Podcasts about Bonsai

There's never a dull moment at Mirai. But one of the most beautiful aspects of the hustle and bustle in the garden is getting to spend time with traveling bonsai vagabonds crossing the country to keep the movement of bonsai activity going. This summer has been particularly busy with guests, students, and friends from old and new. One fateful weekend recently the stars aligned for Steve Varland of the Backcountry Boys and David Cutchin of Wyrdwood Bonsai to both be visiting and dropping off trees at the same time. Mirai has a very deep and long-standing connection with both gentlemen, but the magic truly happens when you get kindred spirits together and further connect the roots of our bonsai culture through shared experiences in the wild, over meals, and spending time working trees together at Mirai. Ryan managed to pack up the crew and take them on a yamadori scouting trip into the wilds of Oregon for the day. Just prior to everyone going their separate ways he was also able to pump them full of caffeine and squeeze out a truly memorable podcast. Enjoy!

Brian and Elliot sit down with Gene Koo, aka Sticky Doodler, the designer of the ENNIE-award-winning The Bonsai Diary. We discuss the accessible design ethos behind The Bonsai Diary, what makes solo play unique, playtesting events like Unpub, and the rise of solo TTRPGs.Talk of the Table is hosted by Elliot Davis and Brian Flaherty.Links:Buy The Bonsai DiaryColor Jam coming soon!Follow GeneKeep an eye out for “These Games Are Not For Sale” at PAXU26Gene Recommends:Game design as a hobbyOur Links:Support TotT on PatreonMany Sided NewsletterMany Sided Media DiscordCredits:Edited by J StrautmanProduced by Many Sided MediaAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Our show did air live today at 9:00 Pacific time on KSFO; Bob Tanem In The Garden with Edie Tanem is the premiere radio talk show about organic gardening, for the whole Bay Area. We took calls, and Edie played a pre-recorded interview she did with Bonsai expert George Haas about the upcoming Redwood Empire Bonsai Society Bonsai Show, which will be in Rohnert Park in August 22 and 23. This podcast is an on-demand replay of the live show, with music edited out for policy compliance.See omnystudio.com/listener for privacy information.

Our show did air live today at 9:00 Pacific time on KSFO; Bob Tanem In The Garden with Edie Tanem is the premiere radio talk show about organic gardening, for the whole Bay Area. We took calls, and Edie played a pre-recorded interview she did with Bonsai expert George Haas about the upcoming Redwood Empire Bonsai Society Bonsai Show, which will be in Rohnert Park in August 22 and 23. This podcast is an on-demand replay of the live show, with music edited out for policy compliance.See omnystudio.com/listener for privacy information.

Kathy and Jim discussed the concept of Bonsai businesses, with Jim sharing his insights on the importance of focus, ideal clients, and high profit margin products. They also discussed the challenges and potential rewards of scaling a business, with Jim emphasizing the need for careful planning, financial management, and seeking guidance from mentors. Lastly, the Pro team shared their plans for funding their upcoming film project through an Indiegogo campaign and discussed the concept of manifestation, with a focus on personal and professional development.Support the show

Every pocket of the country has someone passionate about bonsai adapting their practice to the particulars and extremes of the environment. It's one of the great marvels within the art form, the human ingenuity to create and cultivate bonsai despite the harshness of the weather and location. Jon White carries forward the lineage of pioneers in the Boise region of Idaho who have been doing just that for decades; defying the ruggedness of the landscape in pursuit of tiny trees. Through their tight knit pack, the Boise Bonsai Society has carved out a beautiful oasis of positivity, community, and organic creativity sourced from a local hub of natural beauty and inspiration. Few people think of Boise as a bonsai destination, but Jon's ambitions with High Desert Bonsai have picked up the mantle of previous practitioners as a gathering spot for enthusiasts working with what they have to push bonsai forward and in an authentic direction.  Ryan and Jon sat down recently to talk about the challenges of growing bonsai in the high desert and building a community. From humble beginnings and fruitful mentorship to yamadori treks, field growing, and leading the next generation, Jon made it clear his goal is to raise the level and represent the beauty of an unsung corner of the West in the process. Without further ado, Jon White y'all. Enjoy! You can learn more about Jon and High Desert Bonsai here. If you're traveling through and looking for a bonsai fix you can contact the Boise Bonsai Society for their event schedule. 

In the remasterd inaugural episode of the Bonsai Bonsai Time Podcast, Kevin and Ryan interviewed Ryan's bonsai teacher, Dan Robinson. Dan has had a long career in bonsai where he has assumed many roles. He was a founding member of the Puget Sound Bonsai Association, a pioneer in collecting American native yamadori, a trail-blazer in naturalistic bonsai, a lifelong acolyte of ancient trees, the creator of Elandan Gardens in Bremerton, WA, and the subject of the book Gnarly Branches, Ancient Trees by Will Hiltz. This history of Dan's life in bonsai and more is the subject of this interview.

Listen Now to 020 WTFuture Watch 020 WTFuture Hey folks! We are coming to you from Boulder Creek today, joined by Bobby in sunny San Francisco, before we head off on our annual summer vacation to Montreal! In this week’s chat, we completely geeked out over the future of our “augmented brains” and the rise of local AI, like the mind-blowing new Bonsai tech that squeezes a massive 27-billion parameter model right onto an iPhone. It is a total game-changer because it acts as a private, “sovereign” AI companion that does not need the cloud, saves energy, and ensures nobody steals your ideas. We also had a great time joking about the “dead internet” theory now that bot traffic has surged 7,800%. But don’t worry, human creativity is still king—especially when we use AI as a collaborator to create hilarious, custom animations of floppy disks barfing green data goo, complete with gassy liquid sound fx! Looking to the stars, we were absolutely captivated by SpaceX’s Starship landing beautifully in the Indian Ocean and their wild plans to launch giant AI compute satellites to build an off-planet internet. We also discussed the hilarious twist of a 1972 Soviet Venus probe, Cosmos 482, finally crash-landing back on Earth after a 53-year detour in orbit. To top it all off, astronomers just found LHS1140B, a rocky exoplanet 48 light-years away that has a faint stream of helium, marking the first time we’ve detected an atmosphere on a habitable-zone rocky world! It’s a beautifully abundant, high-tech future ahead of us, and as we pack our bags for the Laurentian Mountains, we can’t wait to see what wildly optimistic paradigm shifts happen next. Enjoy!

Contact Scott from Bonsai MatsuIt is such a small task in the overall scheme of bonsai management and care but it is so important. We repot every few years or maybe longer and the simple job of securing your bonsai at the time of repotting can make a massive difference to the health and ability to achieve your targets for development. There are different methods of securing your bonsai that I'll discuss on top of the reasonings. Support the showBecome a podcast supporter and show the Bonsai Love (it's really appreciated) ❤️https://www.buzzsprout.com/263290/supportWhere to find Bonsai Matsu:InstagramFacebookYouTube Web

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

Recorded at the University of Birmingham's magnificent Elgar Concert Hall, Kathy Clugston is joined by Bethan Collerton of Birmingham Botanical Gardens, Marcus Chilton-Jones of RHS Garden Bridgewater, and Dr Chris Thorogood from Oxford Botanic Garden.The panel tackles a wide range of audience gardening dilemmas, from reluctant peonies and coppiced hazel, to a struggling juniper bonsai and an overgrown black elder. They also discuss how to rescue a drought-stressed blueberry, and keep colour flowing through the garden into late summer.Later in the show, Bethan visits Winterbourne House and Garden to meet local beekeeper Jane Nimmo to discover more about the remarkable lives of honeybees, the challenges facing wild bee populations, and the simple things gardeners can do to help pollinators thrive.Producer: Alison Vernon-Smith Assistant Producer: William NortonA Somethin' Else production for BBC Radio 4* If listening on BBC Sounds and you wish to view the plant list, please go to the Gardeners' Question Time website and open this week's episode page.

Fluent Fiction - Mandarin Chinese: Mystery of the Vanished Bonsai at Beijing's Lush Retreat Find the full episode transcript, vocabulary words, and more:fluentfiction.com/zh/episode/2026-07-12-07-38-20-zh Story Transcript:Zh: 北京植物园，夏天最美的时节，En: Beijing Botanical Garden is at its most beautiful in the summer.Zh: 阳光洒满绿树浓荫，小径旁的花儿随风轻轻摇摆，En: Sunlight fills the lush shade of the green trees, and the flowers by the paths sway gently in the breeze.Zh: 游人络绎不绝。En: Visitors come and go without end.Zh: 然而，就在这个美好的时刻，一件不寻常的事情发生了。En: However, at this beautiful time, something unusual happened.Zh: 一天早上，热心的植物学家家豪刚走进植物园的展厅。En: One morning, a dedicated botanist named Jiahao just entered the exhibition hall of the botanical garden.Zh: 他注意到一件令人震惊的事——一个珍贵的、几百年历史的盆景不见了。En: He noticed something shocking—a precious bonsai, hundreds of years old, was missing.Zh: 这个盆景是植物园的瑰宝，每个来访者都为它的古老和美丽所吸引。En: This bonsai was the gem of the botanical garden, attracting every visitor with its antiquity and beauty.Zh: 家豪急切地四处寻找，心中充满了焦急。En: Jiahao anxiously searched everywhere, his heart filled with worry.Zh: 与此同时，一名本地记者丽芬正寻找她的下一个大新闻。En: Meanwhile, a local reporter named Lifen was looking for her next big story.Zh: 她很快得知了盆景失踪的消息，心想这是一个提升自己职业生涯的绝佳机会。En: She quickly learned about the bonsai's disappearance and thought this was a perfect opportunity to advance her career.Zh: 她决定去植物园了解情况，看看能否挖掘出背后的故事。En: She decided to go to the botanical garden to understand the situation and see if she could uncover the story behind it.Zh: 这时，名叫明的神秘访客引起了家豪的注意。En: At this time, a mysterious visitor named Ming caught Jiahao's attention.Zh: 他似乎对盆景失踪的事情知之甚多，但总是惜字如金。En: He seemed to know a lot about the bonsai's disappearance but was always sparing with his words.Zh: 家豪的直觉告诉他，明可能知道些线索。En: Jiahao's intuition told him that Ming might know some clues.Zh: 起初，家豪并不想和丽芬合作，但他知道自己必须找到失踪的盆景并维护植物园的声誉。En: Initially, Jiahao didn't want to cooperate with Lifen, but he knew he needed to find the missing bonsai and maintain the reputation of the botanical garden.Zh: 经过一番犹豫，他决定与丽芬联手调查。En: After some hesitation, he decided to join forces with Lifen to investigate.Zh: 丽芬也开始信任家豪，并愿意分享她的发现。En: Lifen also began to trust Jiahao and was willing to share her findings.Zh: 两人各司其职，家豪走访园中职员及游客，丽芬则调查园外可疑人士。En: They divided their tasks, with Jiahao interviewing staff and visitors in the garden, and Lifen investigating suspicious individuals outside the garden.Zh: 不久，他们发现明的一些可疑行动。En: Soon, they discovered some suspicious actions by Ming.Zh: 明在盆景失踪当晚出现在现场，并与一位神秘的收藏家有过接触。En: Ming was at the scene the night the bonsai disappeared and had contact with a mysterious collector.Zh: 这些线索让他成为主要嫌疑人。En: These clues made him a prime suspect.Zh: 在掌握足够证据后，家豪和丽芬决定直接面对面问明。En: After gathering enough evidence, Jiahao and Lifen decided to confront Ming directly.Zh: 在面对质疑时，明沉默了片刻。En: When faced with their questioning, Ming was silent for a moment.Zh: 但最后，他叹了一口气，坦白道：“是的，我被一个收藏家雇佣去取走那个盆景。En: But finally, he sighed and confessed, "Yes, I was hired by a collector to take the bonsai.Zh: 但是，当我看到它的真实之美时，我意识到将它私自占有是错误的。”En: But when I saw its true beauty, I realized it was wrong to take it for myself."Zh: 明的承认让家豪和丽芬都松了一口气。En: Ming's admission brought a sigh of relief to both Jiahao and Lifen.Zh: 明表示他已经计划将盆景送回。En: Ming stated that he had already planned to return the bonsai.Zh: 家豪原谅了明，并对他的诚实表示感激。En: Jiahao forgave Ming and expressed gratitude for his honesty.Zh: 最后，盆景安全地回到了植物园，一切恢复了平静。En: In the end, the bonsai was safely returned to the botanical garden, and everything returned to peace.Zh: 家豪学到了信任与合作的重要性，而丽芬明白了新闻真实远胜过夸大其词。En: Jiahao learned the importance of trust and cooperation, while Lifen understood that the truth in journalism is far more valuable than exaggeration.Zh: 在这个故事的结尾，三人都得到了成长。En: At the end of the story, all three grew from the experience.Zh: 在北京植物园的绿树花海中，夏季的阳光仍然灿烂，那盆古老的盆景依然静静地展示着它美丽的故事。En: In the green trees and sea of flowers of Beijing Botanical Garden, the summer sun still shone brightly, and the ancient bonsai continued to quietly tell its beautiful story. Vocabulary Words:botanical: 植物学的lush: 茂盛的breeze: 微风dedicated: 热心的exhibition: 展厅precious: 珍贵的bonsai: 盆景antiquity: 古老anxiously: 急切地reporter: 记者advance: 提升opportunity: 机会mysterious: 神秘的intuition: 直觉hesitation: 犹豫suspicious: 可疑的interview: 走访evidence: 证据confront: 面对面silent: 沉默confess: 坦白relief: 松了一口气admission: 承认honesty: 诚实trust: 信任cooperation: 合作journalism: 新闻业exaggeration: 夸大其词observer: 观察者collector: 收藏家

Contact Scott from Bonsai MatsuWe are meticulous with our bonsai and their design, we spend so long getting even the smallest detail perfect and we should also do this with everything associated with good bonsai. As we commence the repotting season, well I do, it's time to be mindful of the quality and cleanliness of both the work area as well as the tools used in the process. We need to be safe and very importantly we must not have a situation where we transfer any nasties from one bonsai to the next. Also it's balancing only for strong trees? Don't you just leave weak trees alone? Balancing is task we perform continually on all of our bonsai.Support the showBecome a podcast supporter and show the Bonsai Love (it's really appreciated) ❤️https://www.buzzsprout.com/263290/supportWhere to find Bonsai Matsu:InstagramFacebookYouTube Web

In this episode of Bonsai Time, Kevin sits down with Mike Chapman and Zack Wheeler from Nature's Way Nursery in Harrisburg, Pennsylvania. Together, we explore their journeys into horticulture, what it's like balancing bonsai with the demands of working at a professional nursery, and how their experiences have shaped their perspectives on both plants and life.We discuss the day-to-day realities of nursery work, landscaping, and bonsai, along with the dedication and horticultural knowledge required to succeed in each. Mike and Zack share stories from their careers, lessons they've learned along the way, and how working closely with plants has influenced their outlook on the world around them.This conversation goes beyond bonsai, touching on personal growth, finding purpose through meaningful work, and the connections that develop through a shared passion for horticulture.Whether you're a bonsai enthusiast, a plant lover, or simply curious about life behind the scenes at a working nursery, there's something in this episode for you.Enjoy!The video version is ⁠⁠⁠⁠⁠⁠⁠coming soon.Show notes, relevant pictures, and links are ⁠⁠⁠coming soon.See you in the next episode!Guest Info:...Sponsor Info: This episode is sponsored by our co-host via the Kevin Faris moving sale of bonsai pottery, tools, and trees. View these items at In Vivo Bonsai of Columbus, Ohio, USA in-person, or online (shipping available) at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠invivobonsai.etsy.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, or go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.InVivoBonsai.com⁠⁠⁠⁠⁠⁠⁠⁠ and navigate to the consignment ⁠⁠⁠⁠⁠⁠⁠⁠pottery⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠tree⁠⁠⁠⁠⁠⁠⁠⁠ pages.Mailbag submissions/Community:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠NEW BT DISCORD Chat⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BT Facebook Group⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Support the Pod:Anytime you listen, subscribe, rate us, or share us with friends, you help keep us motivated to keep making episodes for you all! If you want to take it to the next level, you can also help keep the podcast going by donating to us through Spotify subscriptions or by sponsoring an episode (contact us directly for that). All donations go back into the podcast such as for our web hosting, recording gear expenses, etc.Podcast Info:The Bonsai Time Podcast is hosted, edited, & produced by Kevin Faris, Ryan Huston, & Kelly Lui. We expect to post new interviews and reflections monthly! Find us on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and our email BonsaiTimePodcast@gmail.com.Submit questions or pictures for future Bonsai Brainstorm episodes to our email, social media DMs, or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Audio editing of this episode and music are by MIDICANCER. Find more music by them on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SoundCloud⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BandCamp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Host info:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ryan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ is a former bonsai apprentice of Elandan Gardens and current operator of In Vivo Bonsai nursery and educational operation in Columbus, OH. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kevin ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠is a bonsai practitioner/teacher now living in Massachusetts. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kelly⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ is a newer bonsai artist volunteering and studying especially in the Los Angeles area.More Bonsai Projects by Ryan:Read more about bonsai on his blog and learn more about his educational services ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Find Ryan's online-available bonsai products, seeds, tools, etc. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Each seed kit sold comes with my full 10-year bonsai-from-seed guide.Find Ryan on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠as well if you need more bonsai in your feed.Also, check out some of my video editing work for the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Puget Sound Bonsai Association⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Columbus Bonsai Society⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠'s demonstration archives.

Happy Sunday from the cast and crew of Bob Tanem In The Garden with Edie Tanem; our show plays live on KSFO.com worldwide and on the radio at 810 KSFO across Northern California, each Sunday at 9:00 local time. This here is the podcast edition, which is just like the original minus the musical segments and most of the ads. This week we took calls from our summer gardening audience, and discussed events and opportunities in the gardening world coming up; Edie will be working with her Bonsai club at the Marin County Fair, July 1-5 at the Marin County Fairgrounds; look for her in the Exhibit Hall.See omnystudio.com/listener for privacy information.

Happy Sunday from the cast and crew of Bob Tanem In The Garden with Edie Tanem; our show plays live on KSFO.com worldwide and on the radio at 810 KSFO across Northern California, each Sunday at 9:00 local time. This here is the podcast edition, which is just like the original minus the musical segments and most of the ads. This week we took calls from our summer gardening audience, and discussed events and opportunities in the gardening world coming up; Edie will be working with her Bonsai club at the Marin County Fair, July 1-5 at the Marin County Fairgrounds; look for her in the Exhibit Hall.See omnystudio.com/listener for privacy information.

This week, artist, sculptor and angler, Ben Keating, makes perfect chicken Parm and gets rappers to give up their bling for charity, we lose in a fishing tournament but win for most diapers reeled in, launch flip-flops at the Verrazano Bridge and then go pheasant hunting in a dirt lot, and get our jean jackets airbrushed while attacking fluke with a cane pole.

Howdy-do, Bonsai Buds. On this episode of Little Things, Evan and Carmen tackle a tough topic and a controversial one at that. Should bonsai be judged and presented awards at exhibitions? Does the bonsai receive the award or does the person who owns the tree receive the award? What about amateurs vs professionals? Listen as Evan and Carmen work their way through these hot takes on bonsai display.Again thanks to our sponsors: Joshua Roth Tools, Underhill Bonsai, and Bonsai BarAlso, a massive thanks to our editor Matt O'Donnell. Thanks for editing every episode and meeting deadlines!Buy Joshua Roth tools on Underhill's online store:underhillbonsaistore.comBook classes at your favorite brewery:bonsaibar.com

Most farms struggle with dust, inefficiency, and labor shortages—but Tyler Niday of Bonsai Robotics reveals how AI-driven machinery is changing the game by transforming existing equipment into autonomous workhorses. Inspired by biology, Bonsai Robotics is making some of agriculture's toughest environments manageable. Tyler shares how autonomous orchard shakers are improving nut harvest efficiency by up to 40%, while converting shuttle trucks into multi-functional farm platforms can save growers hundreds of thousands of dollars in equipment costs. Monte and Tyler explore the industry's evolution from retrofitting machines with autonomous capabilities to developing full-platform solutions, including Bonsai's Amiga series. These adaptable systems support precision spraying, harvesting, and crop scouting. Before co-founding Bonsai Robotics, Tyler began in mechanical engineering and helped develop vision systems at Blue River Technology and gained hands-on learning at Orchard Machinery Corporation. If you've wondered what the future of practical farm automation really looks like, this episode offers a firsthand look at innovations in the field today. Visit Bonsai Robotics: https://bonsairobotics.ai/ Watch episode: https://youtu.be/Cvs7v5FLSN0 Timestamps: 00:10 - Tyler's background and journey into ag tech 02:23 - The role of AI and perception challenges in dust environments 07:48 - Vision-only AI vs LiDAR debate and dust interference 12:04 - Scalability of perception models across crops 12:58 - Training AI models on dusty environments and data fusion 16:16 - Focus on specialty crops as a starting point for autonomy 16:45 - Collaborations with OEMs like Floria and OMC 17:30 - Managing connectivity in remote farm locations 19:07 - Starlink and cellular solutions for remote operations 21:30 - The Amiga platform's different configurations and applications 25:44 - Strategic move into precision spraying and harvest-related automation 28:28 - Future autonomous applications for open-field crops 37:44 - Autonomous nut shaker and harvesting efficiency 41:46 - Future of crop variability mapping and individual plant management 47:13 - Returning value through data management and software ecosystems 49:21 - Industry collaboration, standards, and evolving equipment 52:25 - Closing remarks and future outlook

Wir sprechen über aktuelle Technikthemen rund um Infrastruktur, Open Source und KI. Ein Schwerpunkt ist Sebastians stark automatisierte Kubernetes-Umgebung auf Talos Linux mit GitOps und KI-Agenten unter menschlicher Kontrolle. Außerdem diskutieren wir Plattformfragen, Sicherheits- und Lieferkettenthemen sowie verschiedene KI-Entwicklungen. Zum Schluss greifen wir noch einige kleinere Themen aus dem Entwickleralltag und Werkzeuge für lokale LLMs auf. Blast from the Past Kubernetes Cluster ist nun live! https://www.siderolabs.com/talos-linux https://github.com/kreativmonkey/homelab-gitops payphonetag Froscon Toter der Woche Aus für De-Mail – warum das @ das eingekringelte e besiegte wero Aus für Ubuntu Pastebin – Abschaltung Ende Juni 2026 feedburner Untoter der Woche Stuxnet's Older Brother Revealed After 21 Years (video) fast16 | Mystery Shadow Brokers Reference Reveals High-Precision Software Sabotage 5 Years Before Stuxnet AI der Woche Continue Y/N Torvalds nennt KI Bug Reports “reine Zeitverschwendung” … aber curl Entwickler “zeigt sich versöhnlich” https://hothardware.com/news/new-ai-cyber-worm-thinks-up-its-own-attacks-to-infect-computers Anthropic: Weltweite Pause bei KI-Entwicklung ‘sinnvoll’ Anthropic Bewertung 965 Millarden rsync drama rsync analyse Google Chrome silently installs a 4 GB AI model on your device EU AI Act: Transparenzpflichten ab August 2026 Jakob gewinnt Gemma4 12B Bonsai 4b News Backblaze has quietly stopped backing up your data Debian must ship reproducible packages Cloudflare kauft Vite: Open Source und herstellerneutral – mit Millionenfonds https://arstechnica.com/security/2026/06/dozens-of-red-hat-packages-backdoored-through-its-offical-npm-channel/ https://www.golem.de/news/nur-ein-client-noetig-http-2-bomb-legt-webserver-in-sekunden-lahm-2606-209396.html Blog Post Themen Was eigentlich wenn kein GitHub? Ghostty Is Leaving GitHub Codeberg Gitlab BitBucket (nein!) Hackergarten 3D-Druck der Woche Bambu Lab: I’m reposting your code & I dare you to sue me. (video) Bambu Lab 3D printers: Never again (video) baltobu Zauberstab zum Bezahlen Weltumwelttag “PET Recycling” Mimimi der Woche modules C++20 tooling Python click Nix & SELinux Nix: cross-compiling Updates sind scheiße! Brother Drucker mit neuem Zertifikat Cosmic Desktop Nix Logo Lesefoo I put a datacenter GPU into my PC searchcode.com's SQLite database is probably 6 terabytes bigger than yours How I run multiple $10K MRR companies on a $20/month tech stack Serving a Website on a Raspberry Pi Zero Running Entirely in RAM NixOS auf Flint 2 You don’t love systemd timers enough! Picks IPv8 is finaly here Internet Protocol Version 8 (IPv8) The Unsolved Mystery of Lorem Ipsum (video) ODROID H5 Mechanical Pencil Umweltkosten durch Vibe Coding: Tool berechnet CO₂-Ausstoß für Claude Code Artikel von Heise taken (again)

We sit down with Jim Doyle to explore the timeless art of bonsai. With over 53 years of experience as part of the Susquehanna Bonsai Club, Jim shares the origins and cultural significance of bonsai, along with practical insights into how to care for these living works of art. From shaping techniques to long-term maintenance, he offers a fascinating look into the patience, creativity, and dedication behind bonsai cultivation—revealing why this ancient practice continues to inspire generations today.then,In this medical update, we highlight the launch of a new Celiac Clinic at Penn State Health Milton S. Hershey Medical Center, designed to provide the region's most comprehensive care for patients living with celiac disease. Dr. Kofi Clarke, Chief of Gastroenterology and Hepatology, discusses the clinic's multidisciplinary approach, advanced diagnostic tools, and personalized treatment plans aimed at improving patient outcomes. This episode explores how expanded resources and expert care are transforming the way celiac disease is managed in Central Pennsylvania.

At the home improvement company Lowe's, the marketing team is fittingly led by someone who believes in rolling up your sleeves and getting the job done. CMO Jennifer Wilson describes her approach as "impact over optics." "I care about the brand and I care about the work and I care about the outcome to the customer," Jennifer says. "I put very little value on, well, what will everybody think of that? Or what's the outcome for me in that? ... One could argue maybe you could be in a different place in your career had you taken a you-first angle, but it's not who I am." Today on Building Better CMOs, Jennifer and Greg talk about her experience in merchandising, how Lowe's is adapting to a "K-shaped economy", and leveraging customer data to build loyalty and deepen retention. Plus: Why every marketer should "Be a Sequoia, Not a Bonsai." 00:00 Introduction 01:47 The Macro Landscape and Consumer Mindset 04:12 Innovative Home Services and Subscription Models 09:12 Jennifer's Merchandising Background 14:52 Leading Organizational Transformation 17:49 Leveraging Data to Eliminate Customer Irritants 25:12 Lessons in Building a Retail Media Network 28:47 Career Advice: Humility, Impact, and Lateral Moves 32:21 Emotional Maturity and Understanding Personal Motivators 38:53 Prioritization and Profitable Growth 46:50 Bridging Marketing and Finance 51:10 Customer Experience as the Heart of Modern Branding 56:14 Elevating Service with Generative AI and Mylow 58:34 The Role of the CMO in 2030 Full transcript This episode was produced and edited by Eric Johnson from LightningPod Follow Building Better CMOs in your podcast app⁠⁠⁠ Rate and review the podcast Jennifer's LinkedIn Greg's LinkedIn

Contact Scott from Bonsai MatsuWhen I am working on any of my bonsai but especially my pines at this time of the year I ask myself a few simple questions for every branch that I work on. The integrity of the design I either have built or the design I am working towards rely on me making the correct decisions about removal or reduction. And that applies for all species and every time they are in front of you. The flow and movement of the bonsai is very important and so too is the design and flow of each branch.Support the showBecome a podcast supporter and show the Bonsai Love (it's really appreciated) ❤️https://www.buzzsprout.com/263290/supportWhere to find Bonsai Matsu:InstagramFacebookYouTube Web

Howdy Bonsai Buds! This episode was recorded live at C'est Bonsai 2026. Evan had a panel discussion with Guy Guidry, Dave DeGroot, and Randy Bennett about 50 years of Louisiana clubs. These three gentlemen definitely have a lot of history in Louisiana bonsai and so much more. This is another capture of the history of bonsai in the United States.Thank you Tyler Duplechin for recording this special moment in time! @tylerjduplechinWatch the live recording here with video! https://www.youtube.com/watch?v=eIQwGUtFIns&t=3411sAgain thanks to our sponsors: Joshua Roth Tools, Underhill Bonsai, and Bonsai BarAlso, a massive thanks to our editor Matt O'Donnell. Thanks for editing every episode and meeting deadlines!Buy Joshua Roth tools on Underhill's online store:underhillbonsaistore.comBook classes at your favorite brewery:bonsaibar.com

Hey ya'll, In this episode of Bonsai Time, Kevin shares a major life update that has been quietly taking shape behind the scenes for quite some time — and now, it's finally becoming reality.This next chapter brings new experiences, fresh inspiration, and an entirely new rhythm to daily life for Kevin, Kumi, and Juniper. Kevin opens up about the journey leading to this moment, what sparked the decision, and how this exciting change will shape the future of Bonsai Time Podcast moving forward.There's a lot ahead, and Kevin, Kumi, and Juniper are incredibly excited to share the journey with all of you.Thank you for being part of Bonsai Time.Bonsai ON!! The video version is ⁠⁠⁠⁠⁠⁠⁠soon to comeShow notes, relevant pictures, and links are ⁠⁠⁠soon to comeSee you in the next episode!Sponsor Info: This episode is sponsored by Chris Meade of the Columbus Bonsai Society and by our co-host via the Kevin Faris moving sale of bonsai pottery, tools, and trees. View these items at In Vivo Bonsai of Columbus, Ohio, USA in-person, or online (shipping available) at ⁠⁠⁠⁠⁠⁠⁠invivobonsai.etsy.com⁠⁠⁠⁠⁠⁠, or go to ⁠⁠⁠⁠⁠⁠⁠www.InVivoBonsai.com⁠⁠⁠ and navigate to the consignment ⁠⁠⁠pottery⁠⁠⁠ and ⁠⁠⁠tree⁠⁠⁠ pages.Mailbag submissions/Community:⁠⁠⁠⁠⁠NEW BT DISCORD Chat⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BT Facebook Group⁠⁠⁠⁠⁠Support the Pod:Anytime you listen, subscribe, rate us, or share us with friends, you help keep us motivated to keep making episodes for you all! If you want to take it to the next level, you can also help keep the podcast going by donating to us through Spotify or by sponsoring an episode (contact us directly for that). All donations go back into the podcast such as for our web hosting, recording gear expenses, etc.Podcast Info:The Bonsai Time Podcast is hosted, edited, & produced by Kevin Faris, Ryan Huston, & Kelly Lui. We expect to post new interviews and reflections monthly! Find us on ⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠, our ⁠⁠⁠⁠⁠⁠website⁠⁠⁠⁠⁠⁠, and our email BonsaiTimePodcast@gmail.com.Submit questions or pictures for future Bonsai Brainstorm episodes to our email, social media DMs, or ⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠.Audio editing of this episode and music are by MIDICANCER. Find more music by them on ⁠⁠⁠⁠⁠⁠SoundCloud⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠BandCamp⁠⁠⁠⁠⁠⁠.Host info:⁠⁠⁠⁠⁠⁠Ryan⁠⁠⁠⁠⁠⁠ is a former bonsai apprentice of Elandan Gardens and current operator of In Vivo Bonsai nursery and educational operation in Columbus, OH. ⁠⁠⁠⁠⁠⁠Kevin ⁠⁠⁠⁠⁠⁠is a bonsai practitioner/teacher now living in Massachusetts. ⁠⁠⁠⁠⁠⁠Kelly⁠⁠⁠⁠⁠⁠ is a newer bonsai artist volunteering and studying especially in the Los Angeles area.More Bonsai Projects by Ryan:Read more about bonsai on his blog and learn more about his educational services ⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠.Find Ryan's online-available bonsai products, seeds, tools, etc. ⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠. Each seed kit sold comes with my full 10-year bonsai-from-seed guide.Find Ryan on ⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠TikTok ⁠⁠⁠⁠⁠⁠as well if you need more bonsai in your feed.Also, check out some of my video editing work for the ⁠⁠⁠⁠⁠⁠Puget Sound Bonsai Association⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠Columbus Bonsai Society⁠⁠⁠⁠⁠⁠'s demonstration archives.

Aged to perfection, this episode features Michael Ryan Bell, a professional chef and one of the leading bonsai pot appraisers in America and beyond. We take a deep dive into bonsai pottery—from identifying quality and understanding value to breaking down essential Japanese terminology. Along the way, Michael shares stories from life on the road as a bonsai appraiser and in the kitchen, along with reflections on plants, craft, and the experiences that shape both. It's a rare glimpse into the world behind the pots and the culinary arts.

"That was when I remembered the card from the box..." CREEPYPASTA► "I Tried to Rush a Bonsai" written by Bilbo_Cheated, narrated by ClancyPasta► https://www.reddit.com/r/nosleep/comments/1t06ucy/i_tried_to_rush_a_bonsai/► https://www.reddit.com/user/Bilbo_Cheated/Here are ways to support the channel if you wish ~MERCH ► http://teespring.com/stores/clancypastastorePATREON ► https://patreon.com/clancypastaMEMBERSHIP ► https://www.youtube.com/channel/UCnfg9w5hrnPT7oA1H3uRZEQ/joinHere's where you can find me, and also links to the audio version of the show ~X / TWITTER ► http://x.com/clancypastaINSTA ► https://instagram.com/clancypastaSPOTIFY ► https://open.spotify.com/show/51DHHPsFnEvDAGfRiZPMF7ANCHOR.FM ► https://anchor.fm/clancypastaMUSIC► Background music is original and done in house by my best friend and house audio designer SKEEVY WEEVIL#Creepypasta #scarystories #horrorstories

Sab and Jo discuss rats, aphids and being humble.01:37 What's with all the rats?04:11 Tips on growing a bonsai tree.12:12 Don't prune your climbing guinea flower now - only at the end of summer!Subscribe to the podcast through the ABC Listen App or wherever you like to listen.Listen to the program live on Tuesdays at 2:20PM or on Saturdays at 9:00AM on ABC Radio Perth. Ask your questions by calling in on 1300 22 1025 or text 0437 22 1025.

This week Kevin meets with Roger Steele from the American Bonsai Association Sacramento to learn all about the art of Bonsai. Roger brings his years of wisdom and shares his top tips for beginners so that you can shape up any nursery tree into a vision of ancientness.Check out their website for meetings and more information: American Bonsai Association, SacramentoWatch demonstration videos on their YouTube channel: ABAS YouTubeGreen Acres Garden PodcastGreen Acres Nursery & SupplyGreen Acres Garden Podcast GroupIn the greater Sacramento area? Learn how to make your yard Summer Strong and discover water-saving rebates at BeWaterSmart.info.

Wakey wake, Bonsai Buds! After sleeping off the C'est Bonsai 2026 mayhem, Evan and Carmen are back to talk about pioneer species. Again, thanks to our sponsors: Joshua Roth Tools, Undersell Bonsai, and Bonsai BarA massive thanks to our producer, Matt O'Donnell for editing every episode!Buy Joshua Roth tools on Underhill's online store:underhillbonsaistore.comBook classes at your favorite brewery:bonsaibar.com

Contact Scott from Bonsai MatsuWell we're mid way through the Autumn period and you need to be very careful with watering at this time. Different trees in your collection will be at different stages as we head towards a dormancy period so their water usage will start to vary as well. Overwatering can be a significant problem around now. There are techniques you can use and things to keep an eye out for that will let you know before a problem develops. I have been travelling around this beautiful country of ours recently and had the opportunity to work on amazing material and see loads of bonsai lovers. And there is always something to learn!Support the showBecome a podcast supporter and show the Bonsai Love (it's really appreciated) ❤️https://www.buzzsprout.com/263290/supportWhere to find Bonsai Matsu:InstagramFacebookYouTube Web

Contact Scott from Bonsai MatsuThe approach of Good Structure is Good Bonsai is a great fall back position when you ever get doubt about the next step to take. When you sit there scratching your head and are ready to just leave it until next time, don't leave the issue, don't ignore it, take action. Bend the rules where possible of course and avoid rigidity in your design but always come back to horticultural principles as a priority for the longevity of the design of your bonsai. What looks good initially may be impossible to maintain in the long term.Support the showBecome a podcast supporter and show the Bonsai Love (it's really appreciated) ❤️https://www.buzzsprout.com/263290/supportWhere to find Bonsai Matsu:InstagramFacebookYouTube Web

Is your company optimizing for the wrong kind of success? Almost 90 percent of companies are focused on short term efficiency metrics when the real winners are playing a long term profitability game. The scary part is your numbers can look better than ever while your business is actually getting weaker. In this episode of Insight Out, I sit down with Nicolas Darveau-Garneau, growth strategist, former Google Chief Evangelist, and author of "Be a Sequoia, Not a Bonsai," to unpack why most companies focus on the wrong things and what the top performers do instead. Nicolas has studied growth across more than 1,000 companies, from startups to global giants. What he found is a simple but powerful shift in thinking. Most companies behave like bonsai trees, constantly trimming for short-term efficiency. The ones that win grow like sequoias. They focus on scale, resilience, and long-term customer value. We talk about why return on ad spend can be a risky metric, how St. Jude's raised 46% more money by changing a single KPI, and why the best companies stay open to new ideas. Nicolas also shares how to use AI to predict customer lifetime value, how to build a testing culture that moves faster than your competitors, and why brand building can actually be measured in a meaningful way. From Amazon's early days in Canada to a single gym in Los Gatos that makes its customers far more valuable, the examples bring these ideas to life in a very practical way. If you are a founder, executive, or entrepreneur who wants to build something that keeps growing and compounding over time, this conversation will change how you look at every metric on your dashboard. Let's dive in! In this episode, we discuss: [00:00] Introduction [01:08] Introduction to Nicolas Darveau-Garneau [02:58] The one metric making companies weaker [05:48] The St. Jude's case study [07:43] Examples that reveal right vs wrong metrics [11:17] AI and the right dashboard [15:10] Why customer lifetime value is so controversial [18:37] The three pillars of sequoia growth [22:26] Pillar 2: Increasing value of existing customers [26:37] Pillar 3: Velocity of testing [30:40] Customer lifetime value in everything [33:13] The art of the minimum viable test [35:47] Amazon's customer lifetime value playbook [38:37] Brand building as economic science [47:54] The AI acceleration [52:11] Where to find Nicolas Notable Quotes [03:30] “Would you rather invest a dollar to make 10 or invest a million dollars to make $2 million?” – Nicolas [03:37] “By dividing by the investment as opposed to subtracting the investment, you're making a mistake.” – Nicolas [03:48] “Almost every company, 90% of the companies are focusing on the short term efficiency metrics, and instead they should be focusing on longer term profitability metrics.” – Nicolas [06:23] “Most companies don't have the right KPI and don't use the right data.” – Nicolas [10:45] “The best companies are already much better and they get better faster because they're open to trying new stuff.” – Nicolas [13:58] “For almost every industry that 20% of the customers in the industry drive a hundred percent of the profits in the industry.” – Nicolas [14:35] “Not all customers are created equally.” – Billy [15:31] “Make more money in the long term trying to predict the future and try to acquire the most valuable customers.” – Nicolas [29:42] “If you have customer lifetime value in the middle, and then you spin tests really, really quickly, you're gonna be hard to catch.” – Nicolas Nicolas Darveau-Garneau Website: https://nicolasdarveaugarneau.com/ LinkedIn: https://www.linkedin.com/in/nickdg Book: Be a Sequoia, Not a Bonsai Billy Samoa Saleebey LinkedIn: ⁠https://www.linkedin.com/in/billysamoa/ Email: ⁠billy@podify.com⁠ and ⁠saleebey@gmail.com⁠  Insight Out  Website: ⁠https://www.insightoutshow.com/ Learn more about your ad choices. Visit megaphone.fm/adchoices

03-27-26 Recording of The Friday Night Groove on 88.3 FM WXOU, Auburn Hills, MI. In this episode I sit down with the Detroit-based musician, multi-instrumentalist, producer, and interdisciplinary artist, known as, Na Bonsai. We discuss her latest album, Obsidian Bloom, her EP angel of airfields, upcoming performances and much more.   Set List: Intro: Na Bonsai - Bootstrap Paradox Na Bonsai -  Black As Lead Interview Part 1 Na Bonsai - angel of airfields Interview Part 2 Na Bonsai - Planet Paralysis (String Reprise) Interview Part 3 Na Bonsai - Wings Interview Part 4 Ending: Na Bonsai - Shadow of You    For more on the artist visit: https://nabonsai.com/

Contact Scott from Bonsai MatsuGood structure does not mean rigidity in design. Breaking or bending the rules can be one of the most enjoyable components when designing your bonsai. In this episode I want to offer some good structural advice or guidelines (not rules!). Things like good branch placement, visual weight, negative and positive space, bend distance consistency and branch origin angle are all on the agenda as well as others. Again, they're not rules, just considerations.Support the showBecome a podcast supporter and show the Bonsai Love (it's really appreciated) ❤️https://www.buzzsprout.com/263290/supportWhere to find Bonsai Matsu:InstagramFacebookYouTube Web

Contact Scott from Bonsai MatsuOne of the most important areas of bonsai for me is the structure of our trees, and good structure matters! I talked about this very early on in the podcast back in season one and it's well overdue for a freshen up and some deeper thought. In this podcast I'll talk about why good structure matters and also that good structure starts with taper. Taper of the trunk, roots, primary and secondary branches. Remember, good structure is good bonsai.Support the showBecome a podcast supporter and show the Bonsai Love (it's really appreciated) ❤️https://www.buzzsprout.com/263290/supportWhere to find Bonsai Matsu:InstagramFacebookYouTube Web

How's it going, Bonsai Buds! Evan and Carmen are back at it again. After I short break, the Little Things are returning to bring you more quality content about everything bonsai. From reviews of recent bonsai shows/exhibitions, bonsai critiques, bonsai care guides, special guests, and more. If you're new here, welcome! Many thanks to your Bonsai Buds for supporting us and being patient with us as we took a short break :)Also, C'est Bonsai 2026 is on the horizon! Click the link below to preregister for tickets and workshops here:https://event.fourwaves.com/cestbonsai2026Again thanks to our sponsors: Joshua Roth Tools, Underhill Bonsai, and Bonsai BarAlso, a massive thanks to our editor Matt O'Donnell. Thanks for editing every episode and meeting deadlines!Buy Joshua Roth tools on Underhill's online store:underhillbonsaistore.comBook classes at your favorite brewery:bonsaibar.com

Brendan talks about three games he played for the first time recently, and one game he revisited. Join us, won't you?Bonsai (2023)Crystallo (2018)Rise of Augustus (2013)Take 5! (1998) revisitedWhat games have you been playing? Share your plays over on boardgamegeek in guild #3269.

It's garden talk radio with Edie Tanem on the Bob Tanem In The Garden radio show on KSFO! We were live this morning and taking calls, and the result is available for your listening pleasure on this podcast: 45 minutes of gardening advice, ideas and ruminations. In addition, Edie attended the Bonsai show and sale over the weekend and has that experience to talk about. This podcast edition has been lightly edited to remove musical content and the bulk of the advertising.See omnystudio.com/listener for privacy information.

It's garden talk radio with Edie Tanem on the Bob Tanem In The Garden radio show on KSFO! We were live this morning and taking calls, and the result is available for your listening pleasure on this podcast: 45 minutes of gardening advice, ideas and ruminations. In addition, Edie attended the Bonsai show and sale over the weekend and has that experience to talk about. This podcast edition has been lightly edited to remove musical content and the bulk of the advertising.See omnystudio.com/listener for privacy information.

Transform Your Small Business: Profit, Customers, and Growth Strategies is covered in this podcast, along with the following subjects:How small businesses can improve their sales through targeted customer acquisitionLeveraging AI and digital tools to boost marketing efficiency and customer retentionProven growth frameworks from advising 1,000+ CEOs to scale profitably***************************************Join Andrew Frazier and Nicolas Darveau-Garneau for a livestream unpacking "Transform Your Small Business: Profit, Customers, and Growth Strategies." Drawing from Nick's experience as Google's former Chief Evangelist—advising over 1,000 CEOs—and his upcoming book Be a Sequoia, Not a Bonsai, this session reveals proven tactics for boosting profits, attracting loyal customers, and scaling with AI and digital tools. Andrew's small business expertise complements Nick's strategies with practical steps for immediate impact.Nicolas Darveau-Garneau (“Nick”) is a leading expert in growth, artificial intelligence, and digital transformation with over 25 years of experience in technology and strategy. He is the former Chief Evangelist at Google, where he advised more than 1,000 global CEOs on digital transformation, and previously served as Chief Strategy and Growth Officer at AI company Coveo. An entrepreneur and investor, he has co-founded four internet companies (selling three) and invested in over 20 tech startups. He sits on the boards of TMX Group, McEwen Mining, and Alida, and teaches executive courses on AI in marketing and in the boardroom. His forthcoming book, Be a Sequoia, Not a Bonsai, shares seven growth secrets used by the world's most successful companies, based on his work with top leaders.

Our first guest this morning was Suzanne Muller, a woman of many hats at the Bonsai Garden at Lake Merritt -- a non-profit all-volunteer run garden nurturing an amazing collection of miniaturized trees in the Japanese Bonsai tradition. Top of mind is the Gardens' annual fundraiser event -- the Mammoth Auction and Sale is this coming Saturday and Sunday March 7 and 8. Our second guest was Bonsai enthusiast and podcaster Addison Galambos. Addison is a board member at the Garden and a Bonsai "Builder" himself -- in fact he runs a podcast called the "Bonsai Builders Podcast" that is available at your favorite podcast site or on YouTube. The rest of todays' broadcast was discussion of current gardening stuff, and listener calls. This podcast is a repackaging of our original radio broadcast on 810 KSFO; all music and most ads have been edited out.See omnystudio.com/listener for privacy information.

Our first guest this morning was Suzanne Muller, a woman of many hats at the Bonsai Garden at Lake Merritt -- a non-profit all-volunteer run garden nurturing an amazing collection of miniaturized trees in the Japanese Bonsai tradition. Top of mind is the Gardens' annual fundraiser event -- the Mammoth Auction and Sale is this coming Saturday and Sunday March 7 and 8. Our second guest was Bonsai enthusiast and podcaster Addison Galambos. Addison is a board member at the Garden and a Bonsai "Builder" himself -- in fact he runs a podcast called the "Bonsai Builders Podcast" that is available at your favorite podcast site or on YouTube. The rest of todays' broadcast was discussion of current gardening stuff, and listener calls. This podcast is a repackaging of our original radio broadcast on 810 KSFO; all music and most ads have been edited out.See omnystudio.com/listener for privacy information.

Giữa mùa hè rực nắng của nước Úc, khi Tết không còn là tiết xuân se lạnh như ở quê nhà, người Việt vẫn tìm cách giữ lại mùa xuân trong những chậu cây cảnh. Với nhiều gia đình, chơi bonsai và hoa Tết không chỉ là thú vui, mà là cách lưu giữ ký ức, gửi gắm lời chúc đầu năm và tìm lại sự bình an nội tâm. Từ câu chuyện của hai anh em Hiển và Hoàng tại Happy Bonsai Sydney, bài viết khám phá cách những loài cây đã thích nghi với khí hậu mới nhưng vẫn mang trọn tinh thần Tết Việt nơi đất khách.

OpenAI's Sam Altman recently said that artificial intelligence programs have become more energy efficient than human beings. Google's Larry Page said that it is absurd to think that AI can be stopped in any meaningful way. And Jack Clark of Anthropic said that new Claude models are developing characteristics that its designers do not understand. Artificial intelligence is moving very quickly, and we sit down with someone who works in that world to digest the changes. In studio:Max Irwin, president at Bonsai.io---Connections is supported by listeners like you. Head to our donation page to become a WXXI member today, support the show, and help us close the gap created by the rescission of federal funding.---Connections airs every weekday from noon-2 p.m. Join the conversation with questions or comments by phone at 1-844-295-TALK (8255) or 585-263-9994, email, Facebook or Twitter. Connections is also livestreamed on the WXXI News YouTube channel each day. You can watch live or access previous episodes here.---Do you have a story that needs to be shared? Pitch your story to Connections.

In hour four, Hoch and Crowder discuss if they're patient enough to properly trim a Bonsai tree at Funky Buddha. Hoch reads the lines, injury report, and prop bets for Heat vs Hawks, also for the Bulls, who play in Milwaukee vs the Bucks. Plus, Hoch briefly forgets who the Marlins manager is and thought Skip Schumaker was still the manager of the team.

There's getting to know someone for the first time and the unveiling of personal stories and background that come with introductions; call it a familiarization of sorts. And then there are discussions with old friends and familiar faces where you pick up where you left off and continue to evolve the conversation.  We recently had the opportunity to be a guest on one of our favorite bonsai podcasts, Bonsai Time, hosted by Kevin Faris and Ryan Houston. Kevin goes back a long way with Mirai and has dove deep into bonsai in North America through his experience as well as through his podcast. We knew the conversation would go places other podcasts don't and Kevin and Houston would ask questions of Ryan perhaps other podcasters might not have the familiarity or comfort level to discuss. The boys at Bonsai Time did not disappoint. From family and life outside of bonsai to the depths of Ryan's experiences as an apprentice, a professional, and the bonsai community at large the podcast winds seamlessly through the many aspects of living bonsai as a lifestyle and all the bumps along the way. It was thoroughly enjoyable. If you haven't listened the Bonsai Time be sure to check them out here. Subscribe and support their mission to continue shining light on all the good people and positive stories that exist inside this weird and wacky endeavor of bonsai in North America. We are excited for you all to have a listen and enjoy!

There are many podcasts popping up in bonsai but recently Mirai became aware of one in particular whose host brought a different level to the game of bonsai discussion.  The Bonsai Builders Podcast is the brainchild of bonsai addict and passionate practitioner Addison Galambos. Through Addison's innate curiosity, contemplative wit, and thorough preparation, we found Bonsai Builders to be something new, something different, and something more in depth. Naturally, when Addison approached Mirai to do a deep dive on bonsai culture, aesthetics, judging, and other tasty subject matter, it was tough to turn down.  Mirai x Bonsai Builders are releasing part 1 of our conversation in collaboration on both platforms. We dig deep into trees mirroring people, culture clashes in design, and how judging can make or break a show. However, to hear part 2 of our conversation, and easily the best part of the discussion, head over to the Bonsai Builders podcast here. Give Bonsai Builders a like and subscribe, listen to the rest of Ryan's conversation with Addison, and send the Builders some love to help an up-and-coming talent continue to grow.  Each time we talk about complex subject matter we tease out new themes and evolve our understanding. We are confident this conversation is a major step in the continued journey to uncovering the depth and power of bonsai as an art form.  Enjoy! Subscribe via Apple Podcasts, Stitcher, Spotify, or wherever you get your podcasts.

Today is another classic ep with just the bros! Chad starts us off hot with a new revelation about Disney being haunted. How does the happiest place on earth get so much hidden plasma and goo? We then dive into Baseball and take a call from brookeknowballs. Brooke & Joel break down the most overrated stats in baseball and why the FEEL of the game is more important plus what to expect in the upcoming World Series! Chad talks about ripping aspen this winter but things take a left turn when he admits to 2 planks. JT talks about his experience at Huntington Gardens and why he is now invested in Bonsai trees. We finish the ep by calling our NFL insider, Chudwin. We check up on his 5 hot takes from the start of the season and get 5 new STEAMING HOT takes after week 7. We are live streaming a Fully unedited version of the pod on Twitch, if you want to chat with us while we're recording, follow here: https://www.twitch.tv/chadandjtgodeep Grab some dank merch here:https://appreeshapparel.com/ Come see us on Tour! Get your tix - http://www.chadandjt.com TEXT OR CALL the hotline with your issue or question: 323-418-2019(Start with where you're from and name for best possible advice) Check out the reddit for some dank convo: https://www.reddit.com/r/ChadGoesDeep/ Thanks to our Sponsors: HOUSE OF ATLAS: Razors & Shave cream NOW AVAILABLE IN TARGET NATIONWIDE Get clean shaven and feel fresh today! PRODUCTION & EDITS BY: Jake Rohret