Podcasts about thinky

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

Overthinking has no place in the life God has designed for the believer. Rather than being consumed by every conceivable possibility, we are called to commit every care into His hands, confident in His unfailing faithfulness.

Analizamos el impacto del Mundial 2026 en el marketing digital y cómo las marcas deben adaptarse para aprovechar esta oportunidad única. Descubre las claves para conectar con audiencias modernas y potenciar tu estrategia en tiempos de saturación de contenido.

Its that time of year again and the TWIG Crew are back to discuss our Summer Game Fest/Not-E3 podcast schedule, the indie showcases that have already kicked off!Show Notes:Warhammer Skulls Showcase - Festival of Video Games 2025 - https://www.youtube.com/watch?v=C4A1s4DH78wSix One Indie Showcase 2025 - https://www.youtube.com/watch?v=adI4uizTYN4Thinky Direct 2025 - https://www.youtube.com/watch?v=iXyyhQdlx-MJRPG Indie Quest 2025 - https://www.youtube.com/watch?v=QAanpHTfrAwYour Geekmasters:Alex "The Producer" - https://bsky.app/profile/dethphasetwig.bsky.socialKen Reels - https://bsky.app/profile/kenreels.comFeedback for the show?:Email: feedback@thisweekingeek.netTwitter: https://twitter.com/thisweekingeekBluesky: https://bsky.app/profile/thisweekingeek.bsky.socialSubscribe to our feed: https://www.spreaker.com/show/3571037/episodes/feediTunes: https://podcasts.apple.com/us/podcast/this-week-in-geek/id215643675Spotify: https://open.spotify.com/show/3Lit2bzebJXMTIv7j7fkqqWebsite: https://www.thisweekingeek.netJune 3, 2025

Bienvenidas y bienvenidos a Recarga Activa, el podcast diario de AnaitGames en el que filtramos lo más relevante de la actualidad del videojuego en pildorazos de 15 minutos. La Recarga Activa de hoy: Así fue el Thinky Direct, el evento digital dedicado a los juegos de puzzles I Am Your Beast saldrá en consolas el 25 de junio ¿A qué vamos a jugar este fin de semana? Suscríbete para recibir el siguiente episodio en tu gestor de podcasts favorito. Puedes apoyar nuestro proyecto (y acceder a un montón de contenido exclusivo) en Patreon: https://www.patreon.com/anaitreload ♫ Sintonía del programa: Senseless, de Johny Grimes Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of Indieventure Extra, Rachel chats with puzzle bestie and Thinky Games colleague Joseph Mansfield about - you guessed it - thinky games! Joe is the head puzzle expert over at Thinky Games.com and is the brains behind the Thinky Games database, the Thinky Awards, ThinkyCon AND the brand new and upcoming Thinky Direct puzzle showcase! That's quite the puzzle-focused CV, not to mention his thinky-based YouTube channel Joe Plays Puzzle Games, which you should definitely check out. You can also find Joe on BlueSky and play his games over on itch.io! The two delve into their favourite puzzle games, different puzzle design philosophies, and have a chat about the wonderful thinky community too.  If you'd like to see the Thinky Direct, you can tune in at 10am PDT, 1pm EDT, 7pm CEST, 6pm BST on May 29th. The showcase will be livestreamed over at ThinkyGames.com, the Thinky Games Twitch page, and the Thinky Games YouTube channel.  Joe's Hyperfixation is the YouTube channel Sounds Like! Enjoy the episode! Our music was written and performed by Ollie Newbury! Find him on Instagram at @newbsmusic. Meanwhile, you can find us at indieventurepodcast.co.uk or wherever you listen to podcasts.  

Bienvenidas y bienvenidos a Recarga Activa, el podcast diario de AnaitGames en el que filtramos lo más relevante de la actualidad del videojuego en pildorazos de 15 minutos. La Recarga Activa de hoy: La licencia de WRC vuelve a Nacon, después de un breve escarceo con EA Sports Dragon Quest I & II Remake saldrá el 30 de octubre El 29 de mayo se emite el Thinky Direct, un evento dedicado a juegos de puzzles Suscríbete para recibir el siguiente episodio en tu gestor de podcasts favorito. Puedes apoyar nuestro proyecto (y acceder a un montón de contenido exclusivo) en Patreon: https://www.patreon.com/anaitreload ♫ Sintonía del programa: Senseless, de Johny Grimes Learn more about your ad choices. Visit megaphone.fm/adchoices

Today I am discussing how to share your fantasies and desires {for the bedroom} to your spouse! Bluechew: Visit bluechew.com and use promo code HOUSEWIFE to try one month free! Bathmate: Go to www.bathmatedirect.com/hornyhousewife to get 10% off your purchase Liferx: Use code HOUSEWIFE at www.liferx.md to save $50 on your first order!

Overnight News, 2. Tom's BEST Feeling In The World... 3. The Most Heinous Heist! 4. What's The Coincidence? 5. Creeps On A Plane! 6. Ticked Off Tom - Balenciaga 7. Chinese Unhappy Days??? The ONLY way to wake up in Adelaide is with your best brekkie mates Tom & Callum on Fresh 92.7 Keep up to date on our socials. Instagram - @fresh927 Facebook - Fresh 92.7See omnystudio.com/listener for privacy information.

Bienvenidas y bienvenidos a Recarga Activa, el podcast diario de AnaitGames en el que filtramos lo más relevante de la actualidad del videojuego en pildorazos de 15 minutos. La Recarga Activa de hoy: Super Soccer es el primer juego que se retira de los catálogos de Nintendo Switch Online Lorelei and the Laser Eyes se lleva el premio a Juego del Año en los Thinky Awards 2024 Los lanzamientos destacados de esta semana Suscríbete para recibir el siguiente episodio en tu gestor de podcasts favorito. Puedes apoyar nuestro proyecto (y acceder a un montón de contenido exclusivo) en Patreon: https://www.patreon.com/anaitreload ♫ Sintonía del programa: Senseless, de Johny Grimes Learn more about your ad choices. Visit megaphone.fm/adchoices

#347"A slow meat computer"Roundtable2024.05.16In the episode, Mark, Ellen, and Stephen talk local events, including (don't worry everyone's fine) a fire in the clubhouse's building, construction, and the games they are playing, so if you are just here for the topics, go ahead and skip to minute 23. Ellen learns about King Making, Stephen humble brags about being good at Smash Brothers, and Dale plays kingmaker in naming the second topic!NewsMasu Fire - Paul Walsh, Star TribuneBus Rapid Transit (BRT) - Jared Brey, GoverningWhat We are PlayingDragon's Dogma 2 - WikipediaPrincess Peach Showtime NPR Review - Rakiesha Chase-Jackson, NPRKing Making0:23:00Stephen McGregorGame DesignStephen has been QA testing Harvest Kingdom - Ben Hunder, DiscordThe YouTube video that Stephen references - Distraction Makers, YouTubeKingmaking in Root (Leder Games) video - Cole Wehrle, GDCSmallworld - Board Game GeekThinky Bits (Off-Screen Gameplay)0:49:17Ellen Burns-JohnsonGame DesignGames discussedReturn of the Obra DinSpirit Island - Board Game GeekDeath Drives a Bus - itch.io

Rains Retreat teachings from 9th August to 25th October 2023. Teachings given by the abbot Ajahn Brahm at Bodhinyana Monastery in Serpentine (southeast of Perth, Western Australia). The main audience was the Sangha.   Track  6/10: Feely Feely, Not Thinky Thinky – 13th September 2023 (dates are estimations) See the full set here. The BSWA is now using Ko-fi for donations. Please join us on Ko-fi and cancel your donations via Patreon. Thanks for your ongoing support! To find and download more precious Dhamma teachings, visit the BSWA teachings page: https://bswa.org/teachings/, choose the teaching you want and click on the audio to open it up on Podbean.

In the episode, Mark, Ellen, and Stephen talk local events, including (don't worry everyone's fine) a fire in the clubhouse's building, construction, and the games they are playing, so if you are just here for the topics, go ahead and skip to minute 23. Ellen learns about King Making, Stephen humble brags about being good at Smash Brothers, and Dale plays kingmaker in naming the second topic!NewsMasu Fire - Paul Walsh, Star TribuneBus Rapid Transit (BRT) - Jared Brey, GoverningWhat We are PlayingDragon's Dogma 2 - WikipediaPrincess Peach Showtime NPR Review - Rakiesha Chase-Jackson, NPR0:23:00King MakingStephen has been QA testing Harvest KingdomBen HunderDiscordThe YouTube video that Stephen referencesDistraction MakersYouTubeKingmaking in Root (Leder Games) videoCole WehrleGDCSmallworldBoard Game Geek0:49:17Thinky Bits (Off-Screen Gameplay)Games discussedReturn of the Obra DinSpirit IslandBoard Game GeekDeath Drives a Busitch.io

Bienvenidas y bienvenidos a Recarga Activa, el podcast diario de AnaitGames en el que filtramos lo más relevante de la actualidad del videojuego en pildorazos de 10-15 minutos:1️⃣ Sony Interactive Entertainment despedirá a 900 empleados, el 8% de su plantilla2️⃣ Deck Nine despide al 20% de su personal3️⃣ Leyendas Pokémon: Z-A y otros anuncios del Pokémon Presents4️⃣ Los Thinky Awards anuncian sus ganadoresSuscríbete para recibir el siguiente episodio en tu gestor de podcasts favorito. Puedes apoyar nuestro proyecto (y acceder a un montón de contenido exclusivo) en Patreon: https://www.patreon.com/anaitreload♫ Sintonía del programa: Senseless, de Johny Grimes Hosted on Acast. See acast.com/privacy for more information.

and go morning thoughts of you thoughts of you go let go thinky oh it’s evening music please let go thinky

Books that make you go “Hmmmm” are my favorite kind of books. Whether I'm challenging my current mode of thinking or simply investigating the other side of the argument, more information is almost always better than less. And if what I read can help me understand who I am and who I am trying to become, even help me get there a bit more efficiently, that's a good use of my time and money. I shared this list with Heaven Citizens, my Facebook group, earlier this week. Some of it got through; some of it, for technical reasons, did not. Apologies for that. Here is the complete list. Enjoy.Fahrenheit 451, by Ray BradburyThe Great Divorce, by C.S. LewisDisconnected, by Thomas KerstingThose Who Walk Away, by Patricia HighsmithIn Defense of Food, by Michael PollanAs Nature Made Him, by John ColapintoSan Francisco is Burning, by Dennis SmithKing Leopold's Ghost, by Adam HochschildThe Madness of Crowds, by Douglas MurrayGod's Bestseller, by Brian MoynahanHal Hammons serves as preacher and shepherd for the Lakewoods Drive church of Christ in Georgetown, Texas. He is the host of the Citizen of Heaven podcast. You are encouraged to seek him and the Lakewoods Drive church through Facebook and other social media. Lakewoods Drive is an autonomous group of Christians dedicated to praising God, teaching the gospel to all who will hear, training Christians in righteousness, and serving our God and one another faithfully. We believe the Bible is God's word, that Jesus died on the cross for our sins, that heaven is our home, and that we have work to do here while we wait. Regular topics of discussion and conversation include: Christians, Jesus, obedience, faith, grace, baptism, New Testament, Old Testament, authority, gospel, fellowship, justice, mercy, faithfulness, forgiveness, Twenty Pages a Week, Bible reading, heaven, hell, virtues, character, denominations, submission, service, character, COVID-19, assembly, Lord's Supper, online, social media, YouTube, Facebook.   

 @KidologyCO  Blaming "the gays" for surrogacy https://youtu.be/S2s9NCBytxs?si=XJMwW1qrzmnn9Afz   @consilienceproject3122  The Psychological Drivers of the Metacrisis: John Vervaeke Iain McGilchrist Daniel Schmachtenberger https://youtu.be/-6V0qmDZ2gg?si=42xMwRzVNOfOUrIZ  Surprising Rebirth of Belief in God Podcast https://justinbrierley.com/surprisingrebirth/  High on God: How Megachurches Won the Heart of America https://amzn.to/3v2Qi8q  Paul Vander Klay clips channel https://www.youtube.com/channel/UCX0jIcadtoxELSwehCh5QTg Bridges of Meaning Discord https://discord.gg/eex6RuVC https://www.meetup.com/sacramento-estuary/ My Substack https://paulvanderklay.substack.com/ Estuary Hub Link https://www.estuaryhub.com/ If you want to schedule a one-on-one conversation check here. https://paulvanderklay.me/2019/08/06/converzations-with-pvk/ There is a video version of this podcast on YouTube at http://www.youtube.com/paulvanderklay To listen to this on ITunes https://itunes.apple.com/us/podcast/paul-vanderklays-podcast/id1394314333  If you need the RSS feed for your podcast player https://paulvanderklay.podbean.com/feed/  All Amazon links here are part of the Amazon Affiliate Program. Amazon pays me a small commission at no additional cost to you if you buy through one of the product links here. This is is one (free to you) way to support my videos.  https://paypal.me/paulvanderklay Blockchain backup on Lbry https://odysee.com/@paulvanderklay https://www.patreon.com/paulvanderklay Paul's Church Content at Living Stones Channel https://www.youtube.com/channel/UCh7bdktIALZ9Nq41oVCvW-A To support Paul's work by supporting his church give here. https://tithe.ly/give?c=2160640  

Unedited and unscripted chat about everything from Layover (still not over the music videos or the hair) to Enlistment (sob) and everything in between as Bangtail keeps us on our toes.NEW! Afternoona Army is now on PATREON!Join The BTS Buzz and get access to Afternoona Army's exclusive DISCORD channel, get shout outs on-air in podcast, and receive invitations to quarterly live support groups. Questions? Email afternoonaarmy@gmail.com for more information.Sign Up for Our Newsletter!Want our thoughts on Yunki's hair extensions in a 500-word essay? More book recs? This is the place to get it. Sign up HERE! Are your family and friends sick of you talking about K-drama? We get it...and have an answer. Check out our sister pod www.afternoonadelight.com for more episodes, book recs and social media goodness. And don't forget about the newest members of our network: Build an appetite for juicy living with It's Bananas, a podcast where the fruit maven shares how tasting new and diverse fruits leads to self-discovery, joy, and connection; and Afternoona Asks where diaspora Asians living in the West find ways to reconnect to Asian culture via Asian/KDramas.Want to find more great BTS content? Head over to Afternoona Army for "thinky, thirsty and over thirty" takes on Bangtan life and links to our social media.

Have you heard these common myths about the importance of downtowns and cultural centers in community preservation? Myth #1: Downtown revitalization is only for big cities. Myth #2: Cultural centers are just for entertainment and not essential for the community. Myth #3: Investing in downtown revitalization is a waste of resources. In this episode, our guest Chad Banks sheds light on the significance of downtowns and cultural centers in community preservation. Host Emy DiGrappa and co-host Lucas Fralick engage in conversation with Chad Banks, Director, Rock Springs Main Street/Urban Renewal Agency. They discuss the critical role of downtowns and cultural centers in community preservation and economic growth. The episode highlights the significance of historic preservation in Wyoming, where funds are limited. Chad Banks shares the inspiring story of the restoration of the Wyoming capital, which faced resistance but ultimately preserved the building's beauty and history. Chad emphasizes the need for community-driven initiatives and partnerships between local governments, Main Street programs, nonprofits, and various agencies.  Chad Banks, a dedicated Rock Springs resident, whose roots in the town trace back to five generations, vividly shared his journey of discovering the importance and need for preserving downtowns and cultural centers. It all began with a casual trip to a small town in Utah for his daughter's dance competition. What caught his attention was the town's blandness, its lack of unique identity, a stark contrast to the vibrant and historic downtowns he was familiar with. This experience sparked a realization in Chad. He saw how these downtowns, with their historic buildings and unique stories, were the pulse of the community, providing a sense of identity and continuity. Chad took this realization back to Rock Springs and into his role with the city's Main Street program, where he has since worked to reinvigorate the downtown area, preserve its rich history, and enhance its unique identity.  Links:  FaceBook Youtube  www.Thinkwy.org   LinkedIn Visit Thinkwy.org to listen and learn about the Winds of Change podcast  Sign up for the podcast newsletter and subscribe to the podcast on Thinky.org.   Resources: Explore the Main Street program and its revitalization programs for locally owned, locally driven prosperity.  Learn about the Smithsonian Sparks exhibit, Spark Places of Innovation, which highlights innovation in rural America.  Stay tuned to learn more about the locations around Wyoming that will host the Sparks exhibit.  Explore the Main Street program and its revitalization programs for locally owned, locally driven prosperity.  Stay tuned to learn more about the locations around Wyoming that will host the Sparks exhibit.   Encourage children and adults to learn about and appreciate the history and heritage of their communities.  Contact the Main Street Urban Renewal Agency for information on downtown development and revitalization programs.   Support local businesses and entrepreneurs by shopping and dining in downtown districts.   Encourage children and adults to learn about and appreciate the history and heritage of their communities.    Connect with Emy diGrappa and Wyoming Humanities: FaceBook   Twitter    ThinkWY Website   LinkedIn   Listen on Spotify, Google Podcasts and Apple Podcasts and many more. ThinkWY.org Sign up for our Storytelling Podcast Newsletter! Follow this link or use the QR code Subscribe on Spotify, Apple Podcast and YouTube

Thinky-thoughts and brain weasels defined Thinky-thoughts originate in the subconscious, usually attached to memory. Another form of thinky-thoughts is not based in subconscious memory, but on your beliefs, values, and habits. Brain weasels, squirrels in the brain, and the like are chittering voices that will tell you how much you suck, what you're not good at, and all sorts of similar things that aren't true. They usually are the spawn of your comfort-desiring ego and often reflect the voices of sometimes well-intentioned friends and loved ones. Passive conscious awareness Everyone is of three minds. One that's unconscious, one that's subconscious, and one that's conscious. The unconscious mind is purely automated functions of your body. The subconscious mind is almost entirely run by rote and routine. The conscious mind is your conscious awareness of yourself. The conscious mind can be both passive and active. The passive is when you are aware of yourself and your surroundings – but not doing anything to actively engage. The active is when you practice mindfulness. It's asking questions about what you're thinking, what and how you're feeling, what you intend, and what you are or aren't doing. Passive conscious awareness is where thinky-thoughts live. Because while they originate in the subconscious via memory, belief, value, and/or habit – they can only be engaged by the conscious mind. Thinky-thoughts are the road not taken and other uncertainties Thinky-thoughts might originate in the subconscious with memory, belief, value, and habit. But they're not necessarily focused on the past. They can also be placed in the present and the future. They tend to go deep – hence why I call them thinky-thoughts. What thinky-thoughts aren't is the here and now. They're what was, they are alternative notions of what is, and notions and ideas of what may be. But they're not the present, the here and now, or your true reality. The rodent thoughts of brain weasels most often chitter, squeak, and cause second-guessing, self-esteem issues, and a sense of unworthiness. They're obnoxious, annoying, and unkind. And they are not yours – they are lying liars that lie. Employing mindfulness Active conscious awareness. That is genuine mindfulness. Mindfulness is active conscious awareness both of your inner mindset/headspace/psyche self and the world around you. Rather than passive and wholly internalized, mindfulness uses your 6 senses to naturally engage and bridge the internal with the external. This week's Applied Guidance for Mindfulness Tool: Identifying thinky-thoughts vs brain weasels is easy to do via mindfulness. Dealing with each takes the same process, though brain weasels are arguably easier to destroy since they are not your creation. If you have any chittering, annoying notions floating about your mind, sharing doubt and uncertainty that you're rather sure isn't yours, those are brain weasels. If you have notions based in subconscious beliefs, values, and habits, or tied to memories – those are thinky-thoughts. To deal with either, you just need to set-aside 5 minutes. Spend 2 minutes deep breathing to centered. Look at the brain-weasel or thinky-thought. Ask yourself: ·        How does this make me think? ·        How does this make me feel? ·        What does this make me feel? ·        Is it mine, or an outside influence? Asking and answering these, here and now, tells you what you're working with. And from there, more active conscious awareness – mindfulness – will let you make change to address, remove, or otherwise deal with these. Repeat as necessary.      Learn more about your ad choices. Visit megaphone.fm/adchoices

Sometimes being the only one in your head can be infuriating Thinky-thoughts can insinuate themselves into a strange place between your conscious and subconscious minds. But it's a place both right in front of you and hard to reach. When there are a lot of things happening all at once – both inside and outside of yourself – overwhelm can hit hard and fast. Before you know it, you find yourself flustered and frustrated. Then you start asking negative-leaning questions. Getting out of your own head can be incredibly challenging. But why? Your head is like a buried treasure chest How? Because you have items stored in your “treasure chest” that have been lost, forgotten, and buried for who-knows-how-long? Because the mind is made up of both a conscious and subconscious aspect, there are two sometimes opposed possibilities for everything. Any information you get now is subjected to conscious and subconscious thinking. Mindfulness is active conscious awareness Mindfulness is active use of conscious awareness. That's because mindfulness involves questioning your inner mindset/headspace/psyche self. It's simple questions such as, ·        What am I thinking? ·        What am I feeling? ·        How am I feeling? ·        What do I intend here? ·        What am I doing? Each of the above questions can only be answered here, and now. That's an active act of conscious awareness. And that's what mindfulness is. Your own head isn't just your headspace When you find yourself stuck in your own head, that often involves more than your subconscious and conscious mind. Thinky-thoughts aren't always thoughts. They can also be impressions made from the body and soul. Work on being here now When it comes to getting out of your head – or doing anything to take control of your life – being here, now, is necessary. The best way to do this is via active conscious awareness. Mindfulness. Getting out of your own head is never a one-and-done process. That's because thought, feeling, and all else change due to circumstances, happenstance, choices, environment, and tons of other factors. This week's Applied Guidance for Mindfulness Tool: This week's tool might look familiar. Before you start the steps to use this tool, find and/or create 5 minutes for yourself, alone, in a safe and comfortable space. Make sure you have a timer and some means for writing, digital or otherwise. Step 1: For 2 minutes, practice deep breathing to calm and center yourself. Step 2: After that, ask yourself, aloud, the following questions (and write them down): ·        What am I thinking? ·        What am I feeling? ·        How am I feeling? ·        What do I intend here? Step 3: Once you have made yourself mindful, is there something that's been nagging at you or otherwise bothering you? Now that you're actively, consciously aware, can you identify what it is? Step 4: Write it down. Then, write down what it will take to release and free yourself from this. Step 5: Take another minute of deep breathing to recenter yourself. This can evoke an odd state of being because you're going into your head to work with something you've been having a difficult time working with. Don't allow negative feelings about this to dominate – forgive yourself for being human. Author Website Email Instagram Facebook LinkedIn TikTok Blogs: titaniumdon.com and mjblehart.medium.com Cover artist Fe Mahoney: https://www.etsy.com/shop/TaliasInspirations Learn more about your ad choices. Visit megaphone.fm/adchoices

For art projects. For friendship. For life. Thinky thoughts this week.

Your hosts get caught up after the Yule break! – Send in a voice message: https://anchor.fm/around-grandfather-fire/message Suggest a topic or a guest: https://forms.gle/dYrQUpyE7VPDGnUP8 Our Patreon https://www.patreon.com/aroundgrandfatherfire Our Buy Me A Coffee https://www.buymeacoffee.com/agfpodcast -- Copyright 2022 -- Tinder Tony, Heidi, Claire, Hanna, Kristine, Laura Loki, Imtir, Casper K.*, Blkcat88, Craig, Emi, Voyager, Josie Spark M Anon, Indi Latrani, Katie, Dashifen*, Melkor, Alissa Addy, LaDena, Marco, Amanda S., Annora, Boojumhaus, Kim B.*, Nolan*, Elanor Faithful*, Douglas S. Pierce Books, Jeramie* Kindling Mother Multiverse, Genessa, Maleck Odinsson, Nick H., Jane W., Cynnian*, Jean Cavanaugh, Mach*, Cammy* Flame Victoria* Amanda H., Brannadov* Blaze Kirk Thomas -- Opening voice work Kai Belcher Music “Ophelia” by Les Hayden, provided by the Free Music Archive and used under Creative Commons licenses: freemusicarchive.org/music/Les_Hayden/Proverbs/Les_Hayden_-_Proverbs_-_05_Ophelia_1785 -- Our discord community https://discord.gg/3fFdYPnrVk Find us on FaceBook http://wwwfacebook.com/Around Grandfather Fire -- Sarenth's Patreon https://sarenth.wordpress.com/patreon/ Wordpress https://sarenth.wordpress.com/ Twitter: @Sarenth Three Pagans On Tap -- Jim TwoSnakes Wordpress: https://themoonlitsanctuary.wordpress.com/ Instagram: @jimtwosnakes TikTok: jimtwosnakes2 -- Caitlin Storm Breaker FaceBook: http://www.facebook.com/caitlin.terry.5099 Blog: https://stormpaqo.home.blog --- Send in a voice message: https://anchor.fm/around-grandfather-fire/message

Paul Vander Klay clips channel https://www.youtube.com/channel/UCX0jIcadtoxELSwehCh5QTg Bridges of Meaning Discord https://discord.gg/g4R8eHVX https://www.meetup.com/sacramento-estuary/ My Substack https://paulvanderklay.substack.com/ Estuary Hub Link https://www.estuaryhub.com/ If you want to schedule a one-on-one conversation check here. https://paulvanderklay.me/2019/08/06/converzations-with-pvk/ There is a video version of this podcast on YouTube at http://www.youtube.com/paulvanderklay To listen to this on ITunes https://itunes.apple.com/us/podcast/paul-vanderklays-podcast/id1394314333  If you need the RSS feed for your podcast player https://paulvanderklay.podbean.com/feed/  All Amazon links here are part of the Amazon Affiliate Program. Amazon pays me a small commission at no additional cost to you if you buy through one of the product links here. This is is one (free to you) way to support my videos.  https://paypal.me/paulvanderklay To support this channel/podcast with Bitcoin (BTC): 37TSN79RXewX8Js7CDMDRzvgMrFftutbPo  To support this channel/podcast with Bitcoin Cash (BCH) qr3amdmj3n2u83eqefsdft9vatnj9na0dqlzhnx80h  To support this channel/podcast with Ethereum (ETH): 0xd3F649C3403a4789466c246F32430036DADf6c62 Blockchain backup on Lbry https://odysee.com/@paulvanderklay https://www.patreon.com/paulvanderklay Paul's Church Content at Living Stones Channel https://www.youtube.com/channel/UCh7bdktIALZ9Nq41oVCvW-A To support Paul's work by supporting his church give here. https://tithe.ly/give?c=2160640

Just chatting https://youtu.be/7OodrXH-hIc  Matthiew pageau porch https://youtu.be/uIGLrx7APb4  Pageau JBP https://youtu.be/8R-vkbxX8r4  Friday Morning Nameless https://www.youtube.com/channel/UCiJmdXTb76i8eIPXdJyf8ZQ/videos  Vlog brothers Green Elon Musk https://youtu.be/MLBYtwu0gkY    Paul Vander Klay clips channel https://www.youtube.com/channel/UCX0jIcadtoxELSwehCh5QTg My Substack https://paulvanderklay.substack.com/ Estuary Hub Link https://www.estuaryhub.com/ If you want to schedule a one-on-one conversation check here. https://paulvanderklay.me/2019/08/06/converzations-with-pvk/ There is a video version of this podcast on YouTube at http://www.youtube.com/paulvanderklay To listen to this on ITunes https://itunes.apple.com/us/podcast/paul-vanderklays-podcast/id1394314333  If you need the RSS feed for your podcast player https://paulvanderklay.podbean.com/feed/  All Amazon links here are part of the Amazon Affiliate Program. Amazon pays me a small commission at no additional cost to you if you buy through one of the product links here. This is is one (free to you) way to support my videos.  https://paypal.me/paulvanderklay To support this channel/podcast with Bitcoin (BTC): 37TSN79RXewX8Js7CDMDRzvgMrFftutbPo  To support this channel/podcast with Bitcoin Cash (BCH) qr3amdmj3n2u83eqefsdft9vatnj9na0dqlzhnx80h  To support this channel/podcast with Ethereum (ETH): 0xd3F649C3403a4789466c246F32430036DADf6c62 Blockchain backup on Lbry https://odysee.com/@paulvanderklay https://www.patreon.com/paulvanderklay Paul's Church Content at Living Stones Channel https://www.youtube.com/channel/UCh7bdktIALZ9Nq41oVCvW-A To support Paul's work by supporting his church give here. https://tithe.ly/give?c=2160640

(Please note this episode originally aired on Afternoona Delight podcast on May 11th, 2022)What happens when four members of Afternoona Army gather around the proverbial podcast campfire to discuss BTS? Things get thinky...but also, ahem, thirsty. Come for the real talk on why Yoongi/Suga looks so fine in a skirt or how Hoseok/J-Hope works the stage harder than anyone. But stay for a discussion on parasocial relationships and why fangirling over BTS can help heal wounds inflicted by toxic masculinity and patriarchal frameworks. But also, we all want to call Namjoon/RM "Daddy." Also features hot takes on Permission to Dance concerts in Los Angeles and Las Vegas. For all you long time fans, please note: We are all Pandemic Army so still learning!Want to keep the discussion going? Hop on over to the Afternoona Army Instagram at @afternoonaarmy. Want to recommend a topic? Reach out to us at afternoonarmy@gmail.com. Borahae!

My review of this Dual Rondel & Tile Laying Game That is Much More Thinky Than it Looks!   0:00 - Introduction 0:37 - Overview 07:52 - Modes of Play   The Game Boy Geek Helps You “Find & Enjoy the Next Board Game You'll Love” with new content at least every other day.  Meet up on these Web & social media platforms: Website - www.GameBoyGeek.com Facebook - http://www.Facebook.com/TheGameBoyGeek Twitter - http://www.Twitter.com/TheGameBoyGeek Instagram - http://www.Instagram.com/TheGameBoyGeek Podcast - RSS - http://gameboygeek.podbean.com/feed/ Podcast iTunes - https://podcasts.apple.com/us/podcast/game-boy-geek-hi-quality-hi/id1042741475

The Joshes are back on that #SweatLife plus a special listener request because I guess we do that now? It's a great time!

Thinky Thoughts, Big Feelings, and Energetic Layers with Lindsay Jani   Episode #80 Lindsay Jani helps heal people's pasts, so it stops impacting their every day lives while helping them transform from where they are to where they want to be.  She's a hypnotherapist, energy healer, intuitive, wife and mom to the happiest 5 year old on the planet and truly enjoys helping people discover and harness their life's truest potential.    You'll learn about...   Your “thinky thoughts” and limiting beliefs   Being with your big feelings   The Layers of energetic trauma   Receiving abundance and the law of reciprocity   @LindsayJani Lindsay Jani Website

What happens when four members of Afternoona Army gather around the proverbial podcast campfire to discuss BTS? Things get thinky...but also thirsty. Come for the real talk on why Yoongi/Suga looks so fine in a skirt or how Hoseok/J-Hope works the stage harder than anyone. But stay for a discussion on parasocial relationships and why fangirling over BTS can help heal wounds inflicted by toxic masculinity and patriarchal frameworks. But also, we all want to call Namjoon/RM "Daddy." Also features hot takes on Permission to Dance concerts in Los Angeles and Las Vegas. For all you OG fans, please note: We are all Pandemic Army so still learning!Are your family and friends sick of you talking about K-drama? We get it...and have an answer. Join our AfterNoona Delight Patreon and find community among folks who get your obsession. And check out www.afternoonadelight.com for more episode, book recs and social media goodness.★ Support this podcast on Patreon ★

Oh just me rambling again during my live stream. Stop by some time twitch.tv/wizardvee

More deep thinky thoughts on Peace, how the manifest it, and the lack thereof we are experiencing.

In this episode, Christian sits down with Liz Burow, founder of Thinky Space, an insights and innovation practice working at the intersection of human-centered design and architecture. Liz is a 2020 LinkedIn Top Voice, a global leader in design strategy, and former vice president of client insights & product innovation at WeWork. Liz is an expert facilitator, communicator, and educator, frequently writing and speaking on design research and the future of work. She has published in the Harvard Business Review and is a visiting speaker at MIT, Harvard Business School, and the University of Michigan. She's also a visiting professor in design thinking at Cornell University, Parsons School of Design, and the University of Minnesota. https://thinkyspace.com/ https://theantiarchitect.com/

Intro Banter - This is the (mostly) unscripted intro to the show! We catch up, play games, talk about games that perhaps did not make the main show, and just gab, Welcome!  Holiday List of Lists Extravaganza! - Our annual segment wherein we share shopping and playing lists for all kinds of gamers on all budgets!    Alex:   New Hotness Unfathomable Cubitos Hero Quest Final Girl Oath World of WarCraft: Wrath of the Litch King Pandemic Picture Perfect Dungeon Party Clash of Cultures Land Vs Sea Hadrians Wall Cascadia Sleeping Gods   Alex Approved Dune Imperium Eclipse: Second Dawn of the Galaxy Terraforming Mars Aes Expedition My City Roll Camera! The Filmmaking Board Game MicroMacro: Crime City   Etsy/Crafty Sling Puck/Finger Hockey Stackable Trays with Bagging Funnel GripMats  BoardGameBoost (Caddy Max, Stockade) Larry:    No Budget: Eclipse 2nd Dawn Roll Player Adventures Dinosaur World w/ Expansions Detective City of Angels Thinky: Maglev Metro Castles of Burgundy Steam Works   New & Shiny: GI Joe Deck Building Game Deep Space D6 Armada Ashes Reborn: Rise of the Phoenixborn   Small But Mighty (could double as stocking stuffers): RailRoad Inc (Challenge or any regular color) Agropolis In Vino Morte' Cartographers  Not just Good, but SUPER Good - Top 3 lists themed by genre This time we ALSO include our top three Christmas Gift Gaming presents!   Alex: 3 - Stackable trays with bagging funnel 2 - Picture Perfect/Land vs Sea 1 - Cubitos   Larry:  Dinosaur Island Rawr n Write  (small but mighty) Dune Imperium  (Thinky)  Folded Space (Inserts & Accessories)  Naughty Nice List - Some Games or some companies have made the nice list for meritorious reasons, and some are getting a lump of coal!    Alex:   Naughty - Asmodee  Naughty - Hasbro (Hero Quest)   Nice - TMG (Thanks for all the fish) Nice - Eggertspeile (GWT) Plaid Hat (Summoner Wars) Nice - Kolossal Games Larry:    Naughty: Asmodee Renegade Abusive & Divisive figures in the BG World Nice:  TMG Conventions (TGD, Origins, Gen Con) Crowdfunding Special Thank you to our sponsor: VanRyder Games     Links:  Podstudio1  BGG        Music:  As always from the amazing community of gifted musicians, arrangers, and composers over at ocremix.org go visit and support them for the full project and so much more amazing music!   Intro / Interludes:  Carol of the Bells / by sephfire From the  Album - An Overclocked Christmas V.I Project Page - https://williammichael.info/aocc/     Outtro:  Christmas Time is Here / by Fratto From the  Album - An Overclocked Christmas V.I Project Page - https://williammichael.info/aocc/        

On a daily basis, my partner and I discuss deeply spiritual topics and current events. We have often talked about offering our own unique perspective on these things. I am happy to invite you to follow our new series, Thinky and Feely Discuss. We invite you join us to consider with us how to contain love and service in thought and deed during these times of anxiety and civilisational transformation. Please do join the conversation on our telegram group @blackswansibylofficial and submit your thoughts and questions in the comments. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/blackswansibyl/support

Keri and Carter mock John Leguizamo's dimwitted yet condescending description of Critical Race Theory, and then discuss the anti-Communist protests in Cuba. The video version of this episode is available here: https://unsafespace.com/ep0603 Links Referenced in the Show: "Thinky Talk" from John Leguizamo: https://youtu.be/LAuxny50mEA NYT Reports on Cuba: https://twitter.com/nytimes/status/1414338725207752708 Cuba is Trump's Fault: https://www.reuters.com/world/americas/cubas-president-blames-discontent-us-sanctions-2021-07-12/ Thanks for Watching! The best way to follow Unsafe Space, no matter which platforms ban us, is to visit: https://unsafespace.com While we're still allowed on YouTube, please don't forget to verify that you're subscribed, and to like and share this episode. You can find us there at: https://unsafespace.com/channel For episode clips, visit: https://unsafespace.com/clips Other video platforms on which our content can be found include: LBRY: https://lbry.tv/@unsafe BitChute: https://www.bitchute.com/channel/unsafespace/ Also, come join our community of dangerous thinkers at the following social media sites...at least until we get banned: Censorship-averse platforms: Gab: @unsafe Minds: @unsafe Locals: unsafespace.locals.com Parler: @unsafespace Telegram Chat: https://t.me/joinchat/H4OUclXTz4xwF9EapZekPg Censorship-happy platforms: Twitter: @unsafespace [currently suspended without any reason given] Facebook: https://www.facebook.com/unsafepage Instagram: @_unsafespace MeWe: https://mewe.com/p/unsafespace Support the content that you consume by visiting: https://unsafespace.com/donate Finally, don't forget to announce your status as a wrong-thinker with some Unsafe Space merch, available at: https://unsafespace.com/shop

Keri and Carter mock John Leguizamo's dimwitted yet condescending description of Critical Race Theory, and then discuss the anti-Communist protests in Cuba. The video version of this episode is available here: https://unsafespace.com/ep0603 Links Referenced in the Show: "Thinky Talk" from John Leguizamo: https://youtu.be/LAuxny50mEA NYT Reports on Cuba: https://twitter.com/nytimes/status/1414338725207752708 Cuba is Trump's Fault: https://www.reuters.com/world/americas/cubas-president-blames-discontent-us-sanctions-2021-07-12/ Thanks for Watching! The best way to follow Unsafe Space, no matter which platforms ban us, is to visit: https://unsafespace.com While we're still allowed on YouTube, please don't forget to verify that you're subscribed, and to like and share this episode. You can find us there at: https://unsafespace.com/channel For episode clips, visit: https://unsafespace.com/clips Other video platforms on which our content can be found include: LBRY: https://lbry.tv/@unsafe BitChute: https://www.bitchute.com/channel/unsafespace/ Also, come join our community of dangerous thinkers at the following social media sites...at least until we get banned: Censorship-averse platforms: Gab: @unsafe Minds: @unsafe Locals: unsafespace.locals.com Parler: @unsafespace Telegram Chat: https://t.me/joinchat/H4OUclXTz4xwF9EapZekPg Censorship-happy platforms: Twitter: @unsafespace [currently suspended without any reason given] Facebook: https://www.facebook.com/unsafepage Instagram: @_unsafespace MeWe: https://mewe.com/p/unsafespace Support the content that you consume by visiting: https://unsafespace.com/donate Finally, don't forget to announce your status as a wrong-thinker with some Unsafe Space merch, available at: https://unsafespace.com/shop

This one is terrible but I promised myself I’d do one a week, on Fridays, so here it is. In all it’s terrible glory and it was so fun to make it.

Watch before you listen! https://www.netflix.com/title/70273401 --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

Happy Monday! Today we are brining you all a dating episode discussing an universal experience: unrequited love. In this episode, we talked about types of unrequited love, the chill girl phenomenon and getting over people who you've never really dated! Once again we are asking to please normalize heartbreaks of nothingness. Tune in now to get a good laugh and spice up your work entangled Monday morning!!

Happy Monday! Today we are brining you all a dating episode discussing an universal experience: unrequited love. In this episode, we talked about types of unrequited love, the chill girl phenomenon and getting over people who you've never really dated! Once again we are asking to please normalize heartbreaks of nothingness. Tune in now to get a good laugh and spice up your work entangled Monday morning!!

Happy Monday! Today we are brining you all a dating episode discussing an universal experience: unrequited love. In this episode, we talked about types of unrequited love, the chill girl phenomenon and getting over people who you've never really dated! Once again we are asking to please normalize heartbreaks of nothingness. Tune in now to get a good laugh and spice up your work entangled Monday morning!!

Happy Monday! Today we are brining you all a dating episode discussing an universal experience: unrequited love. In this episode, we talked about types of unrequited love, the chill girl phenomenon and getting over people who you've never really dated! Once again we are asking to please normalize heartbreaks of nothingness. Tune in now to get a good laugh and spice up your work entangled Monday morning!!

THIS WEEK: Dane just can't leave the Jag's headlights alone, Levi spends quality time with the family & Anthony puts more miles on his new bike in the first week than the previous owner did in years! VIDEO VERSION AVAILABLE HERE: https://youtu.be/3mUt3btU2Rc  ​ #BMW​ #Motorcycles​ #Jaguar​ #JDM​ #Acty​ #KeiCar​ #KeiTruck​ #UKCut​ #WeightLoss​ #Diet​ #Exercise​ #DetailProducts​ #TRCPodcast​ #Detailing​ #DetailingChannel​ #TheRagCompany ​ ►Subscribe to our MAIN channel here: https://bit.ly/3cSRixY ►Subscribe to our FAQ channel here: https://tinyurl.com/ycm3ctuk ►Subscribe to our PODCAST channel here: https://bit.ly/2WNs12i   RATE & REVIEW: https://itunes.apple.com/us/podcast/the-rag-company-podcast/id1269337267?mt=2 SPOTIFY: https://open.spotify.com/show/4gIO6hW4cp3LuHwUn0MSEM INSTAGRAM: https://www.instagram.com/theragcompanypodcast YOUTUBE: https://www.youtube.com/c/TheRagCompanyPodcast https://www.youtube.com/c/TheRagCompany FACEBOOK: https://www.facebook.com/TheRagCompanyPodcast/ https://www.facebook.com/TheRagCompany/ https://www.facebook.com/groups/TheRagCompanyPodcast https://www.facebook.com/groups/DeTalks   SHOW INFO: Hosts: Dane Hennen, Levi Gates, Anthony Fisher Produced by: Nick Kovach Content provided courtesy of The Rag Company ©2021

All rambling, no cuts. This is not art! This is just a draft. Might delete later. AKA Hannah Hart rips off the bandaid and tries to post to podcast. --- Support this podcast: https://anchor.fm/hannahlyze-this-with-hannah-hart--hannah-gelb/support

Artsy Smartsy

In this episode of the Heromakers Podcast, Laurie and Ann talk with Allen Langford, former collegiate athlete and founder of Think Y Organization, which focuses on positively impacting youth from different backgrounds and socioeconomic statuses, with the main goal of guiding them into manhood. In this episode, Allen talks about growing up in Detroit and the role of his mentor to help him become the person he is today. We talk fatherhood and why sports is a natural connection to character and life skills development. You can find ThinkY on IG at @thinkyorganization or on Facebook at: https://www.facebook.com/ThinkYOrganization. You can visit their website here: https://www.thinkyorganization.com. Heromakers can be found on IG @heromakersmovement and on FB @heromakersmovement. You can also visit our website at: www.heromakersmovement.com. Like us and follow us today!We are now on PATREON and we would love if you'd become a partner! Check it out here: https://www.patreon.com/heromakersmovement.

Kidzania, Kuriakon, Papalote Museo del Niño, Thinky Box y Universum platican sobre la Educación STEAM en espacios no formales en este Panel de Expertos.

https://thementalmasteryalliance.com/ https://www.instagram.com/thementalmasteryalliance/ https://twitter.com/TMMA3746 How can you properly process into your future if your style of thought is fundamentally flawed?

Tentacled alien sea monsters swarm across the Irish coast with a thirst for human blood. There's just one problem - they're allergic to alcohol, and to anyone drunk enough to fight them. Marcus and Melissa discover that scotch mixes well with this warm 80s-style creature feature. Also, what horror characters would you rather have on a monster slaying dream team? Motion Picture Terror Scale: 1. Quality: 4. Personal Enjoyment: 5. Articles mentioned in this episode: "INTERVIEW: Grabbing A Word With Director Jon Wright," by Steve Cummins for IFTN "WHY YOU NEED TO SEE IRISH MONSTER MOVIE GRABBERS," by Sarah Dobbs for SciFiNow "Kevin Lehane interview: Grabbers, Twitter, and filming in the rain," by Sarah Dobbs for Den of Geek "Jon Wright interview: Grabbers, Christmas movies, body parts," by Sarah Dobbs for Den of Geek

Q looks back on Season 1 of Warehouse 13 and pulls out some themes that ran across the whole thing, like a bolt of electricity from a Tesla! Are themes just for book reports? Thanks for asking, no they're not! Themes give you a central idea around which to build a narrative! And give you thinky thoughts! Twitter: twitter.com/Under_ratedPod Facebook: www.facebook.com/UnderratedPod/ Instagram: instagram.com/under_ratedpod Music: freemusicarchive.org/music/Les_Sans_Culottes/ Cover image: billbeard.net/blog3/ --- Send in a voice message: https://anchor.fm/uppod/message

Website - Tumblr - Twitter We look back on 2019 and the various television we watched this year and what we are excited about in 2020. 

Halloween is upon us and so is another episode of the Poor Variety Show. Listen to the boys get confused AND scared.

https://thementalmasteryalliance.com/ppa We really dove into this one! Very topical topics.

Edward reveals his Top 20 Thinky Fillers0:00:50 - What’s been going on0:07:56 - Poker updateWhat I’ve been playing?0:13:44 - Churchill / Mark Herman0:19:40 - Grand Con0:21:35 - Glass Road0:22:30 - Yedo0:23:15 - Taj Mahal0:24:00 - Rome: City of Marble0:24:42 - What we’ve acquired?0:25:35 - What we’re anticipating / looking forward to playing?0:27:00 - Top 20 Thinky FillersiTunes link for Supplemental Feed: https://itunes.apple.com/us/podcast/heavy-cardboard-supplemental/id1398708343?mt=2RSS link for Supplemental Feed: https://podcast.heavycardboard.com/supplemental.xmlYouTube channel!Support the show! PledgeHC.comThe Herd map is here: Click to add your mark!Be sure to join the discussion over at theguild to discuss the episode with us & your fellow Elephants!

Thinky-doers are those of us whose work spans the spaces between thought, through the messy middle, into doing. I'm a thinky-doer, and I'm here to help others create less friction, and more flow in our work.

This week, on Tea with Robin, The ego upset and its stinky voice. While we may never fully transcend the ego, we don’t need to freak and it certainly does not mean we aren't doing the spiritual journey right. Here’s how we deal and love ourselves through it. The inspiration today is about helping yourself out of the hole when the upset has taken you over but you still know you want to choose a better feeling. We'll talk joyful distractions and a win list. I'll read a letter from a friend searching for a little #lightworkerlove to decide on an entrepreneurial path that fits well with her highest self. All this and more. Grab a cuppa yum yum and meet me here.  XO. Robin Send a letter to Robin: https://goo.gl/forms/BIIEsdfTJsIMouuA2 Courses with me: https://robinhallett.com/classes-retreats/ Subscribe to this podcast: https://robinhallett.libsyn.com/ Work with me privately: https://robinhallett.com/work-with-robin-hallett/ Shop my art and courses: https://www.robinhallett.com/shop Love & inspo mail: https://www.robinhallett.com/subscribe Instagram: https://www.instagram.com/robinhallett/ Facebook: https://www.facebook.com/RobinHallettIntuitive/ YouTube: https://www.youtube.com/c/RobinHallett

A healthy faith achieves a balance between intellect and emotion. But the Christian world has lost this balance, and often seeks to manufacture a contrived emotionalism, while seeing itself as being at war with intellectuals.  Donate at: www.patreon.com/christianitywithoutthecrap Facebook: www.facebook.com/christianitywithoutthecrap Instagram: www.instagram.com/christianitywithoutthecrap Email: cwoc.mark@gmail.com           cwoc.tammy@gmail.com

This week Jimmy Diresta, Bob Clagett and David Picciuto talk about making products.

This week Jimmy Diresta, Bob Clagett and David Picciuto talk about making products.

This week Jimmy Diresta, Bob Clagett and David Picciuto talk about making products.

This week Jimmy Diresta, Bob Clagett and David Picciuto talk about making products.

In the pilot episode of Thinky, we discuss free will. Do free thinkers get punished? What will the modern definition of free will be in different countries? Should we have free will in every portion of our lives? We touch on these matters and more on Thinky, the podcast think tank.

This podcast was recorded at the now world famous Hot Water Comedy Club in Liverpool. Myself and Eddie Hoo were joined by Moses Ali Khan, Chris Kehoe (who does a podcast called Mondeo Law) and Token Woman. We covered a lot of ground and had a right old laugh whilst doing so. Topics were the European Court of Human Rights ruling that you can't mock the Prophet Muhammed, fat shaming, why mental people are better at sex, stalking, "Thinky wank", my Facebook ban, Ryanair racism, Paedophile Optimus Prime, Strictly Come Dancing, sex god Stephen Hawking and how we'd murder someone.  Leave a review on iTunes and like us Facebook so that we know you exist.  Like the page on Facebook: https://www.facebook.com/ArguingForTheSakeOfArguing/  Follow Dave on Twitter: https://twitter.com/davidL0NGLEY  For mostly weightlifting videos, follow Dave on Instagram: https://www.instagram.com/david_longley_/?hl=en  Website here: https://davidlongley.squarespace.com/ 

Website - Tumblr - Twitter Here is a very special live episode, reporting from Manhattan (and away from our usual recording set up), to give you a full run down on our recent weekend at New York ComicCon. 

Are you ready for some deep thoughts?  This episode is full of thinky thinky.  We talk about the upcoming Venom movie on whether we are excited for it or not.  We review Ant-Man and the Wasp, Lake Placid, and Hold the Dark.  Join us for all of the intellectual talk! 

Today, “Chip” Karma joins Corban and Sofia to talk about their favorite early 2000’s kid’s TV shows! They also introduce a new segment, “Colloquial Saying of the Week”.   Show Notes: The Perks of Being a Wallflower by Stephen Chbosky Little Secrets - 2001 Film Majuscule - Merriam-Webster   Recommendations of the Week: Corban: Watch “Faithville” Sofia: Watch “The Beverly Hillbillies” Karma: Watch “Nailed It”   Follow Us: Corban on Twitter Sofia on Instagram Rocket to Anywhere on Twitter Rocket to Anywhere on Instagram   Music By: Podington Bear at the Sound of Picture Library  

It's now been two years since David picked "5 Winners in a Thinking World" here on the podcast. Those stocks were indeed winning after one year, but how have they fared to date against the stiff competition of a raging bull market? Then, continuing this month's theme of Conscious Capitalism, we bring you a conversation with the remarkable CEO of Middleby Corp., Selim Bassoul.

We've been waiting, and you've been waiting, for the middle of the week when the GameBytes Show crew brings you their impressions of the great video games they've been playing! Wait no longer! In a two-man show, Dale and Jeremy bring the goods, and you will be delighted and entertained! Dale kicks it off with a roundup of the latest updates to the very popular and fashionable team-based competitive first person shooter Team Fortress 2, and its trans-media tie-ins. He then switches to the very old-and-nobody-is-playing-it-anymore Destiny 2 (people are still playing it -ed.). Jeremy reaches back into the VR library a bit with Driveclub, but spices things up with a discussion of using a force-feedback-enabled racing wheel in virtual reality! Intro: "Intruder Alert" - Team Fortress 2, by Mike Morasky Outro: "Be Here Now (Full Version)" - Driveclub, by Hybrid Check out our Discord community at https://discord.gg/ZTzKH8y

Dalam episod ini kami bersembang tentang pulang ke Kedah, membuat persembahan di If Walls Could Talk, Wonder Woman, kumpulan Divergent yang akan kami pilih, syurga atau neraka, soalan yang akan kami tanya serta kenapa orang tak suka bentuk hiburan yang memerlukan pemikiran. Semoga bermanfaat dan semoga terhibur! Ajukan soalan anda kepada kami di buahmulutpodcast@gmail.com

We begin our study at the four basic attrait combinations (think “spiritual personality types”) with an in-depth look at the kataphatic-speculative quadrant. In short, these are people who relate to God primarily by means of connection to the created world (kataphatic) and for whom things need to “make sense” (speculative). If you are listening to this podcast, odds are […]

The All Roads Tavern Inaugural Adventure into the Void Ocean! The crew comes together as we develop some of our favorite characters we've ever played! (Sorry about the early episodes audio quality we were just getting started!)

So last week the Sixers acted like an African Dictatorship and had a profound shakeup in a front office which was already under all kinds of national and local fire because of "The Process". I sat down with Jim Adair of CrossingBroad.com who kind of looks like he's sporting Groucho Grease in this photo. Jim spelled out and nicely recapped the whole she-bang to date in a way that an NBAvoider like myself could wrap his tiny little head around. I think you'll find it interesting even if you don't like basketball, or sports, or me. You can find Jim on the Twitters here and you can check out his clever tee shirts at OptionaliTEES. As always, like us on Facebook, and leave a review in iTunes. Subscribe and enjoy, thanks. Adam

One man's banger is another man's not banger. The gang's intellectual expertise is called into question. But don't worry, we more than make up for it with a combination of turtle facts and turtle ungrounded speculation. You can follow our Instagram or Facebook accounts to check up on our daily shuffles, for more of this sort of nonsense. See acast.com/privacy for privacy and opt-out information.

It’s a heckuva night back at the house. Drinky drinks for the first time, and thinky thinks for the next time. We’re a little stirred up due to some noisy clouds hanging over the homestead, all heavy with portent and ill-omen. We gird ourselves up and, with a little liquid courage, discuss some of the … Continue reading Episode 4: Drinky Drink, Thinky Think →

Episode 6 is here to invade your Soul!!!! Show Notes: We dive into Ghost in the shell as if we knew what the heck we are talking about. Also Sgt. Frog and Brynhildr in the Darkness Click Here to Download Click Here to Stream Please Subscribe!!! ITunes Subscription Filed under: Uncategorized Tagged: A-kon, anime, Brynhildr […]

Curtis & Boet give a spoiler-free review of 22 Jump Street before diving into their favorite game trailers and news from E3 and then topping it off with some Nerd News. The post Episode 106 – Exciting Parkour Thinky Game appeared first on Clinically Inane.