Podcasts about KV

It's EV News Briefly for Friday 13 March 2026, everything you need to know in less than 5 minutes if you haven't got time for the full show.Patreon supporters fund this show, get the episodes ad free, as soon as they're ready and are part of the EV News Daily Community. You can be like them by clicking here: https://www.patreon.com/EVNewsDailyRIVIAN REVEALS R2 PRICINGThe Rivian R2 launches in four trims, all sharing an 87.9 kWh usable battery, ranging from the $57,990 Performance AWD (656 hp, 330 miles) arriving this Spring to a ~$45,000 base RWD variant in late 2027 with 275+ miles of range. All trims charge 10–80% in 29 minutes via a native NACS port, with a $1,495 destination charge across the board.FORD CUTS EXPLORER ENTRY PRICE WITH LFP BATTERYFord has revised its European Explorer EV with a new LFP battery pack, growing usable capacity from 52 kWh to 58 kWh and boosting WLTP range 17% to 444 km (276 miles), while a stronger APP350 motor lifts output to 140 kW and cuts the 0–100 km/h time to 8.0 seconds. The updated model starts at €39,990 in Germany and adds vehicle-to-load charging, refreshed infotainment, expanded driver assistance features, and standard one-pedal driving, though peak DC charging drops from 145 kW to 110 kW.LUCID NAMES MIDSIZE SUVS COSMOS AND EARTHLucid revealed at Investor Day 2026 that its two upcoming midsize electric SUVs will be called Cosmos and Earth, targeting a ~$50,000 starting price and production before end of 2026. Both will use 800V architecture, bidirectional charging, the new in-house Atlas drive unit (23% lighter, 30% fewer parts), and Lucid claims just 69 kWh would be sufficient for 300 miles of range thanks to a 0.22 drag coefficient.LUCID GRAVITY ADDS CARPLAY AND ANDROID AUTOLucid has rolled out an OTA update (UX 3.5) bringing wireless and wired Apple CarPlay and Android Auto to the Gravity SUV for North American owners now, with Europe and the Middle East to follow in late March. Both systems display on the Gravity's 6K Clearview Cockpit screen, addressing one of the most requested features from Lucid customers.JAECOO 8 UK SALES START IN MAYThe Jaecoo 8, a three-row flagship SUV, goes on sale in the UK in May priced from £45,500, using Chery's Super Hybrid System pairing a 1.5-litre turbo petrol with a three-speed auto for 422 bhp, 83 miles of electric-only range, and over 700 miles of combined range. Two trims are offered — Luxury (seven seats, £45,500) and Executive (six Nappa leather captain's chairs, £47,500) — with DC fast charging up to 40 kW for a 30–80% charge in about 20 minutes.EU EV PRICES FALL AS SMALL CARS RETURNAverage EU electric car prices dropped €1,800 to €42,700 in 2025 — the first decline since 2020 — driven by a surge in affordable B-segment BEVs like the Citroën ë-C3 and Renault 5, whose average segment prices fell 13%. T&E expects further price pressure in 2026 as Volkswagen Group prepares a small-car family including the ID. Polo, Cupra Raval, and Skoda Epiq, all targeting around €25,000.HONDA AXES THREE US EVSHonda has cancelled the 0 Series SUV, 0 Series Saloon, and Acura RSX for U.S. production, warning of losses up to ¥2.5 trillion ($15.8 billion) as it reverses its EV strategy amid rollbacks of U.S. fossil fuel regulations and removal of EV incentives. CEO Toshihiro Mibe said the priority is to "stop the bleeding," with operating losses now expected up to ¥1.12 trillion in the current fiscal year; the Sony-Honda Afeela brand is unaffected.VOLKSWAGEN SETS ID. POLO FROM €25,000Volkswagen will world-premiere the entry-level ID. Polo next month, starting at €25,000 and marking the first ID model to carry an established VW brand name. The range spans 37 kWh LFP and 52 kWh NMC battery options with outputs from 85 kW to 166 kW, and includes an R-Line (~€35,000, ~211 hp) and a GTI variant, with up to 450 km (280 miles) of WLTP range from the larger pack.ENEL COMPLETES 3,730 CHARGING STATIONSEnel has finished installing 3,730 EV charging stations across five Italian regions under the first tender of Italy's PNRR recovery plan, with each station offering two points capable of up to 90 kW each. The network is accessible via Enel's app or card and integrates with around 160 mobility service providers, with a further 1,200 stations already contracted under subsequent tenders.ELECTREON COMPLETES INDUCTEV ACQUISITIONElectreon has finalized its acquisition of U.S.-based InductEV, combining dynamic in-road wireless charging with InductEV's high-power stationary wireless charging for heavy-duty transit and freight. The merged portfolio now covers highway and urban corridor charging (LINE), burst charging at stops (DASH), depot charging (DOT), and heavy-duty freight charging (Ultra DOT).SCANDLINES STARTS BALTIC WHALE SERVICEScandlines launched the Baltic Whale on 10 March 2026, claiming it as the world's largest electric freight ferry in operation at 147 metres, running the 18.5 km Rødby–Puttgarden route carrying 66 freight units. Its 10 MWh battery can fully recharge in just 12 minutes via a dedicated 50 kV / 25 MW cable, with an automated docking tower connecting in 15 seconds, while a hybrid diesel mode reduces crossing time from one hour to 45 minutes.

In this ICYMI episode of the Line Life Podcast, we are sharing the narrated version of an article from the November 2025 issue of T&D World magazine on how Ameren Illinois is using new technology to inspect aging sub-transmission conductors and prioritize line rebuilds. The utility manages more than 45,000 miles of power lines supported by about 1.3 million utility poles, including 2,000 high-strength composite poles. Ameren Illinois adopted the LineVue device to perform comprehensive, cost-effective inspections of conductor spans, improving accuracy over traditional visual methods. LineVue technology enables remote, real-time assessment of conductor health, identifying issues such as rust, pitting and broken strands on energized lines up to 500 kV.  To read the full story, go to T&D World's website.

Květinářky Dita Pejcha Nováková a její maminka Ilona Nováková se rozhodly pro trochu jiné kvítí, jak říkají s nadsázkou. Nejen pro nevěsty vážou tzv. stabilizované květiny.

Květinářky Dita Pejcha Nováková a její maminka Ilona Nováková se rozhodly pro trochu jiné kvítí, jak říkají s nadsázkou. Nejen pro nevěsty vážou tzv. stabilizované květiny.

Join Kyle, Nader, Vibhu, and swyx live at NVIDIA GTC next week!Now that AIE Europe tix are ~sold out, our attention turns to Miami and World's Fair!The definitive AI Accelerator chip company has more than 10xed this AI Summer:And is now a $4.4 trillion megacorp… that is somehow still moving like a startup. We are blessed to have a unique relationship with our first ever NVIDIA guests: Kyle Kranen who gave a great inference keynote at the first World's Fair and is one of the leading architects of NVIDIA Dynamo (a Datacenter scale inference framework supporting SGLang, TRT-LLM, vLLM), and Nader Khalil, a friend of swyx from our days in Celo in The Arena, who has been drawing developers at GTC since before they were even a glimmer in the eye of NVIDIA:Nader discusses how NVIDIA Brev has drastically reduced the barriers to entry for developers to get a top of the line GPU up and running, and Kyle explains NVIDIA Dynamo as a data center scale inference engine that optimizes serving by scaling out, leveraging techniques like prefill/decode disaggregation, scheduling, and Kubernetes-based orchestration, framed around cost, latency, and quality tradeoffs. We also dive into Jensen's “SOL” (Speed of Light) first-principles urgency concept, long-context limits and model/hardware co-design, internal model APIs (https://build.nvidia.com), and upcoming Dynamo and agent sessions at GTC.Full Video pod on YouTubeTimestamps00:00 Agent Security Basics00:39 Podcast Welcome and Guests07:19 Acquisition and DevEx Shift13:48 SOL Culture and Dynamo Setup27:38 Why Scale Out Wins29:02 Scale Up Limits Explained30:24 From Laptop to Multi Node33:07 Cost Quality Latency Tradeoffs38:42 Disaggregation Prefill vs Decode41:05 Kubernetes Scaling with Grove43:20 Context Length and Co Design57:34 Security Meets Agents58:01 Agent Permissions Model59:10 Build Nvidia Inference Gateway01:01:52 Hackathons And Autonomy Dreams01:10:26 Local GPUs And Scaling Inference01:15:31 Long Running Agents And SF ReflectionsTranscriptAgent Security BasicsNader: Agents can do three things. They can access your files, they can access the internet, and then now they can write custom code and execute it. You literally only let an agent do two of those three things. If you can access your files and you can write custom code, you don't want internet access because that's one to see full vulnerability, right?If you have access to internet and your file system, you should know the full scope of what that agent's capable of doing. Otherwise, now we can get injected or something that can happen. And so that's a lot of what we've been thinking about is like, you know, how do we both enable this because it's clearly the future.But then also, you know, what, what are these enforcement points that we can start to like protect?swyx: All right.Podcast Welcome and Guestsswyx: Welcome to the Lean Space podcast in the Chromo studio. Welcome to all the guests here. Uh, we are back with our guest host Viu. Welcome. Good to have you back. And our friends, uh, Netter and Kyle from Nvidia. Welcome.Kyle: Yeah, thanks for having us.swyx: Yeah, thank you. Actually, I don't even know your titles.Uh, I know you're like architect something of Dynamo.Kyle: Yeah. I, I'm one of the engineering leaders [00:01:00] and a architects of Dynamo.swyx: And you're director of something and developers, developer tech.Nader: Yeah.swyx: You're the developers, developers, developers guy at nvidia,Nader: open source agent marketing, brev,swyx: and likeNader: Devrel tools and stuff.swyx: Yeah. BeenNader: the focus.swyx: And we're, we're kind of recording this ahead of Nvidia, GTC, which is coming to town, uh, again, uh, or taking over town, uh, which, uh, which we'll all be at. Um, and we'll talk a little bit about your sessions and stuff. Yeah.Nader: We're super excited for it.GTC Booth Stunt Storiesswyx: One of my favorite memories for Nader, like you always do like marketing stunts and like while you were at Rev, you like had this surfboard that you like, went down to GTC with and like, NA Nvidia apparently, like did so much that they bought you.Like what, what was that like? What was that?Nader: Yeah. Yeah, we, we, um. Our logo was a chaka. We, we, uh, we were always just kind of like trying to keep true to who we were. I think, you know, some stuff, startups, you're like trying to pretend that you're a bigger, more mature company than you are. And it was actually Evan Conrad from SF Compute who was just like, you guys are like previousswyx: guest.Yeah.Nader: Amazing. Oh, really? Amazing. Yeah. He was just like, guys, you're two dudes in the room. Why are you [00:02:00] pretending that you're not? Uh, and so then we were like, okay, let's make the logo a shaka. We brought surfboards to our booth to GTC and the energy was great. Yeah. Some palm trees too. They,Kyle: they actually poked out over like the, the walls so you could, you could see the bread booth.Oh, that's so funny. AndNader: no one else,Kyle: just from very far away.Nader: Oh, so you remember it backKyle: then? Yeah I remember it pre-acquisition. I was like, oh, those guys look cool,Nader: dude. That makes sense. ‘cause uh, we, so we signed up really last minute, and so we had the last booth. It was all the way in the corner. And so I was, I was worried that no one was gonna come.So that's why we had like the palm trees. We really came in with the surfboards. We even had one of our investors bring her dog and then she was just like walking the dog around to try to like, bring energy towards our booth. Yeah.swyx: Steph.Kyle: Yeah. Yeah, she's the best,swyx: you know, as a conference organizer, I love that.Right? Like, it's like everyone who sponsors a conference comes, does their booth. They're like, we are changing the future of ai or something, some generic b******t and like, no, like actually try to stand out, make it fun, right? And people still remember it after three years.Nader: Yeah. Yeah. You know what's so funny?I'll, I'll send, I'll give you this clip if you wanna, if you wanna add it [00:03:00] in, but, uh, my wife was at the time fiance, she was in medical school and she came to help us. ‘cause it was like a big moment for us. And so we, we bought this cricket, it's like a vinyl, like a vinyl, uh, printer. ‘cause like, how else are we gonna label the surfboard?So, we got a surfboard, luckily was able to purchase that on the company card. We got a cricket and it was just like fine tuning for enterprises or something like that, that we put on the. On the surfboard and it's 1:00 AM the day before we go to GTC. She's helping me put these like vinyl stickers on.And she goes, you son of, she's like, if you pull this off, you son of a b***h. And so, uh, right. Pretty much after the acquisition, I stitched that with the mag music acquisition. I sent it to our family group chat. Ohswyx: Yeah. No, well, she, she made a good choice there. Was that like basically the origin story for Launchable is that we, it was, and maybe we should explain what Brev is andNader: Yeah.Yeah. Uh, I mean, brev is just, it's a developer tool that makes it really easy to get a GPU. So we connect a bunch of different GPU sources. So the basics of it is like, how quickly can we SSH you into a G, into a GPU and whenever we would talk to users, they wanted A GPU. They wanted an A 100. And if you go to like any cloud [00:04:00] provisioning page, usually it's like three pages of forms or in the forms somewhere there's a dropdown.And in the dropdown there's some weird code that you know to translate to an A 100. And I remember just thinking like. Every time someone says they want an A 100, like the piece of text that they're telling me that they want is like, stuffed away in the corner. Yeah. And so we were like, what if the biggest piece of text was what the user's asking for?And so when you go to Brev, it's just big GPU chips with the type that you want withswyx: beautiful animations that you worked on pre, like pre you can, like, now you can just prompt it. But back in the day. Yeah. Yeah. Those were handcraft, handcrafted artisanal code.Nader: Yeah. I was actually really proud of that because, uh, it was an, i I made it in Figma.Yeah. And then I found, I was like really struggling to figure out how to turn it from like Figma to react. So what it actually is, is just an SVG and I, I have all the styles and so when you change the chip, whether it's like active or not it changes the SVG code and that somehow like renders like, looks like it's animating, but it, we just had the transition slow, but it's just like the, a JavaScript function to change the like underlying SVG.Yeah. And that was how I ended up like figuring out how to move it from from Figma. But yeah, that's Art Artisan. [00:05:00]Kyle: Speaking of marketing stunts though, he actually used those SVGs. Or kind of use those SVGs to make these cards.Nader: Oh yeah. LikeKyle: a GPU gift card Yes. That he handed out everywhere. That was actually my first impression of thatNader: one.Yeah,swyx: yeah, yeah.Nader: Yeah.swyx: I think I still have one of them.Nader: They look great.Kyle: Yeah.Nader: I have a ton of them still actually in our garage, which just, they don't have labels. We should honestly like bring, bring them back. But, um, I found this old printing press here, actually just around the corner on Ven ness. And it's a third generation San Francisco shop.And so I come in an excited startup founder trying to like, and they just have this crazy old machinery and I'm in awe. ‘cause the the whole building is so physical. Like you're seeing these machines, they have like pedals to like move these saws and whatever. I don't know what this machinery is, but I saw all three generations.Like there's like the grandpa, the father and the son, and the son was like, around my age. Well,swyx: it's like a holy, holy trinity.Nader: It's funny because we, so I just took the same SVG and we just like printed it and it's foil printing, so they make a a, a mold. That's like an inverse of like the A 100 and then they put the foil on it [00:06:00] and then they press it into the paper.And I remember once we got them, he was like, Hey, don't forget about us. You know, I guess like early Apple and Cisco's first business cards were all made there. And so he was like, yeah, we, we get like the startup businesses but then as they mature, they kind of go somewhere else. And so I actually, I think we were talking with marketing about like using them for some, we should go back and make some cards.swyx: Yeah, yeah, yeah. You know, I remember, you know, as a very, very small breadth investor, I was like, why are we spending time like, doing these like stunts for GPUs? Like, you know, I think like as a, you know, typical like cloud hard hardware person, you go into an AWS you pick like T five X xl, whatever, and it's just like from a list and you look at the specs like, why animate this GP?And, and I, I do think like it just shows the level of care that goes throughout birth and Yeah. And now, and also the, and,Nader: and Nvidia. I think that's what the, the thing that struck me most when we first came in was like the amount of passion that everyone has. Like, I think, um, you know, you talk to, you talk to Kyle, you talk to, like, every VP that I've met at Nvidia goes so close to the metal.Like, I remember it was almost a year ago, and like my VP asked me, he's like, Hey, [00:07:00] what's cursor? And like, are you using it? And if so, why? Surprised at this, and he downloaded Cursor and he was asking me to help him like, use it. And I thought that was, uh, or like, just show him what he, you know, why we were using it.And so, the amount of care that I think everyone has and the passion, appreciate, passion and appreciation for the moment. Right. This is a very unique time. So it's really cool to see everyone really like, uh, appreciate that.swyx: Yeah.Acquisition and DevEx Shiftswyx: One thing I wanted to do before we move over to sort of like research topics and, uh, the, the stuff that Kyle's working on is just tell the story of the acquisition, right?Like, not many people have been, been through an acquisition with Nvidia. What's it like? Uh, what, yeah, just anything you'd like to say.Nader: It's a crazy experience. I think, uh, you know, we were the thing that was the most exciting for us was. Our goal was just to make it easier for developers.We wanted to find access to GPUs, make it easier to do that. And then all, oh, actually your question about launchable. So launchable was just make one click exper, like one click deploys for any software on top of the GPU. Mm-hmm. And so what we really liked about Nvidia was that it felt like we just got a lot more resources to do all of that.I think, uh, you [00:08:00] know, NVIDIA's goal is to make things as easy for developers as possible. So there was a really nice like synergy there. I think that, you know, when it comes to like an acquisition, I think the amount that the soul of the products align, I think is gonna be. Is going speak to the success of the acquisition.Yeah. And so it in many ways feels like we're home. This is a really great outcome for us. Like we you know, I love brev.nvidia.com. Like you should, you should use it's, it's theKyle: front page for GPUs.Nader: Yeah. Yeah. If you want GP views,Kyle: you go there, getswyx: it there, and it's like internally is growing very quickly.I, I don't remember You said some stats there.Nader: Yeah, yeah, yeah. It's, uh, I, I wish I had the exact numbers, but like internally, externally, it's been growing really quickly. We've been working with a bunch of partners with a bunch of different customers and ISVs, if you have a solution that you want someone that runs on the GPU and you want people to use it quickly, we can bundle it up, uh, in a launchable and make it a one click run.If you're doing things and you want just like a sandbox or something to run on, right. Like open claw. Huge moment. Super exciting. Our, uh, and we'll talk into it more, but. You know, internally, people wanna run this, and you, we know we have to be really careful from the security implications. Do we let this run on the corporate network?Security's guidance was, Hey, [00:09:00] run this on breath, it's in, you know, it's, it's, it's a vm, it's sitting in the cloud, it's off the corporate network. It's isolated. And so that's been our stance internally and externally about how to even run something like open call while we figure out how to run these things securely.But yeah,swyx: I think there's also like, you almost like we're the right team at the right time when Nvidia is starting to invest a lot more in developer experience or whatever you call it. Yeah. Uh, UX or I don't know what you call it, like software. Like obviously NVIDIA is always invested in software, but like, there's like, this is like a different audience.Yeah. It's aNader: widerKyle: developer base.swyx: Yeah. Right.Nader: Yeah. Yeah. You know, it's funny, it's like, it's not, uh,swyx: so like, what, what is it called internally? What, what is this that people should be aware that is going on there?Nader: Uh, what, like developer experienceswyx: or, yeah, yeah. Is it's called just developer experience or is there like a broader strategy hereNader: in Nvidia?Um, Nvidia always wants to make a good developer experience. The thing is and a lot of the technology is just really complicated. Like, it's not, it's uh, you know, I think, um. The thing that's been really growing or the AI's growing is having a huge moment, not [00:10:00] because like, let's say data scientists in 2018, were quiet then and are much louder now.The pie is com, right? There's a whole bunch of new audiences. My mom's wondering what she's doing. My sister's learned, like taught herself how to code. Like the, um, you know, I, I actually think just generally AI's a big equalizer and you're seeing a more like technologically literate society, I guess.Like everyone's, everyone's learning how to code. Uh, there isn't really an excuse for that. And so building a good UX means that you really understand who your end user is. And when your end user becomes such a wide, uh, variety of people, then you have to almost like reinvent the practice, right? Yeah. You haveKyle: to, and actually build more developer ux, right?Because the, there are tiers of developer base that were added. You know, the, the hackers that are building on top of open claw, right? For example, have never used gpu. They don't know what kuda is. They, they, they just want to run something.Nader: Yeah.Kyle: You need new UX that is not just. Hey, you know, how do you program something in Cuda and run it?And then, and then we built, you know, like when Deep Learning was getting big, we built, we built Torch and, and, but so recently the amount of like [00:11:00] layers that are added to that developer stack has just exploded because AI has become ubiquitous. Everyone's using it in different ways. Yeah. It'sNader: moving fast in every direction.Vertical, horizontal.Vibhu: Yeah. You guys, you even take it down to hardware, like the DGX Spark, you know, it's, it's basically the same system as just throwing it up on big GPU cluster.Nader: Yeah, yeah, yeah. It's amazing. Blackwell.swyx: Yeah. Uh, we saw the preview at the last year's GTC and that was one of the better performing, uh, videos so far, and video coverage so far.Awesome. This will beat it. Um,Nader: that wasswyx: actually, we have fingersNader: crossed. Yeah.DGX Spark and Remote AccessNader: Even when Grace Blackwell or when, um, uh, DGX Spark was first coming out getting to be involved in that from the beginning of the developer experience. And it just comes back to what youswyx: were involved.Nader: Yeah. St. St.swyx: Mars.Nader: Yeah. Yeah. I mean from, it was just like, I, I got an email, we just got thrown into the loop and suddenly yeah, I, it was actually really funny ‘cause I'm still pretty fresh from the acquisition and I'm, I'm getting an email from a bunch of the engineering VPs about like, the new hardware, GPU chip, like we're, or not chip, but just GPU system that we're putting out.And I'm like, okay, cool. Matters. Now involved with this for the ux, I'm like. What am I gonna do [00:12:00] here? So, I remember the first meeting, I was just like kind of quiet as I was hearing engineering VPs talk about what this box could be, what it could do, how we should use it. And I remember, uh, one of the first ideas that people were idea was like, oh, the first thing that it was like, I think a quote was like, the first thing someone's gonna wanna do with this is get two of them and run a Kubernetes cluster on top of them.And I was like, oh, I think I know why I'm here. I was like, the first thing we're doing is easy. SSH into the machine. And then, and you know, just kind of like scoping it down of like, once you can do that every, you, like the person who wants to run a Kubernetes cluster onto Sparks has a higher propensity for pain, then, then you know someone who buys it and wants to run open Claw right now, right?If you can make sure that that's as effortless as possible, then the rest becomes easy. So there's a tool called Nvidia Sync. It just makes the SSH connection really simple. So, you know, if you think about it like. If you have a Mac, uh, or a PC or whatever, if you have a laptop and you buy this GPU and you want to use it, you should be able to use it like it's A-A-G-P-U in the cloud, right?Um, but there's all this friction of like, how do you actually get into that? That's part of [00:13:00] Revs value proposition is just, you know, there's a CLI that wraps SSH and makes it simple. And so our goal is just get you into that machine really easily. And one thing we just launched at CES, it's in, it's still in like early access.We're ironing out some kinks, but it should be ready by GTC. You can register your spark on Brev. And so now if youswyx: like remote managed yeah, local hardware. Single pane of glass. Yeah. Yeah. Because Brev can already manage other clouds anyway, right?Vibhu: Yeah, yeah. And you use the spark on Brev as well, right?Nader: Yeah. But yeah, exactly. So, so you, you, so you, you set it up at home you can run the command on it, and then it gets it's essentially it'll appear in your Brev account, and then you can take your laptop to a Starbucks or to a cafe, and you'll continue to use your, you can continue use your spark just like any other cloud node on Brev.Yeah. Yeah. And it's just like a pre-provisioned centerswyx: in yourNader: home. Yeah, exactly.swyx: Yeah. Yeah.Vibhu: Tiny little data center.Nader: Tiny little, the size ofVibhu: your phone.SOL Culture and Dynamo Setupswyx: One more thing before we move on to Kyle. Just have so many Jensen stories and I just love, love mining Jensen stories. Uh, my favorite so far is SOL. Uh, what is, yeah, what is S-O-L-S-O-LNader: is actually, i, I think [00:14:00] of all the lessons I've learned, that one's definitely my favorite.Kyle: It'll always stick with you.Nader: Yeah. Yeah. I, you know, in your startup, everything's existential, right? Like we've, we've run out of money. We were like, on the risk of, of losing payroll, we've had to contract our team because we l ran outta money. And so like, um, because of that you're really always forcing yourself to I to like understand the root cause of everything.If you get a date, if you get a timeline, you know exactly why that date or timeline is there. You're, you're pushing every boundary and like, you're not just say, you're not just accepting like a, a no. Just because. And so as you start to introduce more layers, as you start to become a much larger organization, SOL is is essentially like what is the physics, right?The speed of light moves at a certain speed. So if flight's moving some slower, then you know something's in the way. So before trying to like layer reality back in of like, why can't this be delivered at some date? Let's just understand the physics. What is the theoretical limit to like, uh, how fast this can go?And then start to tell me why. ‘cause otherwise people will start telling you why something can't be done. But actually I think any great leader's goal is just to create urgency. Yeah. [00:15:00] There's an infiniteKyle: create compelling events, right?Nader: Yeah.Kyle: Yeah. So l is a term video is used to instigate a compelling event.You say this is done. How do we get there? What is the minimum? As much as necessary, as little as possible thing that it takes for us to get exactly here and. It helps you just break through a bunch of noise.swyx: Yeah.Kyle: Instantly.swyx: One thing I'm unclear about is, can only Jensen use the SOL card? Like, oh, no, no, no.Not everyone get the b******t out because obviously it's Jensen, but like, can someone else be like, no, likeKyle: frontline engineers use it.Nader: Yeah. Every, I think it's not so much about like, get the b******t out. It's like, it's like, give me the root understanding, right? Like, if you tell me something takes three weeks, it like, well, what's the first principles?Yeah, the first principles. It's like, what's the, what? Like why is it three weeks? What is the actual yeah. What's the actual limit of why this is gonna take three weeks? If you're gonna, if you, if let's say you wanted to buy a new computer and someone told you it's gonna be here in five days, what's the SOL?Well, like the SOL is like, I could walk into a Best Buy and pick it up for you. Right? So then anything that's like beyond that is, and is that practical? Is that how we're gonna, you know, let's say give everyone in the [00:16:00] company a laptop, like obviously not. So then like that's the SOL and then it's like, okay, well if we have to get more than 10, suddenly there might be some, right?And so now we can kind of piece the reality back.swyx: So, so this is the. Paul Graham do things that don't scale. Yeah. And this is also the, what people would now call behi agency. Yeah.Kyle: It's actually really interesting because there's a, there's a second hardware angle to SOL that like doesn't come up for all the org sol is used like culturally at aswyx: media for everything.I'm also mining for like, I think that can be annoying sometimes. And like someone keeps going IOO you and you're like, guys, like we have to be stable. We have to, we to f*****g plan. Yeah.Kyle: It's an interesting balance.Nader: Yeah. I encounter that with like, actually just with, with Alec, right? ‘cause we, we have a new conference so we need to launch, we have, we have goals of what we wanna launch by, uh, by the conference and like, yeah.At the end of the day, where isswyx: this GTC?Nader: Um, well this is like, so we, I mean we did it for CES, we did for GT CDC before that we're doing it for GTC San Jose. So I mean, like every, you know, we have a new moment. Um, and we want to launch something. Yeah. And we want to do so at SOL and that does mean that some, there's some level of prioritization that needs [00:17:00] to happen.And so it, it is difficult, right? I think, um, you have to be careful with what you're pushing. You know, stability is important and that should be factored into S-O-L-S-O-L isn't just like, build everything and let it break, you know, that, that's part of the conversation. So as you're laying, layering in all the details, one of them might be, Hey, we could build this, but then it's not gonna be stable for X, y, z reasons.And so that was like, one of our conversations for CES was, you know, hey, like we, we can get this into early access registering your spark with brev. But there are a lot of things that we need to do in order to feel really comfortable from a security perspective, right? There's a lot of networking involved before we deliver that to users.So it's like, okay. Let's get this to a point where we can at least let people experiment with it. We had it in a booth, we had it in Jensen's keynote, and then let's go iron out all the networking kinks. And that's not easy. And so, uh, that can come later. And so that was the way that we layered that back in.Yeah. ButKyle: It's not really about saying like, you don't have to do the, the maintenance or operational work. It's more about saying, you know, it's kind of like [00:18:00] highlights how progress is incremental, right? Like, what is the minimum thing that we can get to. And then there's SOL for like every component after that.But there's the SOL to get you, get you to the, the starting line. And that, that's usually how it's asked. Yeah. On the other side, you know, like SOL came out of like hardware at Nvidia. Right. So SOL is like literally if we ran the accelerator or the GPU with like at basically full speed with like no other constraints, like how FAST would be able to make a program go.swyx: Yeah. Yeah. Right.Kyle: Soswyx: in, in training that like, you know, then you work back to like some percentage of like MFU for example.Kyle: Yeah, that's a, that's a great example. So like, there's an, there's an S-O-L-M-F-U, and then there's like, you know, what's practically achievable.swyx: Cool. Should we move on to sort of, uh, Kyle's side?Uh, Kyle, you're coming more from the data science world. And, uh, I, I mean I always, whenever, whenever I meet someone who's done working in tabular stuff, graph neural networks, time series, these are basically when I go to new reps, I go to ICML, I walk the back halls. There's always like a small group of graph people.Yes. Absolute small group of tabular people. [00:19:00] And like, there's no one there. And like, it's very like, you know what I mean? Like, yeah, no, like it's, it's important interesting work if you care about solving the problems that they solve.Kyle: Yeah.swyx: But everyone else is just LMS all the time.Kyle: Yeah. I mean it's like, it's like the black hole, right?Has the event horizon reached this yet in nerves? Um,swyx: but like, you know, those are, those are transformers too. Yeah. And, and those are also like interesting things. Anyway, uh, I just wanted to spend a little bit of time on, on those, that background before we go into Dynamo, uh, proper.Kyle: Yeah, sure. I took a different path to Nvidia than that, or I joined six years ago, seven, if you count, when I was an intern.So I joined Nvidia, like right outta college. And the first thing I jumped into was not what I'd done in, during internship, which was like, you know, like some stuff for autonomous vehicles, like heavyweight object detection. I jumped into like, you know, something, I'm like, recommenders, this is popular. Andswyx: yeah, he did RexiKyle: as well.Yeah, Rexi. Yeah. I mean that, that was the taboo data at the time, right? You have tables of like, audience qualities and item qualities, and you're trying to figure out like which member of [00:20:00] the audience matches which item or, or more practically which item matches which member of the audience. And at the time, really it was like we were trying to enable.Uh, recommender, which had historically been like a little bit of a CP based workflow into something that like, ran really well in GPUs. And it's since been done. Like there are a bunch of libraries for Axis that run on GPUs. Uh, the common models like Deeplearning recommendation model, which came outta meta and the wide and deep model, which was used or was released by Google were very accelerated by GPUs using, you know, the fast HBM on the chips, especially to do, you know, vector lookups.But it was very interesting at the time and super, super relevant because like we were starting to get like. This explosion of feeds and things that required rec recommenders to just actively be on all the time. And sort of transitioned that a little bit towards graph neural networks when I discovered them because I was like, okay, you can actually use graphical neural networks to represent like, relationships between people, items, concepts, and that, that interested me.So I jumped into that at [00:21:00] Nvidia and, and got really involved for like two-ish years.swyx: Yeah. Uh, and something I learned from Brian Zaro Yeah. Is that you can just kind of choose your own path in Nvidia.Kyle: Oh my God. Yeah.swyx: Which is not a normal big Corp thing. Yeah. Like you, you have a lane, you stay in your lane.Nader: I think probably the reason why I enjoy being in a, a big company, the mission is the boss probably from a startup guy. Yeah. The missionswyx: is the boss.Nader: Yeah. Uh, it feels like a big game of pickup basketball. Like, you know, if you play one, if you wanna play basketball, you just go up to the court and you're like, Hey look, we're gonna play this game and we need three.Yeah. And you just like find your three. That's honestly for every new initiative that's what it feels like. Yeah.Vibhu: It also like shows, right? Like Nvidia. Just releasing state-of-the-art stuff in every domain. Yeah. Like, okay, you expect foundation models with Nemo tron voice just randomly parakeet.Call parakeet just comes out another one, uh, voice. TheKyle: video voice team has always been producing.Vibhu: Yeah. There's always just every other domain of paper that comes out, dataset that comes out. It's like, I mean, it also stems back to what Nvidia has to do, right? You have to make chips years before they're actually produced.Right? So you need to know, you need to really [00:22:00] focus. TheKyle: design process starts likeVibhu: exactlyKyle: three to five years before the chip gets to the market.Vibhu: Yeah. I, I'm curious more about what that's like, right? So like, you have specialist teams. Is it just like, you know, people find an interest, you go in, you go deep on whatever, and that kind of feeds back into, you know, okay, we, we expect predictions.Like the internals at Nvidia must be crazy. Right? You know? Yeah. Yeah. You know, you, you must. Not even without selling to people, you have your own predictions of where things are going. Yeah. And they're very based, very grounded. Right?Kyle: Yeah. It, it, it's really interesting. So there's like two things that I think that Amed does, which are quite interesting.Uh, one is like, we really index into passion. There's a big. Sort of organizational top sound push to like ensure that people are working on the things that they're passionate about. So if someone proposes something that's interesting, many times they can just email someone like way up the chain that they would find this relevant and say like, Hey, can I go work on this?Nader: It's actually like I worked at a, a big company for a couple years before, uh, starting on my startup journey and like, it felt very weird if you were to like email out of chain, if that makes [00:23:00] sense. Yeah. The emails at Nvidia are like mosh pitsswyx: shoot,Nader: and it's just like 60 people, just whatever. And like they're, there's this,swyx: they got messy like, reply all you,Nader: oh, it's in, it's insane.It's insane. They justKyle: help. You know, Maxim,Nader: the context. But, but that's actually like, I've actually, so this is a weird thing where I used to be like, why would we send emails? We have Slack. I am the entire, I'm the exact opposite. I feel so bad for anyone who's like messaging me on Slack ‘cause I'm so unresponsive.swyx: Your emailNader: Maxi, email Maxim. I'm email maxing Now email is a different, email is perfect because man, we can't work together. I'm email is great, right? Because important threads get bumped back up, right? Yeah, yeah. Um, and so Slack doesn't do that. So I just have like this casino going off on the right or on the left and like, I don't know which thread was from where or what, but like the threads get And then also just like the subject, so you can have like working threads.I think what's difficult is like when you're small, if you're just not 40,000 people I think Slack will work fine, but there's, I don't know what the inflection point is. There is gonna be a point where that becomes really messy and you'll actually prefer having email. ‘cause you can have working threads.You can cc more than nine people in a thread.Kyle: You can fork stuff.Nader: You can [00:24:00] fork stuff, which is super nice and just like y Yeah. And so, but that is part of where you can propose a plan. You can also just. Start, honestly, momentum's the only authority, right? So like, if you can just start, start to make a little bit of progress and show someone something, and then they can try it.That's, I think what's been, you know, I think the most effective way to push anything for forward. And that's both at Nvidia and I think just generally.Kyle: Yeah, there's, there's the other concept that like is explored a lot at Nvidia, which is this idea of a zero billion dollar business. Like market creation is a big thing at Nvidia.Like,swyx: oh, you want to go and start a zero billion dollar business?Kyle: Jensen says, we are completely happy investing in zero billion dollar markets. We don't care if this creates revenue. It's important for us to know about this market. We think it will be important in the future. It can be zero billion dollars for a while.I'm probably minging as words here for, but like, you know, like, I'll give an example. NVIDIA's been working on autonomous driving for a a long time,swyx: like an Nvidia car.Kyle: No, they, they'veVibhu: used the Mercedes, right? They're around the HQ and I think it finally just got licensed out. Now they're starting to be used quite a [00:25:00] bit.For 10 years you've been seeing Mercedes with Nvidia logos driving.Kyle: If you're in like the South San Santa Clara, it's, it's actually from South. Yeah. So, um. Zero billion dollar markets are, are a thing like, you know, Jensen,swyx: I mean, okay, look, cars are not a zero billion dollar market. But yeah, that's a bad example.Nader: I think, I think he's, he's messaging, uh, zero today, but, or even like internally, right? Like, like it's like, uh, an org doesn't have to ruthlessly find revenue very quickly to justify their existence. Right. Like a lot of the important research, a lot of the important technology being developed that, that's kind ofKyle: where research, research is very ide ideologically free at Nvidia.Yeah. Like they can pursue things that they wereswyx: Were you research officially?Kyle: I was never in research. Officially. I was always in engineering. Yeah. We in, I'm in an org called Deep Warning Algorithms, which is basically just how do we make things that are relevant to deep warning go fast.swyx: That sounds freaking cool.Vibhu: And I think a lot of that is underappreciated, right? Like time series. This week Google put out time. FF paper. Yeah. A new time series, paper res. Uh, Symantec, ID [00:26:00] started applying Transformers LMS to Yes. Rec system. Yes. And when you think the scale of companies deploying these right. Amazon recommendations, Google web search, it's like, it's huge scale andKyle: Yeah.Vibhu: You want fast?Kyle: Yeah. Yeah. Yeah. Actually it's, it, I, there's a fun moment that brought me like full circle. Like, uh, Amazon Ads recently gave a talk where they talked about using Dynamo for generative recommendation, which was like super, like weirdly cathartic for me. I'm like, oh my God. I've, I've supplanted what I was working on.Like, I, you're using LMS now to do what I was doing five years ago.swyx: Yeah. Amazing. And let's go right into Dynamo. Uh, maybe introduce Yeah, sure. To the top down and Yeah.Kyle: I think at this point a lot of people are familiar with the term of inference. Like funnily enough, like I went from, you know, inference being like a really niche topic to being something that's like discussed on like normal people's Twitter feeds.It's,Nader: it's on billboardsKyle: here now. Yeah. Very, very strange. Driving, driving, seeing just an inference ad on 1 0 1 inference at scale is becoming a lot more important. Uh, we have these moments like, you know, open claw where you have these [00:27:00] agents that take lots and lots of tokens, but produce, incredible results.There are many different aspects of test time scaling so that, you know, you can use more inference to generate a better result than if you were to use like a short amount of inference. There's reasoning, there's quiring, there's, adding agency to the model, allowing it to call tools and use skills.Dyno sort came about at Nvidia. Because myself and a couple others were, were sort of talking about the, these concepts that like, you know, you have inference engines like VLMS, shelan, tenor, TLM and they have like one single copy. They, they, they sort of think about like things as like one single copy, like one replica, right?Why Scale Out WinsKyle: Like one version of the model. But when you're actually serving things at scale, you can't just scale up that replica because you end up with like performance problems. There's a scaling limit to scaling up replicas. So you actually have to scale out to use a, maybe some Kubernetes type terminology.We kind of realized that there was like. A lot of potential optimization that we could do in scaling out and building systems for data [00:28:00] center scale inference. So Dynamo is this data center scale inference engine that sits on top of the frameworks like VLM Shilling and 10 T lm and just makes things go faster because you can leverage the economy of scale.The fact that you have KV cash, which we can define a little bit later, uh, in all these machines that is like unique and you wanna figure out like the ways to maximize your cash hits or you want to employ new techniques in inference like disaggregation, which Dynamo had introduced to the world in, in, in March, not introduced, it was a academic talk, but beforehand.But we are, you know, one of the first frameworks to start, supporting it. And we wanna like, sort of combine all these techniques into sort of a modular framework that allows you to. Accelerate your inference at scale.Nader: By the way, Kyle and I became friends on my first date, Nvidia, and I always loved, ‘cause like he always teaches meswyx: new things.Yeah. By the way, this is why I wanted to put two of you together. I was like, yeah, this is, this is gonna beKyle: good. It's very, it's very different, you know, like we've, we, we've, we've talked to each other a bunch [00:29:00] actually, you asked like, why, why can't we scale up?Nader: Yeah.Scale Up Limits ExplainedNader: model, you said model replicas.Kyle: Yeah. So you, so scale up means assigning moreswyx: heavier?Kyle: Yeah, heavier. Like making things heavier. Yeah, adding more GPUs. Adding more CPUs. Scale out is just like having a barrier saying, I'm gonna duplicate my representation of the model or a representation of this microservice or something, and I'm gonna like, replicate it Many times.Handle, load. And the reason that you can't scale, scale up, uh, past some points is like, you know, there, there, there are sort of hardware bounds and algorithmic bounds on, on that type of scaling. So I'll give you a good example that's like very trivial. Let's say you're on an H 100. The Maxim ENV link domain for H 100, for most Ds H one hundreds is heus, right?So if you scaled up past that, you're gonna have to figure out ways to handle the fact that now for the GPUs to communicate, you have to do it over Infin band, which is still very fast, but is not as fast as ENV link.swyx: Is it like one order of magnitude, like hundreds or,Kyle: it's about an order of magnitude?Yeah. Okay. Um, soswyx: not terrible.Kyle: [00:30:00] Yeah. I, I need to, I need to remember the, the data sheet here, like, I think it's like about 500 gigabytes. Uh, a second unidirectional for ENV link, and about 50 gigabytes a second unidirectional for Infin Band. I, it, it depends on the, the generation.swyx: I just wanna set this up for people who are not familiar with these kinds of like layers and the trash speedVibhu: and all that.Of course.From Laptop to Multi NodeVibhu: Also, maybe even just going like a few steps back before that, like most people are very familiar with. You see a, you know, you can use on your laptop, whatever these steel viol, lm you can just run inference there. All, there's all, you can, youcan run it on thatVibhu: laptop. You can run on laptop.Then you get to, okay, uh, models got pretty big, right? JLM five, they doubled the size, so mm-hmm. Uh, what do you do when you have to go from, okay, I can get 128 gigs of memory. I can run it on a spark. Then you have to go multi GPU. Yeah. Okay. Multi GPU, there's some support there. Now, if I'm a company and I don't have like.I'm not hiring the best researchers for this. Right. But I need to go [00:31:00] multi-node, right? I have a lot of servers. Okay, now there's efficiency problems, right? You can have multiple eight H 100 nodes, but, you know, is that as a, like, how do you do that efficiently?Kyle: Yeah. How do you like represent them? How do you choose how to represent the model?Yeah, exactly right. That's a, that's like a hard question. Everyone asks, how do you size oh, I wanna run GLM five, which just came out new model. There have been like four of them in the past week, by the way, like a bunch of new models.swyx: You know why? Right? Deep seek.Kyle: No comment. Oh. Yeah, but Ggl, LM five, right?We, we have this, new model. It's, it's like a large size, and you have to figure out how to both scale up and scale out, right? Because you have to find the right representation that you care about. Everyone does this differently. Let's be very clear. Everyone figures this out in their own path.Nader: I feel like a lot of AI or ML even is like, is like this. I think people think, you know, I, I was, there was some tweet a few months ago that was like, why hasn't fine tuning as a service taken off? You know, that might be me. It might have been you. Yeah. But people want it to be such an easy recipe to follow.But even like if you look at an ML model and specificKyle: to you Yeah,Nader: yeah.Kyle: And the [00:32:00] model,Nader: the situation, and there's just so much tinkering, right? Like when you see a model that has however many experts in the ME model, it's like, why that many experts? I don't, they, you know, they tried a bunch of things and that one seemed to do better.I think when it comes to how you're serving inference, you know, you have a bunch of decisions to make and there you can always argue that you can take something and make it more optimal. But I think it's this internal calibration and appetite for continued calibration.Vibhu: Yeah. And that doesn't mean like, you know, people aren't taking a shot at this, like tinker from thinking machines, you know?Yeah. RL as a service. Yeah, totally. It's, it also gets even harder when you try to do big model training, right? We're not the best at training Moes, uh, when they're pre-trained. Like we saw this with LAMA three, right? They're trained in such a sparse way that meta knows there's gonna be a bunch of inference done on these, right?They'll open source it, but it's very trained for what meta infrastructure wants, right? They wanna, they wanna inference it a lot. Now the question to basically think about is, okay, say you wanna serve a chat application, a coding copilot, right? You're doing a layer of rl, you're serving a model for X amount of people.Is it a chat model, a coding model? Dynamo, you know, back to that,Kyle: it's [00:33:00] like, yeah, sorry. So you we, we sort of like jumped off of, you know, jumped, uh, on that topic. Everyone has like, their own, own journey.Cost Quality Latency TradeoffsKyle: And I, I like to think of it as defined by like, what is the model you need? What is the accuracy you need?Actually I talked to NA about this earlier. There's three axes you care about. What is the quality that you're able to produce? So like, are you accurate enough or can you complete the task with enough, performance, high enough performance. Yeah, yeah. Uh, there's cost. Can you serve the model or serve your workflow?Because it's not just the model anymore, it's the workflow. It's the multi turn with an agent cheaply enough. And then can you serve it fast enough? And we're seeing all three of these, like, play out, like we saw, we saw new models from OpenAI that you know, are faster. You have like these new fast versions of models.You can change the amount of thinking to change the amount of quality, right? Produce more tokens, but at a higher cost in a, in a higher latency. And really like when you start this journey of like trying to figure out how you wanna host a model, you, you, you think about three things. What is the model I need to serve?How many times do I need to call it? What is the input sequence link was [00:34:00] the, what does the workflow look like on top of it? What is the SLA, what is the latency SLA that I need to achieve? Because there's usually some, this is usually like a constant, you, you know, the SLA that you need to hit and then like you try and find the lowest cost version that hits all of these constraints.Usually, you know, you, you start with those things and you say you, you kind of do like a bit of experimentation across some common configurations. You change the tensor parallel size, which is a form of parallelismVibhu: I take, it goes even deeper first. Gotta think what model.Kyle: Yes, course,ofKyle: course. It's like, it's like a multi-step design process because as you said, you can, you can choose a smaller model and then do more test time scaling and it'll equate the quality of a larger model because you're doing the test time scaling or you're adding a harness or something.So yes, it, it goes way deeper than that. But from the performance perspective, like once you get to the model you need, you need to host, you look at that and you say, Hey. I have this model, I need to serve it at the speed. What is the right configuration for that?Nader: You guys see the recent, uh, there was a paper I just saw like a few days ago that, uh, if you run [00:35:00] the same prompt twice, you're getting like double Just try itagain.Nader: Yeah, exactly.Vibhu: And you get a lot. Yeah. But the, the key thing there is you give the context of the failed try, right? Yeah. So it takes a shot. And this has been like, you know, basic guidance for quite a while. Just try again. ‘cause you know, trying, just try again. Did you try again? All adviceNader: in life.Vibhu: Just, it's a paper from Google, if I'm not mistaken, right?Yeah,Vibhu: yeah. I think it, it's like a seven bas little short paper. Yeah. Yeah. The title's very cute. And it's just like, yeah, just try again. Give it ask context,Kyle: multi-shot. You just like, say like, hey, like, you know, like take, take a little bit more, take a little bit more information, try and fail. Fail.Vibhu: And that basic concept has gone pretty deep.There's like, um, self distillation, rl where you, you do self distillation, you do rl and you have past failure and you know, that gives some signal so people take, try it again. Not strong enough.swyx: Uh, for, for listeners, uh, who listen to here, uh, vivo actually, and I, and we run a second YouTube channel for our paper club where, oh, that's awesome.Vivo just covered this. Yeah. Awesome. Self desolation and all that's, that's why he, to speed [00:36:00] on it.Nader: I'll to check it out.swyx: Yeah. It, it's just a good practice, like everyone needs, like a paper club where like you just read papers together and the social pressure just kind of forces you to just,Nader: we, we,there'sNader: like a big inference.Kyle: ReadingNader: group at a video. I feel so bad every time. I I, he put it on like, on our, he shared it.swyx: One, one ofNader: your guys,swyx: uh, is, is big in that, I forget es han Yeah, yeah,Kyle: es Han's on my team. Actually. Funny. There's a, there's a, there's a employee transfer between us. Han worked for Nater at Brev, and now he, he's on my team.He wasNader: our head of ai. And then, yeah, once we got in, andswyx: because I'm always looking for like, okay, can, can I start at another podcast that only does that thing? Yeah. And, uh, Esan was like, I was trying to like nudge Esan into like, is there something here? I mean, I don't think there's, there's new infant techniques every day.So it's like, it's likeKyle: you would, you would actually be surprised, um, the amount of blog posts you see. And ifswyx: there's a period where it was like, Medusa hydra, what Eagle, like, youKyle: know, now we have new forms of decode, uh, we have new forms of specula, of decoding or new,swyx: what,Kyle: what are youVibhu: excited? And it's exciting when you guys put out something like Tron.‘cause I remember the paper on this Tron three, [00:37:00] uh, the amount of like post train, the on tokens that the GPU rich can just train on. And it, it was a hybrid state space model, right? Yeah.Kyle: It's co-designed for the hardware.Vibhu: Yeah, go design for the hardware. And one of the things was always, you know, the state space models don't scale as well when you do a conversion or whatever the performance.And you guys are like, no, just keep draining. And Nitron shows a lot of that. Yeah.Nader: Also, something cool about Nitron it was released in layers, if you will, very similar to Dynamo. It's, it's, it's essentially it was released as you can, the pre-training, post-training data sets are released. Yeah. The recipes on how to do it are released.The model itself is released. It's full model. You just benefit from us turning on the GPUs. But there are companies like, uh, ServiceNow took the dataset and they trained their own model and we were super excited and like, you know, celebrated that work.ZoomVibhu: different. Zoom is, zoom is CGI, I think, uh, you know, also just to add like a lot of models don't put out based models and if there's that, why is fine tuning not taken off?You know, you can do your own training. Yeah,Kyle: sure.Vibhu: You guys put out based model, I think you put out everything.Nader: I believe I know [00:38:00]swyx: about base. BasicallyVibhu: without baseswyx: basic can be cancelable.Vibhu: Yeah. Base can be cancelable.swyx: Yeah.Vibhu: Safety training.swyx: Did we get a full picture of dymo? I, I don't know if we, what,Nader: what I'd love is you, you mentioned the three axes like break it down of like, you know, what's prefilled decode and like what are the optimizations that we can get with Dynamo?Kyle: Yeah. That, that's, that's, that's a great point. So to summarize on that three axis problem, right, there are three things that determine whether or not something can be done with inference, cost, quality, latency, right? Dynamo is supposed to be there to provide you like the runtime that allows you to pull levers to, you know, mix it up and move around the parade of frontier or the preto surface that determines is this actually possible with inference And AI todayNader: gives you the knobs.Kyle: Yeah, exactly. It gives you the knobs.Disaggregation Prefill vs DecodeKyle: Uh, and one thing that like we, we use a lot in contemporary inference and is, you know, starting to like pick up from, you know, in, in general knowledge is this co concept of disaggregation. So historically. Models would be hosted with a single inference engine. And that inference engine [00:39:00] would ping pong between two phases.There's prefill where you're reading the sequence generating KV cache, which is basically just a set of vectors that represent the sequence. And then using that KV cache to generate new tokens, which is called Decode. And some brilliant researchers across multiple different papers essentially made the realization that if you separate these two phases, you actually gain some benefits.Those benefits are basically a you don't have to worry about step synchronous scheduling. So the way that an inference engine works is you do one step and then you finish it, and then you schedule, you start scheduling the next step there. It's not like fully asynchronous. And the problem with that is you would have, uh, essentially pre-fill and decode are, are actually very different in terms of both their resource requirements and their sometimes their runtime.So you would have like prefill that would like block decode steps because you, you'd still be pre-filing and you couldn't schedule because you know the step has to end. So you remove that scheduling issue and then you also allow you, or you yourself, to like [00:40:00] split the work into two different ki types of pools.So pre-fill typically, and, and this changes as, as model architecture changes. Pre-fill is, right now, compute bound most of the time with the sequence is sufficiently long. It's compute bound. On the decode side because you're doing a full Passover, all the weights and the entire sequence, every time you do a decode step and you're, you don't have the quadratic computation of KV cache, it's usually memory bound because you're retrieving a linear amount of memory and you're doing a linear amount of compute as opposed to prefill where you retrieve a linear amount of memory and then use a quadratic.You know,Nader: it's funny, someone exo Labs did a really cool demo where for the DGX Spark, which has a lot more compute, you can do the pre the compute hungry prefill on a DG X spark and then do the decode on a, on a Mac. Yeah. And soVibhu: that's faster.Nader: Yeah. Yeah.Kyle: So you could, you can do that. You can do machine strat stratification.Nader: Yeah.Kyle: And like with our future generation generations of hardware, we actually announced, like with Reuben, this [00:41:00] new accelerator that is prefilled specific. It's called Reuben, CPX. SoKubernetes Scaling with GroveNader: I have a question when you do the scale out. Yeah. Is scaling out easier with Dynamo? Because when you need a new node, you can dedicate it to either the Prefill or, uh, decode.Kyle: Yeah. So Dynamo actually has like a, a Kubernetes component in it called Grove that allows you to, to do this like crazy scaling specialization. It has like this hot, it's a representation that, I don't wanna go too deep into Kubernetes here, but there was a previous way that you would like launch multi-node work.Uh, it's called Leader Worker Set. It's in the Kubernetes standard, and Leader worker set is great. It served a lot of people super well for a long period of time. But one of the things that it's struggles with is representing a set of cases where you have a multi-node replica that has a pair, right?You know, prefill and decode, or it's not paired, but it has like a second stage that has a ratio that changes over time. And prefill and decode are like two different things as your workload changes, right? The amount of prefill you'll need to do may change. [00:42:00] The amount of decode that you, you'll need to do might change, right?Like, let's say you start getting like insanely long queries, right? That probably means that your prefill scales like harder because you're hitting these, this quadratic scaling growth.swyx: Yeah.And then for listeners, like prefill will be long input. Decode would be long output, for example, right?Kyle: Yeah. So like decode, decode scale. I mean, decode is funny because the amount of tokens that you produce scales with the output length, but the amount of work that you do per step scales with the amount of tokens in the context.swyx: Yes.Kyle: So both scales with the input and the output.swyx: That's true.Kyle: But on the pre-fold view code side, like if.Suddenly, like the amount of work you're doing on the decode side stays about the same or like scales a little bit, and then the prefilled side like jumps up a lot. You actually don't want that ratio to be the same. You want it to change over time. So Dynamo has a set of components that A, tell you how to scale.It tells you how many prefilled workers and decoded workers you, it thinks you should have, and also provides a scheduling API for Kubernetes that allows you to actually represent and affect this scheduling on, on, on your actual [00:43:00] hardware, on your compute infrastructure.Nader: Not gonna lie. I feel a little embarrassed for being proud of my SVG function earlier.swyx: No, itNader: wasreallyKyle: cute. I, Iswyx: likeNader: it's all,swyx: it's all engineering. It's all engineering. Um, that's where I'mKyle: technical.swyx: One thing I'm, I'm kind of just curious about with all with you see at a systems level, everything going on here. Mm-hmm. And we, you know, we're scaling it up in, in multi, in distributed systems.Context Length and Co Designswyx: Um, I think one thing that's like kind of, of the moment right now is people are asking, is there any SOL sort of upper bounds. In terms of like, let's call, just call it context length for one for of a better word, but you can break it down however you like.Nader: Yeah.swyx: I just think like, well, yeah, I mean, like clearly you can engage in hybrid architectures and throw in some state space models in there.All, all you want, but it looks, still looks very attention heavy.Kyle: Yes. Uh, yeah. Long context is attention heavy. I mean, we have these hybrid models, um,swyx: to take and most, most models like cap out at a million contexts and that's it. Yeah. Like for the last two years has been it.Kyle: Yeah. The model hardware context co-design thing that we're seeing these days is actually super [00:44:00] interesting.It's like my, my passion, like my secret side passion. We see models like Kimmy or G-P-T-O-S-S. I'm use these because I, I know specific things about these models. So Kimmy two comes out, right? And it's an interesting model. It's like, like a deep seek style architecture is MLA. It's basically deep seek, scaled like a little bit differently, um, and obviously trained differently as well.But they, they talked about, why they made the design choices for context. Kimmy has more experts, but fewer attention heads, and I believe a slightly smaller attention, uh, like dimension. But I need to remember, I need to check that. Uh, it doesn't matter. But they discussed this actually at length in a blog post on ji, which is like our pu which is like credit puswyx: Yeah.Kyle: Um, in, in China. Chinese red.swyx: Yeah.Kyle: It's, yeah. So it, it's, it's actually an incredible blog post. Uh, like all the mls people in, in, in that, I've seen that on GPU are like very brilliant, but they, they talk about like the creators of Kimi K two [00:45:00] actually like, talked about it on, on, on there in the blog post.And they say, we, we actually did an experiment, right? Attention scales with the number of heads, obviously. Like if you have 64 heads versus 32 heads, you do half the work of attention. You still scale quadratic, but you do half the work. And they made a, a very specific like. Sort of barter in their system, in their architecture, they basically said, Hey, what if we gave it more experts, so we're gonna use more memory capacity.But we keep the amount of activated experts the same. We increase the expert sparsity, so we have fewer experts act. The ratio to of experts activated to number of experts is smaller, and we decrease the number of attention heads.Vibhu: And kind of for context, what the, what we had been seeing was you make models sparser instead.So no one was really touching heads. You're just having, uh,Kyle: well, they, they did, they implicitly made it sparser.Vibhu: Yeah, yeah. For, for Kimmy. They did,Kyle: yes.Vibhu: They also made it sparser. But basically what we were seeing was people were at the level of, okay, there's a sparsity ratio. You want more total parameters, less active, and that's sparsity.[00:46:00]But what you see from papers, like, the labs like moonshot deep seek, they go to the level of, okay, outside of just number of experts, you can also change how many attention heads and less attention layers. More attention. Layers. Layers, yeah. Yes, yes. So, and that's all basically coming back to, just tied together is like hardware model, co-design, which isKyle: hardware model, co model, context, co-design.Vibhu: Yeah.Kyle: Right. Like if you were training a, a model that was like. Really, really short context, uh, or like really is good at super short context tasks. You may like design it in a way such that like you don't care about attention scaling because it hasn't hit that, like the turning point where like the quadratic curve takes over.Nader: How do you consider attention or context as a separate part of the co-design? Like I would imagine hardware or just how I would've thought of it is like hardware model. Co-design would be hardware model context co-designKyle: because the harness and the context that is produced by the harness is a part of the model.Once it's trained in,Vibhu: like even though towards the end you'll do long context, you're not changing architecture through I see. Training. Yeah.Kyle: I mean you can try.swyx: You're saying [00:47:00] everyone's training the harness into the model.Kyle: I would say to some degree, orswyx: there's co-design for harness. I know there's a small amount, but I feel like not everyone has like gone full send on this.Kyle: I think, I think I think it's important to internalize the harness that you think the model will be running. Running into the model.swyx: Yeah. Interesting. Okay. Bash is like the universal harness,Kyle: right? Like I'll, I'll give. An example here, right? I mean, or just like a, like a, it's easy proof, right? If you can train against a harness and you're using that harness for everything, wouldn't you just train with the harness to ensure that you get the best possible quality out of,swyx: Well, the, uh, I, I can provide a counter argument.Yeah, sure. Which is what you wanna provide a generally useful model for other people to plug into their harnesses, right? So if youKyle: Yeah. Harnesses can be open, open source, right?swyx: Yeah. So I mean, that's, that's effectively what's happening with Codex.Kyle: Yeah.swyx: And, but like you may want like a different search tool and then you may have to name it differently or,Nader: I don't know how much people have pushed on this, but can you.Train a model, would it be, have you have people compared training a model for the for the harness versus [00:48:00] like post training forswyx: I think it's the same thing. It's the same thing. It's okay. Just extra post training. INader: see.swyx: And so, I mean, cognition does this course, it does this where you, you just have to like, if your tool is slightly different, um, either force your tool to be like the tool that they train for.Hmm. Or undo their training for their tool and then Oh, that's re retrain. Yeah. It's, it's really annoying and like,Kyle: I would hope that eventually we hit like a certain level of generality with respect to training newswyx: tools. This is not a GI like, it's, this is a really stupid like. Learn my tool b***h.Like, I don't know if, I don't know if I can say that, but like, you know, um, I think what my point kind of is, is that there's, like, I look at slopes of the scaling laws and like, this slope is not working, man. We, we are at a million token con

Blížící se Velikonoce bývají pro trh s vejci každoroční zkouškou. Letos se přitom krátce před svátky sešlo hned několik negativních vlivů, od ptačí chřipky přes povinnou přestavby chovů až po změny v obchodních pravidlech. To vše může mít dopad na dostupnost vajec i jejich cenu. Podle předsedkyně Českomoravské drůbežářské unie Gabriely Dlouhé se do nabídky v první řadě promítá pokles stavů tuzemských nosnic. Kvůli ptačí chřipce bylo jen například v Královéhradeckém kraji utraceno zhruba 240 tisíc mladých slepic, obnova těchto chovů potrvá měsíce. V součtu s odstávkami chovů kvůli přestavbám technologií před zákazem klecových chovů může dosáhnout letošní pokles stavů nosnic před Velikonocemi kolem deseti procent. „Opravdu každé procento je na tom obchodu vidět,“ říká Dlouhá. Agenda. Rozhovory s top lídry českého byznysu, zakladateli firem, odborníky. Čtvrthodinka o byznysu z první ruky. Každý všední den na SZ Byznys a ve všech podcastových aplikacích. Odebírejte na Podcasty.cz, Apple Podcasts nebo Spotify.

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Pouštíte si na veřejnosti videa nahlas? Posíláte psy do školky? Kvůli čemu bezdůvodně panikaříme? Fajn ruleta i nový bangery na víkend. Ve WAKE UP SHOW.

Tottenham förlorade ännu en match och är nu på riktigt med i bottenstriden, kommer de åka ur? En spännande fredagskväll med intressanta matcher från alla hörn. Real Madrids svåra måstematch mot Celta Vigo. Liverpool med chans att avkräva revansch av Wolves efter förlusten i tisdags. Fransk stormatch på Parc des Princes mellan PSG och Monaco. Bayern ska försöka avgöra ligan hemma mot Mönchengladbach. Napoli tar emot Torino som precis fått ny tränare. Dessutom helgens match - Derby della Madonnina som kanske kan avgöra titelstriden i Serie A, eller är den redan avgjord?Programledare: August SpångbergExperter: Robin Bylund & Ricard NormanViva Fotboll görs i samarbete med ATG:Gå med i Viva Fotbolls Tillsammanslag på ATG, där vi varje helg skickar in en välkalibrerad Big 9-kupong där vi försöker fälla någon av dom stora favoriterna för att stå där med miljongarantin på ensam vinnare med 9 rätt. Här har ni laget: https://www.atg.se/tillsammans/inbjudan/XKZI-CGTW-319315/tDhBPMy5pbFG8uzq%3AaJrSG_tO82Uf1mO6Zm4Fpw%3A7b2V4nqE-g4m1k4fuwZJ3VAKVv-2dCMKgw?gameId=BIG9_2025-08-23_725344240_2060735806Du hittar alltid dom senaste tripplarna, andelarna, Big 9 och annat från oss på https://www.atg.se/tutto/18+ Regler & villkor gäller. Stödlinjen.seI samarbete med TV4 Play:Unikt erbjudande ger dig som lyssnare möjligheten att ta del av ännu en spännande säsong av La Liga och Serie A hos TV4 Play, paketet TV4 Play Sport för enbart 174 kr/mån i 6 månader. Utöver det serier, film, tennis, rally, hästhoppning och mycket annat.Följ länken för att ta del av erbjudandet: https://www.tv4play.se/kampanj/vivaKontakta redaktionen: linus@k26media.seVill ditt företag samarbeta med Viva fotboll? samarbete@tutto.seSociala Medier:Instagram - Viva_fotbollTwitter - VivafotbollTikTok - VivafotbollTidskoder:00:00 Intro11:00 Tottenham - Crystal Palace26:50 Kvällens matcher29:30 Celta Vigo - Real Madrid32:50 Milan - Inter39:00 Big 9 Hosted on Acast. See acast.com/privacy for more information.

What could happen if a renewable energy project fails to meet new NERC compliance requirements? In some cases, penalties can reach up to $1 million per day per violation.In this special episode of Navigating the Grid, we're sharing a conversation originally recorded for the Clean Energy Edge Podcast, where Kellie Macpherson, EVP of Compliance & Security at Radian Generation, joined host Russ Bates to discuss the evolving compliance landscape for renewable energy.Starting May 1, 2026, inverter-based resources (IBRs) rated 20 MW or greater and connected at 60 kV or higher must register under updated North American Electric Reliability Corporation Category 2 requirements. The compliance threshold is dropping from 75 MW to 20 MW, dramatically expanding federal oversight across solar, wind, and battery storage projects.In this episode, Kellie breaks down what the rule change means, which projects will be affected, and why compliance now extends beyond paperwork to include operational readiness, cybersecurity monitoring, and audit preparedness.As renewables take on a larger role in grid reliability, understanding these requirements is becoming essential for operators, developers, and investors alike.

MOZART: Requiem en Re menor KV 626 (47.03). C. Schäfer (sop.), B. Fink (con.), K. Streit (ten.), G. Finley (baj.), Coro Arnold Schönberg, Concentus Musicus Wien. Dir.: N. Harnoncourt.Escuchar audio

Ve druhé polovině 19. století se v Rakousku-Uhersku zavádí alternativa k sňatku církevnímu pro ateisty nebo páry rozdílných vyznání. Mezi prvními se na Staroměstské radnici žení pokrokový český vlastenec Vojta Náprstek. Úřední osobou se nechá oddat také budoucí vojevůdce a císař Napoleon Bonaparte. Kvůli naléhání papeže si ale svou první manželku Josefínu vezme ještě před oltářem. Co Bůh spojí, člověk nerozlučuj! Co spojí úředník, úředník také rozvede. A v tom tkví výhoda.Všechny díly podcastu Příběhy z kalendáře můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Real Madrid står för ett fiasko i derbyt mot Getafe. Hur stor kommer krisen bli om man tappar La Liga-titeln? Hur allvarlig är Mbappés skada? Räddas Fiorentina av den dåliga kvalitén i botten av Serie A? Hiljemarks usla Pisa. Hugo Larssons oroväckande form i Frankfurt. Borde Amin Boudri vara aktuell för landslaget? Der Klassiker bjöd på både underhållning och Bayern München visar hög klass. Kan Barcelona stå för en remontada i cupen mot Atletico Madrid? Lewandowskis märkliga skada. Fabregas tar hjälp av Bodø för att slå Inter i semifinalen av Coppa Italia.Programledare: Fabian NorlundExperter: Leonard Jägerskiöld Velander & Anel AvdićViva Fotboll görs i samarbete med ATG:Gå med i Viva Fotbolls Tillsammanslag på ATG, där vi varje helg skickar in en välkalibrerad Big 9-kupong där vi försöker fälla någon av dom stora favoriterna för att stå där med miljongarantin på ensam vinnare med 9 rätt. Här har ni laget: https://www.atg.se/tillsammans/inbjudan/XKZI-CGTW-319315/tDhBPMy5pbFG8uzq%3AaJrSG_tO82Uf1mO6Zm4Fpw%3A7b2V4nqE-g4m1k4fuwZJ3VAKVv-2dCMKgw?gameId=BIG9_2025-08-23_725344240_2060735806Du hittar alltid dom senaste tripplarna, andelarna, Big 9 och annat från oss på https://www.atg.se/tutto/18+ Regler & villkor gäller. Stödlinjen.seI samarbete med TV4 Play:Unikt erbjudande ger dig som lyssnare möjligheten att ta del av ännu en spännande säsong av La Liga och Serie A hos TV4 Play, paketet TV4 Play Sport för enbart 174 kr/mån i 6 månader. Utöver det serier, film, tennis, rally, hästhoppning och mycket annat.Följ länken för att ta del av erbjudandet: https://www.tv4play.se/kampanj/vivaKontakta redaktionen: linus@k26media.seVill ditt företag samarbeta med Viva fotboll? samarbete@tutto.seSociala Medier:Instagram - Viva_fotbollTwitter - VivafotbollTikTok - VivafotbollTidskoder:00:00 Intro01:25 Real Madrid - Getafe13:10 Udinese - Fiorentina20:40 Swedes of the Week27:30 Dortmund - Bayern München39:10 Team of the Week44:35 Barcelona - Atletico Madrid ikväll50:00 Kvällens matcher53:20 Avrundning Hosted on Acast. See acast.com/privacy for more information.

In Q4/2025 ist die Budgetierung für Hausärzte weggefallen. Im „ÄrzteTag“-Podcast berichtet Arzt und Praxisberater Georg Lübben, wie sich das auf die Quartalsabrechnung ausgewirkt hat und worauf Praxisinhaber nun achten sollten.

Vyskočilo na mě onehdy na iksku krátké video. Ukazovalo koně s lyžemi na nohou na skokanském můstku. Kůň se odpíchne, jede po můstku a pak letí a letí do krajiny. Tohle celoživotně obdivuji a nechápu, jak se to může někdo naučit. Jednou na tom můstku musíte stát poprvé a když cokoli děláte poprvé, neumíte to. No a u toho videa byla stručná poznámka: Kvůli tomuhle stojí 64 giga RAM tisíc dolarů.

Vyskočilo na mě onehdy na iksku krátké video. Ukazovalo koně s lyžemi na nohou na skokanském můstku. Kůň se odpíchne, jede po můstku a pak letí a letí do krajiny. Tohle celoživotně obdivuji a nechápu, jak se to může někdo naučit. Jednou na tom můstku musíte stát poprvé a když cokoli děláte poprvé, neumíte to. No a u toho videa byla stručná poznámka: Kvůli tomuhle stojí 64 giga RAM tisíc dolarů.Všechny díly podcastu Glosa Plus můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

„Květy jsou sexuální orgány rostlin, projektujeme do nich svoje představy a probouzí naše smysly,“ říká v hotcastu Pot umělkyně a zahradnice Barbora Lungová. Jako loňská laureátka Ceny Jindřicha Chalupeckého prezentovala svou veřejně přístupnou Duhovou zahradu v Kameníkách u Kyjova, která je věnovaná queerness a lidem dobré vůle. S Karlem Vladykou mluvila o květinách a zahradničení, vlastním coming outu, i roli umělkyně v dnešním světě.

Tři paralympiády, tři medaile – zlatá, stříbrná, bronzová. A k tomu řada dalších z evropských i světových šampionátů. Velkých sportovních úspěchů dosáhl handicapovaný plavec Arnošt Petráček z Jankova na Českobudějovicku. Kvůli rehabilitaci trávil od dětství hodně času v bazénu a plavání mu pomáhalo překonat nejrůznější životní situace.Všechny díly podcastu Jihočeši můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Primule nebo třeba bramboříky. Květiny, které symbolizují příchod jara, už plní skleníky. A taky saláty či bylinky. Připraveni na jaro jsou také v hlučínském zahradnictví, kde spoustu rostlinek pěstují od semínka, a tak s výsadbou začínají už v zimě.

Střevní mikrobiom je soubor bilionů mikroorganismů v trávicím traktu. Kvůli obrovskému vlivu na celé zdraví, imunitu, psychiku i chování je označován jako druhý mozek lidstva. Klíčové studie naznačují, že jeho zdraví souvisí s vegetariánskou nebo veganskou stravou. Proč? Důvodů je více, ale některé studie označují za stěžejní rozmanitost stravy. „Ukazují, že je klíčové, aby člověk měl třicet různých rostlinných potravin týdně, pak je jedno, jestli je vegetarián nebo jí třeba středomořskou stravu,“ vysvětluje v novém díle Longevity podcastu mikrobiolog z Fakultní nemocnice Motol Jakub Hurych. Ten detailně popisuje, jak na tělo působí veganská strava a jaké problémy při ní mohou nastat. Zároveň varuje před komerčním testováním mikrobiomu. „Stojí poměrně hodně peněz, ale má řadu překážek. Testuje se z jednoho malého vzorku, který toho příliš neřekne, a navíc vůbec nevíme, jak výsledky interpretovat. Nevíme, co je zdravý mikrobiom,“ dodává Hurych a popisuje, co s ním dělají antibiotika.

Chillout Classic w Radiu Spin #122 Urodziny Mozarta 1. J.S. Bach - Aria z Wariacji Goldbergowskich - Jean Rondeau.2. W. A. Mozart - Divertimento D-dur KV 136, I. Allegro, Hagen Quartet.3. W.A. Mozart - Koncert na flet i harfę KV 299 cz.II Andantino, Samuel Coles, Naoko Yoshino, English Chamber Orchestra.4. W.A. Mozart - Kwartet fortepianowy Es- dur KV 493, cz. II Larghetto, Yo-Yo Ma, Emanuel Ax, Isaac Stern, Jaime Laredo.5. W.A. Mozart - Kwintet klarnetowy A-dur KV 581 "Stadler" cz. II Larghetto, Michel Portal, Cherubini Quartet.6. W.A. Mozart - Symfonia g-moll KV550, cz. IV Allegro assai Berliner Philharmoniker, Sir Simon Rattle.7. W.A. Mozart - Eine Kleine Nachtmusik KV 525, cz. II Romance. Andante, Academy of St. Martin in the Fields, Sir Neville Marriner.8. Golden Cage - Dardust.

Primule nebo třeba bramboříky. Květiny, které symbolizují příchod jara, už plní skleníky. A taky saláty či bylinky. Připraveni na jaro jsou také v hlučínském zahradnictví, kde spoustu rostlinek pěstují od semínka, a tak s výsadbou začínají už v zimě.Všechny díly podcastu Odpolední interview můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Český student vězněný v Číně. Kvůli čemu? Co o něm všechno víme? Proč se o jeho případ zajímá zrovna Miloš Zeman? A proč právě teď? A jak to může souviset s nedávno zadrženým čínským špionem v Česku? Otázky pro Sabinu Slonkovou z projektu Neovlivní, který se tématu věnuje ve svém březnovém čísle. Ptá se Matěj Skalický.Všechny díly podcastu Vinohradská 12 můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Český student vězněný v Číně. Kvůli čemu? Co o něm všechno víme? Proč se o jeho případ zajímá zrovna Miloš Zeman? A proč právě teď? A jak to může souviset s nedávno zadrženým čínským špionem v Česku? Otázky pro Sabinu Slonkovou z projektu Neovlivní, který se tématu věnuje ve svém březnovém čísle. Ptá se Matěj Skalický.

Programledare: Fabian NorlundExperter: Siavoush Fallahi & Leonard Jägerskiöld VelanderViva Fotboll görs i samarbete med ATG:Gå med i Viva Fotbolls Tillsammanslag på ATG, där vi varje helg skickar in en välkalibrerad Big 9-kupong där vi försöker fälla någon av dom stora favoriterna för att stå där med miljongarantin på ensam vinnare med 9 rätt. Här har ni laget: https://www.atg.se/tillsammans/inbjudan/XKZI-CGTW-319315/tDhBPMy5pbFG8uzq%3AaJrSG_tO82Uf1mO6Zm4Fpw%3A7b2V4nqE-g4m1k4fuwZJ3VAKVv-2dCMKgw?gameId=BIG9_2025-08-23_725344240_2060735806Du hittar alltid dom senaste tripplarna, andelarna, Big 9 och annat från oss på https://www.atg.se/tutto/18+ Regler & villkor gäller. Stödlinjen.seI samarbete med TV4 Play:Unikt erbjudande ger dig som lyssnare möjligheten att ta del av ännu en spännande säsong av La Liga och Serie A hos TV4 Play, paketet TV4 Play Sport för enbart 174 kr/mån i 6 månader. Utöver det serier, film, tennis, rally, hästhoppning och mycket annat.Följ länken för att ta del av erbjudandet: https://www.tv4play.se/kampanj/vivaKontakta redaktionen: linus@k26media.seVill ditt företag samarbeta med Viva fotboll? samarbete@tutto.seSociala Medier:Instagram - Viva_fotbollTwitter - VivafotbollTikTok - Vivafotboll#vivafotboll Tidskoder:00:00 Intro00:47 Inter - Bodø/glimt27:57 Atletico Madrid - Club Brugge35:47 Newcastle - Qarabag37:47 Leverkusen - Olympiacos40:07 Kvällens Champions League45:19 Real Madrid - Benfica57:37 Avrundning Hosted on Acast. See acast.com/privacy for more information.

Český student vězněný v Číně. Kvůli čemu? Co o něm všechno víme? Proč se o jeho případ zajímá zrovna Miloš Zeman? A proč právě teď? A jak to může souviset s nedávno zadrženým čínským špionem v Česku? Otázky pro Sabinu Slonkovou z projektu Neovlivní, který se tématu věnuje ve svém březnovém čísle. Ptá se Matěj Skalický.

Programledare: Fabian NorlundExperter: Adam Pinthorp & Anel AvdićViva Fotboll görs i samarbete med ATG:Gå med i Viva Fotbolls Tillsammanslag på ATG, där vi varje helg skickar in en välkalibrerad Big 9-kupong där vi försöker fälla någon av dom stora favoriterna för att stå där med miljongarantin på ensam vinnare med 9 rätt. Här har ni laget: https://www.atg.se/tillsammans/inbjudan/XKZI-CGTW-319315/tDhBPMy5pbFG8uzq%3AaJrSG_tO82Uf1mO6Zm4Fpw%3A7b2V4nqE-g4m1k4fuwZJ3VAKVv-2dCMKgw?gameId=BIG9_2025-08-23_725344240_2060735806Du hittar alltid dom senaste tripplarna, andelarna, Big 9 och annat från oss på https://www.atg.se/tutto/18+ Regler & villkor gäller. Stödlinjen.seI samarbete med TV4 Play:Unikt erbjudande ger dig som lyssnare möjligheten att ta del av ännu en spännande säsong av La Liga och Serie A hos TV4 Play, paketet TV4 Play Sport för enbart 174 kr/mån i 6 månader. Utöver det serier, film, tennis, rally, hästhoppning och mycket annat.Följ länken för att ta del av erbjudandet: https://www.tv4play.se/kampanj/vivaKontakta redaktionen: linus@k26media.seVill ditt företag samarbeta med Viva fotboll? samarbete@tutto.seSociala Medier:Instagram - Viva_fotbollTwitter - VivafotbollTikTok - Vivafotboll#vivafotboll TIDSKODER:00:00 Intro02:00 Everton - Man United12:40 Serie A31:00 Swedes of the Week45:00 TOTW52:50 Kvällens Champions League Hosted on Acast. See acast.com/privacy for more information.

Kvůli škrtům v rozpočtu ministerstva životního prostředí se mají národním parkům snížit finance na provoz a některé mají i propouštět. Nižší rozpočty pak mohou ovlivnit některé plánované investice.

Bývalý šéf Motola promluvil. Miloslav Ludvík trvá na nevině a vzkazuje, že u soudu se ukáže. Policie podle něj bude překvapená. Kvůli čemu? V čem měla pochybit? Jak se Ludvík hájí? A proč ještě nebyl obžalován? Poví Artur Janoušek z investigativního týmu Radiožurnálu. Ptá se Matěj Skalický.

Bývalý šéf Motola promluvil. Miloslav Ludvík trvá na nevině a vzkazuje, že u soudu se ukáže. Policie podle něj bude překvapená. Kvůli čemu? V čem měla pochybit? Jak se Ludvík hájí? A proč ještě nebyl obžalován? Poví Artur Janoušek z investigativního týmu Radiožurnálu. Ptá se Matěj Skalický. Všechny díly podcastu Vinohradská 12 můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Bývalý šéf Motola promluvil. Miloslav Ludvík trvá na nevině a vzkazuje, že u soudu se ukáže. Policie podle něj bude překvapená. Kvůli čemu? V čem měla pochybit? Jak se Ludvík hájí? A proč ještě nebyl obžalován? Poví Artur Janoušek z investigativního týmu Radiožurnálu. Ptá se Matěj Skalický.

Kvalitní vosková mezistěna je základem pro stavbu včelího plástu. Je možné ji buď koupit, nebo vyrobit svépomocí. K tomu je však nezbytná dobrá výbava. Kvůli finanční úspoře ji lze například vypůjčit od většího sdružení. Postup výroby se pak dále dělí podle množství mezistěn, které bude včelař potřebovat.

Kvalitní vosková mezistěna je základem pro stavbu včelího plástu. Je možné ji buď koupit, nebo vyrobit svépomocí. K tomu je však nezbytná dobrá výbava. Kvůli finanční úspoře ji lze například vypůjčit od většího sdružení. Postup výroby se pak dále dělí podle množství mezistěn, které bude včelař potřebovat.

Kvalitní vosková mezistěna je základem pro stavbu včelího plástu. Je možné ji buď koupit, nebo vyrobit svépomocí. K tomu je však nezbytná dobrá výbava. Kvůli finanční úspoře ji lze například vypůjčit od většího sdružení. Postup výroby se pak dále dělí podle množství mezistěn, které bude včelař potřebovat.

This week on International Feature, we took a trip back to one of the most talked-about holiday rom-coms of all time: Love Actually. Often held up as a seasonal classic, the film has remained a staple of Christmas viewing for years — but for us, revisiting it raised a lot more questions than warm fuzzy feelings. We dug into why the movie's reputation doesn't match our experience, and how some of its most celebrated moments have aged… poorly.• Only really connecting with one or two of the relationship dynamics presented • Our genuine disbelief that this is considered a romantic comedy “classic” • The blatant fatphobia and the way several characters are mistreated for laughs • The uncomfortable and inappropriate dynamic between Keira Knightley and Andrew Lincoln's character • Too many characters, too little depth, and barely enough time spent with any storyline • The randomness of certain casting choices — and how Martin Freeman ends up in movies like this • Why ensemble rom-coms just don't work for us when the emotional payoff isn't thereA holiday staple for some… but for us, this was more frustrating than festiveLetterbox'd Synopsis: Eight very different couples deal with their love lives in various loosely interrelated tales all set during a frantic month before Christmas in London.

Televize Nova řeší, jak naložit s víkendovou debatou Za pět minut dvanáct. Poté, co se neprosadila v neděli, přišel přesun na sobotu. Teď mění moderátora. „Koho jiného do prvního dílu než Andreje Babiše, “ říká nová tvář pořadu Bára Divišová.Původně nedělní debatu Nova spustila v říjnu 2023 s moderátorem Martinem Čermákem. Kvůli velké konkurenci Otázek Václava Moravce na ČT i Partie Terezie Tománkové na Primě, ale televize pořad přesunula na sobotu. Debatu navíc v pátek předtáčí, aby usnadnila zvaní atraktivních hostů.„Ten pořad měl možná velké ambice, ale naskočil do hodně konkurenčního prostředí. Když se ani nebudeme soustředit na sledovanost, tak jsme tam hrozně bojovali o hosty. Tři diskuze na neděli v jeden čas to si myslím, že už je moc. Tak jsme ustoupili na sobotu,“ vysvětluje Bára Divišová, která byla hostem podcastu Mediální cirkus.Ze Střepin do debatyDivišová, která na Nově pracuje už 18 let a v poslední době byla spojená především s pořadem Střepiny nebo Napřímo, se stala novou moderátorkou debaty Za pět minut dvanáct v únoru. Jako prvního hosta si do studia pozvala premiéra Andreje Babiše z hnutí ANO.„Nedávno jsme měli na rozhovoru pana prezidenta, takže jsem si říkala, koho jiného do prvního dílu než Andreje Babiše. Až potom mi došlo, co jsem si na sebe zase ušila,“ popisuje Divišová, která patří mezi pár novinářů, s nimiž Andrej Babiš mluví.„My se známe dlouho. Jednou, někdy v roce 2015, jsem měla s Andrejem Babišem živý vstup do poledních Televizních novin. A on si přinesl pokladničku a mluvil asi sedm minut. Kolegyně Emma Smetana mě za to tehdy strašně kritizovala. Po vstupu jsem Babišovi říkala: ‚Co to mělo být?‘ A on se začal hrozně smát. Ale od té doby si pamatoval, jak se jmenuji,“ vzpomíná Divišová v Mediálním cirkusu na vysílání, ve kterém Babiš vysvětloval elektronickou evidenci tržeb.Exkluzivní prostor dostala Divišová i krátce po loňských sněmovních volbách, když k ní Andrej Babiš přišel na rozhovor tehdy ještě na TN.cz a přinesl s sebou návrh programového prohlášení vlády ANO, SPD a Motoristů. Ten moderátorce ve vysílání i předal. „Kdybych to věděla, tak bych se na to lépe připravila. Kolegové si ze mě dělali legraci, že jsem ho normálně přitlačila, aby mi to dal. Že jsem mu řekla: ‚To mi dáte!‘ Já jsem to řekla, ale na konci věty byl otazník. Ale nečekala jsem to. Kdybych to čekala, tak ten rozhovor povedu trošku jinak. Ale Andrej Babiš takové nečekané věci rád dělá,“ říká novinářka.Babiše umí naštvat každýSama říká, že jí diváci občas vztahy s Babišem předhazují, třeba na sociálních sítích, což ji mrzí.„Diváci spekulují, proč to tak je, že tady nedostane otázky na tělo, že jsem k němu servilní, že má nějaké výhody. Ale na moji obranu - on takhle mluví i s některými dalšími novináři. Na plénu mluví třeba o Petru Vaškovi z České televize. Petr za ním může přijet pro vyjádření, odepíše mu na esemesku. Tak snad doufám, že když dám tyhle dva příklady vedle sebe, že mě lidé nebudou tolik obviňovat,“ říká Divišová a pokračuje:„Andreje Babiše umí naštvat každý. To není mým cílem. Chci z toho člověka dostat informace, hlavně ve chvíli, kdy těch informací má ze svého postu dost. My spolu nesouhlasíme, i se pohádáme, ale smějeme se u toho, je to taková výměna názorů. Na někoho je lepší takový ten sofistikovaný postup, že potom sám na sebe řekne všechno.“Jak se vyrábí zpravodajství pro Novu? Jak zábavné je dělat Střepiny? A jak se dívá na debatu o veřejnoprávních médiích? --Mediální cirkus. Podcast Marie Bastlové o dění na mediální scéně. Zajímá ji pohled do redakcí, za kulisy novinářské práce – s předními novináři i mediálními hráči.Sledujte na Seznam Zprávách, poslouchejte na Podcasty.cz a ve všech podcastových aplikacích.Archiv všech dílů najdete tady. Své postřehy, připomínky nebo tipy nám pište prostřednictvím sociálních sítí pod hashtagem #medialnicirkus nebo na e-mail: audio@sz.cz.

CELÝ DÍL NAJDETE NA HEROHERO.CO/STUDION Nové dokumenty z kauzy Jeffreyho Epsteina ukázaly, jak rozsáhlá vlivová síť tohoto odsouzeného sexuálního predátora byla. S finančníkem se přátelili milardáři i politici, radila se s ním evropská šlechta, kromě toho získával utajované vládní informace. „Epstein vytvořil novodobý Olymp, svět, kde neplatila běžná pravidla. Překonal řadu konspirací,“ říkají ve Studiu N Jiří Sobota a Barbora Chaloupková z podcastu Amerika, bejby. Kvůli novým informacím ze složek Jeffreyho Epsteina se rozpadá britská vláda a zkompromitovaní muži se omlouvají či odstupují z vlivných pozic. „Sledujeme konec jedné generace elit,“ říká Barbora Chaloupková. Přesto to zatím nevypadá, že v Americe někdo další zamíří do vězení. Dočkají se oběti někdy spravedlnosti? A vystoupí Donald Trump ze stínu skandálu? „Podstatou kauzy je, že už v tuto chvíli naleptává důvěru celé společnosti v systém. Jestliže jsme se dříve smáli těm, kteří věřili v konspirace, jako je QAnon, dnes vidíme, že reálná situace může být ještě horší,“ říká Jiří Sobota. Na druhé straně ale materiály vycházejí na světlo a část elity, jež považovala samu sebe za nedotknutelnou a nad zákonem, teď čelí celospolečenskému odsouzení. Kauza Epstein je děsivá, může se ale stát i zárodkem budoucí očisty. K tomu ale vede v tuto chvíli ještě dlouhá cesta.

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

Chcete-li podpořit Studio Svobodného přístavu, můžete tak učinit v krypto i korunách! Pravidelná podpora a LN: https://opristavu.urza.cz/ BTC: bc1qwy8l3w0v826amd69h4awpt9hee6srxn4gk2cpg LTC: ltc1q2w2zezyj4anh3v428msf9kqvzelt76n62ys93h Číslo účtu: 2201359764/2010; variabilní symbol: 6 -------- V únorovém livestreamu Svobodného přístavu vystoupí Karolína K.; věnovat se budeme (nejen) veřejnoprávním médiím. Čím se liší od těch státních? Ta otázka může vést k demokratickým versus autokratickým režimům; a možná i k důvěře v instituce. A co stojí za rušením koncesionářských poplatků budoucí (v únoru už možná aktuální) vládou? Jaké to může mít dopady na naši společnost? Navíc se možná do termínu livestreamu objeví další závažná témata s novou vládou související. – Karolína Kváš (https://karolinakvas.cz/); tarotová rebelka; spisovatelka; cestovatelka; antropoložka; bloggerka; tvůrkyně; koučka – Urza (www.urza.cz); autor knihy Anarchokapitalismus; tvůrce Svobodného přístavu; spoluzakladatel a hlava Institutu Ludwiga von Misese; člen předsednictva Svobody učení; učitel ve svobodné škole Ježek bez klece

Professor Agnes Wold är tillbaka i Kvällspassets studio för att svara på lyssnarfrågor! Lyssna på alla avsnitt i Sveriges Radios app. Ett nyfiket och underhållande aktualitetsprogram med lyssnaren i fokus.Ellen undrar om hon borde vaccinera sina barn mot vattkoppor, Tor har funderingar kring RS-vaccin och bältrosvaccin och Carina frågar om det är värt att dricka mycket honungsvatten när man är förkyld.

Vláda podle exministra kultury Martina Baxy nepracuje pro občany. Proto opozice vyvolala jednání o nedůvěře vládě. „Každý měsíc nedůvěra nebude, protože doufám, že Andrej Babiš odvolá Petra Macinku. Věřím tomu,“ řekl Baxa. Hostem Ptám se já byl bývalý ministr kultury, poslanec Martin Baxa (ODS). Poslanci pokračují v jednání o vyslovení nedůvěry vládě ANO, SPD a Motoristů druhým dnem. Opozice schůzi vyvolala kvůli kontroverzním krokům vládní koalice, zejména kvůli vyostření sporu mezi Motoristy a prezidentem Petrem Pavlem. Kvůli zprávám ministra zahraničí a lídra Motoristů Petra Macinky hlavě státu, které Pavel označil za pokus o vydírání, opoziční strany požadují také Macinkovo odvolání. Exministr kultury Martin Baxa věří, že je povinností opozice v takové situaci jednání o nedůvěře vyvolat. Zároveň doufá v Macinkův konec ve vládní funkci. „Pevně doufám, že Andrej Babiš odvolá Petra Macinku. Věřím tomu. Možná je to naivní, ale je naší povinností to říkat, protože místopředseda vlády nemá být člověk, který píše vyděračské esemesky prezidentovi.“Baxa v Ptám se já také komentoval poslední kroky dalšího zástupce Motoristů ve vládě, svého předchůdce v čele ministerstva kultury Oto Klempíře. Ten si po nedělní demonstraci spolku Milion chvilek na podporu prezidenta pozval na resort umělce, kteří na akci vystoupili, aby si to s ním přišli vyříkat na ministerstvo. Na ministrovo pozvání zástupci kulturní obce reagovali s výzvou, aby šéf resortu přišel na veřejnou debatu do divadla Palace v centru Prahy. Klempíř to odmítl s tím, že chtěl řešit budoucnost kultury, nikoliv politiku a pro umělce tedy stále platí pozvání na ministerstvo. Umělci ovšem trvají na setkání před veřejností.  „Do obdobné situace jsem se také dostal. Z částí umělecké obce jsem tímhle způsobem řešil téma statusu umělce. Dostal jsem možná obdobné pozvání jako pan ministr Klempíř  na takovou otevřenou debatu do nějakého kulturního prostoru. A já jsem to pozvání tehdy přijal,“prohlásl exministr. Kam míří česká kultura? Jak to dopadne s veřejnoprávními médii? A jaký smysl má hodiny trvající snaha o nedůvěru vládě s předem jasným výsledkem?--Podcast Ptám se já. Rozhovory s lidmi, kteří mají vliv, odpovědnost, informace.Sledujte na Seznam Zprávách, poslouchejte na Podcasty.cz a ve všech podcastových aplikacích.Archiv všech dílů najdete tady. Své postřehy, připomínky nebo tipy nám pište prostřednictvím sociálních sítí pod hashtagem #ptamseja nebo na e-mail: audio@sz.cz.

När stod du upp för din partner, en nära vän eller någon i din familj som älskar någon och behövde stöd? Hör lyssnarnas berättelser! Lyssna på alla avsnitt i Sveriges Radios app. Kvällens program handlar om modet att välja kärleken, även när omgivningen säger något annat. Vad drev dig, vilken reaktion fick du – och hur ser du på det idag? Vi öppnar Sveriges största samtalsrum där lyssnarnas berättelser är allt.Kanske gick du emot traditioner, stod upp inför släkt och vänner eller vågade säga ja fast allt kändes osäkert. Ditt samtal kan ge tröst, styrka och igenkänning för andra. Ring in, skriv till oss eller delta i samtalet på sociala medier. Ordet är ditt – ring och berätta. Om att stå upp för sin kärlek med Hanna SihlmanRing eller mejla oss, på karlavagnen@sverigesradio.se eller skriv till oss på Facebook och Instagram. Telefonslussen öppnar kl. 21.Programmet startar kl. 21:40.

Az előfizetők (de csak a Belső kör és Közösség csomagok tulajdonosai!) már szombat hajnalban hozzájutnak legfrissebb epizódunk teljes verziójához. A hétfőn publikált, ingyen meghallgatható verzió tíz perccel rövidebb. Itt írtunk arról, hogy tudod meghallgatni a teljes adást. Forradalmi követelés: drogmentes rendőrséget! Miért nem tudnak parkolni a kínaiak? Mit akar Lázár János a vécékefével? A választás titkos sztárjai a BlackRock támogatásával: Hiller haver, Jakab Péter, Humanisták, Vona Gábor. 00:53 A Blackrock szponzorálásával. A kínai néni és a tisztogatás. A szívószálpápa rendszáma.05:47 Breaking: Humanisták.07:21 Jakab Péter Borsod 01-ben. Life coach lennél inkább, vagy DK-s? Így múljon el minden náci! Nem maradt hely a Fidesztől jobbra.12:30 Hiller haver nem adja fel. A HVG MSZP-tesztje. Antiszemita plakátrongolás a XI. kerületben.17:09 Kínaiak, kecskék, birkák, hüvelyesek, kézjelek.22:28 Kvíz: TFR. A kínai egykepolitika vége.26:45 Ivan Krastev és a szláv népesedési háború. A kollektív parkolási képességek szerepe a geopolitikai játszmákban, különös tekintettel Tajvan lerohanására. Rommel és Guderian bezzeg tudtak párhuzamosan parkolni!34:52 Honosítások a 2030-as vébére.37:25 A legtávolabbi hallgató. Randevúk szociológusokkal és gyökerekkel.41:04 Visszatér a Heti hetes. Új idők új Bajor Imréje. Amikor Simicska szerint Orbán meg akarta venni az RTL-t, csak nem volt pénze.46:30 Vitézy Dávid és a KRESZ.50:01 Együgyű párt a drogmentes rendőrségért.See omnystudio.com/listener for privacy information.

Kvällspasset gästas av Sveriges Radios utrikeskorrespondenter, Simon Isaksson (USA), Andreas Liljeheden (Bryssel) och David Rasmusson (Norden) som svarar på lyssnarnas frågor om det spända världsläget. Lyssna på alla avsnitt i Sveriges Radios app. Ett nyfiket och underhållande aktualitetsprogram med lyssnaren i fokus.Neda undrar hur EU kan jobba mot styret i Iran och Anita undrar om man kan se en trend i bojkott mot amerikanska produkter i Skandinavien. Vi hör också Thomas som undrar vad gemene amerikan tycker om Trumps styre och Charlotte undrar vad Trumps nya fredsråd är för något?

What happens when the hobby you love starts feeling like a chore? We go straight at that question with Jim Bates, exploring how burnout creeps in, why favorite subjects can become fear targets, and what it takes to rediscover honest joy at the bench. Jim shares how a demanding year pushed modeling to the margins, why armor felt freer than aircraft, and the simple mindset shift that turned “perfect or quit” into “finish and learn.” Along the way, we unpack airbrush avoidance, photoetch dread, and the tiny victories that rebuild momentum—like stripping a botched primer, repainting, and choosing progress over paralysis.We also get practical. You'll hear how keeping short journal notes, and accepting weekend-only bench time can remove friction and make modeling sustainable again. We talk about the limits of step-by-step boilerplate articles, why video excels at teaching technique, and how personal writing can spark creativity in ways a camera can't. Jim's revived blog, A Scale Canadian, is his sandbox for that approach: short, thoughtful posts that value honesty over hype.There's fresh inspiration too. We walk through Model Mania at the Museum of Flight—a display-only, public-forward event with seminars, demos from Rick Lawler, and zero contest pressure—plus a quick tour of new kit announcements that caught our eye, from Airfix's Canberra and JU 52 to MiniArt's Opel Maultier. To close, we share bench updates: Shermans and Cromwells, a Hellcat edging toward weathering, a T-33 off the shelf of doom, and a KV-85 waiting on brass.If you've been stuck, second-guessing, or saving “the good kit” for a better version of yourself that never seems to arrive, this conversation is your nudge. Build for you. Finish something small. Protect your joy. Then tell us what you're tackling next. Subscribe, share with a friend who needs the push, and leave a quick review to help others find the show.Model Paint SolutionsYour source for Harder & Steenbeck Airbrushes and David Union Power ToolsSQUADRON Adding to the stash since 1968Model PodcastsPlease check out the other pods in the modelsphere!Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Give us your Feedback!Rate the Show!Support the Show!PatreonBuy Me a BeerPaypalBump Riffs Graciously Provided by Ed BarothAd Reads Generously Provided by Bob "The Voice of Bob" BairMike and Kentucky Dave thank each and everyone of you for participating on this journey with us.

For our first International Feature of 2026, we dive into Anatomy of a Fall, the 2023 French courtroom drama, directed by Justine Triet. It became a major awards-season standout, winning the Palme d'Or at Cannes and sparking wide conversation for its unconventional approach to truth, perspective, and performance. Rather than leaning on flashy courtroom theatrics, the film builds tension through ambiguity, character study, and meticulous visual choices.In this episode, we break down what made Anatomy of a Fall resonate so strongly with audiences and critics alike, including what worked for us and why the film continues to linger long after the credits roll.The across-the-board performances, from the lead actors all the way down to the dogHow the film's open-ended storytelling challenges the audience to sit with uncertaintyDirect, intentional cinematography choices that quietly shape how we interpret eventsA courtroom drama focused on truth and perception, without over-the-top legal theatricsWhy restraint and realism make the emotional beats hit harderWhether you loved the ambiguity or found it frustrating, this was a film that gave us plenty to unpack.Letterbox'd Synopsis: A woman is suspected of her husband's murder, and their blind son faces a moral dilemma as the sole witness.

Welcome to episode 339 of The Cloud Pod, where the forecast is always cloudy! Justin and Matt are in the studio today to bring you all the latest in cloud and AI announcements, including more personnel shifts (and it doesn't seem like it was very friendly), a new way to get much needed copper, and Azure marketplace advertising 4,000 different models. What's the real story? Let's get into it and find out!  Titles we almost went with this week US-EAST-1: Still the Least Reliable Friend You Keep Inviting to Parties **OpenAI 0⃣ From Zero to Inference: BigQuery Makes Open Models a Two-SQL Problem AWS Goes Full Brandenburg Gate: Sovereign Cloud Opens for Business Seven Ate Nine: AWS Skips G7 and Goes Straight to G7e Instances From Crawling to Calling: Cloudflare Buys Human Native to Fix AI’s Data Problem Finally, an AI That Actually Listens to Your War Room Panic Tag, You’re Governed: AWS Automation Takes the Wheel Cloudflare Reaches for the Stars: Astro Framework Acquisition Lands Gemini Gets Personal: Google AI Finally Reads Your Email (With Permission) AWS Strikes Ore: Amazon Cuts Out the Middleman in Copper Supply Chain When Your Region Goes Down More Often Than Your Kubernetes Cluster ChatGPT Go: OpenAI’s New Middle Child Gets $8 Allowance Cloudflare’s Space-Age Acquisition: Astro Gets Jetsons-Level Upgrade Rosie the Robot Fired: Cloudflare Brings Astro Framework Into the Family It took 5 years, and now we have ads in our AI.  AI now with Ads EU says hands off my data General News  00:50 Heather's data is not unreliable  Maybe it's unreliable. I blame Matt for having screwed up his outtro (as he did today), in which case I no longer recognize his participation.  01:11 Astro is joining Cloudflare Cloudflare acquires The Astro Technology Company, bringing the popular open-source web framework in-house while maintaining its MIT license and multi-cloud deployment capabilities.  Major platforms like Webflow Cloud, Wix Vibe, and Stainless already use Astro on Cloudflare infrastructure to power customer websites. Astro 6 introduces a redesigned development server built on Vite Environments API that runs code locally using the same runtime as production deployment. When using the Cloudflare Vite plugin, developers can test against workerd runtime with access to Durable Objects, D1, KV, and other Cloudflare services during local development. The framework focuses on content-driven websites through its Islands Architecture, which renders most pages as static HTML while allowing

Kvůli komiksu Zelený Raoul se s nimi kdysi soudil Jiří Paroubek, kvůli karikatuře na obálce to stejné dělal Tomio Okamura. Přesto si z politiků utahují dál. „Nejlépe prodávají časopis,“ říká šéfredaktor Reflexu Martin Bartkovský.„Tomio Okamura je samozřejmě Pitomio a my všichni to můžeme říkat. Jak konstatoval soud, musí snést vyšší míru kritiky. Navíc to, kdy jako první věc ve funkci předsedy Sněmovny podrží štafle, aby někdo jiný sundal ukrajinskou vlajku, která se mu nelíbí, je podle mě krystalickou ukázkou chování někoho, komu by se dalo říkat Pitomio,“ vysvětluje Martin Bartkovský, šéfredaktor časopisu Reflex, který byl hostem nejnovějšího dílu podcastu Mediální cirkus.Právě vyobrazení Tomia Okamury jako klauna s nápisem Pitomio je asi nejslavnější obálka Reflexu. A to i díky následnému soudnímu sporu.Vyšla 7. listopadu 2012 v době, kdy se Okamura stal senátorem a pracoval na znásobení známosti svého jména tím, že kandidoval v prvních přímých prezidentských volbách. Po vydání karikatury dal Okamura na Reflex žalobu. Tu ale po mnohaleté soudní tahanici předloni definitivně prohrál.Na obálce fungují Babiš, Zeman a OkamuraČasopis Reflex vychází v Česku už 35 let a na výrazných titulních stranách si zakládá.„Fungují čeští politici. Byla doba, kdy vládl Andrej Babiš s Milošem Zemanem a do toho tam jako třetí vzadu pobíhal Tomio Okamura nebo komunisté, o které se vláda tehdy opírala. Tam stačilo kohokoliv z téhle vlády dát na obálku a hned tam prodeje byly. Dneska už to takhle není,“ říká šéfredaktor Bartkovský. „Filip Turek a jeho volební blitzkrieg v eurovolbách zafungoval velmi dobře. Motoristé fungují. I Andrej Babiš. Funguje i prezident Petr Pavel a vždycky funguje Volodymyr Zelenskyj. Stejně tak Vladimir Putin nebo Donald Trump, ale to jsou jediné zahraniční persony,“ popisuje Bartkovský taktiku při výrobě titulních stran.Vůbec nejúspěšnější titulní strany Reflexu za rok 2025 se ale nakonec politiky vůbec netýkaly. „Byly to obálky in memoriam našich dlouholetých spolupracovníků. Tím jedním byl psycholog Cyril Höschl a tím druhým byl šéf karlovarského filmového festivalu pan Jiří Bartoška,“ dodává Martin Bartkovský.Ten se šéfredaktorem Reflexu stal v prosinci 2023, po půl roce nejistoty ve vedení časopisu. Před ním ho řídil Marek Stoniš a časopis se často dostával na hranu kritiky za texty a titulky zavánějící někdy až xenofobií.„Kvůli obálce s černým Hitlerem chtěla odejít polovina redakce. Občas už jsme byli prostě zbytečně zlí. Vtipné je být vtipní, satiričtí, jízliví, ale když jste vyloženě zlí, a dávali to vědět i čtenáři, tak to vtipné není,“ říká Bartkovský a naráží na titulní stranu s portrétem Adolfa Hitlera coby černocha s monstrózním afro účesem, která vyšla v roce 2020.Babiš jako Slabiš. Nevíme, z koho si utahovat dřívS novou vládou je podle Bartkovského stále těžší si z ministrů dělat legraci, protože v redakci neví, koho karikovat dříve.„Je to vlastně bezprecedentní situace, kdy byste mohli každý týden udělat na každého člena vlády jednu obálku, tedy kromě těch členů SPD, kteří nic neříkají. Ale ti ostatní jsou velmi plodní,“ žertuje novinář s tím, že na obálce tento čtvrtek by chtěl mít Andreje Babiše. „Bude na té obálce jako Slabiš, bude se opírat o Macinku s Turkem a Okamurou a bude se snažit udělat Česko lepší, nejlepší zemí na této planetě,“ směje se Bartkovský.I humor má ale v Reflexu hranice.„Nemáme hranice v tom, z kterého politika si udělat legraci, ale nechceme si dělat legraci ze všedních lidí, obyčejných Čechů, z někoho, kdo se nemůže bránit. My si vždycky děláme legraci z lidí, kteří mají nějakou moc nebo mají pocit, že drží nějakou moc a my je chceme trošku uzemňovat,“ říká šéf oblíbeného časopisu.Co čeká od vlády Andreje Babiše? Co kabinet změní a dotáhne za příští čtyři roky? A jak se vlastně dostal do čela Reflexu?--Mediální cirkus. Podcast Marie Bastlové o dění na mediální scéně. Zajímá ji pohled do redakcí, za kulisy novinářské práce – s předními novináři i mediálními hráči.Sledujte na Seznam Zprávách, poslouchejte na Podcasty.cz a ve všech podcastových aplikacích.Archiv všech dílů najdete tady. Své postřehy, připomínky nebo tipy nám pište prostřednictvím sociálních sítí pod hashtagem #medialnicirkus nebo na e-mail: audio@sz.cz.

A slow start, a full heart, and a clear plan. We kick off 2026 by resetting our modeling habits, sharpening the skills that matter most, and putting dates on the calendar to turn ideas into finished work. HeritageCon is pulling us forward, but it's the day-to-day that will make the difference: tighter bench time, better canopies, and bases that finish strong instead of phoning it in.One photo sent us down a rabbit hole—captured Soviet armor at Kummersdorf with mysterious inventory rectangles. We trace similar markings across other vehicles and share why the rectangle's color might be yellow, then ask armor specialists for hard provenance rather than AI guesses. That curiosity fuels the whole episode. The dojo keeps paying dividends, from canopy wax tips and stencil-cutter know-how to encouragement from modelers solving the same problems. We celebrate KitMask extending mojo30 for 30% off through HeritageCon and spotlight how small breaks in cost and friction can nudge more projects across the line.We lay out our goals for the year. Aircraft need spotless canopies—polished clear parts, confident masking, and frames that sit sharp and true. Speed is focus: fewer distractions, more finishes without losing joy. Armor projects get a base upgrade with cleaner edges, smarter terrain transitions, and groundwork that complements the model instead of competing with it. On the adjacent front, we commit to mastering a Cameo stencil cutter for crisp markings and layered paint effects, and we push to launch phase two of our website so the community can learn and share even more.On the bench, the Hellcat weathers the tiny-stencil storm, the Moosaroo rally build earns custom decals and a clever mixed-material interior, and the KV-85 stacks sub-assemblies toward primer. Our 2026 wish list is ambitious but grounded: MiniArt T-34/76 variants, a modern JSU-152, an early D3A1 Val, a 1/72 Privateer, and a 14-meter Daihatsu for Pacific dioramas. If you've got insight on Kummersdorf markings or a kit rumor we should track, jump in. Subscribe, share the show with a modeling friend, and leave a quick review—then tell us your top skill goal for 2026.SQUADRON Adding to the stash since 1968Model Paint SolutionsYour source for Harder & Steenbeck Airbrushes and David Union Power ToolsModel PodcastsPlease check out the other pods in the modelsphere!Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Give us your Feedback!Rate the Show!Support the Show!PatreonBuy Me a BeerPaypalBump Riffs Graciously Provided by Ed BarothAd Reads Generously Provided by Bob "The Voice of Bob" BairMike and Kentucky Dave thank each and everyone of you for participating on this journey with us.

Send us a textWe are headed to Shot Show in 2026.  We will be publishing episodes every day going over what we saw and some of the coolest things at the show.  Make sure you like, subscribe and share so you get all the latest info from the show.Intro/Outro Music: Quest by KV    / kvmusicprod  License: Creative Commons — Attribution 3.0 Unported  — CC BY 3.0 Free Download / Stream: https://audiolibrary.com.co/kv/questMusic promoted by Audio Library:    • DAILY No Copyright For You – Quest by KV