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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

Chalked Cast and chill with ApparentlyJack and the Chalked Squad - Support this podcast: https://podcasters.spotify.com/pod/show/chalked-cast/support0:00 - Intro2:00 - ApparentlyJack's thoughts on the RLCS 2v2 event, the 2v2 Skill Ceiling and Jack's experience with tennis and Rocket League13:20 - What were Jack's 2v2 predictions, will we see Zen and Atow team?21:59 - NA RLCS 2v2 Preview23:53 - EU TEAMS - RLCS 2v2 Global team tierlist32:45 - SAM TEAMS - RLCS 2v2 Global team tierlist44:16 - MENA TEAMS - RLCS 2v2 Global team tierlist1:00:10 - OCE TEAMS - RLCS 2v2 Global team tierlist1:02:22 - NA TEAMS - RLCS 2v2 Global team tierlist1:24:11 - Oski Nass gatekeep conversation, general discussion

In this episode of the Full Circl Podcast, Alice Burks, Director of People Success at Deal, shares insights from her career. Working at Deel, with over 7,000 team members in more than 120 countries, Alice has firsthand experience navigating the complexities of a fully remote organisation. Her insights are invaluable for anyone looking to understand how to succeed in this new work environment.

In this episode of the In-House Outliers podcast, host Anna Richards speaks with Dan Wate, Legal Operations Manager at Associated British Foods, about managing legal operations solo across a global team of 150 lawyers in 14 countries.Dan shares his journey from joining ABF before legal ops had a name, to building a function that spans a complex, multinational business. He covers two core strategies for scaling as a team of one: having a clear plan to stay proactive rather than reactive, and building an unofficial team through trusted relationships across finance, IT, HR, and procurement. The conversation also touches on why being nice is a genuine professional skill, how to approach vendor and outside counsel relationships as partnerships, and what those new to legal ops should know about building transferable skills.

One of my clients leads quarterly meetings with teams across Brazil, APAC, Europe, and the US but when it came to explaining recent staff changes, they realised they weren't confident using the right English vocabulary. If you've ever hesitated when talking about promotions, internal moves, or people leaving, this episode is for you. You'll learn the key phrases senior managers use to describe company movements clearly, professionally, and confidently in global team meetings. Enjoy! Anna GET MY FREE WEEKLY NEWSLETTER - Become a free member and get my weekly round up of tips in the newsletter and extra bonus content INTERESTED IN COACHING WITH ME? Register interest to be informed of future places on my 3-month programme THIS PODCAST IS MADE POSSIBLE BY OUR FANTASTIC SUPPORTERS. WANT TO BECOME A SUPPORTER TOO? TRANSCRIPTS - do an in-depth review of the episode content LinkedIn @AnnaConnellyYouTube @annabusinessenglish

On the 15-year anniversary of Gabelli Japan K.K., President and COO Mitsuyoshi Kikuchi shares his outlook on 2026 from the Tokyo Office. Moderated by Grace Boyd (Video Production Manager, Gabelli Rye Office). 0:00 Mitsuyoshi Kikuchi, President & COO 0:53 Gabelli Japan K.K. 2:30 Japanese Markets 3:34 Global Team - connecting around the world 4:14 Focus of Research 5:35 Outlook for 2026 6:08 Deals 7:36 Important Disclosures To learn more about Gabelli Funds' fundamental, research-driven approach to investing, visit https://m.gabelli.com/gtv_cu or email invest@gabelli.com. Connect with Gabelli Funds: • LinkedIn - https://www.linkedin.com/company/investgabelli/ • X - https://x.com/InvestGabelli • Instagram - https://www.instagram.com/investgabelli/ • Facebook - https://www.facebook.com/InvestGabelli http://www.Gabelli.com Invest with Us 1-800-GABELLI (800-422-3554)

Leading a team that spans countries and time zones brings communication challenges that go far beyond working remotely. Tsedal Neeley, a professor at Harvard Business School, explains why global teams are especially vulnerable to misunderstandings and why leaders often don't realize there's a problem until collaboration starts to suffer. Neeley shares advice on how leaders can reduce those misunderstandings by being intentional about how people communicate and connect.

How do you protect high-risk operations when every second counts?Join host Dave Bosco and guest Josh Jones from AFEX Fire Suppression Systems as they explore the critical role of fire suppression systems in the oil & gas sector. Recorded live from the Daniel Energy Partners Barbecue in the Permian Basin, Josh shares how Apex evolved from forestry roots to becoming a key safety partner in energy. With practical insights on reducing risks and protecting assets in high-hazard environments, this episode offers a clear look at the future of safety and innovation in the industry.00:00 Welcome to Energy Espresso01:08 Introduction to AFEX and Fire Suppression Systems04:44 The Genesis and Evolution of AFEX11:07 Josh's Journey into the Oil and Gas Industry17:33 Global Expansion and Challenges22:14 Fire Suppression in Mining and Oil & Gas22:38 Global Team and Market Expansion24:16 Management Style and Team Culture28:17 Industry Challenges and Innovations32:26 Leadership and Industry Evolution41:01 Podcast Conclusion and Final Thoughts

In this inspiring episode of Mirror Talk: Soulful Conversations, Tobi sits down with Lori Pappas, a pioneer, humanitarian and author whose life reflects true courage, deep resilience and the beauty of transformation. From being one of the first female computer sales reps in the world to becoming an award-winning entrepreneur, Lori's early years were filled with trailblazing breakthroughs. Yet even after achieving what many describe as the American Dream, she discovered that purpose is far more profound than success.Her journey eventually led her to found the Global Team for Local Initiatives and relocate to Ethiopia, where her humanitarian work has touched more than 100,000 lives. In her book The Magic of Yes, she teaches the DREAM framework, a transformational guide for reconnecting with inner wisdom and living in alignment with the truth of who you are.Together, Tobi and Lori explore themes of personal growth, intuition, love, spirituality and the constant unfolding of purpose. Lori also shares her beautiful story of finding love at 67 and reflects on how nature, presence and spiritual practice nourish her calling to serve.This episode is perfect for listeners seeking clarity, empowerment and a deeper connection to the wisdom within.What You Will LearnHow to define yourself by who you are rather than what you doWhy challenges often become gateways into deeper wisdomThe power of reflection in cultivating self-awarenessThe meaning of living in alignment with your valuesWhat Lori learned about love and vulnerability in later lifeWhy saying yes to life opens doors to transformationEpisode Chapters00:00 Introduction to Resilience and Transformation02:50 Self-Reflection and Personal Growth05:38 Navigating Challenges as a Female Pioneer08:18 Knowing Your Worth10:53 Finding Purpose Beyond Success13:35 The Role of Reflection in Gaining Wisdom22:18 Overcoming Noise and Baggage23:41 The DREAM Framework Explained27:50 Living in Alignment30:36 The Magic of Yes: Embracing Life34:42 Finding Love at 6741:48 The Value of Relationships42:38 Practicing Presence and MindfulnessWho is Lori Pappas?Lori Pappas is a pioneer, humanitarian, and author whose life reflects the power of resilience and reinvention. In her 20s, she became one of the first female computer sales reps in the world, later becoming an award-winning entrepreneur. After discovering that retirement did not bring fulfilment, she founded the Global Team for Local Initiatives and moved to Ethiopia, where her work has touched more than 100,000 lives. Her reflections and wisdom inspired her book, The Magic of Yes and the transformational DREAM framework that guides individuals to embrace their inner wise woman.Website: loripappas.com Listen on your favourite podcast platform: ⁠https://lnkfi.re/mirrortalk⁠Don't forget to subscribe, rate, share, and comment. Thank you!CONFESSIONS is now available: https://mirrortalkpodcast.com/confessions-book/ Thank you for joining me on this MIRROR TALK podcast journey. Please subscribe to any platform and remember to leave a review and rating.Stay connected: https://linktr.ee/mirrortalkpodcast More inspiring episodes and show notes are here: https://mirrortalkpodcast.com/podcast-episodes/ Your opinions, thoughts, suggestions, and comments are important to us. Please share them here: https://mirrortalkpodcast.com/your-opinion-matters/ Could you support us by becoming a Patreon? Please consider subscribing to one or more of our offerings at http://patreon.com/MirrorTalk All proceeds will help enhance the quality of our work and outreach, enabling us to serve you better.We use and trust these podcasting tools, software, and gear. We've partnered with amazing platforms to give our Mirror Talk community exclusive deals and discounts: https://mirrortalkpodcast.com/mirror-talks-recommended-podcasting-tools-exclusive-discounts/ 

In this powerful and deeply personal conversation, we meet Lori Pappas, one of the first female computer sales reps in the world, an award-winning entrepreneur, and founder of the Global Team for Local Initiatives, where she spent over a decade in Africa helping more than 100,000 people.After years of chasing success, Lori discovered that the true magic of life comes from saying yes—yes to forgiveness, boundaries, curiosity, and self-discovery. Her journey inspired the D.R.E.A.M. Framework, a practical guide for transforming challenges into wisdom.In this episode, Lori and Yo talk about reinventing yourself at every stage of life, the myths that hold women back from self-advocacy, and how forgiveness and reflection unlock our deepest wisdom.

I went from running a student storage company out of college to owning 63 self-storage facilities, building a global team across five continents, and acquiring a multi-million dollar recruiting business. In this episode, I sat down with Will Smith and Niklas from Acquiring Minds to break down how I structure deals, why offshoring changed everything for my operations, and how I think about building in public, despite the hate that comes with it. We also talk through the tradeoffs of chasing internet fame vs. quietly building real businesses. This one goes deep. Grow your business:   https://sweatystartup.com/events   Book:   https://www.amazon.com/Sweaty-Startup-Doing-Boring-Things/dp/006338762X     Newsletter:   https://www.nickhuber.com/newsletter     My Companies:   Offshore recruiting – https://somewhere.com   Cost segregation – https://recostseg.com   Self storage – https://boltstorage.com   RE development – http://www.boltbuilders.com   Brokerage – https://nickhuber.com   Paid ads – https://adrhino.com   SEO – https://boldseo.com   Insurance – https://titanrisk.com   Pest control – https://spidexx.com     Sell a business:   http://nickhuber.com/sell     Buy a business:   https://www.nickhuber.com/buy     Invest with me:   http://nickhuber.com/invest     Social Profiles:   X – https://www.x.com/sweatystartup   Instagram – https://www.instagram.com/sweatystartup   TikTok – https://www.tiktok.com/404?fromUrl=/sweatystartup   LinkedIn – https://www.linkedin.com/in/sweatystartup     Podcasts:   The Sweaty Startup & The Nick Huber Show   https://open.spotify.com/show/7L5zQxijU81xq4SbVYNs81     Free PDF – How to analyze a self-storage deal:   https://sweatystartup.ck.page/79046c9b03  

#EP317 The solar industry faces a defining moment in Q4 2025. While regulatory challenges create uncertainty, well-capitalized players treat this period as a buying opportunity. Daniel Dus, CEO and founder of Cleantech Industry Resources (CIR), breaks down the market dynamics and explains how his company's global expertise positions it to thrive during industry consolidation.Daniel Dus founded CIR to provide development, construction, and operations services for solar and battery storage projects. His team of 140 professionals across three offices in India and the United States brings proven experience from building some of the world's largest solar installations, including work with Adani Green Energy as it scaled from startup to 16 gigawatts in operation.Key Discussion Points:The current solar market has split into thirds: one-third undercapitalized and struggling, one-third in wait-and-see mode, and one-third aggressively acquiring projects with strong capital backing - creating opportunities for companies like CIR that can execute complex transactions.CIR serves 150 clients across the solar value chain, providing everything from interconnection studies and permit-ready planset packages to full EPC services, with particular expertise in rescuing distressed projects and navigating complex utility requirements.CIR's competitive advantage comes from its India-based technical teams, who have worked on massive international projects, bringing world-class expertise at competitive rates to US developers who struggle with margin compression.Solar Fight Night, Dus's passion project since 2008, has raised nearly $2 million for clean energy nonprofits through 24+ events. The 2025 event at Las Vegas's Zouk Nightclub drew over 3,300 attendees, the largest crowd in the event's history.Tim Montague is an affiliate of CIR and welcomes developers and EPCs to contact him for more information about working with CIR. [Book here: https://calendly.com/tim-montague/30min]Connect with Daniel Dus, CIR LinkedIn: www.linkedin.com/in/danielrdusWebsite: cleantechindustryresources.comSolar Fight Night: www.solarfightnight.org/ Support the showConnect with Tim Clean Power Hour Clean Power Hour on YouTubeTim on TwitterTim on LinkedIn Email tim@cleanpowerhour.com Review Clean Power Hour on Apple PodcastsThe Clean Power Hour is produced by the Clean Power Consulting Group and created by Tim Montague. Contact us by email: CleanPowerHour@gmail.com Corporate sponsors who share our mission to speed the energy transition are invited to check out https://www.cleanpowerhour.com/support/The Clean Power Hour is brought to you by CPS America, maker of North America's number one 3-phase string inverter, with over 6GW shipped in the US. With a focus on commercial and utility-scale solar and energy storage, the company partners with customers to provide unparalleled performance and service. The CPS America product lineup includes 3-phase string inverters from 25kW to 275kW, exceptional data communication and controls, and energy storage solutions designed for seamless integration with CPS America systems. Learn more at www.chintpowersystems.com

In this insightful episode of the Authors on Mission podcast, host Danielle Hutchinson sits down with Ken Taylor, founder of OwnerRez and author of the upcoming book Working in Slippers. Ken shares how growing up around tech entrepreneurship shaped his leadership philosophy—and how building a remote-first company taught him the value of trust, autonomy, and measurable outcomes.They dive into:

In this episode of the Grow A Small Business Podcast, host Troy Trewin interviews Nathan Baws, founder of NumberFied, joins the Grow a Small Business podcast to share his incredible journey from launching his first “business” at age six to building 15 diverse ventures across industries. He opens up about scaling his catering company after Shark Tank success and transforming failures into seven-figure wins. Nathan explains how Number Five grew from one VA to a global team of 80 across five countries, providing affordable growth solutions for small businesses. He dives deep into the power of mindset, creative lead generation, and embracing AI to accelerate growth. This episode is packed with lessons on resilience, marketing, and scaling with purpose from a true serial entrepreneur. Why would you wait any longer to start living the lifestyle you signed up for? Balance your health, wealth, relationships and business growth. And focus your time and energy and make the most of this year. Let's get into it by clicking here.   Troy delves into our guest's startup journey, their perception of success, industry reconsideration, and the pivotal stress point during business expansion. They discuss the joys of small business growth, vital entrepreneurial habits, and strategies for team building, encompassing wins, blunders, and invaluable advice.   And a snapshot of the final five Grow A Small Business Questions: What do you think is the hardest thing in growing a small business? Nathan Baws shared that the hardest part of growing a small business is having the right skill set in growth and marketing, and trying to navigate it while being on your own. What's your favorite business book that has helped you the most? Nathan Baws shared that his favorite business book is “Never Split the Difference” by Chris Voss, which deeply influenced him through its powerful negotiation strategies and practical takeaways. Are there any great podcasts or online learning resources you'd recommend to help grow a small business? Nathan Baws shared that he's a big fan of Alex Hormozi's content and often uses YouTube to dive into whatever business topic he's focused on at the time. He also mentioned that he uses AI tools as a kind of “mentor,” asking questions and generating solutions on the go. What tool or resource would you recommend to grow a small business? Nathan Baws shared that one of the most valuable tools to grow a small business is automation software, especially for lead generation. He mentioned using tools like Instantly to automate outreach, book more appointments, and scale sales efficiently. What advice would you give yourself on day one of starting out in business? Nathan Baws shared that the advice he would give himself on day one of starting out in business is: “Learn lead generation and marketing early – spend most of your day finding ways to generate more sales.” Book a 20-minute Growth Chat with Troy Trewin to see if you qualify for our upcoming course. Don't miss out on this opportunity to take your small business to new heights! Enjoyed the podcast? Please leave a review on iTunes or your preferred platform. Your feedback helps more small business owners discover our podcast and embark on their business growth journey.     Quotable quotes from our special Grow A Small Business podcast guest: Sales fix almost everything – focus on generating leads every single day — Nathan Baws Creative marketing and consistent lead generation are the true lifelines of any small business — Nathan Baws If you're not actively growing your business, you're already falling behind — Nathan Baws      

saas.unbound is a podcast for and about founders who are working on scaling inspiring products that people love, brought to you by https://saas.group/, a serial acquirer of B2B SaaS companies.In episode #35 of season 5, Anna Nadeina talks with Dave, founder of AhaSlides, a platform to help you make interactive presentations.--------------Episode's Chapters----------------0:00 - Introduction1:03 - Dave's Background & Founding Aha Slides2:36 - Building a Global Team & Leadership Philosophy9:16 - Team Culture & Routines14:36 - Hybrid Work & Office vs. Remote19:47 - Sales Motion & Growth Strategy24:39 - Product-Led Growth & Importance of Product25:14 - Going Global & Customer Diversity27:17 - AI in SaaS: Opportunities & Challenges46:44 - Biggest Wins & Lessons LearnedDave - https://www.linkedin.com/in/davebui/AhaSlides - https://ahaslides.com/Subscribe to our channel to be the first to see the interviews that we publish twice a week - https://www.youtube.com/@saas-groupStay up to date:Twitter: https://twitter.com/SaaS_groupLinkedIn: https://www.linkedin.com/company/14790796

Blake Harris is the founder of Blake Harris Law, the largest exclusively offshore asset protection law firm in the United States. With a team spread across four continents, Blake has built a niche global practice helping high-net-worth individuals legally protect their assets through Cook Islands trusts and other offshore strategies. In this episode, Blake breaks ... Read more The post Offshore Trusts, Global Team, Media Mindset: Blake Harris Is Not Your Typical Lawyer | EP191 appeared first on KazSource.

In this episode of Grow a Small Business, host Troy Trewin interviews Julia Linehan, founder of The Digital Voice, a UK-based PR and marketing agency specializing in ad tech and martech. Julia shares her journey from a solo consultant to leading a remote team of 36, including 28 full-time equivalents. Over the past six years, she has grown the agency's revenue sixfold and doubled profits, driven by her people-first approach. Julia discusses the challenges of letting go, the power of consistent company culture, and the value of tools like Trello and Slack. She also highlights the importance of work-life balance and strong client relationships in building a sustainable, scalable business. Other Resources: When should a growing small business have a Board of Directors or Advisors? Get a return from an effective Chairperson of a Board An easy way to measure if your customers love you in 21 minutes – use the Net Promoter Score (NPS). And it's FREE. Why would you wait any longer to start living the lifestyle you signed up for? Balance your health, wealth, relationships and business growth. And focus your time and energy and make the most of this year. Let's get into it by clicking here. Troy delves into our guest's startup journey, their perception of success, industry reconsideration, and the pivotal stress point during business expansion. They discuss the joys of small business growth, vital entrepreneurial habits, and strategies for team building, encompassing wins, blunders, and invaluable advice. And a snapshot of the final five Grow A Small Business Questions: What do you think is the hardest thing in growing a small business? According to Julia Linehan, the hardest thing in growing a small business is managing stress. She explains that without effectively handling stress, it can quickly become overwhelming and negatively impact both the individual and the business. She also highlights cash flow management as a significant challenge, noting the delicate balance required between growth, maintaining profitability, and ensuring financial stability. What's your favorite business book that has helped you the most? Julia Linehan's favorite business books that have helped her the most are "Big Impact Without Burnout" by Bianca Best and "Radical Candor" by Kim Scott. She also recommends "The One Minute Manager" and "Monkey Management" for their practical insights on leadership and team communication. Are there any great podcasts or online learning resources you'd recommend to help grow a small business? Julia Linehan recommends several valuable podcasts and online learning resources for small business growth, including her agency's own shows Off Record On Point and Legends of Adtech. She also highlights podcasts by Tamara Littleton and Paul Gubbins with Wayne Blodwell for insights into the ad tech and marketing industries. For ongoing learning, she suggests platforms like Skillshare and Coursera and encourages dedicating regular time, such as a weekly “Boost Your Power Hour,” to continuous professional development. What tool or resource would you recommend to grow a small business? Julia Linehan recommends using the right tools to support remote collaboration and project management when growing a small business. Her top picks are Trello, for organizing tasks and workflows with transparency, and Slack, for maintaining strong team communication and connection. She emphasizes that investing in effective software tailored to your business needs is essential for sustainable growth. What advice would you give yourself on day one of starting out in business? Julia Linehan's advice to herself on day one of starting out in business would be to be present, enjoy the journey, and smile through it. She believes that the more you enjoy what you're doing, the more others around you will too, creating a positive ripple effect in both team culture and client relationships. Book a 20-minute Growth Chat with Troy Trewin to see if you qualify for our upcoming course. Don't miss out on this opportunity to take your small business to new heights! Enjoyed the podcast? Please leave a review on iTunes or your preferred platform. Your feedback helps more small business owners discover our podcast and embark on their business growth journey.     Quotable quotes from our special Grow A Small Business podcast guest: People over profits—invest in your team, and the returns will follow – Julia Linehan Let go, trust your team, and watch them fly – Julia Linehan You don't need to chase every opportunity – protect your culture first – Julia Linehan      

Canva just announced $3 billion in ARR, 230 million monthly active users, and 24 million paying subscribers—including 95% of the Fortune 500. Even more impressive? They've been profitable for seven years while growing at 40–50% per year. In this episode, Canva's Head of Engineering, Brendan Humphreys, reveals how he went from employee #12 to leading 2,300 engineers across continents, and why Canva's “pragmatic excellence” lets them ship AI features at breakneck speed—like launching Canva Code to 100 million users in just three months.Brendan shares the story of Canva's AI journey: building an in-house ML team back in 2017, acquiring visual AI startups like Kaleido and Leonardo AI, and why they use a hybrid of OpenAI, Anthropic, Google, and their own foundation models. He explains how Canva's App Store gives niche AI startups instant access to millions, and why their $200M Creator Fund is designed to reward contributors in the AI era. You'll also hear how AI tools like Copilot are making Canva's senior engineers 30% more productive, why “vibe coding” isn't ready for prime time, and the unique challenges of onboarding junior engineers in an AI-driven world.We also dig into Canva's approach to technical debt, scaling from 12 to 5,000 employees, and why empathy is a core engineering skill at Canva. CanvaWebsite - https://www.canva.comX/Twitter - https://x.com/canvaBrendan HumphreysLinkedIn - https://www.linkedin.com/in/brendanhumphreysX/Twitter - https://x.com/brendanhFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (01:14) Canva's Mind-Blowing Growth and Profitable Journey (03:41) Why Brendan Left Atlassian to Join a Tiny Startup (06:17) What Being a Founder Taught Brendan About Leadership (07:24) Growing with Canva: From 12 Employees to 2,300 Engineers (10:02) How Canva Runs a Global Team from Sydney to Europe (13:16) Is AI a Threat or a Superpower for Canva? (15:22) The Real Story Behind Canva's AI and Machine Learning Team (17:23) How Canva Ships New AI Features So Fast (19:19) A Tour of Canva's Latest AI-Powered Products (21:03) From Design Tool to All-in-One Productivity Platform (26:21) Keeping Up the Pace: How Canva Moves So Quickly (30:22) The Future: AI Agents, Copilots, and Smarter Workflows (33:14) How AI Tools Are Changing the Way Engineers Work (35:47) Rethinking Hiring and Training in the Age of AI (37:01) Why Empathy Matters in Engineering at Canva (39:41) Building vs. Buying: How Canva Chooses Its AI Tech (41:23) Lessons Learned: Technical Debt and Scaling Pains (51:18) Shipping Fast Without Breaking Things (53:08) What's Next: AI Video, New Features, and Big Ambitions

May 14, 2025 Join host Pete Neubig as he talks with Thad Tarkington, CEO and Co-Founder of Second Nature, about the challenges and insights of building and leading a global team. Thad shares his journey from lifeguard to tech entrepreneur, the critical role of alignment and communication in scaling a business, and his company's strategies to reinforce core values across a 300+ person organization. Whether you're an early-stage founder or a seasoned executive, this episode is packed with practical advice on culture, leadership, and growth.

Erica has helped build a global team at Workiva where everyone has a "me vs. me" mentality - working to improve ourlseves, not a competitive "me versus you" attitude. How can you get better? How can you deliver the best to your clients and internal consitutents.  Erica was named "Manager of the Year" and she says it is because of her incredible team.  We talked about holding people accountable - such an important issue for leaders to demonstrate.  I love when Erica discusses what she looks for in top sellers (17 min in)  Erica has people on her team from Amsterdam all the way to the West Coast of the U.S. She spent time at Morgan Stanley in Institutional Wealth Services, a few years at Direxion, and has now been at Workiva for 4+ years.  More about Erica: https://www.linkedin.com/in/ericaettore/ More about Women Sales Pros - we have a website, we are on LinkedIn, Facebook, and Instagram.  Subscribe to our 2x a month news, and share the podcast with others! We'd love a 5 star rating and comments on iTunes if you are so moved! It really makes a difference.  subscribe: https://bit.ly/thewspnews Contribute: https://forms.gle/v9rRiPDUtgGqKaXA6 Past News Issues: bit.ly/past_news_issues https://womensalespros.com/podcast/ 

Send us a textIn this episode of the Amazing Teams podcast, we sat down with Marc Cenedella, CEO of The Ladders,  to explore the evolving landscape of work culture. We discuss the shift to remote work, the key drivers of productivity, and the power of gratitude in the workplace. Marc shares insights on standing out in a competitive job market, the impact of AI on job applications, and the importance of crafting resumes that highlight measurable achievements. The conversation also touches on the role of meetings and how they can influence creativity and efficiency.We dive into:How meetings can hinder productivity and creativity.75% of job seekers prioritize intrinsic rewards over cash. Gratitude fosters a positive work environment and strengthens team cohesion. Tune in to hear Marc's advice on navigating today's job market and building a thriving career. Resources:Paul Graham's Maker's schedule, manager's scheduleLearn more about LaddersConnect with Marc on LinkedInThe Ladders Career Cab from 2011

Vikram Bharati, founder of Draper Startup House, and Jeremy Au talked about how the startup world has shifted since their last conversation. They explored how Draper Startup House has expanded across continents while wrestling with the challenge of scaling both physical spaces and community-driven programming. They discussed how remote and hybrid work are evolving post-pandemic, and how startups are adapting faster than large corporations. They also reflected on parenting and preparing the next generation for a fast-changing world, where original thinking and adaptability may matter more than credentials. Vikram also shared his growing interest in “digital nations,” a concept that could reshape how governments serve people and how individuals relate to borders and institutions. 1. Scaling Draper Startup House globally: Vikram shares that Draper Startup House has grown to 15 locations across South America, India, and Korea, focused on building startup communities in adventurous and underserved places. 2. Finding the right people as a challenge: The model combines real estate ("hardware") and startup programming ("software"), which requires local leaders who can do both—something that's tough to find consistently. 3. Remote work is here to stay: Vikram believes the post-pandemic world has made flexible work a permanent reality, especially for startups and global teams like his, which now span the US, Brazil, India, Portugal, and more. 4. Hybrid models work best: The trend he sees is a mix of in-person and remote work—typically two or three days in the office—which balances productivity and employee satisfaction. 5. Parenting in a changing world: Both Jeremy and Vikram reflect on raising young kids today, and how future success may depend more on adaptability and creativity than traditional credentials or schooling. 6. Unique perspectives come from unplugging: Vikram suggests that stepping outside the common information feed is one way to build original thinking—especially as everyone now consumes the same digital content. 7. Digital nations as the next frontier: Vikram outlines his interest in building “digital nations”—online systems that provide government-like services and community without being bound to geography, potentially expanding opportunity beyond borders. Watch, listen or read the full insight at https://www.braves ea.com/blog/scaling-startup-communities Get transcripts, startup resources & community discussions at www.bravesea.com WhatsApp: https://whatsapp.com/channel/0029VakR55X6BIElUEvkN02e TikTok: https://www.tiktok.com/@jeremyau Instagram: https://www.instagram.com/jeremyauz Twitter: https://twitter.com/jeremyau LinkedIn: https://www.linkedin.com/company/bravesea English: Spotify | YouTube | Apple Podcasts Bahasa Indonesia: Spotify | YouTube | Apple Podcasts Chinese: Spotify | YouTube | Apple Podcasts Vietnamese: Spotify | YouTube | Apple Podcasts

Chanie Gluck is joined by John Gwin, founder and CEO of The Auctus Group, a consulting firm specializing in financial and operational services for plastic surgeons and dermatologists. In this episode, John shares his passion for technology, leadership, and Revenue Cycle Management (RCM). John is also the Vice Chair of the HBMA Innovations Committee and the president of a nonprofit called BRAVE Coalition Foundation.  We delved into the unique structure of The Auctus Group, which employs over 100 team members across eight countries working remotely. John explained his innovative pod model, which fosters collaboration and empowers teams to make decisions collectively, rather than following a traditional hierarchical structure. This approach not only enhances organizational  communication but also promotes a strong company culture.  John then discussed the significant role that technology and AI plays in RCM. He shares insights into the technology tools that have transformed his operations, including DocVocate, Raxia, SuperDial, and more. He highlights the effectiveness of AI tools in improving patient payment rates and streamlining workflows. He also touches on the emerging trend of custom-built tech solutions that can be developed collaboratively, making advanced technology more accessible to smaller billing companies.  This episode is packed with valuable insights for anyone in the healthcare billing space, especially those looking to leverage technology to enhance their offshore operations.   You can learn more about Auctus Group here: https://auctusgroupconsulting.com/    Sponsored by 4D Global, empowering medical billing companies through offshore staffing. 

Uncover the unexpected truth about leading global teams! This global team leader shares six powerful strategies for success, from cultural agility to building trust across time zones. Discover how leveraging diversity can drive innovation and team performance, and how to create a work environment where everyone feels valued and supported. But the real surprise lies in the simple yet profound actions that can make a world of difference in your global team. Want to know the surprising key to building trust across borders? It's not what you think! Dive into this episode to uncover the unexpected secrets to leading global teams and making a lasting impact. In this episode, you will be able to: Mastering the art of leading global teams across borders can transform your leadership approach and drive remarkable team performance. Discover the key to building trust in multicultural teams and unlock the potential for unparalleled collaboration and synergy. Uncover powerful strategies for cross-cultural communication to enhance team dynamics and foster a more inclusive work environment. Explore the potential of leveraging diversity for team innovation and witness the impact it can have on creativity and problem-solving. Learn effective techniques for managing time zones in global teams to streamline operations and maximize productivity across different geographical locations. The key moments in this episode are: 00:00:02 - Welcome and Introduction 00:01:17 - Leading Across Borders 00:03:11 - Cross-Cultural Communication 00:06:19 - Leveraging Diversity 00:07:12 - Building Trust and Respect Boundaries 00:13:01 - Supporting the Podcast Community 00:13:14 - Listener Appreciation 00:13:31 - Making a Difference 00:13:40 - Final Words of Encouragement Leading across borders is a rewarding challenge of modern leadership. When we embrace cultural agility, foster inclusivity, and align around a common vision, we can unlock the potential of teams with cognitive diversity, allowing them to innovate and thrive together. - Lori Adams Brown Mastering global team leadership In leading global teams across borders, mastering global team leadership is crucial for success. Understanding cultural differences and adapting leadership styles accordingly fosters collaboration. Effective communication and respect for diverse perspectives are key components in leading high-performing global teams. Connect with Lori Adams Brown on LinkedIn to send a direct message or email the show to share your thoughts and experiences in leading global teams. Join the Patreon community of difference makers to engage in deeper conversations and direct message Lori Adams Brown with your tips and experiences in leading globally. Subscribe to the podcast, leave a review, and share the episode with someone who might find it helpful to make a difference in their own way. Use Microsoft Planner or notion boards to coordinate work across time zones and ensure effective collaboration in global teams. Reach out to Lori Adams Brown on Instagram by sending a direct message or commenting on any post related to the episode to share your thoughts and experiences in leading across borders. Connect with us: https://www.aworldofdifferencepodcast.com Linkedin YouTube FaceBook Instagram Threads Patreon (for exclusive episodes just for Difference Makers) Bluesky TikTok Subscribe to the podcast, leave a review, and share this episode with someone who might need to hear it. Your support helps the community grow and keeps these important conversations going. If you need professional help for your worklife: https://www.betterhelp.com/difference Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of the Know Your Shit podcast, Josh Cadillac welcomes Daniel V, an inspiring entrepreneur and founder of Global Team. Daniel shares his journey from Peru to the United States, his passion for helping U.S. businesses scale, and his mission to create meaningful opportunities for professionals in Latin America. They discuss the importance of living life by design, overcoming challenges, and building a purpose-driven business. This conversation is filled with actionable insights on entrepreneurship, personal growth, and making a global impact.

About this EpisodeDexter Cousins welcomes Rob Lincolne, Founder & CEO of PayDock to the latest episode of the Fintech Chatter Podcast.Rob shares Paydock's unique position in the payments ecosystem with a deep-dive into payments orchestration and the importance no-code solutions.He discusses the challenges of attracting top talent, and the complexities of building a global team. He highlights the resilience his team in Ukraine who have operated under tough conditions since 2022.Rob shares with Dexter his daily routines and the importance of maintaining a healthy work-life balance as a founder of a global Fintech venture.About Rob LincolneRob Lincolne is an accomplished entrepreneur with extensive experience in the payments industry. Founder and Co-CEO of Paydock, he is dedicated to transforming how we transact by creating the world's most trusted payment orchestration platform.His journey began in Sydney 2007 when he founded a digital marketing agency specialising in the not-for-profit sector.The Paydock vision arose from the need for better integration between financial institutions and their merchant clients. Their innovative approach aims to unlock the fintech market's true benefits.Rob has also founded a successful "near-shore" talent placement agency in Ukraine, connecting software engineers with fintechs around the globe.About PaydockPaydock is a payments orchestration platform, leading the way in resolving costly merchant issues with its innovative API-first technology that harmonises payment, fraud, identity and other vendors (such as Stripe, PayPal, etc.) through a single interface.Key TakeawaysPayDock addresses the technology gaps between large banks and fintech.Adopting a no-code solution within the payments industry is essential for merchant adoption of fintech.Orchestration should be about enabling merchants, not commoditising services.Building a global team has been crucial for scaling the business.Customer relationships are vital in the fintech space.The future of payments is modular and interoperable.Attracting talent requires offering challenging problems to solveCharacter and resilience are more important than skills.The hype in fintech is over; it's time for real business.Chapters00:00  Introduction to Fintech Chatter01:59   Understanding PayDoc and Its Unique Position06:06  Innovation and the No-Code Approach10:58   Defining Payments Orchestration14:58   The Journey of PayDoc: From Australia to the UK19:09   Building a Global Team and Talent Acquisition21:54   Supporting Teams During Crisis: The Ukraine Experience25:32   Attracting Top Talent in Fintech29:26   The Importance of Character in Hiring33:56   Resilience and Overcoming Adversity38:33   Current Trends in Fintech42:14   Daily Routines and Habits of Founders45:06  Looking Ahead: PayDoc's FutureLinksVisit Paydock Connect - Rob LincolneConnect - Dexter CousinsSend us a textSubscribe Newsletter: https://www.linkedin.com/newsletters/fintech-leaders-7092732051488980992/Connect on Linkedin: https://bit.ly/3DsCJBp

Are you struggling to navigate the challenges of leading a global team?Meet Dirk De Smaele!Dirk is a Leadership Coach, Trainer, Consultant and Keynote speaker. He is a Former J&J Innovative Medicine R&D Executive and Board MemberAfter a fulfilling 28-year career in a global healthcare company, he made the difficult decision to leave his corporate, executive role and fully dedicate himself to his true passion—coaching and consulting people, teams, and fostering transformational leadership at individual or organisational level.With 28 years of leadership experience in R&D at J&J Innovative Medicine, Dirk had the privilege of leading high-performing global teams in end-to-end CMC process and product development for both synthetics and biologics. His career has been built on a foundation of scientific expertise, strategic thinking, and a passion for empowering people. As a seasoned executive, he has successfully navigated complex challenges, driven innovation, and delivered results by fostering a culture of collaboration, accountability, and growth.Dirk is excited to bring these experiences and insights into his new role as a leadership coach, trainer, and consultant. His mission is to empower leaders to unlock their potential, inspire their teams, and drive sustainable success. Dirk understand the pressures and nuances of leadership. He has seen firsthand the transformative power of clear vision, empathy, and strategic alignment, and is eager to help others apply these principles in their own leadership journeys.Whether you're looking to elevate your leadership skills, develop high-performing teams, or navigate organizational change, Dirk is here to offer practical guidance, grounded in decades of real-world experience.Key Points:- A deeper understanding of others,- Connect with people on a more human level, seeing them beyond their roles or titles- Reach your fullest potential- Leadership to create collaborative environments built on trust- Cultivate long-term, successful relationships and collaborative teams- Enhance your self-awareness, openness, self-accountability, and conflict resolution skills- Psychological safety and high-performance teamwork...and so much more!Connect with Dirk:Website: https://www.atransformationaljourney.comListen to the Podcast, subscribe, leave a rating and a review:Apple: https://podcasts.apple.com/us/podcast/how-to-lead-a-global-team-w-dirk-de-smaele/id1614151066?i=1000678430073Spotify: https://open.spotify.com/episode/4pZ3fXKl5ACWPVFNXLa9B4?si=dBfOa2yNRY68lDQCOEm8swhttps://open.spotify.com/episode/4pZ3fXKl5ACWPVFNXLa9B4YouTube: https://youtu.be/npQ5s9EPNaU

Brittany Brewer, a senior account executive at FreeUp, discusses the benefits and challenges of building a global team for e-commerce businesses. She shares how FreeUp was created to address the need for reliable freelancers in the e-commerce space and offers tips on effectively delegating tasks to a global team. Brittany emphasizes the importance of documenting tasks, establishing clear communication protocols, and creating an action plan for utilizing freed-up time. She also highlights the significance of regular meetings and open communication between clients and freelancers. Managing a global team requires clear lines of communication and consideration for different time zones and cultures. It is important to find the best lines of communication that your team is already comfortable with. Tools like Slack, Loom, Zoom, and Google Meet can facilitate effective communication. When working with international freelancers, it can be beneficial to delegate tasks that can be completed while you sleep. It is also important to establish clear communication preferences and use tools like WhatsApp or other communication platforms that work best for both parties. Additionally, using technologies like Time Doctor for productivity management and LastPass for secure password sharing can enhance team efficiency and security. Key Episode Takeaways: Building a global team can help e-commerce businesses scale and free up time for big picture tasks. Documenting tasks and creating clear communication protocols are essential for effective delegation. Regular meetings and open communication between clients and freelancers are crucial for project success. Utilize available tools like screen recorders to create standard operating procedures and provide resources for freelancers. Clear lines of communication are essential when managing a global team. Consider different time zones and cultures when working with international freelancers. Use tools like Slack, Loom, Zoom, and Google Meet for effective communication. Delegate tasks that can be completed while you sleep to maximize productivity. Establish clear communication preferences and use platforms like WhatsApp that work best for both parties. Utilize technologies like Time Doctor for productivity management and LastPass for secure password sharing. If you feel Brittany and her team at FreeUp can help you, you may visit: https://www.freeup.net  For show transcript and past guests, please visit https://www.ecommercemarketingpodcast.com Or on YouTube at: https://www.youtube.com/channel/UC3PgT0NOGzpdPGQtBK0XLIQ  Follow Arlen: Twitter: https://twitter.com/askarlen   Facebook: https://www.facebook.com/arlen.robinson.7   Instagram: https://www.instagram.com/arlenyohance/   LinkedIn: https://www.linkedin.com/in/arlenrobinson/   Past guests on the ecommerce marketing podcast include Neil Patel, Nemo Chu, Luke Lintz, Luke Carthy, Amber Armstrong, Kris Ruby and many more. Thanks for listening. Be sure to subscribe and leave a review.

In our latest episode of Web3 Unpacked, Rich Pasqua of @mvmtmedia, speaks with Dan Kaizer (CTO) of Azuro, a powerful prediction layer, offering tooling, oracle & liquidity solutions for any app and EVM chain.…"We are completely transparent and our solution is completely trustless, so you don't need to trust us. You only need to trust the code. Our code is audited by many, many entities, so we usually have very a few audits for each release. - Dan KaizerABOUT AZUROAzuro is the on-chain predictions layer. It consists of modular tooling, oracle and liquidity solutions for EVM chains to host powerful prediction and gaming apps. With its unique infrastructure layer approach Azuro makes on-chain predictions and gaming portable and composable. It allows anyone to engage and monetize users by building apps, integrations, and products quickly, permissionlessly and with zero upfront or running costs.Website:  https://azuro.orgLinkedIn:   https://www.linkedin.com/company/azuroprotocolDiscord:  https://discord.gg/azuroVIDEO PODCASThttps://youtu.be/6JofC5usl14Chapters00:00 Dan's Journey into the Crypto World05:14 Azuro: Prediction Markets and Liquidity Solutions26:02 Building a Global Team and Community Engagement35:17 The Launch of the Azuro Token and DecentralizationLEARN MORE ABOUT MVMT/Web3 Unpackedhttps://linktr.ee/mvmt.mediahttps://mvmt.media#mvmt #blockchain #richpasqua #richardpasqua #web3unpacked #prediction #Coinsub #blockchain #cryptopayments #stablecoins #EVM #evmcompatible #chains #userexperience #decentralized #societalchange  #borderlesseconomy #publishing #contentcreation, blockchain #accounting #financialmanagement #regulatoryclarity #unknownpossibilities #azuro #DanKaizer Liquidity #global #web3news

Colin chats with Chris Parker about building a thriving culture in a fully distributed team and scaling globally. Chris shares insights on hiring the right people, promoting internal talent, and strategically expanding into new markets. They discuss the importance of customer-centric strategies and minimizing risks in business growth.Follow the Host:Collin Mitchell (Partner, Leadium)Our Episode Guest:Chris Parker (VP of Global Sales, Customer.io)Sponsored By:Leadium | The leader in outbound sales appointment setting*If you'd like to be a guest on the show or have any questions, email us at guest@salestransformation.co - Just tell us why you're reaching out and we'll contact you as soon as we can!

In this conversation, Neil Bhuiyan interviews James Ski, the CEO and founder of Sales Confidence, a B2B sales community. James shares his journey in sales, his motivation for success, and his experience with bipolar disorder. He discusses navigating stress in sales and shares insights from his founder's journey. James explains what Sales Confidence is all about, who it is for, and the benefits of becoming a member. He also shares the feeling of building Sales Confidence and how he seeks coaching and mentorship. In this conversation, James shares insights on personal growth, accountability, and success in sales. He emphasises the importance of investing in oneself through following and learning from successful entrepreneurs, seeking coaching and mentorship, and asking the right questions. James encourages individuals to take the first step towards their goals and to aim for 10X growth. He also highlights the journey of building a business and offers advice to his younger self.

C3 Church Global exists to equip and empower our pastors to lead healthy, growing and multiplying churches. We believe in the local church. Out of an apostolic church a movement was birthed, but in the last decade we have transitioned from a church with a movement to a movement of churches. In this weeks podcast Ps Phil Pringle (Found and Leader of C3 Church Global) sits down with Ps John Pearce (Global Executive Director) and Ben Giles (General Manager) to bring some clarity to how our wonderful movement is now structured for growth into the future - including the make up of the Global Team, oversight and accountability, the function and purpose of church levies, and future plans for resourcing and leadership development. GLOBAL TEAM Global Executive - How do we fufil to overall vision and maintain culture and values Global Board - Governance and Finances Regional Director - Local leaders overseeing regions of pastors Global Office - Administration, comms, resources, events For more on the history of who we are as a movement make sure to also check out our We Are C3 Documentary on Youtube. For more information on C3 Church Global www.c3churchglobal.com | @c3churchglobal Find you closest C3 Church: https://c3churchglobal.com/find-a-church COMING UP ‘The Outpouring' C3 Church Global Conference Singapore | May 15-17, 2024 Register now! www.c3churchglobal.com

Today I am joined by Janet Livingstone, founder of Culture Is Key, a consultancy devoted to leadership development, executive coaching and team effectiveness. Having lived and worked on 5 continents, she has a first-hand understanding of the challenges teams can face when communicating across time zones.Janet possesses extensive experience in helping leaders and teams to take ownership of their own development through intentional work and human connection in the workplace.As a multilingual expert on intercultural competence, I invited Janet to join me on The HR L&D Podcast to share her lived experience to demonstrate how leaders can expand dialogue and build trust within hybrid and international teams both in established companies and startups. Here are some key learnings from this episode: Modern Human Resources: We unpack the multifaceted nature of HR and its impact on individuals and organizations.Unraveling Workplace Disconnection: We Investigate the root causes of dissatisfaction and disconnection in the workplace.Pandemic Perspectives: Discussion on how the COVID-19 pandemic has unexpectedly benefited personal and professional growth.Adapting to Life Abroad: Actionable strategies for navigating the challenges and rewards of living and working in foreign countries.Fostering Connection in Meetings: Effective techniques for creating meaningful connections during meetings.Importance of Work Culture: An inside look at a global consulting and coaching practice that leverages diverse disciplines for transformative outcomes.This episode of the HR L&D Podcast is sponsored by Deel, the all-in-one Global People Platform that simplifies how you manage the entire global team lifecycle. Hire and onboard talent in over 150 countries in minutes. Run payroll in over 100 countries with one click. Offer competitive benefits, equipment, and equity from a single dashboard. From contractors, direct employees, EOR, and more, you can manage them all in one place with Deel.Book a demo nowConnect With Janet Livigstone:Website: https://www.cultureiskey.coachContact Info: janet@cultureiskey.coach Connect with Nick Day:Email: nick@jgarecruitment.comPhone: 01727800377Of course, if you are an HR or L&D professional listening to this podcast and you have an HR, HRIS or L&D related vacancy that you would love some specialist HR recruitment support with – please also get in touch with me! I would love to help show you what a great HR recruitment experience feels like! You can reach out to me directly at nick@jgarecruitment.com or give me a call – 01727800377.Thanks for listening folks – I look forward to bringing you the next episode of the HR L&D Podcast real soon!(00:00) Preview (00:43) Sponsor Message from Deal(02:01) Introduction(05:07) Learning from Different Cultures (09:11) Addressing Disconnection at Work(14:53) Post-Pandemic Positives for Cross-Cultural Communication(18:36) Living and Working Across Continents(27:15) Sponsor Message from JGA Recruitment(31:06) Facilitation Skills for Leaders(34:10) Janet's Love for Jazz and Comedy(41:33) American Work Culture Traits(46:20) UK

Are you regularly making time and space for connection in your global team? How well are you and your people connecting with each other today?  Building connection through your communication is a real superpower, and can help you create trust, engagement and better relationships with your people, wherever they are based around the world.  Although connection technically is easier than ever, true human connection can be more challenging with hybrid and remote working. In today's episode I share how to connect well in your multicultural team. What you will learn in this episode: Why is it so hard to build connection in the workplace today Connection is more important than ever Your communication superpower: connection Know your people Start with listening The non-verbal communication secrets Conversation skills pro Develop your cultural intelligence muscles Resources: If you know connection is an area you and your team need to be stronger in, and you would love to receive clarity on how to improve, meet me for a free discovery call. I'll share my insights with you so you can immediately start building connection in your team. Book your time here: https://culturecuppa.com/contact/ Listen to episode 72, the 4 pillars of Global Leader Communication: https://culturecuppa.com/podcasts/the-4-pillars-of-global-leader-communication/ Listen to episode 75, Mastering Clarity: Elevating your Influence: https://culturecuppa.com/podcasts/mastering-clarity-elevating-your-influence/ Listen to episode 76, Empowering Confidence: strategies for your communication: https://culturecuppa.com/podcasts/empowering-confidence-strategies-for-your-communication/ Introduction to Cultural Intelligence, listen to episode 008, How to improve your Cultural Intelligence: https://culturecuppa.com/podcasts/how-to-improve-your-cultural-intelligence/ Sign up to receive future episodes of the podcast as soon as they are released: https://culturecuppa.com/get-free-insights Follow me on LinkedIn for more strategies, skills and tips: https://www.linkedin.com/in/victoria-rennoldson Email me: victoria@culturecuppa.com Website: https://culturecuppa.com

In this insightful podcast episode, hosts Rick and Kaleem delve into various aspects of workplace culture, personal resilience, and career experiences within the tech sector, guided by their esteemed guest, Peter Caputa, CEO of Databox. Peter's journey unfolds as he shares anecdotes highlighting the importance of resilience, stemming from his family's experience building their own house, instilling in him values of determination and perseverance from a young age.Transitioning seamlessly into a reflection on his tenure at HubSpot, Peter reminisces about the company's early days characterized by a flexible and informal work culture. Despite the lack of strict policies, the aggressive goals at HubSpot often led to long working hours, leaving little time for vacations—a testament to the demanding nature of the tech industry.The conversation shifts gracefully towards Peter's company, Databox, a performance analytics software for businesses. Peter elucidates how Databox simplifies performance monitoring, goal setting, and predictive analysis by aggregating data from various sources. Operating with a hybrid model, Databox boasts teams in Slovenia and remote teams globally, emphasizing flexibility and inclusivity in its approach to remote work.Delving deeper into the discussion, Peter underscores the challenges and benefits of remote work, emphasizing the importance of workplace flexibility and cultural inclusivity. Databox's commitment to fostering a sense of community among remote teams is evident through virtual events and holiday celebrations, ensuring that all team members feel valued and connected.Tune in and gain valuable insights into Databox's strategic approach to remote work, emphasizing flexibility, inclusivity, and community building among remote teams. Peter's reflections on navigating the fast-paced tech industry underscore the importance of resilience and perseverance in achieving success, making this episode a must-listen for anyone interested in the evolving landscape of remote work and tech entrepreneurship.Learn more about Peter:Peter's LinkedIn: https://www.linkedin.com/in/pc4media/Databox: https://databox.com/

Don't sleep on the six, the Raptors will be back in no time, give us two years max. No disrespect but we don't need LeBron James. There is no “D” in LeBron. Scottie Barnes is an All-Star and the youngest player to ever get the keys in Toronto, do the Raptors have to keep him happy as our franchise player? The All-Star game is gone, time to make it Team USA VS The World if you want it to be competitive. Did LeBron really ruin the dunk contest by never participating, we are seeing a lack of superstars participate.  Make Finch great again, lets end the gun violence and bring these artists together to really end the violence. Also, shoutout KD for getting behind the mic in the studio but who is the greatest rap hooper? Jerami Grant stop it, you tripping on the drip.

In this episode of the SecurityMetrics podcast, Jen Stone chats with Heidi Babi, an ISA, PCIP, and CISSP at Mars Corporation, about managing PCI compliance in a massive, complex organization with hundreds of data flows.Listen to learn:How to break down overwhelming requirements into manageable steps and design flexible solutions for future growth.How to utilize compensating controls and customized solutions to achieve robust security.How to build rapport with internal teams to create a more functional and effective PCI program for your company.Filmed at the 2023 PCI Community Meeting in Dublin, Ireland.Hosted by Jen Stone, Principal Security Analyst (MCIS, CISSP, CISA, QSA)[Disclaimer] Before implementing any policies or procedures you hear about on this or any other episodes, make sure to talk to your legal department, IT department, and any other department assisting with your data security and compliance efforts.

Fred Johnson and Adrià Passola deeply discuss what it takes to lead transformational change in organizations. Adrià leans on his experience of the InitativeOne Process to show that it takes high-performing leaders to create high-performing organizations. You can't do it on your own.  They also discuss the launching of InitiativeOne's Global Team and the excitement around bringing the InitiativeOne Process to more organizations across the globe. Learn more about InitiativeOne's Leadership Transformation Process: https://www.initiativeone.com/leadership-transformation-organizational.  ¡Quiero Saber Mas!: https://www.initiativeone.com/vincere.  

Building a team with global reach and impact! We sit down with the international training team from the USA, Germany, and Greece to talk about how they create a standard training team across the globe. Everyone is required to become a master trainer. Sonax https://www.sonaxusa.com/ Host:⁠  @JodySedrick ⁠ The RoadFS Podcast RoadFS Software ⁠https://roadfs.com

In this podcast episode, the discussion is with Biftu Yousuf, PhD Candidate and Research Assistant in the Geography Department, in the faculty of environmental and Urban change at York University, plus Certified NVivo Expert with the GenUrb, Urbanization, Gender, and the Global South: A Transformative Knowledge Network.  We discuss her research experience with the GenUrb global project. GenUrb's grant number is SSHRC Partnership Grant (PG) 895-2017-1011.

In this episode Eran Mizrahi discusses the work he is doing with Ingredient Brothers to make the ingredient search and procurement process easier for customers. Listen in as Deborah and Eran also talk about the importance of building a global team and the challenges that come with it. Eran emphasizes the need for a clear focus on building a global team, while Deb adds that it's essential to have a long-term strategy and find team members who align with the company's values and goals. They also discuss the milestones and challenges of entrepreneurship and the importance of networking.   Eran grew up watching his father grow a successful import business that was built on integrity and customer service. A South African transplant, Eran started his career at Deloitte. From there, he came to New York to pursue his MBA at Columbia '14.Eran was an early employee at Plated, where he focused on building planning and sourcing programs. The team's collective efforts led to a $300M sale to Albertsons.He then went to join Nuts.com, one of the world's largest nuts and specialty ingredient e-commerce companies. He was quickly elevated to COO and quadrupled capacity to support growth in 2020.Fun Fact: Eran took a gap during college to attend culinary school, where he solidified his love of food.   You can connect with Eran in the following ways: Website: ingredientbrothers.com Linkedin: https://www.linkedin.com/company/74524657/admin/feed/posts/   Whether you are a C-Suite Leader of today or tomorrow, take charge of your career with confidence and leverage the insights of The CEO's Compass: Your Guide to Get Back on Track.  To learn more about The CEO's Compass, you can get your copy here: https://amzn.to/3AKiflR    Other episodes you'll enjoy: C-Suite Goal Setting: How To Create A Roadmap For Your Career Success - http://bit.ly/3XwI55n Natalya Berdikyan: Investing in Yourself to Serve Others on Apple Podcasts -http://bit.ly/3ZMx8yw Questions to Guarantee You Accomplish Your Goals - http://bit.ly/3QASvymSee omnystudio.com/listener for privacy information.

On this episode of the Scale Up Valley Podcast, Mike Dias speaks with Craig Everett, CEO at Holibob Key Takeaways Opening a world of possibilities for travellers, tour operators, and travel brands by pioneering solutions. How to innovate in a very crowded market like travel Leveraging technology to disrupt an established market Raising a Pre-Seed round during the Pandemic Bringing diversity in a very authentic way Lessons learned working in a remote first environment. The first acquisition just after Series A

773: May Yap, the Chief Information Officer of Jabil, talks about the remarkable modernization and transformation journey she has led within the company. With May based in Singapore, May leads a truly global team with tech centers worldwide and shares her insights into managing this diverse and distributed workforce. She describes the process and benefits of modernization, how digitalization leads to better organization throughout IT, and how her team tests new technologies for potential implementation. May describes the advantages of having a long tenure as CIO, the latest tech trends shaping the industry, and the keys to her career success.

773: May Yap, the Chief Information Officer of Jabil, talks about the remarkable modernization and transformation journey she has led within the company. With May based in Singapore, May leads a truly global team with tech centers worldwide and shares her insights into managing this diverse and distributed workforce. She describes the process and benefits of modernization, how digitalization leads to better organization throughout IT, and how her team tests new technologies for potential implementation. May describes the advantages of having a long tenure as CIO, the latest tech trends shaping the industry, and the keys to her career success.

This week we are excited to welcome Julio Monzon to the Sensei's Corner! Julio is the President of MonetizeMore, which is a leading publisher monetization company helping digital publishers improve their ad operations and yield management practices. This week we are focusing on the topic of how to grow a global team successfully.   Timestamp: 00:01:17 - Who is Julio Monzon 00:02:53 - Company culture 00:04:05 - Diversity within the company 00:04:21 - Does the way he runs the business affect recruitment? 00:06:34 - Employee engagement 00:12:15 - Explosive Growth 00:13:47 - Interview Questions 00:16:58 - DiSC Assessment 00:19:48 - Something Julio's most excited about in the industry 00:23:25 - A fun fact about Julio 00:24:15 - A place Julio visited that was unexpectedly amazing 00:26:05 - Julio's biggest what if  00:27:54 - What would you tell your younger self?   Guest Information: Julio Monzon LinkedIn MonetizeMore  LinkedIn | Website Meet Our Team: Hélène Parker - Chief Programmatic Coach Programmatic Meet Up | Website | LinkedIn | Twitter Programmatic Digest - Youtube | LinkedIn | Instagram The Reach & Frequency Course - Listeners get a 10% discount with code podcast10 Alexa Gabrielle Ramos - Podcast Editor Instagram | Website | LinkedIn  S and S Creative Media - Podcast and Media Manager Instagram | Website | LinkedIn

The world has change due to covid and we are not going back to normal work practises. Work from home and hybrid work is now the norm. This has created many challenges but also endless opportunities. The need to get teams together has moved from nice to have to must have. This is what global team gathering is looking to address Join Tourpreneur+ for even more content: https://tourpreneur.com/plus/ Join our free tour operator community of over 5.3k members: https://www.facebook.com/groups/tourpreneur Check out Global Team Gatherings: https://globalteamgatherings.com/ 

For more stories of remote teams doing great things, visit https://www.collaborationsuperpowers.com.

Our guests are Joelle Sambuc Bloise and Aldo Bloise, who are the co-founders of Ikkyu. Ikkyu is a unique tea company that sells high-quality Japanese tea based in the southern island of Kyushu. If you live outside of Japan, it is not easy to find reasonably priced high-quality Japanese tea. Then I heard about Ikkyu. I ordered some tea from the website and I had some wonderful new discoveries! Of course, there are other great Japanese tea companies but I got particularly interested in Ikkyu's focus on the terroir of Kyushu island. In this episode, we will discuss how Joelle and Aldo decided to move to Japan even though they had a solid professional career in Switzerland, how they discovered the charm of Japanese tea, why you should try Japanese tea beyond matcha, the unique terroir of Kyushu Island, and much, much more!!!Heritage Radio Network is a listener supported nonprofit podcast network. Support Japan Eats by becoming a member!Japan Eats is Powered by Simplecast.