Podcasts about dominant

Ep 123: In this week's AMA episode, I'm answering some of your most recent fitness questions—from whether you actually need to train your "lower core" differently, to exercises to do if you feel like you're quad-dominant and how and why alignment is so important pre workout + little clues that you might not be as aligned as you think.We also dive into one of my more recent favorite topics - how you can actually  build strength and lose fat when life is busy. (Spoiler: it's less than you think.)Plus... I finally announce my brand new Busy Body program, designed specifically for women who want noticeable results without spending hours in the gym.Busy Body launches July 28th and includes: 5 workouts per week  30–45 minute sessions  Structured body-part split  Alignment drills and warm-up exercises included  Progressive strength-focused programming  Designed for busy women who want maximum results in minimal time If you've been waiting for a realistic program you can actually stick with, this is it.__________________________________________________________________________________________________Apply for Advanced Training and Nutrition with Chelsey & Dr Emily Dow HEREStart your 7 day FREE trial of my new app HERE!Programs:8 Week Summer Prep Planhttps://www.trainerize.me/profile/chelseyrosehealth/?planGUID=d48c6fbf116c4e8fb4d31f94b5376fa3Want to work in person with Chelsey?Join a semi-private class in LA here.Email info@chelseyrosehealth.com to inquire about one on one in person training.Shop the things i'm loving HEREFollow Chelsey on Instagram:@Chelseyrosehealth@StrengthtobuildFollow Chelsey on TikTok Here."Submit a question to the show"

Andy and Randy break down Spain's victory over Argentina in the World Cup final, highlighting the dominance of the Spanish squad and Argentina's controversial behavior. They also discuss Trae Young's failed attempt to rip a Jalen Brunson jersey and the impact of LeBron James on the NBA's scheduling. 01:20 - Michigan AD Warde Manuel 04:45 - Cam Skattebo Backflip Mishap 08:08 - Bourbon Street Versus Broadway 11:41 - World Cup Final Reaction 18:20 - Spain Dominates The Pitch 23:44 - Food Safety And E. Coli 28:44 - Athletes Complaining About Taxes 32:33 - NBA Stars At WWE 36:58 - Gwinnett County Football Stars

LinktreeMOMENTUM NEW SHOWFollow the INSTAGRAM to stay up to date with all of the great Rugby League content!! https://www.instagram.com/biglezsleague_/?hl=en Hosted on Acast. See acast.com/privacy for more information.

Are we staring into the abyss of a hiring chaos or on the brink of a revolution? Join us as we dive deep into the messy truth behind current job boards, AI's role in hiring, and how the market is reshaping the talent game. This is raw, real, and everything you need to understand where work is headed today.  In this episode:  The turbulence in the job board market and declining power of giants like ZipRecruiter and Indeed  How AI could finally end ghosting and improve hiring transparency  The impact of economic slowdown and slow hiring strategies on blue vs. white collar jobs  Why employer distrust is rising and how pricing models fuel the chaos  The future of smaller companies, AI assistants, and the redefinition of HR in a tech-driven world    Timestamps: 00:00 - Introduction: Unpacking the chaos in recruiting tech 02:22 - Fun facts about Chris: Kayaking and hiking adventures 04:40 - The real trouble with job boards: Struggling giants and market share battles 07:12 - Is the market slowdown due to fewer jobs or just hiring shifts? 08:41 - Employer behavior shift: Quantity over quality and ghosting culture 09:56 - AI's potential to eliminate ghosting and streamline the recruitment process 11:11 - How companies like Greenhouse are rethinking candidate experience 12:54 - The fundamental business problems facing job boards today 14:29 - Why secondary job boards are failing to overtake giants 17:50 - Trends in blue collar vs. white collar hiring challenges 19:04 - The nightmare of application processes and resume parsing 22:57 - The future of AI agents for job seekers and hiring automation 26:24 - Will smaller, more AI-driven companies dominate the future? 28:52 - How job seekers are feeling: Fear, hope, and AI optimism 30:33 - The human side of AI: From greeting cards to personalized connections 31:49 - The coming era of smaller teams powered by AI efficiency 33:35 - Creative visions: HR Data Doodles and comedy in Human Resources    Resources & Links:  RecTech Media - Follow Chris Russell for cutting-edge insights  ZIPRecruiter - The struggling giant of job boards  Indeed - Dominant market player and trends insight  Greenhouse - HR tech innovator focusing on candidate experience  Europe's AI & Data Privacy Standards - For regulatory context  YouTube: Human Resources TV Show - Satirical look into HR humor  Connect with Chris Russell:  LinkedIn  Twitter  Stay tuned for more episodes where we challenge the status quo, ignite the future of work, and boldly explore what's next in HR and recruitment. 

A busy post-match program from GMHBA as Geelong defeats St Kilda, plus we have an interview with Mark O'Connor. See omnystudio.com/listener for privacy information.

Saint Lucian Olympic champion Julien Alfred continued her outstanding form by winning the women's 100 metres in Budapest with a season's best time of 10.87 seconds, following her record-breaking victory in the 200 metres at the Monaco Diamond League.

Some parts of the media have pronounced MAHA DOA PDA. But maybe they're just FOI? Helena and Theodore discuss. They also tackle the staggering, stunning, Holy Guacamole adoption of GLP-1s by Americans, and the rise – and risks – of compounded weight loss drugs and direct-to-consumer pharma. Finally, a good vibe: a pool full of pickles for a proper purpose!

UAE und Pogacar übernehmen das Kommando Die erste Woche der Tour de France 2026 hat einen klaren Sieger: UAE Emirates und Tadej Pogacar. Nach einem starken Mannschaftszeitfahren von Visma, folgt die Antwort durch Siege von del Toro und Pogacar an den Tagen zwei und drei. Auf der Tourmalet-Etappe, erobert Pogacar das Gelbe Trikot von Ausreißer Torstein Træen zurück und zeigt eindrucksvoll, warum er der Topfavorit ist. Bent und Lukas analysieren die entscheidenden Momente, UAEs Überlegenheit und den Stand im Gesamtklassement nach neun Etappen. Merlier der Schnellste Tim Merlier dominiert das Sprinterfeld und holt zwei Etappensiege – aus teils aussichtsloser Position. Dazu blicken wir auf ... Dieser Podcast wird vermarktet von der Podcastbude.www.podcastbu.de - Full-Service-Podcast-Agentur - Konzeption, Produktion, Vermarktung, Distribution und Hosting.Du möchtest deinen Podcast auch kostenlos hosten und damit Geld verdienen?Dann schaue auf www.kostenlos-hosten.de und informiere dich.Dort erhältst du alle Informationen zu unseren kostenlosen Podcast-Hosting-Angeboten. kostenlos-hosten.de ist ein Produkt der Podcastbude.

Republican Opposition: The Intellectual Force of Robert Taft and the Rise of McCarthy GUEST: Nick BunkerSenator Robert Taft, known as "Mr. Conservative," was a dominant intellectual force in the Republican Party, preparing for a potential 1952 presidential run. Moving away from pre-war isolationism, Taft became a staunch "Asia first" advocate, pushing for the defense of Taiwan with the U.S. Navy. This stance created a sharp divide with Dean Acheson, who feared being branded an "imperialist power." While Taft provided the intellectual foundation for Republican policy, Joe McCarthy emerged as the "wild side" of this movement. Driven by pure ambition, McCarthy hijacked the Republican agenda by shifting the focus from domestic "socialism" to communist subversion within the State Department. Meanwhile, Dwight D. Eisenhower, then president of Columbia University, watched from the sidelines with despair. He worried about the "chaos and confusion" in Washington, the demoralization of the military due to budget cuts, and the lack of a clear strategy for Asia. (3)

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Discussing 3 prospects you need to track this college football season as well into the 2027 NFL draft

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

Joe Giglio and Hugh Douglas react to Zack Wheeler's 14-strikeout masterpiece against the Reds following his exclusion from the MLB All-Star Game. They debate whether Jesús Luzardo should offer his spot to Wheeler and admit they ranked the pitcher too low on the station's Top 11 list. Zack Wheeler himself calls the selection rules "BS" in post-game comments regarding his availability. 01:23 - Intro and Wheeler Performance 06:52 - All-Star Snub Debate 13:24 - Wheeler Blasts Selection Rules 20:49 - Caller Slams Top 11

Western and Japanese meetings often look similar on the calendar, but they operate on very different assumptions. Both may involve reporting, planning, problem-solving and innovation, yet the real work happens in different places. In many Western companies, the meeting room is where people debate, confront, push, defend and decide. In Japan, the meeting is often only one stage in a broader process. Much of the real alignment happens before the meeting through nemawashi, the groundwork that smooths disagreement and builds consensus. Leaders who miss this difference can become frustrated, confused and ineffective when running meetings in Japan. Why do Western and Japanese meetings feel different? Western meetings often treat the room as the decision arena, while Japanese meetings often treat the room as a confirmation stage. The difference is not cosmetic; it changes how leaders should prepare, participate and follow up. In the West, especially in US, Australian and European business cultures, people may expect direct debate during the meeting. They "duke it out," test ideas, challenge assumptions and make decisions in the room. In Japan, open conflict inside the meeting can feel disruptive or embarrassing. The Japanese approach often relies on conversations before the meeting to understand concerns, reduce friction and avoid public confrontation. Do now: Do not assume silence in a Japanese meeting means agreement. Check views privately before and after the meeting. What communication problems appear in Western-style meetings? Western-style meetings often expose communication extremes: passive wishing, aggressive demands, direct debate, hidden stress and power plays. Leaders need a toolbox to manage these behaviours. Some people express desires as vague wishes, while others make blunt demands. Some bulldoze through barriers, while others read the air, or kuki wo yomu, to avoid offending anyone. Stress can show up as aggression, silence or controlled professionalism. In cross-cultural teams, direct communicators may dominate while quieter or more indirect contributors self-censor. This can rob the meeting of valuable perspectives and distort the quality of the decision. Do now: Watch for both over-speaking and under-speaking. The loudest voice is not always the best insight. How do power and accountability affect meetings? Meetings become dysfunctional when people use them to elevate themselves, deflate others or dodge accountability.The leader's job is to stop meetings becoming internal combat arenas. Strong individuals may jockey for position by depressing the status of others. A salesperson belittling an administration colleague as a "cost centre" versus a "profit centre" is a classic example of destructive status play. Accountability can also become uneven. Dominant people may hold everyone else to strict standards while granting themselves a free pass because they are "major producers." In Japanese organisations, these tensions may be less visible in the room, but they still exist under the surface. Do now: Make accountability universal. No one gets a special exemption because of title, ego or results. Why is confrontation handled differently in Japan? Confrontation is handled differently in Japan because preserving harmony, face and decorum often matters more than winning a public argument. Directness is acceptable only when it remains polite, considerate and relationship-aware. In many Western environments, confrontation can be seen as honest, passionate or necessary. In Japan, blunt confrontation may shut people down, damage trust or push disagreement underground. Some people will hide valid concerns rather than risk tension. Others may communicate indirectly, expecting the leader to read between the lines. This does not mean Japanese meetings lack conflict. It means conflict is often managed through timing, setting and private discussion. Do now: Move sensitive disagreement out of the public meeting and into respectful one-on-one conversations. What is nemawashi and why does it matter? Nemawashi is the Japanese practice of doing groundwork before a decision so disagreement can be handled privately and consensus can form before the meeting. It files down the rough edges before everyone gathers. The word originally comes from preparing tree roots before transplanting, and in business it means consulting stakeholders, testing reactions, lobbying for support and solving objections early. Loud people, quiet people, supporters and sceptics should all be contacted before the formal meeting. By the time the meeting occurs, the decision may already be broadly agreed. To Western leaders, this can look slow or overly political. In Japan, it often creates smoother implementation. Do now: Before the meeting, identify stakeholders, consult them privately and resolve objections early. How should Western leaders run meetings in Japan? Western leaders should adapt by combining their meeting discipline with Japanese-style groundwork. Keep clarity, accountability and timing, but do not rely on public debate alone to reveal the truth. Use pre-meeting consultation to uncover hidden concerns. Invite quieter team members to share views privately before the group session. Clarify who supports the proposal, who is worried and what must change before approval. Then use the meeting to confirm direction, summarise commitments and maintain decorum. This approach works well in Japan-based multinationals, local subsidiaries, joint ventures and cross-cultural leadership teams. Do now: Build the meeting before the meeting. Then use the formal meeting to align, confirm and assign action. Final Summary Western and Japanese meetings differ because they place the centre of gravity in different locations. Western leaders often expect debate and decision-making to happen inside the meeting room. Japanese organisations often use the meeting as one step in a broader consensus-building process, with nemawashi doing much of the heavy lifting beforehand. Leaders in Japan should not abandon all Western meeting tools. Clear purpose, participation, accountability and follow-up still matter. But they must add pre-meeting groundwork, private consultation and sensitivity to indirect communication. Do that and many of the usual meeting problems are circumvented before they can explode in public. Author Bio Dr. Greg Story, Ph.D. in Japanese Decision-Making, is President of Dale Carnegie Tokyo Training and Adjunct Professor at Griffith University. He is a two-time winner of the Dale Carnegie "One Carnegie Award" (2018, 2021) and recipient of the Griffith University Business School Outstanding Alumnus Award (2012). As a Dale Carnegie Master Trainer, Greg is certified to deliver globally across all leadership, communication, sales and presentation programs, including Leadership Training for Results. He has written several books, including three best-sellers — Japan Business Mastery, Japan Sales Mastery and Japan Presentations Mastery — along with Japan Leadership Mastery and How to Stop Wasting Money on Training. His works have been translated into Japanese, including Za Eigyō (ザ営業), Purezen no Tatsujin (プレゼンの達人), Torēningu de Okane o Muda ni Suru no wa Yamemashō (トレーニングでお金を無駄にするのはやめましょう), and Gendaiban "Hito o Ugokasu" Rīdā (現代版「人を動かす」リーダー). Greg also publishes daily business insights on LinkedIn, Facebook and Twitter, and hosts six weekly podcasts. On YouTube, he produces The Cutting Edge Japan Business Show, Japan Business Mastery, and Japan's Top Business Interviews, which are widely followed by executives seeking success strategies in Japan.

Brady is back after the Mariners go 5-1 on a six-game homestand, moving themselves to 47-44 on the season. He talks about the massive credit the team deserves, the excellent run of starting pitching and the keys to it and what's coming next in Miami and Tampa. Also, former Mariners infielder Ben Williamson, now with the Rays, joins us ahead of that series and Brady talks about the promotions of Laz Montes and Michael Arroyo to Triple-A Tacoma. See how K12 Powered Schools can help unlock your child’s full potential. Enroll online today at K12.com/foul Use our code for 10% off your next order of MLB tickets on SeatGeek*: seatgeek.onelink.me/RrnK/TERRITORY10 Sponsored by SeatGeek. *Restrictions apply. Max $20 discount. Calling all Mariners fans: Refuse to Lose Territory is your new home for Mariners analysis all year round. Subscribe on Youtube today. Part of the Foul Territory Network.See omnystudio.com/listener for privacy information.

Catch up on all the Rugby League news from NRL 360, Tuesday the 7th of July, with hosts Braith Anasta and Gorden Tallis. The NRL 360 panel looks over all the big stories in Rugby League land. This includes whether Maroons number 7 Sam Walker can continue his immense form into the Origin Decider? Can Cleary finally break his 0-3 Origin decider record? & can Laurie Daley make the right call on substitutes after some very controversial decisions in games 1 & 2 + The NRL announce a record-breaking rights deal with Fox and channel 9. For more of the show, tune in on Fox League CH 502 or stream full episodes on KAYO.See omnystudio.com/listener for privacy information.

-CBS Sports did a great list that we addressed earlier, starting with Notre Dame in the 1920s…but seeing Florida as the top team of the 2000s,Alabama the top team of the 2010s, and Georgia the tops of the 2020s shows how the B1G still has a long ways to go to get that kind ofdominance-Also---Nebraska was honorable mention in both the 70s and 80s---showing just how incredible that run of time was in the programAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Round of 32 delivered everything.

Tapt Media's tennis correspondent Craig Gabriel reports from Wimbledon where several big names have been eliminated. See omnystudio.com/listener for privacy information.

Why your dominant hand seems better at almost everything Hand Dominance Is Driven by Practice, Not Birth Contact the Show: coolstuffdailypodcast@gmail.com Learn more about your ad choices. Visit megaphone.fm/adchoices

• breaking the porn to masturbation loop through clear rules • shifting from selfish impulses to service and obedience • using permission requests to slow down urges and earn rewards • setting penalties for porn use and unauthorized touching • treating mistakes as training data instead of disappearing • reducing porn like a withdrawal habit with audits and tracking • building discipline for sharper focus and better life goals • choosing a chastity cage Go to my Fet Life. You join my chastity society, the Crown and Cage Chastity Society. Go to lockedinlust.com. You can purchase the MACH2 Vice. My PROMO code CHASTITYQUEEN, all caps, all one word and it is stackable. You can leave Me a tip here on my podcast under support the show link Try to connect with your local BDSM community. Fetlife is a great way to see others in similar FLR and chastity lifestyles. You can check out Mine in Fetlife at Chastity-Queen. It's a free to join. Hugs, Chastity Queen Locked In Lust 15% OFF:CHASTITYQUEEN Use Discount Code:CHASTITYQUEEN for 15% OFF ANYTHING at www.lockedinlust.com Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showSUPPORT ME in 2 ways! XOXO1: https://paypal.me/TiffanyTheQueen?country.x=CA&locale.x=en_USOR2: https://www.buzzsprout.com/1992649/supportCHASTITY IS FREEDOMI LOVE YOU 

ESPN FC is up to react to another exciting day in the World Cup. France took care of Sweden. Kylian Mbappe just keeps scoring, and he is one goal behind Lionel Messi for the Golden Boot of the World Cup. Erling Haaland scored late to help Norway advance. They are set up for a date with Brazil. Do they have a chance? We also recap the Mexico vs Ecuador match before closing out with some predictions. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Welcome back to SANDCAST: Beach Volleyball with Tri Bourne and Travis Mewhirter, and welcome back to the Gstaad Elite, the greatest place on Earth. We're recapping an action-packed opening day in Gstaad, where: Trevor Crabb and Chase Budinger delivered an enormous day one, qualifying with a win over Adrian Heidrich and Yves Haussener and then handling Taylor Crabb and Andy Benesh with ease Tina Graudina continues thriving here in Gstaad, making the main draw with young Liv Ebere USA Volleyball's women so-so day, with three teams punching their main draw tickets and four bowing out Switzerland's young guns, Luc Fluckiger and Andrin Kolb upsetting the Grimalt cousins before ALMOST taking David Ahman and Jonatan Hellvig to three And more! SHOOTS! We have a NEW BOOK! Pre-order your copy of Volleyball for Dummies today at Barnes and Noble! Get 25 PERCENT off and FREE SHIPPING on all Mikasa products with our code, SANDCAST and play with the ball. played with the best in the game. Head to Mikasa's website and get your bag of balls today! Get 10 PERCENT OFF VBTV using our discount code, SANDCAST10 Want to get better at beach volleyball? Use our discount code, SANDCAST, and get 10 percent off all Better at Beach products!  Want SANDCAST merch? We got you covered. Check it out here! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

We're entering a new society, is what I said, and then laughed at hearing myself say something so extreme, but…It's what I truly believe!Dominant narratives are losing their zing, filmmakers (and audiences!!) are more discerning than ever about Hollywood's ultimate goals, and something new and non-dependent is rising like a perfectly baked soufflé. For the cost of an oatmilk matcha (pre-tip), you can unlock the vault of Town Hall workshops designed ~specifically~ to help non-dependent artists build audiences.It was an absolute pleasure to get into non-dependence, grief, the creative process, and well.. all of it with Laverne McKinnon, an executive producer, grief coach, and career strategist on her Substack Live a few weeks ago.Below you'll find the timestamps and 5 actions filmmakers can take right now to build their careers in the brand new world we're creating every single day.Timestamps & Topics* 00:03:42 — The “coziness” of Birdman and how it helped me learn screenwriting* 00:08:14 — The influences of seeing space shuttle launches in Florida as a kid* 00:10:10 — Learning that artistic freedom wasn't on my industry career path* 00:11:11 — Accepting reality: no one's looking for breastfeeding mom directors!* 00:15:30 — Career coherence and a resistance to having a “marketable” identity* 00:20:02 — The ambition it takes to chart a difference course* 00:22:53 — Instability brought on by mergers, strikes, and AI* 00:23:44 — Forging genuine relationships between filmmakers and audiences* 00:24:39 — The NonDē infrastructural builds we're currently working on* 00:27:35 — Why my negative self-talk is none of my business* 00:30:34 — The importance of collecting data for filmmakers* 00:32:07 — Collaboration over competition as a necessary strategy for audience-building* 00:32:30 — The current initiatives being run by the Non-Dependent Film Movement on and off FilmStack* 00:37:08 — How to generate momentum when it's lost* 00:41:22 — What folks get very wrong about non-dependence* 00:45:55 — Defining career grief and the language and support to process it* 00:47:41 — My personal story of not working for 3 years* 00:50:43 — Learning to “take the L” as a major life strategy* 00:55:02 — How severed attachments impact the brain's cognition* 00:57:18 — How being with my dad in his final days impacted my work* 01:00:13 — How holding all parts of life, including death and loss, can create meaning and even sometimes fulfillment5 Actions for Filmmakers to Take Right Now * Execute on one thing today. Execution is momentum. Waiting for “20/20 vision” or a perfect plan is a thinking trap because as much as I would la-hove to think my way out of every situation, situations usually require action before you can figure them out. But action doesn't have to be a grand gesture. You can catch a momentous wave by simply sending an email, following up with someone when a ball has been dropped or phoning in a favor. There's always something to do, and it doesn't have to be a big thing for it to have big impact.* Collect whatever data you have access to right now and write it down. We all know we're supposed to be collecting our data, but what does that mean?It could mean:* Your subscriber count* Your subscriber open rate* Your last film's overall seat count* Your conversion rate (for email or ticket sales or podcast eps, or…)* Your ACV (Average Customer Value), meaning, how much does each customer/audience member typically spend with you?* Funding raised across all projectsCollecting this kind of data puts you in the driver's seat whether you're going fully non-dependent or not. Because if you decide you want a traditional distribution deal down the line, when you collect your data now, having that data will give you better leverage when striking that deal.* Strike up a radical collaboration with another filmmaker. For non-dependent filmmakers, when you focus on competing rather than collaborating, you're kneecapping your ability to grow your audience. You aren't in a competitive business, you're in a collaborative one. Audiences do not get sick of movies. If they like your filmmaker friend's film, that is actually GOOD NEWS FOR YOU. It means they're more likely to watch yours. So whatever you can do today to radically collaborate with someone will only benefit you. It could be as simple as a joint Substack post or as complicated as a multi-city collaborative theatrical tour. Collaboration allows artists to collapse timelines, learn from each other's mistakes faster, and offset the risk from going it alone.* Participate in an infrastructural build. Because the traditional Hollywood system has become an unstable place to chart a career, it's incumbent on us to build the infrastructure we need. But we can't build the house without the scaffolding. We need small bits of infrastructure to build the bigger, better system as a whole. Small builds could be: rounding up all of the screening venues in your neighborhood or city and sharing them with NonDē Alternative Venue List, or suggesting an initiative within the NonDē 50 Films Project, or writing a Substack post on your post-production workflow. Small infrastructure is still infrastructure, and we need it to build the entire ecosystem.* Develop your career-grief literacy. The disappointment you feel in the industry (or in the speed at which you're able to build outside of it) creates legitimate physiological events in your body that impact cognition and physical health. Take the time to grieve it. Mourn the things you thought were for you. Mourn the direction you thought you were pointed in. Integrate that loss into your identity, you don't have to always just rise above. Sometimes you can wade directly through, and that's a faster salve. Remember: maintaining your emotional resilience is required to continue making art. Take good care of yourself, always.Psst. If you've made it this far in the post, then you deserve the insider secrets! You can grab a 7-day free trial to test drive past LGA Town Halls to see if they're valuable, before you commit. :)Feeling NonDē curious? Quick Ways to Get Started in the NonDē Film Movement ahead »* Subscribe to FilmStack Daily Digest and start getting your analysis and breakdown from thinkers on this platform with independent voices.* Try non-dependence one of these 64 ways.* Read about the New Film Criticism and subscribe to a swath of amazing film critics who don't just introduce us to new work, but also help us think and write about films in an altogether fresh, original, personal way. Check out Film Soup Zine run by KLA Media Group and C. C. Simmons who will be reviewing specifically-NonDē films.* Join the NonDē 50 Films Project by signing up here and checking out the dashboard here.* Start talking about it. Let's bring these ideas into the conversation. At first people will say we can't do it. That's fine, let's talk about it. Let's bring these big ideas into the rooms with us.* Discuss with your team. Be the leader on your production team and bring non-FilmStack filmmakers into the fold. Share resources and posts with them about how to do things differently.* Share about the NonDē Film Movement far and wide. A better world is inevitable if we make it so.Read the entire NonDē series:* It's not too late to say you were there when it started* You're invited to the table* If you say it, they will come* Went to Slamdance, and all I got was renewed faith in the future of film* The (not so) hidden agenda of non-dependent film (part 1 of 3)* The love of the game is a power move (part 2 of 3)* Who gets to speak about the future of film? (part 3 of 3) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsgoagain.substack.com/subscribe

Monday, June 29, 2026 The Dominant Duo – Total Dominance Hour -The Dominant Duo is full strength, Thunder news (Lou Dort), Hartenstein's future, Berry Trraml checks in from the border of New Mexico & Arizona, Dean's Thunder reaction and more. Follow the Sports Animal on Facebook, Instagram and X PLUS Jim Traber on Instagram, Berry Tramel on X and Dean Blevins on X Follow Tony Z on Instagram and Facebook Listen to past episodes HERE! Follow Total Dominance Podcasts on Apple, Google and SpotifySee omnystudio.com/listener for privacy information.

In tonight's postgame show, ToddFox breaks down another impressive Angels victory as strong starting pitching set the tone before the bullpen slammed the door with four scoreless innings. The offense came through with timely hitting, and the Angels did everything they needed to secure the series against the Athletics. In this episode: Complete game recap Starting pitching analysis Bullpen dominates over the final four innings Key offensive moments Player of the Game What this series win means moving forward Looking ahead to the next matchup If you enjoy Angels baseball coverage, be sure to Like , Subscribe , and leave your thoughts in the comments! Who was your Player of the Game? #Angels #GoHalos #LosAngelesAngels #MLB #Athletics #Baseball #MLBHighlights #Postgame #AngelsBaseball #Halos #Bullpen #StartingPitching #ToddFox #GrindingTrueCrimeAndSports Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

--{ "Dominant Minority and Passive Majority vs Sentient Minority"}-- Detaching from the mainstream and the alternative - The choices we make - Casualties in a long war - Alan Greenspan - Anna Paulina Luna and interdimensional beings - Political routines, promises and "Something for Nothing" - Free flu shots - Differing scripts for each age group - John Edwards speech - MANDATORY Health Care - "Preventative" and "Mental Health" Care - Psychological evaluations - Cradle to Grave - (Permanent) Income Tax - "Temporary" War Tax of Britain - Loopholes for elite - Lawyers-Liars - The "New Freedom" - Good Reasons and Real Reasons - Schmucks - National DNA databases - Criminal, medical blood samples - Information collection, Law enforcement - Predictability for "safety" - Airport biometric scanning - Cashless society, system of punishment and reward - "Total Quality Management" - Problem of elite AND vast majority of population (Land of the Dead) - Democracy to force minority to conform - Governance, Government - Good slaves. Groups and organizations - Conformity, sameness, gregariousness - Intolerance of individualism - Animating force of Spirit. Creatures of instinct (earth bound) - Compassion is a survival mechanism - Global government; Scientific dictatorship under "Benevolence" - Mindset of passive compliance, content to be happy slaves - Mind Control for the Masses. Article: "Judge Wants Everyone in U.K. on DNA Database" by James Orr, Sept. 5, 2007 Guardian Unlimited)

Daniel Norcross is alongside former England captains Michael Vaughan and Sir Alastair Cook, as well as BBC chief cricket commentator Jonathan Agnew for reaction to the third day's play between England and New Zealand at Trent Bridge.Hear from England spinner Shoaib Bashir, and New Zealand allrounder Nathan Smith.

In this episode, Ms. Elle helps a new FemDom who feels intimidated by her own power develop the confidence to lead more naturally and overcome shame and vanilla conditioning surrounding letting her Dominant voice roar. Support the showSUPPORT THIS PODCAST  |  FOLLOW ON SOCIAL

Elite quarterback play & leadership - Dominant defenses or unstoppable offenses - Coaching stability - Roster continuity & age - Injury luck & mental toughness - Regular season dominance leading into the repeat year Then we turn the microscope on the 2025-26 Seahawks — the reigning Super Bowl LX champions — and ask the big question: Do they share the same winning formula as the great repeat teams, or are they missing key pieces? If you're a Seahawks fan wondering if another Lombardi Trophy is coming in 2027, this is the video for you. Link to my YouTube Channel. Live on Wed and Sunday, 5PM PST...https://www.youtube.com/@TheHawksNest12thman?sub_confirmation=1 Sign up for PrizePicks with code: HMA and get $50 in lineups instantly when you play your first $5+ lineup! https://link.prizepicks.com/LME0/HAWK Link to my Patreon....https://www.patreon.com/thehawksnest Twitter...@SeahawksNester Twitch...@TheSeahawksNest Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Shohei Ohtani and rookie catcher Dalton Rushing couldn't seem to get on the same page early in Wednesday's start... and it led to one of the most frustrating innings of Shohei's season. Then everything changed. In this week's 'This Week in Shohei Ohtani News', we break down the miscommunication between Ohtani and Rushing, Shohei's postgame comments explaining exactly what happened, and how he responded by striking out the side and dominating the rest of the game. We also discuss: ⚾ Shohei averaging 100 MPH for the first time in his MLB career ⚾ The fastest pitch of his career (101.7 MPH) ⚾ Another dominant start (6 IP, 2 ER, 8 K) ⚾ Six home runs in his last 12 games ⚾ Becoming a father of two and his first comments after welcoming his son ⚾ Why Shohei continues to prove he's the most incredible player baseball has ever seen. If you enjoyed the episode, don't forget to like, subscribe, and leave a comment! Did the Shohei Ohtani frustration with Dalton Rushing surprise you? #ShoheiOhtani #Dodgers #MLB #Baseball #ThisWeekInShoheiOhtaniNews #DaltonRushing #大谷翔平 Timestamps: 0:00 Intro 0:16 Ohtani vs Rushing Frustration 3:15 Performance vs Twins 5:20 Rushing Struggles 7:42 Shohei Pushing Through Pain 9:35 Postgame Comments 10:54 Shohei Offense 13:38 New Dad x2 15:08 Outro Learn more about your ad choices. Visit megaphone.fm/adchoices

(0:00-21:33) Have we seen a player as dominant as dominant as Johan Duran in Philadelphia? Plus, Andrew DiCecco joins the show for the final three hours(21:33-33:35) Players in Philly that were must watch TV(33:35-43:10) Today's Headlines with Connor Thomas including one athlete that used to hate Baker Mayfield for a valid reasonPlease note: Timecodes may shift by a few minutes due to inserted ads. Because of copyright restrictions, portions—or entire segments—may not be included in the podcast.For the latest updates, visit the show page Kincade & Salciunas on 975thefanatic.com. Follow 97.5 The Fanatic on Twitter, Facebook, and Instagram. Watch our shows on YouTube, and subscribe to stay up-to-date with all the best moments from Philly's home for sports!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Will Anderson Jr., Calen Bullock, Derek Stingley Jr.-we know the usual HARD-HITTIN' Texans suspects on the defensive side of the ball.. How far can those fellas/DeMeco's Defense for the Texans carry the team in 2026 within this AFC..!?!

In what aspects of your life are you as dominant as Shohei Ohtani is at baseball? Chad opens with that discussion and plenty of right and wrong answers.

There's an uptick in ship crossings through the Strait of Hormuz, says Charles Schwab's Kevin Gordon, believing it offers a small reprieve for the energy trade. He sees Micron's (MU) earnings and this week's inflation prints as prominent catalysts to move markets. Michelle Gibley sticks with the AI trade by explaining what the MSCI reclassification means for global stocks, including South Korean tech giants Samsung and SK Hynix. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Jason Smith and Mike Harmon discuss team USA's DOMINANT win against Australia and Paraguay. They are joined by JP Morosi to discuss the World Cup further. See omnystudio.com/listener for privacy information.

Nick Kirby breaks down Hunter Greene after his dominant first rehab start in the Arizona Complex League, where the Reds ace looked sharp in his return to action. Nick also recaps the Dayton Dragons' incredible run to clinch a playoff spot after winning the Midwest League East Division first-half title. Plus, a full rundown of all the Reds minor league action from Thursday and a preview of the Reds' upcoming series against the New York Yankees.   Chatterbox Reds on YouTube: https://www.youtube.com/@ChatterboxSports    OTHER CHATTERBOX PROGRAMING:   Chatterbox Bengals: https://podcasts.apple.com/us/podcast/chatterbox-bengals-a-cincinnati-bengals-nfl-podcast/id1652732141  Chatterbox Bearcats: https://chatterboxbearcats.podbean.com/  The Stone Shields Show: https://podcasts.apple.com/us/podcast/west-4th-and-long/id1828384424  The Flyin Lion (FC Cincinnati): https://podcasts.apple.com/us/podcast/the-flyin-lion-fc-cincinnati-podcast/id1701368522    Chatterbox Reds Sponsors:   Book your appointment at Richter & Phillips today: https://richterphillips.com/pages/cha...   Get 20% off sitewide at Homage with promo code CHATTER at checkout: homage.com/CBOX    The Anthony Muñoz 78 Cigar! Get your box today: https://www.anthonymunoz.com/anthony-munoz-78-cigars     Apply today at Sharefax Credit Union: Sharefax.org or call 513-753-2440   Learn more about our friends at QC Kinetix! Call (513) 655-3356 or visit qckinetix.com    It's OK not to be OK! Need Help? Call or text 988 DAY OR NIGHT or visit mantherapy.org  

In today's episode Scientific Triathlon coach Mikael Eriksson answers listener questions about time-crunched training and lots more…  HIGHLIGHTS AND KEY TOPICS:  How to train effectively on 7 hours per week, and most workout windows being just 45 minutes.  Can you use running power to do reliable shoe testing (for running economy) without a metabolic cart?  How a highly glycolytic (fast-twitch dominant) athlete should adapt their training strategies for Ironman training, and in general to get better at long, sustained efforts.  SHOWNOTES, LINKS, RESOURCES AND RELATED EPISODES:  The shownotes for today's episode can be found here.  The full podcast episode archives (including category filters) can be found here.  Coaching Thoughts: 15 Underrated Factors for Triathlon Performance | EP#440 Q&A on racing and testing | EP#399 Physiological Profiling Explained – Why, How, and Important Training Implications for Triathletes FTP, VO2max and VLaMax: what triathletes need to know with Sebastian Weber | EP#169 SPONSORS: Precision Fuel & Hydration produce our favourite gels, sports drinks, and electrolyte and carbohydrate products here at That Triathlon Show and Scientific Triathlon. Use the free Fuel & Hydration Planner to get a personalised plan for your carbohydrate, sodium and fluid intake in your next event, and get 15% off your first 2026 order by using the code TTS2026 at checkout. Rouvy is hands down the most complete indoor cycling platform for triathletes. Among their thousands of beautiful bike courses from all around the world, all filmed in stunning quality, they have over 75 IRONMAN and IRONMAN 70.3 race courses plus 20+ Challenge Family courses, so you can pre-ride your race from home. Real gradients, real visuals, and real feel! Head to rouvy.com and use the code TTS to get your first month free on top of a 7-day free trial. Effortless Swimming produce the best swim goggles for triathletes and open water swimmers. Their NanoClear anti-fog lenses give you clear, fog-free vision that lasts and doesn't wear off. Don't let foggy or leaky goggles ruin another swim. Go to shop.effortlessswimming.com and use the code TTS15 to get 15% off your goggles, and get a free two-month Effortless Swimming course membership.LEARN MORE ABOUT SCIENTIFIC TRIATHLON: The Scientific Triathlon website is the home of That Triathlon Show and everything else that we doContact us through our contact form or email me directly (note - email/contact form messages get responded to much more quickly than Instagram DMs)Subscribe to our NewsletterFollow us on InstagramLearn more about our coaching, training plans, and training camps. We have something to offer for everybody from beginners to professionals. HOW CAN I SUPPORT THAT TRIATHLON SHOW (FOR FREE)? I really appreciate you reading this and considering helping the show! If you love the show and want to support it to help ensure it sticks around, there are a few very simple things you can do, at no cost other than a minute of your time. Subscribe to the podcast in your podcast app to automatically get all new episodes as they are released.Tell your friends, internet and social media friends, acquaintances and triathlon frenemies about the podcast. Word of mouth is the best way to grow the podcast by far! Rate and review the podcast (ideally five stars of course!) in your podcast app of choice (Spotify and Apple Podcasts are the biggest and most important ones).Share episodes online and on social media. Share your favourite episodes in your Instagram stories, start a discussion about interesting episodes on forums, reference them in your blog or Substack. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Most NBA titles are thought of as dynasties, but what happens when a large-market team storms the Finals with resilience and unexpected brilliance? The New York Knicks' surprising championship run defies the odds—and shows that Heart, Strategy, and Star Power still matter in today's parody-driven league. If you think the Knicks just got lucky, think again: this episode unveils how they outwitted rivals, tired out Wemby and the Spurs, and dominated when it counted most, led by none other than Jalen Brunson's legendary playoff performance.

On this episode of Tip of the Ice-Burgh, Nick Belsky reacts to the Carolina Hurricanes' dominant run through the 2026 Stanley Cup Playoffs and the remarkable streak that will officially come to an end with Jordan Staal. Plus, he dives into the latest Pittsburgh Penguins contract news, examines two names gaining serious traction as potential Penguins draft targets, and more -- Tune in! Check out our latest episodes

Time for Sports Graffiti! Mason and Ireland dive back into the World Cup! Is it good that no NBA Champion has repeated recently? The guys are joined by Max Kellerman! Kellerman discusses the Knicks winning the Championship! Where were you on this day in 1994? Learn more about your ad choices. Visit podcastchoices.com/adchoices

Brennan & Eric are back ahead of NASCAR's highly anticipated San Diego race on Naval Base Coronado. They discuss the recent action at Pocono, driver moves and rumors, and the dominant seasons from Denny Hamlin and Justin Allgaier. Learn more about your ad choices. Visit megaphone.fm/adchoices

Hire Better, Sell More & Communicate Better with DISC What if you could instantly understand how someone thinks, communicates, and makes decisions — before you ever pitch them, hire them, or lead them? In this episode, David Hill breaks down the DISC behavioral assessment and how mastering it will transform the way you sell, hire, and connect with everyone around you. David shares his own DISC profile (DCI), walks you through each of the four behavioral styles — Dominant, Influential, Steady, and Compliant — and reveals how recognizing these patterns in others (and yourself) is the single biggest lever you can pull to become more effective in business and life. Episode: Hire Better, Sell More & Communicate Better with DISC Host: David Hill — Maxwell-certified trainer, coach & speaker | 36 years in sales | 20 years studying & teaching DISC What You'll Learn in This Episode: What the DISC assessment is and why it matters in sales, hiring, and leadership The four DISC behavioral profiles: D (Dominant), I (Influential), S (Steady), C (Compliant) How to recognize each style's patterns so you can communicate more effectively Why 85% of your success comes from people skills — not technical skills (Carnegie Institute study) How David went from being a high-D, task-focused top producer to a well-rounded communicator Real-world examples: elevator test, vacation packing, checklists, and more How to use DISC in your hiring process before bringing someone onto your team What Shark Tank's Robert Herjavec revealed about how they choose who to invest in A walkthrough of David's actual DISC assessment results The history of behavioral profiling — from ancient philosophers to William Marston (1940) Key Quotes: "I would rather have the ability to deal with people than any other ability at all." — John D. Rockefeller "9 out of 10 times on Shark Tank, they pick based on the person, not the project." — Robert Herjavec Resources & Links Mentioned:

Carl and Mike open the show with Braves talk as they react to Spencer Strider having to leave his last outing due to right arm soreness and discuss why they believe it will be tough to trust Strider returning to his dominant form.

#143 Most new Dominants make the same mistake: they water themselves down because they're afraid of becoming the stereotypical Dom everyone rolls their eyes at. In this episode, we're sharing how to step into authority without becoming controlling, arrogant, or mean – and why that fear may be holding your dynamic back. Listen now and discover what real dominance looks like.➡️ Free 'How to be a Good Dom' Quick Start Guide: https://domsubliving.com/dom➡️ Subscribe to our free newsletter: https://domsubliving.com/signup

The USMNT is off to a dream start at the home World Cup following a statement 4-1 win over Paraguay, and the Call It What You Want crew breaks it all down. Live from the Power & Light District in Kansas City, Jimmy Conrad, Charlie Davies, Benny Feilhaber, Chris Wittyngham, Kelley O'Hara, and countless ecstatic U.S. fans react to an incredible night where Folarin Balogun scored twice and Gio Reyna capped things off in style. The crew discusses the vibes around Mauricio Pochettino's team, Christian Pulisic's precautionary exit, and what's left to come in Group D. How much can we read into this opening statement? Are we getting carried away? Or is this just the start of deep run in the tournament?Call It What You Want is available for free on the Audacy app as well as Apple Podcasts, Spotify and wherever else you listen to podcasts. Follow the Call It What You Want team on X:⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@JimmyConrad⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠ @CharlieDavies9⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠@TMeola1⁠⁠⁠⁠⁠⁠⁠For more soccer coverage from CBS Sports, visit ⁠https://www.cbssports.com/soccer/⁠Subscribe to ⁠⁠⁠⁠⁠⁠⁠⁠Paramount+⁠⁠⁠⁠⁠⁠⁠⁠Visit the betting arena on CBS Sports.com: ⁠https://www.cbssports.com/betting/⁠For all the latest in sportsbook reviews and promos: ⁠https://www.cbssports.com/betting/news/sportsbook-promos/

This week on Come Curious, Reed sits down with kink theorist, power exchange mentor and educator Tripp Terrier to unpack one of the most misunderstood roles in BDSM: the dominant. Together they explore why true dominance isn't about control, arrogance or entitlement but instead, it's about presence, responsibility and service. Tripp shares his unique perspective on power exchange, explaining how authority is earned through knowledge, patience and skill rather than demanded through status or ego. They discuss the importance of integrating every part of yourself including the feminine and more uncomfortable aspects of your personality, and why self-awareness is one of the most powerful tools a dominant can develop. Inside the episode: - Why dominance is a giving role, not a taking one- The difference between confidence and entitlement- How power exchange can become deeply transformative- Why consent is more complex than a simple "yes" or "no"- The role of humility in healthy masculinity and leadership- Supporting partners through subdrop and emotional vulnerability Tripp also shares his essential reading list for anyone curious about kink, including The Topping Book and The Bottoming Book by Janet Hardy and Dossie Easton, alongside The Heart of Dominance and The Dominance Playbook by Anton Fulmen. Whether you're dominant, submissive, curious or simply interested in healthier models of masculinity, this conversation offers a fascinating look into power, connection and personal responsibility. Follow Tripp online @tripp.terrier Come watch the full episode, AD FREE over on Patreon: patreon.com/comecurious And tune in on all major streaming platforms - Come Curious. Follow us on Instagram: @comecurious Florence @florencebark Reed @reedamberx Our podcast is released every Thursday. Listen to the full episode wherever you get your podcasts. Spotify: https://open.spotify.com/show/4nQnVqQcEn4ptshlKjtdqo Apple Podcasts: https://podcasts.apple.com/gb/podcast/f-ks-given-by-comecurious/id1444176549 Learn more about your ad choices. Visit megaphone.fm/adchoices

BSJ's Greg Bedard reacts to Day 1 of Patriots Minicamp, including what he saw from Drake Maye, A.J. Brown, Caleb Lomu and others on the Patriots offense, who were impressive today. Greg also discusses the return of Kayshon Boutte and Christian Gonzalez to practice. Patriots on CLNS Media is Powered by:

Tyler Anbinder examines the remarkable industrial success of Phelan and Collender, who mass-produced high-quality billiard tables. Their innovation in table cushions led to a dominant market position, and they famously gifted a custom gold-ornamented table to Ulysses S. Grant. Their massive factory on 10th Avenue symbolized the pinnacle of Irishentrepreneurial achievement. Anbinder concludes by debunking the myth that the famine Irish were permanently stuck in poverty. His research reveals they were a highly ambitious and determined group who successfully utilized networking and grit to climb the socioeconomic ladder in the United States. (8)1861 CHURCH STREET

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