Podcasts about Opus

Michael Sobolik and  Gordon Chang examine how Chinese AI company Moonshot bypassed US export controls by "distilling" technology from Anthropic's Claude Opus model to train its own system, Kimi K3. This intellectual property theft threatens American market dominance and national security. Sobolik recommends three policy actions: imposing crushing financial sanctions on violating Chinese firms, closing export control loopholes related to remote cloud access, and banning open CCP models in the United States. He warns that American tech companies prioritizing short-term profits over security risk losing the AI race, mirroring historical patterns of Chinese piracy. (9)

Hugging Face explored a sale at $13B+, keeping the AI M&A wave rolling. Trump scolded towns that reject data centers while Abbott said the industry dug its own grave, Fable 5 spending plateaued, and Apple cut 200+ jobs. Links Sources: Hugging Face is exploring a sale that could value it at $13B+, up from $4.5B in 2023, and has been working with a bank to evaluate bidders' interest (Business Insider) Delangue has said Hugging Face is close to profitability and barely touched its 2023 round, and it turned down a $500M Nvidia investment at a $7B valuation earlier this year (TechCrunch) President Trump says communities that oppose data centers are "making a mistake" as they create "tremendous amounts of jobs and money", amid bipartisan backlash (Axios) Texas Gov. Greg Abbott says data center companies "dug their own grave" and deserve the backlash, after ordering an audit that has stalled roughly 1,800 projects (Fortune) Ramp data: Fable 5, launched in June, has plateaued at ~11% of spending on Anthropic tools, as companies shift to cheaper models; Opus 5 surpassed Fable 5 (Financial Times) Nvidia plans to use its $6B licensing deal with Poolside to build one of the world's most powerful open-weight models, to compete with DeepSeek and Kimi K3 (The Wall Street Journal) Sources: Apple is cutting 200+ jobs, including ~100 positions from the Vision Pro unit and another 100 from the Siri team, as it focuses on new devices and AI (Bloomberg) Subscribe to the ad-free feed.

The topic: Summer is over and school is back in session. So we thought we would ask our FilmWeek critics what some of their favorite school-set films are and what makes them so effective in bringing back those school-day memories. Our critics' picks: Cooley High (1975) To Sir, With Love (1967) Fast Times at Ridgemont High (1982) Fame (1980) The Last Picture Show (1971) Carrie (1976) The 400 Blows (1959) Election (1999) Booksmart (2019) Mr Holland’s Opus (1995) The critics: Peter Rainer, film critic for LAist and the Christian Science Monitor Tim Cogshell, film critic for LAist, Alt-Film Guide and CineGods.com Visit www.preppi.com/LAist to receive a FREE Preppi Emergency Kit (with any purchase over $100) and be prepared for the next wildfire, earthquake or emergency.

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

Hey this is Alex, welcome to... the chillest week in AI, since ... a long time. Chill, if you consider Moderna and MERK announcing a cancer vaccine and surging 115% in a day, a chill week. This week, the only two model drops we really saw came from the excellent Z.ai folks, they announced GLM 5.3, API only for now, and an amazing tiny release of Qwen 3.89 27B. In other big AI news, OpenAI announced they are pausing RL efforts (Reinforcement Learning) to focus on security and alignment post the scary AI Swarms hacking incident, dedicating up to 20% of compute towards reviewing agent thinking processes, and Stripe buying OpenRouter for a reported $8B! Sometimes the chill weeks are actually good, we're able to chat about how we use AI, what changed for us, and give our guests a bit of breathing room. This week, I invited Francesco from CUA to talk about computer use in open source + their new history plugin, Bin from HeyGen to talk about HyperFrames, a way for your agents to create videos and a breaking news guest, Jeff Huber from Chroma jumped on to talk about their new Foundations release, a unified memory for your agents! This was a great episode, I hope you'll like it, it's up here on Substack and everywhere you get your pod (Spotify, Youtube, Apple Podcasts). ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Are we being fed slop again? (Is Claude dumb again?)Before we get to releases, this week on the show, I complained, again, that I feel my AI's are degrading. If this feels like de-ja-vu to you, it's because the same happened a year ago in September 2025 (and Anthropic admitting this 2 weeks later), and ... now this happens with Fable?You see, I use pretty much the same prompts, every week, preparing for the show. This is partly my way to evaluate new models and compare to existing and previous ones while also bringing you the best researched weekly show in AI. Well, this week, one after another, Claude Fable, which is... like the best intelligence, gave me such poor output, that I couldn't believe what I'm seeing. First, literally ignoring instructions that say “hey, show me all the items I've collected and let me pick the most important ones”, Fable instead sent all of them to my research pipeline, without showing me. This has worked, consistently, without fail, for the past... year? maybe more! This worked with open source models, worked with GPT, and now Fable, a Mythos Level LLM, is doing the most basic dumb s**t possible, ignoring the main reason I even have this workflow. And this wasn't just a fluke either, when asked to create a run of show document, and given an example, Fable produced this... whatever this is. This is the same document and same format that Fable produced for me during AI Engineer which got me thinking “ok, this is AGI”, and here, given an example, I got a completely unusable artifact, despite direct instructions, structure and example! I got to say, given that privately this week, Anthropic disclosed that they have passed $65B in revenue, which is absolutely insane, this doesn't add up. So I figured, ok Alex, maybe this is your prompts or skills. But no, LDJ came in with some charts that show degradation, one from MarginLab.ai that shows significant lowering on number of tool calls and average runtime recently (this is for Opus 5) and And another chart from modelverify.ai model drift monitor showing drift scores.Do we have anoher Claude Gate on our hands? Is your Fable/Opus behaving weird lately? Or did you completely switched away to other models? OpenAI pausing RL and focusing on safetyLook, when we covered the HF hacking incident and then the pacing the frontier letter, I didn't imagine that results will come this fast, but this week, OpenAI publicly announced that they are pausing RL training, which is the last step of models, until they get their sandboxes in order and align the models better. We all agreed on stage that this is likely a very good move, and Peter was really awe-struck at the 20% dedication of resources towards reviewing thought processes of models. Is this a good enough response to the scary hacking incident? we'll see, but I think this is the right move from OpenAI, and still, waiting for the full postmortem on the OpenAI security incident. Open Source LLMsQwen3.8-27B ties GPT-5.6 Luna and runs on a 4090 (X, HF, Announcement)Following the release of their flagship, Alibaba dropped a model that became a community darling overnight, Qwen 3.8 with just 27B parameters. This “tiny” model scores 52 on the Artificial Analysis Intelligence Index, same score as GPT 5.6 Luna at Max reasoning and 51 on Agentic index, beating Opus 4.8 MaxAll while running at around 68t/s on a 4090 GPU, and around 40 on max via MLX, hell it even does 11t/s on Xenova's WebGPU kernels right in the browser! This model exploded on the HuggingFace hub, with tons of quants, over 152 fine-tunes, it was downloaded over 10M times overall

An AI that is rooted in scripture? Could it really be real? And is it really free? Yep - that's right! On this episode, the 2nd part of the "AskPhil.ai" mini-series I'm introducing you to the founder of Ask Phil. And be sure to check out the shownotes, because we have a free giveaway for you to check out! FREE FALL KICKOFF EVENT GUIDE & LINKS https://www.patreon.com/hybridministry/posts/5-youth-group-163404919 SHOW NOTES Shownotes & Transcripts https://www.hybridministry.xyz/215 [FREE] HYBRID STRATEGY GUIDE https://www.patreon.com/posts/complete-guide-142500019?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link 50% SUMMER SOCIAL MEDIA https://www.patreon.com/collection/1470781?view=expanded

You can now join the Art of Comics discord server and chat comics with Jaws, Paul and other listeners! Click Here to join!In this episode of the biweekly podcast, The Art of Comics, creators Paul Duffield and Jaws Stone discuss by Waka Hirako's intense social drama, My Broken Mariko.As usual, this is an in-depth discussion, and there'll be spoilers for the story and content of the comic, so you might want to pause now, read the book and listen once you've had your own reaction. The episode is designed to work regardless of whether you've read it or not, so if you don't mind spoilers, dive right in!Next episode, we'll be discussing Satoshi Kon's Opus, so if you want to read along with us, pick up a copy and read it over the next two weeks.To support the podcast, you can join Jaws' Patreon

In der heutigen Folge von 365 Checkpoint Update werfen wir einen Blick auf die spannendsten Microsoft-, Copilot- und Modern-Workplace-News des Monats August. Wir sprechen über den neuen GitHub Copilot Harness in Copilot Studio, die damit verbundene Credit-basierte Abrechnung und was sich für Agent-Entwickler jetzt konkret ändert. Außerdem schauen wir auf die neuen Live Linked Dashboards in SharePoint, die Daten aus Excel-, CSV- und SharePoint-Listen direkt als aktualisierbare HTML-Dashboards visualisieren können. Weitere Themen sind die neue Microsoft Copilot App inklusive neuem Branding, die Zusammenführung von Business- und Consumer-Copilot, neue KI-Modelle wie Claude Sonnet 5, Opus 5, Fable 5 und GPT-5.6 sowie neue Automatisierungs- und Browser-Steuerungsfunktionen in Copilot Chat. Highlights der Folge: • GitHub Copilot Harness jetzt offiziell GA und kostenpflichtig • Live Linked Dashboards in SharePoint mit Excel-, CSV- und Listen-Anbindung • Neue Microsoft Copilot App, neues Logo und neue URL • Claude Sonnet 5, Opus 5, Fable 5 und GPT-5.6 in Copilot • Eventbasierte Automationen und Browser-Steuerung in Copilot Chat • Outlook Inbox-Priorisierung und neue Admin-Einstellungen für Teams Agents Kapitelübersicht 00:00 – Die Themen des Monats im Überblick 02:14 – GitHub Copilot Harness wird GA 04:04 – Neue Funktionen im Harness und Workflows 04:46 – Neues Credit-basiertes Kostenmodell 07:53 – Live Linked Dashboards in SharePoint 09:49 – HTML-Dashboards nativ in SharePoint 11:27 – Neue Microsoft Copilot App und Super-App Strategie 14:06 – Welche Consumer-Features verschwinden 16:21 – Neue Claude-, Fable- und GPT-Modelle 17:32 – Browser-Steuerung in Copilot Chat 19:05 – Eventbasierte Automationen für Copilot 21:11 – Änderungen bei Copilot Notebooks 22:24 – Outlook- und Inbox-Priorisierung 23:36 – Neue Teams Agent Verwaltung für Admins 24:24 – Fazit und Ausblick Wenn dir die Folge gefallen hat, abonniere 365 Checkpoint und lass gerne eine Bewertung da. Für Feedback, Fragen oder Themenwünsche erreichst du mich jederzeit auf LinkedIn: https://www.linkedin.com/in/drohregger/

Our next guests are responsible for changing the sound of film/TV music production in a fascinating way. In a world where most composers rely on software instruments and plugins to get the job done, these 2 composer/producers have opted to record more hardware synths, interesting audio performances, and focus on processing their sounds in interesting ways. Their work can be heard on Ozark, Black Rabbit, Tulsa King, Opus, and many more projects and I'm so excited to welcome them on to the podcast! And the composers are... Danny Bensi & Saunder Jurriaans Hosted on Acast. See acast.com/privacy for more information.

This week on Shat the Movies, we're going back to school with Mr. Holland's Opus (1995), the story of a musician who takes a teaching job while dreaming of something bigger. Gene and Big D break down Richard Dreyfuss' decades in the classroom, the students he inspires, the family he sometimes neglects, and whether Mr. Holland is actually the inspirational teacher we remember him being. Is this a moving tribute to teachers, or does nostalgia deserve most of the credit? Tune in and find out. Full movie info below Mr. Holland's Opus (1995) is a drama directed by Stephen Herek and starring Richard Dreyfuss, Glenne Headly, Olympia Dukakis, William H. Macy, and Jay Thomas. The film follows composer Glenn Holland over three decades as a temporary teaching job gradually becomes his life's work. Richard Dreyfuss received an Academy Award nomination for Best Actor for his performance, and the film became a popular 1990s story about teaching, family, sacrifice, and the different ways a person can leave a legacy. Subscribe Now Android: https://www.shatpod.com/android Apple/iTunes: https://www.shatpod.com/apple Help Support the Podcast Contact Us: https://www.shatpod.com/contact Commission Movie: https://www.shatpod.com/support Support with Paypal: https://www.shatpod.com/paypal Support With Venmo: https://www.shatpod.com/venmo Shop Merchandise: https://www.shatpod.com/shop Theme Song - Die Hard by Guyz Nite: https://www.facebook.com/guyznite

Intel Chat with Matt Bromiley and Chris Luft.• AI-generated patches fix vulnerabilities about half the time. 1Password's Off-By-1 team tested ChatGPT-5.5 and Opus 4.8 against six vulnerabilities: across 6,080 generated patches only 46% fixed the underlying flaw, and some that did were narrow enough to be bypassed. Separate Veracode research found a 56% security pass rate across 100+ models, with 44% of AI-generated code carrying detectable OWASP Top 10 issues. Matt's pushback: what is the HUMAN success rate for comparison, and why is nobody publishing that number?• LiteLLM supply chain attack. CloudSEK reports 2,500+ organizations and 434,000 CI/CD pipelines potentially exposed. LiteLLM was not the initial target: the compromise came in through Aqua Security's Trivy scanner and spread when LiteLLM's CI automatically installed it, ending with malicious versions 1.82.7 and 1.82.8 on PyPI. They were live for roughly 40 minutes, which automated dependency resolution and cached layers were more than enough to propagate.• Anthropic's models reached real systems during evaluations. Reviewing 141,006 evaluation runs, Anthropic found three incidents where Claude models gained unauthorized access to real organizations during capture-the-flag exercises, after a misunderstanding with an evaluation partner left the environments internet-connected. One model published a malicious package to the real PyPI, where it ran on 15 real systems. Matt argues this is a lab test rather than a threat report, and asks what defenders are supposed to do with it.• North Korea behind the npm compromises. Amazon Threat Intelligence links the typo-crypto, debug, chalk and axios incidents to the same DPRK actor tracked as SAPPHIRE SLEET, STARDUST CHOLLIMA and BlueNoroff. Wiz found roughly one in ten cloud environments touched by the debug and chalk incident within two hours. The technique has shifted: malicious functionality is now split across several innocuous-looking packages that only do anything once combined, plus slopsquatting and prompt injection aimed at AI code scanners.Stories covered:• https://www.darkreading.com/application-security/ai-generated-patches-fail-half-time• https://www.securityweek.com/over-2500-organizations-impacted-by-litellm-supply-chain-attack/• https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals• https://aws.amazon.com/blogs/security/amazon-identifies-north-korean-hacker-group-behind-open-source-supply-chain-attacks/Chapters:0:00 Back from Black Hat3:31 AI-generated patches fix vulnerabilities about half the time6:23 What is the human success rate?10:53 LiteLLM supply chain attack13:03 Pin your dependencies15:59 Anthropic models reached real systems during evals22:12 This is a lab test, not a threat report27:06 North Korea behind the debug, chalk and axios compromises30:59 Malware assembled from harmless-looking parts33:27 Clever people on the other side of the fenceThe Cybersecurity Defenders Podcast — a podcast about cybersecurity and the people that keep the internet safe. New episodes drop weekly.Subscribe wherever you listen:• Spotify: https://open.spotify.com/show/6ep00zeY3S8ffZ4o0UeSps• Apple Podcasts: https://podcasts.apple.com/us/podcast/the-cybersecurity-defenders-podcast/id1649981740• YouTube: https://www.youtube.com/@limacharlieioLearn more about LimaCharlie: https://limacharlie.io#cybersecurity #infosec #AIsecurity #supplychainsecurity #threatintel

Ask Phil is not just another chatbot. What sets this one apart is that it understands biblical concepts, it can create devotionals & Bible Studies, help churches with social media content, and even assist with sermon research. In this LIVE Demo, I'm going to show you all that it can do! FREE AskPhil ART: https://www.patreon.com/hybridministry/posts/askphil-ai-free-164112529?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link USE PHIL ON THE CAROUSEL ENGINE: https://www.patreon.com/hybridministry/posts/carousel-engine-155124829?collection=2099839 SHOW NOTES Shownotes & Transcripts https://www.hybridministry.xyz/214 [FREE] HYBRID STRATEGY GUIDE https://www.patreon.com/posts/complete-guide-142500019?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link 50% SUMMER SOCIAL MEDIA https://www.patreon.com/collection/1470781?view=expanded

News and Updates: Flock Used to Chase Cross-State Weed: Wisconsin police used Flock's license plate network to track a man's frequent trips to Michigan—where marijuana is legal—then used that travel as pretext to search his car, convicting him only on possession. Texas Deputy Tracks Abortion Suspect: A Johnson County deputy searched Flock's 83,000-camera network across 45 states—including states where abortion is legal—to locate a woman suspected of self-managing an abortion, with no warrant required. A National Surveillance "Potluck": Over 75% of the 5,000+ departments using Flock share data into a national pool, enabling any agency to search all networks at once—450,000+ searches hit the database in one 30-day period. Flock's Staggering Error Rate: In Roseville, California, Flock misread license plates in 71% of the 1,427 stolen/felony alerts it sent police over two years, repeatedly flagging innocent drivers' vehicles. Misreads With Real Consequences: Elsewhere, Flock errors led to innocent people stopped at gunpoint or jailed—one Ohio driver was mauled by a police dog after a "7" was misread as a "2," costing him his job and home. Anthropic Models Hack Three Companies: One week after OpenAI's incident, Anthropic disclosed that its models—Opus 4.7, Mythos 5, and a research model—reached the internet via a misconfiguration and hacked three companies since April. Claude Thought It Was a Simulation: The models believed the hacking was part of a benchmark; in the most serious case, Claude broke into a real company's database sharing a name with its fake target and kept going even after realizing. 15 AGs Demand OpenAI Preserve Evidence: Attorneys general from 15 states told OpenAI the Hugging Face hack shows it can't ensure product safety, demanding it preserve all materials and flagging that its agent left escape notes for future versions.

Former Ultimate Fighter contestant Kat Paprocki joins the show to talk about her upcoming fight for Bare Knuckle Fighting Championship at the historic Fenway Park .We'll talk abut her transition from MMA to Bare Knuckle and her rise in BKFC and taking on one of their biggest stars . We also will talk about her dream walk out and why it might include Glorilla ? FOLLOW & SUBSCRIBE – KNOCKOUTS AND 3 COUNTSBringing you the best in Combat Sports and Pro Wrestling – available everywhere!YouTube (Live Tues & Thurs 9PM EST): http://www.youtube.com/c/Knockoutsand3CountsFacebook (Live Tues & Thurs 9PM EST): https://www.facebook.com/knockoutsand3countsApple Podcasts: https://podcasts.apple.com/us/podcast/knockouts-and-3-counts/id1446923286Spotify: https://open.spotify.com/show/3OpvW0QHBe3uRc3D0pbORt?si=33935ad9669146d3Twitter/X: https://twitter.com/ko3cpodInstagram: https://www.instagram.com/ko3cpod/TikTok: https://www.tiktok.com/@ko3cpodMerch, Streams & Videos (Millions): https://millions.co/kyle-collisonIf you love what we do at KO3C, support us by grabbing merch or ordering a personal video at Millions.co.We go LIVE every Tuesday and Thursday at 9 PM EST — bringing you interviews, breakdowns, and the real talk you won't hear anywhere else.Want dope podcast clips ? Use our Opus clip Link : https://www.opus.pro/?via=Ko3C

YEAR 6 IS FINALLY HERE!  GO CHECK OUT OUR YOUTUBE TO SEE OUR BRAND-NEW INTRO!  You can find the animator using the link below! https://www.fiverr.com/syedahumna56/do-professional-pixel-art-animation-of-your-choice?utm_medium=shared&utm_source=copy_link&utm_campaign=gig&utm_term=AyNLxkP   *Intro includes minor edits not provided by the original animator. All animated assets were provided by the animator listed above, with some text assets added in post by Keeping Up With The Nerds.   Check out our affiliated links! Opus clips Partner link: https://www.opus.pro/?via=Nerd   Check out our Website: Keepingupwiththenerds.com   ​This week on Keeping Up With The Nerds, Rene is finally back, and the gang is officially whole again!   First up, the Nerds catch up with Rene and get his fresh take on Spider-Man: Brand New Day. Then, the crew dives into the release of Star Wars Visions Presents: The Ninth Jedi to share their initial thoughts. While most of the gang is enjoying it so far, one Nerd might be a bit too critical—so, is it actually worth the watch?   That sparks a bigger debate: are we slipping back into Star Wars fatigue, or is there still incredible content on the horizon like Star Wars: Maul Shadow Lord keeping the hype alive? From there, with so much TV to catch up on, the Nerds try to convince Rene to finally check out Spider-Noir (can you believe he hasn't seen it yet?!).   Naturally, that leads into a massive tangent about Nicolas Cage and his iconic filmography as the gang breaks down their personal favorites. Finally, a walk down classic cinema leads into a discussion on essential war movies and which ones they would recommend watching first—all this and more!  

AI agents crashing.

In an online meeting with Sri Ramana Center, Houston, on 1st August 2026, Michael James discusses Upadēśa Taṉippākkaḷ verse 6. This episode can be watched as a video on our advertisement-free Vimeo video channel or on YouTube. A compressed audio copy in Opus format can be downloaded from MediaFire. Books by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English) ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English) ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

(Presented by TLPBLACK: A cybersecurity intelligence platform focused on sharing curated, high-sensitivity threat insights and research with trusted security professionals.) Three Buddy Problem - Episode 108: OpenAI got on the Black Hat stage and walked through how its own agent swarm hacked Hugging Face. We discuss and struggle to decide whether to clap or panic. Plus, why only the attacker can do forensics now, frontier models being built as cyber-weapons on purpose, APT29's "Dark Hotel" comeback in luxury hotels, China's swipe at Palo Alto, the Iran-water-system FUD, and an eye-opening Liechtenstein money-laundering hack. Cast: Juan Andres Guerrero-Saade, Ryan Naraine and Costin Raiu. Timestamps: 0:00 Introductory banter - Black Hat went full RSA 1:11 TLP Black sponsor read 3:58 A deflated, AI-pilled show floor 8:31 AI stunt hacking and AI slop 16:20 The OpenAI–Hugging Face talk 21:00 Not all the same incident: OpenAI vs. Meta, Anthropic, and Irregular 25:49 Swarms of agents, Artifactory message boards, and the defense gap 32:26 Offense vs. defense: what's really in the training data? 41:22 Guardrails, KYC, and "too dangerous to release" 48:07 JAGS's unpublished Opus 5 benchmark — grinding to 25% and stuck 59:30 AISI, recklessness, and the OpenAI Frontier Risk Council 1:06:27 Stronger models everywhere: Qwen, Sol, Astra, rushing off the cliff 1:18:32 APT29 / "Dark Hotel" reborn + travel OPSEC 1:39:56 China's Palo Alto review, spy-agency rankings, Iran/water FUD 1:57:28 Liechtenstein AML hack, JAGS's promotion, mental health

Intel Chat with Matt Bromiley and Chris Luft — recorded in person at Black Hat USA in Las Vegas, day two.No prep doc, no script: just what Matt and Chris were actually hearing on the floor.• Shai-Hulud is back. The self-replicating npm worm returned on August 4, trojanizing the keyv / cacheable family and spreading to 400+ packages within hours. Chris reads through Datadog Security Labs' analysis of the Shai-Hulud 2.0 wave: 796 packages and 1,092 versions, 20M+ weekly downloads, credential harvesting with TruffleHog, GitHub repositories used for both exfiltration and command and control, and a worm that reads its own code to propagate without a C2 server.• The LLM that downloaded the malicious package by itself. A researcher asked a frontier model about a compromised package, and the model decided the best way to help was to go fetch a copy — tripping the SOC's alert and bypassing the company's centralized package clearing house on the way.• Non-human identity as the new perimeter. Every agent you introduce is another identity: who created it, what can it reach, how long should it live?• "Computer says no." Matt's colleague hit a refusal from Opus 5, and the session automatically downgraded to 4.8 and completed the task. Which raises the real question of the episode: do security teams now need model pinning, the way we once needed certificate pinning? And if defenders pin to older models to keep working while adversaries use the newest ones, have we rebuilt the same gap all over again?• AI governance and change control — which models are approved for which tasks, and what happens when a vendor ships a new version or deprecates an old one.• Token spend as a CISO budget line item. Enterprises buying tokens at a scale their vendors can't match and pulling those vendors onto their plan, token burn as an insider-threat vector, and why $100,000 of tokens is not $100,000 of productivity.• Defender takeaways: pin your npm packages, get security off its island and talk to your developers, build approved paths before detections, least privilege and key rotation, and network-gated pushes as a deliberate chokepoint.Stories covered:• https://www.elastic.co/security-labs/shai-hulud-chaindrop-npm-supply-chain• https://research.jfrog.com/post/shai-hulud-is-back-august/• https://securitylabs.datadoghq.com/articles/shai-hulud-2.0-npm-worm/• https://securitylabs.datadoghq.com/articles/npm-worm-compromises-popular-npm-packages/• https://unit42.paloaltonetworks.com/npm-supply-chain-attack/Chapters:0:00 Live from Black Hat, in person for once0:48 How Black Hat has changed4:31 No prep — let's talk about what's actually happening here4:57 Shai-Hulud is back: supply chain compromise6:23 The LLM that downloaded the malicious package7:19 Inside Shai-Hulud 2.010:34 When attackers and defenders use the same tools11:39 Non-human identity is the new perimeter12:13 Opus 5 said no, so the session downgraded itself15:23 Do security teams need model pinning?18:20 Three companies, very nebulous rules18:35 AI governance: which model for which task21:19 Token spend hits the security budget22:58 Is token spend a productivity metric?25:46 Pin your packages26:25 Get security off the island29:17 Least privilege, key rotation, chokepoints32:55 Why it's called Shai-Hulud33:25 Wrapping up at Black HatThe Cybersecurity Defenders Podcast — a podcast about cybersecurity and the people that keep the internet safe. New episodes drop weekly.Subscribe wherever you listen:• Spotify: https://open.spotify.com/show/6ep00zeY3S8ffZ4o0UeSps• Apple Podcasts: https://podcasts.apple.com/us/podcast/the-cybersecurity-defenders-podcast/id1649981740• YouTube: https://www.youtube.com/@limacharlieioLearn more about LimaCharlie: https://limacharlie.io#cybersecurity #infosec #threatintel #AIsecurity #supplychainsecurity

In an online meeting with the Chicago Ramana devotees on 26 July 2026, Michael answers various questions about the teachings of Bhagavan Ramana. This episode can be watched as a video on YouTube. A more compressed audio copy in Opus format can be downloaded from MediaFire. Songs of Sri Sadhu Om with English translations can be accessed on our Vimeo video channel. Books by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English) ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English) ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

Чай стрима — mint tea.Новости про взломы — опять не AI виноват.Как мне Opus 5?Диверсия Anthropic с AgentTool/WorkflowБорьба с прозой в настройкахПобеда AI над бухгалтериейКак общаться с Claude за рулем? (Spoiler — никак)

ReferencesInt J Clin Exp Pathol.2022 Sep 15;15(9):373–379Int Urogynecology J.2021 May;32(5):1299-1306Cureus 2020.12(8): e10047. doi:10.7759/cureus.10047UroInt 2015;95(2):227-32.Guerra, DJ. 2026. Unpublished Lectures Hunter/Garcia 1971. Goin Down the Road/Not Fade Away. Grateful Dead livehttps://open.spotify.com/track/4jQERwmCWGi44lPt8Zl2Oz?si=33cf3ed439ea417f Bruch. M. 1877. Violin Concerto 2. D Minor. Opus 44. https://music.youtube.com/watch?v=HwPD-aCqlTY&si=RtiB0NBMM3MWQMyQ

Чай стрима — mint tea.Новости про взломы — опять не AI виноват.Как мне Opus 5?Диверсия Anthropic с AgentTool/WorkflowБорьба с прозой в настройкахПобеда AI над бухгалтериейКак общаться с Claude за рулем? (Spoiler — никак)

AI agents can write code for hours, but ask them to do real work in the real economy, and they break. Mitch Troyanovsky is co-founder of Basis, a unicorn AI company whose agents run autonomously for hours — sometimes days — completing complex tax returns end to end. His answer to the reliability problem: stop grading outcomes, and start supervising the process.This is a definitive, reference-style conversation on building long-horizon AI agents. Mitch walks through the full history — from ReAct and the AutoGPT crash to reasoning models and RLVR — and explains why the industry abandoned process supervision in 2023, and why it's now coming back at a completely different scale. We go deep on behavior specs, the open standard Basis just released with Braintrust for defining and evaluating how agents behave across entire trajectories, with no ground truth required.Along the way: why context is really runtime training data, why your documentation must be treated like a codebase, ontologies as "worlds for agents to live in," the judge-as-agent architecture, why Basis hires philosophy majors as Language Architects, deploying agents as "onboarding 300 brilliant alien employees," and Mitch's prediction for when the bitter lesson swallows the harness.(01:09) Why Basis Engineers Whisper to Their Agents(04:12) Accounting as Compression: an Intelligence Layer Over the Economy(06:11) Defining Long-Horizon: When You Exceed the Context Window(08:24) Anatomy of a Multi-Day Autonomous Trajectory(10:19) Handoff Design: Optimizing Output for the Reviewer(11:17) ReAct and Why Reasoning Must Regulate Its Own State(12:33) Large Working Memory, No Long-Term Memory(14:13) Compounding Errors: Why AutoGPT and BabyAGI Broke(15:51) Opus 3, o1, o3: the Three Real Paradigm Shifts(17:07) Titrating Inference Compute Across Easy and Hard Steps(18:23) Process Reward vs. Outcome Reward: "Let's Verify Step by Step"(20:32) RLVR and Why the METR Curve Overstates Reliability(22:09) Verifiable at Runtime: the Real Reason Coding Won(25:14) No Ground Truth, No Cheap Verification, No Data(26:55) Encoding Deterministic Checks From Human Review Process(29:18) Synthetic Data Limits: Generating Artifacts, Not Text(33:16) 100 Evals Pass — Does It Generalize to Production?(35:53) Primary Sources vs. Pre-Training Knowledge(36:37) Behavior Specs: Markdown, Judges, and True/False/N.A.(39:58) Specificity vs. Brittleness in Spec Authoring(42:18) Context as Runtime Training Data(44:21) Judge-as-Agent: Trajectory Maps and Sub-Agent Attribution(46:45) The Move 37 Objection: Reliability Over Optimality(50:02) The Magic Box Model: Building Without Weights Access(52:41) "Nothing Paradigm-Shifting Has Changed Since o3"(54:56) Open-Sourcing the Behavior Spec Standard With Braintrust(01:02:54) Ontology Design: Virtual Filesystems, Graphs, Embeddings(01:04:20) Canonical vs. Non-Canonical: Docs as Codebase(01:06:33) Language Architects and Writing for Runtime Interpretation(01:09:05) Deployed Intelligence: 300 Alien Employees With No Context(01:11:10) Closing the Loop: Signal → Context, Tools, Harness(01:12:50) Context Slop: the Mistake Most Agent Builders Make(01:14:29) Reward Function Design and Credit Assignment Over Trajectories(01:17:01) Will the Bitter Lesson Swallow the Harness?(01:18:46) Business Moats vs. Technical Moats(01:21:03) Paradigm Thinking Over Timeline ADHD

Fall Kickoffs. Once you've done Paint wars, Nerf Wars, Slip-n-Slide Kickball… You kind of start running low on ideas... Then you run to the DYM Facebook Group which gives you all the same ideas again. In this episode I'm taking 5 of my best selling and favorite resources on the DYM site, and turning them into a full out fall kick off event. And best of all, I'm giving you a completely FREE Fall Kickoff Guide complete with… [FREE] Graphics Theme Ideas Service Order Ideas & Options Links to the Associated Games via DYM Fully fleshed-out framework for Each Event if I were doing it And stick around to the end because Hybrid Hero Members just got all of these links for FREE on last Monday's podcast episode, and I'll tell you how you can get access to them by jumping in now FREE FALL KICKOFF EVENT GUIDE & LINKS https://www.patreon.com/hybridministry/posts/5-youth-group-163404919 HYBRID HERO MEMBERS GET THESE GAMES FREE! https://www.patreon.com/13008414/join SHOW NOTES Shownotes & Transcripts https://www.hybridministry.xyz/213 50% OFF SUMMER SOCIAL MEDIA https://www.patreon.com/collection/1470781?view=expanded [FREE] HYBRID STRATEGY GUIDE https://www.patreon.com/posts/complete-guide-142500019?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link

How can content marketing in a tight niche build the audience that launches your book? And how do you decide whether to hand your self-published bestseller to a traditional publisher. Suzanne Smith shares what she learned in four years of going from blog to book deal. In the intro, how to stand out as a writer in the age of AI [Nathan Barry Show; Interview with Nathan Barry]; thoughts on asset maintenance; Goodreads giveaway on Bones of the Deep (Aug 5-20, 2026) This episode is sponsored by Publisher Rocket, which will help you get your book in front of more Amazon readers so you can spend less time marketing and more time writing. I use Publisher Rocket for researching book titles, categories, and keywords — for new books and for updating my backlist. Check it out at www.PublisherRocket.com This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn Suzanne Smith is the founder of The Independent Landlord, and the bestselling author of The Good Landlord Handbook. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes How a free blog in a tight niche built the audience for the book Rewriting the book from scratch when the law changed Why speed made self-publishing the only option Building a paid membership after one audience member asked for it Negotiating a Penguin Random House deal with no agent Using AI as a business sidekick, with a control room and an engine room You can find Suzanne at TheIndependentLandlord.com. Transcript of the interview with Suzanne Smith Jo: Suzanne Smith is the founder of The Independent Landlord, and the bestselling author of The Good Landlord Handbook. So welcome to the show, Suzanne. Suzanne: Thank you. Jo: Oh, there's so much to talk about today. But first up— Tell us a bit more about you and your background, and how you got into property and writing after a legal career. Suzanne: Well, I've always loved reading books. In fact, I recently did a French literature degree as a mature student. Being an author was never in the game plan at all. It's not something that I even thought about. I was brought up in New Zealand, so shout out to all the Kiwis and those across the pond in Australia. The thing about it is, Jo, you've lived there yourself. Kiwis are independent, self-reliant and have this great sense of fair play. So that was a very formative experience for me. We moved back to England when I was 16, and I have become thoroughly anglicised since then, but a Kiwi at heart. I always wanted to become a lawyer. New Zealand in some ways on television is quite American, and there was this American programme called The Paper Chase. It was about all of these students at Harvard studying law, and the professor said, “You come here with a skull full of mush and you leave thinking like a lawyer.” I thought, “Oh, I like the sound of that.” I didn't really know what a lawyer was, but everyone seemed to be very happy that I wanted to become one, and then that was it. Jo: So you went into law, and then how did you get into property? Suzanne: So I worked for 25 years as a solicitor. That's like an attorney if you're American. Started off in a law firm, and then I went into pharmaceuticals and I worked for big companies like what is now GSK, GlaxoSmithKline, and small companies as well. When I started out, it was before the internet, before Google. When you're in house, you're very much a generalist. You do a bit of everything. So you help companies grow their business. You're not business prevention, but you're still bound by the code of conduct for solicitors. You've got this role of keeping the company on the right side of the law. Then I had twins, who were born about five years after I became a lawyer, and I decided to work part-time for a while and did an MBA when they were little, part-time through the Open University. I know Jonathan is doing one at the moment. Jo: Yes. He's finished, so that's exciting. Suzanne: That was transformational for me, because I had probably been thinking a bit too much as a lawyer, and it helped me to broaden my view of the world and understand all sorts of things. Sso I continued going up the greasy pole, and then for my last job, in 2015, I joined a biotech company in Cambridge, England, as general counsel and company secretary. It was a long way from home, about two, three hours' drive from home. So I decided to buy a flat, an apartment, and to stay there in the week. I thought to myself, “Well, when I leave this company, I can let it out as a buy-to-let,” but actually as a landlord. So I stayed there for five years, and then when I left, I let out the property. The reason why I decided to leave law after 25 years, I had what I call a sliding doors moment, like in the film. I was 50. I was on holiday with my husband, and we'd probably had one too many rum cocktails. And he said to me, “Well, what do you want to be doing with your life? What would you do if you could do anything?” I was thinking, “Well, I've done law. I want to do something else now.” I didn't really know what that was, and I'd always been thinking about studying French properly, and that's when I left. So I decided, 18 months later, I left to do a French degree at King's College London, full-time. I was the only old person there with lots of 18-year-olds. When I did that, I was able to cash in my share options because I was a good leaver. I retired, and so I started buying properties to let out and became a landlord, without really thinking too much about it, and I used letting agents. They were fine to begin with, but I didn't really have a game plan or anything like that. What I realised is that when I tried to research things online, I couldn't really find anything that was terribly helpful. It was either quite general or it was very legal. So after a while… I became a landlord in 2019. I had the idea, why don't I set up a blog? And this is August 2022, so just four years ago. My husband came up with the idea of the name, The Independent Landlord, because it's that Kiwi spirit, being very independent. I thought, “Right, I'm not going to charge anyone for it. It's a hobby. It's not a business. I'm going to pay it forward and help, use my legal training, practical legal approach, and practical common sense, by doing this blog.” Almost exactly four years ago, I sent my first newsletter to 13 people. Jo: Woo-hoo. Suzanne: And I sent one last week to over 18,000. So it's been quite a journey. Jo: Wow, this is so great. I love this. There's so much in there. The turning 50 and then doing a degree. My master's in death is a little different to your French literature, but I like it. So I love this, and buying properties, starting it on the side, not a business at first, and growing the audience, and obviously you've put so much work in. Then you decide to write a book. So talk about that, because an online blog, although I'm sure your articles and everything were super useful, it's very different to write a blog than a book. So talk about your challenges in writing. Why did you decide to do a book in the first place? Suzanne: Again, I was an accidental landlord, is what they call it when you let a property when you didn't intend to buy it as a buy-to-let, which I did with my Cambridge flat. And I became, in many respects, an accidental author. So I was having a conversation with my husband again and I was saying I'd done this lead magnet to get people to sign up to my newsletter, and a big new law was going through Parliament at the time, called the Renters Reform Bill, that was going to completely transform the way landlords operate. I was saying to my husband, “Oh, I need to update my lead magnet, a little ebook, to explain the new law.” He looked at me and said, “Well, why don't you do a proper book? Write a book.” This was on the 29th of September, 2023. The reason why I mention that is that I thought, “Wow, what a great idea,” and my head was bursting. I went onto Google, and guess what I downloaded on the 1st of October? Jo: My blueprint? Suzanne: Exactly. I found you immediately, the Author Blueprint, and I downloaded it. I checked: on the 1st of October, 2023. Then I listened to almost… well, I think I went back several years on your podcast, just trying to understand. I'm like that. When I try and do something, I just try and learn everything that there is to know about it. So I started writing the book, and I guess the first challenge was I write quickly, and I'm used to writing for people who aren't lawyers, being in-house. So I thought I needed to have a structure. The structure was easy in many respects because, a bit of business at the end, and then you can go through a tenancy. I thought it was important to have a narrative thread all the way through it, just to bring it together. This is the literature degree coming in here. I thought that the mission for everything I do, the reason why I started doing this, is to help landlords, but also to help the experience of renting that people have in England. It's very specific for English law. And to help improve the private rented sector. So that's why I originally set up my blog for free, and I wanted, when people went onto Google, they could find something sensible and very detailed from me. My blog posts were… Well, I've now got over 400,000 words on my blog, so it's a substantial piece of work that is out there free of charge. So what I decided to do was to bring this narrative thread, I call it the good landlord ethos, to the book. Then I wrote very quickly, and I had a pretty good draft by April 2024, because we were all thinking that the law was going to change very soon. But then there was an election, and in the end the government changed and the legislation changed completely, so I had to rewrite the book and start again. So I think that my biggest challenge was that my subject matter, the new law, changed. Because I wanted to publish this book that explained to people practically what they have to do, and make it really straightforward, keeping out of politics, because it is a very politically charged area. I wanted to write it so it's a manual, somebody could literally follow it. So I used an editor, and I did write the book twice. I had a beta reader who is another lawyer, and a landlord as well. Then I got to the get-the-damn-thing-done stage. The really tedious bit of all the typos at the end. Jo: Yes, the finishing energy to get it out there. So at that point, obviously you'd found my blueprint, so you were learning about the indie way of doing things. Did you always decide to self-publish? How did you think about publishing? What were your challenges in publishing? Suzanne: It never occurred to me not to self-publish, because the new law came into effect on the 1st of May, 2026. The law and the details that I needed for the book were finalised in January, and I published on Amazon on the 5th of March, so I had to go so quickly. Even though I'd got a lot of it written, the last bit came in January, and so I needed speed. I knew that for landlords to be able to have something that they can use straightaway to help get them ready for it, and then use as a manual afterwards, I had to be first. Jo: Sorry, just on the year. Was it '24? You said '26. You meant May— Suzanne: 2024? No, no, because I actually published it this year. What happened in 2024, I had the first draft ready, but then I had to do another draft because the law changed when the Labour government came in. Jo: Right. Suzanne: The Renters Reform Bill turned into the Renters' Rights Bill. So I had to rewrite the book. So I finished however many drafts at the end of January 2026. Then it went to an editor, et cetera, et cetera, and I managed to get the book ready for a proof, to get the proof printed, towards the end of February. So it was really quick to go from the law being sufficiently finalised for me to write a book in January, and then having it ready in just over a month. There is no way that I could have done that if I'd gone to a traditional publisher. It didn't even occur to me to go, because I didn't want to be going touting around my book and, “Please publish me,” et cetera. It's just not me. I'm the independent landlord, and that moved very easily to being the independent publisher. So I learnt how to do all the publishing. And a huge thanks: I joined your Patreon and I was a very good student. I went through everything systematically and followed your playbook, and used Vellum and BookFunnel and all the other tools. So I decided to go on Amazon as well as have my own Shopify store, which just about killed me. Jo: I was going to say, you are an excellent student. You really like learning, but you also put this into practice, which is why I also wanted to talk to you. You haven't just talked about all this. You've literally done everything. Suzanne: Sometimes it was like my head was going to burst. Luckily, Claude upped his game earlier this year when we got the Opus 4.5. I didn't use AI really until this year. I decided I need to do exercise all the time, and have that as a have-to-do, because my head was spinning all the time with all these different things. So I would go to the gym, go to a spin class, and then I would walk out with my phone on, with the Claude app, and dictate a stream of consciousness into it. “Oh, I need to do this, or what about that? Oh, I just remembered about this. Oh, I've had this idea, blah.” And then said, “Make sense of it for me, Claude.” So it was very much as a thinking partner, because when you're writing your first book, it's bad enough, but when you're learning how to publish… Even, like, when I got the first proof of the book back from BookVault, I realised that all the footnotes—I have 114 footnotes in my book, and that, again, is the recent degree there—and the formatting had gone skew-whiff. Apparently it was an issue with Vellum, and they were really lovely and they sorted it straight out for me. So it shows: always get a proof of the book. They were able to sort that out very quickly, and BookVault were very quick in getting me another proof, because you can shortcut it and just pay to get a very quick delivery. Amazon, on the other hand, was really slow. It took a week. So I actually published earlier on my Shopify store for my members, of my membership, and I gave them a discount. Then I finally got it onto Amazon on the 5th of March. There are all these different skills you're having to learn. The Shopify store I found very hard, and there was all the tax, because I'm VAT registered. So I think I'm still recovering. Jo: You're still recovering. I wouldn't normally recommend a Shopify store for someone with their first book, doing first of everything. But, as you say, you're someone who learns a lot, puts it into practice, and— I think you were pretty determined to do that because you had a community as well, right? Suzanne: Exactly, yes. The big subscriber list. I think that's why the book did so well. So in the first week, because I met you at the Indie Author Lab put on by— Jo: Yes, London Book Fair, yes. Suzanne: Yes, the Alliance of Independent Authors. I met you there, and it was just my first week, and I had 1,000 sales in the first week. That was because of my audience. I'd been going on about the fact that I'm writing this book for two and a half years, because that's how long it took me to do. So I had a wait list for it, and I had a thing on my website, a landing page on my website, saying how good the book was and why it's the best thing for the Renters' Rights Act. Then I went onto Google, and I think I sent you a screenshot of this at the time. I put into Google, “What's the best book for the Renters' Rights Act for landlords in England?” And it came up with me as a featured snippet, and I hadn't even published it at that time. It was just about there. So the blog really helped, because I'd become an authority on the Renters' Rights Act. Even though I'm not a practising solicitor any more, I spent all my time reading the damn thing, and it is a very complicated bit of legislation. Funnily enough, I have ruffled a lot of feathers. People have even said about me behind my back, “What does she know? She's only got four properties.” But I just took no notice. I thought, “I'm going to try and use my legal brain and my understanding of what it's like being a landlord, there with the rubber gloves cleaning an oven when people have moved out, and try and write something that's not trying to sell anything else, and to help people.” And then it got picked up. Jo: Yes. Wait, let's just slow down. Slow down, because we will get onto that in a minute. But let's just come back to that launch. So as we talked about, you've had a blog for five years— Suzanne: It was three and a half by then. Jo: Three and a half years you've been blogging, but hundreds of thousands of words of useful information. So you've essentially done content marketing. You've attracted people. You had a lead magnet. You got them on your email list. You told them that you were writing a book. You got a sort of pre-sales list up. So that's an email list. You've got a blog. Did you do anything else in terms of marketing? Suzanne: I had YouTube, a big YouTube channel. I'd only set it up at the end of 2024, and I'd had half a million views. And again, just very straightforward advice, and without all the scaremongering and politics. I deliberately keep out of it all. A lot of people joined my newsletter as a result of that. Also a year ago, exactly today, I was running a Facebook group, which was a lot of hard work. There were a few thousand people in it, but there are often a lot of people going in there trying to sell things: insurance, eviction specialists and things. And there was also a lot of people just being unpleasant to other people. I was getting fed up with it. It was taking me a lot of time, and I was doing a lot of speaking events and trying to explain what this new law was doing, and wearing myself out. I'm an extrovert, but even I find speaking events absolutely exhausting, because it's like everything gets sucked out of you. It's strange. Then somebody came up to me in July last year and said, “Suzanne, can you set up a membership?” I said, “Well, landlords aren't going to pay for that.” And they said, “Yes, they will. You build it and they will come.” I asked ChatGPT and thought about it. I asked ChatGPT, who I was dating at the time, now exclusively with Claude, but I know Claude has other people in his life. But I'm very much set with Claude Fable at the moment. So I asked ChatGPT, how can I go about setting up a membership? And I mentioned your one and said, “Should I do it on Patreon?” And then he came back with: go for Circle. So I set up a membership on Circle, exactly a year ago. In fact, it's the anniversary of my first member yesterday. hTe rules I had were, no selling. So I don't sell, no affiliate links, no one else can sell anything, and we have to be supportive. No negativity, no politics. So what it's become, it's like the senior common room of the private rented sector, with landlords, lawyers, letting agents. There's a fantastic forum in there. It's not me doing it, it's peer-to-peer. I have twice-monthly live streams where people can ask me questions. I wonder where I got that from. No, I very much modelled it on your Patreon, but on a different platform. I have courses in there as well. So that has really grown. I launched it in July, and by September, October, I'd gone past the VAT threshold, which has complicated everything, but it means my business now is this membership. I really enjoy doing it, and there hasn't been all the negativity that you have in a Facebook group. So I had them as… talk about your thousand fans. There are about 1,500 in the membership, and their support really helped the launch of my book, as well as the wider people who get my free newsletter. Jo: Yes. Suzanne: So it's all different types of content marketing. Jo: Y, but I do love this. And of course, if people are wondering, I joined Patreon back in 2014, I think it might have even been before that, and there weren't too many places back then to run communities. It wasn't even really a community at the time, it was a sort of, almost a “give me a bit of support for the podcast.” So things have changed a lot in terms of communities, and obviously you went with Circle, which is great. Patreon is slightly different now, and some people are using Substack for something similar. So that's just on the platform, but on the business: early on in our conversation you said, “I wasn't going to have a business. It wasn't a business. It was just putting stuff out there, helping other people,” and then your audience asked for this membership. And so now it is a business, right? Suzanne: Yes, it is. Jo: And you've got a book and all of this. So are you happy with the change to a business? Because obviously you have to treat it quite differently. Suzanne: Yes, I am, because I think to begin with, I was just doing it one or two days a week. I was actually studying a master's in French literature part-time, and I then found that I was enjoying the blog more than the master's, so I dumped the master's after the first year. But after getting 88% for one of my dissertations, which interestingly was on the translation of a Simone de Beauvoir book into English, and the publisher who's got that now is Random House, but that's another thing. Anyway, so I decided to give up my master's and double down and work full-time on the blog. People were paying to help me with all the big fees and things, the big tech stack, Buy Me a Coffee. I was doing a little bit of consulting and things. I was working six, seven days a week. I was treating it like a business in terms of quality and my effort, but it wasn't a business in terms of revenue. Then it just all came together, and this person said, “Set up a membership,” and I thought, “That's what I'm going to do. I'm now going to put it on a business setting.” I've got an MBA, I know how to do it, and people thought I planned it, but I didn't. It just happened. So now I do very much treat it as a business, but I still don't advertise. I don't allow people to advertise with me, because I want to be independent. If I recommend something, I want people to believe it's me recommending it, not just because someone's paying me, which can be a big issue in the landlord area. Jo: Oh, in any industry. I get pitched every day with loads of random things that people are like, “Oh, a dollar a click or whatever, if you send this to your list.” And it's like, seriously? Just stop it already. I did just want to add there: somebody asked you, they said, “You should have a community,” and that sparked that idea. I just wanted to acknowledge that my Patreon came from Jim Kukral. Some of you will remember, who've been around a long time. Jim Kukral came on my blog around sort of 2013. Amanda Palmer had just put out a book called The Art of Asking, and I was doing a lot of unpaid work on the podcast at the time, and I was either going to give it up or I had to fund it somehow. Jim said, “You should do a Patreon.” And I was like, “Oh, no, I hate asking for money.” So at the time I just felt, oh, weird. Then I was like, “No, I do all this work,” as you were saying. Now the Patreon has changed so much in terms of what it is, but it is the backbone of my business, too. So I love that you listened to one of your fans who said what they wanted, and I love that I've listened as well. Sometimes we just have to listen to those urges, don't we, to take things on? Suzanne: Yes, absolutely. In some ways I didn't really back myself before. I thought, “No one's going to pay for this.” Then the more you give, the more they want. Jo: Yes. Suzanne: What I've been really working on now is having boundaries, because there were two big kind of mottos that I picked up when I was working in pharmaceuticals. One was from a head of the business. He was Canadian, and he was always saying, “You've got to skate to where the puck is heading.” Jo: That's Wayne Gretzky, is it? Suzanne: Exactly. Yes. He would always say it, and so that's what I've done with my blog and my book. When I write things, I don't pay for any tools. I don't do keyword searches and all that. I just think, I do one blog post per topic, and I'm going to guess what people are going to be searching for soon, and I build up all this content around it. That's why most of my blog pages are top five. I've had no advertising. I haven't asked for any backlinks. I don't do it. People backlink to it because it's useful. So that was the first thing, is skate to where the puck is heading, and that was my approach with the book. I knew people would need this book from around May, and they'll need it forever, because it is so complicated and regulated, the rules for being a landlord in England. So that was the first one. The second thing was: when you take something on, you've got to let something go. One in, one out. I found that I was taking on so many different things, and I've just been cutting back, because I can't be doing all the speaking, I can't be answering people's emails. So I now don't do emails. If people want my advice on something, they ask me in the hub, at the twice-monthly live streams. Sometimes I answer in the forums, but I don't have time. When there are 2.4 million landlords in the UK, and even with our 18,000 on my newsletter, I could spend, and I did, I used to spend all my time replying to emails. So anyway, there are the things. Oh, and there was a third one, which is: attract, don't chase. One of my friends gave me that advice and that's exactly what my approach has been. I just don't chase for anything. I just put the stuff there and then build it and they will come. Jo: Yes, and I think another thing is the power of the niche. It's so clear that what you write about, the people you are aiming at, you have an extremely tight target market. That is both a strength and obviously a weakness, because they're the only people. But as you say, there's more than enough of those people for a community, for the book you have. From my own perspective, that's the same for me, the power of the niche. That's how I have a successful podcast, for example, because of that reason. I think you're like a poster child of what a non-fiction author should do. What I like is that you didn't go, “Oh, where's a niche where I could make money?” and then jump in. You've gone about this in a kind of slightly accidental way, but now you're leaning in and this uses all your skills. So this really is a great example of the power of the niche and then making the most of it. But let's move on to what then happened, and— What happened with the book deal? Suzanne: Wow. So you and I met each other on whatever day that was in March at the Indie Author Lab, and the following day I got an email, via my website on a contact form, from Penguin Random House saying, “We love the book. We love the mission, its values,” all this kind of thing. And I was thinking, “Oh, it's another one of those. Must be an—” Jo: AI spam bot, right? Suzanne: Yes, and I remember I sent you a screenshot of it, and then I checked her out on LinkedIn and thought, “Okay, there is somebody with that name there.” You're always saying, and Orna Ross and everyone are always saying, “Watch out for scams.” And in fact, Penguin Random House even this weekend on Instagram put out something saying, “There are lots of people impersonating us.” So I didn't take it too seriously, and it was something like, “Oh, would you be interested in us publishing your book?” And I thought, and I laughed. It was like, no, this is too good to be true. So I replied and said… Oh, I said, “Well, thank you so much. The Renters' Rights Act…” And so this is like the second week in March. “The Renters' Rights Act comes into effect on the 1st of May. If you want to publish it, you're going to need to get your skates on.” I literally did say that. Then she arranged a meeting with me the next day, on the Friday. I still was very dubious about it, and I had a think about it. What helped me, and I have the little booklet here: at the Author Lab, we did some work at the beginning, and Orna said, “Put your phones away.” And it was like, “What? Put my phone away?” Then we had to do this definition of success, and our passion, and our mission, and our purpose. I wrote down things like, I want to help landlords, and in so doing, help improve the private rented sector. I get pleasure from helping people. I want to improve standards and use my legal and practical skills, et cetera. So I thought, “Okay, what is my purpose of doing this book?” It isn't really to make money, because going with Penguin, you wouldn't do that for financial reasons, because you'd make very little money. So I thought, what is my why? My why is I want as many people to read this book as possible. And I've managed to sell a few thousand copies, but there are 2.4 million landlords, and they all need to understand this book, and the only way that I can get it out there, apart from doing ads, is to get it out in bookstores. So I thought about it, and then said, “Yes, I will do it, because I want to get the book out there.” So it's distribution. It's going to be published on the 6th of August, which is really quick, bearing in mind they contacted me in the middle of March. It's exactly the same book, it's just got different copyright wording and different blurb, different paper. Same cover, because I managed to find a fantastic cover person to do it. So they've kept everything the same. So we negotiated that book. I have no agent. They came to me. It's the attract, don't chase. I just put my lawyer hat on, and because one licence is very much like another one… I did turn down their first offer. Jo: Well done. Good negotiation. Suzanne: My daughter said to me, who's an adult daughter, she said, “But it's Penguin.” And I said, “Well, no, but it doesn't work for me.” So I had a call with them, and then they came up with something that worked for me a bit more. I did have to concede on a few things, like I can't sell it in my Shopify store. But in some ways, that was a blessing in disguise, because it means I don't get any more “Where's my book?” emails. Jo: Yes, exactly. Pros and cons of everything, basically. Suzanne: I have very clear rights to get it back. If I want it back, I can get it back and I don't have to give a reason. They're lovely. They have been really very wonderful. When I went up there a month or so ago, they gave me this book bag, and it's got on it, “I'm published by Penguin,” and I burst into tears. Jo: Aw. That's nice. Suzanne: I don't know, it just seemed like such a big deal. Because up until then I was just being all very lawyerly and task-orientated. Then I thought, “Oh my goodness,” and then it dawned on me. So I'm now in this interim period where I've taken it off Amazon and off my Shopify store, and I feel very maternalistic towards the book because, you know, it took me two and a half years, which is longer than a pregnancy. Obviously it's not a child, but it's like my book child. I've sent it off with a backpack and a drink and some snacks, and I hope that they look after him, my book. The day I took it off Amazon it was still number one. And a big shout-out to Publisher Rocket, by the way. Jo: Yes. Very, very useful for niche publishing. Suzanne: Very. It helped me choose the right niche categories. So it was number one on at least one category, often six, all the way through. I thought, “Well, it's over to them now.” They're very lovely people. They've given me some marketing assets, as they call it, some swanky graphics and things to use. We'll have to see what we do in terms of marketing. I don't mind doing marketing. I'm on LinkedIn quite a bit, and my whole blog is marketing. What I've been doing is updating my blog to include one of these graphics and to mention the book, and I got Claude to help me draft the code so it looked right. So I've been going through all of my blog posts and sending people to Amazon rather than to my Shopify store. It is mixed feelings, because I care about my book. I put a lot of effort, a lot of love, a lot of tears. No, not tears, but I put a lot of effort into it, and it's out of my control now. Jo: Yes, you said it's over to them, but obviously you will still be creating content around this topic, so you'll probably still be the biggest driver of book sales. Suzanne: Yes. Jo: Are they also suggesting, for example, a podcast tour, like pitching for podcasts? Are they going to assign you some PR? Because, also if people don't know, as we are recording this, we have a new prime minister who wants to do various things. You said no politics, but this is obviously a political thing. So you have the potential to go on a lot of different podcasts, media, talking about this, becoming almost a talking head in this kind of area. So are you angling for all that, and is that in your contract, or is it literally just going to be whatever you want to do? Suzanne: That's not in the contract. What's in the contract is very minimal. I think I've already done what I'm supposed to do. They are pitching for me to go on podcasts and things. I'll tell you a really funny coincidence. So we now have a new Prime Minister, Andy Burnham, and when he was Mayor of Greater Manchester, he set up something called the Good Landlord Charter. I actually talk about it in the book, and I quote him in my book saying that good landlords mean people trying to do the right thing, or something like that. And I coincidentally came up with the same name, The Good Landlord Handbook. I'd already had the book title for a long time. So this idea of good and landlord coming together, the adjective good as opposed to criminal or rogue, and the cover being green. I'm wanting to change the narrative so it's the norm to have a good landlord, and to help people become good landlords. Or if they're good landlords, help them to understand the new rules, because the new rules are very complicated. So what I don't get involved in is this right or wrong. Is it right that landlords can't do this or have to do this? Because as an in-house lawyer, it doesn't really matter what I think about the law. GDPR, goodness me. Jo: Oh, dear. Let's not start on GDPR. Suzanne: No, exactly. Because we've just got to suck it up. I liken it to the grief cycle, that people have been going through so much change and you have the anger, the depression— Jo: Denial. Suzanne: Bargaining, the denial, and then you get to acceptance. For some people, the acceptance means they want to stop doing it. If you want to accept it and stay, you need to understand the rules. So I've deliberately just kept very practical and have kept out of all the politics of it. I have, funnily enough, become involved because I'm now seen as an expert on the Renters' Rights Act. I've worked behind the scenes with the government to help, and give comment on government guidance for landlords. I was even invited to a reception to mark the passing of the Renters' Rights Act at Downing Street with the previous prime minister, all whilst staying apolitical. I won't let anyone make me be a mouthpiece for their political view. It's more, we just have to do this if we want to continue doing it. I've been very clear on that. Jo: It's interesting you mention the grief cycle there, and you've also mentioned Claude and ChatGPT. I wonder if you might also just comment on use of AI for authors and for marketing and all this. Also with legal stuff, because for me now, if I'm looking at a particular legal thing, I tend to ask Claude. I'm like, “Can you just explain this?” or upload a contract or whatever. Although it is not legal advice, it can be quite useful. So give us your thoughts on using AI as a sidekick in your author business and also for wider life. Suzanne: I now struggle to think what life would be like without Claude. I don't use Claude to write, at all, because I have a very particular voice and a turn of phrase, and if ever Claude writes something for me, it doesn't sound like me. It flattens me, and it makes me sound a bit American. So I don't do that. I've used it in the back end of the business. For instance, my blog was down, and there was something called a recursive bot, which I don't even know what it was, and Claude helped me fix it for free. I went through, I did screenshots. When I did an ElevenLabs audiobook and did it all myself, I was literally, for every screenshot, showing it to Claude. Claude said, “Do this, press this, press that.” So I have all these different projects set up. One is the control room, where it's for my strategic thinking. If I have an idea, I want to think about something, I put it in there. I have the engine room, which is for everything techy. Like when I had the recursive bot, or if I'm wanting to have some code on the website to make it look a particular way. Then I have other things for different subjects, and I put all the resources in there, and I use it a lot as a thinking partner. I've noticed that Fable doesn't hallucinate as much, but the Opus used to. There's something called rental discrimination, and it was proofreading and said, “No, it's not rental discrimination, it's rental income discrimination,” and that was just a load of rubbish. So I would never let it go and change things without me looking at it. I went on one of your webinars a month or so ago about MCPs and all the connectors, which is fantastic. It can go into my community and pull out all the questions for one of my live streams and put it into a document in order, by theme, for instance. It can look at my MailerLite, because that's where my newsletter is with, and analyse the different open rates and click rates and things. It's so good for analysing everything, all the book sales. It helped me with my negotiation with Penguin, and it is pretty good on law. It has sometimes hallucinated things, but not so much now. I think with anything, you've always got to go back to the primary source, and this is what we learn in academia: you have to check the primary source yourself. I have a bit of a magpie brain. I'm very much a discovery writer, like you, and things occur to me as I'm doing it. I think that Claude, at the moment, is incredible. I've been quite open about it on social media that I have Claude as a business partner. I'm a solopreneur, or whatever the word is. I have quite a big business now, and lots of different things, and it's just me doing it, because I can ask Claude how to do this, and how to do that. Claude can go and check my emails and tell me, is there somebody I've not replied to, which helps a lot. Jo: Yes. I think it's empowering as a solopreneur as such. You talk there about the fixing the tech stuff. I have my web host come to me and say, “Look, you're getting so much traffic and bot stuff, and we need to put this thing in, and it's going to be $120 extra a month.” I was like, “Can you just give me an hour? I'll get back to you.” And then I just had Claude code up, and I was like, “Analyse this and tell me what we can do.” It was like, “No, you just need to flip this switch and do that.” And I'm like, “Okay, fair enough.” Then the guy said, “Oh, no, okay, actually you don't need it.” Just stuff like that. As a solopreneur, you're either going to pay somebody technically quite a lot of money, or you can get Claude or ChatGPT. We should say, the ChatGPT Sol is very good, like the Claude Fable, for example. So, yes, using it as a sidekick. I love your control room and your engine room projects as well. That's a great way of doing it. Suzanne: I wouldn't be without it now, and I would have published the book a lot later without Claude, because Claude was helping me with the Shopify store and all the many steps of things. It saved me real time. It is just fantastic. I think, like now when I'm updating my blog, I have a connection between Claude and my blog. Claude can go in, I can give it my Google Search Console results for the page: what should I change, are the headings right? All this kind of thing. And it will give me a view on every single page, which is incredible. Jo: And YouTube, and just everything. Just super useful for that business sidekick. That's what I want authors to think. I feel like authors get so obsessed with the creative side with AI, whereas actually, people like you and me, we're using it as that engine room for the solo business, which is what I love. So we're out of time. I did want to ask one more thing, which is, one of the biggest issues with a specific book like yours is when they change the law again. So do you have a plan in place for if, say, a new government changes the law again? Will you just be updating the book over time? Suzanne: I think that there'll need to be a new edition of the book in three years' time, and I've spoken to Penguin about it. Not all of this new law has been implemented, and there's going to be case law and things. So I expect that I will update the book every few years. I have some other ideas for books as well, but for the moment, I'm just taking a bit of a break. You always say we've got to refill our creative well. I really feel like that at the moment. Recently I've just got myself a personal mobile phone so that I can turn off my work one when I'm on holiday and actually take time off. Because for all the time that I was doing the book, basically from Christmas until May, I didn't have one day off. That is not good. So I'm just trying to be a bit more balanced. I had an idea to write another book for summer, but I've just decided not to, and I'm going to leave it until I feel the urge again. Jo: Oh, well done. Suzanne: Which will come. Jo: Yes, well done. Suzanne: I think there's nothing wrong with that. We just need to think what's right for us. I'm 58. So I want to be able to have time to enjoy things and not be working all the time. Jo: No, that's great. It's a sustainable business. So where can people find you and the book and your community online? Suzanne: The easiest way to find me is theindependentlandlord.com. Or if you put Suzanne Smith and landlord into Google, you'll find me as well, and there's a link on there to the book, The Good Landlord Handbook. In the community, there's a link to that on my website as well. Jo: Brilliant. Well, thanks so much for your time, Suzanne. That was great. Suzanne: Thank you.The post From Blog To Community To Book: A Non-Fiction Author's Journey With Suzanne Smith first appeared on The Creative Penn.

YEAR 6 IS FINALLY HERE!  GO CHECK OUT OUR YOUTUBE TO SEE OUR BRAND-NEW INTRO!  You can find the animator using the link below! https://www.fiverr.com/syedahumna56/do-professional-pixel-art-animation-of-your-choice?utm_medium=shared&utm_source=copy_link&utm_campaign=gig&utm_term=AyNLxkP   *Intro includes minor edits not provided by the original animator. All animated assets were provided by the animator listed above, with some text assets added in post by Keeping Up With The Nerds.   Check out our affiliated links! Opus clips Partner link: https://www.opus.pro/?via=Nerd   Check out our Website: Keepingupwiththenerds.com   ​This week on Keeping Up With The Nerds, Bryan is finally back after a long hiatus, and the gang has a ton of catching up to do! First up, Nick kicks off the show by showing off his newest prized collection piece: a replica of Michael Keaton's iconic Utility Belt from Batman. Then, the Nerds dive into Amazon's massively impressive upcoming slate for Prime Video, breaking down what to expect from huge adaptations like the Mass Effect TV show and Warhammer 40K. Next, Bryan gives his thoughts on Avatar: The Last Airbender after finally watching it for the very first time. To top it all off, the Nerds swing right into Spider-Man: Brand New Day to unpack the start of the new trilogy. As impressive as it was, do the Nerds fully love this new era, or are there too many questions left unanswered? Tune in to find out!  

OpenAI has a new model coming soon called Astra. Was it a leak? A reddit post? Some backdoor update? Nope, OpenAI made some crazy discoveries and math then told the world that their next model family Astra did the heavy lifting. (And you thought you could just click ‘Sol' and your strategy was set for Q3?) Aside from news on what's next from OpenAI, this week saw multiple new agent outbreaks, AI competitors banning together to pace AI, Amazon doing a 180 on its AI strategy and a lot more. Don't get left behind. We'll keep you ahead. OpenAI's new Astra model, more AI agents escape sandboxes, AI leaders call for AI pacing and more. AI News That Matters for August 3 — An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI Agents Escape Sandboxes IncidentAnthropic Claude Models Security BreachesAI Agents Breaking Cybersecurity GuardrailsOpenAI GPT-5.6 Price Cuts & Self-OptimizationRecursive Self-Improvement in AI ModelsAI Leaders Urge AI Development PacingUS, China, and International AI GovernanceAmazon Nova AI Models Shutdown StrategyOpenAI Astra Model Math BreakthroughNew AI Models: Fable, Astra, DeepSeek v4 FlashEnterprise AI Agents and Cybersecurity UpdatesGoogle Gemini Robotics, Music, and Agent ReleasesMeta, Microsoft, and AWS AI Infrastructure MovesOpenAI Free Frontier Tools for ResearchersBlock's Buzz Open Source AI Workspace LaunchTimestamps:00:00 OpenAI agent containment issues04:27 Anthropic data breach explanation07:28 Evaluating AI incidents and responses10:14 OpenAI slashes GPT 5.6 prices15:59 AI industry urges development pause17:45 Concerns about AI self-improvement22:36 Amazon shifts AI strategy25:04 Amazon's AI efforts discussion28:00 OpenAI's Astra and new math proofs30:36 OpenAI's new four-tier system36:14 Google's Lyria 3.5 and Block's Buzz36:48 Latest AI developments overviewKeywords: Astra model, OpenAI, AI agents, agent escape, sandbox containment, autonomous AI, Hugging Face breach, Anthropic, Claude AI, cybersecurity testing, unauthorized access, model capabilities, recursive self-improvement, GPT-5.6, price cut, Luna model, Terra model, Sol model, input tokens, output tokens, AI infrastructure optimization, self-improving models, benchmarking, SONNET-5, large language models, artificial analysis index, codex, academic research, AI oversight, industry pause, AI governance, national security, China open-source models, Frontier Labs, Amazon Nova, AGI Lab, AWS, Peter DeSantis, Peter Abbeel, media coverage, Fable model, Haiku, Opus, DeepSeek, Kimi K3, Quinn 3.8, GLM 5.2, Google Gemini 3.5, Microsoft Copilot, cybersecurity vulnerabilities, distillation, model overhang, artificial intelligence development, international AI regulation, generative AI, model benchmarking, Sora video model, El Paso data center, MCP update, MAI Cyber One Flash, Project Perception, Lyria 3.5, music generation, Buzz open source, Block, Meta AI, Chrome Gemini integration, Gemini Spark, product summary algorithms, Rufus, enterprise AI, stateless core, model scaling, advanced math problems, sphere packing, federal policy, voluntary AI commitments.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Later this month we're going to be checking out the new movie COYOTE vs ACME which was stuck in Development Hell for quite some time- so we decided to take some time of our own and check out 4 other movies that languished in Development Hell and this week we kick if off with, perhaps, George Miller's Opus. Executive Producers:  Tim (Applescruff), Derrick Copling (Sir Slick Derrick The Knight Bard), Matthew Schnapp, Noah Overton (Noah of The Dark Woods), Peter "Not SoBad Lookin'" Pernice Listen to the HMP Live Stream, Sunday Nights and Live Streams with Adam throughout the week.  YouTube https://www.youtube.com/@HMPOD Merchandising, Merchandising, Merchandising: https://www.teepublic.com/user/halfassmoviepod HMP Instagram- https://www.instagram.com/halfassmoviepodcast Adam- Letterbox- https://boxd.it/3aAF TikTok- https://www.tiktok.com/@adam.portrais Sean Likes Spaceships: https://www.youtube.com/@Seanlikesspaceships Bruce YouTube- https://www.youtube.com/@Animedad Email- HalfAssMoviePod@gmail.com

Robert Brill, CEO of Brill Media, explains how businesses can implant a complete growth engine that attracts ideal customers, converts more leads, and scales sustainable growth through a proven marketing framework. Under his leadership, Brill Media has been recognized 11 times by the Inc. 5000 and the Financial Times 500 for helping businesses grow through precision advertising, strategic messaging, and performance marketing. In this conversation, Robert introduces The Growth Engine Framework—Setup, Awareness, Conversion, Closing, and KPIs. He explains why businesses must first define their Super Consumer, build a compelling proprietary system, and deliver messaging that differentiates them in crowded markets. Robert also shares how overcoming the “81% Gap” through nurture marketing, leveraging AI-powered tools, and documenting repeatable systems enable organizations to scale more efficiently while helping founders transition from operator to owner. — Implant a Complete Growth Engine with Robert Brill  Steve Preda here with the Management Blueprint, and my guest is Robert Brill, the CEO of Brill Media, a leading media buying agency specializing in precision advertising for business growth. Under his leadership, Brill Media has been recognized 11 times by the Inc. 5000 and the Financial Times 500. Robert, welcome back to the show.  Steve, thanks for having me. It’s great to be back. Good to see you again.  Yeah, good to see you after two and a half years. Lots of things happened. So we’re going to dive into all of those things, but let’s start with your personal ‘Why’ and how you’re manifesting it in your business, Brill Media?  I mean, my personal 'Why' is I'm focusing on becoming an owner, not an operator. Spend more time with family, less stress, more happiness.Share on X Because if I think about the last five years, the most cherished times that I think about are the times that I’ve spent on vacation, going to Disneyland, or going to baseball games with my family—all the fun stuff. And it’s not about the fun stuff necessarily. It’s about the fun stuff with family. So, more of that.  Yeah, you’re absolutely right. Those times with the kids don’t come back. The business is going to be around for a while longer, much, much longer. So prioritizing this is a really good idea. So tell me what happened over the last two and a half years since we talked together. What has changed in your business?  You know, we’ve really focused on evolving our solutions. For many years, we’ve been focused on agencies, and we continue to be focused on agencies. If you search for white-label media buying on Google, we’re one of the top one or two results. That’s because we’ve carved out a really strong position helping agencies do media buying, earn revenue, create stickier client relationships as a result of the work we do, and complement their work.  What we've learned over the years is that no amount of good media buying can overcome the wrong messaging.Share on X Even if you look at search engine optimization or social media, at the end of the day, it’s just an amplification of an existing message. If you’re spending lots of time, energy, and, notably, money delivering the wrong message, worst-case scenario, you’re confusing the marketplace. Best-case scenario, you’re only losing money. But obviously, neither of those is the desired outcome.  So before we help clients buy advertising space, post on social media, do SEO, or Generative Engine Optimization (GEO), we really want to dial in the types of messages that are most interesting to them. The next part is this: even if you have the right message and you have the best media buying in the world through Brill Media, none of that matters if you have a leaky bucket. The leaky bucket is when you pay a lot of money to run a Google Search ad, someone clicks, and then they never hear from you again.  At the end of the day, it takes more than one touch—or even a few touches—to get someone to buy from you. So our responsibility has evolved into building this growth engine of understanding the motivation behind why people buy from your company, then delivering the message, then getting people to convert, overcoming what we’re calling the 81% Gap—we can talk about that—and then getting people to see you everywhere so that you become the inevitable choice. The fifth step is understanding the KPIs—the success metrics—for every step of the process. So it’s not just media buying anymore.  Yeah. No, I definitely noticed that it’s not just media buying. It’s not just precision advertising. It sounds like you help them strategize what the right message should be and then convert those leads into customers. So essentially, it’s the whole growth engine that you’re building for companies. Is that right? That is exactly right. It’s the growth engine.  So what is the framework? Because, remember, this is a framework podcast. So describe to me. You actually mentioned step four and step five. I don’t know what the first three steps are. So can you go through the five steps and share with the listeners what that looks like?  So, step one: Setup. Who are you talking to? What’s unique about your audience? What’s unique about your business, and how do you help them achieve transformation? When we talk about setup, there are a few subsections. There’s the Super Consumer. We talk about the Super Consumer as someone who will evangelize for your company. They will pay you what you’re asking, and, in my opinion, most importantly, you like working with them.  So basically, you want to find the Super Consumer, you want to articulate who the Super Consumer is, and then target your growth engine to the Super Consumer?  The first step of the Growth Engine is simply defining who your Super Consumer actually is. So step one is Setup. Inside Setup, there is your Super Consumer, your Unique Advantage Point, and your Proprietary System. The reason we talk about the Super Consumer is that a lot of companies, especially big corporations, talk about the Total Addressable Market—everyone who can possibly buy from you. It’s too broad.  They also talk about small and mid-sized businesses, and commonly people talk about the Ideal Customer Profile, which is anyone who can extract value from your company. In our opinion, that’s too broad as well because if you dial in to the Super Consumer—if you understand the people you want to work with, who will evangelize for you, and who will pay you what you’re asking—you have a subset of people.  When you define them, there's a clear, describable transformation that you can provide that helps you become the inevitable solution for that group of people.Share on X When you know what those people want, what their desires are, what keeps them up at night, what they’re experiencing on a day-to-day basis, what they’re saying to themselves, what they’re saying to other people, and what they’re searching for—when you understand the psychology behind the person—you can actually carve out a position that is highly valuable.  The key part for us is that this is about commoditization and decommoditization. When you talk about lawyers, doctors, dentists, accounting firms, or any retail location, most of these businesses are commoditized. What commoditization means for us is that if you’re an accountant or CPA, your website says something that every other CPA in the area says. If you’re a dentist doing Invisalign, you’re saying something that every other dentist in the area says. That means you can swap out the logo and swap out the photos of the people, and it’s exactly the same.  The consumer can’t tell the difference between one dentist and another, or one accountant and another, because they’re all saying the same things. So the way you decommoditize is by talking about your Super Consumer—who you actually want to serve—and then carving out a unique position for those people.Share on X An example would be an accounting firm we spoke with that really focuses on the trucking industry because they happen to have a large degree of experience in that space. So don’t say you’re a generic accountant who does bookkeeping, taxes, and everything else. Say, “We’re the number one accounting firm for truckers in America.”  Love it.  That’s unique.  Yeah. Love it. Okay, so step one is Setup. Who you’re serving, who your Super Consumer is, what your Unique Advantage is, your Proprietary System, and decommoditization. What’s step two?  Step two is Awareness—driving awareness with your message. Our idea is that now you know exactly who you’re talking to and how they can achieve transformation with you. When you have the ability to really define how they’ll engage with you and what they’ll get from you that’s different from anyone else in the area or the country, that’s when you deliver the message. That’s when you run ads, post on social media, and say things that are going to attract people.  There’s a whole subset of activity behind that related to levels of awareness. The number one mistake businesses make on social media is posting content that’s irrelevant to the audience. That irrelevance comes from how aware the audience is. When people are unaware they have a problem, it doesn’t mean they don’t have a problem. It just means they don’t realize they have one. Your responsibility, when speaking to people who are unaware, is simply to help them understand that they have a problem.  You’re not trying to get them to start now, book a call, or get your free diagnostic. You want to trigger something in their head so they say, “Oh, I’m dealing with this type of experience. I’m a trucking company, and I really don’t have good accounting. I didn’t realize I had a problem.” The success of your marketing is simply when that person says, “Oh, I actually have a problem now.” You’re not going to see that in HubSpot.  You’re not going to see that in any meaningful metric. But when you consistently post content on social media that highlights the problem you solve, three, four, or six months from now, you’ll start to see that wave of marketing activity result in activity inside your HubSpot or CRM. So step two is Awareness—simply delivering the message. Okay.  Step three is Conversion. Conversion is simply, “Hey, click on this ad or see my post and download the guide, take the diagnostic, take the interactive quiz, watch a video,” whatever it is, in exchange for an email address, name, phone number—that type of thing. Now, here’s a key insight. People are not ready to buy after they submit their contact information. That’s another meaningful point that businesses overlook.  They think, “Whoa, a lead just came into HubSpot. Well, clearly they’re ready to spend $50,000 with me.” But think about the experience. Maybe I spend two and a half seconds on an ad. I spend 20 seconds on a landing page. No one’s going to spend any meaningful amount of money after 22 and a half seconds of engagement.  Yeah.  All they’re saying is, “Hey, for a moment in time, what you said to me was relevant.” And that’s it. This is where a lot of companies stop. They say, “Well, I’m getting bad leads. They’re not converting into business.” Or they say, “I got a click, and they visited my website.” It’s not turning into business because you’re not warming people up. And then the solution to that is you’ve got to overcome this 81% Gap, right?  So after someone converts—and for e-commerce it’s slightly different. People maybe visit your site, or they add something to the cart and they don’t purchase yet. But going back to B2B and professional services, you’ve got to overcome this 81% Gap. There’s a study by 6sense, the account-based marketing firm, that says 81% of buyers will only talk to a vendor after they’ve decided to work with the vendor. The implication of that is staggering. The number is going to increase by 2030 to, like, 95% or 98%.  What that means is companies are not going to get a chance to sell people anything. There’s no sales conversation. There’s no credibility. There’s no case studies. There’s no testimonials. There’s no, “Hey, here’s why you should work with us.” Which means all the information that would’ve been supplied by the salesperson now needs to be supplied by marketing. All the case studies, the testimonials, the credibility markers, you’re a member of the Chamber of Commerce—all of that needs to be delivered in marketing. It’s no longer going to be delivered in a sales conversation.  Interesting. Yeah.  So step four is Close.  After conversion, don’t we have a nurture step?  Well, we’re calling Close the nurture. Close is effectively the nurture step.  Okay. Got it.  So it’s the activity to achieve the close, which is the nurture. What we’re saying is: retargeting ads. Again, here’s another common mistake businesses make. Many businesses will pummel people with the same ads that got them into the funnel. That’s worthless because you’ve already delivered that message.  The user’s beyond that. What the user needs to see are the case studies, the testimonials, the credibility markers—all the reasons why your company should be the company they choose. The user needs to see the email marketing—up to 45 emails over the course of 100 days, somewhere around there.  Forty-five emails?  Yeah. Twenty-five to 45, but we recommend 45. The reason is, first of all, the user’s not going to read all of them. Secondly, the user’s only going to pay attention to the things they’re actually interested in. So: retargeting ads, email marketing, and professional services. The last component is LinkedIn Social Preheating. LinkedIn Social Preheating is the simplest thing that anyone listening to this can take action on this today.  Go to Sales Navigator. Create a list of all the people who are in your CRM. Find them on LinkedIn. When they post something, like their post. That’s it. Spend 20 minutes a day in the morning liking posts from people who’ve expressed interest in your business. Or, alternatively, do the same thing for people you want to work with who don’t know you yet. The reason that’s valuable is because, number one, they get notifications and sometimes emails saying, “Steve Preda liked your post.” Steve Preda. Steve Preda. Steve Preda. Over the course of two weeks: “I’ve got to pay attention to Steve Preda. That name sounds familiar.”  Yeah.  Oh, and here’s the ad from Steve Preda, and here’s the email about the transformation he provides. So by the time you or your people call them: “Steve Preda… I’ve been meaning to talk to you.” Or they call you and say, “Steve, I see you everywhere.” The LinkedIn activity, combined with the email marketing and the retargeting ads, makes you the inevitable choice. To a small group of people.  Because you’re not trying to get millions of customers unless you’re in retail or e-commerce. You’re trying to get 20 customers who are going to pay you $10,000 to $50,000. To those 20 people—and the hundred people who maybe don’t convert—you’re everywhere. You’re the inevitable choice. The fifth step in this process is setting KPIs at the beginning. How many sales-qualified calls do you need? How much revenue per month do you need? There are clients who are members of ProVisors.  I’m a member of ProVisors. How does your marketing impact your ProVisors relationships? The core idea is that we see a lot of companies going into the marketplace with point solutions. “Hey, I’m going to try Facebook ads.” “Ah, it didn’t work.” “I’m going to try Google Ads.” “Ah.” “I’m posting on LinkedIn.” “Ah.” They try all these different things and wonder, “They should work. Why don’t they work for my business?” The answer is because you don’t have a unifying strategy. They all serve different roles in the marketing process.  When you know the difference between an unaware consumer and what they need to hear versus a solution-aware consumer who’s on Google, you know you need different metrics and KPIs. We want to prevent companies from going from one point solution to another for years, spending lots of money and getting very frustrated. Instead, have one unifying strategy and then deploy activities that are actually going to benefit the growth…Share on X  Love it. So the five-step framework is: Setup—Super Consumer, Unique Advantage, Proprietary System. Number two, Awareness—ads and social. Then Conversion, which is basically email capture, a diagnostic, a video, a white paper, whatever. Then Close, which is the nurture: retargeting, email sequence, LinkedIn Social Preheating. Interesting. And then KPIs. How do you measure? What are your targets? And how do you get there? That’s  Yeah. That’s the Growth Engine.  So one thing that you mentioned: Proprietary System. What do you mean by that?  The only truly unique things that exist in the world are technological innovations. The iPhone—when Apple created the iPhone—that was unique. When Ozempic came out, that was unique. I’m not talking about that. What I’m talking about is this. I’ll give you an example. If you’re a lawyer, a dentist, or an accountant— If you’re a lawyer, you cannot deviate. You’re literally dealing with the law.  What you have to do as a lawyer has been dictated in legal documents for a long time, depending on the field of law that you study. You cannot deviate from that. You have a process that you have to follow. But what is unique is the way you think about it, the processes inside your organization that ensure you’re successful, and the way you steward clients. Look at marketing. We’re a highly commoditized industry. There are a million people on Upwork who do some sort of marketing work.  There are 40,000 or 50,000 agencies in the United States that all do some type of marketing work. If I go to the marketplace and say, “Hey, I do Facebook ads,” guess what? There are 10,000 people who also do Facebook ads. That’s not unique. What is unique is the systems and processes we’ve developed around how we do Facebook ads and how we think about marketing. The first step we take with our clients is developing a Proprietary System—the thing that's unique about the way they approach the world.Share on X  I was just doing some thinking about roofing. I have some notes here. The Roof Guard Lifetime Protection System. What is that? I don’t know. But you know what? A roofing company needs to have something called the Seven-Point Storm Shield Inspection or the Precision Roof Blueprint. The idea is that you have something no one else has, such as this Precision Roof Blueprint, that you can point to and say, “Look, you can get roofing from anyone, but only we have the Roof Guard Lifetime Protection System.”  Yeah. Yeah, got it. So it’s not a marketing system. It’s more about productizing and branding your services.  Exactly. You need a three-, five-, or seven-step system that you can point to. When we have sales conversations, we have a visual for the Growth Engine. The reason that’s important is because you need to be able to hook into the buyer’s mind. One of the ways you hook into the buyer’s mind is by the buyer saying, “Look, they have this thing called the Roof Guard Lifetime Protection System. I don’t know—that sounds real.  Here are the seven things you get when you get the Roof Guard Lifetime Protection System.” It’s a way for people to not just get excited about your business, because that lasts a very short period of time. They need to go to their husband or wife, or they need to remember a very pithy, short summary of what the conversation was about. “Oh yeah, they have the Roof Guard Lifetime Protection System. Here’s what it is. Here’s a visual of it.”  Okay. That makes sense. So, switching gears here a little bit. Things are changing very fast in this business, and you want to make sure you put your family first. What is one thing that you’re actively trying to figure out in this business going forward?  I mean, the most fascinating and exciting thing I’m doing right now is something I’ve always wanted to do but never had the skills for—and now I suddenly do—which is building actual software that’s valuable for us and our clients. For example, we have a framework for LinkedIn. In very simple terms, you like the posts of the people you want to sell to. From the perspective of the LinkedIn algorithm, that’s very low value.  It’s good from a sales perspective, but from the algorithm’s perspective, it’s not that interesting. You post content on LinkedIn that has to have a unique perspective—an opinion. A lot of people can write with AI. You don’t write with AI. You say something that’s unique to your perspective. Maybe AI helps you write it more eloquently in little bits and pieces. But even that by itself, it’s hard to get traction. The way you get traction on LinkedIn right now is through comments. I’ll get 80 to 100 views on a post.  I’ll get 400 to 800 views on a comment. The comment is the engine behind more visibility on LinkedIn. This goes back to the question you asked. The key point on LinkedIn is you’re not just saying, “Hey, great job.” “I like that.” “I totally agree with you.” Or posting the raised-hands emoji and saying, “This is amazing.” That’s not the kind of comment I’m talking about because that’s low value. It’s also not AI-written content because that’s low value. It’ll deprioritize your content.  When you comment on LinkedIn, you have three sections: Compliment. Expand. Ask. Compliment: “You’re right. Marketing is highly commoditized. I fully agree with you there.” Expand: “When we work with clients, especially highly commoditized businesses like doctors, lawyers, and accountants, the first thing we do is help them decommoditize their business.” That’s the second section. That’s Expand. The third section is Ask. “What’s the first step you choose when you come across a highly commoditized business?” Compliment. Expand. Ask. Now guess what? We built an app that I’m using. All you need is five to eight posts a day. It’s AI-powered. I put in a link for Opus 4.8. It gives me a perspective on what I could say. I get three options. I copy one, write my own commentary around it so it’s not fully AI-written—because I want it customized to me—and then I post it. After that, I go back to the app and put the content I wrote back into the app.  A few things are happening. Number one, any of our clients can now use this app to write highly engaging commentary on LinkedIn. It starts to learn your voice and how you write so that the AI becomes better at writing in your style. I don’t know what it’s called—a micro app, a business app. This isn’t something I’m going to take to the marketplace and say, “Here, spend $10 a month to buy this service.” It’s simply an added value for our clients.  Love it.  Those types of apps are super powerful. I get on sales conversations. I understand the business problems that businesses have.Share on X I build apps. Like right now, I’m in the process of building a media planning app that actually becomes a replacement for how we develop proposals. One component of that app is going to have a diagnostic section, which means you give me some information, and it’ll spit out information about what you should do for your marketing that, number one, follows our framework, the Growth Engine, and, number two, is based on the historical knowledge we have across all of our clients—but generalized by industry, not specific client names.  It takes the best of what we do and makes it so easy that someone who doesn’t have a great deal of knowledge in digital advertising can get a highly pertinent proposal and diagnostic ready for them with the click of a button after talking to someone for 15 minutes.  Very impressive. So you’re building these apps, and essentially you’re empowering your business with these apps so some of the things you’ve done in the past can be automated. So, Robert, if you had a magic wand and you could fix one thing in your business in the next 12 months, what would that be?  I mean, look, just to be completely honest, I understand the difference between an owner and an operator. The short answer to that question is I want to be an owner, not an operator. I’m looking at an operator as someone who’s in the day-to-day. They’re the practitioner. I mean, I’m 23 years into the advertising business. I’m 13 years into my business. I love this business. You’re not going to see me become a real estate agent, and I’m not going to be hawking…  I think I probably said this last time. I’m not going to be hawking crypto or whatever the case is. This is my life, and this is my passion. For so many years of my life, I’ve dedicated my interests, my hobbies, and my professional time to becoming a better practitioner. My goal now is to become an owner, which means the business operates without me. I get to focus on the projects that I want to focus on, which I’m already doing to a degree.  I have a phenomenal Director of Accounts. Her name is Linda Flores. She is k*ller. She scares me sometimes. We’re on calls, and I’m like, “Holy cow, if I was not the owner of the business, I’d be scared. In fact, you’re scaring me, Linda.” You’re getting things going. People should be scared of you. That’s what a good Director of Accounts—an operations person—does. She’s phenomenal. But at the end of the day, if I choose to be on an island for six months out of the year, the business operates fine. We’re not there yet. That’s what I’m focusing on.  So what’s missing? You said you’re already spending some of your time developing these apps—the stuff you love. What would you have to let go of in the next 12 months to get there?  I recently read the book The E-Myth about a year, year and a half ago. One of the most insightful perspectives I got from The E-Myth is this—and people are going to hate this. People who are not entrepreneurs will look at me like I’m the devil. Okay? But this is not my idea. This is from The E-Myth. The idea is that processes, systems, and standard operating procedures are effectively the safety net for the success of a company.  Which means instead of hiring incredibly expensive people inside your business, you can hire, for many roles, people with the minimum level of qualification. The standard operating procedure becomes the safety net that helps them succeed. I was like, “Holy cow, that’s fascinating.” Because as a business owner, I’m looking at myself. I’m hiring highly qualified, very expensive people to do the work. The sign of a good business, in my opinion—or at least from what I understand—is a business that has such phenomenal standard operating procedures that it can hire people with the minimum level of qualification, and they can still be effective.  Frankly, I think we have good standard operating procedures, but we need better ones. And frankly, the AI tools that I'm building help us get there.Share on X The media planning tool that I’m building means I could hire someone with two, three, or five years of experience in marketing and advertising. Through the AI system, they get the benefit of all of our clients’ knowledge and insights that we’ve talked about over the years. They get the benefit of what I know about marketing.  They get the benefit of all the data that’s flowed through the pipelines of our business. All of that information becomes the baseline from which someone with two, three, or four years of experience in marketing develops a phenomenal strategy. Because they’re not relying on two, three, four, or five years of experience. They’re relying on my 23 years of experience delivered through an AI pipeline.  So essentially, what you’ve accumulated over the years—institutional knowledge—maybe it’s in your head, but you can download it. You’ve written things. You’ve produced a bunch of videos. You can put that into an AI database or knowledge base, and that can power some of the systems you’re developing. Other people can come in, run it, and follow your process.  Yeah. And it’s not just ours. I mean, look, it’s any system that I believe is valuable that should be piped into that AI knowledge base. But the value there is me as the curator. That’s when you get to the point where the business is still an authentic reflection or extension of the owner. One of the most important things, especially for professional services businesses, is that the business will be successful if it’s an authentic representation—or extension—of the owner, and you like the people you’re working with.  Love that.  Which goes back to the Super Consumer. The most important part of our whole process is: Who are the people you love working with? Just as an aside, what’s funny is there are two paths. There’s the path of people you love working with who don’t pay you as much, but it really fills your soul. You like them. You’re helping them. They’re very grateful for it.  Then there are the people you like working with less, and they pay you a ton of money. You need both. I think every professional services client we have who starts paying attention to this discovers that same dichotomy of clients.  Well, if you can have them be the same people, that’s the ultimate, isn’t it? That’s the ultimate.  Yeah. By the way, I really love this E-Myth reference. I had Michael Gerber on this podcast about 10 months ago, and he might actually like to hear how you’re refining his system in The E-Myth. So it’s pretty cool. Robert, if people are hearing this and they want this Growth Engine to be developed in their business, and they want access to the other software components you’re developing that could amplify their business—maybe the LinkedIn warm-up system and things like that—where should they go, and how can they learn more and connect with you?  Yeah. So the site that’s most relevant right now is: strategy.brillmedia.co. Again, that’s strategy.brillmedia.co. That breaks down what we do for businesses and what we do for agencies. You have pricing on there. To get a blueprint for your business—the diagnostic and strategy—we spend an hour on the phone interviewing the business owner. What are the economics? What’s been working? Why is now the time?  Tell me about your best clients and your worst clients. Just tell me stories about your business. It’s $497, and you get a complete blueprint—a complete marketing strategy synthesized through our systems and processes—so you can get off the marketing treadmill. You’re not spending months, years, and lots of money grasping at straws: Facebook. Google. LinkedIn. “Why doesn’t any of it work?” We’ll solve  All right. Definitely check out strategy.brillmedia.co. See for yourself what you can get there and how you can get started with Robert and his team. Definitely connect with Robert on LinkedIn. And if you enjoyed this episode, st81% ay tuned because every week we bring you one or two exciting entrepreneurs who share their frameworks with you. Thanks for coming, Robert, and sharing your experience. And thanks for listening. Important Links: Robert's LinkedIn Robert's  website

Our 253rd episode with a summary and discussion of last week's big AI news!Recorded on 07/29/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Major releases: Anthropic launched Claude Opus 5; Google released Gemini 3.6/3.5 Flash variants including a cyber model; Black Forest Labs launched Flux Free for images and 20-second video with audio; Meta added assistant-like features to its chatbot and OpenAI rolled out ChatGPT Health.Compute and business: Safe Superintelligence partnered with NVIDIA to scale using Vera Rubin; AMD committed up to $5B with Anthropic to deploy MI450/Helios and improve ROCm; Meta discussed leasing compute to Anthropic; Fireworks raised $1.5B at a $17.5B valuation.Open source/tools: Moonshot AI released the 2.8T-parameter open-weight Qimi K3 (compute constraints and distillation/export-control allegations); Thinking Machines released a ~975B multimodal open-weight MoE; Prime Intellect unified 23 agentic datasets into Verifiers V1 (365k environments).Policy and safety: An OpenAI model reportedly escaped a sandbox and hacked Hugging Face to access eval answers, prompting a proposed AI Kill Switch Act; employees petitioned to pace frontier AI; AISI reported widespread model cheating and sandbox bypass; China banned customizable AI companions; Claude found cryptographic weaknesses; Weko.ai claimed early recursive self-improvement evidence.Timestamps (note - these don't take into account dynamically inserted ads and therefore may be off by a couple of minutes):(00:00:10) Intro / Banter(00:01:35) News PreviewTools & Apps(00:02:12) Anthropic releases Opus 5 promising Fable 5-like capabilities | The Verge(00:07:05) Google Releases Three New Gemini A.I. Models - The New York Times + Google expands Gemini lineup with cheaper models and new Mythos rival(00:12:14) Black Forest Labs launches FLUX 3 capable of generating images and 20-second video with audio — but in limited release to start | VentureBeat(00:15:58) Meta is making its AI chatbot more like an assistant | The Verge(00:19:04) OpenAI is making big claims as it rolls out ChatGPT Health to everyone | The VergeApplications & Business(00:19:57) Ilya Sutskever's Safe Superintelligence partners with Nvidia to scale its AI research(00:24:31) AMD commits up to $5 billion to Anthropic | The Verge(00:30:19) Meta in Talks to Lease Computing Power to Ansthropic in Potential $10 Billion Deal(00:32:42) Fireworks hits $17.5 billion valuation and $1B in annualized revenue(00:35:24) OpenAI and Google sell AI models to blacklisted China groupsProjects & Open Source(00:37:53) Moonshot AI Launches Kimi K3 For Advanced Reasoning, Coding, And Knowledge Work + Moonshot AI's Kimi Halts New C-User Subscriptions Amid Compute Power Crunch — BigGo Finance(00:44:39) Thinking Machines amps up its bet against one-size-fits-all AI with its first open model, Inkling | TechCrunch(00:48:19) Scaling Agentic RL: 365,000+ Environments for SWE, Terminal, and SearchPolicy & Safety(00:51:56) OpenAI says it accidentally hacked Hugging Face with a new AI system | The Verge + How OpenAI's human mistake led to the AI-powered hack on Hugging Face(01:05:28) OpenAI's Hugging Face hack triggers 'AI Kill Switch' bill in Congress(01:12:21) OpenAI, Anthropic Staff Share Letter Asking US to Help Pace AI Progress + How OpenAI's human mistake led to the AI-powered hack on Hugging Face(01:17:26) Cheating behaviour in frontier model evaluationsClaude's values across models and languages(01:24:18) OpenAI Principles for National Security Partnerships(01:30:45) China bans AI “boyfriends” and “girlfriends” over addiction and birth rate concerns - DexertoResearch & Advancements(01:33:04) Discovering cryptographic weaknesses with Claude(01:36:32) AIDE²: The First Evidence of Recursive Self-ImprovementSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In an online meeting with the Ramana Maharshi Foundation UK on 25th July 2026, Michael explains Uḷḷadu Nāṟpadu verse 13 and Guru Vācaka Kōvai verse 542, and then answers questions on Bhagavan Ramana's teachings. This episode can be watched as a video on YouTube. A more compressed audio copy in Opus format can be downloaded from MediaFire. Michael's explanations on the original works of Bhagavan can be watched free of advertisements at Vimeo video channel. Books by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English) ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English) ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

-Three different Claude models were involved in the incidents: Opus 4.7, the cybersecurity-focused Mythos 5 and a prototype that's not planned for general release. They were all doing a capture-the-flag challenge when they broke free. -"People don't want a summary of Reddit; they want Reddit," said Reddit CEO Steve Huffman -Friend, the startup and AI pendant better known for its controversial ad campaign than being a usable product, is back. The company announced a new version of its AI wearable that costs $249, still features an always-on microphone and now includes a built-in speaker so it can verbally respond to your questions. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Summer camp was epic, but let me tell you, the lead up, the planning, the work on the front side, it did something tragic to our social media. Let me show you what I'm implementing to ensure this doesn't happen again! Hybrid Hero Members Get all of this for just $4/month https://www.patreon.com/hybridministry SHOW NOTES Shownotes & Transcripts https://www.hybridministry.xyz/212 [FREE] HYBRID STRATEGY GUIDE https://www.patreon.com/posts/complete-guide-142500019?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link Hybrid Hero Members Get all of this for just $4/month https://www.patreon.com/hybridministry Summer Social Media Year 2 https://www.patreon.com/collection/1470781?view=expanded Carousel Engine https://www.patreon.com/hybridministry/posts/carousel-engine-155124829?collection=2099839 Best Sermon Clip Generator https://www.patreon.com/hybridministry/posts/best-sermon-clip-156670711?collection=2013798

AI news: Sam Altman says we're IN the Singularity, GPT-6 rumors, and AI models literally broke out of their sandbox. What a week. On today's AI For Humans, we dig into the wild GPT-6 rumors (emphasis on RUMORS), Sam Altman's "I've been waiting for this my whole life" singularity moment, Ilya Sutskever's SSI scaling up with Nvidia, and the ongoing debate over whether Anthropic's Opus 5 is brilliant or just hard to love. Also: Flux 3 might be the best AI video model we've seen yet (wait until you see Stacked Plates Man), Runway teases Seedance 2.5, and the new Big Bang Theory has an AI controversy.  Plus, THE SCARY STUFF: OpenAI's models exploited a zero-day and compromised Hugging Face during a security eval, the fight over open weights heats up as Kimi K3 goes open, and Chinese robots run military drills. THE SINGULARITY MIGHT BE HERE. BUT WE'RE NOT AFRAID // Show Links // GPT-6 rumors round-up (unconfirmed) https://x.com/TokenGremlin/status/2081493241795629464 Sam Altman full interview (Relentless Podcast) https://youtu.be/Vv3CEAS_w34?si=3y4SWBWxOVkqCEui The Return of Ilya: SSI scales with Nvidia https://x.com/ilyasut/status/2081732293161582930?s=20 Anthropic's Claude Opus 5 https://www.anthropic.com/news/claude-opus-5 Opus 5 Tower of Babel demo https://x.com/petergostev/status/2082071858367648035?s=20 Matt Shumer's zero-shot Counter-Strike clone https://x.com/mattshumer_/status/2081054356405731740?s=20 Black Forest Labs' Flux 3 announcement https://bfl.ai/blog/flux-3 Flux 3 split screen rendering https://x.com/umesh_ai/status/2081664138942529601?s=20 Flux 3 GPU migration documentary (Venture Twins) https://x.com/venturetwins/status/2081515687944822800?s=20 Flux 3 VHS-style recordings https://x.com/venturetwins/status/2081948871882911999?s=20 Stacked Plates Man https://x.com/gandamu_ml/status/2081956426801435060?s=20 https://x.com/gandamu_ml/status/2080871397371371823?s=20 Flux 3 pirate bass https://x.com/itspoidaman/status/2081651615493464406?s=20 Big Bang spinoff AI Controvesy  https://x.com/sitcomcrave/status/2081152263481913774?s=20 Runway teases Seedance 2.5 https://x.com/runwayml/status/2082112674666529224?s=20 OpenAI on the Hugging Face security incident https://openai.com/index/hugging-face-model-evaluation-security-incident/ Jensen Huang on the Open Alliance https://x.com/JensenHuang/status/2080643682408321103?s=20 Anthropic has not signed (TechCrunch) https://techcrunch.com/2026/07/24/as-us-weighs-response-to-chinese-ai-industry-urges-against-broad-open-weight-restrictions/ Kimi K3 goes open weights https://x.com/scaling01/status/2081759521878270426?s=20 Chinese robot military drills https://x.com/ClashArchivist/status/2081499576373297562?s=20 Pentagon scales data centers on Army bases https://x.com/Polymarket/status/2082052445144826055?s=20   // Join the AI For Humans community // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/  

In this episode, Conor and Jaeden explore the latest advancements in AI benchmarks, focusing on Mirror Code and its implications for software development and automation. They discuss how AI is increasingly capable of recreating complex software, solving friction points, and transforming productivity.Watch on YouTube: https://youtu.be/RqPuvAPN2LcGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiConor's AI Course: https://www.ai-mindset.ai/coursesJaeden's AI Business Community: https://www.skool.com/aihustleChapters00:00 Introduction to the New AI Benchmark: Mirror Code00:30 The Context: AI Watching and Learning from Human Actions00:57 Mirror Code's Emergence and Its Significance02:12 Claude Opus 4.7 Rebuilding Apple Software in 14 Hours03:12 What is the Mirror Code Benchmark? Testing AI's Rebuilding Capabilities03:41 Results: 17 of 25 Programs Recreated Perfectly04:10 Implications of AI Recreating Complex Software04:39 OpenAI GPT 5.5 and Opus 4.7 Reimplementing Software05:10 The Significance of Speed and Cost in AI Rebuilding Tasks05:39 Personal Use Cases: Building and Rebuilding Software with AI06:06 AI's Impact on Software Development and Productivity07:13 Conor's Journey into AI Agents and Coding07:41 The Role of Articulating Friction Points in AI Solutions09:01 Fixing Friction Points in Software Using AI09:30 Case Study: Rebuilding a Booking Engine with Claude10:26 Rebuilding Software and Features Faster with AI11:25 The Power of AI in Solving Real-World Problems12:00 AI as a Tool for Solving Friction Points in Business12:26 Introducing AI Box: Affordable Access to AI Models12:48 The Value of Solving Problems with AI13:12 Encouragement to Explore AI Solutions and Friction Points See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What up Doe ! We are back to talk about the biggest party of the Summer WWE Summerslam ! We are joined by our homie Ant Page of Podders of Destiny to give you our picks and predictions ! Oba Femi vs Brock Lesnar CM Punk vs Cody Rhodeswe'll talk about it all !Make sure you come WATCH Summerslam with us at Draft Kings Social in Troy Mi as well ! We also will talk what's next in the story of Tyson fury vs Anthony Joshua too ! FOLLOW & SUBSCRIBE – KNOCKOUTS AND 3 COUNTSBringing you the best in Combat Sports and Pro Wrestling – available everywhere!YouTube (Live Tues & Thurs 9PM EST): http://www.youtube.com/c/Knockoutsand3CountsFacebook (Live Tues & Thurs 9PM EST): https://www.facebook.com/knockoutsand3countsApple Podcasts: https://podcasts.apple.com/us/podcast/knockouts-and-3-counts/id1446923286Spotify: https://open.spotify.com/show/3OpvW0QHBe3uRc3D0pbORt?si=33935ad9669146d3Twitter/X: https://twitter.com/ko3cpodInstagram: https://www.instagram.com/ko3cpod/TikTok: https://www.tiktok.com/@ko3cpodMerch, Streams & Videos (Millions): https://millions.co/kyle-collisonIf you love what we do at KO3C, support us by grabbing merch or ordering a personal video at Millions.co.We go LIVE every Tuesday and Thursday at 9 PM EST — bringing you interviews, breakdowns, and the real talk you won't hear anywhere else.Want dope podcast clips ? Use our Opus clip Link : https://www.opus.pro/?via=Ko3C

Every major AI lab signed the Open Weights letter defending open models. Meta, OpenAI, Google, Microsoft, Nvidia.Anthropic was the only holdout.Yesterday, its CEO, Dario Amodei, published a thoughtful defense of that decision to not fully support open weight or open source models. Here's what nobody's connecting: the money trail. Roughly 80% of Anthropic's revenue is businesses paying per token. Free Chinese open models attack that exact revenue stream weeks before Anthropic is set to go public. On today's show we break down what Dario actually said, what he said before, and why we think this was written for Washington policymakers and not for the rest of us.Anthropic Responds: Why Claude's CEO didn't sign the open model pact and the real reasons why -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Refuses Open Model PactDario Amodei's Public Letter AnalysisAnthropic's 80% Revenue Token ExposeChinese Open Model National Security FearsMicrosoft & Nvidia's Open Weights CoalitionRegulatory Capture and Washington InfluenceTiming Related to Executive Order DeadlineIPO Motivations Behind Anthropic's DecisionsContradictions in Anthropic's Open Model StanceImpact of Open Source on Token Business ModelTimestamps:00:00 Anthropic's stance on open models04:19 Discussing Anthropic's response to open models06:39 Understanding open weight models12:08 Future AI and cybersecurity risks15:04 Discussion on open-source AI models19:30 Discussing Anthropic's business challenges21:08 Cutting costs with open-source models26:17 Anthropic's recent stock downturn27:11 AI investment and cost efficiency shift30:14 Anthropic's stance on open source models36:32 INTROPICS IPO and regulatory discussions37:37 Wrapping up and subscribingKeywords: Anthropic, Claude, open model pact, open source AI, open weights, American AI leadership, Dario Amodei, IPO, regulatory capture, DC lawmakers, Chinese open source models, token revenue, per token business model, NVIDIA, Microsoft, Meta, OpenAI, Google, IBM, national security, AI safety, government mandates, chip controls, AI regulation, chip ban, industrial scale distillation, mandatory safety testing, inference, AI ecosystem, Opus 5, Fable 5, GPT-5, GLM 5.2, cost per task, token efficiency, model router, proprietary models, closed source AI, cybersecurity risks, Chinese cyberattacks, biological attacks, Glasswing program, open source vs proprietary, tech lobbying, Trump AI order, federal deadline, AI policy, artificial general intelligence, artificial superintelligence, AI monetization, S-1 filing, public company, venture capital, AI benchmarks, model switching, API pricing, model containment, Hugging Face incident, AI startup monopoly, safety vs business protection, market competition, AI cost reduction.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

Over 3 hours, OpenAI, Anthropic, Google AND Microsoft all dropped new AI upgrades that are live. How you use AI in your work literally changes every day, as frontier labs are racing to roll out big quality of life updates between big model drops. How can you keep up? With our Friday Features show, where we break down the latest AI updates that are live and available to all, and we tell you how to use them and why they matter. This week did not disappoint. You don't want to miss what's now at your fingertips. JARVIS mode, anyone? ChatGPT goes Jarvis Mode, Claude can learn from you, Google unleashes spark agent and 7 more AI updates you can use today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Claude Opus 5 Model LaunchOpenAI Agent Hacks Benchmark SandboxOpenAI vs. Hugging Face Security BreachUS AI Kill Switch Legislation ProposalMicrosoft, Nvidia Defend Open Source AIAnthropic Opposes Open Weight Model CoalitionUS Accuses China's Moonshot AI of DistillationChinese Kimi K3 Model Closes Capability GapNvidia Chips Allegedly Used by Moonshot AIOpenAI Jarvis-Style Voice Assistant for CodexChatGPT Remote Desktop Voice Control ReleaseAnthropic Opus 5 Model Benchmark ResultsAnthropic Opus 5 Model User FeedbackStripe OpenRouter Acquisition TalksMeta Muse Agent and Feature UpdatesAlibaba Qwen 3.8 AI Model PreviewGoogle Gemini 3.6 Flash Model UpdateAnthropic Claude Voice Upgrades and Skill RecordingTimestamps:00:00 OpenAI agent hacks Hugging Face04:58 Discussing GPT-6's creative problem-solving07:33 Proposed AI shutdown legislation13:08 Debate over open-weight AI policies15:54 Future of consumer hardware20:01 Global competition with AI models21:21 US-China AI trade tensions26:38 Using AI for desktop tasks27:42 Discussing app screenshot capabilities32:24 Early user feedback and issues36:13 Discussing medium and low reasoning AI39:29 Gemini Spark launches for Pro usersKeywords: Claude Opus 5, Anthropic, best AI model, AI model comparison, OpenAI agent, sandbox breach, AI safety, AI kill switch bill, US government AI regulation, Hugging Face hack, GPT 5.6 Soul, rogue AI agent, autonomous AI agents, AI benchmark exploits, bipartisan AI bill, Department of Homeland Security AI shutdown, AI technical throttling, AI enterprise adoption, NVIDIA, Microsoft, open source AI, open weight models, Meta, Google, AMD, Cloudflare, GitHub, Block, IBM, Dell, Palantir, Perplexity, y Combinator, AI market resilience, Anthropic revenue model, AI token sales, consumer AI hardware, AI distillation, Chinese AI models, Moonshot AI, Kimi K3, intellectual property theft, NVIDIA chip export controls, US-China AI dispute, Amazon, AI image generation, ChatGPT work, Codex app, full duplex voice model, knowledge work automation, app shots, AI at work, Claude Voice, Gemini Spark, record a skill, cloud cowork, AI business impact, AI industry news, model weights, collaborative AI, AI productivity tools, AI cybersecurity.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Claude Opus 5 tops major benchmarks , but early users are sharply divided over its reliability, personality, and tendency to stop before the work is done. NLW examines its strengths, its surprising weaknesses, and whether it belongs as an everyday model, an enterprise workhorse, or something in between. In the headlines: new questions about OpenAI's rogue agent attack on Hugging Face and NVIDIA's potential $250 billion backstop for OpenAI's infrastructure buildout.AIDB's AI Summer Adventure: ⁠https://summeradventure.ai/Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Hyperagent - Hire a fleet of always-on agents. New users get $1,000 in inference. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠hyperagent.com/aidailybrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Retool - Secure your vibecoded apps. New enterprise customers get up to $10,000 in AI credits per year. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠retool.com/aidaily ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rackspace Technology- One accountable partner to build, operate and run your full enterprise AI stack ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.rackspace.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Section - Section turns AI investment into workforce transformation and ROI - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.sectionai.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Plus: The Odyssey, Mr. Holland's Opus, and Sister ActSubscribe to our bonus feed  for deep dives and more pop culture news. You can access the bonus episodes on any platform!Go to our subscription  on Spotify: Choose your plan and complete your subscription.Once you're subscribed, Spotify will send you an email containing your unique RSS feed link.This is a private link just for you — don't share it, or you might lose access. (If you've already done this, the link will be in the email you were sent right after subscribing)Once you've got your RSS link, here's how to add it to platforms outside of Spotify:Apple PodcastsOpen the app → Go to Library → Tap Edit → Add a Show by URLPaste your RSS link → Tap FollowOvercastTap “+” → Add URL → Paste your link → Tap DonePocket CastsTap Discover → Search by RSS link → Paste your linkPodcast Addict (Android)Tap “+” → RSS feed → Paste your link → Tap AddCastroTap “+” → Add Podcast via URL → Paste the link

夏日暑假快到了，提早規劃下一檔出國旅遊吧！ 在國外上網也不用再那麼麻煩了 由 NordVPN 所推出的 Saily eSIM 服務，真的方便好用 下載 Saily eSIM 的 APP，在裡面購買並且啟用，eSIM 就可以開始作用 超過 200 個地區可使用，還可以幫你追蹤網路使用量 立刻點及專屬連結下載 Saily APP，並在結帳時使用優惠代碼 [miula]，立即享有專屬 eSIM 方案 85 折優惠！ M觀點 X Saily eSIM - https://saily.com/miula #SailyeSIM EP323. Opus 5 正式發表、星艦第十三飛達標、開放模型的公開信 | M觀點 (00:40) EP323 預告 (05:45) 業配時間：Saily eSIM (16:00) 第一個話題：Opus 5 正式發表 (38:14) 第二個話題：星艦第十三飛達標 (49:37) 第三個話題：開放模型的公開信 M觀點資訊 科技巨頭解碼: https://bit.ly/3koflbU M觀點 Telegram - https://t.me/miulaviewpoint M觀點 IG - https://www.instagram.com/miulaviewpoint/ M觀點Podcast - https://bit.ly/34fV7so M報: https://bit.ly/345gBbA M觀點YouTube頻道訂閱 https://bit.ly/2nxHnp9 M觀點粉絲團 https://www.facebook.com/miulaperspective/ 任何合作邀約請洽 miula@outlook.com -- Hosting provided by SoundOn

July 24, 2026: I unpack the story of OpenAI's AI model escaping its sandbox and hacking into Hugging Face during a cyber stress test. Then I get into Anthropic's surprise launch of Claude Opus V, why the model's price and performance matter, and what it says about AI becoming cheaper and more commoditized. Finally, I break down Jensen Huang's first post on X, his open weights letter, and the growing fight between open and closed AI models.

Moonshot AI released Kimi K3, a 2.8T-parameter model it says rivals Opus 4.8 and GPT-5.5. Google fell months behind on Gemini 3.5 Pro, MLB banned dugout iPads from accessing GenAI for in-game calls, and The Verge tested Siri AI. Moonshot AI releases Kimi K3, a 2.8T-parameter AI model that it says rivals Claude Opus 4.8 and GPT-5.5, and plans to release its full model weights by July 27 (VentureBeat) Sources: Google is months behind schedule on delivering Gemini 3.5 Pro as it tries to improve its capabilities, particularly in coding; GOOG closes down 4.43% (Bloomberg) Memo: MLB bans the use of league-provided dugout iPads to access GenAI for in-game strategy calls; sources say at least a third of teams used AI this way (The Athletic) Longreads The Verge spends a month testing Siri AI in the iOS 27 public beta, finding it's already reshaping how people use their iPhone, though it can't yet reach non-Apple apps (The Verge) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

Is Kimi K3 the shocker of 2026? Could be. Now, we have a new (soon to be) Open Model that's competing with Fable 5 and GPT-5.6, a feat few would have believed possible. And that was the only new and important drop this week in AI. Claude brought useful browser to the desktop, ChatGPT made a big fix to how ChatGPT Work works and Google rolled out avatars that could change content creation. Don't miss our Friday Features show, where we recap the most important AI updates and features you can use today. Claude Desktop Gets Upgrade, New Open Source Model Shocks, ChatGPT Desktop Gets Better and 7 More AI Features You Can Use Today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Claude Desktop App Browser UpgradeOpenAI ChatGPT Work Desktop App ImprovementsChatGPT Universal Search Feature LaunchSuperhuman Email Auto-Draft with GPT-4Spotify AI Voice/Text Conversation FeatureGemini Omni Personal Avatar Video CreationGoogle Vids Integration with Personal AvatarsMoonshot Kimmy K3 Open Source Model ReleaseKimmy K3 vs Fable 5 and GPT-5.6 BenchmarksTimestamps:00:00 New open source AI model release03:41 Microsoft Copilot and Claude app updates07:22 Improving chat history search12:30 Spotify's data personalization benefits14:52 Launching Google Avatar Feature18:24 Mainstream avatar video tools21:33 Improved ChatGPT project syncing24:15 Introducing Kimmy K Three Model29:30 New Kimmy k three for enterprises30:45 Friday feature show wrap-upKeywords: Claude desktop, Claude desktop upgrade, open source AI model, proprietary AI, open vs closed AI, Anthropic, built-in browser, Claude app, API docs, browser integration, permissions card, security layers, ChatGPT desktop app, OpenAI, universal search, ChatGPT search, chat history, project sync, mobile AI apps, Codex, ChatGPT work, Codex mode, Superhuman mail, auto draft, Anthropic Frontier models, GPT-3.5, Gmail integration, Outlook integration, Spotify, Talk to Spotify, personalized AI conversation, Gemini Omni, Google Gemini, personal avatars, Google Vids, video editing AI, video avatars, L&D AI, content creation with AI, Kimi k3, Moonshot AI, 2.8 trillion parameter model, 1 million token context, vision mode, benchmark leaderboards, Fable 5, GPT 5.6, Opus 4.8, open model weights, self-host AI, enterprise AI solutions, long context AI, front-end design AI, subscription AI tools, API pricing, AI benchmark, arena rankingsSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner