Podcasts about hoppers

ALEX STULCE RETURNS! And as he joins Spencer on today's episode, the two talk about the hopper fishing strategies that too often get ignored. Things like adding drag on purpose to make the hopper look like it's alive, to fishing them in shallow, nervous water. These are the little things that WILL make a difference in your fishing this summer, but that most folks don't ever discuss.  You'll also hear about:  Everything going on behind-the-scenes at VFC, including updates on products, live events, and new releases Tips for fishing etiquette when you're brand-new How to guesstimate the depth of a river when setting up a nymph rig How to tell if you're getting a bite, or just ticking the bottom The great knot debate between the Clinch and Loop knots LINKS FROM THE SHOW DONATE - Amber Kelly GoFundMe - VIEW HERE Get TroutRoutes PRO Free For One Month - CHECK IT OUT Join the VFC Online Community - CHECK IT OUT QUESTIONS FOR THE SHOW - SUBMIT HERE #LIVEREELLIFE MOMENT - SUBMIT HERE Get the FREE Field Guide - CHECK IT OUT

Undefeated to this point of the home and away season, Coolamon are looking to complete their year with a win over the Swans. Coach Gav McMahon joined The Wooden Spooners...See omnystudio.com/listener for privacy information.

Undefeated to this point of the home and away season, Coolamon are looking to complete their year with a win over the Swans. Coach Gav McMahon joined The Wooden Spooners...See omnystudio.com/listener for privacy information.

This week on The Gospel Jubilee Chip & Denny will be playing music by The 3 Heath Brothers, Westward Road, The Hoppers, Sunday Drive, Jim Brady, The Grascals & Dolly Parton, and their mystery artists of the week.   Here are all of the ways you can listen to the Gospel Jubilee On your Echo device say, Alexa, play the Gospel Jubilee on Apple podcast. For a direct download go to: https://api.spreaker.com/v2/episodes/73827536/download.mp3       Ocean Waves Radio ... every Wednesday at 5:00 PM Eastern time., www.OceanWavesRadio.com   Thursday afternoons at 4:00 PM and Sunday mornings at 9:30 AM  EST on Southern Branch Bluegrass Radio, www.sbbradio.org   Playlist:   Artists |Song Title   01. The 3 Heath Brothers - Good Good Life   02. Anthem Edition - He Is Who He Is   03. Loren Talley - Walls   04. Westward Road - My Prayer For You   05. The Jay Stone Singers - Good Thing   06. The Hyssongs - Our God Is Good   07. Karen Peck & New River - I Wanna Know How It Feels   08. The Kingsmen - When The Old, Old Story Was New   09. The Hoppers - Jesus The One   10. The Kramers - He Loved My Soul   11. The Booth Brothers - He Showed His Love   12. Sunday Drive - Sometimes It Takes a Storm   13. Our Mystery Artist of the Week - You'll Never Walk Alone   14. The Ferguson Family - He'll Move Heaven and Earth   15. Blake & Jenna Bolerjack - Storm Walker   16. The Binions - Come To The Table   17. Avenue - Jesus Cares For Me   18. Mark Bishop - Prayer Request & Hard Candy   19. Jim Brady - Turn The Tide   20. Ernie Haase & Signature  Sound - Shout brother Shout   21. the Gaither Vocal Band - Something To say   22. Greater Vision - I Want To Know That You Know   23. The Grascals & Dolly Parton - Broken Angels        

Seeing the Grain Market Futures Move Corn Leaf Hoppers in Kansas Impressive Heat in Kansas   00:01:05 – Seeing the Grain Market Futures Move: K-State grain economist, Daniel O'Brien, gets today's show rolling with a grain market outlook where he discusses futures moving to the lower side and what point crop development is at.    Daniel on AgManager.info   00:12:05 – Corn Leaf Hoppers in Kansas: Keeping the show rolling is K-State Extension agronomist, Tina Sullivn, and K-State Extension entomologist, Anthony Zukoff, as they explain the appearance of corn leaf hoppers in Kansas and the impact it can have on corn. Article - Corn Leaf Hopper  Corn Disease Network   00:23:05  – Impressive Heat in Kansas: Chip Redmond, K-State meteorologist, ends the show as he previews the Kansas forecast that includes hot and dry weather and what could come after.      Send comments, questions or requests for copies of past programs to ksrenews@ksu.edu.   Agriculture Today is a daily program featuring Kansas State University agricultural specialists and other experts examining ag issues facing Kansas and the nation. It is hosted by Shelby Varner and distributed to radio stations throughout Kansas and as a daily podcast.   K‑State Extension is a short name for the Kansas State University Cooperative Extension Service, a program designed to generate and distribute useful knowledge for the well‑being of Kansans. Supported by county, state, federal and private funds, the program has county Extension offices statewide. Its headquarters is on the K‑State campus in Manhattan. For more information, visit Extension.ksu.edu. K-State Extension is an equal opportunity provider and employer.

This week on the Oakley Podcast, Jeremy Kellett sits down with young dispatchers Kyle Williams and Alden Hazelwood to talk about their paths to Oakley Trucking, starting in the shop, and how that hands-on experience now shapes the way they dispatch. They discuss key topics like the 2290 heavy road use tax reminder, the dangers of road rage and speeding, and why communication between drivers, dispatch, and customers is critical. Kyle and Alden explain their day-to-day responsibilities, how they plan loads, manage problems like breakdowns, and track settlements to ensure owner-operators get good miles and pay. They also highlight the importance of relationships, understanding geography and different dry bulk divisions (hoppers, pneumatics, end dumps), and seeing things from both the customer and driver perspective. The main takeaway for listeners is that successful trucking at Oakley depends on safety, clear communication, mutual trust, and a shared commitment between dispatch and owner-operators to pull in the same direction. Key topics in today's conversation include: Reminder About 2290 Heavy Road Use Tax Deadline (1:10) Discussion on Road Rage, Speeding, and Driver Professionalism (2:17) How Alden Found Oakley and His Previous Fiber Optic Job (7:27) How Kyle Pivoted From Physical Therapy to Dispatch at Oakley (8:51) How Shop Experience Helps Them Now in Dispatch (12:14) Learning Geography and Load Planning Challenges (14:03) Daily Responsibilities and Morning Problems in Dispatch (16:49) Importance of Communication vs Confrontation With Drivers (19:42) Bringing Drivers in for Face to Face Meetings About Issues (22:17) Young Dispatchers Working With Veteran Owner-Operators (24:05) Learning Drivers' Hobbies and Building Relationships (27:36) Differences Between Pneumatics, Hoppers, and End Dumps (28:39) What Dispatchers Want From Drivers to Make Jobs Easier (31:12) How Dispatchers Protect Driver Earnings and Check Settlements (32:37) Reviewing Miles, Deductions, and Detention on Settlements (34:09) Final Thoughts and Takeaways (35:11) Oakley Trucking is a family-owned and operated trucking company headquartered in North Little Rock, Arkansas. For more information, check out our show website: podcast.bruceoakley.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Matthew 14:13-21

Thursday, Prototyping Demo Day at Rev Ithaca Startup Works, Movies at the Station with Hoppers at the Cayuga Heights Fire Department, Petty Thieves at Myers Park in Lansing, Jazz & Blues Night at Ithaca 5 [...]

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

If you are looking for a review of the odyssey, you wont find it here, but we have other exciting news. Like Disney cuts Pixar staff, even after making a billion dollars. Or Is Nintendo justified in telling consumers to stuff it after tariff refunds? PlayStation ain't budging on ditching discs, and some sales numbers back them up. But hey, at least we got a trailer for Avengers Doomsday!

Disney just terminated more Pixar animation staff, and they're blaming the failure of Elio and Hoppers for the layoffs. But is that REALLY what's going on here? Toy story 5 is the biggest movie of the year. Watch the podcast episodes on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629 MORE CLOWNFISH TV - Official Merch Store: http://ClownfishMinus.com Facebook - https://facebook.com/ClownfishTV X - https://x.com/ClownfishTVcom Clownfish TV subreddit: https://www.reddit.com/r/ClownfishTVOfficial/ Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #Disney #Pixar #Animation #Movies #Podcast #Commentary #News #Reaction #Gaming #Comedy #Entertainment #Hollywood #PopCulture #Tech #Anime #FYP Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome back to Hangin' at the Hangar Bar! This week, Scott and Candice explore the Disney experiences they were convinced they would dislike—only to discover that some of them eventually became favorites. From classic attractions and Pixar films to bourbon tastings and fireworks shows, this episode is all about keeping an open mind and letting Disney surprise you.In this episode:✨ Why Scott went from avoiding Carousel of Progress to calling it one of his favorite attractions in Walt Disney World—and how its storytelling, history, and connection to Walt Disney changed his perspective.

Coming back from a brief hiatus caused by many things (including Matt working on his movie), Gabe and Matt review the year that has been 2026 so far (up until about July 7th) and talk about three films that they found worthy of discussion, whether it was because the movie was good, bad, horrendous, curious, bloated, misguided, or what-have you. Movies discussed include Wuthering Heights, Project Hail Mary, Obsession, The Backrooms, Hoppers, and Pressure. -  -  -  -  - Listen to us on Apple Podcast and Spotify!Send us a message!Music by Mike. Check out his Soundcloud.Like our content? Visit our website! 

Its the second rewatch recap of 2026, featuring The Wizard of Oz, Wayne's World, Hoppers, Predator, Sliver, Crash (1996), Lara Croft: Tomb Raider, and more!Podcast Details: Season 3, Episode 2Cinema5000.comBluesky: Cinema 5000YouTube: @cinema5000podcastPodcast artwork: James Merolla - Instagram: @far_away_fires

We explain how Pixar's Hoppers fits into the Pixar Theory! Filled with easter eggs and Pixar references, Hoppers adds an incredible amount to the Pixar Theory... or does it? Are these easter eggs an amazing gift or a sarcastic prank? Does Hoppers confirm the Pixar Theory ot make it a joke? Listen to hear our thoughts and comment your own down below! Leave your thoughts in a review on Apple Podcasts or comment on our YouTube or Instagram and we'll give you a shoutout in our next episode!For more Mouse Ears Movie Thoughts content, check out our Instagram, website, and YouTube:Instagram: https://www.instagram.com/mouseearsmoviethoughts?igsh=MTZjYW5ranE0MG0wdQ%3D%3D&utm_source=qrYouTube: https://www.youtube.com/@mouseearsmoviethoughts9688Website: https://memtpodcast.com/Magic Candle Company: https://magiccandlecompany.comIf you have any comments, questions, or episode ideas you would like to share with us, email us at memtpodcast@gmail.com. Hosted on Acast. See acast.com/privacy for more information.

EPISODE #487-- Today we talk the Star Wars also ran and all around mess that is THE ICE PIRATES (1984), directed by Stewart Rafill. It's a bad movie! Don't watch it! It's bad in a way that makes me think "Hey, Black Hole, that was pretty good!" We als otalk about CUTTER'S WAY (1981, dir. Ivan Passer), THE TERMINATOR 2: JUDGMENT DAY 3D (1991, dir. James Cameron), BLADE RUNNER (1981, dir. Ridley Scott), and HOPPERS (2026, dir. Daniel Chong). LINKS-- Join the cause at Patreon.com/Quality. Follow the us on on Bluesky at kislingconnection and cruzflores, on Instagram @kislingwhatsit, and on Tiktok @kislingkino. You can watch Cruz and show favorite Alexis Simpson on You Tube in THEY LIVE TOGETHER. Thanks to our artists Julius Tanag  and Sef Joosten. The theme music is "Eine Kleine Sheissemusik" by Drew Alexander. Also, I've got a newsletter on Substack, so maybe go check that one out, too. Listen to DRACULA: A RADIO PLAY on Apple Podcasts, at dracularadio.podbean.com, and at the Long Beach Playhouse at https://lbplayhouse.org/show/dracula And, as always, Support your local unions! UAW, SAG-AFTRA, and WGA strong and please leave us a review on iTunes or whatever podcatcher you listened to us on!

On this bonus episode, we're breaking down Hoppers, a weird, funny, and surprisingly unpredictable original that feels like a throwback to when Pixar wasn't afraid to experiment. It's not top-tier, but it's exactly the kind of risk-taking, offbeat storytelling we've been missing. Is this the direction Pixar should be heading in?____Please consider joining our Patreon: https://www.patreon.com/wwibofficialYouTube: https://www.youtube.com/@whywasntitbetterLetterboxd: https://letterboxd.com/wwibpodcastInstagram: https://www.instagram.com/wwib_officialX: https://x.com/WWIBpodcastSubscribe! Rate! Review! Tell a friend!

Little Sister joins us this week as we talk about 1776, Bad Jokes, Outlander, Game of Thrones, Parade of Horribles, Crowns of Nyaxia, Romantacy, Mel Brooks, Minions, Animaniacs, The Man Who Died Seven Times, The Music Man, Hoppers, LitRPG, Darling of Fate, Iced Tea, Death Has Joined the Party, PlayStation Physical Media, Vegas, Project Phoenix on Kickstarter, and the Xbox layoffs. So get out your pen and call your mom, it's time for a GeekShock!

Today, we're celebrating Disney and Pixar's Hoppers with two exclusive interviews featuring the filmmakers and cast behind one of our favourite animated films of the year. First, we sit down with director Daniel Chong and producer Nicole Paradis Grindle to discuss creating Pixar's latest original adventure, the film's heartfelt themes, bringing an entirely new world to life, and the creative journey behind Hoppers. Then, we're joined by Piper Curda, the voice of Mabel, and Bobby Moynihan, the voice of King George, to talk about finding these characters, collaborating through animation, and what they're most excited for audiences to experience now that Hoppers is streaming on Disney+. If you enjoyed this episode, be sure to subscribe, leave a review, and follow Geekcentric for more interviews, reviews, and conversations celebrating the best in movies, television, gaming, and pop culture. Hoppers is now streaming on Disney+. Watch Our Interview with Daniel and Nicole Here Watch Our Interview with Piper and Bobby Here Check out Geekcentric onYouTube | Instagram | Twitter | TikTokJoin the Geekcentric Discord HEREFollow Eatcentric - Same geeks. New Eats

In today's episode, Haylie Pomroy welcomes back Dr. Melanie Hoppers of the Bateman Horne Center for a deep dive into post-exertional malaise, or PEM, the defining feature of ME/CFS that turns the conventional wisdom about exercise completely upside down. Dr. Hoppers explains why pushing through fatigue can cause lasting and sometimes permanent damage in patients with ME/CFS and long COVID. She breaks down what PEM actually is at the physiological level, how mitochondrial exhaustion limits the body's ability to produce energy, and why willpower has nothing to do with it. She also shares practical, real-world pacing strategies, including resting heart rate tracking, scheduled rest breaks, and how to stay within what she calls the energy envelope to protect the body's potential for healing. If you or someone you love has been told to just push through it, this episode will change how you think about energy, healing, and what your body actually needs. Tune in to Fast Metabolism Matters. If your body feels like it's running on empty, overburdened, or just not responding the way it used to, Haylie's latest book, Toxic Overload, tells you exactly what to do. Download your free digital copy today and start understanding what your body is trying to tell you. Free Download: Get Your Copy of Toxic Overload

Shelby and Andrew are back with a classic Trip it to Me Mess Around. They create a game out of thin air and the results are surprisingly decent. They also talk about Lord of the Flies, Hoppers and The Moment!

We review Pixar's Hoppers! We share our favorite moments and scenes we wish were better from this hit movie! Listen to hear our ratings! Leave your thoughts in a review on Apple Podcasts or comment on our YouTube or Instagram and we'll give you a shoutout in our next episode!For more Mouse Ears Movie Thoughts content, check out our Instagram, website, and YouTube:Instagram: https://www.instagram.com/mouseearsmoviethoughts?igsh=MTZjYW5ranE0MG0wdQ%3D%3D&utm_source=qrYouTube: https://www.youtube.com/@mouseearsmoviethoughts9688Website: https://memtpodcast.com/Magic Candle Company: https://magiccandlecompany.comIf you have any comments, questions, or episode ideas you would like to share with us, email us at memtpodcast@gmail.com. Hosted on Acast. See acast.com/privacy for more information.

Welcome to TV Break, where Pop Break's Director of Podcasting Alex Marcus is joined by editor-in-chief Bill Bodkin, and TV Columnist Josh Sarnecky to talk about the happenings in television.Here's the format of the show:The Best Thing I Saw on TV Last Month  – Alex, Bill, & Josh talk about a show/episode/event they liked from the last month including Prime Video's Spider-Noir, HBO Max's House of the Dragon and My Adventures with Superman, AppleTV's Widow's Bay and Maximum Pleasure Guaranteed, and Disney Plus's The Bear, Not Suitable For Work, and X-Men ‘97. Newsbreak – This month, they are discussing the news that Comcast plans to spinoff NBCUniversal into a stand alone media corporation, potentially as a first step in a larger media merger.Streaming Wars – Alex, Bill, & Josh each selected their top streamer of the month. Josh opted for Peacock on the strength of Love Island, as well as their being the exclusive streaming home of Super Mario Galaxy and Spanish Language World Cup coverage. Alex went all in on Tubi after it's parent company Fox purchased the to digital TV distributor and a key Tubi rival in the FAST market, Roku. Meanwhile, Bill handed it to Disney Plus after the historically high NBA Finals ratings, new season drops from The Bear and X-Men ‘97, and becoming the exclusive home of Hoppers and Avatar: Fire & Ash!New Series Spotlight  – This month Alex, Bill, and Josh discuss HBO's Life, Larry, and the Pursuit of Unhappiness, a new series from Barack and Michelle Obama and Curb Your Enthusiasm's Larry David that attempts to answer the question, would the 90 historical kids cartoon Hysteria be better if it starred Larry David. 

Happy 4th of July! Brennan, Nicole & Mama K are on to catch-up on a whole bunch of 2026 movies and streaming shows: Toy Story 5, Disclosure Day, Obsession, Backrooms, The Sheep Detectives, Pressure, Hoppers, Louis Theroux: Inside the Manosphere, They Will Kill You, The Other Bennet Sister, Nine Perfect Strangers Season 2, Widow's Bay, Your Friends & Neighbors Season 2, His & Hers, Seinfeld

On this episode of Metal Geeks, Cary the Metal Geek and George Tripsas return with another packed episode covering the latest in movies, TV, gaming, comics, and heavy metal. We discuss everything we've been watching, including Funny AF, Widow's Bay, For All Mankind Season 5, Toy Story 5, Masters of the Universe, House of the Dragon, Little Brother, Disclosure Day, Legends, Last Samurai Standing, Dark Matter and Hoppers. We also talk about what we're reading with Jays of Future Past and Blake Crouch's Recursion, dive into gaming with the latest GTA 6 release date news, 007 First Light, LEGO Batman, and Ghost of Yōtei on PlayStation Portal, and discuss what's tickling our geek, including excitement for X-Men '97 Season 2. George returns with another edition of George Hates Metal, reviewing tracks from Einherjer, C.O.F.F.I.N., and Blarf, before we wrap things up with What We Jammin', featuring new music from Green Carnation, Walg, Masterplan, Entropist, Paul McCartney, Micky Dolenz, Warning, Radiant Black, Gravety, Einherjer, Tarja, Lex Legion, Sleeping Pulse, Junius, and the Haserot album release party! Join us for another episode packed with geek culture, gaming, comics, and plenty of metal! Stay updated on all our geekery by visiting our website at metalgeekspodcast.com or metalgeeks.net. Have opinions or ideas? Share them with us via email at msrcast@gmail.com. Follow us on Twitter, @metalgeeks, and @msrcast. We're also on Instagram at @metalgeeks. Connect with us on Facebook/MetalGeeks and join the Metal Geeks Society group.  Subscribe to Metal Geeks Podcast on iTunes, and if you enjoy the show, please leave a 5-star review and give us a like. You can also catch us on Apple Music, Stitcher, Google Play, and Spotify—add us to your playlist! Tune in and remember: Keep it Metal, Keep it Geeky!

The Gents move down to Beaverton and hop into the 2026 animated movie Hoppers! :25 - Movies We've Seen (Toy Story 5, Supergirl, Jackass: Best and Last, Ladyhawke, Masters of the Universe, Wet Hot American Summer, Project Hail Mary) 25:25 - TV Shows We've Seen (Widow's Bay) 33:14 - Hoppers (2026) Get bonus episodes on our Patreon! Next episode: Minority Report (2002)

Kelsi and Trey discuss what they've been watching lately before diving deep into Toy Story 5 and the larger Toy Story franchise. We also talk about Hoppers as a Gen Z-coded environmental Pixar movie, Voicemails for Isabelle as one of the better traditional rom-coms (and Netflix originals) in years, and Nirvana the Band the Show the Movie as a chaotic miracle of DIY stunt filmmaking. Then we move into Toy Story 5, discussing why the franchise remains so emotionally elastic and what toys means for all audiences. We also get into Jessie's role as the emotional center of the new film, the movie's surprisingly thoughtful take on technology and digital childhood, Bonnie's cyberbullying storyline, and where Toy Story 5 ranks in the franchise.You can also sign up for our Patreon as a free member below to listen to free episodes with Toy Story (1995) now available for free members.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Become a member of The Extra Credits+ on Patreon ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok: The Extra Credits⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Extra Credits YouTube Channel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Letterboxd: ⁠The Extra Credits⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram: ⁠@theextracredits⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter: ⁠@theextracredits⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Send requests, questions, and thoughts to our email: extracreditspod@gmail.com

Lori Alan is known for her voice work throughout the industry as Diane Simmons in Family Guy, Pearl Krabs in Spongebob Squarepants, and so much more. She joins Agents of Fandom to discuss her role as Bonnie's Mom in Toy Story 5, how she has been lucky enough to play a series of moms across Pixar films, and her favorite Toy Story films and characters.Check out https://www.agentsoffandom.com for the latest TV and Movie reviews!

This week on the Oakley Podcast, Jeremy Kellett sits with Michael and Leslie Hopper, a husband-and-wife team at Oakley Trucking, to share their late-career transition into trucking and eventual success as owner-operators. They walk through Michael's move from multi-unit management and daycare operations and Leslie's 31-year restaurant career into trucking in their late 40s, starting with carriers like Werner, Tyson, U.S. Xpress, and FedEx Custom Critical before buying their own trucks and ultimately landing at Oakley. The Hoppers explain how teaming works day vs. night, how they became a top revenue truck pulling an end dump, why they're now moving to pneumatics for longer miles and higher pay, and how trucking allowed them to buy a home, build retirement, and support a large blended family. Their key message: it's never too late to reinvent yourself, owner-operator trucking can be a powerful path to financial freedom, and companies like Oakley can make the leap from company driver to business owner much more achievable. Key topics in today's conversation include: Welcoming the Hoppers to the Show (0:42) Leslie's Restaurant Career and First Talk of Trucking (6:09) Michael's Corporate and Daycare Management Background (9:17) How They Met, First Date, and Long-Distance Relationship (13:18) Convincing Leslie to Change Careers and Go to Truck School (16:44) Training Together at U.S. Xpress and Becoming a Team (18:23) Why Teaming Works for Them, Day vs Night Driving, and Resets (23:44) Taking the Leap to Owner Operator and Early Panic With No Loads (26:35) Running FedEx Custom Critical and Department of Defense Freight (29:59) Leaving FedEx, Short Hazmat Stop, and Choosing Oakley (33:17) Deadheading, Emergency Loads, and Being a Top Revenue Truck (36:27) Leslie's Social Media, Oakley Community, and Dump Video Followers (41:01) Buying Their House on Five Acres And Building Late-Life Retirement (44:02) Moving From End Dump to Pneumatic and Chasing Higher Rates (47:32) Why It's Not Too Late to Start Trucking at 50 and Thrive (52:39) Oakley Trucking is a family-owned and operated trucking company headquartered in North Little Rock, Arkansas. For more information, check out our show website: podcast.bruceoakley.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Bros dish on a recent theater experience, and some of the craziest documentaries of the year thus far. The Bros top 100 movie list: https://docs.google.com/spreadsheets/d/1nunoib8wXZQITbGItRFx_ZnvDUwY6VtgrkGmzuGKqJY/edit#gid=0Keep up with the Bros at: https://broforcesquad.com/https://www.youtube.com/channel/UCCJML5XTKJl2OzGW5HWrJhwhttps://itunes.apple.com/us/podcast/bro-force-squad/id1158546516?mt=2https://twitter.com/BroForceSquadStuff discussed in this episode includes Maternal Instinct, Hoppers, The Truth and Tragedy of Moira Wilson, Backrooms, Untold: The Shooting at Hawthorne Hill, Schindler's List, Toy Story, Blade II

Toy Story 5 just hit theaters, so it seemed like a good time to revisit our episode from last summer where we discussed YOUR picks for the greatest Pixar films of all time. Thousands of you voted, and we've got the results.If you want to hear about other Pixar films we loved, listen to our episodes about:Nature needs a little help in the inventive Pixar movie ‘Hoppers''Turning Red' paints teenage feelings in rich, vibrant colors'Inside Out 2' is a Pixar sequel worth celebratingConnect with Pop Culture Happy Hour:Letterboxd / FacebookOur weekly newsletterSupport Pop Culture Happy Hour+See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

This week, the New York Knicks win their first championship in over 5 decades, and fans are angry at Elmo, Mel recaps the marketplace she recently participated in, Andrew gives his thoughts on Hoppers, sadly, we lost two members of the Disney community, Porsche reveals their Toy Story collaborations and we wonder whether Disney/Pixar should be worried over the Netflix/Skydance partnership. Listen now at: https://www.podketeers.com/626 or watch this episode at: YouTube.com/Podketeers   Our most frequently requested links can be found at: https://www.podketeers.com/links/   Help us make a difference!  Teamboat Willie is the official charity team of the Podketeers Podcast. For more information on the charity that we're currently supporting, head to: http://www.teamboatwillie.com   Check out our series of Armchair Imagineering episodes here:  https://www.podketeers.com/armchair-imagineering/ --- Join the FGP Squad Family! Support for Podkeeters is provided by listeners and viewers like you! We like to call our supporters our Fairy Godparents (they call themselves the FGP Squad). You can find more info on how to become part of the FGP Squad family by going to:  https://www.podketeers.com/fgp --- We're on Discord! Join other members of our community and us on our Discord server! Use the invite link below to join us: https://discord.gg/gG8kJ2a ---

In Disney's Toy Story 5, Jessie, Woody, and Buzz are back to confront a new threat – screen time. When their kid Bonnie gets a new tablet called Lilypad, she forgets about the rest of her toys. But Jessie is determined to win Bonnie back, even after a series of events separates them. Toy Story 5 features a familiar and beloved voice cast with Tom Hanks, Tim Allen and Joan Cusack reprising their roles. Are you a fan of all things Pixar? Check out these episodes!Top 5 Pixar movies, ranked by listeners Nature needs a little help in the inventive Pixar movie ‘Hoppers' Connect with Pop Culture Happy Hour:Letterboxd / FacebookOur weekly newsletterSupport Pop Culture Happy Hour+See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

The Gents crash on a deserted island and assert their dominance and watch the dark comedy Send Help! :24 - Movies We've Seen (Backrooms, Star Wars: The Mandalorian and Grogu, Obsession, Disclosure Day, Keeper, Masters of the Universe, All The Marbles, The Sheep Detectives) 24:35 - TV Shows We've Seen (Jury Duty: Company Retreat, She-Ra and the Princess of Power, Margo Has Money Troubles, Widow's Bay) 34:34 - Send Help (2026) Get bonus episodes on our Patreon! Next episode: Hoppers (2026)

Jim Hill and Drew Taylor look at a packed week in animation, from the box office surprises around Disclosure Day and Obsession to the latest honors for Aardman's founders. They also preview Pixar's Gatto, salute Floyd Norman's upcoming Academy Honorary Award, and remember Tinker Bell reference model Margaret Kerry. The main event is Disney's live-action Lilo & Stitch 2, with Chris Sanders returning to write and direct a brand-new story for everyone's favorite blue alien experiment. NEWS • Disclosure Day opens big for Universal while Obsession continues its remarkable box office run. • Jim and Drew talk physical media, Hoppers on Disney+, and Best Buy's exit from DVDs and Blu-rays. • Aardman founders Peter Lord and David Sproxton receive knighthoods as the studio celebrates with a Royal Mint coin. • Pixar's Gatto gets a closer look, including its Venice setting, painterly style, and voice cast. • Floyd Norman is set for an Academy Honorary Award, while Jim and Drew remember Margaret Kerry's legacy as Tinker Bell's live-action reference model. FEATURE • Disney moves ahead with live-action Lilo & Stitch 2 for May 2028. • Chris Sanders returns to write and direct after voicing Stitch in both the original animated classic and the 2025 live-action remake. • Jim and Drew discuss how Angel, Stitch's pink counterpart from the animated series, may factor into the sequel. • The conversation connects Sanders' busy slate, from The Wild Robot to Lilo & Stitch, with Disney's long-running affection for Experiment 626. HOSTS • Jim Hill - X: @JimHillMedia, Instagram: @JimHillMedia • Drew Taylor - X: @DrewTailored, Instagram: @drewtailored FOLLOW • Facebook: @JimHillMediaNews • YouTube: @jimhillmedia • TikTok: @jimhillmedia • Patreon: https://www.patreon.com/jimhillmedia/ SUPPORT Support the show and access bonus episodes and additional content at https://www.patreon.com/jimhillmedia. PRODUCTION CREDITS Edited by Dave Grey Produced by Eric Hersey - https://strongmindedagency.com SPONSOR If a Disney or Universal trip is on your radar for 2026, UnlockedMagic.com helps you lock in great ticket prices without the stress. Head to UnlockedMagic.com to grab the best ticket deals and make that future trip just a little more magical. If you would like to sponsor a show on the Jim Hill Media Podcast Network, reach out today. https://www.jimhillmedia.com/sponsor/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Jim Hill and Drew Taylor look at a packed week in animation, from the box office surprises around Disclosure Day and Obsession to the latest honors for Aardman's founders. They also preview Pixar's Gatto, salute Floyd Norman's upcoming Academy Honorary Award, and remember Tinker Bell reference model Margaret Kerry. The main event is Disney's live-action Lilo & Stitch 2, with Chris Sanders returning to write and direct a brand-new story for everyone's favorite blue alien experiment. NEWS • Disclosure Day opens big for Universal while Obsession continues its remarkable box office run. • Jim and Drew talk physical media, Hoppers on Disney+, and Best Buy's exit from DVDs and Blu-rays. • Aardman founders Peter Lord and David Sproxton receive knighthoods as the studio celebrates with a Royal Mint coin. • Pixar's Gatto gets a closer look, including its Venice setting, painterly style, and voice cast. • Floyd Norman is set for an Academy Honorary Award, while Jim and Drew remember Margaret Kerry's legacy as Tinker Bell's live-action reference model. FEATURE • Disney moves ahead with live-action Lilo & Stitch 2 for May 2028. • Chris Sanders returns to write and direct after voicing Stitch in both the original animated classic and the 2025 live-action remake. • Jim and Drew discuss how Angel, Stitch's pink counterpart from the animated series, may factor into the sequel. • The conversation connects Sanders' busy slate, from The Wild Robot to Lilo & Stitch, with Disney's long-running affection for Experiment 626. HOSTS • Jim Hill - X: @JimHillMedia, Instagram: @JimHillMedia • Drew Taylor - X: @DrewTailored, Instagram: @drewtailored FOLLOW • Facebook: @JimHillMediaNews • YouTube: @jimhillmedia • TikTok: @jimhillmedia • Patreon: https://www.patreon.com/jimhillmedia/ SUPPORT Support the show and access bonus episodes and additional content at https://www.patreon.com/jimhillmedia. PRODUCTION CREDITS Edited by Dave Grey Produced by Eric Hersey - https://strongmindedagency.com SPONSOR If a Disney or Universal trip is on your radar for 2026, UnlockedMagic.com helps you lock in great ticket prices without the stress. Head to UnlockedMagic.com to grab the best ticket deals and make that future trip just a little more magical. If you would like to sponsor a show on the Jim Hill Media Podcast Network, reach out today. https://www.jimhillmedia.com/sponsor/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Slightly younger versions of Earth's mightiest heroes team up because the one hero's brother, who would later also become one of Earth's mightiest heroes and then ... fix time? Or break it? ... anyway, he's got these space worms, and ... you know what, it's Avengers, you know what it's about. Listen to us revisit the first great team-up of the MCU!Plus, we'll talk about DISCLOSURE DAY, continue the ongoing evaluation of BACKROOMS and OBSESSION, drop in on SCARY MOVIE, HOPPERS, SWAPPED and more!

Support Dis After Dark on Patreon!Love the show and want even more magic in your ears? Join our Patreon community for just $2 a month and unlock exclusive perks: Weekly Mini-Episodes: Get a bite-sized, 10-to-15-minute bonus episode just like this one dropped straight into your feed every single week. Early Access: Skip the queue and listen to the main show before anyone else.How to JoinReady to support the chaos? Head over to ttps://www.patreon.com › afterdarkpodcastnetwork and sign up today. Your support helps us keep the lights on and the drinks flowing!

Earlier this month, Disney released Pixar's latest animated movie, "Hoppers" on Disney+ around the world.  The movie  The wildly imaginative animated adventure follows animal lover Mabel as she uses a groundbreaking technology to "hop" her consciousness into a lifelike robotic beaver, uncovering a hidden animal world shaped by its own rules. Alongside charismatic beaver King George and an unforgettable community of local wildlife, Mabel embarks on an action-packed mission that invites audiences to experience nature like never before. Recently, I got to speak with the film's director, Daniel Chong and producer, Nicole Paradis Grindle, where we spoke about the movie's release on Disney+ and what they are hoping for it.  You can find "Hoppers" available to stream now on Disney+. Did you check out "Hoppers" on Disney+? Let me know on social media!

This is our NEW RELEASE review podcast, ONE HOT TAKE.In an era where even very good Pixar films can feel overlooked, Hoppers deserves better. It's funny, warm, inventive and packed with heart. Synopsis:A 19-year-old animal lover uses technology to place her consciousness into a robotic beaver to uncover mysteries in the animal world beyond her imagination.DREW TAYLOR is a reporter for The Wrap. He has written for The New York Times, Vulture, Vanity Fair, The Playlist, and Collider. He also wrote The Art of Onward, which gives an inside look at the making of the 2020 Pixar film. The book is available to purchase here. He also co-hosts a weekly podcast about animation called Fine Tooning, which is available on all podcast platforms.Follow Drew:Twitter: @drewtailoredInstagram: @drewtailoredPodcast: Light the FuseOne Heat Minute ProductionsWEBSITE: oneheatminute.comTWITTER: @OneBlakeMinute & @OHMPodsMERCH: https://www.teepublic.com/en-au/stores/one-heat-minute-productionsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Summer often exposes a quiet reality in many churches: some members begin shopping for a new congregation. In this episode, Josh and Sam address why this happens and how leaders should respond. If you've faced it, you are not alone. Every long-tenured pastor has. The post The Reality of Church Hoppers: A Pastor’s Honest Perspective appeared first on Church Answers.

This episode is sponsored by Shopify - Go to http://shopify.com/SCB to sign up for your $1-per-month trial period.  A single chalkboard scene in Hoppers appears to reveal the origins of some of Pixar's most important technologies, including talking dog collars, WALL-E, MO, scream canisters, and possibly even the secret behind Cars itself. Today we're breaking down every clue, every Easter egg, and every connection to discover how Hoppers fits into the Pixar Theory timeline. #pixar #Hoppers #SuperCarlinBrothers

Find support in therapy! Sign up and get 10% off at https://BetterHelp.com/SUPER. Is Mabel from Hoppers secretly the mother of Russel from Up? Today J dives into the ever-expanding Pixar Universe find out!  From their love of the outdoors, door-to-door enthusiasm, and relationship with the elderly, these 2 have a lot in common.  They may have both encountered the villainous Charles Muntz as well… #pixar #Hoppers #SuperCarlinBrothers

Jeremy Clarkson bought a farm! He needs a Lamborghini tractor. ‘Hoppers' is now on streaming. Scott Pelley has been fired from CBS. This director has no problem speaking ill of the dead when it's Val Kilmer. Kristin Cavallari talks about her weird run-in with an A-lister. Want to see if any of us are outfit repeaters? Watch Sarah and Vinnie on YouTube! Fitting back into your skinny pants? You could sell your old stuff. You'd be amazed at how fast you adapt. It's Global Running Day! A vandalized Ronald McDonald is selling big on eBay.

Hour 1: Come play Bridge The Gap with us! SarahandVinnie@audacy.com. Peabo Bryson, the Grammy-winning soul singer, has passed away at 75. It reminds Matty of his days making emotional mixtapes. It's a good day for a music tangent. Owain Rhys Davies passed away suddenly. An attempted murder reminds Vinnie of the gang's new favorite show. Would you go to a party celebrating a boob job reveal? A story about a guy with a leaf blower. Hour 2: Jeremy Clarkson bought a farm! He needs a lamborghini tractor. ‘Hoppers' is now on streaming. Scott Pelley has been fired from CBS. This director has no problem speaking ill of the dead when it's Val Kilmer. Kristin Cavallari talks about her weird run-in with an A-lister. Want to see if any of us are outfit repeaters? Watch Sarah and Vinnie on YouTube! Fitting back into your skinny pants? You could sell your old stuff. You'd be amazed at how fast you adapt. It's Global Running Day! A vandalized Ronald McDonald is selling big on eBay. Hour 3: It's time to Bridge The Gap! Jason, our favorite wine bar owner, is back for GenX. He's taking on Reverend Josh for the Zillennials. This might be our toughest battle yet. Vinnie wants the Sharks to get better so he can go to games. There's a new grocery store drama. Ew! Barry's been everywhere, and he says NEVER leave the house without these 5 things. Plus, try this weird travel hack for cheap food and a new experience! Hour 4: Reality star Spencer Pratt moves forward in the mayoral race. Vinnie asks a hypothetical question that breaks Bob: Taylor Swift and your mom are getting married on the same day, and you're invited to both. What are you attending? A 21-year old got the surprise of a lifetime at a live performance of La La Land when the conductor asked if anyone could sight read the piano. 4th of July is just around the corner. Consider these new favorite snacks for your picnic. Plus, How Old Is That Guy?

Drew Taylor and Jim Hill dive into one of the busiest weeks in animation and entertainment news, from early reactions to Pixar's Toy Story 5 and its surprisingly Jessie-focused story to major developments involving Rick & Morty, X-Men '97, Jorge Gutierrez, and Netflix animation. Along the way, they unpack box office surprises, discuss the end of Disney Twenty-Three magazine, and explore what the future may hold for some of animation's biggest franchises. This episode also includes Drew's thoughts after seeing the first 45 minutes of Toy Story 5 and a look at why Pixar may have another blockbuster on its hands. NEWS • Early box office projections suggest Toy Story 5 could set a new franchise opening weekend record. • Pixar's Hoppers arrives on home video as the film continues its strong theatrical run. • Universal launches major Minions & Monsters promotion ahead of the film's July debut. • D23 members react to the discontinuation of Disney Twenty-Three magazine. • Rick & Morty's President Curtis is getting his own Adult Swim spin-off series. FEATURE • Drew shares impressions after seeing the first 45 minutes of Toy Story 5. • New details reveal a deeper continuation of Jessie's story from Toy Story 2. • The film revisits one of the franchise's most emotional storylines in an unexpected way. • Could Toy Story 5 set the stage for even more adventures beyond the fifth film? HOSTS • Jim Hill - X: @JimHillMedia | Instagram: @JimHillMedia | jimhillmedia.com • Drew Taylor - X: @DrewTailored | Instagram: @drewtailored | drewtaylor.work FOLLOW • Facebook: @JimHillMediaNews • YouTube: @jimhillmedia • TikTok: @jimhillmedia • Patreon: @jimhillmedia SUPPORT Support the show and access bonus episodes and additional content at Patreon.com/JimHillMedia. PRODUCTION CREDITS Edited by Dave Grey Produced by Eric Hersey - StrongMindedAgency.com SPONSOR This episode is sponsored by UnlockedMagic.com, the go-to source for great deals on Disney and Universal theme park tickets. If a 2026 theme park vacation is on your radar, their team can help you save money while making the planning process easier. If you would like to sponsor a show on the Jim Hill Media Podcast Network, reach out today. Jim Hill Media Sponsorship Information Learn more about your ad choices. Visit megaphone.fm/adchoices

Preview for Later Today: Doug Messier describes NASA's innovative mission using robotic hoppers to survey the lunar South Pole, seeking water and potential sites for a future moon base through high-resolution imaging in the moon's environment.MAY 1952

It's a lot like Avatar. This week Nando, DJ, and Diggins jack in to watch the Pixar movie about nature with somehow the most and least to say, Hoppers. They nitpick the beavers, the humans, and of course the ants.   Recommendations DJ - Oz the Mentalist, Debunked (video), R.I.P. Comedy Roasts (article) Diggins - Widow's Bay (series), The Sheep Detectives (movie) Nando - Exit 8 (movie), Dead as Disco (video game), Widow's Bay (series), Game Changer (series)   Plugs Mostly Nitpicking on Bluesky The Nando v Movies Discord Roses and Rejections Diggins' Substack - A Little Perspective All of Nando's Links   Mostly Nitpicking theme by Nick Porcaro Logo by Michelle Chapman

Rick Fisher explores the militarization of the Moon, citing Chinese interest in lunar radar and "moon hoppers" for resource discovery. He describes a technological competition with the U.S. involving nuclear power plants, lasers, and satellite constellations intended for both peaceful research and potential offensive or defensive combat. (12/16)1945 TRUMAN ON WILHELMSTRASSE TOUR BERLIN

Beavers are having a moment, thanks to the new Pixar movie “Hoppers.” Amid some body-swapping shenanigans, the film is about humans coexisting with wildlife—particularly oversized rodents capable of reworking landscapes in profound ways.  The beaver science consultant on “Hoppers,” Emily Fairfax, joins Flora to talk about beavers' brilliant, chaotic landscape engineering, and how the creatures show up in the movie. Then, reporter Zac Ziegler walks Flora through a successful beaver-centric engineering project in Oregon.  Guests:  Emily Fairfax is an assistant professor of geography at the University of Minnesota. She was a science consultant for the Pixar movie “Hoppers.” Zac Ziegler is a reporter at KLCC in Eugene, Oregon. Other episodes you may enjoy: How The Humble Beaver Shaped A Continent Beavers Build Ecosystems Of Resilience Transcripts for each episode are available within 1-3 days at sciencefriday.com. Want SciFri gear? Check out our new shop! Subscribe to this podcast. Follow our show on Instagram, TikTok, Facebook, and Bluesky @scifri and sign up for our newsletters. Got a science question that's keeping you up at night? Call us: 877-4-SCIFRI