Podcasts about Prefect

For the latest podcast Ali caught up with returning guest Olga Wojtas to talk about the latest in her Miss Blaine's Prefect series of novels, Miss Blaine's Prefect and the Parcel of Rogues.Olga explains the unusual premise of the series, and exactly who Miss Blaine's Prefect, aka Shona McMonagle, is, and how she came to find herself (spoiler alert - not really) travelling time.The two then discuss why Olga set this latest book in Enlightenment Edinburgh, writing about, and subverting, real historical characters including Robert Burns and Walter Scott, as well as other Enlightenment figures such as Adam Smith, Joseph Black, Henry Mackenzie, and Adam Ferguson, and why she wanted to investigate this time and place in particular.They also talk about the changing face of Edinburgh as a city over the centuries, before Olga details the research behind the novel and what she learnt about her home city.It's always a pleasure to have a chat with Olga who is great company, and we hope this comes across in this episode. If you aren't yet a fan of Miss Blaine's Prefect then we hope this will convince you to get acquainted. And if you are already a regular reader then this will remind you why, and, as an enticement to listen to the end, we also get to learn a little more about Miss Blaine herself!For full details, and all the ways to listen, head over to scotswhayhae.com

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

In this episode, we're joined by Jeremiah Lowin, Founder & CEO at Prefect and the creator of FastMCP, to explore how one of the most influential projects in the MCP ecosystem came to be - and where the protocol is heading next.We discuss the accidental origin of FastMCP, why Anthropic adopted it into the official SDK, what developers are getting wrong about MCP, and why Chris believes the biggest opportunity for AI agents isn't customer-facing applications, but internal enterprise systems. We also dive into MCP Apps, developer experience, protocol design, AI tooling, Python, and why building great abstractions is often more valuable than exposing more configuration.Along the way, we explore the rapid growth of the MCP ecosystem, how FastMCP became the default way many developers build MCP servers, why "too much magic" can actually hurt developer experience, and what the next generation of AI-powered applications will look like as agents move beyond simple tool calling into rich, interactive experiences.Prefect: https://www.prefect.ioJeremiah Lowin: https://www.linkedin.com/in/jlowinDemetrios: https://www.linkedin.com/in/dpbrinkmTimestamps:00:00 Lost My Entire Talk00:47 The Story Behind FastMCP02:08 Anthropic Adopted FastMCP02:34 When MCP Took Off04:10 FastMCP vs The Official SDK05:43 Is MCP Actually Dead?06:42 What Everyone Gets Wrong About MCP08:11 MCP's Biggest Use Case10:25 Building Internal AI Systems12:00 Why FastMCP Exploded13:29 Making Complex Software Simple15:10 Can Software Be Too Magical?20:11 MCP Apps Explained23:42 Why Python Needed MCP Apps27:54 The Future of AI Interfaces34:18 AI Should Generate UIs40:11 AI Deleted My Presentation43:30 The AI Assistant We Actually Need48:00 Personal AI vs SaaS52:28 The Future of AI Agents55:06 Final Thoughts

Potter Revisited Episode #112 Parentified Children and Emotionally Stunted Adults AKA Harry Potter and the Order of the Phoenix, Chapter 14 "Percy and Padfoot" Everyone needs therapy in this series  Harry and Cho finally are able to have a moment  Harry's conversation with Cho is so important for his survivor's guilt  Filch trying to read Harry's mail is shady and we know Umbridge is behind it  Tori and Shay debate who Cho had feelings for first - Harry or Cedric Sirius has been outed as being in London - he really has no regard for his safety and how much Harry worries about it It would of been nice if Fred and George could of been more encouraging to Ron at his first Quidditch practice, knowing how insecure he is  Draco and co have nothing better to do with their time so they crash the Gryffindor practice  Percy's owl shows up in the Gryffindor common room - a bad omen?  Percy's letter to Ron... is not great It's nice that Percy wants to congratuate Ron on being Prefect, but then doubling down on cutting out Harry...  Was Percy persuaded to write this letter, as he urges Ron to confide in Umbridge about Harry Percy glosses over the estrangement with his family in this letter quite a bit  It's got to be so hard for Harry to see this as Percy was someone he was close to for four years prior to this  Shay thinks as a Slytherin, Ron should of pretended to cut Harry off and speak with Umbridge to find out what she was up to  Sirius randomly pops up in the Gryffindor common room fire - despite the Ministry knowing he is in London Sirius gives the trio the lowdown on Umbridge - and how racist she is  Fudge thinks that Dumbledore is training students for combat against the Ministry - is this dumb?  Sirius has no regard for his safety or how much Harry and co worry about him - planning to turn up in Hogsmede?? Sirius saying that Harry isn't like his father feels like such a low blow and immature for Sirius  Does Sirius have an idealized version of James he remembers? We think married, father James in wartime was a lot more responsible that teenage James  Harry really has to parent Sirius which is so annoying.  Snape Sucks count for Chapter 14: 0 Umbridge Sucks count for Chapter 14: 2 Email any thoughts, questions or feedback to  potterrevisitedpodcast@gmail.com Music: Shelter Song by Alexander Nakarada (www.serpentsoundstudios.com) Licensed under Creative Commons BY Attribution 4.0 License https://creativecommons.org/licenses/by/4.0/ Follow Us:  Facebook  https://www.facebook.com/potterrevisited Twitter https://twitter.com/potterevisited Instagram https://www.instagram.com/potterrevisited_/ Youtube https://www.youtube.com/channel/UC4v2Xt0OIQ8_LCVYhKf2S5A TikTok https://www.tiktok.com/@potterrevisited  

Tonight on Brett Orchard's Old Time Radio Show, we're settling in with the timeless comfort of Dad's Army: A Stripe for Frazer. One of Britain's best-loved classic comedies, Dad's Army is full of gentle humour, wartime nostalgia and that wonderfully familiar feeling of Sunday evenings gone by. Whether you grew up with Captain Mainwaring, Sergeant Wilson, Corporal Jones and Private Pike, or you're discovering Walmington-on-Sea for the first time, this is classic British comedy at its warmest. Make yourself comfortable, put the kettle on, dim the lights and enjoy another cosy journey back into the golden age of radio. Subscribe for classic British comedy, old time radio, vintage mysteries and calm night-time listening. dayslikethis.life #DadsArmy #ClassicBritishComedy #OldTimeRadio #SundayNightListening #BedtimeListening

Tonight on Brett Orchard's Old Time Radio Show, we're settling in with the timeless comfort of Dad's Army: Sargeant Wilson's Little Secret. One of Britain's best-loved classic comedies, Dad's Army is full of gentle humour, wartime nostalgia and that wonderfully familiar feeling of Sunday evenings gone by. Whether you grew up with Captain Mainwaring, Sergeant Wilson, Corporal Jones and Private Pike, or you're discovering Walmington-on-Sea for the first time, this is classic British comedy at its warmest. Make yourself comfortable, put the kettle on, dim the lights and enjoy another cosy journey back into the golden age of radio. Subscribe for classic British comedy, old time radio, vintage mysteries and calm night-time listening. dayslikethis.life #DadsArmy #ClassicBritishComedy #OldTimeRadio #SundayNightListening #BedtimeListening

We continue to sift through Pope Leo's 'Magnifica Humanitas' with Professor Paolo Carozza of Notre Dame's Law School. Also serving as chair of the Meta Oversight Board, Carozza explained why he thinks the encyclical is "a profound and prophetic document," and why there is a need now to "develop a new politics" for the "whole persons and for all persons," as the Holy Father wrote. As we look ahead to the beatification of soon-to-be-Blessed Fulton Sheen, Msgr. Jason Gray joins, executive director of the Archbishop Fulton Sheen Foundation, to share insights about his life of virtue, and all the step-by-step documentation that is collected and vetted when preparing a cause for sainthood, including details about the many possible miracles that are being explored. With big news from the Vatican this week. Msgr. Roger Landry shares his moments with Pope Leo, and reacts to the big news of Montse Alvarado being appointed to the position of Prefect of Dicastery for Communication, the first laywoman to ever hold that position.

Some good news finally for the Tigers?

OCD and Bi-Polar - I'm ruining my prefect romantic relationship due to my OCD and Bi-Polar emotional outbursts. Listen to caller's personal dramas four times each week as Dr. Kenner takes your calls and questions on parenting, romance, love, family, marriage, divorce, hobbies, career, mental health - any personal issue! Call anytime, toll free 877-Dr-Kenner. Visit www.drkenner.com for more information about the show (where you can also download free chapter one of her serious relationships guidebook).

The Model Context Protocol, or MCP, gives developers a common way to expose tools, data, and capabilities to large language models, and it has quickly become an important standard in agentic AI. FastMCP is an open source project stewarded by the team at Prefect, which is an orchestration platform for AI and data workflows. The The post FastMCP with Adam Azzam and Jeremiah Lowin appeared first on Software Engineering Daily.

The Model Context Protocol, or MCP, gives developers a common way to expose tools, data, and capabilities to large language models, and it has quickly become an important standard in agentic AI. FastMCP is an open source project stewarded by the team at Prefect, which is an orchestration platform for AI and data workflows. The FastMCP project builds on MCP to provide high-level, ergonomic abstractions for Python developers to rapidly build and deploy MCP servers and applications. Jeremiah Lowin is the founder and CEO of Prefect, and Adam Azzam is the VP of Product at the company. In this episode, Jeremiah and Adam join Gregor Vand to discuss the origin story of FastMCP, the three pillars of the framework, the architectural decisions behind FastMCP 3.0, and much more. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post FastMCP with Adam Azzam and Jeremiah Lowin appeared first on Software Engineering Daily.

The Model Context Protocol, or MCP, gives developers a common way to expose tools, data, and capabilities to large language models, and it has quickly become an important standard in agentic AI. FastMCP is an open source project stewarded by the team at Prefect, which is an orchestration platform for AI and data workflows. The FastMCP project builds on MCP to provide high-level, ergonomic abstractions for Python developers to rapidly build and deploy MCP servers and applications. Jeremiah Lowin is the founder and CEO of Prefect, and Adam Azzam is the VP of Product at the company. In this episode, Jeremiah and Adam join Gregor Vand to discuss the origin story of FastMCP, the three pillars of the framework, the architectural decisions behind FastMCP 3.0, and much more. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post FastMCP with Adam Azzam and Jeremiah Lowin appeared first on Software Engineering Daily.

The Model Context Protocol, or MCP, gives developers a common way to expose tools, data, and capabilities to large language models, and it has quickly become an important standard in agentic AI. FastMCP is an open source project stewarded by the team at Prefect, which is an orchestration platform for AI and data workflows. The FastMCP project builds on MCP to provide high-level, ergonomic abstractions for Python developers to rapidly build and deploy MCP servers and applications. Jeremiah Lowin is the founder and CEO of Prefect, and Adam Azzam is the VP of Product at the company. In this episode, Jeremiah and Adam join Gregor Vand to discuss the origin story of FastMCP, the three pillars of the framework, the architectural decisions behind FastMCP 3.0, and much more. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post FastMCP with Adam Azzam and Jeremiah Lowin appeared first on Software Engineering Daily.

The Model Context Protocol, or MCP, gives developers a common way to expose tools, data, and capabilities to large language models, and it has quickly become an important standard in agentic AI. FastMCP is an open source project stewarded by the team at Prefect, which is an orchestration platform for AI and data workflows. The The post FastMCP with Adam Azzam and Jeremiah Lowin appeared first on Software Engineering Daily.

In the near future, artificial intelligence could do the thinking, and a robotic combination of human and machine do the lifting. But it's a potentially dystopic scenario – the poor, the weak, and those who simply believe in humanity as nature intended could be abandoned. These are just some of major ethical challenges posed by so-called transhumanism. Professor JASON EBERL is a bioethicist at St Louis University in Missouri. On the 8th of April, he'll deliver the annual lecture to the Plunkett Centre for Ethics in Sydney. Each Easter, we bring you stories from places where war and other disasters can try people's faith but where, despite great despair, their religious beliefs survive. ASUNTHA CHARLES has worked with the faith-based aid organisation World Vision in Afghanistan under the Taliban and Cox's Bazaar in Bangladesh, where 1.1 million Rohingya Muslims fled the military regime in Myanmar/Burma. She was in Australia recently with the Micah Women's Network.Israel's prime minister Benjamin Netanyahu has backed down after the Vatican and the governments of France, Italy and even the United States protested a decision to block the Latin Patriarch of Jerusalem, Cardinal Pizzaballa, from entering the Church of the Holy Sepulchre in Jerusalem on Palm Sunday. After Israel's president Isaac Herzog intervened, Netanyahu said the cardinal would have access to all holy sites this Easter.A Sydney bishop is set to take up a top Vatican job. Pope Leo has announced Bishop Anthony Randazzo will become the new Prefect of the Dicastery for Legislative Texts, meaning he'll be responsible for interpreting and applying church law throughout the Catholic world. He'll become the most senior Australian cleric in the Vatican since the late George Pell, who held was Prefect for the economy.And finally, the pope had some pointed words for the residents of the world's wealthiest state. In a day-long visit to Monaco, where one in three people are millionaires, Leo condemned the “idolatry of power and money” and reminded them Jesus was “an advocate” for the poor and outcast.

Lesson #5 – Why We Believe the Bible John 17:17 Isaiah 40:8 The Fourfold Power of Scripture 1. The Scriptures Are Inspired 2 Timothy 3:16 inspiration comes from the Greek word meaning: Theopneustos = God-breathed ● Psalm 119:89 ● Matthew 4:4 2. The Scriptures Are Instructive 2 Timothy 3:16 Four Wonderful Areas Of Instruction: • Doctrine – What Is Right • Reproof – What Is Wrong • Correction – How To Get Right • Instruction – How To Stay Right John 4:14 3. The Scriptures Are Instrumental 2 Timothy 3:17 The Bible works in three great ways: A. For Salvation 2 Timothy 3:15 “Which are able to make thee wise unto salvation.” ● Romans 10:17 ● 1 Corinthians 5:3–4 B. For Sanctification 2 Timothy 3:17 John 17:17 C. For Service 2 Timothy 3:17 4. The Scriptures Are Irrefutable Hebrews 11:1 “the evidence of things not seen.” A. Historical Evidence Examples where archaeology confirmed the Bible: • Belshazzar Confirmed by the Nabonidus Cylinders found in Iraq. • The Hittites Thousands of tablets discovered at Boghazköy. • Pontius Pilate – confirmed by a limestone inscription reading “Pontius Pilate, Prefect of Judea.” • Pool of Bethesda – excavated near the Church of St. Anne, showing the five colonnades exactly as described in Scripture. B. Scientific Evidence Ancient Egyptian medicine included things like: ● lizard blood ● donkey dung poultices ● rotten meat applications ● human and animal excrement as medicine ✔ Quarantine for infectious disease (Leviticus 13–14) ✔ Washing after contact with sickness (Leviticus 15) ✔ Proper disposal of human waste outside the camp (Deuteronomy 23) ✔ Isolation of diseased individuals (Numbers 5) C. Preservation Evidence Psalm 12:6–7 The Bible has been printed in millions of copies and over 3,500 languages. ● Voltaire, who predicted Christianity would vanish (his home later housed the Geneva Bible Society). ● Diocletian's edict in A.D. 303 ordering Scriptures burned. ● Nazi book burnings ● Communist censorship ● Modern attempts to remove the Bible from public life. Mark 13:31 D. Prophetic Evidence Over 300 prophecies about Jesus Christ were written centuries before His birth. • Born in Bethlehem • Betrayed for 30 pieces of silver • Hands and feet pierced • Buried with the rich Psalm 34:8 How to Get Into the Bible 1. Read it daily 2. Study it carefully 3. Meditate on it slowly 4. Share it freely 5. Apply it immediately

He was born in Rome to a wealthy senatorial family. He received a good education in secular and spiritual learning, and became Prefect of Rome. While still in the world, he used his great wealth mostly for the good of the Church, building six monasteries in Sicily and another in Rome itself. At this monastery, dedicated to the Apostle Andrew, Gregory was tonsured a monk. He was appointed Archdeacon of Rome, then, in 579, Papal legate to Constantinople, where he lived for nearly seven years. He returned to Rome in 585 and was elected Pope in 590.   He is famed for his many writings, his generous charity (he gave almost all his income to the poor, and often invited the poor to share his table), and for initiating missionary work among the Anglo-Saxon peoples. The Liturgy of the Presanctified Gifts, celebrated on Wednesday and Friday evenings during Great Lent, was compiled by him. St Gregory introduced elements of the chanting that he had heard in Constantinople into Western Church chant: The Gregorian Chant which beautified the Western churches for many years is named for him. Its system of modes is related to the eight tones of the Eastern church. He is called 'the Dialogist' after his book The Dialogues, an account of the lives and miracles of Italian saints.   Saint Gregory reposed in peace in 604.

The Ransom of Canton.The lame-duck Superintendent watches helplessly as a triumvirate of Qing officials arrives to reverse every compromise his predecessor had wrought... & promptly launches the most ambitious Chinese military operation of the entire war. In the midst of that rain-soaked battlefield, a brief skirmish between British soldiers and peasant militiamen plants the seed of a legend that will haunt Chinese politics for the next century. Time Period Covered:Feb. 1841–Oct. 1841 Major Historical Figures: The Qing Empire:The Daoguang Emperor (Aisin-Gioro Minning) [r. 1820–1850]Yishan, Imperial Commissioner and Pacifier-General of the Rebellious (靖逆) [1790–1878]Longwen, Manchu nobleman and ministerial attaché [d. 1841]Yang Fang, Governor-General and military commander [c. 1770–1846]She Baoshun, Prefect of Canton [fl. 1840s]Yuqian, Imperial Commissioner for Military Operations in Zhejiang [fl. 1841] The British Empire:Queen Victoria [r. 1837–1901]Henry John Temple, 3rd Viscount Palmerston, Foreign Secretary [1784–1865]Charles Elliot, Chief Superintendent of British Trade in China [1801–1875]Sir Henry Pottinger, incoming Plenipotentiary to China [1789–1856]Sir Hugh Gough, Commander of British Land Forces [1779–1869]Captain William Hutcheon Hall, commanding HMS Nemesis [c. 1797–1878] Major Sources Cited:Wakeman, Frederic Jr. "Canton Trade and the Opium War." The Cambridge History of China, Vol. 10.Wakeman, Frederic Jr. Strangers at the Gate: Social Disorder in South China, 1839–1861.Fay, Peter Ward. The Opium War, 1840–1842.Lovell, Julia. The Opium War: Drugs, Dreams and the Making of China. Learn more about your ad choices. Visit megaphone.fm/adchoices

He was born sometime in the mid-fourth century on an island in the Aegean. For a time he lived successfully in the world, receiving a good education in Constantinople, then serving for a time for the Prefect of the Praetorium. But, becoming aware of the vanity of worldly things, he answered Christ's call, gave away all his goods to the poor and entered a monastery in Syria. After four years in obedience, he came to feel that the security of monastic life was inconsistent with the Gospel command to take no thought for the morrow; so he withdrew to the desert, taking with him only his garment and the Book of the Gospel. There he lived alone for seven years.   At the end of this period he set out on an apostolic mission to Mesopotamia, where he brought many to Christ: the city prefect Rabbula was converted after Alexander brought down fire from heaven, and a band of brigands who accosted the Saint on the road were transformed into a monastic community. He finally fled the city when the Christians there rose up demanding that he be made bishop. He once again took up a solitary life in the desert beyond the Euphrates, spending the day in prayer and part of the night sheltered in a barrel. There he remained for forty years. His holiness gradually attracted more than four hundred disciples, whom Alexander organized into a monastic community. Each disciple owned only one tunic, and was required to give away anything that they did not need for that day. Despite this threadbare life, the monastery was able to set up and run a hospice for the poor!   Alexander was perplexed as to how the admonition Pray without ceasing (1 Thess. 5:17) could be fulfilled by frail human flesh, but after three years of fasting and prayer, God showed him a method. He organized his monks into four groups according to whether their native language was Greek, Latin, Syriac or Coptic, and the groups prayed in shifts throughout the day and night. Twenty-four divine services were appointed each day, and the monks would chant from the Psalter between services. The community henceforth came to be known as the Akoimetoi, the Unsleeping Ones. (Similar communities later sprang up in the West, practicing what was there called Laus Perennis; St Columban founded many of these.)   Always desiring to spread the holy Gospel, Saint Alexander sent companies of missionaries to the pagans of southern Egypt. He and a company of 150 disciples set out as a kind of traveling monastery, living entirely on the charity of the villages they visited. Eventually they settled in some abandoned baths in Antioch, setting up a there a monastery dedicated to the unceasing praise of God; but a jealous bishop drove them from the city. Making his way to Constantinople, he settled there with four monks. In a few days, more than four hundred monks had left their monasteries to join his community. The Saint organized them into three companies — Greeks, Latins and Syrians — and restored the program of unsleeping prayer that his community had practiced in Mesopotamia. Not surprisingly, his success aroused the envy and anger of the abbots whose monasteries had been nearly emptied; they managed to have him condemned as a Messalian at a council held in 426. (The Messalians were an over-spiritualizing sect who believed that the Christian life consisted exclusively of prayer.) Alexander was sent back to Syria, and most of his monks were imprisoned; but as soon as they were released, most fled the city to join him again. The Saint spent his last years traveling from place to place, founding monasteries, often persecuted, until he reposed in 430, 'to join the Angelic choirs which he had so well imitated on earth.' (Synaxarion)   The practice of unceasing praise, established by St Alexander, spread throughout the Empire. The Monastery of the Akoimetoi, founded by a St Marcellus, a successor of Alexander, was established in Constantinople and became a beacon to the Christian world. 'Even though it has not been retained in today's practice, the unceasing praise established by Saint Alexander was influential in the formation of the daily cycle of liturgical offices in the East and even more so in the West.' (Synaxarion)

The Greek Tyron means "conscript." This holy Martyr of Christ came from Pontus and was a Roman legionary during Maximian's persecution (~303). Though he had been a Christian since childhood, he kept his faith secret while in the army. While his cohort was stationed near a town called Euchaita, he learned that the people there were being terrorized by a dragon which lived in the neighboring forest. He set off to face the dragon, praying to God that the outcome of the contest would be a sign to him of whether the time had come to offer himself for martyrdom. He found the fire-spitting monster and, arming himself with the sign of the Cross, drove his spear through its head and killed it.   His success convinced him that, having vanquished this fleshly dragon, he was ready to vanquish the spiritual dragon, the Devil. When the commander of his camp next ordered a sacrifice to the Gods, Theodore boldly refused, saying "I am a Christian!" Further, he encouraged the other Christians in his company to do the same. That night he went to a nearby pagan temple of Rhea, mother of the gods, and burned it down. He was seen by the caretaker of the temple and was brought unresisting to the governor Publius. Theodore was thrown into a solitary dungeon cell; there he refused bread and water, saying that Christ had promised him food from heaven. He spent his time there chanting hymns with the angels, so that the guards were convinced that other Christians had somehow joined him in his cell.   When all argument, cajolery, bribery and threat had failed to turn the soldier from Christ, the governor resorted to torture, subjecting the Saint to terrible mutilations; but when Theodore endured them calmly and resolutely, the governor began to fear that his example would encourage other Christians, and ordered that he be burned. Taken to the stake, the Martyr walked freely into the flames, where he gave back his soul to God. When his body was ransomed and taken from the ashes by a pious Christian, it was found to be untouched. A church was built in Euchaita in honor of the Martyr; many pilgrims came there for the healing of soul and body.   In 361, the Emperor Julian the Apostate ordered the Prefect of Constantinople to have all foods in the marketplaces sprinkled with blood of animals sacrificed to the pagan gods during the first week of Lent, so that Christians would be unable to escape contact with idolatry. But St Theodore appeared in a vision to Patriarch Eudoxius (360-364), warned him of the plan and told him to instruct his flock not to buy any food in the marketplace, but to eat kolyva made from boiled wheat grains. So, through the Saint's intervention, the people were preserved from the stain of idolatry. Ever since, the Church has commemorated the miracle on the first Saturday of Great Lent. Since that time kolyva has come to be offered also in honor of the Saints and in memory of the departed. The whole grain represents the body, sown corruptible, which will be raised incorruptible (2 Cor. 15:37); it is usually sweetened with honey to signify the delights of Paradise.

TRADCAST EXPRESS - Episode 222 Topics covered: Society of St. Pius X (SSPX) announces new episcopal consecrations for July 1, 2026. Vatican reacts. SSPX Superior General meets with Vatican doctrine chief 'Cardinal' Victor Fernandez. Conditions for dialogue outlined. Latest developments. Links: Diane Montagna, "SSPX Announces Intention to Proceed with Episcopal Ordinations" (Feb. 2, 2026) "Society of St. Pius X Announces New EPISCOPAL CONSECRATIONS for July 1, 2026" Novus Ordo Watch (Feb. 2, 2026) "Video Discussion: On the Upcoming SSPX Episcopal Consecrations", Novus Ordo Watch (Feb. 4, 2026) "Meeting scheduled between Society of St. Pius X and Prefect of the DDF", Vatican News (Feb. 5, 2026) "Is It 1988 All Over Again? Watching a New SSPX Drama Unfold", Novus Ordo Watch (Feb. 6, 2026) "Communiqué From the General House: Meeting in Rome", FSSPX.news (Feb. 12, 2026) "Vatican Proposal to SSPX: Call Off the Bishops' Consecrations and We'll Talk!", Novus Ordo Watch (Feb. 12, 2026) "Can the SSPX Be Forbidden To Do What Is Permitted to the Chinese Communist Party?", FSSPX.news (Feb. 13, 2026) Sign up to be notified of new episode releases automatically at tradcast.org. Produced by NOVUSORDOWATCH.org Support us by making a tax-deductible contribution at NovusOrdoWatch.org/donate/

"This Martyr was the daughter of most distinguished and noble parents named Philip and Claudia. Philip, a Prefect of Rome, moved to Alexandria with his family. In Alexandria, Eugenia had the occasion to learn the Christian Faith, in particular when she encountered the Epistles of Saint Paul, the reading of which filled her with compunction and showed her clearly the vanity of the world. Secretly taking two of her servants, Protas and Hyacinth, she departed from Alexandria by night. Disguised as a man, she called herself Eugene [Eugenios -ed.] while pretending to be a eunuch, and departed with her servants and took up the monastic life in a monastery of men. Her parents mourned for her, but could not find her. After Saint Eugenia had laboured for some time in the monastic life, a certain woman named Melanthia, thinking Eugene to be a monk, conceived lust and constrained Eugenia to comply with her desire; when Eugenia refused, Melanthia slandered Eugenia to the Prefect as having done insult to her honour. Eugenia was brought before the Prefect, her own father Philip, and revealed to him both that she was innocent of the accusations, and that she was his own daughter. Through this, Philip became a Christian; he was afterwards beheaded at Alexandria. Eugenia was taken back to Rome with Protas and Hyacinth. All three of them ended their life in martyrdom in the years of Commodus, who reigned from 180 to 192." (Great Horologion)

She was the daughter of a prominent family in Nicomedia during the reign of the persecutor Maximian (286-305). Her parents betrothed her to a nobleman named Eleusius, but without his knowledge, or that of her parents, she had already committed her life to Christ, and consecrated her virginity to him. To put off her suitor, she told him that she would not marry him until he became Prefect. Eleusius went to work using his fortune to bribe and influence those in power, and succeeded in being appointed Prefect of Nicomedia. When he went to Juliana to claim her as his wife, she was forced to confess herself a Christian, saying that she would never marry him unless he gave up the worship of idols and embraced the faith of Christ. For her confession, she was arrested and taken before the Prefect: Eleusius, her once-ardent suitor. He was now filled with an ardent rage toward her and, when she would not renounce her faith, had her subjected to the most sadistic tortures imaginable. Miraculously, she endured these without harm. Witnessing this wonder, 500 men and 130 women from among the pagans confessed Christ. The enraged Prefect had all of them beheaded immediately, followed by Juliana herself. She was eighteen years old when she won the Martyr's crown.

He grew up in Milan and became an army officer, where he distinguished himself so well that the Emperor Diocletian made him captain of the Praetorian Guard not suspecting that Sebastian was a Christian. In Rome, while fulfilling the duties of a courtier, he used his position to comfort and encourage his imprisoned fellow-Christians. By his labors and example he brought many to faith in Christ, including Chromatius, the Prefect in charge of persecuting the Roman Christians.   Sebastian had upheld two brothers, Mark and Marcellinus, who were awaiting execution for their faith. When the day of execution came, their father Tranquillinus, who had been a pagan but through Sebastian's example had converted, presented himself to Chromatius and announced that he too was a Christian. His testimony was so powerful that the hard heart of the Prefect was melted, and he himself resolved to become a Christian.   Caius, Bishop of Rome, gathered the new brethren (both men and women — not all of Sebastian's converts have been mentioned here) to embrace them and baptize them, but also to warn them of their coming Martyrdom. He instructed some to flee the city and others, headed by Sebastian, to remain in Rome, devoting their days to fasting, prayer and thanksgiving as they awaited their death. As the "company of Martyrs" did this, many came to them and were healed of ailments, and many joined them in confessing Christ.   When the time of martyrdom came, each member of the company was subjected to imaginatively cruel tortures before his execution. Sebastian himself was made to witness the deaths of all his companions, then to endure his own trial. He serenely confessed his unshaken faith before Diocletian himself before being taken to the place of execution. There he was tied to a post and made the target of a band of archers until his body bristled with arrows like the quills of a porcupine. He was left for dead, but when Irene, widow of St Castulus, came to bury him, she found him alive and tended his wounds. Amazingly, he recovered, and presented himself once again to the Emperor. Astonished and outraged, the tyrant ordered that Sebastian be beaten to death with clubs and thrown into the city's sewer. That evening, a pious Christian woman was told in a vision to retrieve his body and bury it in the catacombs. After St Constantine brought peace to the Church, Pope Damasus built a church over the site in the Saint's honor. For hundreds of years, many miracles were worked there through St Sebastian's intercessions.

The Perfect Time for Prefect Gifts | Galatians 4.4-7 by Christ Church Presbyterian

More lurid writings by Cardinal Victor Manuel Fernández, Prefect for the Dicastery for the Doctrine of the Faith (DDF), have been discovered by the Argentine Catholic website El Wanderer. These obscene texts come from the cardinal's books: Heal Me with Your Mouth: The Art of Kissing and Mystical Passion: Spirituality and Sensuality. Learn more about your ad choices. Visit megaphone.fm/adchoices

The timing of this news is critical. Pope Leo XIV is reportedly deciding the future of the Roman Curia and key positions therein, including the position of Prefect for the Dicastery of the Doctrine of the Faith, the post Cardinal Fernandez currently holds.Sources:https://www.returntotradition.orgorhttps://substack.com/@returntotradition1Contact Me:Email: return2catholictradition@gmail.comSupport My Work:Patreonhttps://www.patreon.com/AnthonyStineSubscribeStarhttps://www.subscribestar.net/return-to-traditionBuy Me A Coffeehttps://www.buymeacoffee.com/AnthonyStinePhysical Mail:Anthony StinePO Box 3048Shawnee, OK74802Follow me on the following social media:https://www.facebook.com/ReturnToCatholicTradition/https://twitter.com/pontificatormax+JMJ+#popeleoXIV #catholicism #catholicchurch #catholicprophecy#infiltration

The timing of this news is critical. Pope Leo XIV is reportedly deciding the future of the Roman Curia and key positions therein, including the position of Prefect for the Dicastery of the Doctrine of the Faith, the post Cardinal Fernandez currently holds.Sources:https://www.returntotradition.orgorhttps://substack.com/@returntotradition1Contact Me:Email: return2catholictradition@gmail.comSupport My Work:Patreonhttps://www.patreon.com/AnthonyStineSubscribeStarhttps://www.subscribestar.net/return-to-traditionBuy Me A Coffeehttps://www.buymeacoffee.com/AnthonyStinePhysical Mail:Anthony StinePO Box 3048Shawnee, OK74802Follow me on the following social media:https://www.facebook.com/ReturnToCatholicTradition/https://twitter.com/pontificatormax+JMJ+#popeleoXIV #catholicism #catholicchurch #catholicprophecy#infiltration

José Tolentino de Mendonça is a theologian, university professor, and one of the most original voices in modern Portuguese. Born in the island of Madeira in 1965, today he is Prefect of the Dicastery for Culture and Education, an administrative unit of the Roman Curia. He is Grand Chancellor of the Pontifical Institute for Christian Archaeology and a Bishop since 2018. From 2018 to 2022 he was Librarian of the Holy Roman Church. He was made a Cardinal in October 2019. "The common thread running through my story is the importance of attention to culture, dialogue, and the power of knowledge and science." "This is a time when the desire for peace, the stubborn search for a path of peace, should be the primary concern of contemporary culture and society." "My vision as a man of the church, as a believer, as a Christian, is ultimately a poetic vision and ends up being my way of inhabiting reality."

Did Pope Benedict XVI believe the Magisterium can err—or that it has erred in the past? In this video, we explore his views across different stages of his life: his early reflections as a young theologian at Vatican II, his role as Prefect of the Congregation for the Doctrine of the Faith, and finally, his […]

"Saint Romanus was a deacon and exorcist in the Church of Caesarea in Palestine. He happened to be at Antioch in 303 when the Emperor Diocletian's edicts for the general persecution of Christians were published. He could not bear to see so many Christian men, women and children denying their faith in the true God for fear of suffering. As they went to sacrifice to the idols, he ran up, consumed with zeal for righteousness, crying shame on them with a loud voice. He was immediately arrested and brought before the city Prefect. He faced interrogation boldly and to prove the stupidity of the pagan cult, he asked for a child to be brought in, taken at random from the crowd in the public square. Romanus enquired of the lad whether it was more sensible to worship the one and only God and Creator of the world, or the many gods of the pagans. Showing himself wiser than the pagans, the child unhesitatingly decided for the God of the Christians. The Prefect flew into a rage at being made to look ridiculous and ordered the young confessor to be put to the torture straight away in the presence of his mother. The child endured the torments without flinching but told his mother he was thirsty and wanted a drink. '0 my dear son', the admirable woman answered, 'do not drink corruptible and temporal water, but keep up your courage so as to drink living and eternal water in the Kingdom of God!' The child was beheaded, and Saint Romanus was condemned to be burnt to death. He welcomed the sentence joyfully, and with a shining face was led unresistingly to the stake. Since the Emperor was in the city, the executioners awaited his decision before lighting the fire and the valiant Martyr exclaimed at the delay, 'Where is the fire that is prepared for me?' But the execution was stayed so that he could be brought before the Emperor in person. Aware that Christians rejoice over the death of a Martyr as the entrance to everlasting life, the tyrant wanted to increase the suffering of Christ's athlete by delaying the moment of deliverance. He ordered the executioners to tear out his tongue, which Romanus freely offered, and he miraculously went on praising God and encouraging the faithful after it was cut away. After this torment, he was imprisoned for a long time in chains until the Emperor's birthday. This was celebrated all over the Empire and a general release of prisoners was customary. But Romanus was not freed; with his feet crushed in the stocks, he was secretly strangled in his dungeon and thus received the adornment of martyrdom, as he had desired."(Synaxarion)

"Saint Romanus was a deacon and exorcist in the Church of Caesarea in Palestine. He happened to be at Antioch in 303 when the Emperor Diocletian's edicts for the general persecution of Christians were published. He could not bear to see so many Christian men, women and children denying their faith in the true God for fear of suffering. As they went to sacrifice to the idols, he ran up, consumed with zeal for righteousness, crying shame on them with a loud voice. He was immediately arrested and brought before the city Prefect. He faced interrogation boldly and to prove the stupidity of the pagan cult, he asked for a child to be brought in, taken at random from the crowd in the public square. Romanus enquired of the lad whether it was more sensible to worship the one and only God and Creator of the world, or the many gods of the pagans. Showing himself wiser than the pagans, the child unhesitatingly decided for the God of the Christians. The Prefect flew into a rage at being made to look ridiculous and ordered the young confessor to be put to the torture straight away in the presence of his mother. The child endured the torments without flinching but told his mother he was thirsty and wanted a drink. '0 my dear son', the admirable woman answered, 'do not drink corruptible and temporal water, but keep up your courage so as to drink living and eternal water in the Kingdom of God!' The child was beheaded, and Saint Romanus was condemned to be burnt to death. He welcomed the sentence joyfully, and with a shining face was led unresistingly to the stake. Since the Emperor was in the city, the executioners awaited his decision before lighting the fire and the valiant Martyr exclaimed at the delay, 'Where is the fire that is prepared for me?' But the execution was stayed so that he could be brought before the Emperor in person. Aware that Christians rejoice over the death of a Martyr as the entrance to everlasting life, the tyrant wanted to increase the suffering of Christ's athlete by delaying the moment of deliverance. He ordered the executioners to tear out his tongue, which Romanus freely offered, and he miraculously went on praising God and encouraging the faithful after it was cut away. After this torment, he was imprisoned for a long time in chains until the Emperor's birthday. This was celebrated all over the Empire and a general release of prisoners was customary. But Romanus was not freed; with his feet crushed in the stocks, he was secretly strangled in his dungeon and thus received the adornment of martyrdom, as he had desired."(Synaxarion)

What do we talk about when talk about empathy & remorse in regards to Mack's role as DOS master and recruiter in NXIVM? How is remorse expressed in Allison Mack's new podcast about her time in and life after Keith Raniere's destructive cult? Where's Mack's empathy for those that she victimized? What does the podcast tell us about Mack's view of her role in NXIVM. What does the future hold for Mack?Get access to exclusive content & support the podcast by a Patron today! https://patreon.com/robertaglasstruecrimereportThrow a tip in the tip jar! https://buymeacoffee.com/robertaglassSupport Roberta by sending a donation via Venmo. https://venmo.com/robertaglassBecome a channnel member for custom Emojis, first looks and exclusive streams here: https://youtube.com/@robertaglass/joinThank you Patrons!Beth, Shelley Safford, Carol Mumumeci, Therese Tunks, JC, Lizzy D, Elizabeth Drake, Texas Mimi, Barb, Deborah Shults, Debra Ratliff, Stephanie Lamberson, Maryellen Sudol, Mona, Karen Pacini, Jen Buell, Marie Horton, ER, Rosie Grace, B. Rabbit, Sally Merrick, Amanda D, Mary B, Mrs Jones, Amy Gill, Eileen, Wesley Loves Octoberfest, Erin (Kitties1993), Anna Quint, Cici Guteriez, Sandra Loves GatsbyHannna, Christy, Jen Buell, Elle Solari, Carol Cardella, Jennifer Harmon, DoxieMama65, Carol Holderman, Joan Mahon, Marcie Denton, Rosanne Aponte, Johnny Jay, Jude Barnes, JenTheRN, Victoria Devenish, Jeri Falk, Kimberly Lovelace, Penni Miller, Jil, Janet Gardner, Jayne Wallace (JaynesWhirled), Pat Brooks, Jennifer Klearman, Judy Brown, Linda Lazzaro, Suzanne Kniffin, Susan Hicks, Jeff Meadors, D Samlam, Pat Brooks, Cythnia, Bonnie Schoeneman-Dilley, Diane Larsen, Mary, Kimberly Philipson, Cat Stewart, Cindy Pochesci, Kevin Crecy, Renee Chavez, Melba Pourteau, Julie K Thomas, Mia Wallace, Stark Stuff, Kayce Taylor, Alice, Dean, GiGi5, Jennifer Crum, Dana Natale, Bewildered Beauty, Pepper, Joan Chakonas, Blythe, Pat Dell, Lorraine Reid, T.B., Melissa, Victoria Gray Bross, Toni Woodland, Danbrit, Kenny Haines and Toni Natalie.

Jeremiah Lowin, founder of Prefect , returns to the show to discuss the seismic shift in the data and AI landscape since our last conversation a few years ago. He shares the wild origin story of FastMCP, a project he started to create a more "Pythonic" wrapper for Anthropic's Model Context Protocol (MCP).Jeremiah explains how this side project was incorporated into Anthropic's official SDK and then exploded to over a million downloads a day after MCP gained support from OpenAI and Google.He clarifies why this is an complementary expansion for Prefect, not a pivot , and provides a simple analogy for MCP as the "USB-C for AI agents". Most surprisingly, Jeremiah reveals that the primary adoption of MCP isn't for external products, but internally by data teams who are using it to finally fulfill the promise of the self-serve semantic layer and create a governable, "LLM-free zone" for AI tools.

Friends of the Rosary,Today, October 22, is the feast of St. John Paul II (1920-2005).Born in 1920 in Wadowice, Poland, and elected pope in 1978, Karol Jozef Wojtyla demonstrated exceptional apostolic zeal throughout his earthly life, particularly for families, young people, and the sick, and exhibited an intensified pastoral commitment with heroic generosity.He promulgated the Catechism of the Catholic Church, the Code of Canon Law for the Latin Church and for the Eastern Churches, and the Luminous Mysteries.Pope John Paul II made 146 pastoral visits in Italy and 104 international apostolic journeys. He produced 14 Encyclicals, 15 Apostolic Exhortations, 11 Apostolic Constitutions, and 45 Apostolic Letters.He also wrote five books: Crossing the Threshold of Hope (October 1994); Gift and Mystery: On the Fiftieth Anniversary of My Priestly Ordination (November 1996); Roman Triptych, meditations in poetry (March 2003); Rise, Let Us Be on Our Way (May 2004), and Memory and Identity (February 2005).Pope John Paul II celebrated 147 beatifications, during which he proclaimed 1,338 blesseds, and 51 canonizations, for a total of 482 saints.On May 13, 1981, an attempt was made on Pope John Paul II's life in Saint Peter's Square. Saved by the maternal hand of the Mother of God, following a lengthy stay in the hospital, he forgave the attempted assassin.John Paul II was beatified on May 1, 2011, by Pope Benedict XVI, his immediate successor and, for many years, his valued collaborator as Prefect for the Congregation for the Doctrine of the Faith. He was canonized by Pope Francis on April 17, 2014.Ave Maria!Come, Holy Spirit, come!To Jesus through Mary!Here I am, Lord; I come to do your will.Please give us the grace to respond with joy!+ Mikel Amigot w/ María Blanca | RosaryNetwork.com, New YorkEnhance your faith with the new Holy Rosary University app:Apple iOS | New! Android Google Play• ⁠October 22, 2025, Today's Rosary on YouTube | Daily broadcast at 7:30 pm ET

Pope Leo made a surprise appointment to fill the position he left behind as Prefect for the Vatican's Dicastery for Bishops, the office that vets and recommends candidates to lead dioceses around the world. The man he chose, Archbishop Filippo Iannone, age 67, is an Italian bishop and member of the Carmelite Order. He was not, per Vatican correspondent Gerard O'Connell, among the names circulated before the appointment was made. In this short episode of “Inside the Vatican”—abbreviated because of a forthcoming deep dive episode—Colleen and Gerry discuss Archbishop Iannone's background and what he may bring to this influential Vatican role. Links from the show: Pope Leo makes a surprise pick for his first major Vatican appointment Deep Dive: Inside the top secret process of appointing a Catholic bishop King Charles III to make delayed state visit to Vatican 6 months after death of Pope Francis NYC choir performs at the Vatican for Pope Leo—in his own words Credits: Producer: Ricardo da Silva, SJ Audio engineering and video editing: Kevin Christopher Robles Executive producer: Sebastian Gomes Inside the Vatican is a production of America Media. Support Inside the Vatican by subscribing to America Magazine! Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us a textIn this episode, I am joined by His Eminence, Cardinal Marc Ouellet, former Prefect of the Congregation for Bishops and author of Word, Sacrament, Charism: The Risks and Opportunities of a Synodal Church.Drawing on decades of leadership within the Church—including his roles in seminary formation, global episcopal appointments, and multiple synods—Cardinal Ouellet brings a deeply theological and pastoral lens to one of the most debated processes in the modern Church: synodality.In This Episode, You'll Hear:The theological and spiritual roots of the Synod on SynodalityCardinal Ouellet's reflections on why synodality must begin in faith, not functionThe risks of reducing the Church to NGO logicHow Mary, Mother of the Church, is the true archetype of synodalityThe need for both hierarchical leadership and charismatic creativityWhat lay people can do to live out the Church's mission with fidelity and joyThis Episode is For You If You've Ever Asked:What exactly is synodality—and why is the Church talking so much about it?How do we walk together without compromising truth?What role do lay people play in a “synodal” Church?How can we discern the Holy Spirit in communal dialogue?Resources Mentioned:Word, Sacrament, Charism: The Risks and Opportunities of a Synodal Church — Available at Ignatius PressFollow Gotta Be Saints:Instagram: @gottabesaintsWebsite: gottabesaints.comSubscribe, rate, and leave a review to help others discover the call to holiness through the voices of the Church.Sponsored by TruthlyThis episode is brought to you by Truthly — the world's first Catholic action app helping you reflect and share your faith confidently. Start your free trial at truthly.ai and use code gottabesaints for a special listener offer. Support the show

Today's saint was born in Rome and studied law before entering into public service as a Prefect. Two years later he became a monk. Who was this great saint who eventually become a Pope? Find out on today's reflection from Fr. Kubicki.

The Catholic Church celebrated the 10th anniversary of Pope Francis' election. Cardinal Michael Czerny, Prefect of the Dicastery for the Promoting Integral Human Development, reflected on Pope Francis' vision for the Church. This year, on March 19, many dioceses in the United States will observe the traditional Lenten practice of almsgiving by participating in The Catholic Relief Services Collection. Funds from this collection support the work of Catholic organizations that aid victims of war and natural disaster, provide pastoral care to migrant workers, and help welcome refugees fleeing violence and persecution. Bishop Seitz spoke about the role that this collection plays in vital Catholic programs that share in the Catholic mission of promoting the sacredness of human life and the dignity of the human person. Read the CRS Collection annual report. https://usccb.cld.bz/crs-2021-annual-report

On July 9, Pope Francis named twenty-one new cardinals, including two with deep ties to the United States: U.S.-born Archbishop Robert Prevost, Prefect of the Dicastery for Bishops, and Archbishop Christophe Pierre, Apostolic Nuncio to the United States. The cardinals-designate will be installed during a consistory in Rome on September 30. The Vatican has released names of several hundred participants in the upcoming Synod on Synodality in October. Along with bishops and cardinals from around the world, the Holy Father personally appointed other participants, including some non-bishop voting members. Catholic Current spoke with Archbishop Paul Etienne of Seattle, one of delegates appointed by Pope Francis.

"This Saint, who was born in Spain, was the Archdeacon of the Church of Rome, caring for the sacred vessels of the Church and distributing money to the needy. About the year 257, a harsh persecution was raised up against the Christians by Valerian. Pope Sixtus, who was from Athens, was commanded to worship the idols, and refused; before his martyrdom by beheading, he committed to Laurence all the sacred vessels of the Church. When Laurence was arrested and brought before the Prefect, he was questioned concerning the treasures of the Church; he asked for three days' time to prepare them. He then proceeded to gather all the poor and needy, and presented them to the Prefect and said, "Behold the treasures of the Church." The Prefect became enraged at this and gave command that Laurence be racked, then scourged with scorpions (a whip furnished with sharp iron points — compare II Chron. 10:11), then stretched out on a red-hot iron grill. But the courageous athlete of Christ endured without groaning. After he had been burned on one side, he said, "My body is done on one side; turn me over on the other." And when this had taken place, the Martyr said to the tyrants, "My flesh is now well done, you may taste of it." And when he had said this, and had prayed for his slayers in imitation of Christ, he gave up his spirit on August 10, 258." (Great Horologion). His icon shows him stretched on the grill.

"This Saint, who was born in Spain, was the Archdeacon of the Church of Rome, caring for the sacred vessels of the Church and distributing money to the needy. About the year 257, a harsh persecution was raised up against the Christians by Valerian. Pope Sixtus, who was from Athens, was commanded to worship the idols, and refused; before his martyrdom by beheading, he committed to Laurence all the sacred vessels of the Church. When Laurence was arrested and brought before the Prefect, he was questioned concerning the treasures of the Church; he asked for three days' time to prepare them. He then proceeded to gather all the poor and needy, and presented them to the Prefect and said, "Behold the treasures of the Church." The Prefect became enraged at this and gave command that Laurence be racked, then scourged with scorpions (a whip furnished with sharp iron points — compare II Chron. 10:11), then stretched out on a red-hot iron grill. But the courageous athlete of Christ endured without groaning. After he had been burned on one side, he said, "My body is done on one side; turn me over on the other." And when this had taken place, the Martyr said to the tyrants, "My flesh is now well done, you may taste of it." And when he had said this, and had prayed for his slayers in imitation of Christ, he gave up his spirit on August 10, 258." (Great Horologion). His icon shows him stretched on the grill.

Patreon: https://www.patreon.com/c/miccheckwaifuwaifu   Episodes of the week: To Be Hero X, Evil Lord of a Galactic Empire   Rascal Does Not Dream of Bunny Girl Senpai   Old Country Bumpkin   Lazarus/Once Upon a Witches Death   The Brilliant Healers New Life in the Shadows   Unaware Atelier Meister   Can this boy-girl relationship survive   WindBreakers Season 2   Shoshimin Season 2   Get to Know MCWW? Favorite anime director?   Review of The Too-Perfect Saint: Tossed Aside by My Fiance and Sold to Another Kingdom https://anilist.co/anime/183275/The-TooPerfect-Saint-Tossed-Aside-by-My-Fianc-and-Sold-to-Another-Kingdom/   Animation: Polow - 7 Telliano: 7.5 Sound Design: Polow - 8 Telliano - 8 Pacing: Polow - 9.5 Telliano - 8.5 Plot: Polow - 9.5 Telliano - 10 Characters: Polow - 9.5 Telliano - 9.5   Too-Perfect Saint Spoilers  

Overnight a story emerged from Rome from credible sources: That Pope Leo XIV had kept Fernandez on as Prefect for the Dicastery of the Doctrine of the Faith. Then the story got very strange.Sources:https://www.returntotradition.orgSponsored by Pray Latinhttps://praylatin.comContact Me:Email: return2catholictradition@gmail.comSupport My Work:Patreonhttps://www.patreon.com/AnthonyStineSubscribeStarhttps://www.subscribestar.net/return-to-traditionBuy Me A Coffeehttps://www.buymeacoffee.com/AnthonyStinePhysical Mail:Anthony StinePO Box 3048Shawnee, OK74802Follow me on the following social media:https://www.facebook.com/ReturnToCatholicTradition/https://twitter.com/pontificatormax+JMJ+

Overnight a story emerged from Rome from credible sources: That Pope Leo XIV had kept Fernandez on as Prefect for the Dicastery of the Doctrine of the Faith. Then the story got very strange.Sources:https://www.returntotradition.orgSponsored by Pray Latinhttps://praylatin.comContact Me:Email: return2catholictradition@gmail.comSupport My Work:Patreonhttps://www.patreon.com/AnthonyStineSubscribeStarhttps://www.subscribestar.net/return-to-traditionBuy Me A Coffeehttps://www.buymeacoffee.com/AnthonyStinePhysical Mail:Anthony StinePO Box 3048Shawnee, OK74802Follow me on the following social media:https://www.facebook.com/ReturnToCatholicTradition/https://twitter.com/pontificatormax+JMJ+

Join Lost Roman Heroes for Part 2 of our Honorable Mentions series, and meet some of the most remarkable Romans you never heard of: Pope Leo I (stood up to Attila), Anthemius the Prefect (built the Theodosian Walls), Constantine III (Britannia's last hope), Flavius Constantius (one of the West's last magister militums), and our favorite, Marcellinus (THE LAST JEDI)!  

It was an election that seems to have been faster than that of his predecessor, or at least as fast, since Francis had been elected in the sixth round on the second day. It once again took two days and five or six votes to elect the 267th Pope in history, and the first American Pope. At 6:08 p.m., white smoke rose above the roof of the Sistine Chapel, announcing to the crowd gathered in St. Peter's Square that the Church had a new Pope. But it wasn't until 7:12 p.m. that Cardinal Dominique Mamberti appeared on the Loggia and proclaimed the expected formula: "Annuntio vobis gaudium magnum: Habemus papam" (I announce to you a great joy, we have a (new) pope), Cardinal Robert Francis Prevost, who took the name Leo XIV. The new Roman Pontiff then stepped forward to pronounce his first Urbi et Orbi blessing. He began with a short speech in which he expressed his desire to promote a synodal Church. Born on September 14, 1955, in Chicago, he is 69 years old and an American citizen. He entered the Order of Saint Augustine in 1977 and made his perpetual profession in 1981. He was ordained a priest on June 19, 1982. He served as Prior General of the Augustinians from 2001 to 2013. He was appointed Bishop of Chiclayo, Peru, in 2014, a position he would hold until 2023. Since January 30, 2023, he has been Prefect of the Dicastery for Bishops. On September 30, 2023, he was created Cardinal Deacon of Santa Monica, before being elevated to the title of Cardinal Bishop of Albano on February 6, 2025. The question that arises, for those who wish to move beyond the emotion of the moment, is, "What will be the future of the Church in the Pontificate that is just beginning?" In 2013, only a small number of clergymen and Vaticanists who knew Cardinal Jorge Bergoglio from Argentina expressed any serious apprehension about his election. Thanks to the website "Cardinium Collegii recensio," it is possible to find out what each cardinal has said or written on sensitive issues such as the ordination of women to the diaconate, the blessing of same-sex couples, the option of priestly celibacy, the restriction of the traditional Mass, the agreement between China and the Vatican, and the promotion of a synodal Church. Regarding the new Pope, he declared that "the clericalization of women" would not solve the problems of the Catholic Church. He insisted, stating that "the apostolic tradition is something that has been very clearly stated, especially when we are talking about the issue of the ordination of women to the priesthood." However, he pointed out that Pope Francis recently appointed Sister Simona Brambilla Prefect of the Dicastery for Institutes of Consecrated Life and Societies of Apostolic Life. He commented: "I think it will continue to be recognized that women can contribute a great deal to the life of the Church at various levels." Regarding the blessing of same-sex couples, he presented somewhat ambiguous formulations. However, he appears to be a fervent supporter of the synodal Church. As reported above, he expressed his support for this idea from the loggia. The speed of the election suggests that it was a compromise election. There were so many divisions among the cardinals who entered the conclave that it may have been necessary to consider promoting a compromise cardinal. On the other hand, the choice of Leo XIV as a name may reflect a desire to move beyond the period that began with John XXIII. But ultimately, more about this new Pope will be discovered when he is at work, beginning with the appointments to head the various Roman dicasteries. Let us pray for the new Sovereign...

nsti.com/checkout Click to receive your $1 jump start at New Saint Thomas Institute for your Catholic Bible in a Year, Catholic Bible Cheat Sheet, and Catholic LIfetime Reading List and 10 Catholic Courses from Dr. Taylor Marshall on Catholic Bible, Catholic Philsophy, Latin Mass, Church Fathers, Mariology and more. https://www.birchgold.com/taylor — Get your FREE copy of the Ultimate Guide to Gold in the Trump Era today. Become a student member of New Saint Thomas Institute and get all 10 courses to begin at $1 by clicking here: 2025 Catholic Game Plan  Offer ends January 10 2025. https://amzn.to/4gpLIDI Get the book Nikolaos: A Retelling of the Saint Nicholas Story (discounted for Christmas)

Harry has been cleared of all charges! Ron and Hermione have been made Prefects! We're throwing a banger of a party at Grimmauld Place! Come join us as we discuss The Woes of Mrs. Weasley, just leave the boggart at home, please! Welcome, Slug Club Member Rex!  It's the holiday season and the MuggleCast Merch Store is open for all your gifting needs! Grab brand-new, original MuggleCast designs. We have t-shirts, sweaters, hoodies, hats, and more! And don't forget, you can now gift a Patreon subscription! Mark your calendars! A special holiday edition of Quizzitch Live is headed your way Sunday, December 15 at 12 PM ET. Stay tuned for more details! We discuss some exciting Harry Potter TV Show news: Papa Essiedou is rumoured as the favorite for Snape, while we get confirmation that filming is set for Summer 2025. David Holmes, the stuntman for Daniel Radcliffe, who was injured on the set of Deathly Hallows: Part 1 has released a new book called The Boy Who Lived. Chapter-by-Chapter continues with Order of the Phoenix, Chapter 9: The Woes of Mrs. Weasley. Our Time Turner segment has us spinning some old recordings from past discussions! Be sure to check out Episode 443 and Episode 241 for prior Chapter 9 analysis! Why was Dumbledore in such a rush to exit the trial chamber? And why didn't he bother to give Arthur the good news about Harry? What was Lucius Malfoy doing down on Level 9? How does he know enough about Harry's situation to call him "Patronus Potter"? We analyze his level of (financial) influence over Ministry officials. What does it say about Fudge that despite being surrounded by Death Eaters, Dumbledore believes he's acting of his own free will? Arthur explains Muggle Baiting to Harry. Do Fred and George's actions from last summer with ton-tongue toffee fall into this category? Why doesn't Sirius seem to be happy for Harry? When is he going to grow up? Are we surprised that everyone was surprised that Ron was made Prefect? Who would have made a better choice? Neville? Dean? Seamus? What are the pros and cons if Dumbledore had made Harry a Prefect? Molly's Greatest Fear and the many lessons it teaches us as readers What would each of our boggarts be (within the context of Grimmauld Place)? Lynx Line: Should Dumbledore have made Harry a Prefect instead of Ron? Why or why not? Quizzitch: What fountain gets the most money thrown into it per year, in the real world? Don't forget you can support the show over at Patreon.com/MuggleCast! You'll get great benefits such as Bonus MuggleCast, live streams, yearly stickers, Lynx Line participation, a physical gift, a video message from one of the four of us made just for you, our private Facebook and Discord groups where you can hang out with fellow Potter fans, and so much more! On this week's Bonus MuggleCast, we debate whether or not the Harry Potter Movies are Christmas movies? Visit MuggleCast.com for episode transcripts, social media links, our full episode archive, our favorite episodes, and to contact us! Learn more about your ad choices. Visit megaphone.fm/adchoices