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The crew are joined by Heidi Watney ahead of tonight's Red Sox game. How important the series against the Yankees is this weekend and more!

Recorded 2026-08-28 12:00:09

Recorded 2026-08-21 12:05:22

A California lifeguard and the boy he rescued off the coast of Santa Cruz last month were reunited at the White House yesterday as the president honored both as heroes. Reporter: Angela Corral, The California Report Governor Gavin Newsom has set a goal to add 2.5 million new units to California's housing stock by 2030. Reporter: Ben Christopher, CalMatters A state bill heading for a final vote as early as this week would permanently extend a fund to help oil industry workers affected by California's transition away from fossil fuels. Reporter: Julie Small, KQED A federal judge in Oakland is weighing whether a Trump administration plan to replace a network of seasoned legal aid organizations with new groups is an adequate way to represent unaccompanied children in immigration court. Reporter: Tyche Hendricks, KQED Learn more about your ad choices. Visit megaphone.fm/adchoices

Recorded 2026-08-14 11:59:37

Which Milwaukee restaurant was named in the top 10 of Mediterranean restaurants in America?

Official Emailtalkinwithtopher@gmail.comCryptid and Kin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://cryptidandkin.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠(instagram) https://www.instagram.com/cryptidandkin/?hl=en=(YouTube) www.youtube.com/@CryptidAndKinTopher's The Mail Box Guys⁠⁠⁠⁠(facebook) https://www.facebook.com/share/1C6cbtm8eA/⁠⁠⁠⁠(instagram) https://www.instagram.com/the_mailbox_guys/?hl=enSocial Media(linktr.ee) ⁠⁠https://linktr.ee/talkinwithtopher⁠⁠(instagram) ⁠⁠https://www.instagram.com/talkinwithtopher/?hl=en⁠⁠(twitter) ⁠⁠https://twitter.com/_conderman⁠⁠(snap chat) ⁠⁠https://www.snapchat.com/add/cconderman?share_id=HiV14moKPns&locale=en-US⁠⁠(tik tok) ⁠⁠https://www.tiktok.com/@talkinwithtopher?lang=en⁠⁠(Facebook) ⁠⁠https://www.facebook.com/christopher.conderman⁠⁠Time Stamps(00:00:00) Start(00:01:37) my new morning prayer(00:07:05) Olaf enjoys a higher power(00:13:58) Vampires in 1857 Bible(00:21:19) The middle finger represents Either(00:32:57) Flock is the fall guy(00:45:14) Jared Kushner is the anti Christ(00:50:58) King of the Jews(00:58:22) German Human Regenerator(01:03:06) Could Nukes be Faked(01:12:25) Islam's lying strategy(01:16:33) Michigan is allowing animal sacrifice(01:20:28) 5G was the dress rehearsal and 6G is the kill switch(01:24:32) reset your phone before the authorities get a hold of it(01:27:52) don't let Epstein files get you stuck in a world of anger(01:33:11) Pilots confirm earth is Flat(01:38:00) William Shatner believes Earth is Flat(01:41:50) WAZE is owned by Israel(01:46:22) Earthquake in Japan explodes mall(01:50:22) this is mental illness(01:57:44) Tucker agrees that data centers are bad for our future(02:02:27) STOP celebrating you're birthdayEpisode Linkshttps://www.facebook.com/share/r/1Dm1XJgisF/https://www.facebook.com/share/v/19GFyGJmRD/https://www.facebook.com/share/v/19HPQRKFnj/https://www.facebook.com/share/r/1ALQFF9XHT/https://www.instagram.com/reel/DZ7MGFOBGrw/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==https://www.facebook.com/share/v/17vbhB13Bt/https://www.facebook.com/share/v/1e8WdFphUm/https://www.instagram.com/reel/DZFu3z0iZLl/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==https://www.facebook.com/share/v/1BzdLEnx78/https://www.instagram.com/reel/DZ5m03wt77M/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==https://www.instagram.com/reel/DbJXKWrnOc0/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==https://www.facebook.com/share/r/1BqXUvw3EJ/https://www.instagram.com/reel/Da2tPlbBywr/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==https://www.facebook.com/share/v/1Hs6nUcvay/https://www.instagram.com/reel/DbDTvKFAUQX/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==https://www.facebook.com/share/r/17eS6yeVo7/https://www.instagram.com/reel/DamRYotNE-s/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==https://www.facebook.com/share/r/1Bq3aDabfd/https://www.bbc.com/news/videos/c9d8ggpdgqlohttps://www.instagram.com/reel/DYXu9rODUCu/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==https://x.com/bigwhitelb/status/2081470414107332930?s=20https://www.instagram.com/reel/DaLKVECoDXn/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

Every year, hundreds of athletes from across the UK and Ireland come together to celebrate the life-changing impact of organ and stem cell donation at the British Transplant Games. Among those representing Ireland this year is a Limerick man whose journey from battling cancer to competing on the track is nothing short of extraordinary. Limerick native and Transplant Sport Ireland athlete, Trevor Lynch, joins the programme.Image via Getty. Hosted on Acast. See acast.com/privacy for more information.

✌️ Standing fifty meters tall, the Arc de Triomphe has dominated the Champs-Élysées for nearly two centuries. Originally erected to celebrate Napoleon's military victories, the Arch has seen its symbolism evolve over the decades to now embody peace. But do all the sculptures adorning it truly convey this message of peace? Join this week's conversation on https://www.frenchconversationgroup.comRelated episodes: la Marseillaise (https://youtu.be/m4CNOqtRpn0), la tour Eiffel (https://youtu.be/51x_-ZB4SJI), Napoléon (https://youtu.be/8YQ2XI8vBnA)

In today's episode, we welcomed Douglas W. Sborov, MD, MS, to discuss the significance of the July 2026 FDA approval of isatuximab-irfc (Sarclisa Escena) for subcutaneous injection for multiple myeloma indications. Dr Sborov is a tenured professor of medicine in the Department of Internal Medicine in the Division of Hematology and Hematologic Malignancies and an adjunct associate professor in the Departments of Molecular Pharmaceutics and Biomedical Engineering at the University of Utah Huntsman Cancer Institute (HCI) in Salt Lake City, as well as director of the HCI Hematology Disease Center and Plasma Cell Dyscrasias (PCD) Program, co-leader of the Hematologic Malignancies Clinical Trials Research Group, and member of the HCI Experimental Therapeutics Program and International Myeloma Working Group (IMWG).As part of the July 10, 2026, approval, subcutaneous isatuximab is indicated for use: in combination with pomalidomide (Pomalyst) and dexamethasone for the treatment of adult patients with multiple myeloma who have received at least 1 prior line of therapy, including lenalidomide (Revlimid) and a proteasome inhibitor in combination with carfilzomib (Kyprolis) and dexamethasone for the treatment of adult patients with relapsed or refractory multiple myeloma who have received 1 to 3 prior lines of therapy in combination with bortezomib (Velcade), lenalidomide, and dexamethasone for the treatment of adult patients with newly diagnosed multiple myeloma who are not eligible for an autologous stem cell transplant In our exclusive interview, Dr Sborov outlined how the subcutaneous formulation of isatuximab and the ability to administer the agent via an on-body delivery system could affect patient quality of life. He also detailed key findings from the phase 3 IRAKLIA trial (NCT05405166) that supported the approval and explained how the use of isatuximab may shift in clinical practice following the subcutaneous approval.

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

Liz Peek. Peek critiques New York politician Zohran Mamdani for failing to define the working class, arguing the DSAprimarily represents white, college-educated liberals. She also discusses Kevin Warsh's influence and the likelihood of the Federal Reserve maintaining interest rates despite inflationary pressures and global instability in the Middle East. (2)1900

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In this episode of Wake Up, Look Up, Pastor Zach reflects on Vice President JD Vance's argument for a more Christian America and explores how faith has shaped the nation's values throughout history. He discusses the challenges of describing America as a "Christian nation" while emphasizing the importance of recognizing the God-given dignity and value of every person. Pastor Zach encourages believers to thoughtfully engage cultural and political conversations through the lens of biblical truth, love, and shared moral principles.Have an article you'd like Pastor Zach to discuss? Email us at wakeup@ccchapel.com!

Jonathan SAYEH— The naval blockade on Iran represents the "maximum pressure" campaign, effectively dropping Iranian oil exports to zero; this economic strangulation poses an existential threat to the regime, which struggles to pay its personnel, and recent direct missile exchanges suggest diplomatic understandings between the U.S. and Iran have collapsed. (14)

Indiana University will represent Team USA in the FISU games in Lima, Peru later this summer. Team USA opens play on July 15th with an exhibition game with Team Canada at Assembly Hall. Hoosier Huddle looks at what fans should expect and what they are looking at with the Hoosiers despite a major piece not playing. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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The show OPEN... America at 250... Ella and Gretchen... and deaths!

Send us Fan MailWe look at the Obama (checks notes) Center and revisit its namesake's legacy.  

MJ Sutton, an Irish Hyrox athlete and coach, discusses his journey to representing Ireland at the Hyrox World Championships.

Pippa Hudson speaks to Bella Strydom, a 16-year old learner at Fish Hoek High School, who has been selected to represent South Africa at 3 international championships in the coming year. Lunch with Pippa Hudson is CapeTalk’s mid-afternoon show. This 2-hour respite from hard news encourages the audience to take the time to explore, taste, read and reflect. The show - presented by former journalist, baker and water sports enthusiast Pippa Hudson - is unashamedly lifestyle driven. Popular features include a daily profile interview #OnTheCouch at 1:10pm. Consumer issues are in the spotlight every Wednesday while the team also unpacks all things related to health, wealth & the environment. Thank you for listening to a podcast from Lunch with Pippa Hudson Listen live on Primedia+ weekdays between 13:00 and 15:00 (SA Time) to Lunch with Pippa Hudson broadcast on CapeTalk https://buff.ly/NnFM3Nk For more from the show go to https://buff.ly/MdSlWEs or find all the catch-up podcasts here https://buff.ly/fDJWe69 Subscribe to the CapeTalk Daily and Weekly Newsletters https://buff.ly/sbvVZD5 Follow us on social media: CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567 See omnystudio.com/listener for privacy information.

Professor John Yoo critiques the "Thucydides Trap" analogy used by Xi Jinping to describe US-China tensions. He argues China resembles militaristic Sparta, while the US represents the democratic, commercial Athens. Yoo warns that China's declining population and stolen technology make it a declining power compared to the booming US. (3)SPARTA

Kevin, Grayson, and The Chief are here on World Cup Eve to give you a final Miles Robinson send off before he goes on to represent FC Cincinnati on the United States Men's National Team and World Cup. Then In Part Two it's a return to the Film Room with the BBC Mini Series “Dear England”, the dramatic re-telling of the Southgate-Era English National Team. Is it more Ted Lasso or United Passions? Either way, it's pretty good!    Timestamps: (3:34) - Pedestrians vs Cars (19:35) - Miles Robinson (43:30) - Dear England in the Film Room   Links:  Looking for an MLS podcast? Check out The World's GAM Visit our friends at Streetside Brewery Our friends at E&L Roofing have all your Gutter, siding, and roofing needs covered!  Check Out Oliver's Desserts and use code CHIEFSWEETS for 20% off! Check out The Post at www.thepostcincy.com Music by Jim Trace and the Makers Join the Discord Server and jump into the conversation Follow us on BlueSky, Twitter,  Facebook, Instagram, and YouTube Support us on Patreon https://www.patreon.com/ThePostCincy

Chase Iron Eyes discusses the importance of vision quests throughout history to many different peoples and cultures over thousands of years, Donald Trump’s appearance at the NBA Finals and what it represents to common New Yorkers, Pete Hegseth’s racist screed at a D-Day memorial in Europe, and the fascist takeover of the United States. #NativeRights… The post Native American Patriot Talk – Ep. 9: What Trump Represents To New York And More Broadly first appeared on AM 950.

Nate Slack and Mitch Harper bring you the latest BYU news and notes, including updates on Cougar basketball and football recruiting.

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B.C. Conservatives lurch right: who speaks for the middle? (0:55) Brad West, Mayor of Port Coquitlam Has B.C.'s youth been shut out of the workforce? (11:47) Jairo Yunis, Director of Policy at the Business Council of British Columbia Our Energy Future: Are electric vehicles in retreat? (21:22) Jeremy Cato, Automotive Journalist at CatoCarGuy.com Vancouver Park Board considers jetski ban (38:03) Tom Digby, Vancouver Park Board Commissioner and Board Chair Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to Episode 167 of the Garlic Fries and Baseball Guys Podcast. Sam Lubman and Steven Rissotto react to the lowest moment of the season (so far) from Wednesdays game when Willy Adames got thrown at at home courtesy of an aggressive Hector Borg send. Plus, what the hell do the Giants do with Trevor McDonald and a new trend in this team that is concerning.

Judy Dempsey discusses how the AfD has become Germany's leading political party by capitalizing on public anger over housing shortages and the government's handling of the wars in Iran and Ukraine. The party represents a growing threat to the established political order in Europe. (16)1948

The new CD by Flutes and Low represents is the Result of the songs being crafted one at a time by the composers, Cambria Haen and Ben Pichler. When the two got together with Phil Nusbaum, Cambria first told why the name, Flutes and Low, was chosen for a group that does not have a flautist. Then, the talked turned to musical inspiration.

PREVIEW for Later Today: Rick Fisher examines China's moon hopper project, a dual-use device for the Chang'e-7mission. While ostensibly searching for water ice, the unmanned vehicle represents a potential shift toward surveillance and artillery capabilities in space.

If you don't mind the imports the land you love might be unrecognizable in just a few years time. You want stupid enemies in life. Learning valuable lessons in school. Follow The Jesse Kelly Show on YouTube: https://www.youtube.com/@TheJesseKellyShowSee omnystudio.com/listener for privacy information.

Volodymyr Zelenskyy and the Evolution of Ukrainian IdentityVolodymyr Zelenskyy, a Russian-speaking Jewish entertainer and businessman, represents a shift toward a civic Ukrainian identity rather than an ethnic one. His 2019 election victory was rooted in a desire for an outsider to fix the failing political system and find a path to peace with Moscow. Because of his background, Zelenskyy initially believed he could negotiate directly with Putin. His presidency highlights that being Ukrainian is now defined by a commitment to the state rather than language or religion, directly contradicting Putin's "one people" myth. Guest: Professor Eugene Finkel. (6/8)1890

We reject the current MN state flag because it represents the failure of this administration. An inspector tells MN house subcommittee that he was told not to pursue fraud cases. GL started on this day back in 1993. Johnny Heidt with guitar news. Heard On The Show:Suspect fatally shot during gunfight with Richfield police, officer hospitalized‘Their only goal was to destroy the unit': Former Minnesota fraud investigator speaks outKing Charles III and Queen Camilla visit 9/11 memorial in New YorkSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Send us Fan MailRent up, groceries up, wages stretched thin and somehow the “American Dream” keeps moving farther away. We sat down with Congressman Steven Horsford to talk about what that pressure looks like on the ground in Las Vegas and across Nevada's 4th District, and what government can actually do when working families feel boxed in. He shares his own path from being raised here by an immigrant mother to leading major job training programs, then taking that fight for opportunity to Congress. We get specific about housing affordability in Southern Nevada: how institutional investors and corporate hedge funds can buy homes with cash, outbid first-time buyers and veterans, convert neighborhoods into high-rent portfolios, and leave communities paying the price. Horsford breaks down his role as a representative, including constituent services many people never realize exist, like helping with VA problems, cutting through federal bureaucracy, and pointing small business owners toward SBA resources and capital. The conversation also goes bigger than policy. We talk about the sacrifice and scrutiny of public office, why he calls this a moral moment, what accountability should look like even for the powerful, and how the legacy of leaders like Jesse Jackson connects voting rights to economic justice today. If you care about Las Vegas, Nevada politics, small business, or the housing market, this one is for you. Subscribe, share this with a friend, and leave a review with the one issue you want leaders to fix first.

Send us Fan MailDownload study notes for this chapter.Download study notes for this entire book.**********Scriptures taken from the Holy Bible, New International Version ®, NIV ® Copyright © 1973, 1978, 1984, 2011 by Biblica, Inc. Used with permission. All rights reserved worldwide.The “NIV”, “New International Version”, “Biblica”, “International Bible Society” and the Biblica Logo are trademarks registered in the United States Patent and Trademark Office by Biblica, Inc.  Used with permission.BIBLICA, THE INTERNATIONAL BIBLE SOCIETY, provides God's Word to people through Bible translation & Bible publishing, and Bible engagement in Africa, Asia Pacific, Europe, Latin America, the Middle East, and North America. Through its worldwide reach, Biblica engages people with God's Word so that their lives are transformed through a relationship with Jesus Christ.Support the show

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Paris Fletcher. Entrepreneur, content creator, and founder of Full Bloom, a wellness brand focused on self-discovery and personal growth.

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Paris Fletcher Entrepreneur, content creator, and founder of Full Bloom, a wellness brand focused on self-discovery and personal growth.

During the 5pm hour of today's show Chuck & Chernoff talked about the Braves, the A's, the Hawks and more before getting listeners answers to today's Worst Idea question. See omnystudio.com/listener for privacy information.

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1. Eric Cline discusses the Late Bronze Age through the lens of the Uluburun shipwreck, which represents the era's globalized trade network. The ship's cargo, including copper from Cyprus and tin from Afghanistan, highlights the interconnectedness of civilizations like the Egyptians, Hittites, and Mycenaeans. Cline explains that the collapse around 1177 BC was not caused by a single event but a "perfect storm" of factors, including drought, famine, earthquakes, and the Sea Peoples' migrations. This catastrophic sequence occurred so rapidly that societies lacked the time to recover, leading to a systemic failure of the ancient world's trade routes. (1)

In this groundbreaking episode of the Tick Boot Camp Podcast, we interview Dr. Jayakumar Rajadas, a Stanford Medicine researcher who has discovered multiple breakthrough therapeutic candidates for Lyme disease, Babesia, and Bartonella. His work includes the discovery of Disulfiram's effectiveness against Lyme and Babesia, Azlocillin's potent activity against Lyme and Bartonella, and advanced targeted drug-delivery systems designed to preserve the gut microbiome. Dr. Jay's research has been featured in TIME Magazine (Azlocillin) and Forbes (Disulfiram), and connects deeply with the work of leading Lyme researchers, including Dr. Monica Embers (Tulane), Dr. Kim Lewis (Northeastern), Dr. Kenneth Liegner, and Dr. Brian Fallon (Columbia University). This interview delivers hope, science, and unprecedented detail on what may become the next generation of Lyme disease treatments. Key Topics Covered 1. How the Stanford Tick Initiative Sparked a New Era of Drug Discovery In 2012, Stanford launched a major initiative in response to community demand for better Lyme treatments. Dr. Rajadas was selected to lead drug development, focusing specifically on persistent/chronic Lyme disease, where few researchers were working. 2. Understanding Borrelia: Active vs. Stationary Forms & Why Chronic Lyme Persists Dr. J explains the three key survival modes of Borrelia burgdorferi: Active Phase The bacteria are replicating and metabolically active. Easier to kill with standard antibiotics. Stationary Phase Bacteria reach population limits and slow down growth. Represents early persistence mechanisms. Persister Forms Triggered by stressors like antibiotics (e.g., doxycycline). Bacteria fold into round bodies, spiral forms, or compact “cement-like” protective balls. These forms: Shut down metabolic pathways Resist penetration Survive antibiotic exposure Why Doxycycline Can Fail Doxycycline can induce persisters, causing Borrelia to form impenetrable protective shells rather than die. This is why many patients initially feel better, then relapse. 3. Disulfiram (Antabuse): Lyme + Babesia Breakthrough Featured in Forbes One of the biggest scientific shocks of the last decade: Discovery Through Stanford's high-throughput screening of FDA-approved drugs, Disulfiram emerged as a top hit. Clears Borrelia (including persistent forms) Clears Babesia — a major advantage over standard antibiotics Does NOT harm the gut microbiome Is already FDA-approved and widely used for alcohol aversion therapy Highly potent but requires careful dosing due to side effects in inflamed patients. Why Some Patients Improve, and Others Suffer Chronic Lyme patients already have heightened inflammation. Disulfiram is a powerful molecule whose polymorphic forms behave differently in different people. His lab developed: Less toxic formulations Buccal & sublingual delivery systems Rectal delivery options These may reduce neuropsychiatric side effects reported by some patients. Clinical Connections Dr. Kenneth Liegner pioneered clinical use and published cases Dr. Brian Fallon conducted NIH-listed clinical trials. Many clinicians now use Liegner's protocols. Real-world example: Matt shares the story of Brooke Stoddard (Generation Lyme), who regained his life after Disulfiram treatment under Dr. Liegner. 4. Azlocillin: The Antibiotic That TIME Magazine Called a Gamechanger If Disulfiram is the Lyme and Babesia weapon, Azlocillin may be the frontline tool for Lyme and Bartonella. Why Azlocillin Is Revolutionary Eradicates both active and persister forms of Borrelia. Destroys doxycycline-induced “cement ball” persisters by drilling into their vulnerable cell-wall synthesis pathways. Proven effective against Bartonella when paired with azithromycin, based on research by Dr. Monica Embers (Tulane) . The Cell-Wall Vulnerability Breakthrough Persisters STILL must maintain minimal cell-wall synthesis to survive. Azlocillin exploits this tiny vulnerability: It penetrates the protective sphere Breaks the “cement wall” Forces the bacteria out of hibernation Kills them rapidly This discovery is one of the biggest scientific leaps in Lyme research in a decade. The Delivery System That Protects the Gut Microbiome Azlocillin is extremely hydrophilic, making absorption difficult.Dr. Jay fixed this by creating: A magnesium-lipid nanoparticle formulation Designed to release in the upper intestine Avoiding the colon (where most microbiome lives) This allows: High bloodstream absorption Minimal microbiome damage Oral availability of a drug previously only available via IV Why Azlocillin May Be Better Than Disulfiram Hits Borrelia + Bartonella Stronger anti-inflammatory effects No polymorphism issues Fewer side effects Potent against persisters A company is preparing to bring his oral formulation to clinical trials by next year. 5. Loratadine (Claritin): The First Clue from 2012 Before Disulfiram and Azlocillin, Dr. Jay's lab identified Loratadine (Claritin) as a manganese transporter inhibitor of Borrelia. Why it mattered: Borrelia uniquely relies on manganese, not iron. Blocking manganese uptake may weaken the bacteria. The discovery went viral, with many patients reporting improvement even at OTC doses—though the binding affinity was weak. This project introduced the concept of drug repurposing for Lyme to the scientific community. 6. Melittin (Bee Venom) — The Micro-Needle Patch Alternative Bee venom therapy is widely used in the Lyme community, but risks stings and allergic reactions. Dr. J is developing: Melittin micro-needle patches Delivering the active peptide without stinging Using dissolvable, painless needles A safe, controlled, pharmaceutical-grade delivery approach This could modernize bee venom therapy and make it more accessible. 7. Mechanism of Brain Fog & Fatigue in Lyme: A Major Breakthrough Dr. Jay's lab published a neuroscience paper demonstrating: Outer Surface Protein (Osp) Nanoparticles Borrelia sheds lipid-coated outer membrane particles. These form stable nano-vesicles that: Enter the bloodstream Cross into the brain Cause mitochondrial dysfunction Reduce ATP production Result: Brain Fog, Fatigue, Cognitive Dysfunction This explains why neurological Lyme can persist even after bacterial levels drop. This work ties strongly to ongoing research at Columbia University under Dr. Brian Fallon. 8. Collaborations With World Leaders in Lyme Research Dr. J's research intersects with: Dr. Kim Lewis (Northeastern University) Reproduced and validated Disulfiram findings publicly. Helped launch interest in persister-killing therapies. Dr. Monica Embers (Tulane University) Demonstrated Azlocillin + Azithromycin effectiveness against Bartonella. One of the world's foremost experts in persistent infection models. Dr. Kenneth Liegner Early clinical pioneer of Disulfiram therapy. Published stunning recovery cases. Dr. Brian A. Fallon (Columbia University) Leading psychiatrist specializing in post-treatment Lyme. Conducted planned Disulfiram clinical trials. These collaborations form a powerful network accelerating treatment development. 9. New Anti-Inflammatory Discoveries: Galangin & More Dr. Jay recently co-authored a 2025 paper on: Galangin (Thai ginger rhizome extract) Which may reverse cardiac inflammation and fibrosis His team is also exploring other nutraceutical molecules for chronic inflammation relief in Lyme patients. 10. Dr. Jay's Personal Story of Illness and Hope He reveals for the first time: He was diagnosed with Stage 3 Multiple Myeloma Lost the ability to walk Suffered unbearable pain After cutting-edge therapies and research, he is now in full remission His message to Lyme patients: “There is ALWAYS hope.”

Jayden Daniels has officially made his mother his agent. Is this a big deal or non-story?

The endless analysis of the Nancy Guthrie suspect has focused on his apparent amateurism — the cheap backpack, the bad holster placement, the improvised camera obstruction. Former FBI Special Agent Robin Dreeke offers a corrective: this is what most criminals look like. We've just been conditioned by fiction to expect something else.Dreeke spent over two decades with the Bureau, including serving as Chief of the FBI's Counterintelligence Behavioral Analysis Program. He's seen the full spectrum of criminal operations — from trained intelligence officers to desperate opportunists. And most of what he's seen looks closer to this than to anything Hollywood produces.The expectation gap matters because it affects how everyone — investigators, media, public — interprets evidence. When footage doesn't match the fictional standard, people assume something's unusual. They look for explanations that aren't there. They misread desperation as stupidity or luck as skill.Dreeke addresses the uncomfortable reality that sloppy execution doesn't always mean quick capture. This suspect has evaded identification for four weeks despite massive resources, a $1.3 million reward, and round-the-clock national coverage. That's not necessarily sophistication. It might just be circumstance. But distinguishing between the two requires understanding what baseline criminal behavior actually looks like — and that baseline is far messier than most people realize.From his counterintelligence background, Dreeke explains what a genuinely professional operation would have done differently. The gap between tradecraft and what's on the Guthrie footage is real. But that gap exists in almost every case. This one just has cameras on it.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1Instagram https://www.instagram.com/hiddenkillerspod/Facebook https://www.facebook.com/hiddenkillerspod/Tik-Tok https://www.tiktok.com/@hiddenkillerspodX Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#NancyGuthrie #SavannahGuthrie #RobinDreeke #TrueCrimeToday #FBI #BehavioralAnalysis #CriminalBehavior #TucsonArizona #Kidnapping #HiddenKillers

Guests: Marianna Yarovskaya and Lyuba Sobol. Lyuba Sobol represents democratic Russian forces at the Council of Europe, aiming to delegitimize Putin, while facing continued threats and surveillance alongside other exiled activists.1917 MOSCOW

It was January 24, 2022. The King of Spain was coming to Puerto Rico. But everyone woke up to some unexpected news: the statue of the Spanish conquistador Juan Ponce de León in Old San Juan had been toppled. When the mayor promised to restore it that same day, it raised big questions: Who deserves to be put up on a pedestal? Who represents Puerto Ricanness? Who are our champions? This season, we're going to learn about Puerto Rico through the people who represent us. And we'll ask: what does it take to champion Puerto Rico?Want to support our independent journalism? Join Futuro+ for exclusive episodes, sneak peaks and behind-the-scenes chisme on La Brega and all our podcasts. https://bit.ly/joinfuturoplus Hosted on Acast. See acast.com/privacy for more information.

Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Teri Williams. President & COO (and owner) of OneUnited Bank, from Money Making Conversations Masterclass: Purpose of the Interview The interview aimed to: Showcase OneUnited Bank’s role as the largest Black-owned bank and its commitment to financial empowerment. Educate listeners on digital banking solutions, financial literacy, and generational wealth strategies. Promote OneUnited Bank’s services and initiatives, including its youth financial literacy contest and “One Transaction” wealth-building concept. Key Takeaways Origins & Growth of OneUnited Bank Started as a community bank in Boston, later acquired four Black-owned banks (Miami, LA, Boston) and merged into OneUnited. Became the first Black-owned digital bank and now serves customers nationwide. Digital Banking & Accessibility Customers can open accounts online in minutes. Features include: Mobile check deposit (take a photo of your check). Direct deposit with early pay (up to 2 days early, no fees). Largest surcharge-free ATM network (100,000 ATMs, including Walgreens, 7-Eleven, Chase, Citibank). Combatting Financial Deserts Addresses lack of brick-and-mortar banks in Black communities and reliance on predatory check-cashing services. Emphasizes that check-cashing services never improve credit scores and often harm financial health. Financial Literacy & Wealth Building Advocates automatic savings as a key wealth-building habit. Introduced WiseOne, a tool that aggregates financial data to: Track net worth, income, expenses. Identify duplicate charges and suggest savings. Provide debt-reduction strategies. Youth Financial Literacy Initiative “I Got Bank” Contest for ages 8–12: Read a financial literacy book (free download available). Submit an essay or artwork on what they learned. 10 winners receive $1,000 savings accounts. One Transaction Concept Six key transactions to build generational wealth: Homeownership (OneUnited offers $25K–$50K down payment assistance). Life Insurance (affordable way to transfer wealth). Investments (automatic contributions). Profitable Business (entrepreneurship or side gigs). Credit Score Improvement. Savings (automatic transfers). Focus on one transaction at a time for sustainable progress. Economic Advice for Uncertain Times Anticipates stagflation (inflation + rising unemployment). Recommendations: Hold on to your job (avoid unnecessary job changes). Save more, spend less. Notable Quotes “We were the first Black-owned digital bank—and now the largest Black-owned bank in the country.” “Check cashers only report to credit bureaus when you don’t pay them. That’s crazy.” “If it goes in your pocket, you’re more likely to spend it. Wealthy people automate savings.” “One transaction can make the difference between being wealthy or not.” “We have the largest surcharge-free ATM network in the country—100,000 ATMs.” “Hold on to your job. Start saving more and spending less.” #SHMS #STRAW #BESTSupport the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.