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

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This show has been flagged as Clean by the host. https://rufus.ie/en/ Kasa Smart Plug Mini with Energy Monitoring, Smart Home Wi-Fi Outlet Works with Alexa, Google Home & IFTTT, Wi-Fi Simple Setup, No Hub Required (KP115), White INIT STUFF Sudoers Apps apt update && apt install -y psmisc screen net-tools snapd pipx xbindkeys xbindkeys-config git nmap mono-runtime etherwake cloudflare-ddns mlocate samba # python pipx gahh su plex - pipx install python-kasa # sudo mkdir /root/PYTHONVENV/ python3 -m venv /root/PYTHONVENV/ pipx install python-kasa pipx ensurepath # update db mount -a sed 's//media//g' -i.bak /etc/updatedb.conf updatedb Contab 0 0 * * * /usr/local/bin/cloudflare-ddns --update-now 0 7 * * * /usr/local/bin/etherwake -i enp1s0 -D "d8:bb:c1:a2:2c:0b" 0 3 * * * /usr/bin/veracrypt -d 0 0 5 * * /usr/local/sbin/BSA.sh Mounts cat /etc/fstab UUID=2317187b-c592-46a7-8d8e-45c7d1eae7fc / ext4 errors=remount-ro 0 1 UUID=61A0-586A /boot/efi vfat umask=0077 0 1 UUID=db166a45-1afb-47dc-85cd-cbfcb06a9766 none swap sw 0 0 # data UUID=dbb20dc6-9487-4510-9ab1-c7bbc3014cdb /media/data ext4 defaults,nofail,noatime 0 2 # moredata UUID=5df24408-36ae-4205-8ecb-9d523dc4d820 /media/moredata ext4 defaults,nofail,noatime 0 2 # backup UUID=c1aac0d2-73ca-4d95-883f-5e1f43b5cd13 /media/backup ext4 defaults,nofail,noatime 0 2 Tunefs sudo tune2fs -c 5 -i 7d -C 1 /dev/sdd2 sudo tune2fs -c 5 -i 7d -C 1 /dev/sdb1 sudo tune2fs -c 5 -i 7d -C 1 /dev/sda1 sudo tune2fs -c 5 -i 7d -C 1 /dev/sdc1 XFCE Autologin # /etc/lightdm/lightdm.conf [Seat:*] autologin-user=plex autologin-user-timeout=0 # Enable service systemctl enable lightdm UPower ( do not run pwrstatd ) cat ./UPower/UPower.conf [UPower] EnableWattsUpPro=false NoPollBatteries=false IgnoreLid=false UsePercentageForPolicy=true PercentageLow=10 PercentageCritical=3 PercentageAction=2 TimeLow=1200 TimeCritical=300 TimeAction=120 CriticalPowerAction=HybridSleep PLEX RESTORE/BACKUP /home/plex/Library /media/moredata/_PLEX/usr/lib/plexmediaserver/ Backup ? https://github.com/sinicide/ansible-vm/blob/master/roles/plex/files/pms-backup.sh Autostart Plex /home/plex/.config/autostart# cat PLEX_STARTUP.desktop [Desktop Entry] Encoding=UTF-8 Version=0.9.4 Type=Application Name=KODI_STARTUP Comment=KODI_STARTUP Exec=/home/plex/.local/bin/Plex.sh OnlyShowIn=XFCE; StartupNotify=false Terminal=false Hidden=false RunHook=0 OMBI Sabnsbd+ IDK ... ?!!?!?!? 251 pipx install git+https://github.com/sabnzbd/sabnzbd.git 252 pipx install sabnzbd 253 pipx inject sabnzbd feedparser configobj cherrypy portend chardet cheetah3 puremagic guessit babelfish tmdbsimple 254 pipx install sabnzbd 255 python3 -m venv venv 256 source venv/bin/activate 257 pip install -r requirements.txt 258 cat > ~/sabnzbd/sabnzbd-wrapper.sh ~/sabnzbd/sabnzbd-wrapper.sh

The Daily Gwei Refuel gives you a recap every other week day on everything that happened in the Ethereum and crypto ecosystems - hosted by Anthony Sassano. Timestamps and links to topics discussed: https://daily-gwei-links.vercel.app/recent 00:10 Pectra live on mainnet https://x.com/ethereum/status/1919794615827280126 03:33 Ethereum Foundation changes https://x.com/ethereumfndn/status/1916883771280040264 06:53 EOF and core dev governance chat https://x.com/tkstanczak/status/1920380135225618907 12:09 Ethereum execution layer scaling roadmap https://x.com/nero_eth/status/1916740828376006820 14:16 New slot lifecycle dashboard https://x.com/samcmAU/status/1919996765568147773 16:54 Gas limit to 60 million soon? https://x.com/sassal0x/status/1920611528744509526 18:28 Track validator consolidations https://x.com/sassal0x/status/1920615893584740377 https://x.com/PierTwo_com/status/1920074963597631817 23:09 BuilderNet chat https://x.com/beaverbuild/status/1919752626935087368 https://explorer.rated.network/builders?network=mainnet&timeWindow=1d&page=1 27:48 Stablecoins and rwa are gonna be big https://x.com/sassal0x/status/1920622678072869243 34:00 Hanniabu's thoughts on scaling L1 and L2 https://x.com/hanni_abu/status/1920475130754371630 36:46 Top 4 layer 2's are now stage 1 https://x.com/sassal0x/status/1917770736137298104 39:01 Unichain integrates TEEs for block building https://x.com/unichain/status/1918290979368407403 44:11 Ethereum R1 neutral rollup https://x.com/ethereumR1/status/1917915035516498025 51:14 Infinex Connect revealed https://x.com/infinex_app/status/1917532851433791694 52:31 Where I'm at with my involvement in crypto https://x.com/sassal0x/status/1920264568946712694 This episode is also available on YouTube: https://youtu.be/X8TrVp1JWjU Subscribe to the newsletter: https://thedailygwei.substack.com/ Subscribe on YouTube: https://www.youtube.com/channel/UCvCp6vKY5jDr87htKH6hgDA/ Follow Anthony on Twitter: https://twitter.com/sassal0x Follow The Daily Gwei on Twitter: https://twitter.com/thedailygwei Join the Discord Channel: https://discord.gg/4pfUJsENcg DISCLAIMER: All information presented across all of The Daily Gwei's communication channels is strictly for educational purposes and should not be taken as investment advice.

Ethereum developers propose exponential gas limit increases. A federal court bans OFAC from sanctioning Tornado Cash. The DEF launches a petition for Tornado Cash developers. And EOF is removed from Fusaka. Read more: https://ethdaily.io/692 Disclaimer: Content is for informational purposes only, not endorsement or investment advice. The accuracy of information is not guaranteed.

A daily update on what's happening in the Rocket Pool community on Discord, Twitter, Reddit, and the DAO forum. #RocketPool #rpl #Ethereum #eth #crypto #cryptocurrency #staking #news Podcast RSS: https://anchor.fm/s/cd29a3d8/podcast/rss Anchor.fm: https://anchor.fm/rocket-fuel Spotify: https://open.spotify.com/show/0Mvta9d2MsKq2u62w8RSoo Apple Podcasts: https://podcasts.apple.com/us/podcast/rocket-fuel/id1655014529 0:00 - Welcome Rocket Pool news 0:50 - Community call with Langers https://discord.com/channels/405159462932971535/405163713063288832/1366941315434156094 https://docs.google.com/document/d/15UebhUh9rU1_S4czTiRn_w8W8me558g2FGObEUXS68s/edit?usp=sharing 2:38 - GMC updates previous grant rewards https://dao.rocketpool.net/t/gmc-grant-updates-april-2025/3605?u=shfryn 5:38 - AlphaGrowth secure new deal https://discord.com/channels/405159462932971535/405163713063288832/1366547867405254778 6:58 - Big RPL stake withdrawal from Constellation https://discord.com/channels/405159462932971535/894377118828486666/1366911446616772669 8:41 - $rETH in Hermes gauges https://x.com/HermesOmnichain/status/1917232427208184151 9:59 - Hodja shutting down node https://discord.com/channels/405159462932971535/405163713063288832/1367072984417828874 Staking news 10:40 - Wander tells the exit arb story https://discord.com/channels/968587363536220252/968589754264346664/1367168661097549854 Ethereum news 14:15 - Pectra fork on Gnosis https://x.com/superphiz/status/1917588658019856637 15:10 - Base stage 1 https://x.com/base/status/1917252389083758689 https://x.com/tkstanczak/status/1917296705029996695 18:29 - Vitalik's 2025 goals https://x.com/vitalikbuterin/status/1917541325072916590 20:19 - Blackrock's HUGE build on Ethereum https://x.com/harveymizzle/status/1917351110139879587? https://x.com/econoar/status/1917403734541209784 24:49 - Vitalik talks hardware/bandwidth requirements https://ethereum-magicians.org/t/formalizing-decentralization-goals-in-the-context-of-larger-l1-gaslimits-and-2020s-era-tech/23942 28:29 - EOF blowback https://x.com/potuz_eth/status/1917363736836255841? https://x.com/potuz_eth/status/1917552734242496779 31:38 - Clouted rallying the troops https://x.com/CloutedMind/status/1917104694650147312 In other news 34:55 - Drama in Bitcoin land https://x.com/basedkarbon/status/1917309726192226342?s=46 https://x.com/grok/status/1917412683948908984 36:41 - Recession more likely https://x.com/firstsquawk/status/1917557952728248389?s=46 https://x.com/deitaone/status/1917559941239386296?s=46

A daily update on what's happening in the Rocket Pool community on Discord, Twitter, Reddit, and the DAO forum. #RocketPool #rpl #Ethereum #eth #crypto #cryptocurrency #staking #news Podcast RSS: https://anchor.fm/s/cd29a3d8/podcast/rss Anchor.fm: https://anchor.fm/rocket-fuel Spotify: https://open.spotify.com/show/0Mvta9d2MsKq2u62w8RSoo Apple Podcasts: https://podcasts.apple.com/us/podcast/rocket-fuel/id1655014529 0:00 - Welcome Rocket Pool news 0:50 - Community call with Langers https://discord.com/channels/405159462932971535/405163713063288832/1366941315434156094 https://docs.google.com/document/d/15UebhUh9rU1_S4czTiRn_w8W8me558g2FGObEUXS68s/edit?usp=sharing 2:38 - GMC updates previous grant rewards https://dao.rocketpool.net/t/gmc-grant-updates-april-2025/3605?u=shfryn 5:38 - AlphaGrowth secure new deal https://discord.com/channels/405159462932971535/405163713063288832/1366547867405254778 6:58 - Big RPL stake withdrawal from Constellation https://discord.com/channels/405159462932971535/894377118828486666/1366911446616772669 8:41 - $rETH in Hermes gauges https://x.com/HermesOmnichain/status/1917232427208184151 9:59 - Hodja shutting down node https://discord.com/channels/405159462932971535/405163713063288832/1367072984417828874 Staking news 10:40 - Wander tells the exit arb story https://discord.com/channels/968587363536220252/968589754264346664/1367168661097549854 Ethereum news 14:15 - Pectra fork on Gnosis https://x.com/superphiz/status/1917588658019856637 15:10 - Base stage 1 https://x.com/base/status/1917252389083758689 https://x.com/tkstanczak/status/1917296705029996695 18:29 - Vitalik's 2025 goals https://x.com/vitalikbuterin/status/1917541325072916590 20:19 - Blackrock's HUGE build on Ethereum https://x.com/harveymizzle/status/1917351110139879587? https://x.com/econoar/status/1917403734541209784 24:49 - Vitalik talks hardware/bandwidth requirements https://ethereum-magicians.org/t/formalizing-decentralization-goals-in-the-context-of-larger-l1-gaslimits-and-2020s-era-tech/23942 28:29 - EOF blowback https://x.com/potuz_eth/status/1917363736836255841? https://x.com/potuz_eth/status/1917552734242496779 31:38 - Clouted rallying the troops https://x.com/CloutedMind/status/1917104694650147312 In other news 34:55 - Drama in Bitcoin land https://x.com/basedkarbon/status/1917309726192226342?s=46 https://x.com/grok/status/1917412683948908984 36:41 - Recession more likely https://x.com/firstsquawk/status/1917557952728248389?s=46 https://x.com/deitaone/status/1917559941239386296?s=46

A daily update on what's happening in the Rocket Pool community on Discord, Twitter, Reddit, and the DAO forum. #RocketPool #rpl #Ethereum #eth #crypto #cryptocurrency #staking #news Podcast RSS: https://anchor.fm/s/cd29a3d8/podcast/rss Anchor.fm: https://anchor.fm/rocket-fuel Spotify: https://open.spotify.com/show/0Mvta9d2MsKq2u62w8RSoo Apple Podcasts: https://podcasts.apple.com/us/podcast/rocket-fuel/id1655014529 0:00 - Welcome Rocket Pool news 0:43 - Devnet 2 update https://discord.com/channels/405159462932971535/405163979141545995/1366298824737292298 2:16 - oDAO members leave https://discord.com/channels/405159462932971535/894377758489210930/1365470863142420490 3:24 - RP delegate power https://discord.com/channels/405159462932971535/405163713063288832/1365593465747935262 5:27 - rETH back at peg https://discord.com/channels/405159462932971535/405163713063288832/1366274629882220604 Staking news 6:46 - Constellation closed https://discord.com/channels/405159462932971535/405163713063288832/1366147071538761849 https://discord.com/channels/405159462932971535/405163713063288832/1366191386734628905 https://discord.com/channels/405159462932971535/894377118828486666/1366145590630682636 https://discordapp.com/channels/968587363536220252/1153574664174579842/1366462876893839391 9:37 - Client update https://github.com/status-im/nimbus-eth2/releases/tag/v25.4.1 https://discord.com/channels/405159462932971535/918351974406172723/1365684462817509496 Ethereum news 10:39 - Ethereum Foundation vision and management https://x.com/ethereumfndn/status/1916883771280040264 14:00 - EOF dead… for now https://x.com/gakonst/status/1916035241799544908 https://x.com/giuliorebuffo/status/1916214087207555373? https://x.com/fabdarice/status/1916150700356341839 https://x.com/tkstanczak/status/1916713080265855444 https://x.com/nixorokish/status/1916863265323335727? https://x.com/hanni_abu/status/1916879615768793480 https://x.com/GiulioRebuffo/status/1916880719466410467 https://x.com/nixorokish/status/1916885363102212330 https://x.com/notnotstorm/status/1916887441090466002? 22:06 - Ethereum focused VC fund coming https://x.com/tkstanczak/status/1916884897496535384 23:39 - Justin bullposting Ethereum scaling https://x.com/drakefjustin/status/1916063798491807832 25:52 - EIP: 9698 - pump up the gas https://x.com/ethresearchbot/status/1916581042317623383 28:24 - ETH ETF Inflows https://x.com/glassnode/status/1916791424823033881 In other news 29:59 - EU blockchain idiocy https://x.com/moo9000/status/1916819407520600478? https://x.com/LefterisJP/status/1916894001770172449

Ethereum developers debate over removing EOF from Fusaka. Gitcoin winds down its Grants Stack. And client teams release Pectra-ready mainnet client versions. Read more: https://ethdaily.io/691 Disclaimer: Content is for informational purposes only, not endorsement or investment advice. The accuracy of information is not guaranteed.

The Daily Gwei Refuel gives you a recap every other week day on everything that happened in the Ethereum and crypto ecosystems - hosted by Anthony Sassano. Timestamps and links to topics discussed: https://daily-gwei-links.vercel.app/recent 00:00 Introductory song 00:10 Pectra is coming https://x.com/TimBeiko/status/1915064357823934944 05:12 AllCoreDevs update + EOF chat https://x.com/abcoathup/status/1915537526364004463 11:03 10,000 TPS on Ethereum L1 https://x.com/drakefjustin/status/1916063798491807832 16:23 Data on gas limit testing https://x.com/notnotstorm/status/1915439259630317995 https://x.com/ben_a_adams/status/1916228722065109195 22:31 L1 near-term roadmap for scaling https://x.com/rudolf6_/status/1914036782121058457 23:18 Why scale the L1? https://x.com/sassal0x/status/1916325531978764726 30:29 Vitalik proposes replaying the EVM with RISC-V https://x.com/pumatheuma/status/1913938547301863630 32:39 First Ethproofs ZK call https://x.com/portport255/status/1915560222950908408 34:11 Ethereumadoption.com gets a refreshed look https://x.com/hanni_abu/status/1915425976219037841 35:27 Strategic ETH Reserve website https://x.com/sassal0x/status/1915029027062223213 39:25 Immutable Ratings now live https://x.com/Ratings_wtf/status/1914679911765434499 43:13 David Hoffman's article on Ethereum's change in strategy https://x.com/TrustlessState/status/1913591660405051432 This episode is also available on YouTube: https://youtu.be/dhWbkXzgOW8 Subscribe to the newsletter: https://thedailygwei.substack.com/ Subscribe on YouTube: https://www.youtube.com/channel/UCvCp6vKY5jDr87htKH6hgDA/ Follow Anthony on Twitter: https://twitter.com/sassal0x Follow The Daily Gwei on Twitter: https://twitter.com/thedailygwei Join the Discord Channel: https://discord.gg/4pfUJsENcg DISCLAIMER: All information presented across all of The Daily Gwei's communication channels is strictly for educational purposes and should not be taken as investment advice.

Ethereum developers propose a separate metadata section for EOF. The first Quadratic Accelerator round goes live. Aztec Network pre-releases Noir 1.0. And Varun introduces the Snapchain data layer for Farcaster. Read more: https://ethdaily.io/604

From the archive: This episode was originally recorded and published in 2021. Our interviews on Entrepreneurs On Fire are meant to be evergreen, and we do our best to confirm that all offers and URL's in these archive episodes are still relevant. Dave Scatchard is a former 14 year NHL Pro, international speaker, author of The Comeback - My Journey Through Heaven and Hell. He is regarded as one of the top peak performance life and business coaches in the industry. Top 3 Value Bombs 1. If you can't bring your mindset and your energy to it, you're going to default back to old patterns. Do something big with the energy and shift the old way of thinking, being, and focusing, and build a new one based off of your champion moment. 2. When somebody is in a state of overwhelm, they can't see the way out. It's almost like it doesn't exist because their energy is so diffused, spread across too many areas. 3. To gain confidence, do things and make it your mission. Know what you can do every single day that will bring you an edge and a better chance to win. All Star Coaching - Learn more about Dave's coaching program! All Star Weekend Experience - Discounted offer for Fire Nation on Dave's live event in Scottsdale. Grab your early bird discount for $297! (Sorry! This link was active when this episode was first published in 2021 but is no longer an active offer.) Sponsors HubSpot INBOUND 2021, hosted with love by HubSpot, takes place online October 12-14. Learn more and register now for FREE at Inbound.com Uprising Food The amount of fake ingredients we consume every single day should be a concern, and the people at Uprising Food are here to help. Get 10 dollars off the starter bundle at UprisingFood.com/EOF

MakerDAO launches a $1 billion RWA competition. A Dutch court denies bail for Alexey Pertsev. A U.S. judge grants the delay of the Tornado Cash trial. And a core developer expresses opposition to EOF in Pectra. Read more: https://ethdaily.io/505 Sponsor: Harpie is an onchain security solution that protects your wallet from theft in real time. Harpie helps you detect and block suspicious transactions before they execute, safeguarding your assets from malicious attacks and scams. Try Harpie for free at harpie.io/ethdaily.

In today's episode Will is joined by his past client Caleb, who is has a unique approach to coaching his clients through the "Experience-Oriented Fitness" (EOF) process to get holistic health results. Enjoy the episode and take on a few points to your own coaching as you see fit that Caleb shares with us. Check out Caleb's podcast here: https://tr.ee/Tp3Y1r_IHP His Instagram here: https://www.instagram.com/coachcalebk/ Enjoy!

Core developers agree to include EOF in the Pectra upgrade. Celestia supports Arbitrum Orbit on mainnet. Taiko enables permissionless proving. And Base surpasses OP Mainnet in TVL. Read more: https://ethdaily.io/481 Sponsor: Harpie is an onchain security solution that protects your wallet from theft in real time. Harpie helps you detect and block suspicious transactions before they execute, safeguarding your assets from malicious attacks and scams. Try Harpie for free at harpie.io/ethdaily.

Core developers release EOF Meta EIP-7692. CoW Protocol introduces programmatic orders. Privy now supports passkey MFA. And Ethena releases its 2024 roadmap. Read more: https://ethdaily.io/462 Sponsor: Harpie is an onchain security solution that protects your wallet from theft in real time. Harpie helps you detect and block suspicious transactions before they execute, safeguarding your assets from malicious attacks and scams. Try Harpie for free at harpie.io/ethdaily.

The Daily Gwei Refuel gives you a recap every week day on everything that happened in the Ethereum and crypto ecosystems over the previous 24 hours - hosted by Anthony Sassano. Timestamps and links to topics discussed: https://daily-gwei-links.vercel.app/recent 00:00 Introductory song 00:39 ACDE: EIPs 2935 & 3074 into Pectra, EOF & 7623 shortlisted https://warpcast.com/tim/0xc7b63af0 01:11 EIP-3074 Explained https://domothy.com/eip3074/ 04:42 EIP-3074 wallet attack vectors & mitigation procedures https://twitter.com/danfinlay/status/1778601633381065167 https://twitter.com/haydenzadams/status/1778583141638168698 https://twitter.com/Ivshti/status/1778682389113233691 11:30 Verkle Implementers Call 16: bringing stateless clients to Ethereum https://twitter.com/rudolf6_/status/1778087343611884011 14:47 Ethereum Staking From Home Survey https://warpcast.com/remyroy.eth/0x97d9bceb 15:42 Omni airdrop w/ 50% to Solo Stakers https://twitter.com/sassal0x/status/1778600244466557104 17:38 Eigenlayer AVS mainnet launch  https://twitter.com/eigenlayer/status/1778486504542597480 19:43 BlackRock BUIDL to Circle for USDC transfers enabled https://twitter.com/Securitize/status/1778410122605150280 This episode is also available on YouTube: https://youtu.be/bvhGk90OjFE Subscribe to the newsletter: https://thedailygwei.substack.com/ Subscribe on YouTube: https://www.youtube.com/channel/UCvCp6vKY5jDr87htKH6hgDA/ Follow Anthony on Twitter: https://twitter.com/sassal0x Follow The Daily Gwei on Twitter: https://twitter.com/thedailygwei Join the Discord Channel: https://discord.gg/4pfUJsENcg DISCLAIMER: All information presented across all of The Daily Gwei's communication channels is strictly for educational purposes and should not be taken as investment advice.

The Daily Gwei Refuel gives you a recap every week day on everything that happened in the Ethereum and crypto ecosystems over the previous 24 hours - hosted by Anthony Sassano. Timestamps and links to topics discussed: https://daily-gwei-links.vercel.app/recent 00:00 Introductory song 00:39 ACDE: EIPs 2935 & 3074 into Pectra, EOF & 7623 shortlisted https://warpcast.com/tim/0xc7b63af0 01:11 EIP-3074 Explained https://domothy.com/eip3074/ 04:42 EIP-3074 wallet attack vectors & mitigation procedures https://twitter.com/danfinlay/status/1778601633381065167 https://twitter.com/haydenzadams/status/1778583141638168698 https://twitter.com/Ivshti/status/1778682389113233691 11:30 Verkle Implementers Call 16: bringing stateless clients to Ethereum https://twitter.com/rudolf6_/status/1778087343611884011 14:47 Ethereum Staking From Home Survey https://warpcast.com/remyroy.eth/0x97d9bceb 15:42 Omni airdrop w/ 50% to Solo Stakers https://twitter.com/sassal0x/status/1778600244466557104 17:38 Eigenlayer AVS mainnet launch  https://twitter.com/eigenlayer/status/1778486504542597480 19:43 BlackRock BUIDL to Circle for USDC transfers enabled https://twitter.com/Securitize/status/1778410122605150280 This episode is also available on YouTube: https://youtu.be/2vWSq8VLB_A Subscribe to the newsletter: https://thedailygwei.substack.com/ Subscribe on YouTube: https://www.youtube.com/channel/UCvCp6vKY5jDr87htKH6hgDA/ Follow Anthony on Twitter: https://twitter.com/sassal0x Follow The Daily Gwei on Twitter: https://twitter.com/thedailygwei Join the Discord Channel: https://discord.gg/4pfUJsENcg DISCLAIMER: All information presented across all of The Daily Gwei's communication channels is strictly for educational purposes and should not be taken as investment advice.

Escalation of Force Spotify https://open.spotify.com/artist/5LlxKqktZMjOxrEegvcc3n?si=oa4wnciJTCqYr50RBupmgg Youtube Playlist: https://youtube.com/playlist?list=OLAK5uy_m1wPwAuV0lo7XyP7oSYkW07UvOe9e-V9w&si=19hirKa7JSnXyFwv One of Detroits newest heavy bands with talented and badass detroit hc veterans. TJ fucking rules, EOF rules, let's get em out there!

Introduction Tina Kuhr is an Emergency Management Leader in the Nuclear Industry with the following experience: • Emergency Plan, Emergency Plan Program Procedure and Emergency Plan Implementing Procedure maintenance • Emergency preparedness regulatory change process • Drill and Exercise evaluation and critique report preparation • Training Emergency Organization Members Tina currently serves as the Emergency Preparedness Program Lead for GE Hitachi Nuclear Energy. She supported the transition from event based to position based emergency response procedures, including preparing regulatory change justifications. She is currently converting procedures used by her site's Fire/HAZMAT/EMT responders to our company's standard format. She ensures that emergency response facilities are maintained in a state of readiness and coordinate training for the new Emergency Organization Members. Prior to coming to GEH in 2019, she worked in Nuclear Emergency Preparedness at Duke Energy for ~25 years. There she developed and maintained fleet standard nuclear emergency response procedures and emergency preparedness program procedures for six nuclear stations and the corporate office, including preparing regulatory change justifications. Her accomplishments included: • Reducing the number of nuclear emergency response procedures and emergency preparedness program procedures by 75% by implementing standard procedures across the company following a merger. • Updating surveillance procedures for the common Emergency Operations Facility to include requirements for the three additional sites and performing those surveillances. • Revising the standard emergency response procedures based on lessons learned from drills and exercises following implementation. Before the merger she standardized nuclear emergency preparedness program procedures for the Duke Power fleet and maintained the procedures in the common EOF, incorporating drill and exercise lessons learned and industry best practices. Prior to that, she was the corporate EP “group,” responsible for projects of a generic nature such as bringing Oconee Nuclear Station into the common EOF, which required prior approval by the NRC Commissioners and moving the EOF from one building to another without prior NRC approval. Contact Information https://www.linkedin.com/in/tina-kuhr-b6585930/

Andy Schoonover is founder and CEO of CrowdHealth, a company founded to give people the tools to free themselves from the tyranny of health insurance. Top 3 Value Bombs 1. The sellers of healthcare, which are big hospital systems, primarily want the price to go up. What people don't understand is buyers of healthcare, which are the insurance plans, actually also want the price to go up. 2. Each doctor has about three people that he or she needs to build health insurance plans. He or she spends about 30% of their time fighting with health insurance companies about what they can or cannot do with their patients. 3. Instead of you having to give your monthly check to a big, cold insurance company - that's actually your enemy when it comes to getting your bills paid - you're giving your money directly to another human being. Visit and use the promo code EOF to get $99 a month for the first 3 months - Join Crowd Health Sponsors HubSpot Meet HubSpot's new AI-powered Campaign Assistant, a totally free-to-use AI tool tailor-made for the marketers and business builders who spend hours each day on content creation. Head to HubSpot.com/campaign-assistant to test-drive Campaign Assistant for free Thought-Leader Ever thought about giving a TEDx talk. Visit Thought-Leader.com/fire to join a free training and learn how to land a TEDx Talk and spread your message to millions

Andy Schoonover is founder and CEO of CrowdHealth, a company founded to give people the tools to free themselves from the tyranny of health insurance. Top 3 Value Bombs 1. The sellers of healthcare, which are big hospital systems, primarily want the price to go up. What people don't understand is buyers of healthcare, which are the insurance plans, actually also want the price to go up. 2. Each doctor has about three people that he or she needs to build health insurance plans. He or she spends about 30% of their time fighting with health insurance companies about what they can or cannot do with their patients. 3. Instead of you having to give your monthly check to a big, cold insurance company - that's actually your enemy when it comes to getting your bills paid - you're giving your money directly to another human being. Visit and use the promo code EOF to get $99 a month for the first 3 months - Join Crowd Health Sponsors HubSpot Meet HubSpot's new AI-powered Campaign Assistant, a totally free-to-use AI tool tailor-made for the marketers and business builders who spend hours each day on content creation. Head to HubSpot.com/campaign-assistant to test-drive Campaign Assistant for free Thought-Leader Ever thought about giving a TEDx talk. Visit Thought-Leader.com/fire to join a free training and learn how to land a TEDx Talk and spread your message to millions

Dave Willson is an entrepreneur with 20 years of experience. He founded, operated, and sold numerous businesses. As CFO advisor, he helped optimize over 200 businesses, ensuring they produce relevant, reliable, and real-time information. Top 3 Value Bombs 1. Success is not solely about financial achievements but also about a well-rounded view of accomplishment, like working with loved ones. 2. To succeed in the future, as an entrepreneur, you must abandon siloed and desktop-based approaches, opting for integrated systems that provide real-time data and future insights. 3. Data and accounting should be more than just looking at the past and reporting taxes. It should be the foundation for making informed decisions about where to focus your time and energy in your business. Visit and get your FREE financial system analysis. EOF listeners get one month of service for FREE - Proven CFO website Sponsors HubSpot There's a better way to win, and it all starts with the new HubSpot Sales Hub. It's smart software for smart sales teams that feels good to use! Try it for yourself at HubSpot.com/sales! Thrivetime Show Is now your time? Clay Clark's business coaching has helped over 2,000 entrepreneurs to dramatically increase profitability! Schedule your free consultation today at ThrivetimeShow.com ZipRecruiter The most effective way to find the best people for your roles! Try ZipRecruiter for free before you commit at ZipRecruiter.com/fire.  

Dave Willson is an entrepreneur with 20 years of experience. He founded, operated, and sold numerous businesses. As CFO advisor, he helped optimize over 200 businesses, ensuring they produce relevant, reliable, and real-time information. Top 3 Value Bombs 1. Success is not solely about financial achievements but also about a well-rounded view of accomplishment, like working with loved ones. 2. To succeed in the future, as an entrepreneur, you must abandon siloed and desktop-based approaches, opting for integrated systems that provide real-time data and future insights. 3. Data and accounting should be more than just looking at the past and reporting taxes. It should be the foundation for making informed decisions about where to focus your time and energy in your business. Visit and get your FREE financial system analysis. EOF listeners get one month of service for FREE - Proven CFO website Sponsors HubSpot There's a better way to win, and it all starts with the new HubSpot Sales Hub. It's smart software for smart sales teams that feels good to use! Try it for yourself at HubSpot.com/sales! Thrivetime Show Is now your time? Clay Clark's business coaching has helped over 2,000 entrepreneurs to dramatically increase profitability! Schedule your free consultation today at ThrivetimeShow.com ZipRecruiter The most effective way to find the best people for your roles! Try ZipRecruiter for free before you commit at ZipRecruiter.com/fire.

Ben Swart is the Product Manager at a first response tech startup by day. By night, he's an entrepreneur focusing on creative funding strategies, passive income opportunities and educating others. Top 3 Value Bombs: 1. Fear of success is what happens when you do make it. You get the business you want. You get the clients. What does that success look like and how do you maintain that success? That can be scary as well. 2. You're going to take that upfront work to get started and put in the time, put in the effort and it's going to take a lot of your day. But once you start getting systems that work, you're going to want to scale that to make passive income or reality. 3. Leveraging credits for starting a business is key. You don't have to go into insane amounts of debt. You can strategically use credit cards to fuel and grow your business. Visit and learn about being an affiliate. Use the code EOF75 for a 75% discount of Ben's full course only for the first 50 EOF listeners! - ERC Profit Scores Sponsor: HubSpot: Learn how HubSpot can help your business grow better and get a special offer of 20% off on eligible plans at HubSpot.com/eof!

Braves pitcher Mike Soroka stops by to update David and Eric on his rehab and recovery, plus, he explains what mechanical tweaks he's made and why he's done so. Later, DOB and EOF predict where key free agents, including Dansby Swanson, will land as well as which postseason award nominees will win their respective honors. Follow David on Twitter: @DOBrienATL Follow Eric on Twitter: @EOF34 Learn more about your ad choices. Visit megaphone.fm/adchoices

From the archive: This episode was originally recorded and published in (year). Our interviews on Entrepreneurs On Fire are meant to be evergreen, and we do our best to confirm that all offers and URL's in these archive episodes are still relevant. Paul Daly is the founder and CEO of Congruent and host of the Clarity Compressed podcast. After the acquisition of his first business he is focused on empowering others to connect with their audience through his brand-first approach. Top 3 Value Bombs: 1. Brand is a feeling, and that feeling is the reflection of your viewers values back at them. 2. Brand building does detract from sales, and that is why you need to have the vision for what your brand should and could be. 3. Brand is gravity. Realize that the strength of your brand will attract the right things to you. A workshop that teaches you how to define and deploy brand in the real world! Get 50% off with code EOF! - Brand Is Gravity Sponsors: HubSpot: Learn how HubSpot can help your business grow better at HubSpot.com. Speakeasy: An app that allows you to organize your own live talk show! Visit GetSpeakEasy.com to download the app and start interacting with your audience LIVE!

Braves pitcher Mike Soroka stops by to update David and Eric on his rehab and recovery, plus, he explains what mechanical tweaks he's made and why he's done so. Later, DOB and EOF predict where key free agents, including Dansby Swanson, will land as well as which postseason award nominees will win their respective honors. Watch the episode on YouTube: https://youtu.be/KQ2Ap9xIoTo Follow David on Twitter: @DOBrienATL Follow Eric on Twitter: @EOF34 Learn more about your ad choices. Visit megaphone.fm/adchoices

David and Eric reassemble for a live room edition of 755 Is Real, answering fan questions about the Braves on The Athletic app. The duo canvasses a disappointing end to Atlanta's 2022 season. What went wrong against the Phillies? Michael Harris II was a Gold Glove snub. The guys try to make sense of why the electric young star wasn't recognized for his stellar year. Plus, what's next for Dansby, and how does that factor into Atlanta's decision-making in the SS market? DOB and EOF share their World Series predictions before fielding some listening questions, including a query about some Braves prospects, and potential splash free-agent signings. Check out 755 Is Real on YouTube: https://www.youtube.com/755isreal Follow David on Twitter: @DOBrienATL Follow Eric on Twitter: @EOF34 Learn more about your ad choices. Visit megaphone.fm/adchoices

Steffen Schebesta is the CEO at Sendinblue. He is passionate about business and technology and always open to new ideas, discussions and contacts. Top 3 Value Bombs: 1. You can look back at how much work it takes and overcoming obstacles to become successful. 2. Hiring is essential because the company's heart is the people you will work with. 3. People are everything; they are the heart and soul of the company. The key is to hire the best people and find the perfect match. Focus on who you work with and who you hire. Visit and get your all-in-one solution. Use the EOF code to get 50% off for the first three months! - Send In Blue Website Sponsors: HubSpot: A platform that's easy for your entire team to use! Learn how HubSpot can make it easier for your business to grow better at Hubspot.com! Roll by ADP: Ready for a lot less stress in your life? Get 3 free months of unlimited payroll processing when you visit RollByADP.com/fire! Terms and conditions apply.

Rich Cardona is a Retired Marine Corps aviator, got his MBA and worked for Amazon but abruptly quit at 37 without a plan. He now interviews C-Suite executives, entrepreneurs, and veterans. He is alive! Top 3 Value Bombs: 1. We only live once and we need to make the most of it 2. You need to be ready to fail a million times and just get back up every single time. 3. Slow and steady wins the race. No rush. Be Patient. Visit Rich's website - Rich Cardona Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Katie Wood is an author, entrepreneur, mom of 4, & fire wife who believes there's nothing more powerful than investing in the next generation to prepare them for the unknown future. Top 3 Value Bombs: 1. You become what you believe. At the end of the day, whether you think you can or you can't, you're correct. Believe in ourselves to the fullest, authentically deep down in the soul. Anything is possible. 2. When you tell your mind what's mostly important, it will let that in. Whatever you put to the front of your brain, your mind will take that and attract that throughout the day. 3. Simple powerful lessons done over breakfast teaches kids how to become their best selves, and learn overcome obstacles, take responsibilities, persevere, and love themselves for who they are. Get the A Simple Seed Journal and empower your kid to START the day right - A Simple Seed of Growth, Gratitude & Giggles Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Colton Bollinger and the team over at Jumper Media help over 4,000 businesses tell their stories through Instagram. From zero to 6 million in annual recurring revenue in 2 years and from 3 to 50 employees! Top 3 Value Bombs: 1. The biggest thing when handling an internal team is communication and being able to be transparent with them. 2. Scaling a company will never happen if you don't have a really effective lead generation strategy. 3. Evaluate relationships and what you can give to people; it will come back to you ten-fold. Visit Colton's website - JumperMedia Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Josh Little is a teacher turned serial entrepreneur with four successful tech companies, multiple exits, and hundreds of millions of users.  He's currently on a mission to bring more connection and community into the world with his latest product, Volley–a video messaging app. Tim Schmoyer's company, Video Creators, helps creators, brands, and business' reach a new audience on YouTube. So far their clients have grown by over 18 billion views and 81 million subscribers. Top 3 Value Bombs: 1. Success is about reaching people and changing lives, not about how much money you can make. 2. Volley is the faster way to true human connection. 3. The limits of connection and communication only exist in our minds. Sign up and get access to their FREE Forever plan, plus join the Fire Nation Volley Space! Bonus: get access to Josh & Tim's free micro-masterclass, How to Ignite Your Audience Engagement - Fire Nation Volley Space Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Rush Sadiwala is the founder & CEO at Framework, a platform to run challenges on. He worked as an investor prior to becoming a founder. He is an immigrant who graduated from Duke University and got an MBA from Columbia. Top 3 Value Bombs: 1. You should work on your strengths rather than your weaknesses. 2. Having people do some work before joining your community creates more value. 3. Challenges are an incredible mechanism to achieve your goals. Check out and engage your community with a challenge - Framework Website Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Philip Blows is passionate about improving the world's financial health, having spent the last 15 years building and scaling Fintechs. Philip is the author of a book helping people gain financial freedom - The Money Triangle. Top 3 Value Bombs: 1. Crypto 2.0 is crypto hitting the mainstream. It's becoming much more than just a speculative asset. It's becoming something that people can build into a portfolio and help them achieve long term financial objective. 2. Decentralized finance is a mirror of the traditional world of financing, but brought into small contracts, removing loads of friction, making it more efficient overall. 3. Focus and plan. Find the one activity that will get you closest to your goal, block out everything else. Focus on what really matters. Download AQRU in Playstore or in the App store - AQRU.io Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Angelo Poli is an internationally recognized expert in fitness and nutrition. He's the Founder of MetPro, the world's first algorithm based transformation engine. MetPro specialize's in developing customized nutrition and training programs that are specific to an individual's metabolism, goals and lifestyle needs. Their clientele range in scope from Olympic athletes, NFL MVPs to physique models,  business leaders, and your very own JLD! Angelo has been featured in Men's Health, Sports Illustrated, and The Wall Street Journal and is a Wellness Consultant for universities and hospitals around the country. Top 3 Value Bombs: 1. The best way to build a routine is to anchor it into something critical in your schedule. 2. The secret I love to share with everyone: contrast is leverage. 3. If you're looking to start a transformation, the first and most important step is to understand your body type. Learn How to Unlock Your Metabolism - MetPro Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Paul Wiltshire is a music and technology entrepreneur with over 30 years experience across the music and media industries. He Songtradr launched Songtradr in March 2016 and has since rapidly grown to service 750,000 artists and music creators around the globe, licensing music to advertisers, brands, films, TV and other media. Top 3 Value Bombs: 1. Having an education may not be a pre-requisite. Instead, he focuses on the proper outcomes and backs them up with good hard work. 2. Wherever there is music, there is licensure. 3. If you want to fuel growth, do something you love and are passionate about. Visit and check out all your music needs in one place - Songtradr Website Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Jaspreet "Jas" Mathur is an accomplished Canadian entrepreneur, venture capitalist and lifestyle influencer. He launched his first business at age 12 and has since founded and reinvented a variety of successful companies. Top 3 Value Bombs: 1. You have to identify your definition of success and find the purpose driving you to become the most successful version of yourself. 2. Limitless means you can achieve anything and everything you set your mind to. 3. Evaluate your friends; stick with the people who inspire you, and learn from them. Surround yourself with the right people. Connect with Jas on Instagram - Jas' Instagram Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Coda.io: The doc that brings it all together! Get your team all working together on the same page for free at Coda.io/fire! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Frankie Lane went from sleeping in his car three years ago, to owning an 20 million dollar business. Now he shares his story to inspire others to become entrepreneurs. Top 3 Value Bombs: 1. Most have people in their lives or in their business because they have a need for them. But when you start to realize who they are and what they're about, that's when they'll start to put in the extra effort - they'll fight for you. 2. Time doesn't ask permission. It doesn't ask to see if it's ok to stop or to start. It just does what it always does: it keeps on ticking. 3. It's not just you - we all commit these sins. It's about cutting them out - getting rid of them as quickly as possible. Check out what Frankie's LinkedIn - Frankie's LinkedIn Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Ferret: Are you an investor or high-net-worth individual who is regularly involved in high-stake deals? Then you need Ferret! Visit Ferret.ai and use the code EOF to get access to Ferret's exclusive early beta app!

Jordan Harbinger is the host of The Jordan Harbinger Show where he throws value bombs on all topics that are relevant to being awesome. Today, Jordan and JLD talk about how to create and maintain connections, gamify the process, and turn it to one of your largest competitive advantages. Top 3 Value Bombs: 1. They cannot take away, by operation of law, your relationships, connections, network, and support. It's the one thing you cannot lose. 2. You are not immune to the consequences of not creating and maintain relationships. If you decide not to do this, you're just willfully being ignorant of the secret game that's being played around you. 3. Introverts make 3 or 4 connections in a day, or in a weekend, and those people become friends for life. Extroverts, on the other hand, can meet 50 people and can't remember a single one; they reintroduce themselves every single year at the same event because they can't remember anyone. They find they have a very wide network, but not very many deep relationships. Join Jordan in his FREE networking class - Six-Minute Networking Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Ferret: Are you an investor or high-net-worth individual who is regularly involved in high-stake deals? Then you need Ferret! Visit Ferret.ai and use the code EOF to get access to Ferret's exclusive early beta app!

Lauren and Dr. Geoffrey Trenkle founded Total Testing Solutions and Total Health, which provide equitable access to comprehensive testing for all - through the pandemic and beyond. Top 3 Value Bombs: 1. We have to be able to adapt. Every time we see a challenge, look at it as an opportunity. 2. Virtual proctoring seems to be the easiest way to get people test for results without leaving their home. 3. Expand what we can offer for people, not just the type of health care, but the amount of help we can give to people. The longer the medicines stay antiquated, the more we will stay in the past instead of moving into the future. Visit their e-commerce and use the promo code FIRE for FREE SHIPPING on orders - TTS Total Testing Solutions Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Kevin Lancaster is an award-winning tech entrepreneur who scaled and exited the fastest growing cybersecurity company in the IT Channel. He recently launched ChannelProgram.com to centralize the $2 Trillion IT Channel. Top 3 Value Bombs: 1. Reinvention is key to success in your career. 2. Use opportunities to help eliminate chaos in rapidly evolving industries. 3. If you have an idea and passion for making a change, believe in it, and take action. Check out the website, register for a FREE profile, and start influencing, connecting, and growing - Channel Program Website Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Ferret: Are you an investor or high-net-worth individual who is regularly involved in high-stake deals? Then you need Ferret! Visit Ferret.ai and use the code EOF to get access to Ferret's exclusive early beta app! ZipRecruiter: Find the right employees for your workplace with ZipRecruiter, the #1 rated hiring site in the US, based on G2 ratings! Try it for free at ZipRecruiter.com/fire!

John Day quit his job and sold everything to launch AV HERO in March 2020. AV HERO is now the largest network of audio visual experts in the United States. Top 3 Value Bombs: 1. Don't let fear stop you. Everyday you show up, do the scariest thing first. Once you do that, fear will have much less grasp in your life. 2. Everybody is rethinking their lives, creating issues in the workplace. People realize that their value is more than what they were being compensated of. 3. AV Hero is taking advantage of the freelance gig economy, providing people options about their schedule and compensation. It's going to be the obvious choice for people who may have issues with audio-visual technology. First 10 people to send an email to John will get a complimentary AV Hero visit - Email John Day Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Conversion Fanatics: If you're generating 7 to 8 figures + from your website, and you're not doing conversion optimization, you're leaving revenue on the table! Visit ConversionFanatics.com for a free proposal today and tell them EOF sent you!

Dr. Isaac Jones has been coined “The Doctor of the Future” by Jeff Arnold the founder of WebMD and Sharecare.com. He has upgraded the health, energy and brain function of the worlds leading executives and entrepreneurs from around the world through live full immersion retreats and one-on-one concierge programs. He is also an international #1 best selling author and the author of the new book Superhuman Entrepreneur - High Performance Evolved. You can check out what he's got going on at elevays.com and SuperhumanEntrepreneur.com. Top 3 Value Bombs: 1. When someone is dealing with a large level of stress, brain disease and disorder start to climb like crazy. 2. Live your life in a state of meditation. If you can't get to a place where you can meditate, calm your brain down and focus on things that bring joy, happiness, and excitement to your life. 3. What's beautiful about your brain is that you can change it. If you have healthy neuroplasticity, you can rewire your brain for high performance. You can upgrade and optimize your brain to enable you to become more productive and be able to get more done in less time. Join Dr. Isaac's masterclass - Super Human Brain Masterclass Sponsors: HubSpot: Learn how to grow better by connecting your people, your customers, and your business at HubSpot.com! Ferret: Are you an investor or high-net-worth individual who is regularly involved in high-stake deals? Then you need Ferret! Visit Ferret.ai and use the code EOF to get access to Ferret's exclusive early beta app!