Podcasts about modular

In this episode we start out with Strega delay circuit noise through the VC F3DB and create an atmospheric textural patch from there!Patreon: https://www.patreon.com/nullphiinfinity Bandcamp:  https://nullphiinfinity.bandcamp.com/Instagram: https://www.instagram.com/nullphiinfinity/Youtube: https://www.youtube.com/@Nullphiinfinity  ★ Support this podcast on Patreon ★

We?re talking about empowering churches to combine practical compassion with long-term community transformation. We?re talking about Church driven solutions to the housing crisis. Life, Culture and Current Events from a Biblical Perspective with Neil Johnson.Your support sends the gospel to every corner of Australia through broadcast, online and print media: https://vision.org.au/donateSee omnystudio.com/listener for privacy information.

What's happening in property investing news this week in Australia? It's time to find out! We remove all the fluff to bring a neatly packaged news show, designed to keep you on the ball as an Australian Property Investor. Let's see what's making property news headlines this week in Australia.

In this episode, Juan Hernandez shares his innovative real estate projects in San Bernardino, including the first modular home community, and offers insights on community impact, law changes, and building investor relationships.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

The Alan Cox ShowSee omnystudio.com/listener for privacy information.

Qualcomm just held its 2026 Investor Day, and the headline number is a new $15 billion AI data center revenue target for 2029 — up from essentially zero two years ago. We break down whether this fabless smartphone-royalty giant can actually pull off a pivot into AI data centers.We cover the AlphaWave Semiconductor acquisition and why Arm-based data center networking IP matters to hyperscaler customers, Qualcomm's 3D-stacked silicon packaging approach designed to work around the high bandwidth memory bottleneck amid the 2026 memory shortage, and the Modular acquisition — an AI-native software platform positioned as a potential CUDA alternative. On fundamentals, we walk through the revenue mix as smartphone growth slows to a 5% CAGR following the Apple wind-down, and why automotive and IoT are beating original targets. We close with a reverse DCF on the ~$190 stock price to see how much growth is already priced in, and why QCOM still holds a place in our portfolio as a diversified, value-oriented semiconductor stock.Content is for general information and entertainment only, not individual investment advice. All investing involves risk, including loss of principal. CSI owns shares of Qualcomm.Semi Insider members get full access to our research platform and tools. Join at chipstockinvestor.com.

you know the look-------------------------------------------------------Follow Modular on twitter: https://twitter.com/TheModularMediaFollow Modular on Bluesky:https://bsky.app/profile/modularmedia.bsky.socialFollow Modular on Tumblr:https://www.tumblr.com/modularmediaAll Modular Media Links:https://linktr.ee/TheModularMediaHub-------------------------------------------------------------------Co-hosted by Chris Gaston: https://www.youtube.com/@BoingoRider https://bsky.app/profile/boingorider.bsky.socialhttps://twitter.com/boingo_rider https://boingo-rider.tumblr.com/https://discord.gg/H83j5PGCo-Hosted by Cody Burke:https://www.youtube.com/@snowburke83https://twitter.com/snowcone83https://snowburke.tumblr.com/https://www.instagram.com/never_robot/https://www.twitch.tv/snowcone83Co-Hosted by Buster Corp: https://www.youtube.com/@BusterCorphttps://bsky.app/profile/bustercorp.bsky.socialhttps://twitter.com/BusterBluey3https://busterscorp.tumblr.com/Co-Hosted by Simeon Scotthttps://simeonslinks.carrd.co/

what if i told you this was a regular, typical podcast for us?-------------------------------------------------------Follow Modular on twitter: https://twitter.com/TheModularMediaFollow Modular on Bluesky:https://bsky.app/profile/modularmedia.bsky.socialFollow Modular on Tumblr:https://www.tumblr.com/modularmediaAll Modular Media Links:https://linktr.ee/TheModularMediaHub-------------------------------------------------------------------Co-hosted by Chris Gaston: https://www.youtube.com/@BoingoRider https://bsky.app/profile/boingorider.bsky.socialhttps://twitter.com/boingo_rider https://boingo-rider.tumblr.com/https://discord.gg/H83j5PGCo-Hosted by Cody Burke:https://www.youtube.com/@snowburke83https://twitter.com/snowcone83https://snowburke.tumblr.com/https://www.instagram.com/never_robot/https://www.twitch.tv/snowcone83Co-Hosted by Buster Corp: https://www.youtube.com/@BusterCorphttps://bsky.app/profile/bustercorp.bsky.socialhttps://twitter.com/BusterBluey3https://busterscorp.tumblr.com/Co-Hosted by Simeon Scotthttps://simeonslinks.carrd.co/

In this podcast, experts discuss how to build durable, future-ready architectures through Modular Open Systems Architecture (MOSA) and modular open architecture, a "disconnected-first" approach and adaptable platforms such as Spectro Cloud Palette. Learn why effective tactical edge and edge computing security must be rooted in architectural decisions, rather than added as features after deployment.

In this episode we start with a guitar/gameboy sample in the Morphagene and build a beat-based patch around it!Patreon: https://www.patreon.com/nullphiinfinity Bandcamp:  https://nullphiinfinity.bandcamp.com/Instagram: https://www.instagram.com/nullphiinfinity/Youtube: https://www.youtube.com/@Nullphiinfinity  ★ Support this podcast on Patreon ★

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

James and Frank dig into Slate's modular electric pickup — a $24,950, 205‑mile, no-frills truck you can customize later — and unpack how factory vs dealer options, aftermarket mods and build quality will make or break the idea. They also cover real-world charging (level 1/2/3), fleet and daily-use practicality, and why a DIY, upgradeable EV could reshape affordable vehicle ownership. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

Send us Fan MailA modular schedule can collapse over one thing: late decisions. That's why cross-laminated timber (CLT) and modular construction fit together so well and why they can also magnify each other's coordination mistakes if you treat them like conventional builds. Sidney Filippis, Director of Design at Sterling Structural, details what it really takes to bring mass timber into a factory-driven workflow without surprises. Sidney explains how modular teams should think about CLT, whether as a substitute for familiar assemblies or as a structural system that changes how loads, tolerances, and interfaces behave. Sidney also shares the first design assumptions to revisit when moving from steel to mass timber, including lighter weight impacts and the reality that wood, steel, and concrete each bring different tolerance expectations. Additionally, Sidney discusses costs: CLT can be more expensive up front, so the smarter comparison is total project cost, where exposed wood finishes can reduce drywall, paint, and labor while faster installs cut schedule risk. From there, Sidney details aspects that can make or break projects: MEP penetrations, connection engineering, acoustics planning, and moisture protection on site, and more.If you're considering a first step, Sidney explains why a plant tour and a pilot project can de-risk adoption, plus what success could look like for mass timber and commercial modular construction in the coming years.Support the showListen to all episodes of MBI's Inside Modular podcast at https://www.modular.org/inside-modular-the-podcast-of-commercial-modular-construction/

Introduction What happens to your health coverage the day you leave a job, go independent, or pick up seasonal work? For more than 90 million Americans who earn outside a traditional employer plan, the answer is usually that it disappears. Felix Ortiz, founder and CEO of Smirk Health, joined host Joshua Hollander to explain why the benefits system built in 1929 for full-time employees no longer fits the way people work, and what it takes to build something that does. The conversation covers portable coverage that moves with the worker, modular plans that start at $19, and an AI layer that does more than answer questions. Guest Bio Felix Ortiz is the founder and CEO of Smirk Health, an AI health benefits infrastructure company in Austin building portable coverage for 1099, part-time, hourly, and seasonal workers, underwritten and insured by Chubb. He is a repeat founder whose earlier companies spanned education technology, talent intelligence, and a banking-and-insurance platform for Americans of modest means. He is also a U.S. Army veteran and a marathoner who has finished five of the seven World Marathon Majors. Much of Smirk traces back to watching insurers decide his younger brother's care during a childhood heart condition, and to his own coverage gap leaving the military. Key Topics The 1929 problem - Group benefits were designed for full-time employees, so the fastest-growing part of the workforce gets priced out or left ineligible. Coverage that follows the worker - When a member changes employers, the plan, the price, and the benefits stay the same instead of ending with the job. Modular plans from $19 - Smirk rebuilt the plan chassis so members can add or remove coverage and see what is covered and what it costs before they buy. From supplemental to fully insured - Smirk took a product category damaged by bad actors, re-engineered it into a fully insured medical plan, and stacked it on an AI layer. An AI layer, not a chatbot - Because the plan is tied into Smirk's infrastructure, the concierge can take a member from a question all the way to a booked appointment with known costs. The AI divide - Ortiz argues a gap is opening between people on free AI models and people on paid ones, and that health is the wrong place to let that gap decide outcomes. Getting a carrier to say yes - How Smirk earned Chubb as an underwriting partner, and what Ortiz tells founders about approaching a large carrier. Notable Quotes "Somebody will have a Toyota, somebody else will have a Mercedes-Benz, but ultimately they still have access to a car. And when it comes to health, they don't. That's the big pain point." "We've gutted the whole thing and re-engineered it to be a fully insured medical plan." "You actually are starting to get a divide in AI infrastructure. That's a topic no one talks about." "Three years out, Smirk will be the infrastructure stack for AI within the health and financial intersection." Resources Guest: Smirk Health: https://www.smirkhealth.com Smirk Health on LinkedIn: https://www.linkedin.com/company/smirk-health Felix Ortiz on LinkedIn: https://www.linkedin.com/in/fwoiii/ Host & Organization: Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ Horton International (USA): https://www.horton-usa.com/ Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If this episode was useful, subscribe and leave a review. The Insurtech Leadership Podcast is on YouTube, Podbean, Apple Podcasts, and Spotify.

Big Tech and the space industry are completely rewriting the rules of infrastructure, from low-Earth orbit down to the safety guardrails of generative AI. In this episode of the Tech Field Day News Rundown, Tom Hollingsworth and Alastair Cooke tackle Rocket Lab's massive $8 billion acquisition of satellite operator Iridium and its play for total space infrastructure dominance. They also dive into Qualcomm's strategic buyout of Modular to challenge enterprise AI giants, China's deployment of its first fully renewable-powered AI data center, and the Trump administration's cybersecurity-centric AI Executive Order.Finally, the team breaks down Couchbase's new AI Data Plane, Microsoft's surprising decision to extend free Windows 10 security updates until 2027, and a controversial shift from Anthropic that might just turn AI safety into a premium, paid feature.Time Stamps: 0:00 - Cold Open 0:27 - Welcome to the Tech Field Day News Rundown1:29 - Rocket Lab Buys Iridium in $8 Billion Space Industry Deal4:20 - Qualcomm Buys Modular to Expand Open AI From Edge to Cloud6:57 - China Opens First Fully Renewable-Powered AI Data Center10:14 - Trump AI Order Signals New Focus on Cybersecurity14:11 - Couchbase Expands Database Platform for AI Agents17:30 - Microsoft Extends Windows 10 Security Updates to 202722:26 - Anthropic's New AI Models Raise Big Questions About Safety30:50 - The Weeks Ahead: Upcoming Events32:37 - Thanks for Watching the Tech Field Day News RundownFollow our hosts ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Tech Field Day ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X/Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mastodon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

Today's show is sponsored by The Cost Segregation Guys. To learn more, click on the link and you can qualify for a discount on your cost seg study. -----------I'm usually pretty skeptical when someone tells me they've found a breakthrough in homebuilding.We've heard this story many times before. Modular construction was going to change everything. 3D printing was going to make housing dramatically cheaper. Mass timber was going to replace steel and concrete. In each case, there may be a place for the technology, but the promised revolution often fails to account for the entire building process.Construction is not one problem. It is a collection of hundreds of small problems, each with code requirements, scheduling constraints, labor dependencies, financing implications, and supply chain risks.But every once in a while, something comes along that deserves a closer look.That's the case with Plantd, a North Carolina company founded by former SpaceX engineers, including Nathan Silvernail and Huade Tan. Their target is not the whole house. They're not trying to replace every trade on the job site. They're going after one of the most common materials in homebuilding: OSB, or oriented strand board.Traditionally, OSB is made from wood strands pressed together with resin under heat and pressure. Plantd is making a direct alternative from fast-growing perennial grass. According to the source material, the grass can grow about six inches a day, can be harvested twice a year, and has a much higher carbon sequestration rate than pine. The company's internal testing claims the panels are two times more moisture resistant and one and a half times stronger than traditional softwood OSB. A pine forest takes 40 years to mature. These grasses can supply the same home building in 90% less acreage. And you can grow the grass next to the factory. ------------**Real Estate Espresso Podcast:** Spotify: [The Real Estate Espresso Podcast](https://open.spotify.com/show/3GvtwRmTq4r3es8cbw8jW0?si=c75ea506a6694ef1)   iTunes: [The Real Estate Espresso Podcast](https://podcasts.apple.com/ca/podcast/the-real-estate-espresso-podcast/id1340482613)   Website: [www.victorjm.com](http://www.victorjm.com)   LinkedIn: [Victor Menasce](http://www.linkedin.com/in/vmenasce)   YouTube: [The Real Estate Espresso Podcast](http://www.youtube.com/@victorjmenasce6734)   Facebook: [www.facebook.com/realestateespresso](http://www.facebook.com/realestateespresso)   Email: [podcast@victorjm.com](mailto:podcast@victorjm.com)  **Y Street Capital:** Website: [www.ystreetcapital.com](http://www.ystreetcapital.com)   Facebook: [www.facebook.com/YStreetCapital](https://www.facebook.com/YStreetCapital)   Instagram: [@ystreetcapital](http://www.instagram.com/ystreetcapital)  

In this episode, we explore innovative modular systems for safe and efficient offshore well abandonment, focusing on James Fisher Energy's SeaBass system and global regulatory frameworks.

 This episode of The Circuit covers a mix of tech industry tributes and major semiconductor financial updates. Hosts Ben and Jay begin by paying their respects to pioneering tech blogger and journalist Om Malik, who recently passed away. They then dive into Cerebras's first earnings report as a public company, highlighting strong top-line demand for AI inference but noting investor concern over their complicated gross margins. Next, the hosts unpack Qualcomm's Investor Day, focusing on the company's aggressive $15 billion data center revenue guidance for fiscal 2029, their "Dragonfly" custom ARM CPU roadmap, and their strategic acquisition of software startup Modular. Finally, they analyze Micron's massive earnings report, detailing a staggering 60% quarter-on-quarter memory price surge that has caught major buyers like Apple by surprise, concluding with a lively discussion on the "Game of Thrones" style market dynamics driving the memory industry. 

Origami blablabla to finish 24 pieces modular cube

In this episode, Corwyn Melette shares his insights on real estate development, affordable housing, and building a legacy through strategic investments. Discover how he balances community service with business growth and the importance of systems and leadership in scaling success.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

Guest host Robin Gill talks to Paul Binotto, director of Modular B.C. Learn more about your ad choices. Visit megaphone.fm/adchoices

Plus: Qualcomm to acquire AI software firm Modular in $3.9 billion stock deal. And Zoox debuts redesigned robotaxi for large-scale production. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this reactor deep dive, Chris Keefer is joined by Navid Badie, Chief Nuclear Engineer at Candu Energy Inc, to explain the inner workings of the distinctive CANDU reactor. They trace how Canada's decision to use natural uranium led to heavy-water moderation, horizontal pressure tubes, separate coolant and moderator systems, and the ability to refuel continuously while operating. Navid breaks down the reactor's fuel channels, steam generators, online fueling machines, and the remarkable operating experience behind a technology that has completed nearly a million online refuelling operations.The conversation also examines CANDU safety and longevity: large inventories of water, natural thermosiphon cooling during a station blackout, and the engineering behind reactor refurbishment. Chris and Navid discuss pressure-tube metallurgy, robotic inspection and maintenance, cobalt-60 and other isotope production, and why locally manufacturable natural-uranium fuel can offer importing countries greater fuel-cycle sovereignty. A technically grounded introduction to one of the world's most unusual and capable reactor lineages.Listen to Decouple on:• Spotify: https://open.spotify.com/show/6PNr3ml8nEQotWWavE9kQz• Apple Podcasts: https://podcasts.apple.com/us/podcast/decouple/id1516526694?uo=4• Overcast: https://overcast.fm/itunes1516526694/decouple• Pocket Casts: https://pca.st/ehbfrn44• RSS: https://anchor.fm/s/23775178/podcast/rssWebsite: https://www.decouple.media

OpenAI amplía Daybreak para cerrar vulnerabilidades con IA y presenta GPT-5.5-Cyber. Qualcomm se acerca a comprar Modular para competir con Nvidia también en software. Nvidia anuncia 35 superordenadores de IA en Europa, Oracle reconoce recortes ligados a la IA y una enana blanca podría explicar señales de radio repetitivas desde el espacio.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

Bloomberg reported that Qualcomm is nearing a deal to acquire Modular, an AI software startup known for the Mojo programming language and an inference engine for cross hardware deployment. The reported move aligns with Qualcomm's push to expand on device AI on Snapdragon platforms, including PCs that meet Microsoft's Copilot Plus NPU requirements. Competitive pressure from Nvidia, Apple, Intel, and AMD is driving chipmakers to pair silicon with software to lower developer friction. Recent AI transactions such as Databricks' acquisition of MosaicML and investments in Anthropic show a broader consolidation of tools and compute. Regulators in the United States and Europe have increased scrutiny of AI deals, raising interoperability and licensing questions. Founders and IT buyers should evaluate portability, licensing, and performance baselines as potential ownership changes develop.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Today's BizNews Daybreak covers the US granting Iran a 60-day oil sale license following Switzerland peace talks. Tech stocks retreat as Alphabet slips 5% and Qualcomm eyes a $4 billion Modular acquisition. In the UK, Keir Starmer resigns as PM, clearing the path for Andy Burnham. Plus, the ISS warns of Mexican drug cartels in South Africa, Naspers reports surging core earnings, and Nouriel Roubini turns bullish on US productivity.

In this episode, real estate investor Erik shares his journey in land development, modular homes, and building strategic relationships to grow his business. We explore opportunities, challenges, and key insights for aspiring investors.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

Learn more at TheCityLife.org

Preview for Later Today: Amelia Bruno discusses modular electro-spray thrusters about the size of a postage stamp. Utilizing green monopropellant and solar power, this compact technology allows small satellites, like CubeSats, to change orbits or planes efficiently. It offers an affordable propulsion solution for universities and researchers exploring space using modular, scalable components.1962

Key TakeawaysThe biggest value-add opportunity in self-storage isn't always raising rents—it's adding units. Expanding a facility can create significantly more value than operational improvements alone.Look for excess land when buying self-storage. Vacant land, truck parking, RV storage, or underutilized areas can often be converted into additional storage units.Modular storage containers allow you to expand in phases. Instead of investing heavily upfront, operators can add units as demand grows, reducing risk and vacancy.Simple site designs often outperform maximized layouts. Customer experience, ease of access, safety, and traffic flow can be more valuable than squeezing in a few extra units.Small business customers are often the best tenants. Contractors, HVAC companies, home stagers, and other service businesses tend to stay longer and expand into additional units over time.Unit mix matters. Offering a combination of different sizes can help attract a broader customer base and maximize occupancy.Appearance affects leasing. New, well-maintained units create a better customer experience and can command stronger demand than older, worn containers.Run the numbers before expanding. In Tyler's example, a relatively small capital investment in additional units had the potential to create hundreds of thousands of dollars in additional property value.Think beyond cash flow. Every dollar of NOI created through expansion can dramatically increase a property's value through cap rate compression and future refinancing opportunities.The best self-storage deals often have hidden expansion potential. What looks like excess parking, RV storage, or unused land today may become the highest-return portion of the investment tomorrow

The government has spent €1.3 billion on modular buildings for schools in the past five years. One TD told the Public Accounts Committee that these modular prefabs, were not conducive to teaching or learning, that they were "a blight on communities across this country” and should not be seen as a permanent solution to classroom space.John Boyle, the Irish National Teachers Organisation General Secretary and Eoin Dolan, Principal of Mother of Divine Grace National School in Finglas joined Ciara and Shane to discuss their function.

Stephan Bredell – CEO and co-founder, Platō Coffee SAfm Market Update - Podcasts and live stream

We talk to Patrick Heyn, VP of Marketing for Q-SYS about what we can find at their booth at C8737 in the Central Hall. We also discuss the latest addition to their RoomSuite Modular System and providing a top-to-bottom collection of solutions for meeting spaces.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Amy Lokken shares insights on self-respect, self-confidence, and navigating heavy seasons of life, especially for women and caregivers. Amy Lokken is the founder of The Amy Factor™ andcreator of The Sophisticated Caregiver™ and designer of Müd Modular™. She serves as Private Counsel to high-performing women navigating heavy seasons, helping them live and lead in a way that makes their self-respect visible. With a background in industrial design, psychology, spatialintelligence, and decades of experience shaping environments and professional presence, Amy brings a distinct perspective to leadership, credibility, and theunseen weight many women carry while still showing up for everyone else. Her work centers on the belief that self-respect is not aluxury. It is the standard. And when a woman's standards remain intact during pressure, transition, caregiving, grief, or change, her credibility, steadiness, and leadership become unmistakable. Amy's philosophy is simple: Self-Respect Is the Standard.Credibility Is the Result. Grace Is Strength.GET IN TOUCH WITH AMY:https://theamyfactor.com/amy-factor/ https://theamyfactor.com/the-sophisticated-caregiver/LinkedIn

Tony Martens, co-founder of Plantible Foods, joins Neal to walk through the company's eight-year arc - from a free greenhouse in San Marcos to a commercial-scale rubisco protein facility in West Texas. They get into the science of duckweed and why rubisco is both the most abundant and most bioavailable protein on the planet, the modular “crawl, walk, run” scaling philosophy that kept Plantible from getting buried under capex, and how landing in El Dorado, Texas lifted the surrounding county's median household income by 62%. Plus: why the Taco Stand in Encinitas remains the most-mentioned spot on the pod.Key Topics* Why our food supply chain hasn't been updated in 3,000 years* How rubisco from duckweed competes with eggs, dairy, and meat* The “crawl, walk, run” approach to commercial scale-up* Why avian flu volatility is driving bakery and egg-replacement demand* Modular agriculture vs. billion-dollar capex projects* Living on the San Marcos farm in RVs through COVID lockdown* Lifting a West Texas county's median household income by 62%* Where Plantable products are showing up on shelves todayLinks & ResourcesPlantible Foods Connect on LinkedInTony MartensNeal Bloom This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit risingtidepartners.substack.com/subscribe

On this week's episode, NK News Senior Analytic Correspondent Colin Zwirko discusses a busy week of developments in North Korea. He examines Pyongyang's latest missile test of a new modular launcher and tactical cruise missile system, as well as what these weapons could mean for military planning near the inter-Korean border. He also talks about satellite imagery suggesting North Korea's Choe Hyon-class destroyer may be undergoing final testing near Nampho ahead of possible deployment. The episode also looks at signs Pyongyang may be preparing to welcome a foreign leader visit amid reports of a possible Xi Jinping trip, and at how North Korea's first large-scale solar farm aims to address energy shortages. About the podcast: The NK News Podcast is a weekly podcast hosted by Alannah Hill exclusively for NK News, covering the latest developments in and around North Korea. Each episode breaks down the week's news cycle with NK News journalists, analysts and expert guests.

Zoning codes run to 3,000 pages, contradict themselves, and change without warning — and right now, they're the single biggest reason most housing projects never leave a napkin.Recorded live at World of Modular 2026, this episode brings in Avi Kaufman, co-founder and Chief Real Estate Officer of FutureLot, to unpack what it actually takes to answer "what can I build here?" across 30,000 US jurisdictions. Avi started with a light-bulb moment during refugee resettlement — a carriage house behind a main house, housing a family no one knew could be housed there — and built a platform to make that question answerable at scale. **What we get into:** - The pre-feasibility gap: more housing projects die from discouragement than from bad economics — nobody's counting the permits never filed - Massachusetts alone has 200+ definitions of "gross floor area." Multiply that across 30,000 jurisdictions and you understand why builders stall - FutureLot's traffic-light system (green/yellow) tells builders whether a project clears each zoning criterion before a dollar goes to plans - Why the homeowner-builder conversation is broken — and how a shared interface changes "let me drive by" into a real-time answer at any US address - The tension between local zoning control and the tyranny of whoever has time to show up to meetings - What a customer who lives in the tool 4–5 hours a day looks like — and why that feedback loop is the product **Chapters:** - 00:00 — Intro: What FutureLot does - 00:50 — Origin story: Afghanistan, carriage houses, and untapped housing potential - 02:01 — Why zoning data, not building? - 03:35 — What real estate taught Avi: visuals and ease of use aren't nice-to-haves - 04:30 — Product walkthrough: the builder experience - 07:14 — The homeowner interface and what 8 minutes of dwell time means - 09:10 — Connector, not replacement: the role of FutureLot in the stack - 10:38 — 30,000 jurisdictions and the data complexity behind one screen - 12:11 — Two years in: the regulatory maze is worse than you think - 14:00 — Should we standardize zoning? The tension between local control and paralysis - 15:19 — AI + human review: how the sauce gets made - 16:28 — Trust mechanisms: overrides, alarms, source citations - 18:42 — The customer as collaborator: ground truth flows both ways - 20:30 — Roadmap: 50-state coverage, multifamily, lot splits - 21:07 — The inflection point: why now feels different- 22:18 — Find FutureLot **Find FutureLot:** - [futurelot.com](https://futurelot.com) — free account, 3 property searches - [avi@futurelot.com](mailto:avi@futurelot.com) - [YouTube](https://www.youtube.com/@FutureLot) - [Instagram](https://www.instagram.com/tryfuturelot) - [LinkedIn](https://www.linkedin.com/company/futurelot) - [X / Twitter](https://x.com/futurelot) - [Facebook](https://www.facebook.com/futurelot/) **Most Podern:** - [Spotify](https://open.spotify.com/show/3zYvX2lRZOpHcZW41WGVrp) - [Apple Podcasts](https://podcasts.apple.com/us/podcast/most-podern-podcast/id1725756164) - [Instagram](https://www.instagram.com/most.podern) - [YouTube](https://www.youtube.com/@MostPodern)

Analysts Don Kellogg and Roger Entner unveil insights from Fiber Connect 2026 on data centers and material shortages, and discuss AT&T's new Build-A-Plan rollout as well as their legal fight to sunset legacy copper networks in California. 00:00 Episode intro 00:25 Fiber Connect data center insights 02:51 AI video is driving network requirements 04:41 AT&T's new Build-A-Plan rollout and implications 07:40 Will the plan expand in the future? 08:27 AT&T sues California to sunset copper and DSL 11:00 Satellite has become a reliable backup 12:28 Regulators should embrace the future 13:16 Episode wrap-upTags: telecom, telecommunications, wireless, prepaid, postpaid, cellular phone, Don Kellogg, Roger Entner, Fiber Connect, AI, network, data centers, BEAD, fiber, data, video, DOCSIS 4.0, AT&T, Build-A-Plan, Mint, multi-line, convergence, DSL, California, copper, FCC, satellite, Starlink, T-Mobile, regulation

The big beasts arise as we talk EVE Frontier, MapleStory Universe and how Wemade's approach bests Ubisoft. [00:34] Jon attended EVE Fanfest 2026 in Iceland. What are his takeaways?[02:40] Why EVE Fanfest works beyond being just an event for players.[05:05] EVE Frontier is EVE Online if made from scratch now.[06:46] In Cycle 6 (out 25th June), EVE Frontier finally becomes more of an actual survival game.[09:28] Modular shipbuilding replaces fixed ships.[10:40] EVE Frontier is a game that rewards players who improve their manual gameplay skills. [11:55] “This is a game that makes EVE Online feel cuddly.”[13:58] Does EVE Frontier need non-EVE players?[15:30] The fundamental approach is blockchain as a unified API.[16:38] Why some CCP/Fenris developers want to work on EVE Frontier, not EVE Online. [17:38] How EVE Frontier is using AI for coding and prototyping.[19:10] Nexon is talking about MapleStory Universe, MSU 2.0 and VIBE IP.[22:11] MapleStory Universe did $31 million in revenue in year 1. [23:22] The real KPI for MSU 2.0 is the revenue third-party devs make. [25:55] Average EVE Fanfest attendee had played 7,900 hours of EVE Online. [30:50] Legend of Ymir has released the ability to mint and trade character NFTs.[32:00] Legend of Ymir NFT character trading was $77,000 on day 1. [34:40] Ubisoft is shutting down Champions Tactics' web3 features on 27th May. [36:00] Ubisoft's blockchain problems are a minor part of much wider issues for the company. [39:00] Champions Tactics was beautifully made, but too narrow in its addressable audience. [42:29] Wemade is iteratively learning. Ubisoft is scattergun, lacking learning loops. [44:30] The post-crash shape of blockchain gaming is now becoming apparent.

The newspaper comic strip didn't go extinct — it evolved. But if your work doesn't keep up, your career may be fossilized! From Reddit-ready square comics to vertical-scroll storytelling, they explore how creators are adapting to phones, social media, and changing reading habits while keeping the heart of the comic strip alive. Topics covered The evolution of newspaper comic strips Why horizontal strips existed in the first place How phones changed comics formatting Square-format comics on Reddit and social media Vertical-scroll storytelling Why readers won't rotate their phones Charles Schulz and the flexible-format origins of Peanuts Newspaper syndication vs. modern web distribution YA graphic novels as the next evolution for newspaper strips Lincoln Peirce and the success of Big Nate books Why comic strips are still thriving online Modular comic formatting for webcomics The launch of The Comic Scout  Dave Kellett's Hugo Award nomination anticipation Tips for maintaining visual consistency in comics Workflow advice for newer cartoonists   You get great rewards when you join the ComicLab Community on Patreon$2 — Early access to episodes$5 — Submit a question for possible use on the show AND get the exclusive ProTips podcast. Plus $2-tier rewards.If you'd like a one-on-one consultation about your comic, book it now!Brad Guigar is the creator of Evil Inc and the author of The Webcomics Handbook. He is available for personal consultations. Dave Kellett is the creator of Sheldon and Drive. He is the co-director of the comics documentary, Stripped.

In ep 174 of “How Do You Say That?!” sponsored by britishvoiceover.co.uk, Toby Ricketts joins Sam and Mark in a special as-live episode from the One Voice Conference 2026 in Stratford upon Avon. We talk about commmercial scripts that seem a bit abstract, and do a four handed script that plunges us into a sci-fi crisis! There's a studio/hotel bedroom audience of voice actors, and it's all on video too - so make sure you check our YouTube channel to see us in action - https://www.youtube.com/@howdoyousaythatThe wildcards are chosen by our audience - and there's real peril from ten thousand feet, a real-estate nightmare, and an unusual bird sighting!Our question this week comes from Ben Wake in the audience, asking about accents you wear like a glove.Get involved! Have you got a Wildcard suggestion that we should try or an idea for the show? Send it to us via Mark or Sam's social media or email it directly to podcast@britishvoiceover.co.ukScript 1Hey. The Earth moves. We respond.Macro to micro. Systems rebuild, forms transform.The world's being reshaped. Constantly.Make sustainability real.Fifty years. One mission:Turn imagination into reality.Where science meets craftsmanship—Endless R&D. Relentless breakthroughs.From chemical to physical. From supplier to partner.We don't follow. We lead trends.Physical foaming with jet-speed expansion. Efficient and integrated.Elevating material performance.More elastic and controllable.Stable and comfortable.Strong and recyclable.All-in-one machine.Redefining next-gen manufacturing.Modular, customizable, scalable.Our platform. Your creativity.Across industries and possibilities.We co-create solutions.This moment. React.We shape change.We drive transformation.WE LEAD NEXTWE ARE KINGSTEELScript 2NARRATOR INTRODUCTION:The year is 2367, and chaos reigns. The Earth - dying from climate collapse - is lost forever. Humanity has spread to the stars. Scout ships with minimal crews are sent into the cosmos to seek out viable worlds. The rest of humankind sleeps in cryogenic stasis aboard vast colony ships that will require decades, even centuries, to reach their new homes.This is the beginning of The Scattering. The Great Human Exodus.STRICKLAND [yelling]Stick's dead, I've lost all control, we're coming in way too hot. Kordek, what have you got back there?KORDEK [yelling, clearly frustrated]I don't know, Strickland, the engine's going critical, safeties failing. It's as if the entire system just crashed.COMPUTERWARNING…WARNING…KORDEK [yelling, panic setting in]Drive failsafes collapsing, containment overrides down, we've got an intermix chamber bleed and no way to reroute.BONAR [yelling, sarcastic]ENGLISH!KORDEK [yelling]We're about three minutes from becoming a small sun!BONAR [yelling]Yeah - well, I've got some news on that three-minute deadline! We're going to crash in one!STRICKLAND [yelling]Get to the lifepods! Now. Move, move! Abandon ship. Go!COMPUTER VOICELaunching Lifepod. Launching Lifepod.STRICKLANDMy God, I didn't honestly expect that to work. I can't believe we're alive.KORDEKThose life pods are re-enforced titanium alloy, and the inertia gel is rated for hypersonic impacts…..BONARYeah. No one cares, Kordek. We're alive, that's what matters.We'd love your feedback - and if you listen on Apple Podcasts or Spotify, hit the follow button today!**Listen to all of our podcasts here - you can also watch on YouTube, or say to your smart speaker "Play How Do You Say That?!"About our guest: Toby Ricketts is a multi-award-winning voiceover artist specialising in British, Australian, New Zealand, American and Global international non-regional or mid-Atlantic voice overs. Woof! In the last 25 years of his career, Toby has managed to create a global client base of big-name brands and loyal customers - and pretty much all from his secluded hi-tech studio deep in the New Zealand jungle.Just a few of his impressive clients include Facebook, VISA, Samsung, BMW, Audi, Lexus, Airbus, Lenovo and Google. As well as lecturing on Voiceover topics at international conferences (this one included), Toby has been nominated for 5 SOVAS awards, and has won 7 One Voice Awards, including Male Voiceover of the Year twice in 2018 and 2019, and a GEMA Award in 2025.Toby's websiteToby on FacebookToby on InstaToby's YouTube channelResources: Click here for the Wildcard Generator and don't forget to think of an action your character can be doing!About your hosts:With over 40 years representing major international clients such as Google, Emirates and HSBC; Mark Ryes has been trusted to be the voice for some of the world's biggest brands. If your business needs a fresh voice to represent you, then make it Mark's British voice. As a voiceover, TV presenter, podcaster or product demonstrator - Mark makes your brand truly sparkle!Mark's demos & contact details: https://linktr.ee/britishvoiceovermarkElegantly British with an intelligent, warm and seductive voice, Samantha Boffin helps creatives and production companies create great audio that really connects with their audience. BBC-trained and with over 20 years of broadcast experience on both sides of the mic, she's created award-winning promos, narration and commercials for companies all around the globe, including the BBC, Sky, Games Workshop, John Lewis, Audible and Penguin Random House.Samantha's demos & contact details: https://linktr.ee/samanthaboffinMany thanks to our studio audience... especially Kate De Quidt, Karen Esposito and Ben Wake.

Send us Fan MailPermitting can turn “faster with modular” into “stuck in review” long before the first module leaves the plant. Sam Taylor, founder of Crosscheck Modular Consulting in Ontario, explains what actually slows modular projects down and what teams can do differently on their next build. If you've ever wondered why approvals feel unpredictable from one town to the next, this conversation puts real structure around the problem. Sam walks through the modular approvals process in Ontario, where building permits are submitted to local authorities having jurisdiction and shaped by local bylaws as much as code. She explains why early engagement with building officials matters so much, especially in smaller communities that may not see modular often, and how proactive communication can reduce redesigns, clarify inspections, and keep projects moving. Additionally, she also compares province-to-province realities, from Alberta's deeper familiarity with modular to BC's Step Code energy efficiency requirements.Support the showListen to all episodes of MBI's Inside Modular podcast at https://www.modular.org/inside-modular-the-podcast-of-commercial-modular-construction/

An airhacks.fm conversation with Ian Rogers (@Ian Rogers) about: ZX Spectrum 128K with rubber keys and a burning side grill, Basic programming competitions, REM commands as ASCII art, PC versus Amiga and Archimedes era in the UK, fractal landscape generators for Wing Commander 4 cut scenes, Ocean Software in Manchester and the Head Over Heels game, Manchester Baby and Williams tube as the first stored-program computer, Steve Furber and ARM origins at the University of Manchester, Cosworth and Pi Research Formula One telemetry, transputers and embedded PowerPC data loggers, dynamic binary translation with the Dynamite simulator, ICL 2900 emulation for the Israeli tax system, MIPS to Itanium binary translation for SGI machines, Transitive Corporation and the PowerPC to x86 product that became Apple Rosetta, the Steve Jobs era at Apple, Spark to Power binary translation and the IBM acquisition of Transitive, JDBC versus ODBC API design observations, java.util.Vector and java.util.Hashtable synchronization decisions, StringBuilder array copying overhead from removing synchronization, DARPA HPCS languages Fortress, Chapel, X10, just-in-time parallelization from Java bytecode, LCC compiler from Princeton and the iBerg backend, JikesRVM as a metacircular Java VM written in Java, GNU Classpath and Sable VM by Etienne Gagnon, Apache Harmony port of JikesRVM to Windows, Maxwell and Maxine VMS as GraalVM precursors, Bernd Mathiske and the Sun acquisition by Oracle, GNU Classpath impact of the openJDK GPL release at FOSDEM 2006, Mark Wielaard and Rémi Forax FOSDEM stories, trace compilation and de-optimization parallels with JIT, Azul Systems Vega hardware and concurrent garbage collection, C4 collector design influencing ZGC and Shenandoah, Gil Tene's telephone exchange mentality for JVM responsiveness, page unmapping and signal handler memory pressure problems in HotSpot, Cliff Click and Modular, Google Android Runtime (ART) replacing Dalvik, transactional memory for class initializers in ART, ELF files and OAT format for ahead-of-time compilation, WhatsApp bytecode obfuscation breaking the ART verifier, lock balance verification for speculative lock optimizations, D8 and R8 Android compilers, Goit internal Google bytecode optimizer, Jeremy Manson and Google's OpenJDK variant, Linux kernel performance work and perf tooling, JikesRVM stack trace format making exception-heavy DaCapo benchmarks faster than HotSpot, Energy Efficiency across Programming Languages study comparing Java and Go, Ian Rogers on twitter: @Ian Rogers

Yes folks, it's that time again! We have a talk back!! Also, we have a very special surprise guest that I don't think ANY of you will see coming!!!! Please be sure to listen to the whole thing as we dig in to this past season of Modular!!

My guests are two voices from the brine world. Murray Brooker and Clint Van Marrewijk. Topics:Why do brine projects take so long?WA's hard rock advantagesWill brine development be faster in this cycleThe impact of big balance sheetsWhat makes a great brine project?Brine recovery ratesThe "promise" of DLENo DLE, no Smackover"Brine without borders"Modular brine productionThe brine talent poolRapid fire

Why Your Amazon Dashboard Is Lying to You + Remotion & Voice Cloning Reality Check | Claude Sessions Amazon Dashboard Brain, Remotion Video & ElevenLabs Voice Cloning | Claude Sessions SEO Description Shubhash Sharma on building a data brain behind your Amazon dashboard. Danny McMillan on Remotion video and ElevenLabs voice cloning realities. Episode Summary Week 3 of the month means Claude Sessions, and Danny McMillan and Shubhash Sharma are back with a double feature for Amazon and TikTok Shop sellers building their own AI tooling. Shubhash picks up from last episode's SP API and Ads API walkthrough with a hard lesson learned the wrong way: a polished dashboard wired straight into Amazon is a window with no room behind it. The numbers will lie, and you will not know when a feed silently dies. He walks through the fix: a "brain" sitting between the data sources and the dashboard. Supabase as the long term store, pgvector for unstructured stuff like contracts and reviews, n8n as the orchestration layer. Six core domains every seller shares (orders, products, analytics, ads, finance, affiliates and creators) plus an optional documents layer. He closes with a dual write migration pattern so you can flip between old and new without taking the business offline. Then Danny turns to video and voice. Remotion looks like toy town out of the box, but with the right plugins (motion blur, transitions, captions, shapes, fonts, rendering) and Claude doing the orchestration, it becomes a serious production tool that can pull in your footage, branding and design system. On the voice side, he has tested VoiceBox and F5TTS and come back to ElevenLabs Multilingual v2 at £22 a month. The missing gap everywhere is cadence. He also names the deeper bet: as the market floods with AI generated content, authentic voice becomes the differentiator that cannot be cloned. Key Topics Why dashboards lie when wired straight into Amazon, TikTok and Shopify The "brain" pattern: Supabase, pgvector and n8n as a centralised data layer The six core data domains every seller needs (plus a 7th for documents) Dual write migration so the old system and brain run in parallel Remotion as a code based video tool, and what it needs to stop looking toy town The four layer creative workflow: brief, story skeleton, treatment, scene by scene ElevenLabs vs VoiceBox vs F5TTS for voice cloning your own voice Why cadence is the last hard problem in synthetic voice The authenticity premium in an AI flooded market Timestamps [00:00] Intro and welcome back to Claude Sessions [00:34] Shubhash kicks off: where to put the data you pulled last week [01:04] "Your dashboard is lying to you" and the polished dashboard pitfall [02:32] Dashboard is a window. The brain is the room behind it [04:54] Tech stack: Supabase (Postgres), pgvector, n8n [05:54] The six fundamental data domains [06:26] Orders, products, analytics, ads, finance, affiliates and creators [08:30] The optional 7th layer: unstructured documents via pgvector [09:44] Dual write pattern for safe migration [10:48] Three takeaways: audit, list domains, build one table at a time [12:28] Danny on Remotion: code based video and why it is toy town out of the box [13:51] What is missing: motion blur, transitions, captions, shapes, beat detection [14:54] The 80+ plugin packages that turn it into a real tool [16:56] Pulling in footage, logos, design systems and free music from Pixabay [18:30] The 4 layer creative workflow: brief, story skeleton, treatment, scenes [21:15] Voiceovers: ElevenLabs Pro setup and why the £22 is worth it [22:12] VoiceBox and F5TTS field test: garbage and 5 rounds of tuning later [23:22] Why cadence is the hardest thing for AI voice to fake [25:42] How much reference audio you actually need (30 min min, 2 hours ideal) [27:25] ElevenLabs UI parameters: speed, stability, similarity, exaggeration [28:52] The authenticity premium when the market floods with AI [30:30] Key takeaways, ElevenLabs API usage and locking in your voice once [34:24] Aside: "insane" and "most" as the new AI tells [36:31] SSL 2026 wrap, 18 days out, Ritu returns next week with Japan Key Takeaways Build a brain, not just a window. A dashboard wired straight to Amazon, TikTok or Shopify has no memory. When a feed silently fails, the dashboard happily lies. Sit a Supabase + pgvector + n8n layer in between, and your dashboard becomes a view on top of a real source of truth. Six domains cover almost every seller. Orders, products, analytics, ads, finance, and affiliates / creators. Map every place each one currently lives, then consolidate one domain at a time. Start with one table (orders) and let Claude do the heavy lifting. Use dual write when migrating. Write to the old store and the new brain in parallel for a week. Compare. Flip the dashboard's read side via a feature flag. If something breaks, flip back. Zero downtime, zero fear. Remotion is a system, not a tool. Out of the box it is bare. Add the plugins (motion blur, transitions, captions, fonts, rendering), bring your own footage and design system, and let Claude orchestrate the four layer workflow: brief, story skeleton, treatment, scene by scene. ElevenLabs Multilingual v2 still wins for voice cloning. VoiceBox and F5TTS were not close. Pay the £22, use Model 2, feed it 30 minutes minimum (2 hours ideal) of clean reference audio, and lock the setup in once. Cadence is the last mile. AI can match tone and timbre. It still cannot match the rises, falls and micro pauses that make a sentence sound like you. Use scripts split into short paragraphs, generate three variants, and tune the language you use to talk to Claude until the cadence lands. Authenticity becomes the moat. As written, visual and audio AI floods every channel, the brand voice that is unmistakably human becomes the differentiator. Do not give that away to save 22 dollars a month on a podcast. Notable Quotes "Dashboard is a window. We need a room behind the window. So the brain is going to be the room behind this window." Shubhash Sharma "If any of our SaaS went offline tomorrow, will our business still have its memory? The answer is no, because we haven't stored it. All we have is rented attention." Shubhash Sharma "When you migrate to your brain, don't rip out your old system. Use dual write. Run them in parallel for a week. If something breaks, flip it back. Zero downtime, zero fear." Shubhash Sharma "Remotion out of the box isn't great. It's almost like building some slides, just one step up. You have to build it as a system of what you need." Danny McMillan "The hardest part for AI to represent is cadence. It can get the tone of your voice. That's the easy bit. But the speed and the up and down of how you talk, that's where these models still fail." Danny McMillan "In our rush to use AI, you've got to remember the market floods with it. When everything sounds like AI, the only thing left is the authentic voice for your brand." Danny McMillan Resources Mentioned Supabase : Postgres backend used as the long term data store for the seller "brain" pgvector : Postgres extension for semantic search over unstructured data (contracts, reviews, supplier emails) n8n : Orchestration layer for scheduled pulls and cron jobs with a UI Amazon Selling Partner API (SP API) : Source for orders, inventory and finance data (covered in last episode) Amazon Ads API : Source for ad spend, campaign and keyword data Remotion : Code based, React powered video creation framework ElevenLabs : Voice cloning and text to speech. Model used: Multilingual v2 (Pro plan, £22 / month) F5 TTS : Open source text to speech model tested for voice cloning VoiceBox by Jamie Pine : GitHub voice cloning desktop app tested by Danny Pixabay : Free music and sound effects used inside the Remotion workflow Loom : Source of clean voice reference audio if you record team walkthroughs Seller Sessions Live 2026 : Conference 9 May 2026, 18 days out at recording Hosts Danny McMillan : Host of Seller Sessions and Claude Sessions, founder of DataBrill, building AI native tooling for Amazon sellers. Website: https://sellersessions.com LinkedIn: https://www.linkedin.com/in/dannymcmillan Shubhash Sharma : Engineer building data infrastructure for Amazon and TikTok Shop sellers. Returning Claude Sessions co host. What's Next Next week: Ritu returns from Japan with three subjects covered in this month's rotation. In 18 days: Seller Sessions Live 2026 in London on 9 May. Modular format, new venue confirmed. About Seller Sessions Seller Sessions is the leading podcast for serious Amazon sellers, hosted by Danny McMillan since 2017. Claude Sessions is the AI focused monthly strand where Danny and rotating co hosts work through the practical wins, false starts and engineering reality of building with Claude, MCPs and the wider AI stack inside real seller businesses.

"You don't have to start at the top—gear up with purpose, innovate, and don't let price keep you out of the game." Episode Summary: In this episode of The Gun Experiment, I sit down with Big Keith and our special guest, Rob Godek, the Director of AceTac Gear. We dive deep into the origins of AceTac, from its humble roots in a garage to becoming a respected name in modular and adaptable tactical gear. Rob opens up about the importance of balancing affordability, reliability, and innovation for everyday Americans and professionals alike. We also share stories, review some killer gear (like the low-profile plate carrier and skeleton placard), and unpack the realities of starting and growing a gear company in today's competitive landscape. Expect laughs, honest feedback, and plenty of behind-the-scenes wisdom for both new shooters and seasoned operators. Call to Action: 1. Join our mailing list: Thegunexperiment.com 2. Subscribe and leave us a comment on Apple or Spotify 3. Follow us on all of our social media: Instagram   Youtube 4. Grab some cool TGE merch 5. Ask us anything at AskMikeandKeith@gmail.com 6. Be sure to support the sponsors of the show. They're a big part of making the show possible. Show Sponsors: HSM Ammunition: Official ammo sponsor, tested and recommended by us for reliability and performance. Onsite Firearms Training (OFT): Our go-to for responsible gun ownership and hands-on, real-world defensive training. New York Tac Defense: Offering prepaid legal plans for self-defense—peace of mind when you need it most. Key Takeaways: AceTac Gear is about making purpose-driven, affordable tactical equipment without sacrificing function or quality. Rob Godek's journey illustrates the power of persistence, honest customer feedback, and staying true to the roots—especially supporting everyday Americans and first responders. Not every innovation means reinventing the wheel; often, it means listening to users and perfecting what works. The firearms and tactical gear community thrives on transparency, supporting new shooters, and embracing the “climb” rather than just chasing high-end gear. Modularity, affordability, and durability are at the heart of great EDC and tactical gear. The customer experience at AceTac goes beyond just sales—offering a genuine lifetime warranty and community engagement. Balance your gear needs with honest self-assessment; sometimes a good “middle ground” product outperforms luxury when it comes to real-world use. Guest Information: Rob Godek is the Director and CEO of AceTac Gear. With a background in law enforcement, NASCAR engine building, and a passion for hands-on innovation, Rob brings a no-nonsense, customer-focused approach to tactical gear. AceTac is dedicated to empowering users with modular, reliable kits—ensuring “Gucci” quality is accessible to all, not just the elite. Find AceTac at AceTacGear.com and on Instagram for the latest updates. SEO Keywords: AceTac Gear, Rob Godek, The Gun Experiment, tactical gear review, modular tactical gear, affordable plate carrier, law enforcement equipment, EDC gear, gear innovation, customer feedback tactical, veteran owned gear brand, lifetime warranty tactical, SHOT Show, HSM Ammunition, Onsite Firearms Training, New York Tac Defense, 2A community, plate carrier review, skeleton placard, cummerbund review, Gun Podcast, gear for everyday Americans, product development tactical gear, training for gun owners

Looking for daily inspiration?  Get a quote from the top leaders in the industry in your inbox every morning.   Mike Whincup is the Head of Design and Marketing at Galaxy Multi Rides. Growing up in a family business that pioneered mechanical bull innovation, Mike has worked across both manufacturing and operations, helping expand the company globally while also launching Do The Beach, an inflatable park franchise concept. His experience spans decades in the attractions industry, blending creativity, entrepreneurship, and operational insight. In this interview, Mike talks about modular attractions, profitability through efficiency, and trends in inflataparks. Modular attractions “If you can think it, we've probably made it, as long as we can make it safe.” Mike explains how Galaxy Multi Rides evolved from a single mechanical bull into a fully modular attraction system with interchangeable ride attachments. What began as a practical solution to improve setup efficiency turned into a versatile product line that allows operators to swap themes and experiences easily. From surfboards to sharks to branded activations, the modular concept enables venues to refresh offerings without major capital investment. This adaptability also makes the attractions appealing across multiple markets, from party rentals to permanent installations. The ability to customize and iterate has attracted major brands and entertainment venues, reinforcing the value of flexibility in attraction design. Profitability through efficiency “We're trying to create profitability through efficiency.” A central theme in Mike's philosophy is designing attractions and facilities that maximize revenue while minimizing operational strain. He highlights how inflatable parks can achieve up to 90 percent playable space compared to around 60 percent in trampoline parks, immediately increasing capacity and revenue potential within the same footprint. Efficiency also extends to staffing and layout. By designing attractions that require less supervision and optimizing facility flow, operators can reduce labor costs while maintaining safety and guest experience. This balance between design, operations, and guest flow is what drives sustainable profitability. Trends in inflataparks “It's the evolution of the concept.” Mike describes the current inflatable park movement not as a new trend, but as an evolution. Earlier versions were disconnected attractions placed side by side, while modern inflataparks are fully integrated environments where guests remain engaged continuously. This shift toward immersive, interconnected design improves safety, increases engagement, and enhances overall guest satisfaction. The model also benefits from lower build costs and greater adaptability, making it attractive for operators entering the market. As a result, inflataparks are gaining renewed momentum as a scalable and efficient alternative within the family entertainment center space.   Mike can be reached on LinkedIn. To learn more about Galaxy Multi Rides, visit www.galaxymultirides.com. To learn more about Do The Beach, visit www.dothebeach.com. This podcast wouldn't be possible without the incredible work of our faaaaaantastic team:   Scheduling and correspondence by Kristen Karaliunas   To connect with AttractionPros: AttractionPros.com AttractionPros@gmail.com AttractionPros on Facebook AttractionPros on LinkedIn AttractionPros on Instagram AttractionPros on Twitter (X)

And at Google Next, Google splits its TPUs and unleashes more powerful workplace agents. Plus, did Anthropic's Mythos escape containment?Starring Tom Merritt, Sarah Lane and Andy Beach.Links to stories discussed in this episode can be found here. Hosted on Acast. See acast.com/privacy for more information.