Podcasts about Sidecar

THE FANTASTIC POUR Brett welcomes FW All-Star Siskoid to the Fantasti-Lounge for a new edition of the Fantastic Pour! We talk Godzilla, mix up a brandy sidecar cocktail, and read Godzilla: King of the Monsters #16. Join us in the Fantasti-Lounge as we discuss What does Godzilla eat? Improv in Canada. Cattle rustlin'. And much, much more! Secret Pour-igins: Sidecar Cocktail: The Kaiju Cowboy Ingredients (per drink): 2 oz. brandy 1 oz. orange liqueur (such as Cointreau) ½ oz. lemon juice, freshly squeezed Garnish: orange twist Garnish: sugar rim (optional) Instructions (per drink) Coat the rim of a coupe glass with sugar, if desired, and set aside. Add the cognac, orange liqueur and lemon juice to a shaker with ice and shake until well-chilled. Strain into the prepared glass. Garnish with an orange twist. Comic: Godzilla: King of the Monsters vol.1 #16, Marvel Comics, 1978 Have a question or comment? E-MAIL: fwpodcasts@gmail.com You can find The Fantastic Pour on these platforms: Apple Podcasts Amazon Music Spotify The Fantastic Pour podcast is a proud member of the FIRE AND WATER PODCAST NETWORK: Fire & Water website: http://fireandwaterpodcast.com Fire & Water Facebook page: https://www.facebook.com/FWPodcastNetwork Fire & Water on Instagram: https://www.instagram.com/fireandwaterpodcast Fire & Water on Bluesky: https://bsky.app/profile/fwpodcasts.bsky.social Fire & Water Podcast Network on Patreon: https://www.patreon.com/fwpodcasts Use our HASHTAG online: #FWPodcasts Brett can be found on Instagram at https://www.instagram.com/imagine8design/ and Bluesky at https://bsky.app/profile/imagine8design.bsky.social

La banda 'Sidecars' llega a Mallorca el próximo 6 de agosto en el marco de 'Es Jardí', en pleno veinte aniversario y con Juancho Conejo a la cabeza. Puedes escuchar la entrevista en 'A vivir Baleares'.

Only in Spain a few hours and Mark has already gotten burnt. But not by the sun. Am I right?! Nevertheless, get involved in the roasts here: https://www.instagram.com/mehiganmark/?hl=en

On this week's episode of The MacRumors Show, we discuss the high-end of Apple's increasingly tangled MacBook lineup, including the entry-level MacBook Pro, the redesigned high-end models, and the rumored “MacBook Ultra."1:39 Why Apple Is Skipping M6 Pro and M6 Max4:01 MacBook Ultra Timing and First-Generation Problems6:00 How MacBook Pro and MacBook Ultra Could Coexist9:59 Sponsor: Claude11:20 Untangling Apple's Upcoming MacBook Lineup12:47 Which MacBook Models Are Worth Waiting For?18:00 The MacBook Pro Redesign and Dynamic Island23:10 Could MacBook Ultra Replace High-End MacBook Pros?28:18 What Is a Touchscreen Mac Actually For?30:08 Samsung's New Foldable and Apple's iPhone Ultra33:03 MacBook Ultra Pricing, Features, and Dream Apple DevicesApple's chip roadmap for the Mac is reportedly set to take an unusual turn over the next year. The company is said to be skipping the M6 Pro and M6 Max entirely, jumping from the M5 generation straight to the M7 for its high-end laptops. A standard M6 chip will still arrive this year in an entry-level ‌MacBook Pro‌, but there will apparently be no Pro or Max variant in that family.As a result, Apple's first high-end OLED laptop will use the existing M5 Pro and M5 Max chips rather than newer silicon. First-generation buyers would therefore be paying a premium for a redesigned machine featuring the same processors already found in the current ‌MacBook Pro‌, with M7 Pro and M7 Max models expected to follow in the second half of 2027.The launch window remains fluid. The device was long expected to arrive in late 2026, but memory chip constraints and Apple's recent price increases have pushed it toward early 2027. A second-generation model with M7 chips is already planned for late 2027, meaning the first Ultra could remain on sale for a relatively short window.The overlapping releases make for a crowded and confusing roadmap. Across roughly a year, Apple is expected to ship a base M6 ‌MacBook Pro‌, a redesigned base M7 model in the first half of 2027, two M5 Pro and M5 Max MacBook Ultramodels, their eventual M7 Pro and M7 Max successors, and perhaps new high-end ‌MacBook Pro‌ models with the M7 Pro and M7 Max. Notably, the entry-level M7 model is set to get the new design first, ahead of the pricier high-end ‌MacBook Pro‌ models.The headline changes are reserved for the top-tier "Ultra" model. It is expected to be the first Mac with an OLED display, using the same hybrid tandem ‌OLED‌ technology as the iPad Pro, along with the first touchscreen on a Mac, a Dynamic Island in place of the notch, and a thinner chassis. Both 14-inch and 16-inch sizes are expected. Built-in cellular connectivity for the first time on a Mac is also rumored.Apple is reportedly positioning touch as “touch-friendly, not touch-first," letting users move between touch, trackpad, and keyboard rather than treating the Mac like an iPad. That marks a reversal for a company that long resisted the idea. Steve Jobs argued in 2010 that vertical touchscreens cause arm fatigue, and as recently as 2021 hardware chief John Ternus said the Mac was "totally optimized for indirect input."Signs of the shift are already visible in macOS 27 Golden Gate, which adds direct touch control to Sidecar, so users can tap and interact with macOS elements using a finger on an ‌iPad‌. A reinforced hinge is also expected, so the display does not wobble when tapped. Pricing is likely to be steep. Apple raised prices across the Mac lineup in June, and the current 14-inch ‌MacBook Pro‌ now starts at $1,999, rising to $2,499 with the M5 Pro chip and $4,099 for an M5 Max. The 16-inch M5 Max reaches $4,399, and a fully specced configuration already exceeds $10,000. The high-end ‌OLED‌ model is expected to start higher still.Ready to tackle bigger problems? Get started with Claude today at — https://www.Claude.ai/mac

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

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Diese Woche stand die Apple WWDC 2026 an, wo das Unternehmen ihre Pläne zu AI und Siri mitteilten. Dank der Vorarbeit von Google ist man jetzt auch im AI-Rennen angekommen, zumindest wenn man in den USA wohnt. Denn nicht nur die EU, sondern auch China ist erstmal außen vor.

Every deal document tells a story, and in an antitrust merger review, overbroad and imprecise language can cost you months of investigation and millions of dollars in legal fees. In this Committed Capital Sidecar episode, Dechert antitrust attorneys Rani Habash, Brian Hanna and Greg Luib draw on their combined experience at the firm, the FTC and the DOJ to break down the "hot documents" that attract unwanted regulatory attention and explain how companies can better prepare for their next merger filing.

Isle of Man TT wrap up: Weather continues to be an issue, gorgeous weather during practice week has degraded into classes being cancelled entirely. https://www.cyclenews.com/2026/06/article/2026-isle-of-man-tt-results/Sidecars are officially cancelled, also at the Southern 100  but will that hold for the IOM Classic in August? https://www.iomttraces.com/latest/news/statement-sidecar-2026/https://www.motorcyclenews.com/sport/tt-road-races/2026/june/shaun-parker-gofundme-fundraising-page-launched/?recirculation=taboola_nativehttps://www.motorcyclenews.com/sport/tt-road-races/2026/june/maria-costello-paralysed-crowdfunding-launched/?recirculation=taboola_nativeDown to earth Manx news here: https://www.youtube.com/@manxradioiomDefinitely worth a listen. HUGE SHOUT OUT - To our friends Liza Miller and Hayley Bell leading the Legacy Lap at the IOM TT this year.  https://www.rideapart.com/news/797558/norton-manx-r-first-iom-tt-womens-relay-ride/Our friends Patrick and Luke are obviously enjoying the TT immensely: https://www.facebook.com/100071561585042/videos/pcb.1014734610921916/2145045499373521A  NEW 2-stroke from Kawasaki - and you'll never have to dick with the jetting? BLASPHEMY!https://www.visordown.com/news/kawasaki-kx327-motocrosser-and-kx327x-enduro-two-strokes-revealedAMA Vintage Days Preview - What are you bringing? Where / How are you sleeping? What are you going to ride around on when you get there? Support the showRemember folks...Ride Fast and Take Chances! check out our Youtube channel at https://www.youtube.com/c/ClevelandMoto

Por que la salida de Tim Cook coincide con la llegada de Apple Intelligence y los nuevos modelos fundacionales de la compañía, eso sí, dejando claro que la tecnología es prácticamente 100% Apple, y solo se apoya de forma puntual en servidores externos para tareas de razonamiento complejo. Explicamos cómo esta evolución técnica marca un antes y un después, especialmente con la introducción de Siri AI, un asistente que ahora cuenta con contexto personal, conciencia de lo que ocurre en la pantalla y una capacidad de interacción conversacional muy superior a la que conocíamos.Abordamos la gran problemática regulatoria que impedirá la llegada de estas nuevas funciones de inteligencia artificial a los iPhone y iPad en Europa. Comentamos cómo las normativas de la Ley de Mercados Digitales (DMA) y los roces con las autoridades europeas han forzado a Apple a pausar este lanzamiento, ante el temor de que la apertura obligatoria del sistema a inteligencias artificiales de terceros comprometa la seguridad estructural y la privacidad de los usuarios. Evaluamos las complejas implicaciones de esta situación, debatiendo sobre el difícil equilibrio entre cumplir con la competencia abierta que exige la ley y mantener el ecosistema cerrado y seguro que caracteriza a la marca.Finalmente, repasamos las novedades funcionales más destacadas de las nuevas versiones de los sistemas operativos, como las asombrosas herramientas de edición fotográfica y la espectacular vista isométrica en la aplicación de Mapas, ambas impulsadas por tecnología de splat gaussiano. Destacamos también la notable mejora de rendimiento y fluidez en el uso diario gracias a las optimizaciones internas del procesador, aunque observamos con cierta sorpresa cómo algunos dispositivos relativamente recientes de Apple Watch se han quedado sin soporte en esta actualización. Apple explica la nueva arquitectura de Siri: Google está dentro, pero no como muchos imaginaban Tim Cook se despide reparando la mayor promesa pendiente de Apple Tecnología La nueva Siri AI usa tecnología de Google, pero no es Gemini Creadores Apple escondió una lista enorme de mejoras y la hemos cazado: el iPhone con iOS 27 vuela Tim Sneath on X: "One of my personal favorite features announced at WWDC will I suspect be a sleeper hit: container machines, allowing your Mac to run a lightweight, persistent Linux environment with your home directory and repos automatically mounted: https://t.co/dOBdfOOVxC" / X container/docs/container-machine.md at main · apple/container No tech rule exemption for Apple, EU regulators say amid spat over Siri AI delay | Reuters (2) kitze · supermac.io

Owen Hatherley ist ein britischer Publizist mit Fokus auf Architektur (Brutalismus & Modernismus), Politik & Kultur. Er schreibt hauptsächlich für Architectural Review, Jacobin, die London Review of Books, Sidecar & Tribune und hat viele tolle Bücher zu Ästhetik & Politik veröffentlicht. owenhatherley.co.uk Sendung auf Deutsch und Englisch overdubbed, Skript weiter unten Part I is online at https://www.mixcloud.com/ittym/the-alienation-effect-w-owen-hatherley-ptii/ Ausgehend von drei Persönlichkeiten, die sehr unterschiedliche Wege eingeschlagen haben, jedoch alle im Konstruktivismus verwurzelt waren, haben wir im April das geistige und intellektuelle Klima in GB in der Zwischenkriegszeit im Gegensatz zu Zentraleuropa beleuchtet: Die Architekten Erno Goldfinger und Berthold Lubetkin sowie der Kunsthistoriker Nikolaus Pevsner standen im Fokus. Ein unrühmliches Kapitel, nämlich die antisemitisch und fremdenfeindlich motivierte Internierung der meisten Exilanten habe ich am Beispiel Kurt Schwitters umrissen. In seinem Buch gelingt es Owen, die Trope des 'guten Einwanders' zu vermeiden, deren Kehrseite die der 'unerwünschten, schlechten' Immigration ist - die Heterogenität der Exilant:innen spricht für sich. Sie einte Fluchterfahrung und Othering. Viele von ihnen waren überzeugte Antifaschist:innen und ihr Leben und Werk bezeugt das. Owens Begeisterung für einige der Protagonist:innen, seiner 'Held:innen', denn die gibt es durchaus, ist mitreißend. In diesem Teil der Sendung sollen einige von ihnen und ihr Einsatz für öffentlichen Luxus im Mittelpunkt stehen. Und das ist auch die Brücke zur Gegenwart und zu den letzten Sendungen - die Frage nach antifaschistischer Theorie und Praxis. Quellen: Tschichold https://de.wikipedia.org/wiki/Jan_Tschichold Stefan Lorant / Lilliput / Picture Post https://tribunemag.co.uk/2022/10/picture-post-stefan-lorant-edward-hulton-central-europe https://www.fulltable.com/VTS/m/mag/lill/zzzzz/hfield/a.htm https://iconicphotos.wordpress.com/2017/02/20/back-to-the-middle-ages-picture-post/ https://youtu.be/cmCkvxGnCr0?si=T1ayc6XwCXRAnEGs The Themersons https://www.luxonline.org.uk/artists/stefan_and_franciszka_themerson/calling_mr_smith.html https://monoskop.org/images/b/b4/Kubasiewicz_Jan_1993_The_Themersons_and_the_Gaberbocchus_Press_An_Experiment_in_Publishing_1948-1979.pdf https-//vimeo.com/177270179 Ruth Glass https://uclurbanlab.medium.com/urban-lab-walk-ruth-glass-1964-london-route-in-2024-d56cce80baf2 https://optimism-modernity.org.uk/documents/contact1946.html https://archive.ph/V2KUu Naum Gabo https://de.wikipedia.org/wiki/Das_Realistische_Manifest https://berlinischegalerie.de/sammlung/kuenstlerinnen-archive/das-realistische-manifest-von-naum-gabo/ https://www.nationalgalleries.org/art-and-artists/377 https://artuk.org/discover/stories/eva-frankfurther-the-forgotten-german-artist-who-captured-a-changing-london https://optimism-modernity.org.uk/documents/contact1946.html Hans Feibusch https://de.wikipedia.org/wiki/Entartete_Kunst_(Ausstellung) https://www.historisches-lexikon-bayerns.de/Lexikon/Entartete_Kunst_(Ausstellung)#Inszenierung https://lostgen.art/thema/ausstellung.pdf https://stjohnswaterloo.org/hans-feibusch-a-focus/

WWDC 2026 Watch the Keynote (alternatively, on YouTube) Craig Hockenberry on Spotlight Word wall Rob Rhyne’s toot Weird holes Change Detection .well-known Dear EU,

Cocktail sour (categoria di drink composti da un distillato, limone e un dolcificante) elegante e senza tempo, caratterizzato da un perfetto equilibrio tra cognac, triple sec e succo di limone, il Sidecar è un grande classico della miscelazione internazionale ancora attualissimo a oltre un secolo dalla nascita. E infatti, secondo l'annuale classifica stilata da Drinks International, nel 2026 è ancora fra i 50 più venduti nei migliori bar del mondo.

One more visit to the right side of historyWhat's at the farmers market these days?A little complaining about the weatherJen and Ben go to a wedding! Early Fathers Day appreciation 20th Anniversary Food Holidays

Eva Strehler had already learned what drew her to the road on a motorcycle: freedom, movement, and a way of living outside the usual shape of things. Then she built a sidecar for her dog, Polly, and headed east. What followed was meant to be another long motorcycle journey — through Turkey, into Iran, and across landscapes that changed as quickly as the people she met along the way. But somewhere during the trip, the journey became about something else entirely. This is a conversation about travel, companionship, risk, solitude, and the moments that quietly change the meaning of a journey while you're still inside it.

Chris Pritchard and Lee Johnston sit down with key figures behind the scenes including TT Chief Technical Officer Trevor Denning and Rider Liaison Officer John Barton alongside leading competitors past and present, such as Michael Evans, Tom Birchall, Ryan and Callum Crowe, and Ben Birchall with new passenger Mark Wilkes. It's a look at the TT from every angle. Hosted on Acast. See acast.com/privacy for more information.

This week we gain the power of revolution! But first, Joe recaps his adventure to Chicago and Sarah retracts a proclamation. In Utena, we get some long awaited backstory. That's right, the Kashira shadow troupe take the main stage! Then we finish off our duels with a re-rematch with Touga as he tries to prove he's more than just a pretty face.

Greetings and welcome to this week's edition of the Flavors of Northwest Arkansas podcast. Appreciate you being here. We're in Tontitown today at Mama Z's talking with owner Jesse Bishop, but before we get to her?!?! FOOD NEWS!! Goat Lab Red has closed in Fayetteville. We'll tell you why. Table Au Centre has opened on the Walmart Campus. They have temporary hours now and we'll tell you why. NWA Burger Week is next week, and Big Daddy's Burgers will be open for it! Food and bev for Crystal Bridges will be outsources, according to Axios NWA. The Bentonville Farmer's Market has been ranked the 4th best in the country. We'll explain the ratings system by the USA Today. There are two new Cureate winners in Northwest Arkansas! No Kid Hungry had another big night in Northwest Arkansas! The DISH event raised BIG money for ACNW! Happy Anniversary to Sidecar in Fayetteville! Want to advertise on Flavors of Northwest Arkansas? Email for info! (FlavorsofNWA@Gmail.com) In today's Flavors Flashback, a local James Beard Best Chef of the South nominee tells you how to make your food taste better! Jesse Bishop is the 3rd generation owner/operator of Mama Z's in Tontitown. Her Nona was Edna Zulpo who started Mama Z's. Jesse has some stories about her and the early days of the restaurant. Jesse will also talk about her path from growing up in the restaurant to owning it. She'll talk about what the hardest part has been. Finally, the food, she'll hit the classics including the national dish of Tontitown, fried chicken and spaghetti. For those that don't know, they also have food that's not Italian and she'll also talk about serving breakfast and lunch. We're talking Mama Z's next here on the Flavors of Northwest Arkansas!

On political capitalism and divided workers. Sociology professor at UC Berkeley, Dylan Riley, talks to Alex and Lee about economic stagnation, the state propping up capitalism, and class politics. What is "political capitalism"? And is it true that plunder and predation matter more now than exploitation? Why hasn't the ruling class purged the system through mass bankruptcies and unemployment? How does Chinese state capitalism fit into the story of stagnation and excess capacity? What is the difference between economic interests and class interests? How is the working class divided today? Is there a way out of the impasse? What possibility is there of a pro-growth politics? –> For more like this, subscribe: patreon.com/bungacast

Join us for a conversation with Philip Greene, acclaimed spirits historian and author of Sours: A History of the World's Most Storied Cocktail Style. Philip traces the time-tested combination of citrus and sugar when paired with spirits, from the humble Whiskey Sour to the Daiquiri, Sidecar, Margarita, and Cosmopolitan. He unpacks the fascinating history and lore behind these universally beloved beverages, revealing how a simple formula has captivated drinkers for centuries. Philip shares the stories of the bartenders, bars, and cultural moments that shaped these iconic drinks and offers practical wisdom for mastering them behind the bar. Get his book:  https://www.hachettebookgroup.com/titles/philip-greene/sours/9781454946021/ WATCH OUR VIDEOS ON YOUTUBE: https://www.youtube.com/bartenderatlarge FOLLOW US ON INSTAGRAM: Erick Castro: www.instagram.com/HungryBartender Bartender at Large: www.instagram.com/BartenderAtLarge FOLLOW US ON TIKTOK: Erick Castro: https://www.tiktok.com/@hungrybartender?_t=ZT-8uBekAKOGwU&_r=1 Bartender at Large: www.tiktok.com/BartenderAtLarge  

It's Birthday Month! (mine!) The garden is looking pretty good, even after that crazy frost we had.Farmers Market: Strawwwwwwwberries and rhubarbIt's a hard time to be an environmentalistAs of Wednesday April 29, I am done with cancer. (hopefully it is also done with me) HOW TO CELEBRATE?! With cake? Plants? What? Join the patreon! patreon.com/twochocolatecakesHere are all my links to everything else (although the two chocolate cakes site is currently down while I figure out what to do with it. )

Live from Fort Worth, Texas! The Bardtenders head to Fort Worth for the third annual Heard House to bring you live episodes with some amazing hospitality professionals. The Bardtenders had the chance to stay at the Heard House sponsored by Heard Card Game where bartenders from around the country came together to share their stories, gain access to education opportunities, and create some amazing memories along the way. Join us over the next several weeks as these bartenders share their experiences in the hospitality industry!In this episode of "The Mixing Glass", Trey Fincher shares his passion for music and vinyl records. Trey also shares the story of how he opened his dream bar, Sidecar, in Fayetteville, Arkansas! ------------Trey Fincher is a seasoned bartender with over 20 years of experience in the hospitality industry. He has worked at various establishments including bars, restaurants, and distilleries where he showcased his expertise in creating memorable guest experiences and crafting innovative cocktails.  Trey also competed in the USBG Presents World Class Competition in 2025 and made it to the Top 100 for the second time and advanced to the U.S. Top 30, competing for U.S. Bartender of the Year at the national finals in Atlanta.  Trey opened Sidecar Cocktail Lounge in 2025 with his partners Corey and Reese. Sidecar is his dream bar and truly a love letter to Fayetteville. They describe it as a refined dive — a neighborhood bar that just happens to make world-class cocktails. Their seasonal menus are inspired by vinyl records from their personal collections, with music serving as the creative muse behind everything they do.----------Don't miss out on any of the action!  Head to www.bardtender.com to stay up to date with all of the Bardtender content, find resources for mental and physical well-being, get access to education materials, and check out what all of our bards are up to!Support the show

Send us Fan Mail200 MPH on a sidecar Harley with a pushrod motor is like a tall tale until you hear how Randy Speranza actually did it. We sit down with Randy to trace the full arc: a kid in Chicago reading about Bonneville in an old Hot Rod magazine, a first heartbreak visits when the salt is underwater, and then the long, stubborn journey that turns curiosity into real land speed racing results. We talk through the hands-on years that build a racer, from street cars and drag nights to joining a lakester team and chasing records at El Mirage and the Bonneville Salt Flats. Randy breaks down what keeps him hooked, how the land speed community shares knowledge instead of hiding it, and why records are “on loan” no matter whose name is on the page. If you love motorsport history, grassroots engineering, and the practical reality of going faster on the salt. Then the conversation gets personal. Randy opens up about the moment he finally breaks 200 mph, the family emotions tied to that red hat, and the terrifying day his aorta dissected at Bonneville. He walks us through the life flight, surgery, rehab mindset, and what it means to come back to racing with stents, scars, and a different sense of purpose. We also get into what he's chasing next, including goals on two wheels, three wheels, and four wheels, plus helping his wife Diet build her own speed story. Subscribe for more Land Speed Legends, share this with a friend who lives for Bonneville land speed racing, and leave a review with your favorite moment from Randy's journey.Support the show

Minakshi Singh started bartending at events and parties and fell in love... but legally, she couldn't bartend in an actual bar. It was the '00s in India, and despite that obstacle, she forged a career in the spirits industry and opened several bars. Starting with Cocktails and Dreams Speakeasy, she followed with Sidecar (awarded as one of Asia's Best), The Brook, The Old House (in Nepal) and the later cofounded the India Bartender Show, creating the premiere gathering for bartenders and spirits folk in the nation. (Women are now allowed to work in bars in several Indian states... but not all!) Live music plays a big part in her venues, and she loves to rock. Check out her playlist here: https://open.spotify.com/playlist/43XOfGjnoKvsRlxvhah8hk?si=_349LflpTO63rWQOaN1P6w

WHAT They come in loud and the first few minutes are dense. Things overlap, then separate. Space shows up. They listen more than they talk. But it's always evolving. By the end it's stretched out, slightly odd, and steady after pushing through the mess. Kevin Brown: bass Dan Rosenstark: drums Mike Rosenstark: guitar GEAR Control: All rigs performed and routed via MIDI Designer Pro X on iPad. Guitar and sources: Bleep Labs Thingamagoop, electric kalimba, Moog Little Phatty into Neural DSP Quad Cortex, Fractal VP-4 units, AM4, dual Eventide PitchFactor, Boomerang Phrase III with Sidecar. Augmented with Pianoteq, Native Instruments FM8, four spoken word channels, and six internet radio feeds, running on an Apple M1 MacBook Air with a full plugin chain. Drums and percussion: Native Instruments Maschine MK3 and Jam, YouTube sound sources, ValhallaDelay, iZotope StutterEdit (the first one, thanks Devine). Bass: 7-string Conklin fretted bass with Bartolini pickups into Fractal Audio AX8 and VP-4. Thanks to Ableton Link for keeping us together.

WHAT This one is a short, 46-minute journey into the world below, but always ascending. And the world above, but drooping and perking up. Listen as these humans improvise in a no-holds barred explosion from the rocket-launch pad. Minio Class: keys Dan Rosenstark: drums Mike Rosenstark: guitar GEAR Control: All rigs performed and routed via MIDI Designer Pro X on iPad. Guitar and sources: Bleep Labs Thingamagoop, electric kalimba, Moog Little Phatty into Neural DSP Quad Cortex, Fractal VP-4 units, AM4, dual Eventide PitchFactor, Boomerang Phrase III with Sidecar. Augmented with Pianoteq, Native Instruments FM8, four spoken word channels, and six internet radio feeds, running on an Apple M1 MacBook Air with a full plugin chain. Drums and percussion: Native Instruments Maschine MK3 and Jam, YouTube sound sources, ValhallaDelay, iZotope StutterEdit (the first one, thanks Devine). Keys: Sequential Circuits Take5 and Arturia AstroLab 37. Thanks to Ableton Link for keeping us together.

Connect: Email the show at bobsburgersreheated@gmail.com Follow the show @bobsburgersreheated on Instagram

In this episode, Kevin Knight, Chief Marketing Officer at Sidecar Health, joins the podcast to discuss making healthcare more intuitive and consumer-friendly. He shares how giving money back to consumers through tools like HSAs can improve engagement, and explains why competition and technology are key drivers of meaningful system-wide improvement.

In this episode, Kevin Knight, Chief Marketing Officer at Sidecar Health, joins the podcast to discuss making healthcare more intuitive and consumer-friendly. He shares how giving money back to consumers through tools like HSAs can improve engagement, and explains why competition and technology are key drivers of meaningful system-wide improvement.

(updated file!) Where you were 6, 5, 4, 3, 2, 1 years ago? North Shore FloodsFarmers Market and garden newsDon't fear your colonoscopy!Food HolidaysA heartfelt thank you xoxoJoin the best little community on the internet https://patreon.com/twochocolatecakes?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink

The U.S. Congress has extended Medicare telehealth coverage through 2027, changing the calculus for telehealth deals in several concrete ways. In this Sidecar, Healthcare Transactional Practice co-heads Markus Bolsinger and Jennifer Hutchens and associate Brooke Meadowcroft discuss the principles that will help investors act on the opportunities brought by this increased deal window while also building strategies to outlast any single policy.

For MobileViews 603, recorded on March 29, 2026, I decided to return to my classic Blue Yeti Nano microphone, which I used for hundreds of episodes in years past. Much of our hardware discussion this week centered on my ongoing fascination with the MacBook Neo. I discovered that while it officially only supports one external display, you can effectively run a three-screen setup by using an iPad as a wireless third display through the MacOS Sidecar feature. This configuration, utilizing Mac OS Continuity, allows me to control the iPad using the MacBook's keyboard and mouse, creating a highly functional workstation without the need for extra cables. Jon has adopted a similar workflow in his classroom, using an iPad alongside his MacBook to handle student attendance while presenting his slides. On the software side, we discussed the release of iOS 26.4, which introduced a "Playlist Playground" feature in Apple Music on mobile devices. This tool uses AI to generate playlists from simple text prompts, and it serves as an excellent discovery tool for investigated genres where you might not be an expert. Looking further ahead, we looked at reports that iOS 27 may finally allow Siri to integrate with third-party AI chatbots like Gemini or ChatGPT. Since neither of us is a major fan of the current Siri, being able to choose a preferred chatbot would be a welcome change. As we approached Apple's 50th anniversary as an incorporated entity on April 1st, I reflected on the history of "tiny teams" in technology. While modern projects often involve hundreds of people, many of the most foundational tools—such as Apple DOS, CPM, and VisiCalc—were built by just one or two individuals. For instance, Paul Laughton built the first disk operating system for Apple in just 35 days by himself. We even saw this principle in action this week with Jon's new project, "Different Enough". He built this statistical testing website using GitHub Pages, TypeScript, and React in just 90 minutes. His secret was using ChatGPT to "interview" him about his requirements before generating a prompt for OpenAI Codex to build the final application. We followed up on the Adobe Podcast video test from last week; while the speaker identification worked well for the transcript, I had to boost the output volume significantly in post-production because it was surprisingly low. Jon also shared a bug he encountered with the Plaud Note platform, which misidentified a speaker by tagging the same student profile 20 times across different meetings with different students.. On a more aesthetic note, I shared Casio's announcement of a Japanese Lacquer Edition calculator. It is such a beautiful piece of craftsmanship that I'm now hoping Apple considers a lacquer edition for their MacBook line. What I found truly remarkable was that Jon was able to build a working model in only 90 minutes. He used what he calls a "one-two punch" with AI tools: The Interview: He first used regular ChatGPT to "interview" him about his specific requirements and ideas. The Build: Once the requirements were fleshed out, he had the AI write a high-quality prompt for OpenAI Codex, which then built the actual application using TypeScript and React. The project is currently hosted on GitHub Pages, which Jon set up so that the site automatically rebuilds and deploys in about a minute every time he pushes a change to his repository. To make the tool more accessible, he included real-world examples, such as independent t-tests for tutoring programs and chi-squared independence tests for marketing surveys

Join the Latin Boys as they welcome in Dusty Rhodes from Beavercreek and discuss the NWSL coming to Cowtown. PS Aerosmith rules and Prince sucks.

CADENA 100 celebra el fin de semana con la mejor variedad musical. ¡Buenos días, Javi y Mar! presenta éxitos de Lady Antebellum, “No se me da bien odiarte”, “Échame la culpa” y temas de Bruno Mars con Rosalía, además de Jennifer López. Se anuncia La Noche de CADENA 100, que se celebra mañana, con actuaciones de David Bisbal, Antonio Orozco, Maldita, Nil Moliner, Lori Meyers y Sidecars. Marta Soto visita la emisora para presentar “Reinicio”, su tercer disco, donde explora la sencillez. La emisora emite 45 minutos de música sin interrupción. Suenan canciones como “I Kissed a Girl” de Katy Perry y lo nuevo de Shakira y BZRP, “Algo tú”. CADENA 100 acompaña a sus oyentes con la mejor música y sus programas, disponibles también en la aplicación móvil.

Watch Us On YouTube! Announcing a new, ongoing benefit for annual subscribers of our Slack community. Annual subscribers receive a free Points Path Alerts subscription OR a 30% discount on Points Path Pro. A dream spring break in Hawaii… that lasted barely two days. This week on Miles To Go, Ed is joined by Summer Hull (Mommy Points) to break down a trip that went sideways fast — from relaxing poolside plans in Hawaii to emergency weather alerts that forced a same-day decision to leave the island. What followed was a real-time travel pivot: rebooking flights, finding a new destination, and rebuilding a vacation on the fly using points, miles, and every available credit. The result? A last-minute shift to Disneyland, complete with creative booking strategies, stacked credits, and a reminder of why flexibility is everything in modern travel. Plus, a discussion on the new Disney credit card, lounge access strategies, and how to think about backup plans when travel doesn't go as expected.   Get hydrated like Ed in Vegas with Nuun Use my Bilt Rewards link to sign-up and support the show! If you enjoy the podcast, I hope you'll take a moment to leave us a rating. That helps us grow our audience! If you're looking for a way to support the show, we'd love to have you join us in our Travel Slack Community.  Join me and other travel experts for informative conversations about the travel world, the best ways to use your miles and points, Zoom happy hours and exciting giveaways. Monthly access Annual access Personal consultation plus annual access We have witty, funny, sarcastic discussions about travel, for members only. My fellow travel experts are available to answer your questions and we host video chats multiple times per month. Follow Us! Instagram: https://www.instagram.com/milestogopodcast/ TikTok: https://www.tiktok.com/@milestogopodcast Ed Pizza: https://www.instagram.com/pizzainmotion/ Richard Kerr: https://www.instagram.com/kerrpoints/ ✈️ What We Cover in This Episode ✈️ A Hawaii trip cut short Severe weather alerts and flooding concerns Deciding to leave after just two nights Why waiting it out wasn't an option ✈️ Rebooking flights in real time Finding last-minute award availability Booking multiple backup flights across programs Why flexible points made the difference ✈️ The pivot to Disneyland Choosing a new destination on the fly Booking hotels using Bilt credits and points Saving money with stacked travel credits ✈️ Smart strategies for travel disruptions Why you should always have backup options Using multiple airline programs for flexibility The value of transferable points currencies ✈️ Disneyland Hotel and DVC tower review Room quality, location, and amenities Pool, dining, and overall experience When Disney hotels are worth the cost ✈️ The new Disney Inspire credit card $300+ in potential annual value Statement credits and earning structure Who this card makes sense for ✈️ Lounge access and Sidecar discussion The 90-minute lounge access debate Turning lounge space faster vs guest experience Whether this model works long-term ✈️ Planning future travel Booking trips years in advance Balancing ambitious trips with simpler vacations Why flexibility matters more than ever   ⏱️ Episode 429 Timestamps 0:48 – Summer joins the show and trip setup 4:05 – Arriving in Hawaii and early warning signs 6:23 – Severe weather alerts and decision to leave 10:17 – Booking last-minute flights out of Hawaii 13:01 – Pivoting to Disneyland and rebuilding the trip 18:04 – Disneyland Hotel and DVC tower experience 21:43 – Why flexible points made this trip possible 24:39 – Points Path and monitoring award pricing 26:06 – New Disney Inspire credit card breakdown 30:18 – Sidecar lounge debate and access rules

In this Sidecar, Dechert partners Jarlath Pratt, Clemens York and Mike Okkonen break down the European Commission's proposed Industrial Accelerator Act and its implications for foreign direct investment in the EU's emerging strategic sectors, including batteries, solar, EVs, and critical raw materials. The draft IAA proposes a mandatory pre-closing notification regime with strict conditions, including ownership caps, joint venture requirements and technology transfer obligations, targeting investors from countries controlling more than 40% of global manufacturing capacity in the relevant sectors. Non-EU investors caught by the proposed new regime are well-advised to monitor related developments and to start considering partnerships with EU investors ahead of the legislation being finalized.

On this episode of The Association Podcast, we welcome Erica Salm Rench, Chief Marketing Officer at Sidecar, to explore how associations can move beyond AI buzzwords and into meaningful adoption. Erica shares her career journey from digital marketing to leading AI-driven innovation in the association space, along with the experiences that shaped her approach to technology, community, and leadership.We dive into practical strategies for AI adoption, including building a culture of experimentation, creating internal learning cohorts, and identifying workflows ripe for automation. Erica also discusses the growing role of agents, the balance between personalization and privacy, and how associations can use data more effectively to improve member engagement.Throughout the conversation, Erica highlights the importance of collaboration, curiosity, and continuous learning as associations navigate rapid technological change. She also shares insights from her work at Sidecar, emerging trends in AI, and what leaders should focus on today to prepare their organizations for the future.

Watch Us On YouTube! Announcing a new, ongoing benefit for annual subscribers of our Slack community. Annual subscribers receive a free Points Path Alerts subscription OR a 30% discount on Points Path Pro. Announcing a new, ongoing benefit for annual subscribers of our Slack community. Annual subscribers receive a free Points Path Alerts subscription OR a 30% discount on Points Path Pro. Ed and Richard are back on home turf this week, kicking things off with a small milestone for the podcast: crossing 5,000 YouTube subscribers and continuing to grow the audience across video and audio platforms. From there, Ed shares his experience visiting American Express's new Sidecar lounge concept in Las Vegas, a much smaller lounge designed for short visits. The service and food impressed — but the strict 90-minute access rule raises questions about how useful the concept really is. The discussion then turns to loyalty program updates. Marriott is increasing the flexibility of its free night certificates, allowing members to top them off with more points than before — a change that could make certificates significantly easier to use at higher-end properties. They also break down a new welcome offer on the World of Hyatt Business Credit Card, and whether the increased bonus is enough to offset rising award prices. Finally, Richard brings a mystery topic to the show that sparks a lively debate: should travelers tip hotel housekeeping? The conversation dives into tipping culture, hotel labor practices, and where the line should be between employer responsibility and guest generosity. Get hydrated like Ed in Vegas with Nuun Use my Bilt Rewards link to sign-up and support the show! If you enjoy the podcast, I hope you'll take a moment to leave us a rating. That helps us grow our audience! If you're looking for a way to support the show, we'd love to have you join us in our Travel Slack Community.  Join me and other travel experts for informative conversations about the travel world, the best ways to use your miles and points, Zoom happy hours and exciting giveaways. Monthly access Annual access Personal consultation plus annual access We have witty, funny, sarcastic discussions about travel, for members only. My fellow travel experts are available to answer your questions and we host video chats multiple times per month. Follow Us! Instagram: https://www.instagram.com/milestogopodcast/ TikTok: https://www.tiktok.com/@milestogopodcast Ed Pizza: https://www.instagram.com/pizzainmotion/ Richard Kerr: https://www.instagram.com/kerrpoints/ ✈️ What We Cover in This Episode ✈️ Podcast milestone Miles To Go passes 5,000 YouTube subscribers Why the video audience continues growing Meeting listeners in airport lounges ✈️ Amex's new Sidecar lounge in Las Vegas A smaller lounge concept with limited seating Fast service and made-to-order food The controversial 90-minute entry rule ✈️ Marriott certificate flexibility changes Top-off limit increasing from 15k to 25k points What that means for 35k, 50k, and 85k certificates Whether hotels will adjust award pricing in response ✈️ New World of Hyatt Business Card offer 80,000-point welcome bonus after $10k spend How it compares to previous offers The impact of rising Hyatt award prices ✈️ The hotel housekeeping tipping debate Should guests tip housekeeping? Why tipping culture has expanded The argument for hotels paying higher wages ✈️ Travel updates and upcoming trips Biloxi March Madness trip A whirlwind LA–DC–Vegas itinerary Upcoming travel schedules and bonus content plans ⏱️ Episode 428 Timestamps 0:50 – Podcast intro and YouTube milestone 3:30 – Running into listeners in airport lounges 5:00 – Flighty data and flying the same plane multiple times 7:30 – Ed's "overnight to Chicago" travel strategy 9:50 – Inside the new Amex Sidecar lounge in Las Vegas 13:40 – Food, service, and the 90-minute access rule 16:50 – Marriott increasing certificate top-off limits 19:45 – New Hyatt Business Card welcome offer 22:50 – Richard's mystery topic: tipping hotel housekeeping 28:30 – Upcoming travel and bonus episode plans

Explore Bordeaux, France's legendary wine region, on this episode of the Travels with Darley Podcast. From cycling through the Médoc wine route to riding through Bordeaux in a retro sidecar, Darley Newman shares how to experience the best of the region—plus tips for savoring its food, culture, and historic charm.Using a river cruise as your floating home base, you'll step off to discover the walkable streets of Bordeaux, sip wines in Saint-Émilion, enjoy scenic châteaux views along the water, and even taste cognac in the surrounding region.Darley also highlights a can't-miss local treat: canelés, the rum-and-vanilla pastry that's a Bordeaux classic. France expert Loic Bailly of Uniworld Boutique River Cruises joins the episode to share why Bordeaux is ideal for slow travel—think local markets, curated regional wines, and immersive excursions that connect you with the place. Uniworld is celebrating its 50th anniversary, and travelers can find 50th anniversary specials and savings on select cruises, making it a great time to plan a Bordeaux and France river cruise adventure.

Alex Coonce (Chief People Officer) and Patrick Quigley (CEO) from Sidecar Health joined us on The Modern People Leader. We talked about building a strong CEO–CPO partnership, why culture must scale before headcount does, and how companies can become AI-native while staying transparent with employees.----  Downloadable PDF with top takeaways: https://modernpeopleleader.kit.com/episode287Sponsor Links:

Today we are talking with Drew Wahlgren, Senior Vice President of Capital Markets at MAG Capital Partners. With more than $1.5B in Assets Under Management, MAG is a preeminent sponsor of industrial real estate. Their primary focus is single tenant sale leaseback, and they are now buying multi-tenant industrial assets as well as some operating businesses.www.magcp.com Email Jonathan with comments or suggestions:podcast@thesourcecre.comOr visit the webpage:www.thesourcecre.com*The audio of this podcast is never generated by AI. However, some of the show notes and images may have been generated using AI tools.

BestWire News Editor Dave Pilla reports how renewed investor interest and a push for capital efficiency are fueling rapid growth in reinsurance sidecars, reshaping capacity strategies and adding new dynamics to traditional pricing and long-term capital stability.

CADENA 100, en '¡Buenos días, Javi y Mar!', ofrece 45 minutos de música sin interrupción. Suenan temas como "I'll Be There For You" de The Rembrandts, "Flowers" de Miley Cyrus, "Let Me Love You" de Justin Bieber y "Summer Love" de David Tavare. La Noche de CADENA 100, a beneficio de Manos Unidas, celebra su gala el 28 de marzo con David Bisbal, Beret, Melendi (que estrena tema), Efecto Pasillo, Sidecars, Maldita Nerea y DePol en el Movistar Arena. El programa destaca buenas noticias: "Mamás en acción" ya asiste a niños en 54 hospitales españoles. Además, los vecinos de Hortaleza, Madrid, homenajean con aplausos al farmacéutico Fausto en su jubilación. Una oyente, Sara, comparte su éxito al aprobar dos exámenes de Criminología. Carlos Baute presenta su nuevo sencillo "Quién Mejor Que Tú", manteniendo su esencia con un sonido actual y resaltando a su familia como "personas vitaminas". También se escucha música de Taylor Swift, Lady Gaga y Bruno Mars.

What happens when a health plan stops trying to optimize a legacy system and instead rebuilds the model itself? In this episode of Bright Spots in Healthcare, Eric Glazer sits down with Patrick Quigley, CEO and co-founder of Sidecar Health, for a candid conversation about redesigning health insurance around transparency, incentives, and consumer agency. Rather than focusing on incremental reform, this discussion explores what changes when members can see prices before they receive care, when benefits are structured around clear dollar amounts instead of opaque contracts, and when savings can be shared directly with the individual making the decision. Patrick walks through why traditional insurance design obscures cost and distorts behavior, how employers are responding to rising spend and limited visibility, and what it takes operationally to challenge long standing assumptions about how plans should work. Using examples from employer adoption, member purchasing behavior, and provider pricing dynamics, the conversation surfaces how transparency becomes more than a feature. It becomes the foundation for accountability and market discipline. This episode is designed for health plan leaders, employers, and innovators who are no longer asking whether affordability is a problem, but are questioning whether the current structure can solve it. In this episode, we cover: Why price opacity persists in traditional insurance models What changes when members see real time, upfront pricing How defined benefit structures alter purchasing decisions Why employers are increasingly open to alternative plan design How financial alignment influences utilization patterns The operational realities of building a new insurance model What industry leaders must unlearn to create sustainable affordability About Patrick Quigley: Patrick Quigley is the CEO and co-founder of Sidecar Health, a health insurance company built on a transparency first model. Under his leadership, the organization has focused on creating plans that show members clear prices, allow them to choose providers freely, and share savings when care costs less than expected. His work centers on restoring consumer visibility and aligning incentives across members, providers, and employers to address the structural drivers of healthcare cost growth. Learn more about Patrick Quigley - https://www.linkedin.com/in/quigleyp/ Partner with Bright Spots Ventures: If you are interested in speaking with the Bright Spots Ventures team to brainstorm how we can help you grow your business via content and relationships, email hkrish@brightspotsventures.com. About Bright Spots Ventures: Bright Spots Ventures is a healthcare strategy and engagement company that creates content, communities, and connections to accelerate innovation. We help healthcare leaders discover what's working, and how to scale it. By bringing together health plan, hospital, and solution leaders, we facilitate the exchange of ideas that lead to measurable impact. Through our podcast, executive councils, private events, and go-to-market strategy work, we surface and amplify the "bright spots" in healthcare—proven innovations others can learn from and replicate. At our core, we exist to create trusted relationships that make real progress possible. Visit our website at www.brightspotsinhealthcare.com. Visit our website:  www.brightspotsinhealthcare.com. Follow Bright Spots in Healthcare: https://www.linkedin.com/company/shared-purpose-connect/

(0:00) Felger, Mazz, and Murray open the second hour of the show reacting to what Drake Maye had to say at the podium following last night's loss. (11:12) More callers reaction to the Patriots loss in Super Bowl LX. (21:13) Some thoughts on what Mike Vrabel had to say postgame following the loss. Plus, more callers. (31:15) Was Sam Darnold a sidecar to the Seahawks winning Super Bowl LX? See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

CADENA 100 pone 45 minutos de música sin interrupción. Las entradas para La Noche de CADENA 100, el 28 de marzo en el Movistar Arena de Madrid a beneficio de Manos Unidas, están agotadas. El cartel incluye a Nil Moliner, David Bisbal, Antonio Orozco, Sidecars, Beret, Efecto Pasillo, Maldita Nerea, De Pol, Melendi y Lauriin. En '¡Buenos días, Javi y Mar!', suena "Dile que yo sigo soltero" de Manuel Turizo y Yatra. Se habla de canciones de desamor y despecho, mencionando a Rosalía en este contexto. Jordi Sánchez de OBK presenta "Maldita Mujer" y comparte su pasión por seguir componiendo. Además, se informa de nuevos podcasts con Leira Martínez, Yera Taylor, James Arthur y Shakira.

Czabe and PAUL CHARCHIAN cut it up over being goaded to buy a Utah rich man's toy. The Epstein email dump and the NFL's quandary. Bill Gates is a scumbag, but his ex-wife is filthy rich. Robert Kraft denied the HOF. A "cheese-backed economy" is a thing. Cheese wheel pasta, for the win! PGA Tour prediction pools. CzabeVegas update. MORE.....Our Sponsors:* Cheesesteaks from Philly? Deep dish from Chicago? Go to Goldbelly and use my code CZABE for a great deal: https://www.goldbelly.comAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Uncle Brad and Jules explore the sophisticated and slightly scandalous Between the Sheets cocktail in this episode of The Art of Drinking. Uncle Brad walks through a few potential origin stories for this Prohibition-era classic - a variation on the Sidecar that balances cognac, rum, triple sec, and lemon juice into a drink worthy of its suggestive name. Meanwhile, Jules transforms the 1930s original with her signature tropical twist, bringing sun-soaked flavors to the elegant base. Whether you prefer your Between the Sheets straight from the Jazz Age or with a vacation vibe, this episode delivers both history and innovation in equal measure.    Between The Sheets  Glass: Chilled coupe glass  Garnish: Lemon peel  Directions & Ingredients  In shaker add  ¾ oz Cognac  ¾ oz Plantation 3 star rum  A scant ounce of Cointreau  2 tsp of fresh lemon juice (also known as 1/3 oz)  Pinch of sea salt or 2 drops of saline solution (20g salt to 80g water)  ¼ oz simple syrup (optional) Shake 20 seconds  Double Strain into chilled coupe glass  Add lemon peel garnish     Tropical Sheets  In shaker add  3/4 ounce cognac  1 ounce Coconut Fat Washed Rum  3/4 ounce Orange Curacao  1/2 ounce Mexican Lime juice  ¼ ounce orgeat syrup  Double Strain into chilled coupe glass   The Art of Drinking  IG: @theartofdrinkingpodcast   Website: www.theartofdrinkingpodcast.com     Join Jules  IG: @join_jules  TikTok: @join_jules   Website: joinjules.com    Uncle Brad   IG: @favorite_uncle_brad    This is a Redd Rock Music Podcast  IG: @reddrockmusic  www.reddrockmusic.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Will your next deal trigger a state “mini-HSR” (Hart-Scott-Rodino) filing? In this episode of Committed Capital Sidecar, Dechert partner James Fishkin unpacks the rise of state mini-HSR premerger filing laws under the Uniform Antitrust Premerger Notification Act – who must file, the key nexus thresholds and what materials must be submitted. He spotlights Washington and Colorado's early adoption, outlines civil penalties for noncompliance and shares practical guidance for dealmakers on coordinating multi-state filings and managing state attorneys general review risk.

What's going on:In the gardenThe cat's mouse friendAt the dentistBird Feeder UpdateWith the farmers marketsDISAPPOINTING BAKLAVAUnforgivable ThingsSnowstorm (not to be confused with ICE)Food Holidays for the rest of JanuaryI teased it and then forgot to mention but thanks to the listeners and friends-I set up the A Little Extra Food Pantry and it is full of snacks, warm gloves and hats, crayons and books, juice, wipes, masks, spaghettios,coffee, tea, handwarmers. support the podcast and other content I create around the interwebs by joining the patreon at patreon.com/twochocolatecakes