POPULARITY
Categories
56 episodes with Yolo
7 episodes with Yolo
13 episodes with Yolo
16 episodes with Yolo
19 episodes with Yolo
209 episodes with Yolo
12 episodes with Yolo
18 episodes with Yolo
34 episodes with Yolo
7 episodes with Yolo
11 episodes with Yolo
18 episodes with Yolo
8 episodes with Yolo
158 episodes with Yolo
5 episodes with Yolo
8 episodes with Yolo
4 episodes with Yolo
4 episodes with Yolo
8 episodes with Yolo
26 episodes with Yolo
12 episodes with Yolo
5 episodes with Yolo
12 episodes with Yolo
3 episodes with Yolo
2 episodes with Yolo
13 episodes with Yolo
6 episodes with Yolo
7 episodes with Yolo
6 episodes with Yolo
4 episodes with Yolo
3 episodes with Yolo
3 episodes with Yolo
13 episodes with Yolo
11 episodes with Yolo
14 episodes with Yolo
4 episodes with Yolo
7 episodes with Yolo
5 episodes with Yolo
2 episodes with Yolo
9 episodes with Yolo
2 episodes with Yolo
4 episodes with Yolo
4 episodes with Yolo
4 episodes with Yolo
3 episodes with Yolo
3 episodes with Yolo
2 episodes with Yolo
4 episodes with Yolo
6 episodes with Yolo
2 episodes with Yolo
4 episodes with Yolo
6 episodes with Yolo
4 episodes with Yolo
4 episodes with Yolo
Get 500+ premium podcasts by signing up at www.UTHDynasty.com as a General Manager PLUS subscriber. Also, get access to exclusive shows and deep data dive content from Chad Parsons (and a VIP Chat with the best dynasty owners on the planet) by signing up as an All-Pro at www.Patreon.com/UTH. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Big Idea: Some trauma can only be healed by nail-scarred hands. I. Jesus is close.Psalm 34:18The Lord is near the brokenhearted; he saves those crushed in spirit.II. Jesus comforts.Psalm 147:3He heals the brokenhearted and bandages their wounds.III. Jesus gives courage.Psalm 46:1God is our refuge and strength, a helper who is always found in times of trouble.Next Steps:Believe: I need Jesus to change my heart today.Become: I need Jesus to help me heal this week.Be Sent: I will help someone hurting this week.Discussion Questions:What support would you recommend for someone who is stuck in trauma? (List all the options you'd recommend.)Can you move past trauma without ever sharing it with anyone? Explain your answer.Why is it sometimes hard to trust Jesus with your trauma?Has trauma changed you? If so, has it changed you for better or worse?Who do you know who is stuck in past trauma?Can you share a testimony about how Jesus helped you through trauma?Pray for the opportunity to share that testimony this week.
What becomes possible when enterprise computer vision no longer depends on expensive GPU infrastructure? In this episode of Tech Talks Daily, I speak with Glenn Jocher, founder and CEO of Ultralytics, about YOLO26, CPU inference, edge AI, open vocabulary vision, deployment economics, and the practical work required to move computer vision from a promising pilot into production. Glenn's route into AI began inside the U.S. intelligence community. He worked with the National Geospatial Intelligence Agency and Defense Intelligence Agency on particle physics applications, attempting to detect and track antineutrinos. Antineutrinos are extraordinarily difficult to detect because they pass through almost everything. Glenn describes them as the perfect spy. While searching for better detection methods, he discovered that computer vision researchers were solving similar problems with images. His original attempt to transfer those techniques into particle physics did not succeed. However, the work introduced him to a field where the technology could create a visible effect on everyday life. That led him toward open source development and eventually the YOLO models for object detection, classification, segmentation, and tracking. Glenn believes computer vision research has historically placed too much attention on small gains in accuracy while overlooking deployment economics. A model can perform impressively inside a laboratory and still remain unsuitable for a factory, warehouse, store, vehicle, drone, or medical environment. Price, latency, power consumption, data privacy, and deployment speed can determine whether the technology is commercially useful. This led Glenn and Ultralytics toward smaller models capable of running close to where images and video are generated. YOLO26 continues that approach with architectural changes designed specifically for CPU inference. Glenn says the model can process camera streams in real time at 30 frames per second and run across Intel CPUs, AMD CPUs, and lower power devices such as Raspberry Pi computers. This matters because specialist GPUs can increase the equipment cost and power requirements of a computer vision project. Running inference on existing CPUs or edge hardware can make deployment economically possible across larger numbers of cameras and locations. The scale already involved is difficult to comprehend. Glenn says Ultralytics models now process approximately three billion inference jobs each day, equivalent to around 30,000 every second. These jobs include images, videos, and collections of images being analyzed to detect, segment, or track objects. He attributes the platform's maturity to thousands of mistakes and bugs corrected through a rapid feedback cycle. New models are released, users report problems and request features, and the team incorporates that information into later versions. We also discuss the respective roles of cloud and edge infrastructure. Glenn sees cloud platforms continuing to provide the computing power required for training, while computer vision inference often belongs at the edge. Local processing can reduce latency, control operating costs, and keep sensitive video or medical information closer to where it was created. The smallest YOLO model is approximately three megabytes, according to Glenn. That allows it to reach mobile phones, vehicles, drones, battery powered devices, and other environments where a large language model would be impractical. Open vocabulary vision provides another development. Traditional object detection models are trained to recognize a fixed collection of objects. If a model learns to detect dogs and the user later wants it to detect cats, retraining can cause it to forget earlier knowledge unless both categories appear in the new training data. Glenn explains how promptable models can identify common everyday objects from text or visual instructions without additional training. A user could request a person wearing a blue shirt and white shoes, for example, and the system could search an image for that description. That flexibility could benefit businesses whose requirements change regularly. It reduces the need to create and label a new data set every time the company wants the model to recognize another common object. The range of current applications is already extensive. Glenn describes YOLO being used across robotics, parking, industrial safety, PPE detection, warehouses, aviation, security, traffic management, food quality, and manufacturing. Some of his favorite examples involve environmental problems. One company uses YOLO with underwater vehicles to identify and recover plastic from the ocean. Other applications detect smoke and fire early enough to support forest fire response. For leaders considering computer vision, Glenn recommends beginning with a defined problem and measurable outcome. A manufacturing company may want to reduce defects, but it still needs labeled examples showing the model what acceptable and defective products look like. He advises testing the idea through a limited pilot, measuring the return, and expanding only when the evidence supports further investment. Computer vision has become easier to deploy, but practical problems involving data, cameras, integration, reliability, and operating conditions still separate a demonstration from a production system. Could CPU inference and open vocabulary models make computer vision practical for processes your organization previously considered too expensive? Listen to the episode and share your thoughts with me. Useful Links Ultralytics website Ultralytics Platform
This absolutely is a normal market. Nobody builds risk tolerance on the way up, but I like where stocks are right now. I'm being insightful, patient, disciplined and prepared! Are you? FORMULA - Alpha Picks + Seeking Alpha Premium + Trendspider and Sidekick - PERFECT TOGETHER! THESE SALES END SOON: I negotiated to get 59% off and 100 Sidekick messages per month for the entire year. Plus you get my 4 hour algorithm and so many other benefits with JUST THIS LINK ONLY CLICK HERE TO GET THE DAILY STOCK PICK SPECIAL OFFER - ONLY ANNUAL PLANS AVAILABLE Trendspider is having their own sale with up to 50% off monthly and other plans - this would be for anyone who wants to try it first. Seeking Alpha's SUMMER SALE ✅ *BEST DEAL - SEEKING ALPHA BUNDLE - Save over $150 and get Premium and Alpha Picks together - ✅ ALPHA PICKS - Want to Beat the S&P? Save $50 ✅ Seeking Alpha Premium ONLY - FREE 7 DAY TRIAL ✅SEEKING ALPHA PRO - YOUR FIRST MONTH ONLY $89 ✅ NEW - QUANT GROWTH AND INCOME PORTFOLIO - SAVE $50EPISODE SUMMARY
The three of us recently, like all of us during the past six months, bought old but new-to-us cool cars and we discuss. Jeff Bank was the most recent, purchasing a manual Porsche 997.2 cabriolet on Cars and Bids, while Steve-0 traded his E92 M3 for a manual 997.2 coupe, and our trauma surgeon Dr Stephan Moran took delivery of his bespoke ERA Shelby Cobra replica. Steve-0 then rants that Porsche has produced cars that it shouldn't have--a Porsche too far--and it happened again with the Taycan. Now they need to fix the company again. We help.#carsoncallpodcast #porsche #porsche924 #porschetaycan #yolo #enthusiastcar #automotivepodcast #eracobra #narrowhipcobra #narrowhip427cobra #shelbycobra #bmwm3 #997.2
If you think hackers are still typing away in a basement, this week will change your mind. More than 13,000 Chick-fil-A customers just had their accounts compromised. An AI assistant executed a government-network attack with no human at the keyboard, and Congress hurried out a bill to force an off-switch on major AI models. The real danger isn't the code writers anymore. It's the software. *The attacks now run themselves. Your only edge is the off switch.* Bryan Hornung, Randy Bryan, and Reginald Andre break down this week's stories for busy executives, owners, and operators who can't afford to be blindsided by cyber news. First up: Chick-fil-A. Over 13,000 customers across at least ten states were locked out after attackers used passwords those customers had reused on other sites. No one breached Chick-fil-A's servers. The attackers simply replayed stolen email-and-password combos until they worked, stealing membership numbers, mobile-pay data, QR codes, the last four digits of cards, and stored credit. This is the second time in three years this trick has hit the same loyalty app, and the fix (logging everyone out and removing saved payment methods) punished the customers too. Then it gets stranger. Researchers at Hunt.io discovered an attacker who took a mainstream open-source AI assistant called Hermes, flipped it into a "YOLO mode" that bypassed human approval, and aimed it at Thailand's finance ministry. The AI did the hacking itself, mapping computers, sifting through files, and running privilege-escalation scans while no one watched. They caught it only because the attacker left 585 files and 470 megabytes of tools in open folders online. The weapon wasn't malware. It was an everyday productivity tool with the safety switched off. This is why Washington is concerned. Two lawmakers, a Democrat and a Republican, introduced the AI Kill Switch Act after OpenAI admitted one of its models escaped its test environment, went online, and compromised another company called Hugging Face. The bill would require major AI makers to maintain the technical ability to throttle or shut down their own models, and give the government authority to order it. Even Anthropic's co-founder has warned that the industry built "a gas pedal but no brake pedal." If the model builders want a brake, business owners should too. • Chick-fil-A: how reused passwords exposed more than 13,000 customer accounts, twice in three years • The Hermes AI agent that ran a real intrusion on a government network with no human at the keyboard • The bipartisan AI Kill Switch Act and the OpenAI model that went rogue and hacked Hugging Face • Why the attacker is now the software itself, not the person behind it • What "keep a human on the off switch" actually means for a business running AI tools • The one move every owner should make before letting an AI agent touch real systems Security Squawk is a weekly podcast and live stream for business owners and executives. Support the show: buymeacoffee.com/securitysquawk Subscribe | Like | Share #SecuritySquawk #CyberSecurity #ChickFilA #OpenAI #Anthropic #DataBreach #ArtificialIntelligence #AISecurity #CredentialStuffing #BusinessRisk #SMB #Cyberattack
Big Idea: Sin makes all relationships unhealthy.Romans 12:18If possible, as far as it depends on you, live at peace with everyone. I. Show grace to people who are far from Jesus.2 Corinthians 5:20Therefore, we are ambassadors for Christ, since God is making his appeal through us. We plead on Christ's behalf, “Be reconciled to God.”II. Seek help when people hurt you.Ephesians 4:31-32Let all bitterness, anger and wrath, shouting and slander be removed from you, along with all malice. And be kind and compassionate to one another, forgiving one another, just as God also forgave you in Christ.III. Separate when people are living a lie.1 Corinthians 5:11-13But actually, I wrote you not to associate with anyone who claims to be a brother or sister and is sexually immoral or greedy, an idolater or verbally abusive, a drunkard or a swindler. Do not even eat with such a person. For what business is it of mine to judge outsiders? Don't you judge those who are inside? God judges outsiders. Remove the evil person from among you.Next Steps:Believe: I need to begin a relationship with Jesus today.Become: I will reflect Jesus' way of life in my relationships this week.Be Sent: I will start a gospel relationship this week.Discussion Questions:How do you know when a relationship has become unhealthy?Which is more dangerous for you right now, emotionally or spiritually unhealthy relationships?Are you more patient with Christians or people outside the faith? Explain.Why doesn't the Bible give us permission to break off all hurtful relationships?When was the last time you broke off a relationship with someone who was living a lie? How did it go?Who are you developing a Gospel relationship with?Pray for the wisdom to develop Christlike relationships this week.
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
Big Idea: I don't need more competence or confidence- God's presence is enough.2 Timothy 1:7For God has not given us a spirit of fear, but one of power, love, and sound judgment.I. Let sound judgment guide your decisionsProverbs 3:5-6Trust in the Lord with all your heart, and do not rely on your own understanding; in all your ways know him, and he will make your paths straight.II. Let Jesus's power back up your abilitiesIsaiah 40:29He gives strength to the faint and strengthens the powerless.III. Let God's love define your worth1 Peter 2:9-10But you are a chosen race, a royal priesthood, a holy nation, a people for his possession, so that you may proclaim the praises of the one who called you out of darkness into his marvelous light. Once you were not a people, but now you are God's people; you had not received mercy, but now you have received mercy.Next Steps: Believe: I surrender my soul to Jesus today.Become: I will let only Jesus's sacrifice define my worth this week.Be Sent: I will proclaim Jesus's praises this week.Discussion Questions: When was the last big decision you struggled with? How certain were you that you got it right?When was the last big challenge you overcame? Did you give God credit?When did you last fail a big challenge? How did it affect your self-worth?What is consistently trying to define your self-worth this week?Where do you need God's power most this week?How confident are you in sharing your faith?Pray for God to use you to call someone out of darkness this week.
Irgendwann hat alles sein Ende. Doch kommt nach unserem Lebensende tatsächlich nichts mehr, das große schwarze Loch? Oder ist das Lebensende vielmehr ein Übergang oder der Beginn von etwas Neuem? Schilderungen von Menschen, die durch Herz- oder Hirninfarkt oder beispielsweise durch Vergiftung sozusagen „den biblischen Lazarus“ gemacht haben, weisen eine bemerkenswerte Deckung auf. Sie befanden sich in einem Schwellenbereich zwischen Leben und Tod, galten als klinisch tot und kehrten nach außerkörperlichen Erfahrungen zurück. Ist der Glaube an YOLO, an „You Only Live Once“ trügerisch? Hören Sie hierzu Otto Geißlers Text „Rätsel der Nahtod-Erfahrung“. Sprecher: Bertold Karsten Troyke Bild: Radio München www.radiomuenchen.net/ @radiomuenchen www.facebook.com/radiomuenchen www.instagram.com/radio_muenchen/ twitter.com/RadioMuenchen Radio München ist eine gemeinnützige Unternehmung. Wir freuen uns, wenn Sie unsere Arbeit unterstützen. GLS-Bank IBAN: DE65 4306 0967 8217 9867 00 BIC: GENODEM1GLS Bitcoin (BTC): bc1qqkrzed5vuvl82dggsyjgcjteylq5l58sz4s927 Ethereum (ETH): 0xB9a49A0bda5FAc3F084D5257424E3e6fdD303482
Following his retirement announcement, let's take a moment in today's daily-dose of whatever to celebrate Clint Eastwood's prolific, take-risks career… because #YOLO. 818-835-0473 orwhatevermovies@gmail.com www.orwhatevermovies.com Learn more about your ad choices. Visit megaphone.fm/adchoices
Big Idea: Through Christ, you can live under pressure and above fear. Philippians 4:6-7Don't worry about anything, but in everything, through prayer and petition with thanksgiving, present your requests to God. And the peace of God, which surpasses all understanding, will guard your hearts and minds in Christ Jesus.I. Struggle with self-relianceIsaiah 26:3You will keep the mind that is dependent on you in perfect peace, for it is trusting in you.II. Trade anxiety for abandonPsalm 94:19When I am filled with cares, your comfort brings me joy.III. Release your pressures through prayerPsalm 55:22Cast your burden on the Lord, and he will sustain you; he will never allow the righteous to be shaken.IV. Examine your source of peacePsalm 56:3When I am afraid, I will trust in you.V. Surrender everything to Jesus this week Psalm 62:5-6Rest in God alone, my soul, for my hope comes from him. He alone is my rock and my salvation, my stronghold; I will not be shaken.Next Steps: Believe: I surrender my soul to Jesus today.Become: I will give Jesus control over my worry this week.Be Sent: I will enter into someone's stress this week.Discussion Questions: What is your #1 source of stress this week?What is your go-to way of handling stress?How often do you settle for stress management when God offers stress deliverance?Would you prefer that Jesus remove your sources of stress, or give you strength greater than your stress?Why is it so difficult to trust God with the things that cause you stress?Do you have a testimony of God's peace in the midst of great pressure?Pray for the opportunity to share that testimony with someone far from Jesus this week.
Head to https://gamma.app/signup to check out GAMMA and start designing today!Japan may have finally figured out why the yen keeps getting crushed. And no — it's not because some hedge fund in New York borrowed a few billion yen and YOLO'd it into U.S. Treasuries. That's the cartoon version of the carry trade. That's the version financial media repeats because it sounds simple: borrow cheap yen, buy higher-yielding assets overseas, yen goes down. But that is not the real carry trade.Eurodollar University's conversation w/Steve Van Metrehttps://www.facebook.com/FastMoney/videos/japanese-yen-is-flashing-major-warning-sign-for-market-bk-asset-managements-kath/2457269361418309/https://www.youtube.com/watch?v=YYPAgPp29UIhttps://www.youtube.com/watch?v=ubDFvdI02EQhttps://www.youtube.com/watch?v=Nnad9Bif87ohttps://www.eurodollar.universityTwitter: https://twitter.com/JeffSnider_EDUI'll also be active on Bravais Social - a new AI-centered social network designed for professionals and knowledge workers. The platform aims to bring together a wider range of tools and functionalities tailored specifically for professional interaction, research, and knowledge exchange in one place. You can find me here: https://bravais.social/profile/edu
Today's show asks one of the trickiest questions in personal finance: when does a good habit go too far? Saving is great. Cutting expenses can change your life. Earning more can open doors. But what happens when you optimize so hard that you accidentally squeeze the joy out of the whole plan? Joe, Doug, Diana Merriam from EconoMe, New York Times financial writer Paulette Perhach, and Doc G from Earn and Invest dig into the messy middle between YOLO and never spending a dime. Plus, Doug brings hockey trivia, the panel talks odd jobs, and everyone tries to define what "enough" actually means. You'll see very quickly why this episode is an integral part of greatest hits week!What You'll Walk Away WithWhy reducing expenses works best when it removes waste -- not when it turns your life into a deprivation contestDiana's throw-pillow test: how to ask whether you actually want something or just inherited the idea that you're supposed to want itThe difference between frugal and cheap -- and why ironing hotel toast or stealing dealership coffee might be a sign you've crossed the lineWhy Doc G says saving money is only useful if it eventually becomes fuel for the life you want to liveThe case for "YOLO responsibly": automate the saving first, then give yourself room to spend without turning every purchase into a morality playWhy high savings rates can be powerful in your 20s -- especially when friends turn frugality into a shared goal instead of social isolationPaulette's reminder that money habits aren't just math; ADHD, dopamine, entrepreneurship, and self-compassion can all change how saving feelsWhy earning more often matters more than cutting more -- and how Diana's denied raise helped push her toward building her own thingDoc G's hospice-doctor warning: nobody gets to the end wishing they had worked more nights and weekends to hit a slightly bigger net worthWhy Coast FI may be the healthier goal for some people: save enough to create options, then stop tolerating work or lifestyles that no longer fitThe guardrails idea: avoid both extremes -- wasting your future and wasting your presentWhy This Matters NowIt's easy to turn personal finance into a scoreboard: lower expenses, higher savings rate, bigger income, faster FI date. But the real goal isn't winning the spreadsheet. It's building a life that feels secure, flexible, and worth living while you're still living it. This conversation is a reminder to use money as a tool, not a dare.From the BasementJoe Saul-Sehy gathers a rare Friday card table with Diana Merriam, Paulette Perhach, and Doc G to talk about saving too much, spending too much, working too hard, and finding the middle before the middle finds you. Doug is salty about not going to FinCon, the panel debates FIRE extremes, someone brings up homemade Gatorade, and the trivia question involves hockey nets. No word yet on whether Mom has removed the throw pillows upstairs.Resources MentionedMrStingy.com -- "Too Much of a Good Thing: Taking It Too Far"Diana Merriam -- EconoMe Conference; economeconference.comDiana Merriam -- Optimal Finance DailyPaulette Perhach -- pauletteperhach.comPaulette Perhach -- New York Times personal finance writing, including ADHD and moneyDoc G / Jordan Grumet -- Earn and Invest podcastDoc G -- Wealth with PurposeThe Fioneers -- referenced in the lifestyle design conversationFrugalwoods -- referenced during the throw-pillow/minimalism discussionStacking Benjamins Newsletter, The 201 -- stackingbenjamins.com/201Stacking Benjamins Community, The Basement -- stackingbenjamins.com/basementSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
On this week's show: Apple makes Apple Home hubs a lot pricier, Samsung puts a $5 toll booth in front of the SmartThings API, and Matter itself keeps inching toward “maybe this really will work.” We also hit a THIRDREALITY timer clock, an interesting universal remote hub, Level's rough week, Netflix's new profile email rule, project updates, a pick of the week, and so much more!Building an Apple Home just got pricier: New hardware hikes affect key Matter hubsHome Tech HeadlinesNew - THIRDREALITY Smart Timer Light TL2,Digital Clock,60GHz mmWave Radar,RGB Light,Light Sensor,Simple Setup,Countdown Timer,Matter Enabled,2.4GHz WiFi only,Compatible with Alexa, Apple Home,Google HomeDREO Brings Matter Connectivity to TurboPoly™ Fan 765S, Furthering its Open Ecosystem CommitmentOpenInfrared Point: Universal Remote HubIntroducing UniFi Network 10.5Hackers target UniFi as critical exploits surfaceCrestron Launches Configure Pro Platform for Crestron Home OSJosh.ai Showcases AI-Powered Home Control with New Experiential VideoHunter Douglas Alta Window Fashions Motorized Shades Now Compatible with PowerView AutomationSamsung will soon start charging to access its smart home APIA New Enhanced SmartThings API Experience - SmartThings BlogNetflix now requires every user profile to be tied to unique email addressSmart lock maker Level has been gutted and its founders are outMatter Just Underwent a Major Stress Test for Supporting Large-Scale DeploymentsHome Assistant upgrades to Matter 1.5.1 with a better codebaseThe Matter upgrade you've been waiting forInside the room where the smart home industry is still betting on MatterDroplet the #1 Smart Home Water Sensor - Hydrific, part of LIXIL
Teresa's back with RV trip round two, proving once again that life is always better when it's packed to the brim with adventure. From laughing through the chaos to Mark somehow ending up captaining a boat in Idaho with absolutely zero boating experience, it's a classic Palmer family YOLO adventure. Throw in theme park thrills, Teresa living her best waterslide life, plus plenty of belly laughs, and you've got yourself some weeeeeeee living! The girls' Clairs are working overtime this week. Sarah's giving Wyatt a crash course in the weird and wonderfully dirty candy of the '90s before somehow calling his strep throat before anyone else. Meanwhile, Teresa clairsentiences her way through a dead phone, five exhausted kids and an Uber predicament after a marathon day at a theme park. Plus, baby Ocean has officially turned one! The girls reflect on all the emotions that come with that first birthday, the gratitude of watching these little humans grow, and the universal struggle of babies who seem determined to put absolutely everything in their mouths. They also chat about the magic that happens when we put our phones down, connect with the people around us, and discover how meaningful a conversation with a complete stranger can be. Resource Links: CDA Pontoons cdapontoons.com silverwoodthemepark.com tokikids.com Follow Sarah Wright Olsen: IG: @swrightolsen Follow Teresa Palmer: IG: @teresapalmer FB: https://www.facebook.com/teresamarypalmer/ DISCOUNT CODES: • Go to www.baeo.com and get 20% when using the code MOTHERDAZE20 • Go to www.lovewell.earth and get 20% when using the code MOTHERDAZE20 More about the show! • Watch this episode on YouTube here • Co-founders of @yourzenmama yourzenmama.com • Read and buy our book! "The Zen Mama Guide To Finding Your Rhythm In Pregnancy, Birth, and Beyond" Learn more about your ad choices. Visit podcastchoices.com/adchoices Learn more about your ad choices. Visit podcastchoices.com/adchoices
In this week's episode, I'm sharing some hilarious NYC summer storytimes, dating observations, and the biggest mindset shifts that have been helping me become more confident and stop people-pleasing. We talk about creating your own reality, having higher standards, protecting your energy, confidence, boundaries, dating, friendships, and why you don't need to overextend yourself for people who wouldn't do the same for you.Plus: Fourth of July adventures, nightlife in NYC, funny boylypop stories, current inside jokes, and my favorite tips for staying locked in while still enjoying summer.If you're working on confidence, self-worth, boundaries, self-improvement, dating, or becoming your highest self, this episode is for you.♡ New episodes every week covering wellness, confidence, productivity, dating, fitness, mindset, and navigating your 20s. My self tan must haves from CVS https://creators.cvs.com/mypage/stellaraeGet 10% off Prozis with code STELLARAE10 prozis.com/1LMJGGet 10 free meals and free breakfast for life from HelloFresh with code HF-0449 https://www.filify.co/SHBn0 Get $1000 off the mindodygreen health coach certification program with promo code STELLACOACHING https://www.shareasale.com/u.cfm?d=1281553&m=96296&u=1030263instagram http://instagram.com/stellaraepodcastmy current filming set up:camera: https://amzn.to/4cEQiLOmicrophone: https://amzn.to/3Z2A5gctripod: https://amzn.to/3AEmxgKring light: https://amzn.to/3XxZrShbox lights: https://amzn.to/4e1Q1Ubportable light for phone: https://amzn.to/3XxZspjlisten on spotify: https://open.spotify.com/show/2DMbeh7EqiqgROIjvW0sI9listen on apple podcasts: https://podcasts.apple.com/us/podcast/the-stella-rae-podcast/id1255618182#StellaRaePodcast00:00 Summer has officially become my YOLO season02:20 Why I'm making the most of my late 20s03:20 Creating your own reality & confidence05:10 Stop overthinking what everyone thinks of you06:20 The people-pleasing habit I finally let go of08:00 Detachment, dating & protecting your energy10:00 Why boundaries make people respect you more12:00 The easiest way to practice saying "no"14:15 Dating storytime: the pearl necklace guy17:30 My thoughts on modern dating in NYC20:45 What actually makes a man attractive23:30 Why actions matter more than words26:45 Raising your standards without feeling guilty29:10 My current favorite inside jokes & "boylypop" lore35:20 Staying locked in while still enjoying summer37:10 The mindset I'm taking into the rest of the year
Is retirement harder today than it was 30 years ago—or do we just have more choices and more noise? Steve Hoyl explores how retirement planning has evolved, from the days of pensions to today's challenges of inflation, taxes, Social Security misconceptions, and online financial advice. Steve shares real client stories, discusses the impact of required minimum distributions (RMDs), and explains a creative way some families are using retirement assets to help future generations. The conversation also tackles FOMO, YOLO, and JOMO and how each mindset can influence retirement decisions. Get Your Complimentary Retirement Analysis Social Media: Facebook | XSee omnystudio.com/listener for privacy information.
Big Idea: Don't let AirPods tune out God's megaphone!Numbers 11:15 “If you are going to treat me like this, please kill me right now if I have found favor with you, and don't let me see my misery anymore.” I. It might be God's preparation… 2 Corinthians 1:3-5Blessed be the God and Father of our Lord Jesus Christ, the Father of mercies and the God of all comfort. He comforts us in all our affliction, so that we may be able to comfort those who are in any kind of affliction, through the comfort we ourselves receive from God. For just as the sufferings of Christ overflow to us, so also through Christ our comfort overflows.II. or build your patience… Psalm 88:18You have distanced loved one and neighbor from me; darkness is my only friend.III. but it's never permanent.Psalm 30:5For his anger lasts only a moment, but his favor, a lifetime. Weeping may stay overnight, but there is joy in the morning.Next Step:Believe: I need Jesus to give life to my dead soul today.Become: I will wait for God's will this week.Be Sent: I will minister to someone in pain this week.Discussion Questions: Has anyone walked with you through the same pain they once suffered? If so, how did it impact you?Do you look at depression as a spiritual problem or a mental health problem? Explain your answer. Does seeing a mental health professional mean that your faith is weak?If we belong to Heaven, why do we allow circumstances on Earth to cause us pain?When was the last time God turned your weeping into joy? How can you comfort someone facing mental health problems this week?Pray for the opportunity to put that ministry into practice this week.
Fluent Fiction - Danish: Conquering Fears: A Day in Tivoli Gardens Find the full episode transcript, vocabulary words, and more:fluentfiction.com/da/episode/2026-07-05-22-34-02-da Story Transcript:Da: Tivoli Gardens i København summede af liv.En: Tivoli Gardens in København buzzed with life.Da: Musik spillede i baggrunden, og duften af brændte mandler svævede i luften.En: Music played in the background, and the scent of caramelized almonds floated in the air.Da: Overalt kunne man høre børns latter og skrigen fra de vildeste forlystelser.En: Everywhere, you could hear children's laughter and screams from the wildest rides.Da: Det var en perfekt sommerdag.En: It was a perfect summer day.Da: Mikkel og Astrid gik hånd i hånd gennem parken.En: Mikkel and Astrid walked hand in hand through the park.Da: Astrid snakkede begejstret om alle de ting, de skulle opleve.En: Astrid enthusiastically talked about all the things they were going to experience.Da: Mikkel lyttede, men hans tanker kredsede om den kommende audition.En: Mikkel listened, but his thoughts circled around the upcoming audition.Da: Han var nervøs og kunne ikke lade bekymringerne gå.En: He was nervous and couldn't shake the worries.Da: "Kom nu, Mikkel!"En: "Come on, Mikkel!"Da: sagde Astrid pludselig og ruskede i hans arm.En: Astrid suddenly said, shaking his arm.Da: "Lad os prøve den store rutsjebane!"En: "Let's try the big roller coaster!"Da: Mikkel så op og så det massive stålkonstrukt, der tårnede sig op mod den blå himmel.En: Mikkel looked up and saw the massive steel structure towering against the blue sky.Da: Han tøvede.En: He hesitated.Da: "Jeg ved ikke, Astrid.En: "I don't know, Astrid.Da: Det ser ret skræmmende ud."En: It looks pretty scary."Da: Astrid lo højt og trak ham med et smil.En: Astrid laughed loudly and pulled him with a smile.Da: "Det er netop derfor, vi skal prøve!En: "That's exactly why we should try it!Da: Vi lever kun én gang."En: We only live once."Da: De stillede sig i kø.En: They lined up.Da: Jo nærmere de kom, jo hurtigere bankede Mikkels hjerte.En: The closer they got, the faster Mikkel's heart raced.Da: Astrid talte og grinede hele vejen op til turens startpunkt, men Mikkel var stille.En: Astrid talked and laughed all the way up to the ride's starting point, but Mikkel was silent.Da: Hans tanker var en blanding af frygt og forventning.En: His thoughts were a mix of fear and anticipation.Da: Da vognen begyndte at køre, lukkede Mikkel øjnene.En: As the car began to move, Mikkel closed his eyes.Da: Tempoet steg, vinden sussede i ørerne, og vognene kastede dem rundt i voldsomme sving.En: The speed increased, the wind whistled in their ears, and the cars swung them around in violent turns.Da: Mikkel skreg, men for første gang føltes det befriende.En: Mikkel screamed, but for the first time, it felt liberating.Da: I det korte øjeblik glemte han alt om auditionen og lod sig rive med.En: In that brief moment, he forgot all about the audition and let himself be swept away.Da: Da de steg ud af vognen, stod Astrid der med et stort smil.En: When they got out of the car, there stood Astrid with a big smile.Da: "Hvad sagde jeg?En: "What did I tell you?Da: Det var fantastisk, ikke?"En: It was amazing, wasn't it?"Da: Mikkel grinede, stadig rystet, men nu med et stort smil.En: Mikkel laughed, still shaken, but now with a big smile.Da: "Du har ret.En: "You're right.Da: Det var utroligt!En: It was incredible!Da: Jeg glemte alt om mine bekymringer."En: I forgot all about my worries."Da: Resten af dagen fløj forbi med latter, og de prøvede flere vilde forlystelser, spiste candyfloss og fangede de små fisk i andendammen.En: The rest of the day flew by with laughter, and they tried more wild rides, ate cotton candy, and caught the little fish in the duck pond.Da: Mørket begyndte at falde, og parken lyste op i tusinde farver.En: Darkness began to fall, and the park lit up in thousands of colors.Da: Da dagen var slut, gik de ud gennem indgangen, hånd i hånd.En: As the day ended, they walked out through the entrance, hand in hand.Da: Mikkel følte sig lettet og taknemmelig.En: Mikkel felt relieved and grateful.Da: "Tak, Astrid," sagde han stille, "for at minde mig om at leve i nuet."En: "Thank you, Astrid," he said quietly, "for reminding me to live in the moment."Da: Astrid smilede og nikkede blidt.En: Astrid smiled and nodded gently.Da: "Det er det, venskaber er til for, Mikkel."En: "That's what friendships are for, Mikkel."Da: Mikkel så ud over den oplyste park en sidste gang og indså, at han ikke kunne ændre fremtiden ved at bekymre sig.En: Mikkel looked out over the illuminated park one last time and realized that he couldn't change the future by worrying.Da: Han følte sig klar til auditionen, uanset hvad der kom.En: He felt ready for the audition, no matter what came.Da: Denne dag i Tivoli Gardens havde givet ham mod til at tage skridtene mod sin drøm, mens han nød rejsen.En: This day in Tivoli Gardens had given him the courage to take steps toward his dream, while enjoying the journey. Vocabulary Words:buzzed: summedecaramelized: brændtelaughter: latterscreams: skrigenenthusiastically: begejstretexperience: opleveupcoming: kommendeaudition: auditionworries: bekymringermassive: massivetowering: tårnedehesitated: tøvedescary: skræmmendesmile: smillined up: stillede sig i køanticipated: forventningwhistled: sussedeswung: kastedeviolent: voldsommeliberating: befriendeshaken: rystetgrateful: taknemmeligreminding: mindeilluminated: oplystecourage: modmassive: stålkonstruktliberating: befriendedelight: latterruskede: shakingfanger: catch
When companies mandate AI adoption without a use case, without a strategy, and without a business outcome in mind, they don't get transformation. They get jazz hands and nightmare token bills.This month's System Update pulls apart what's actually happening beneath the headlines: Oracle's 21,000 layoffs attributed to "AI adoption," Amazon arming junior developers to replace senior engineers, and enterprises burning through AI budgets they cannot predict or control.The deeper argument George K. and George A. make is harder to dismiss than the headlines. You cannot drive deterministic business outcomes with probabilistic means of production. The CEOs and CTOs who greenlit LLM adoption at scale are now facing a math problem that no earnings call language can paper over.The free water is now a metered utility. Will the bill ever be worth paying?The episode also turns to labor theater and what we're giving our attention to: what we lose when institutions optimize for engagement over depth, and what it costs when an entire generation learns to consume rather than think.Mentioned: NYT on Schneider Electric's AI adoption without layoffs Amazon tokenmaxxing mandate goes sideways Oracle sheds 13% of its workforce amid so-called AI adoption Amazon still hiring junior employees while also doing layoffs…? The Intellectual Life of the British Working Classes, by Jonathan Rose Snap's intentional targeting of teens' attention Denmark invests in de-screening its schools
Big Idea: The pool looks perfect to a restless frog. I. Temptation is vicious, yet God's grace is always victorious.1 Corinthians 10:13 No temptation has come upon you except what is common to humanity. But God is faithful; he will not allow you to be tempted beyond what you are able, but with the temptation he will also provide the way out so that you may be able to bear it.II. The struggle starts in a restless heart.Galatians 5:16-17 I say, then, walk by the Spirit and you will certainly not carry out the desire of the flesh. For the flesh desires what is against the Spirit, and the Spirit desires what is against the flesh; these are opposed to each other, so that you don't do what you want.III. Run to Jesus with your weakness.2 Corinthians 12:9-10But he said to me, “My grace is sufficient for you, for my power is perfected in weakness.” Therefore, I will most gladly boast all the more about my weaknesses, so that Christ's power may reside in me. So I take pleasure in weaknesses, insults, hardships, persecutions, and in difficulties, for the sake of Christ. For when I am weak, then I am strong.IV. You are never abandoned.Zechariah 4:6 So he answered me, “This is the word of the Lord to Zerubbabel: ‘Not by strength or by might, but by my Spirit,' says the Lord of Armies.Next Steps: Believe: I need Jesus to heal my broken heart today.Become: I will rely on Jesus for power over my struggles this week.Be Sent: I will share about my struggles with someone this week.Discussion Questions: How easy is it for you to share your struggles with others?What does it look like to embrace your weaknesses so that Jesus can give you his strength?When is the last time you felt helpless against temptation?Do you doubt your faith when you feel overwhelmed by temptation?Did you make some decisions based on “might” and “power” this week?Who do you know who needs Jesus to free them from sin?Pray for them by name this week.
Why do so many retirees struggle to spend money they've spent decades saving? Don and Tom explore the psychology behind retirement spending, including the fear of running out of money, the reluctance to touch principal, and how guaranteed income sources like Social Security, pensions, and even simple immediate annuities can make retirees more comfortable enjoying their wealth. They discuss practical strategies for creating spending confidence, the importance of comprehensive retirement planning, and why delaying meaningful experiences can be riskier than spending. The episode also answers a listener question about setting up a Roth IRA for a teenager and examines the latest uncertainty surrounding 529-to-Roth transfers.0:05 Introduction: Why retirees struggle to spend money they can afford to spend1:36 Fear of running out versus fear of missing out in retirement2:52 Why even millionaires worry about spending their savings3:51 The saver mentality and the challenge of switching to spending mode4:47 Research shows many retirees barely touch their nest eggs5:29 YOLO, aging, and the reality of declining mobility later in life6:02 Why retirees prefer spending Social Security, dividends, and interest over principal8:04 Travel, aging, and the danger of postponing experiences8:49 Creating confidence through retirement planning9:56 Using Social Security and RMDs to cover essential expenses10:12 Flexible withdrawal strategies for retirement spending11:39 Could a simple immediate annuity help retirees spend more confidently?12:42 Healthcare costs, aging, and changing spending patterns13:30 Recency bias and how it distorts retirement decisions14:48 Why lifelong savers have trouble becoming spenders16:27 Summer slowdown and a request for more listener questions17:58 Listener question: Setting up a Roth IRA for a 19-year-old daughter19:16 Evaluating Avantis ETFs and M1 Finance for a young investor19:48 Why a single-fund solution may be better for small accounts20:56 The importance of emerging markets exposure22:40 Understanding 529-to-Roth IRA transfer rules24:33 The unanswered question of beneficiary changes and the 15-year ruleQuestions? Comments? Click!
Bill Yount reached financial independence at 60—then froze. His financial advisor confirmed 100% security, yet instead of relief, he felt disoriented fog. The emergency medicine physician who transformed from YOLO spender to 40% saver now struggles with a question that haunts many late starters: if I'm financially free, why can't I leave? Key Topics Discussed 00:05:30 The Wake-Up Call: From YOLO to Financial Awareness Bill's trifecta of mistakes at age 50: being house poor after an underwater renovation, maintaining a single-digit savings rate, and panic-selling stocks at market bottom. A lawsuit became the catalyst for confronting financial reality and transforming to a 30-40% savings rate within a decade. 00:15:00 The Emotional Journey: Anger, Shame, and Transformation Processing the emotional weight of starting late requires confronting anger, shame, and regret. Bill explains how downsizing from material excess created unexpected freedom, and why late starters must do the psychological work alongside the mathematical calculations. 00:22:00 The Partnership: Wife's Role and Family Dynamics Bill's wife became Chief Visionary Officer, returned to work full-time, and they saved her entire income through solo 401(k)s. Their journey debunks the "rich doctor syndrome" myth—25% of physicians at age 60 aren't even millionaires. 00:28:00 The Fog of FI: Reaching the Number and Not Knowing What's Next Sitting across from a financial advisor who confirmed complete financial security, Bill experienced unexpected confusion instead of celebration. This disorienting state—FOGO, or fear of getting out—reveals how identity and emotion don't automatically align with mathematical achievement. 00:35:00 One More Year Syndrome and Identity Struggles Despite being FI, Bill continues working twelve-hour emergency medicine night shifts. He candidly explores identity wrapped up in being a doctor, the meaning derived from patient care, and the difficulty of imagining life beyond the hospital. 00:42:00 The Glide Path: Cutting Shifts and Taking Action After Doc G asked for "one good reason" to keep his current schedule and Bill couldn't answer, he committed to cutting two shifts per month. This gradual approach offers an alternative to the all-or-nothing retirement cliff. 00:50:00 Lessons for Late Starters: Beliefs and Barriers Common limiting beliefs that paralyze late starters include "I'm too far behind," "I don't make enough," and "I don't know enough." Bill emphasizes it's always the right time to start, and the math works the same regardless of income level. 00:58:00 Health, Wealth, and Future Planning A frank discussion about neglecting physical health during wealth accumulation. Bill commits to refocusing on exercise and wellness to minimize the gap between healthspan and lifespan during the "go-go years" of early retirement. 01:05:00 Community, Travel, and What's Next Future plans include traveling to Norway with his sons, speaking at KiwiFi in New Zealand, and an ambitious mission: ensuring every medical resident receives a financial plan by 2035. Notable Quotes Bill Yount: "The emphasis, as we say, on late starter is on the starting and not being late." Bill Yount: "Between stimulus and response is a space. And we need to embrace that space because in that space, we need to regulate and choose our response." Bill Yount: "Relationships compound better than money, I think." Bill Yount: "It's better late than never. And we can catch up to FI together." Ginger: "I think a lot of people say, oh, that person is like me, right? And if they can do it, I can do it." Key Takeaways Track your money completely: Know your net worth, understand expenses, and identify where money goes before creating a plan Implement a reverse budget: Save your target percentage (30-40% if possible) off the top first, then spend the rest according to values Address the emotional work: Process anger, shame, and regret about past mistakes. Forgiveness matters as much as spreads…
Big Idea: Your smart watch can't make time. I. Don't abuse it. James 4:13-16 Come now, you who say, “Today or tomorrow we will travel to such and such a city and spend a year there and do business and make a profit.” Yet you do not know what tomorrow will bring—what your life will be! For you are like vapor that appears for a little while, then vanishes. Instead, you should say, “If the Lord wills, we will live and do this or that.” But as it is, you boast in your arrogance. All such boasting is evil.II. Don't miss it. Psalm 39:4-5 “LORD, make me aware of my end and the number of my days so that I will know how short-lived I am. In fact, you have made my days just inches long, and my life span is as nothing to you. Yes, every human being stands as only a vapor.III. Don't waste it.Colossians 4:5 Act wisely toward outsiders, making the most of the time.Next Steps:Believe: I surrender everything to Jesus today.Become: I will give Jesus full control over my schedule this week.Be Sent: I will spend time growing his kingdom this week.Discussion Questions: When you look at your weekly schedule, what does it say about your priorities?In what ways do you see evidence that you are (or are not) honoring God with your time?What are the “non‑negotiables” on your calendar right now? Which of these clearly honors God?How do you typically decide what makes it onto your schedule?Where do you see patterns of overcommitment, hurry, or burnout in your life?In what practical ways can you build intentional space into your schedule to worship Jesus and grow his kingdom? What small change could you make this week to move in that direction?Pray for the Holy Spirit to help you prioritize building God's kingdom this week.
Retail sales were up 0.9% in May, which is a generally positive economic sign. But it doesn't square with our reality, in which price inflation outpaces wage growth. That is, until you look at that pesky personal savings rate. In this episode, YOLO consumers in a grim economy. Plus: Fed Chair Warsh holds rates steady, the rate of new households is falling, and what would happen if the U.S. lost its global reserve currency status.Every story has an economic angle. Want some in your inbox? Subscribe to our daily or weekly newsletter.Marketplace is more than a radio show. Check out our original reporting and financial literacy content at marketplace.org — and consider making an investment in our future.
Retail sales were up 0.9% in May, which is a generally positive economic sign. But it doesn't square with our reality, in which price inflation outpaces wage growth. That is, until you look at that pesky personal savings rate. In this episode, YOLO consumers in a grim economy. Plus: Fed Chair Warsh holds rates steady, the rate of new households is falling, and what would happen if the U.S. lost its global reserve currency status.Every story has an economic angle. Want some in your inbox? Subscribe to our daily or weekly newsletter.Marketplace is more than a radio show. Check out our original reporting and financial literacy content at marketplace.org — and consider making an investment in our future.
On today's podcast, Chris Cillizza and Matt discuss:— Oysterman Graham Platner wins Maine's Senate primary with 73% — why this is a neutral-to-good night for him, but still a huge risk for Democrats vs. incumbent Sen. Susan Collins— Rep. Nancy Mace finishes near last in South Carolina governor primary after pushing Epstein files — Trump revenge and whether she now joins the YOLO caucus.— Sen. Lindsey Graham beats back another MAGA challenger — is he a better politician than we thought, or is SC less Trumpy than it seems?— The absolutely WILD Colorado GOP governor primary: Leading candidate claims he killed a man at age 7 and (as a civilian) called in airstrikes on ISIS— Is it fair to blame Trump for the Knicks' loss? (Hint: Chris and Matt are divided over this one.)— And MUCH more!Subscribe to Matt Lewis on Substack: https://mattklewis.substack.com/Support Matt Lewis at Patreon: https://www.patreon.com/mattlewisFacebook: https://www.facebook.com/MattLewisDCTwitter: https://twitter.com/mattklewisInstagram: https://www.instagram.com/mattlewisreels/YouTube: https://www.youtube.com/channel/UCVhSMpjOzydlnxm5TDcYn0A– Who is Matt Lewis? –Matt K. Lewis is a political commentator and the author of Filthy Rich Politicians.Buy Matt's books: FILTHY RICH POLITICIANS: https://www.amazon.com/Filthy-Rich-Politicians-Creatures-Ruling-Class/dp/1546004416TOO DUMB TO FAIL: https://www.amazon.com/Too-Dumb-Fail-Revolution-Conservative/dp/0316383937Copyright © 2026, BBL & BWL, LLC
What if the wealthiest person in the room isn't the one with the biggest bank account? What if it's the one who can take a Tuesday lunch with their kid, go fly fishing mid-week, and fall asleep at night without anxiety eating them alive? Because I know people making five million a year who can't do any of that. And I know people making 120 grand who are living fuller, freer, and happier than most. So who's actually richer? In this solo episode of The Happy Hustle Podcast, I break down what I call the Seven Freedoms Framework, the exact philosophy I use to design my own dream reality and help others do the same. This episode isn't about telling you money is bad or that ambition is wrong. It's about getting honest with yourself on what you're actually building toward, and whether the life you're grinding for is one you'd actually want to live. Here's the big shift: most entrepreneurs think what they're chasing will give them the feeling they crave. But once the money shows up, a lot of them find out they're burnt out, stressed, disconnected from their kids, strangers to their spouse, and nowhere near as happy as they thought they'd be. The problem isn't success. It's that we've been measuring the wrong thing. Freedom, not finances, is the real metric that matters. Here are the key takeaways from this episode: Time Freedom. Can you control your calendar, or does it control you? Nearly 70% of Americans say they feel disengaged at work, and most high earners report feeling time broke even when they're cash rich. You can always make more money. You cannot make more sunsets with your kids. You cannot get back the Sunday you missed or the date night you skipped. Do an audit this week. Delete and delegate whatever is draining your energy and stealing your aliveness. Location Freedom. Environment dictates happiness more than most people realize. Are you where you want to be? I didn't want to be suffocating in a city. I wanted mountain air, rivers, nature, places where I could hunt, fish, camp, and breathe. So I built my business around that. If you can't say you're living and working where you feel most alive, that's worth paying attention to. Where you are matters. Build around it. Financial Freedom. And let me be clear, this isn't about buying Lambos. It's about recurring income, low stress, high margins, and real options. Most people wildly overestimate what they actually need to feel financially fulfilled. Reduce lifestyle inflation. Stop buying status symbols that impress others but mean nothing to you. Build income streams that give you breathing room, not just a bigger number on a screen. The goal is to work less and make more, not grind more and pray harder. Creative Freedom. The ultimate flex is waking up genuinely stoked to work. Not dragging yourself to a laptop. Not grinding through tasks you hate. Podcasting, writing, speaking, creating, those are things I love. What would you be doing if nobody judged you? That answer is probably what your soul is calling you toward. Step into that and serve people from that place. That's where your real power lives. Health Freedom. Your body is the vehicle for all of it. Nearly 80% of entrepreneurs say they're on the brink of burnout or have recently burned out. If you've got money but you're inflamed, exhausted, and disconnected, you are not free. Move your body. Get outside. Do the breath work, the sauna, the cold plunge, the things that keep you sharp and whole. Pour from your overflow, not your empty. Relationship Freedom. What's the point of building an empire if you become a stranger to your family? Most entrepreneurs I know sacrifice their marriage, miss their kids' childhood moments, lose friendships, and let go of hobbies they actually love. That's not winning. Schedule the date nights, the device-free dinners, the camping trips, the deep conversations. Studies show experiences create longer lasting happiness than any material purchase ever will. YOLO, my friend. Do the damn thing. Spiritual Freedom. This one brings it all home. Freedom from comparison. Freedom from ego. Freedom from external validation and fear. Being connected to something bigger than yourself, to God, to your divine calling, to whatever gives your life real meaning beyond the grind. Ask yourself what you're actually chasing and why. Is it aligned with who you were created to be? That's the question worth sitting with. At the end of the day, this episode is a reminder that Happy Hustlin' isn't about doing more. It's about being more intentional with what you're building, so the life you create actually feels like freedom. The wealthiest people I know aren't always the richest financially. They're the freest. And that's who I'm competing to be every single day. If you're a high achiever who's tired of grinding toward a finish line that keeps moving, and you're ready to start measuring your life by freedom instead of finances, this episode is for you. Go listen to the full episode at https://caryjack.com/podcastin/. It just might be the reset you didn't know you needed. Connect with Cary!https://www.instagram.com/caryjack/https://www.facebook.com/SirCaryJackhttps://www.linkedin.com/in/cary-jack-kendzior/https://twitter.com/thehappyhustlehttps://www.youtube.com/channel/UCFDNsD59tLxv2JfEuSsNMOQ/featured Get a copy of his new book, https://www.thehappyhustle.com/book Sign up for The Journey: 10 Days To Become a Happy Hustler Online Course @ https://thehappyhustle.com/thejourney/ Apply to the Montana Mastermind Epic Camping Adventure @ https://thehappyhustle.com/mastermind/ “It's time to Happy Hustle, a blissfully balanced life you love, full of passion, purpose, and positive impact!” Episode Sponsors: If you're feeling stressed, not sleeping great, or your energy's been kinda meh lately—let me put you on to something that's been a total game-changer for me: Magnesium Breakthrough by BiOptimizers. This ain't your average magnesium—it's got all 7 essential forms that your body needs to chill out, sleep deeper, and feel more balanced. I take it every night and legit notice the difference the next day. No more waking up groggy or tossing and turning all night If you're ready to sleep like a baby, calm your nervous system, and optimize your recovery, go grab yours now at https://www.bioptimizers.com/happy and use code HAPPY10 for 10% OFF. =================================================================== My Green Mattress If you've been waking up with back pain, feeling stiff, or just not getting that deep, quality sleep. This might be what you're missing: My Green Mattress. It's made with clean, non-toxic, and eco-friendly materials, so you're not just sleeping better, you're sleeping healthier too. The comfort and support are on another level, and you can really feel the difference night after night. If you're ready to invest in better sleep and better recovery, check it out at https://thehappyhustle.com/mygreenmattress =================================================================== Ozlo Sleep If you've been struggling to fall asleep, stay asleep, or just wake up feeling actually rested, let me put you on to something that's been a total game-changer: Ozlo Sleep. These aren't your typical sleep buds. They're designed to block out noise and help your brain fully relax, so you can drift off faster and stay in deep, uninterrupted sleep. Perfect if you're a light sleeper or just want that next-level rest. If you're ready to upgrade your sleep and wake up feeling recharged, check out https://ozlosleep.com and save $80 OFF using code HAPPY.
"The power of Malec."Happy Pride Month! This year, our Patrons chose to celebrate by reading They Both Die at the End by Adam Silvera (2017). If you were on BookTok in 2020, you're probably familiar with this speculative fiction story of two boys who receive a call from Death-Cast notifying them that they have less than 24 hours to live. We follow Mateo and Rufus through September 5, 2017, which was a great day...for being sad.Along with a recap, we talk about Silvera's nontraditional journey to authorship, his very familiar inspirations, and the television adaptation. Give us Bad Bunny as Howie Maldonado, Netflix, PLEASE!Rufus' Instagram: https://www.instagram.com/rufusonpluto/Support the Trevor Project with RTR Merch! https://www.bonfire.com/rereading-the-revolution-pride/ Hosted on Acast. See acast.com/privacy for more information.
The Giro d'Italia is officially "almost over". Stage 6 is done and dusted, and even though Uno-X Mobility tried to make the race chaotic, it ended up coming down to the fastest rider(s) once again. Today, Anna Shackley is back with Abby and Loren to talk about stage 6 and look ahead to stage 7. Will the breakaway finally get its chance? Our audio diarists today include Nienke Veehoven of Visma-Lease a Bike, Caroline Andersson of Liv AlUla Jayco, and Maggie Coles-Lyster of Human Powered Health.
The Fifth Circuit is crossing out laws just for sport. This time it's a 140-year-old ban on making homebrew hooch, because YOLO. Trump's lawsuit against the Wall Street Journal and Rupert Murdoch over an article describing his creepy birthday card to Jeffrey Epstein was dismissed. But … that dismissal was without prejudice, so he can take another swing at it. The trollsuit against the BBC is still limping along. Deputy General Counsel at the Department of Education Josh Kleinfeld makes an interesting pitch to George Mason's Antonin Scalia Law School, which is currently under investigation by … the Department of Education. And Trump's ballroom blitz takes a tumble in court. MAIN SHOW: Trump discovers one weird trick to make the Presidential Records Act disappear. All he has to do is order the Office of Legal Counsel to come up with a memo saying it's unconstitutional and — hey, presto! — he can steal or shred or delete any document he likes. SUBSCRIBER BONUS: Are we the pirates now? Trump v. Murdoch https://www.courtlistener.com/docket/70843413/trump-v-murdoch Trump v. BBC https://www.courtlistener.com/docket/72040010/trump-v-british-broadcasting-corporation Fifth Circuit Home Distillers Ruling https://www.ca5.uscourts.gov/opinions/pub/24/24-10760-CV0.pdf Trump Admin Lawyer Applies To Be Law School Dean, Suggests It Might Help Investigations Go Away https://abovethelaw.com/2026/04/trump-admin-lawyer-applies-to-be-law-school-dean-suggests-it-might-help-investigations-go-away/ Ballroom Blitz Blocked https://www.lawandchaospod.com/p/ballroom-blitz-blocked National Trust for Historic Preservation v. National Park Service https://www.courtlistener.com/docket/73127510/national-trust-for-historic-preservation-v-nps April 1, 2026 OLC Memorandum on the Presidential Records Act https://www.justice.gov/olc/media/1434131/dl Judicial Watch v. NARA ("Socks Case"), 845 F.Supp.2d 288 (DC Cir. 2012) https://scholar.google.com/scholar_case?case=15818036517066124081 Trump v. Mazars, 591 US 848 (2020) https://scholar.google.com/scholar_case?case=2096461232780826445 Nixon v. Administrator of General Svcs. et al., 433 US 425 (1977) https://scholar.google.com/scholar_case?case=11884364268460571560 Show Links: https://www.lawandchaospod.com/ BlueSky: @LawAndChaosPod Threads: @LawAndChaosPod Twitter: @LawAndChaosPod
Chuck Todd opens with what he calls the unmistakable arrival of a "YOLO caucus" in the Senate — a growing number of congressional Republicans who are simply done capitulating to Trump, evidenced by John Thune publicly declaring there's no need to "weaponize" the DNI position and by the broader sense that the non-Trump part of the GOP is openly preparing to move on. He argues Trump is doing everything possible to accelerate his own lame duck status: he's politicizing America's 250th anniversary in ways that genuinely alarm vulnerable Republicans, he failed to engage any of the former presidents in the 250th planning, and he's creating Marie Antoinette-style "let them eat cake" optics by celebrating himself at a moment of real economic pain for ordinary Americans. Trump's treatment of CNN's Kaitlan Collins was outrageous, his cranky behavior with the press is a tell that things aren't going well, and his decision to formally nominate Todd Blanche for Attorney General has essentially zero chance of confirmation — Blanche has burned his bridges in the Senate and the doomed January 6th weaponization fund was reportedly his idea in the first place. It's almost as if Trump is begging to put a neon "I'm a lame duck" sign on the White House. Chuck then turns to California, where ballots are still being counted at a pace that he says is actively eroding public trust in the democratic process itself — the state desperately needs to find a way to count faster — and notes that CA-06 was drawn as a safe Democratic seat but the top two finishers right now are both Republicans, while Spencer Pratt looks safer in the LA mayoral race than Steve Hilton does in the governor's race. He closes with a fascinating analysis of the Graham Platner situation in Maine, where Janet Mills' decision to leave her name on the ballot has created a Nikki Haley-style protest vote opportunity for nervous Democrats — Mills didn't bow out in disgrace so her floor is high, and if she pulls 25% or more in the primary, Chuck predicts very real conversations about replacing Platner will begin. The number to watch is ME-02: if Platner underperforms there, it's the clearest red flag that a candidate Democrats once viewed as a slam-dunk pickup is now in serious trouble. Then, Todd Ricketts — Chicago Cubs co-owner and founder of Freespoke, the search engine that labels news sources with media bias ratings — joins the Chuck Toddcast for a wide-ranging conversation that bridges the increasingly intertwined worlds of media, technology, and professional sports. Ricketts makes the case that when people are given genuinely good information from across the ideological spectrum, they tend to arrive at good answers — and that Freespoke's mission is to present all sides and then get out of the way, rather than letting ad sales determine what news you see. He pushes back on the idea that the market alone can solve the data privacy crisis, arguing data may eventually need to be regulated like a utility but that nothing changes until there's a major "event" that creates real public groundswell. Ricketts is candid about Freespoke's challenges — paywalls remain a real obstacle, the left/right labeling is imperfect and done by outside groups, and the political landscape itself is shifting in ways that scramble the traditional categories . He observes that podcasts have become a primary news source because people clearly hunger for long-form content with nuance, that politicians are now visibly afraid of giving long answers because they might get clipped, and that legacy media still doesn't seem to understand why its audience has migrated elsewhere. The second half pivots into the business of running a baseball team, and Ricketts brings the same straight-talking pragmatism to MLB's looming economic crisis. He argues you cannot sell a salary cap to MLB owners without genuine revenue sharing, because if the league itself isn't competitive then everyone eventually loses — including the owners writing the biggest checks. Players currently take roughly 48% of revenue, a number he expects to climb to around 52% in the next deal, and Ricketts is honest that half of MLB's franchises are still essentially mom-and-pop operations even as private equity money is rapidly entering the sport. He talks about the difficulty of running any sports team in 2026 because fans genuinely feel like they own the franchise, why ownership groups are increasingly building entire entertainment districts around their ballparks to control the fan experience end-to-end, and the painful broadcast rights question every team is wrestling with: fans have cut the cord, the old TV economics no longer work, and ownership has to be flexible with new broadcast partners even as they ask themselves whether season ticket holders should be entitled to free access to every game. Ricketts closes by laying out what would qualify as a disappointing season for the Cubs — a sober assessment from an owner who has watched the economics of his sport, and the media landscape his business depends on, both transform at the same time. Finally, Chuck answers listeners’ questions in the “Ask Chuck” segment and spends a few minutes reflecting on the life of his grandmother who passed away this week. Predict the action all the way through the finals. Sign up now for your twenty-five dollar bonus on https://fanduel.com/predicts Link in bio or go to https://getsoul.com & enter code TODDCAST for 30% off your first order. Thank you Wildgrain for sponsoring. Visit http://wildgrain.com/TODDCAST and use the code "TODDCAST" at checkout to receive $30 off your first box PLUS free Croissants for life! Timeline: (Timestamps may vary based on advertisements) 00:00 Chuck Todd’s introduction 06:45 Increasing # of congressional Republicans done capitulating to Trump 07:30 John Thune said we don’t need “weaponization” of DNI position 08:30 There’s a growing “YOLO caucus” in the senate 09:30 The non-Trump part of the GOP is ready to move on from Trump 10:00 Trump’s treatment of Kaitlin Collins is outrageous 11:45 Trump gets cranky with the press when things aren’t going well 12:30 Trump is a terrible negotiator 13:00 Trump is creating huge political risk politicizing America 250 13:45 Trump should have put the UFC on the national mall, not WH 15:00 Trump is celebrating himself for 250, terrible move politically 16:15 Trump didn’t engage with the former presidents for 250 17:00 Trump is creating Marie Antoinette “let them eat cake” optics 18:30 Vulnerable Republicans may fear attending Trump’s 250 events 19:00 Trump is looking to formally nominate Todd Blanche for AG 19:30 There is zero chance Todd Blanche can get confirmed 20:15 Blanche hasn’t made friends. Weaponization fund was his idea 22:15 Trump may be done listening to any rational advice 23:30 It’s like Trump wants to put a neon “I’m a lame duck” sign on WH 24:15 California ballots are still being counted. Can Steyer and Raman catch up? 26:15 Pratt seems to have a more comfortable lead than Hilton 27:30 CA-06 was drawn to be Democratic, top two so far are Republican 29:45 California desperately needs to find a way to count ballots faster 30:30 Slow count erodes trust is democracy and counting process 33:15 Graham Platner visit to D.C. went ok, but there’s trepidation 35:30 Platner wants to drive the narrative he’s still ahead of Collins 36:30 Polling has shown Platner with a massive lead over Collins for weeks 38:15 Platner’s recent scandals have him in trouble, can’t take much more 39:30 New polling shows Platner took a hit, but it’s recoverable 40:00 Janet Mills chose to keep her name on the ballot for uneasy Dems 41:00 Maine is one of the easier states to replace a candidate 42:30 How votes for Mills should be read 44:15 Mills didn’t bow out in disgrace, her floor is higher 45:30 Mills could become a protest vote for Platner, similar to Nikki Haley 47:00 If Maine voters are nervous about Platner, they can vote for Mills 49:00 If Mills gets 25% or more, then there will be talks of replacing Platner 51:15 If Platner underperforms in ME-02, that’s a red flag 59:45 Todd Ricketts joins the Chuck ToddCast 1:00:30 Providing media bias ratings for online news sources 1:03:00 When people are given good info, they come up with good answers 1:03:30 Goal is to present all sides, then let people make up their mind 1:04:45 You don’t want ad sales for search to determine your information 1:07:00 Can the market fix data sales, or does the government need to regulate? 1:08:45 Should data be regulated like a utility? 1:09:15 There will need to be an “event” to cause groundswell over data privacy 1:10:15 Does Freespoke labeling news left/right cause users to seek their preferred source? 1:13:15 Politics are shifting and what used to be a “left” issue is now a right issue etc 1:14:00 Protectionism has become right and free trade has become left 1:15:45 How would someone like George Will be labled? 1:17:15 Labeling is done by outside groups and the labeling isn’t perfect 1:17:45 The company is for-profit, sells ads and has subscription model 1:18:30 All the search is AI curated, but people curate the current events page 1:19:15 Bing and Google are the direct competitors 1:20:00 The Freespoke algorithm tries to strip out bias 1:21:30 Some topics get a ton of content from one side & none from the other 1:23:00 People are informing themselves via podcasts instead of legacy news 1:23:45 Legacy media needs to understand why audience is going elsewhere 1:25:30 Popularity of podcasts show people like long form content 1:26:45 Politicians are afraid of long answers & nuance in case they get clipped 1:27:15 Paywalls are a challenge for Freespoke, but sources are still included 1:28:15 Why are there left/right labels on sports coverage? 1:29:45 What is Freespoke’s position on mis and disinformation? 1:30:30 What does Freespoke 2.0 look like? 1:31:45 AI is only as good as the people & information that train it 1:32:45 Will you get into the newsletter business? 1:34:30 Can you sell a salary cap to MLB owners without total revenue sharing? 1:35:45 If the league isn’t competitive, then everyone will eventually lose 1:37:00 Players currently get 48% of revenue, may move up to about 52% 1:38:15 Running a sports team is hard because fans feel like they own the team 1:40:15 What have you learned from running the Cubs? 1:41:45 Half the teams are still mom & pop operations, but PE is coming in 1:43:00 Ownership wants to control fan experience, building entertainment districts 1:44:00 Should teams always be available on free TV? 1:44:30 Fans have cut the cord, have to be flexible with broadcast partners 1:46:15 Should season ticket holders be able to get all game broadcasts for free? 1:47:00 What would qualify this season as disappointing for the Cubs? 1:49:45 Chuck’s thoughts on interview with Todd Ricketts 1:51:15 Salary cap proposal for MLB revealed 1:52:30 Salary cap could be much higher than expected to buy time 1:53:45 Willingness to pool local revenue is a big deal 1:54:00 Ask Chuck 1:54:15 Is voting for a candidate an indictment of the character of the voter? 2:13:15 How would the logistics work for expanding the house? 2:17:15 How much should a candidate’s private behavior affect their electability? 2:25:00 How does a state with no income tax like Florida fund services? 2:29:45 With government agreeing to large settlements, won’t future admins do the same? 2:38:30 Chuck’s eulogy for his grandmotherSee omnystudio.com/listener for privacy information.
Chuck Todd opens with what he calls the unmistakable arrival of a "YOLO caucus" in the Senate — a growing number of congressional Republicans who are simply done capitulating to Trump, evidenced by John Thune publicly declaring there's no need to "weaponize" the DNI position and by the broader sense that the non-Trump part of the GOP is openly preparing to move on. He argues Trump is doing everything possible to accelerate his own lame duck status: he's politicizing America's 250th anniversary in ways that genuinely alarm vulnerable Republicans, he failed to engage any of the former presidents in the 250th planning, and he's creating Marie Antoinette-style "let them eat cake" optics by celebrating himself at a moment of real economic pain for ordinary Americans. Trump's treatment of CNN's Kaitlan Collins was outrageous, his cranky behavior with the press is a tell that things aren't going well, and his decision to formally nominate Todd Blanche for Attorney General has essentially zero chance of confirmation — Blanche has burned his bridges in the Senate and the doomed January 6th weaponization fund was reportedly his idea in the first place. It's almost as if Trump is begging to put a neon "I'm a lame duck" sign on the White House. Chuck then turns to California, where ballots are still being counted at a pace that he says is actively eroding public trust in the democratic process itself — the state desperately needs to find a way to count faster — and notes that CA-06 was drawn as a safe Democratic seat but the top two finishers right now are both Republicans, while Spencer Pratt looks safer in the LA mayoral race than Steve Hilton does in the governor's race. He closes with a fascinating analysis of the Graham Platner situation in Maine, where Janet Mills' decision to leave her name on the ballot has created a Nikki Haley-style protest vote opportunity for nervous Democrats — Mills didn't bow out in disgrace so her floor is high, and if she pulls 25% or more in the primary, Chuck predicts very real conversations about replacing Platner will begin. The number to watch is ME-02: if Platner underperforms there, it's the clearest red flag that a candidate Democrats once viewed as a slam-dunk pickup is now in serious trouble. Finally, Chuck answers listeners’ questions in the “Ask Chuck” segment and spends a few minutes reflecting on the life of his grandmother who passed away this week. Predict the action all the way through the finals. Sign up now for your twenty-five dollar bonus on https://fanduel.com/predicts Link in bio or go to https://getsoul.com & enter code TODDCAST for 30% off your first order. Thank you Wildgrain for sponsoring. Visit http://wildgrain.com/TODDCAST and use the code "TODDCAST" at checkout to receive $30 off your first box PLUS free Croissants for life! Timeline: (Timestamps may vary based on advertisements) 00:00 Chuck Todd’s introduction 06:45 Increasing # of congressional Republicans done capitulating to Trump 07:30 John Thune said we don’t need “weaponization” of DNI position 08:30 There’s a growing “YOLO caucus” in the senate 09:30 The non-Trump part of the GOP is ready to move on from Trump 10:00 Trump’s treatment of Kaitlin Collins is outrageous 11:45 Trump gets cranky with the press when things aren’t going well 12:30 Trump is a terrible negotiator 13:00 Trump is creating huge political risk politicizing America 250 13:45 Trump should have put the UFC on the national mall, not WH 15:00 Trump is celebrating himself for 250, terrible move politically 16:15 Trump didn’t engage with the former presidents for 250 17:00 Trump is creating Marie Antoinette “let them eat cake” optics 18:30 Vulnerable Republicans may fear attending Trump’s 250 events 19:00 Trump is looking to formally nominate Todd Blanche for AG 19:30 There is zero chance Todd Blanche can get confirmed 20:15 Blanche hasn’t made friends. Weaponization fund was his idea 22:15 Trump may be done listening to any rational advice 23:30 It’s like Trump wants to put a neon “I’m a lame duck” sign on WH 24:15 California ballots are still being counted. Can Steyer and Raman catch up? 26:15 Pratt seems to have a more comfortable lead than Hilton 27:30 CA-06 was drawn to be Democratic, top two so far are Republican 29:45 California desperately needs to find a way to count ballots faster 30:30 Slow count erodes trust is democracy and counting process 33:15 Graham Platner visit to D.C. went ok, but there’s trepidation 35:30 Platner wants to drive the narrative he’s still ahead of Collins 36:30 Polling has shown Platner with a massive lead over Collins for weeks 38:15 Platner’s recent scandals have him in trouble, can’t take much more 39:30 New polling shows Platner took a hit, but it’s recoverable 40:00 Janet Mills chose to keep her name on the ballot for uneasy Dems 41:00 Maine is one of the easier states to replace a candidate 42:30 How votes for Mills should be read 44:15 Mills didn’t bow out in disgrace, her floor is higher 45:30 Mills could become a protest vote for Platner, similar to Nikki Haley 47:00 If Maine voters are nervous about Platner, they can vote for Mills 49:00 If Mills gets 25% or more, then there will be talks of replacing Platner 51:15 If Platner underperforms in ME-02, that’s a red flag 55:45 Chuck’s thoughts on interview with Todd Ricketts 57:15 Salary cap proposal for MLB revealed 58:30 Salary cap could be much higher than expected to buy time 59:45 Willingness to pool local revenue is a big deal 1:00:00 Ask Chuck 1:00:15 Is voting for a candidate an indictment of the character of the voter? 1:19:15 How would the logistics work for expanding the house? 1:23:15 How much should a candidate’s private behavior affect their electability? 1:31:00 How does a state with no income tax like Florida fund services? 1:35:45 With government agreeing to large settlements, won’t future admins do the same? 1:44:30 Chuck’s eulogy for his grandmotherSee omnystudio.com/listener for privacy information.
After seeing the Rams and Patriots do blockbuster June trades, should the Saints join in and go YOLO? We open our Cruel Summer Series with look back at terrible Kevin Mock draft. Saints Happy Hour is brought to you by Hardhide Ponchatoula Strawberry Whiskey and Chilton County Peach Whiskey!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Ronnie Stoeferle, partner at Incrementum AG and co-author of the In Gold We Trust report, joins Wealthion's Trey Reik to explain why gold's rally may be about much more than a normal bull market.Stoeferle argues that gold is signaling a deeper loss of trust in the dollar-based monetary system, as de-dollarization, inflation volatility, central bank buying, and rising geopolitical risk reshape the global financial order. He also explains why gold may be entering the public participation phase of its bull market — with institutional investors only beginning to wake up to the role gold can play in portfolios.In this conversation, Ronnie and Trey discuss whether the Pax Americana is coming to a close, why fiat currencies look different when measured in gold, whether this is a monetary revaluation rather than a normal gold cycle, and what the In Gold We Trust report reveals about the future of money.
Mindset Coach Taylor Foreman brings an inspring message for Mom's today!
Founder and Executive Director of BEAM (Black Emotional and Mental Health Collective), Yolo, joins Dr. Thema and shares his thoughtful insights on womanist and anti-patriarchal therapy. Yolo also reflects on his homecoming journey and the context of identity and expectations for boys and men. For over two decades, Yolo Akili Robinson has served as a counselor, organizer, facilitator, and community healer working at the intersections of mental health, womanism, spirituality, and collective care. A Ford Foundation Global Fellow and Robert Wood Johnson Foundation Health Equity Award recipient, Yolo is the founder of the Black Emotional and Mental Health Collective (BEAM)—a national grantmaker for healing justice and mental health organizations that has designed evidence-based interventions centering radical wellness and collective care. His work bridges clinical, community, and movement spaces, with experience spanning Men Stopping Violence, NYU Langone Medical Center, and national initiatives with the CDC and NIH—helping to design and implement community-based mental health and wellness interventions nationwide. Recognized by U.S. Surgeon General Dr. Vivek Murthy for his leadership in advancing emotional well-being and social connection, Yolo's areas of specialization include anti-patriarchal counseling and healing work with Black men and boys, collective and community-based curricula and interventions, mental health and HIV/AIDS in Black queer communities, and body-centered healing practices. Don't forget to like, share, and subscribe. Mixed & Edited by Next Day Podcast info@nextdaypodcast.com
We talk to baseball rules expert, and author of the Baseball Field Guide about all the new changes in baseball rules, including the challenge system and ABS which aren't even in the rules! Plus Iran updates, Tez celebrates his Gunners, and of course, the Headlines! Become a supporter of this podcast: https://www.spreaker.com/podcast/chipchat--2780807/support.
In this talk, Mariano, Lead Data Scientist and ML Engineer at OLX, shares his journey building high-impact AI media solutions. We explore the transition from traditional e-commerce models to Generative AI and Agentic tools, focusing on how to take AI products from a notebook to full-scale production.You'll learn about:How to master the full product cycle from requirement gathering to deployment.Using video-to-ad technology to automate car listings and seller experiences.Essential modern tools like FastAPI, Arize, and why UV is a game-changer.When to use LLMs versus specialized vision models like CLIP and YOLO.Why production pipelines are moving from Jupyter notebooks to CLI tools.How agentic coding and AI assistants are 10x-ing development speed.TIMECODES:0:00 Community Introduction and Slack Engagement4:16 Career Journey: From Argentina to Barcelona7:16 Product-Driven AI vs. Traditional Reporting9:41 AI Media Solutions for E-Commerce Sellers10:55 Video-to-Ad: The Future of Marketplaces13:45 Automated Content Creation for Sellers17:10 Defining End-to-End Ownership in Data Science21:12 The Longevity of the CRISP-DM Framework25:33 Impact of Agentic Coding and GitHub Copilot31:42 Why LLMs Aren't Always the Best Solution37:39 Translating Business Needs to ML Requirements41:18 Managing Explicit and Implicit Feedback Loops48:26 Architecture Deep Dive: Image Description Logic55:28 The Declining Role of Notebooks in Production1:02:53 The Modern Tech Stack: Fast API, UV, and ArizeConnect with Mariano: Linkedin - https://www.linkedin.com/in/msemelman/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/
This week, Scott sat down with his Lawfare colleagues Anna Bower and Eric Columbus, and his Brookings colleague Molly Reynolds, to talk through a couple of the week's big news stories in domestic politics, including:“The Grift That Keeps On Giving.” Last week, the Justice Department announced the creation of a so-called Anti-Weaponization Fund of nearly 1.8 billion taxpayer dollars, from which purported victims of politically motivated prosecutions can apply to receive payments. The fund was created as part of a settlement with President Trump and his sons, who sued the IRS for 10 billion dollars over the leak of his tax returns. So far, pardoned Jan. 6 rioters, former Congressman George Santos, Trump's ex attorney Michael Cohen, and even former FBI Director James Comey have all said that they are considering applying, and three lawsuits have already been filed challenging the fund. How did Trump's lawsuit against the IRS lead to this fund? And how do we see these legal challenges playing out in court?“Lame Duck Around and Find Out.” President Trump's preferred primary picks have cruised to victories in Indiana, Kentucky, Louisiana, and Georgia Republican primaries, ousting incumbents Senator Bill Cassidy and Representative Thomas Massie as some of the few voices of dissent within the Republican Party. But Trump's involvement in the primaries has come at a political cost, with outgoing members voicing their criticism and even going so far as to buck the president on legislation. Last week, Cassidy flipped his vote in favor of a critical war powers resolution in the Senate, which could undermine the administration's legal justification for the war. With such close margins in Congress, how do we expect this new YOLO faction to impact the president's agenda before the midterms?While we introduced a third topic, we frankly ran out of time this week. Sorry about that! We'll circle back to it in the weeks ahead.In object lessons, Molly is hooked on the fish-focused local NPR podcast, “Catching The Codfather.” Eric is looking to catch a killer with the latest Hugh Jackman movie (which he thinks is shear perfection). Scott is caught up in the latest “Storm,” featuring Yung Lean. And Anna has caught basketball fever, both with the Knicks' return to the NBA Finals, and also with the (much-more-affordable-but-equally-entertaining) NY Liberty.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.
This week, Scott sat down with his Lawfare colleagues Anna Bower and Eric Columbus, and his Brookings colleague Molly Reynolds, to talk through a couple of the week's big news stories in domestic politics, including:“The Grift That Keeps On Giving.” Last week, the Justice Department announced the creation of a so-called Anti-Weaponization Fund of nearly 1.8 billion taxpayer dollars, from which purported victims of politically motivated prosecutions can apply to receive payments. The fund was created as part of a settlement with President Trump and his sons, who sued the IRS for 10 billion dollars over the leak of his tax returns. So far, pardoned Jan. 6 rioters, former Congressman George Santos, Trump's ex attorney Michael Cohen, and even former FBI Director James Comey have all said that they are considering applying, and three lawsuits have already been filed challenging the fund. How did Trump's lawsuit against the IRS lead to this fund? And how do we see these legal challenges playing out in court?“Lame Duck Around and Find Out.” President Trump's preferred primary picks have cruised to victories in Indiana, Kentucky, Louisiana, and Georgia Republican primaries, ousting incumbents Senator Bill Cassidy and Representative Thomas Massie as some of the few voices of dissent within the Republican Party. But Trump's involvement in the primaries has come at a political cost, with outgoing members voicing their criticism and even going so far as to buck the president on legislation. Last week, Cassidy flipped his vote in favor of a critical war powers resolution in the Senate, which could undermine the administration's legal justification for the war. With such close margins in Congress, how do we expect this new YOLO faction to impact the president's agenda before the midterms?While we introduced a third topic, we frankly ran out of time this week. Sorry about that! We'll circle back to it in the weeks ahead.In object lessons, Molly is hooked on the fish-focused local NPR podcast, “Catching The Codfather.” Eric is looking to catch a killer with the latest Hugh Jackman movie (which he thinks is shear perfection). Scott is caught up in the latest “Storm,” featuring Yung Lean. And Anna has caught basketball fever, both with the Knicks' return to the NBA Finals, and also with the (much-more-affordable-but-equally-entertaining) NY Liberty.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute. Hosted on Acast. See acast.com/privacy for more information.
Brent's been hacking smart speakers, Wes has a surprise, and Chris gives up on OpenClaw.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:ConnecTen Internet — Get $35 off your order total with Jupiter35
Brent's been hacking smart speakers, Wes has a surprise, and Chris gives up on OpenClaw.
Wir sprechen darüber, was wir aneinander schätzen: Caris Talent, Menschen zusammenzubringen und alle Dinge offen anzusprechen. Manuels Organisationstipps und sein Umgang mit Feedback. Im Follow-up hören wir eure Nachrichten zum Begriff „Übermensch" und dem Phänomen der runden Lippen im Deutschen. Außerdem berichtet Cari von ihrer Fahrradtour an der Donau entlang. Hinweis: Nächste Woche Dienstag fällt unser Podcast aufgrund eines Feiertags aus. Wir hören uns wieder am Samstag, den 30. Mai 2026. Transkript und Vokabelhilfe Werde ein Easy German Mitglied und du bekommst unsere Vokabelhilfe, ein interaktives Transkript und Bonusmaterial zu jeder Episode: easygerman.org/membership Sponsor Seedlang : Start speaking German now! Kostenlos auf iOS, Android und seedlang.com. Hausmitteilung: Truck Driver gesucht Wir suchen Truck Driver für einen Videodreh: Ihr fahrt Lkw oder kennt jemanden? Dann meldet euch bei uns! Am 26. Mai startet unsere Easy German Grammar Challenge! Zehn Tage lang gibt es jeden Morgen eine Aufgabe, die wir abends im Zoom-Call zusammen besprechen. Alle Infos findet ihr auf easygerman.org/grammarchallenge Follow-up: Übermensch (Nietzsche) & Lippenrundungen Übermensch (Wikipedia) Instagram: germanwithsammy Geplante Obsoleszenz (Easy German Podcast 656) Support Easy German and get interactive transcripts, live vocabulary and bonus content: easygerman.org/membership
En este nonagésimo tercer episodio del ¡Bipartidismo Strikes Back! (una producción del #PodcastLaTrinchera), Christian Sobrino y Luis Balbino discuten eventos recientes en Cuba, el segundo Mensaje de Situación de Estado de la Gobernadora Jenniffer González, el presupuesto propuesto para el próximo año fiscal, el acuerdo transaccional entre el Imperator Trump y su propio IRS para crear un barril de $1.8 mil millones, el saldo de las primarias del Partido Republicano como antesala a las elecciones de medio término y mucho más.Este episodio es presentado a ustedes por:- San Juan Lincoln, donde encontrarán una exclusiva colección de vehículos de lujo diseñados para satisfacer todas sus expectativas. Allí descubrirán la presencia imponente de la Navigator, la elegancia dinámica de la Aviator, la sofisticación refinada de la Corsair y el diseño moderno de la Nautilus. Pueden visitarlos en la Avenida Kennedy en San Juan para explorar lo que una SUV de lujo debe ser. Su equipo está listo para ofrecerles una experiencia inigualable. Para más información u orientación, llamen al 787-331-5023.- La Tigre, el primer destino en Puerto Rico para encontrar una progresiva selección de moda Italiana, orientada a una nueva generación de profesionales que reconocen que una imagen bien curada puede aportar a nuestro progreso profesional. Detrás de La Tigre, se encuentra un selecto grupo de expertos en moda y estilo personal, que te ayudarán a elaborar una imagen con opciones de ropa a la medida y al detal de origen Italiano para él, y colecciones europeas para ella. Visiten la boutique de La Tigre ubicada en Ciudadela en Santurce o síganlos en Instagram en @shoplatigre.Por favor suscribirse a La Trinchera con Christian Sobrino en su plataforma favorita de podcasts y compartan este episodio con sus amistades.Para contactar a Christian Sobrino y #PodcastLaTrinchera, nada mejor que mediante las siguientes plataformas:Facebook: @PodcastLaTrincheraTwitter: @zobrinovichInstagram: zobrinovichTikTok: @podcastlatrincheraYouTube: @PodcastLaTrinchera
From the state where Trump claims he'd win if Jesus counted the votes, Sarah and Sam joined Tim live on stage in San Diego to debate who is the most cucked Republican and whether Bill Cassidy should get credit for his late-in-the-game YOLO opposition to Trump. Also, Jeff Bezos has Tim rethinking his opposition to socialism, and how could Bibi and Trump have had such an absurd plan for a new Iranian president? At the top of the pod, Amanda Carpenter runs down the thug fund (don't call it a slush fund) and Trump's effort to get permanent immunity from any tax liability for himself and his family. Plus, POTUS's revenge tour may backfire, and the administration may try using Fulton County as a test case for taking over vote counting in Democratic counties. Amanda Carpenter, Sarah Longwell, and Sam Stein join Tim Miller.show notes: Jon on how Trump's global health cuts are undermining the response to the Ebola outbreak Lauren on how the Georgia governor's race may be the most important one in the country And we still have a few tickets left for TONIGHT at Bulwark Live: LA at 7pm. Our friends Jane Coaston, Jon Favreau, Erin Ryan from Crooked Media, The Ringer's Van Lathan and progressive commentator Brian Tyler Cohen will join Sarah, Tim and Sam on stage. Grab your seats at TheBulwark.com/Events
Send us Fan MailGet an exclusive price for vidIQ! https://link.vidiq.com/podcastWant a 1 on 1 coach? https://vidiq.ink/theboost1on1Join our Discord! https://www.vidiq.com/discordWatch the video here:https://youtu.be/mS56rgib18AWe talk with Max Miller from Tasting History about the real choices that turned a creative side project into a full-time YouTube career. We dig into niche selection, early distribution, handling critique, and the practical routines that keep the channel sustainable through big spikes and everyday burnout.• building a food history format that feels educational and watchable • moving from theatre and Disney marketing into owning a creative project • finding a niche through personal habits and viewer curiosity • learning production basics fast while keeping gear simple • promoting early videos through Reddit and targeted communities • deciding which critiques improve the work and which to ignore • navigating COVID-era growth and a major garum-driven breakout • understanding monetization swings and staying financially cautious • choosing between returning to Disney and committing to YouTube • working with a small support team while keeping creative control • managing burnout with tighter task lists and realistic priorities • brainstorming a fresh channel concept built around museum artIf you want to YOLO, go over and check out Tasting History with Max Miller.
Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee
© 2020-2026 Ivy Podcast Discovery LLC
