simile est regnum caelorum thesauro abscondito in agro : quem qui invenit homo, abscondit, et prae gaudio illius vadit, et vendit universa quae habet, et emit agrum illum. 45 Again the kingdom of heaven is like to a merchant seeking good pearls.Iterum simile est regnum caelorum homini negotiatori, quaerenti bonas margaritas. 46 Who when he had found one pearl of great price, went his way, and sold all that he had, and bought it.Inventa autem una pretiosa margarita, abiit, et vendidit omnia quae habuit, et emit eam. 47 Again the kingdom of heaven is like to a net cast into the sea, and gathering together of all kind of fishes.Iterum simile est regnum caelorum sagenae missae in mare, et ex omni genere piscium congreganti. 48 Which, when it was filled, they drew out, and sitting by the shore, they chose out the good into vessels, but the bad they cast forth.Quam, cum impleta esset, educentes, et secus littus sedentes, elegerunt bonis in vasa, malos autem foras miserunt. 49 So shall it be at the end of the world. The angels shall go out, and shall separate the wicked from among the just.Sic erit in consummatione saeculi : exibunt angeli, et separabunt malos de medio justorum, 50 And shall cast them into the furnace of fire: there shall be weeping and gnashing of teeth.et mittent eos in caminum ignis : ibi erit fletus, et stridor dentium. 51 Have ye understood all these things? They say to him: Yes.Intellexistis haec omnia? Dicunt ei : Etiam. 52 He said unto them: Therefore every scribe instructed in the kingdom of heaven, is like to a man that is a householder, who bringeth forth out of his treasure new things and old.Ait illis : Ideo omnis scriba doctus in regno caelorum, similis est homini patrifamilias, qui profert de thesauro suo nova et vetera.St Elizabeth, daughter of Andrew, king of Hungary, was given in marriage to the holy landgrave Thuringia, Louis IV. After the death of her husband, she entered the Third Order of St Francis and died in poverty and humiliation, exiled by her brother-in-law A.D. 1231.

Compline by Candlelight provides peace and stillness as one week ends and another begins. Set in the tranquility of St. Paul's Chapel, one of the oldest buildings in New York City, guests find a seat and hold a candle, while 30 minutes of improvised music by The Choir of Trinity Wall Street fill the space. There's nothing to do but listen. Simile est regnum caelorum - Francisco Guerrero

Ajahn Hasapanna discusses sutta 22 from the Majjhima Nikaya: Alagaddūpama Sutta – “The Simile of the Snake” and is using Bhikkhu Bodhi's translation. Read MN22 on Sutta Central here: “One of the monks denies that prohibited conduct is really a problem. The monks and then the Buddha subject him to an impressive dressing down. The Buddha compares someone who understands only the letter of the teachings to someone who grabs a snake by the tail, and also invokes the famous simile of the raft.”, Sutta Central. Support us on https://ko-fi.com/thebuddhistsocietyofwa BSWA teachings are available from: BSWA Teachings BSWA Podcast Channel BSWA DeeperDhamma Podbean Channel BSWA YouTube

1 And Jesus answering, spoke again in parables to them, saying:Et respondens Jesus, dixit iterum in parabolis eis, dicens : 2 The kingdom of heaven is likened to a king, who made a marriage for his son.Simile factum est regnum caelorum homini regi, qui fecit nuptias filio suo. 3 And he sent his servants, to call them that were invited to the marriage; and they would not come.Et misit servos suos vocare invitatos ad nuptias, et nolebant venire. 4 Again he sent other servants, saying: Tell them that were invited, Behold, I have prepared my dinner; my calves and fatlings are killed, and all things are ready: come ye to the marriage.Iterum misit alios servos, dicens : Dicite invitatis : Ecce prandium meum paravi, tauri mei et altilia occisa sunt, et omnia parata : venite ad nuptias. 5 But they neglected, and went their own ways, one to his farm, and another to his merchandise.Illi autem neglexerunt : et abierunt, alius in villam suam, alius vero ad negotiationem suam : 6 And the rest laid hands on his servants, and having treated them contumeliously, put them to death.reliqui vero tenuerunt servos ejus, et contumeliis affectos occiderunt. 7 But when the king had heard of it, he was angry, and sending his armies, he destroyed those murderers, and burnt their city.Rex autem cum audisset, iratus est : et missis exercitibus suis, perdidit homicidas illos, et civitatem illorum succendit. 8 Then he saith to his servants: The marriage indeed is ready; but they that were invited were not worthy.Tunc ait servis suis : Nuptiae quidem paratae sunt, sed qui invitati erant, non fuerunt digni : 9 Go ye therefore into the highways; and as many as you shall find, call to the marriage.ite ergo ad exitus viarum, et quoscumque inveneritis, vocate ad nuptias. 10 And his servants going forth into the ways, gathered together all that they found, both bad and good: and the marriage was filled with guests.Et egressi servi ejus in vias, congregaverunt omnes quos invenerunt, malos et bonos : et impletae sunt nuptiae discumbentium. 11 And the king went in to see the guests: and he saw there a man who had not on a wedding garment.Intravit autem rex ut viderent discumbentes, et vidit ibi hominem non vestitum veste nuptiali. 12 And he saith to him: Friend, how camest thou in hither not having a wedding garment? But he was silent.Et ait illi : Amice, quomodo huc intrasti non habens vestem nuptialem? At ille obmutavit. 13 Then the king said to the waiters: Bind his hands and feet, and cast him into the exterior darkness: there shall be weeping and gnashing of teeth.Tunc dicit rex ministris : Ligatis manibus et pedibus ejus, mittite eum in tenebras exteriores : ibi erit fletus et stridor dentium. 14 For many are called, but few are chosen.Multi enim sunt vocati, pauci vero electi.

 1 And Jesus answering, spoke again in parables to them, saying:Et respondens Jesus, dixit iterum in parabolis eis, dicens : 2 The kingdom of heaven is likened to a king, who made a marriage for his son.Simile factum est regnum caelorum homini regi, qui fecit nuptias filio suo. 3 And he sent his servants, to call them that were invited to the marriage; and they would not come.Et misit servos suos vocare invitatos ad nuptias, et nolebant venire. 4 Again he sent other servants, saying: Tell them that were invited, Behold, I have prepared my dinner; my calves and fatlings are killed, and all things are ready: come ye to the marriage.Iterum misit alios servos, dicens : Dicite invitatis : Ecce prandium meum paravi, tauri mei et altilia occisa sunt, et omnia parata : venite ad nuptias. 5 But they neglected, and went their own ways, one to his farm, and another to his merchandise.Illi autem neglexerunt : et abierunt, alius in villam suam, alius vero ad negotiationem suam : 6 And the rest laid hands on his servants, and having treated them contumeliously, put them to death.reliqui vero tenuerunt servos ejus, et contumeliis affectos occiderunt. 7 But when the king had heard of it, he was angry, and sending his armies, he destroyed those murderers, and burnt their city.Rex autem cum audisset, iratus est : et missis exercitibus suis, perdidit homicidas illos, et civitatem illorum succendit. 8 Then he saith to his servants: The marriage indeed is ready; but they that were invited were not worthy.Tunc ait servis suis : Nuptiae quidem paratae sunt, sed qui invitati erant, non fuerunt digni : 9 Go ye therefore into the highways; and as many as you shall find, call to the marriage.ite ergo ad exitus viarum, et quoscumque inveneritis, vocate ad nuptias. 10 And his servants going forth into the ways, gathered together all that they found, both bad and good: and the marriage was filled with guests.Et egressi servi ejus in vias, congregaverunt omnes quos invenerunt, malos et bonos : et impletae sunt nuptiae discumbentium. 11 And the king went in to see the guests: and he saw there a man who had not on a wedding garment.Intravit autem rex ut viderent discumbentes, et vidit ibi hominem non vestitum veste nuptiali. 12 And he saith to him: Friend, how camest thou in hither not having a wedding garment? But he was silent.Et ait illi : Amice, quomodo huc intrasti non habens vestem nuptialem? At ille obmutavit. 13 Then the king said to the waiters: Bind his hands and feet, and cast him into the exterior darkness: there shall be weeping and gnashing of teeth.Tunc dicit rex ministris : Ligatis manibus et pedibus ejus, mittite eum in tenebras exteriores : ibi erit fletus et stridor dentium. 14 For many are called, but few are chosen.Multi enim sunt vocati, pauci vero electi.All men are called to the heavenly beatific union, but few are chosen: those who wear the nuptial robe of baptism and the state of grace.

 1 And Jesus answering, spoke again in parables to them, saying:Et respondens Jesus, dixit iterum in parabolis eis, dicens : 2 The kingdom of heaven is likened to a king, who made a marriage for his son.Simile factum est regnum caelorum homini regi, qui fecit nuptias filio suo. 3 And he sent his servants, to call them that were invited to the marriage; and they would not come.Et misit servos suos vocare invitatos ad nuptias, et nolebant venire. 4 Again he sent other servants, saying: Tell them that were invited, Behold, I have prepared my dinner; my calves and fatlings are killed, and all things are ready: come ye to the marriage.Iterum misit alios servos, dicens : Dicite invitatis : Ecce prandium meum paravi, tauri mei et altilia occisa sunt, et omnia parata : venite ad nuptias. 5 But they neglected, and went their own ways, one to his farm, and another to his merchandise.Illi autem neglexerunt : et abierunt, alius in villam suam, alius vero ad negotiationem suam : 6 And the rest laid hands on his servants, and having treated them contumeliously, put them to death.reliqui vero tenuerunt servos ejus, et contumeliis affectos occiderunt. 7 But when the king had heard of it, he was angry, and sending his armies, he destroyed those murderers, and burnt their city.Rex autem cum audisset, iratus est : et missis exercitibus suis, perdidit homicidas illos, et civitatem illorum succendit. 8 Then he saith to his servants: The marriage indeed is ready; but they that were invited were not worthy.Tunc ait servis suis : Nuptiae quidem paratae sunt, sed qui invitati erant, non fuerunt digni : 9 Go ye therefore into the highways; and as many as you shall find, call to the marriage.ite ergo ad exitus viarum, et quoscumque inveneritis, vocate ad nuptias. 10 And his servants going forth into the ways, gathered together all that they found, both bad and good: and the marriage was filled with guests.Et egressi servi ejus in vias, congregaverunt omnes quos invenerunt, malos et bonos : et impletae sunt nuptiae discumbentium. 11 And the king went in to see the guests: and he saw there a man who had not on a wedding garment.Intravit autem rex ut viderent discumbentes, et vidit ibi hominem non vestitum veste nuptiali. 12 And he saith to him: Friend, how camest thou in hither not having a wedding garment? But he was silent.Et ait illi : Amice, quomodo huc intrasti non habens vestem nuptialem? At ille obmutavit. 13 Then the king said to the waiters: Bind his hands and feet, and cast him into the exterior darkness: there shall be weeping and gnashing of teeth.Tunc dicit rex ministris : Ligatis manibus et pedibus ejus, mittite eum in tenebras exteriores : ibi erit fletus et stridor dentium. 14 For many are called, but few are chosen.Multi enim sunt vocati, pauci vero electi.Parable of the marriage guests. All men are called to the heavenly beatific union, but few are chosen: those who wear the nuptial robe of baptism and of the state of grace.

44 The kingdom of heaven is like unto a treasure hidden in a field. Which a man having found, hid it, and for joy thereof goeth, and selleth all that he hath, and buyeth that field.Simile est regnum caelorum thesauro abscondito in agro : quem qui invenit homo, abscondit, et prae gaudio illius vadit, et vendit universa quae habet, et emit agrum illum. 45 Again the kingdom of heaven is like to a merchant seeking good pearls.Iterum simile est regnum caelorum homini negotiatori, quaerenti bonas margaritas. 46 Who when he had found one pearl of great price, went his way, and sold all that he had, and bought it.Inventa autem una pretiosa margarita, abiit, et vendidit omnia quae habuit, et emit eam. 47 Again the kingdom of heaven is like to a net cast into the sea, and gathering together of all kind of fishes.Iterum simile est regnum caelorum sagenae missae in mare, et ex omni genere piscium congreganti. 48 Which, when it was filled, they drew out, and sitting by the shore, they chose out the good into vessels, but the bad they cast forth.Quam, cum impleta esset, educentes, et secus littus sedentes, elegerunt bonis in vasa, malos autem foras miserunt. 49 So shall it be at the end of the world. The angels shall go out, and shall separate the wicked from among the just.Sic erit in consummatione saeculi : exibunt angeli, et separabunt malos de medio justorum, 50 And shall cast them into the furnace of fire: there shall be weeping and gnashing of teeth.et mittent eos in caminum ignis : ibi erit fletus, et stridor dentium. 51 Have ye understood all these things? They say to him: Yes.Intellexistis haec omnia? Dicunt ei : Etiam. 52 He said unto them: Therefore every scribe instructed in the kingdom of heaven, is like to a man that is a householder, who bringeth forth out of his treasure new things and old.Ait illis : Ideo omnis scriba doctus in regno caelorum, similis est homini patrifamilias, qui profert de thesauro suo nova et vetera.St Hedwig, duchess of Poland, of royal stock and the maternal aunt of St Elizabeth of Hungary, retired into a Cistercian convent after the death of her husband. She died A.D. 1243.

44 The kingdom of heaven is like unto a treasure hidden in a field. Which a man having found, hid it, and for joy thereof goeth, and selleth all that he hath, and buyeth that field.Simile est regnum caelorum thesauro abscondito in agro : quem qui invenit homo, abscondit, et prae gaudio illius vadit, et vendit universa quae habet, et emit agrum illum. 45 Again the kingdom of heaven is like to a merchant seeking good pearls.Iterum simile est regnum caelorum homini negotiatori, quaerenti bonas margaritas. 46 Who when he had found one pearl of great price, went his way, and sold all that he had, and bought it.Inventa autem una pretiosa margarita, abiit, et vendidit omnia quae habuit, et emit eam. 47 Again the kingdom of heaven is like to a net cast into the sea, and gathering together of all kind of fishes.Iterum simile est regnum caelorum sagenae missae in mare, et ex omni genere piscium congreganti. 48 Which, when it was filled, they drew out, and sitting by the shore, they chose out the good into vessels, but the bad they cast forth.Quam, cum impleta esset, educentes, et secus littus sedentes, elegerunt bonis in vasa, malos autem foras miserunt. 49 So shall it be at the end of the world. The angels shall go out, and shall separate the wicked from among the just.Sic erit in consummatione saeculi : exibunt angeli, et separabunt malos de medio justorum, 50 And shall cast them into the furnace of fire: there shall be weeping and gnashing of teeth.et mittent eos in caminum ignis : ibi erit fletus, et stridor dentium. 51 Have ye understood all these things? They say to him: Yes.Intellexistis haec omnia? Dicunt ei : Etiam. 52 He said unto them: Therefore every scribe instructed in the kingdom of heaven, is like to a man that is a householder, who bringeth forth out of his treasure new things and old.Ait illis : Ideo omnis scriba doctus in regno caelorum, similis est homini patrifamilias, qui profert de thesauro suo nova et vetera.St Bridget, a descendent of the royal house of Sweden, was married to prince Ulfo. After the death of the latter, she founded the Order of the Most Holy Saviour, commonly called Bridgettines. She died at Rome A.D. 1373.

Preview: Jeff McCausland (United States Army retired) details Russia's slow progress and heavy losses (a quarter of a million dead) in the war in Ukraine. He uses the simile that a snail starting in 2022 would already be in Poland, whereas Russian forces are far behind. Russia's broader goal is the destruction of NATO through intimidation. 1960

The need for Lovingkindness www.dhammasukha.org

What is merit, why is dedicating it so important in Buddhist practice, and how do we dedicate to optimize our spiritual heart and path?Lamas Yeshe and Zopa invite us to explore this vital ingredient and enlivening mindset, one of three sublime principles in all Mahayana Buddhist practice.#buddhanature #Mahamudra #buddhism #buddhistmeditation #merit #dedicating meritResources for this episodeMajjhima Nikāya 29. The Greater Discourse on the Simile of the Heartwood (Bodhi trans.)https://suttacentral.net/mn29/en/bodhi?lang=en&reference=none&highlight=false ⁠⁠⁠Make a dana offering⁠⁠⁠PRAJNA FIRE is a United States 501(c)(3) nonprofit religious organization. Your donation is tax-deductible to the extent allowed by applicable law.Learn more about the integrative dharma practice of ⁠⁠⁠listening, contemplating, and meditating ⁠⁠⁠from Prajna Rising, our online journal.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Meet Lama Yeshe & Lama Zopa, in Tricycle Magazine⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://bit.ly/3xRySck⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠PUBLISHED ARTICLES⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.prajnafire.com/media⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Prajna Fire on Substack⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://prajnafire.substack.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠PRAJNA SPARKS follows the lunar calendar. Look for new episodes on the new moons. Tibetan singing bowl interludes by Shivnee RatnaFOLLOW US⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our Global Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for regular updates on Prajna Fire events with Yeshe and ZopaLama Yeshe and Lama Zopa offer individual spiritual counsel on formal Buddhist practice as well as innovative ways to integrate Buddhist perspective into your everyday life. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book Online at Prajna Fire⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ with immediate confirmation (https://www.prajnafire.com/book-online)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Check us out in the media ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.prajnafire.com/mediaEMAIL US sparks@prajnafire.comFIND US on the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Prajna Fire website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.prajnafire.com/sparks⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)@prajnasparks on Facebook, Instagram, and Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ (https://www.youtube.com/channel/UCRUzGmU7c4_TJdLhG9R8IDA/videos)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Lama Yeshe and Lama Zopa⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.prajnafire.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠) IG: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@karmayeshechodron⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@karmazopajigme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Shivnee Ratna, Tibetan singing bowls (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.shivgauree.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)

In this Marketing Over Coffee: Learn about Story, Proverb, Simile, Analogy, Metaphor, and more! Direct Link to File Ron Ploof, Master of Story, Scholar of Proverb, now automating his best prompts After leaving chip design behind, training LLMs on his work The StoryHow Ptich Deck (Get the deck) – Hear the interview The Proverb Construction […] The post Ron Ploof – Master of Metaphor and Scholar of Story appeared first on Marketing Over Coffee Marketing Podcast.

(Insight Meditation Society - Forest Refuge) Exploring how to not hold hatred in our hearts using this sutta.