Podcasts about LM

LM publica cómo implica más control sobre el propietario, restricciones en los contratos, obstáculos para recuperar una vivienda y nuevos castigos.

Why do cars matter? In this episode of Living a Life in Full, Greg Stanley—collector car specialist, Ferrari judge, host of The Collector Car Podcast, and author of The Enthusiast's Guide To Collector Cars—joins the conversation to explore why automobiles are far more than machines. From Enzo-era Ferraris and first-generation Mustangs to Shelbys, survivor cars, resto-mods, and modern hypercars, Greg explains how cars become rolling works of art, cultural time capsules, and emotional vessels that connect us to history, family, identity, and place. Greg brings more than 25 years of market insight and historical expertise to a wide-ranging discussion about what makes a collector car meaningful, valuable, and unforgettable. He shares insights from judging at major events, working on record-setting sales—including the 1962 Ferrari 330 LM / 250 GTO sold for $51.7 million—and helping enthusiasts understand provenance, documentation, auctions, restoration choices, clubs, valuation, and stewardship. At its heart, this conversation is about curiosity, craft, memory, and meaning. Greg helps make the collector-car world accessible to newcomers while reminding seasoned enthusiasts that the best cars are not just owned—they are held, understood, preserved, and passed along. Whether you love concours Ferraris, muscle cars, JDM, barn finds, or simply asking "what's the story?" this episode offers a thoughtful look at why cars move us in more ways than one.

Devocional do dia 01/10/2026 com o tema: Plante árvores! Certa manhã, veio em minha mente uma doce lembrança do meu querido avô materno. Ele amava plantas. Vivia plantando árvores frutíferas, cultivava flores e tinha um grande zelo pelo seu jardim. Até alguns meses antes de falecer, mesmo debilitado, ele seguia em frente fazendo o que amava: plantar e cultivar suas plantas. Muitas vezes, era questionado por que fazia isso, e simplesmente respondia: faço por meus filhos, netos e bisnetos. Refletindo sobre isso, lembrei do povo de Israel, ao ser levado cativo para a Babilônia, entre os anos 586 a.C. e 538 a.C. Foi um tempo muito difícil para todos, mas Deus, em sua infinita bondade, trouxe uma palavra muito especial ao coração deles. Por intermédio do profeta Jeremias, instruiu-os para que continuassem suas vidas, construindo casas, plantando seus próprios jardins, alimentando-se desses frutos, casando e tendo filhos (Jr 29.4-7), apesar de toda dificuldade que estivessem vivendo. LEITURA BÍBLICA: Jeremias 29.4-7 Digo a mim mesmo: A minha porção é o SENHOR; portanto, nele porei a minha esperança (Lm 3.24).See omnystudio.com/listener for privacy information.

UGA Fans ACCUSE Jon Sumrall of STEALING “THIS, Gators Eye 2027 Flips + Coaches on the HOT SEATSign up for PrizePicks with code: HMA and get $150 in lineups if you win when you play your first $5+ lineup! https://link.prizepicks.com/LM...Source: https://www.youtube.com/watch?...

In this throwback episode Dr. Justin Moody joins Alan to chronicle the remarkable evolution of Implant Pathway—from early hotel seminars to establishing a massive, purpose-built 32-operatory clinical training and nonprofit center in Scottsdale . Together, they break down the philosophy of the Fast Track program, emphasizing actionable clinical judgment, live-patient mentorship, and the importance of immediate chairside implementation . The conversation also covers the regulatory landscape of Arizona's Good Samaritan temporary dental licenses, Dr. Moody's newest venture into winemaking with Titanium Winery in Napa Valley! Some links from the show: Implant Pathway : Hands-on and didactic dental implant education, surgical mentorship, and Fast Track programs . Aadland-Moody Winery: Dr. Justin Moody's boutique vineyard venture in the Spring Mountain District of Napa Valley . Arizona State Board of Dental Examiners : State licensing board details regarding Arizona Good Samaritan / temporary volunteer healthcare permits . We're proud to be supported by the folks at Net32! The Very Dental Podcast Network is supported by Garrison Dental Solutions! Garrison isn't just about the gold standard in sectional composite matrix systems—they've got solutions that elevate your entire restorative workflow. From the innovative Loop Curing Light to their incredible lineup of LM instruments, Garrison makes chairside dentistry faster, easier, and a lot more predictable. Go check out everything they have to offer and support the show at garrisondental.com! Crazy Dental has everything you need from cotton rolls to equipment and everything in between and the best prices you'll find anywhere! If you head over to verydentalpodcast.com/crazy and use coupon code "VERYSHIP" you'll get free shipping on your order! Go save yourself some money and support the show all at the same time! The Wonderist Agency is basically a one stop shop for marketing your practice and your brand. From logo redesign to a full service marketing plan, the folks at Wonderist have you covered! Go check them out at verydentalpodcast.com/wonderist! Enova Illumination makes the very best in loupes and headlights, including their new ergonomic angled prism loupes! They also distribute loupe mounted cameras and even the amazing line of Zumax microscopes! If you want to help out the podcast while upping your magnification and headlight game, you need to head over to verydentalpodcast.com/enova to see their whole line of products! I'm a big fan of the Bioclear Method! I think you should give it a try and I've got a great offer to help you get on board! Use the exclusive Very Dental Podcast code VERYDENTAL8TON for 15% OFF your total Bioclear purchase, including Core Anterior and Posterior Four day courses, Black Triangle Certification, and all Bioclear products. CAD-Ray offers the best service on a wide variety of digital scanners, printers, mills and even  their very own browser based design software, Clinux! CAD-Ray has been a huge supporter of the Very Dental Podcast Network and I can tell you that you'll get no better service on everything digital dentistry than the folks from CAD-Ray. Go check them out at verydentalpodcast.com/CADRay!

Sign up for PrizePicks with code: HMA and get $150 in lineups if you win when you play your first $5+ lineup! https://link.prizepicks.com/LM...Jon Sumrall REVEALS the SECRET Workout That CHANGED Florida — Gators vs Mizzou EARLY PREVIEWSource: https://www.youtube.com/watch?...

Sign up for PrizePicks with code: HMA and get $150 in lineups if you win when you play your first $5+ lineup! https://link.prizepicks.com/LM...BREAKING: Gators LAND ELITE OL! Gator Legend REACTS to Ole Miss + Sumrall GETS REAL on SecondarySource: https://www.youtube.com/watch?...

Alan reflects on the stark contrast between social media reach and traditional audio podcast listenership after short Instagram clips featuring guest Dr. Brady Smith quickly garnered tens of thousands of views while overall podcast downloads remained steady . Looking back on the evolution of dental media since co-founding The Dental Hacks in 2014, he examines the rise of short-form video, shifts in podcast download metrics, the role of algorithmic distribution compared to RSS feeds, and whether traditional audio shows can still effectively connect with dental professionals . We're proud to be supported by the folks at Net32! The Very Dental Podcast Network is supported by Garrison Dental Solutions! Garrison isn't just about the gold standard in sectional composite matrix systems—they've got solutions that elevate your entire restorative workflow. From the innovative Loop Curing Light to their incredible lineup of LM instruments, Garrison makes chairside dentistry faster, easier, and a lot more predictable. Go check out everything they have to offer and support the show at garrisondental.com! Crazy Dental has everything you need from cotton rolls to equipment and everything in between and the best prices you'll find anywhere! If you head over to verydentalpodcast.com/crazy and use coupon code "VERYSHIP" you'll get free shipping on your order! Go save yourself some money and support the show all at the same time! The Wonderist Agency is basically a one stop shop for marketing your practice and your brand. From logo redesign to a full service marketing plan, the folks at Wonderist have you covered! Go check them out at verydentalpodcast.com/wonderist! Enova Illumination makes the very best in loupes and headlights, including their new ergonomic angled prism loupes! They also distribute loupe mounted cameras and even the amazing line of Zumax microscopes! If you want to help out the podcast while upping your magnification and headlight game, you need to head over to verydentalpodcast.com/enova to see their whole line of products! I'm a big fan of the Bioclear Method! I think you should give it a try and I've got a great offer to help you get on board! Use the exclusive Very Dental Podcast code VERYDENTAL8TON for 15% OFF your total Bioclear purchase, including Core Anterior and Posterior Four day courses, Black Triangle Certification, and all Bioclear products. CAD-Ray offers the best service on a wide variety of digital scanners, printers, mills and even  their very own browser based design software, Clinux! CAD-Ray has been a huge supporter of the Very Dental Podcast Network and I can tell you that you'll get no better service on everything digital dentistry than the folks from CAD-Ray. Go check them out at verydentalpodcast.com/CADRay!

Sign up for PrizePicks with code: HMA and get $150 in lineups if you win when you play your first $5+ lineup! https://link.prizepicks.com/LM...Florida Gators STEAM ROLL Ole MIss - INSTANT REACTION - CALL IN

“'Co-op first, ask questions later' is down a bit from its peak, though not dead,” observes John Sobolewski, Global Chair of Latham & Watkins' Liability Management Practice, as he addresses the latest dynamic between debtors and lenders in distressed situations. In this episode, Bloomberg Intelligence analysts Negisa Balluku and Phil Brendel sit down with John to explore the current state of Liability Management Exercises (LMEs) and why stakeholders are ultimately motivated to find negotiated resolutions over value-destroying, multiyear litigation. The conversation unpacks the delicate mechanics of these negotiations, including why repeat market players will give minority holdouts a fair shake but “hold the line” against nuisance value to avoid giving an inch that turns into a yard. John also breaks down the massive bid-ask spreads — think “Grand Canyon” — between debtors and creditors, why debt funds continue to resist taking back equity, and the software sector's growing interest in LM solutions.  The podcast concludes (53:10) with BI's Noel Hebert joining Negisa and Phil for a roundup of their latest research on Air Baltic, QVC, DISH DBS, DISH Wireless, Hughes Satellite Systems, Telesat, and Xerox. The State of Distressed Debt podcast is part of BI's FICC Focus series. Listen on Apple Podcasts and Spotify.

Laudetur Jesus Christus - Ngợi khen Chúa Giêsu KitôRadio Vatican hằng ngày của Vatican News Tiếng Việt.Nội dung chương trình hôm nay:0:00 Tông du ngày 130:20 Chia sẻ Lời Chúa: Lm. Gioan Baotixita Phương Đình Toại, dòng Camillo, chia sẻ Lời Chúa Chúa Nhật 26 thường niên.---Những hình ảnh này thuộc Bộ Truyền Thông của Toà Thánh. Mọi sử dụng những hình ảnh này của bên thứ ba đều bị cấm và dẫn đến việc đánh bản quyền, trừ khi được cho phép bằng giấy tờ của Bộ Truyền Thông. Copyright © Dicasterium pro Communicatione - Giữ mọi bản quyền.

Sign up for PrizePicks with code: HMA and get $150 in lineups if you win when you play your first $5+ lineup! https://link.prizepicks.com/LM...Florida Gators vs Ole Miss ULTIMATE PREVIEW — Injury Updates, Keys to Victory & Week 4 Pick'Em!Source: https://www.youtube.com/watch?...

Which BMW X5 should you actually buy? Sean Troy has owned 103 E53 X5s, still keeps 28 of them, and runs Alpine Rennsport, a BMW and Porsche shop in Cleveland, Georgia. In this episode, he walks through every X5 generation from the perspective of someone who fixes them for a living. You'll hear why the E70 35d is his pick at $15,000, why he'd stay away from the twin-turbo V8s, which E53 engine is best for DIY owners, what's starting to fail on the G05, and his picks at $10k, $25k and $50k. Then Sean shares how his mom's X5 started a 103-car obsession, the crashes that convinced his family these SUVs are worth it, his rare Neiman Marcus X5, and the V10, six-speed manual "LM-ish" tribute he's building in honor of the X5 Le Mans. Plus: the ALPINA X5 that BMW should have built. Chapters 00:00 Intro 00:45 The best X5 for $15,000 01:40 E70 35d vs F15 diesel 03:27 E53: which engine to buy 05:15 E53 big-ticket failures 09:30 Pre-facelift vs facelift E53 11:53 E70: why to avoid the twin-turbo V8s 13:30 35i vs 35d and a diesel X5 M swap 15:50 Is the F15 underrated? 17:53 Best X5 for an off-road build 19:52 Most reliable F15 engine 21:28 G05: why the M50i 26:04 G05 common problems 27:49 Most reliable X5 of all time 28:17 Best X5 at $10k, $25k and $50k 30:01 Are plug-in hybrid X5s worth it? 32:19 What to check before buying any used X5 33:52 Sean's first X5 35:53 103 X5s and how the obsession started 40:03 The rarest one: Neiman Marcus X5 40:55 Where he keeps 28 X5s 42:48 X5 #100 and why he keeps saving them 46:46 The X5 BMW should have built 55:00 SAV vs SUV: what's the difference? 57:26 X5 or Range Rover? 59:32 X5 Le Mans and Sean's V10 "LM-ish" build 1:04:47 Strangest X5 spec 1:06:25 Rapid fire 1:09:03 Where to find Sean 1:09:36 Nate's favorite X5 Find Sean on Instagram: @makee53sgreatagain tWatch the full video version on YouTube: https://youtu.be/JAaWIkcnX_U Support BMWBLOG Podcast by contributing to their tip jar: https://tips.pinecast.com/jar/bmwblog-podcast Find out more at https://bmwblog-podcast.pinecast.co

Sign up for PrizePicks with code: HMA and get $150 in lineups if you win when you play your first $5+ lineup! https://link.prizepicks.com/LM...Are the Florida Gators PLAYOFF CONTENDERS?! Sumrall FIRES UP Auburn Fans + Philo TURNING HEADSSource: https://www.youtube.com/watch?...

Sign up for PrizePicks with code: HMA and get $50 in lineups instantly when you play your first $5+ lineup! https://link.prizepicks.com/LM...Florida Gators vs Ole Miss ULTIMATE PREVIEW, Insider REVEALS the Keys to Victory & Who Has the EDGE!Join this channel to get access to perks:https://www.youtube.com/channe...Hightop Sports Merch:https://www.hightopsport.com/s...Social-Insta: hightop_sportsTwitter: https://twitter.com/hightopspo...Watch AR nterview:https://youtu.be/ZG5pRyc1mlUMore videos:https://www.youtube.com/channe...Source: https://www.youtube.com/watch?...

We know we got you guys. Come on. We wouldn't leave you hanging like that. Join Spencer, Ty, Andy, and special guest Jon Gabrus as they get back to the intense duty of rating sexual fetishes. We got you, baby! Support us on Patreon for $5, $7, or $10: www.patreon.com/tgofv. TGOFV Theme by World Record Pace. A big shout-out to our $10/month patrons: Celeste, Yung Zoe, Dane Stephen, Weedworf, James Lloyd-Jones, Sam Thomas, Josh O'Brien, Kilo, David, Sam, T, Rach, Tomix, Adam W, L M, Revidicism, Jennifer Knowles, Jeremy-Alice, Louis Ceresa, Charles Doyle, Dean, Axon, Themandme, Raouldyke, Stephen Tucker, Lawrence, Rebecca Kimpel, Malek Douglas, Jacon Sauber-Cavazos, Bernventers, William Copping, NewmansOwn, Heather-Pleather, Bunknown, Dinosarden, Bedi, Francis Wolf, King Krang, Anthony C, ASDF, Buffoonworld, Bavbiff, D Love, and Tugboat!

Sign up for PrizePicks with code: HMA and get $150 in lineups if you win when you play your first $5+ lineup! https://link.prizepicks.com/LM...Jon Sumrall Gets HONEST About Auburn — Gator Legend EXPOSES the Film + Ole Miss Preview!Source: https://www.youtube.com/watch?...

V nejnovější epizodě Z Letné a Strahova, přesněji u dílu 85 našeho Space, František A Vašek rozebrali cennou a jasnou výhru na Hané.Členové redakce SN se dále rozpovídali o:Demolici na úvod Evropské ligyKdo pojede reprezentovatZvládnou ženy Farul?Úvod sezóny nevyšel dle našich očekávání, prohráli jsme Derby, nedaří se nám venku, vypadli z bojů o LM, před víkendem jsme byli desátí…Nalaďte si nás i někdy příště, den po zápase ve 20:00 na našem Twitteru @spartanske 

Tickets for AIE NYC now open, and apply for the invite-only AIE CODE. Join us!We have an unusual relationship with today's guest: for years since coauthoring the InstructGPT paper, Diogo Almeida had been saying that API-available frontier models have been going down the wrong path, everything from the alignment to refusals to reliability perspectives, that we have dropped every mode other than autoregressive chat-tuned LLMs because of the overwhelming success of ChatGPT.In a launch video now viewed ~40M times (by comparison, GPT4o was 22M, Fable 5 was 15M, Navier Stokes was 74M, and 6 Astra was 137M), Diogo introduced Jev and it immediately took over the AI timeline — we'll skip full Jev explainers because your favorite AI influencer/educator has probably already done one. We also collected:* the official patterns and cookbooks you should see first, from Allie* Jev usecases* speed based - games and computer use* the voice + computer use example we discuss at 1h34 mins* voice + browser control* The must not miss Doom demo* Driving cars in games* Excalidraw* virtual try-ons* “Smart Games”/smart NPCs* guided responses in text messages* Jev for coding agents has an official guide * jev for linting* compacting tool calls* reasonable pushback from Theo - Diogo has published a note on the Tyranny of the KV Cache that you should read as a followup after the pod for Jev + coding agents, because of his belief that Cache Rules Everything* Programming Languages built atop Jev (Diogo's fave)* Jev for analytics replay and user journey review* “dark data”* entity resolution* natural language search* “smart software”* a core goal of Jev is to “disappear into the background” - eg as unremarkable as regex* Jev as a judge* Jev memes* Jev vs LLM capabiltiies* blending transformers and classifiers* about the confidence api* Jev vs GLiNER (note difference/pushback, agreed, agreed, agreed)* Jev on trolley problem* Jev BushInstead we'll focus on what we can uniquely offer — a broader philosophical and mission-based understanding of how and why Jev was created, and what you should expect next in terms of future models from TypeSafe (ReasoningJev?) and what usecases and ideas you should work on vs the 55th low effort clone of Jev's API or doing a generic JevBench benchmark - something Diogo has rejected publicly.Why RLCD: Three kinds of RLHF, and why they are ALL the wrong north starDiogo knows a good deal about RLHF, given that he was on the team that pioneered post-training at OpenAI — and traces the three branches to Christiano et al 2017 (the robot backflip demo), Stiennon et al 2020 (learning to summarize) and his baby, Ouyang et al 2022 (InstructGPT). From there on, every innovation from Function Calling to Structured Outputs to Reasoning felt like a hack on top of the string based, sequence to sequence prediction paradigm. As he mentions on the pod, from 2023-2024 he struggled unsuccessfully, due to both personal and organization underestimation, to train a model that accurately addressed what he saw as the core problem with making LLMs the heart of software: reliability.Jev's core innovation is "Reinforcement Learning for Calibrated Decisions”, a novel, unpublished technique that optimizes for “answers with epistemically honest probabilities on System One tasks” rather than human rated feedback (RLHF) — which causes hallucinations, sycophancy, and permanent reliance on humans — or programmatically verifiable outputs with rubrics (RLVR) — which solves Navier Stokes but exacerbates jagged intelligence and doesn't integrate well with other software.We've talked about the calibration problem before on the pod, but probably the single best place to understand why RLCD became necessary is Diogo's AIE talk, which discusses why a generation of training helpful AI assistants for humans has impaired them for training models for composable, programmable AI for automation.At the end he also teases his contrarian opinion on scaling laws - which teases how to build a modern neolab without the billions of dollars the major labs have…The Bitterest Lesson: Tasks and Data beats ComputeWe spend a good amount of time discussing Diogo's essay on the Bitterest Lesson:His point is that “You get what you optimize for and the bitterest lesson in ML is that the most important part of it isn't ML at all.” - and picking the right north star, eg upvoting for user preference vs being integrated into tool calls - makes everything else fall in line.We're excited to catch up with a freshly dyed Diogo to discuss:* Why AI can solve extraordinarily hard problems but still fail to automate basic work* What System One Models are and why Jev is built for software rather than chat* RLHF, mode collapse, calibration, and the hidden costs of optimizing for human preferences* Why refusals become a problem when AI is buried inside software dependencies* Why TypeSafe rejects public benchmarks and optimizes for intelligence per dollar* The “bitterest lesson”: why the right task and the right data can matter more than compute* Why TypeSafe thinks of itself as a data lab rather than a model lab* RLCD vs. RLHF and RLVR as fundamentally different North Stars for AI* Why reliability and robustness matter more than simple determinism* Jev's programming primitives and how intelligence maps into software control flow* Why developers should decompose AI workflows into small, measurable decisions* How structured state replaces giant prompts and system messages* Why Diogo thinks AI should eventually disappear into the background of software* The “inverse SaaS-pocalypse” and how AI could supercharge existing software* System One vs. System Two intelligence and the limits of reasoning models* Dark data, computer use, real-time intelligence, and Jev's biggest early use cases* Why Jev could reshape coding agents built around a single-model architecture* Why Diogo says he wouldn't pre-train with $1 billion* The OpenAI journey that led to TypeSafe and why he thinks many neo-labs are approaching AI incorrectly* Coding agents beyond the KV cache, shared state, sub-agents, and the multi-agent futureDiogo Almeida* LinkedIn: https://www.linkedin.com/in/diogomda* X: https://x.com/CompleteSkeptic* TypeSafe AI: https://typesafe.ai/Timestamps00:00:00 Jev Launch Week and the AI Economic Revolution00:02:50 What Is Jev? System One Models and Programmable AI00:05:54 RLHF, Mode Collapse, Calibration, and Yann LeCun00:10:29 Programmatic AI, Refusals, and Safety Alignment00:17:21 Why TypeSafe Rejects Public Benchmarks00:20:43 The Bitterest Lesson: Data, Compute, and the Right Task00:24:59 RLCD vs. RLHF and RLVR00:28:42 Why Powerful AI Still Hasn't Automated the Economy00:39:55 Reliability, Robustness, and Determinism00:48:11 Model Versioning, LTS, Speed, and Intelligence per Dollar00:54:04 Inside Jev's API and Programming Primitives00:58:28 How to Build with Jev: Structure, Decomposition, and Small Decisions01:18:28 The Inverse SaaS-pocalypse and AI Disappearing into Software01:33:21 Computer Use, Dark Data, and Jev's Biggest Use Cases01:38:48 How Jev Could Reshape Coding Agents01:41:00 AI Safety, Frontier Pacing, and the Limits of RLVR01:48:03 Why Diogo Wouldn't Pre-Train with $1 Billion01:55:19 The OpenAI Story Behind TypeSafe02:01:41 Why Diogo Thinks Most Neo-Labs Are Getting AI Wrong02:08:00 Coding Agents Beyond the KV Cache and the Multi-Agent FutureTranscriptIntroduction: Jev Launch Week and Developer MomentumSwyx [00:00:00]: Okay, we're in the studio. A special occasion because this week, Diogo, my good buddy, launched Jev, and it's been taking over the complete timeline. How do you feel? What's it like to be you right now?Diogo Almeida [00:00:16]: Emotionally?Swyx [00:00:17]: Yeah.Diogo Almeida [00:00:17]: Never been worse. Like, I'm a ragged corpse of a person right now because there's so much going on, and I'm like a technical CEO, so I have, like, a lot of fires to fight.Swyx [00:00:29]: Yeah.Diogo Almeida [00:00:29]: But mentally, I feel—I say this all the time, and I've been saying this kind of for years in my over-under events. Like, I feel like the entire AI field is like one of those, like, carnival house of mirrors, and everyone is just insane and saying the weirdest stuff that doesn't make sense. And it feels like for just this week, like, I'm on a better in sync with reality and like, oh, people see it now. AI can be so much more than what was once thought.Diogo Almeida [00:01:06]: And like, yes, we are going to make. Like, an AI-based economic revolution is back on the table, and this is f*****g awesome.Diogo Almeida [00:01:17]: I'm so jazzed the developers get it. It's, it's, Yeah, and I want to show my eternal gratitude to the developers andSwyx [00:01:25]: Yeah.Diogo Almeida [00:01:26]: I'm so jazzed about the community and everything. It's so great.Swyx [00:01:28]: Yeah, you were saying yesterday that you decided to prioritize the town hall and not a bunch of, like, VIP, investor-type people because you wanted to make sure that they are the people that you get your most, attention, right? The engineers, the developers.Diogo Almeida [00:01:43]: Yeah, it felt a little like, oh man, I'm talking to, like, really important people right now.Swyx [00:01:47]: Yeah.Diogo Almeida [00:01:47]: I probably shouldn't reveal who.Swyx [00:01:48]: Yeah.Diogo Almeida [00:01:48]: But it feels a little bit dirty for me to, I'm, like, perhaps overly genuine in things. Like, it feels, like, dirty if, like, in my gigantic calendar event of people to talk to, the community isn't one of those.Swyx [00:02:04]: Yeah.Diogo Almeida [00:02:04]: And actually, in my ideal world, it would be, like, community all the time. I was thinking, “Should I host a town hall while walking to your studio?” And I'm like, “No, that's too crazy.”Swyx [00:02:12]: Sure. Yeah. Well, you guys have been hosting town halls on Discord. Discord is now 100,000 people. Your Twitter'sDiogo Almeida [00:02:19]: I don't follow these stats.Swyx [00:02:20]: Yeah.Diogo Almeida [00:02:20]: So holy s**t.Swyx [00:02:21]: Your Twitter's blown up. It was, it was really funny ‘cause, like, at AIE, you were like, “Yeah, follow me please,” and then you didn't, like, provide even your handle.Diogo Almeida [00:02:29]: I'm a noob. I'm a noob.Swyx [00:02:29]: You're such a noob.Diogo Almeida [00:02:30]: I'm a noob.Swyx [00:02:31]: But no, but that, like, that's, like, positive aura that, likeDiogo Almeida [00:02:33]: CoolSwyx [00:02:33]: You don't know how to promote yourself.Diogo Almeida [00:02:35]: Yeah. Someone, like, called me out when I posted, like, “Holy s**t, we're all three twending-- trending topics.” And then they're like, “That's a personal feed.”Swyx [00:02:42]: That's a personal, yeah.Diogo Almeida [00:02:43]: And I'm like, “Oh, no.”Swyx [00:02:44]: Of course, of course it'll trend to you.Diogo Almeida [00:02:45]: Cringe. Yeah.Swyx [00:02:45]: Yes, ‘cause it's what you clicked on.Diogo Almeida [00:02:47]: Yeah.Swyx [00:02:47]: So okay. Let's, Yeah, so congrats on everything.What Is Jev? System 1 Models and Intelligence per DollarDiogo Almeida [00:02:50]: Thank you.Swyx [00:02:50]: We'll talk about more, details as you have them. But let's, for people who are, like, living under a rock or just want, like, the definitive thing, what is Jev?Diogo Almeida [00:03:02]: Whew. Let me think about. That's a hard one.Swyx [00:03:07]: Okay. And I'm happy to, like, re-ask if you wanna kind ofDiogo Almeida [00:03:09]: No. I'm happy toSwyx [00:03:10]: OkayDiogo Almeida [00:03:10]: I'm happy to, like, just jam on it.Swyx [00:03:12]: Yeah.Diogo Almeida [00:03:13]: I will say, like, the first thing that I'm relieved about with this question is now I don't have to answer that question to my parents anymore ‘cause ChatGPT can just explain it.Swyx [00:03:20]: Nice.Diogo Almeida [00:03:21]: So the way I see it is we new-- need a new class of models. We're not attached to naming that class of models. Our-- the most accurate name we've come up with is System 1 models.Swyx [00:03:33]: Yeah.Diogo Almeida [00:03:33]: There will be reasons, but it's-- there's a reason why we don't call them decision models, because, like, they will be. Like, System 1 is beyond that. That's all I can say. We didn't expect this to be our big launch, so we have stuff in the tank.Swyx [00:03:48]: You should have said low-key research preview.Diogo Almeida [00:03:52]: It kind of was, right? It kind of was. But we. So there's a class of models that we describe them as, like, machine-native, System 1, large programmable. I think these are-- is the class of models where the goal is for code to be the consumer. So as opposed to, lar-- pre-trained large language models, which are meant for, like, autocomplete of the internet, or RLHF models, like chatbot instruction-following models, which are meant to, like, reply to text, or RLVR. It's in a weird gray area with RLHF. Like, these are meant to have things that directly are consumed by code, hence the name type safe. So the thing we really want is to have, like, AI, like, be as powerful as possible, and we think the way to do that is to integrate it with software. And we are designing everything, beyond just the outside, the deep internals of the model to be optimized for software. So number one, Jev is our first large programmable model, or a System 1 model, whatever you want to call it. Jev is meant to be optimized for intelligence per dollar, hence the name Jev.Swyx [00:05:03]: Jevons Paradox.Diogo Almeida [00:05:03]: Jevons Paradox, yeah. And it's optimized for intelligence per dollar. I love this debate with people about what is the most important between reliability, cost, calibration, and speed. And Jev is meant to be. Jev will be the name of models that will be on the frontier of intelligence per dollar. There's other ways to optimize it, like, ML, or at least if you're good at ML, it's all about trade-offs. And we are just going all out on that.Calibration, Mode Collapse, and the Limits of RLHFSwyx [00:05:31]: Yeah. And to me, like, calibration is one of the new things that people weren't talking about as much. We've done an episode In the past, with Clementine Foreia of Hugging Face, where they were like, “Yeah, actually, y- they're just.” Or, and this is your whole argument about RLHF, is they're more collapsing towards what you want to hear the mostDiogo Almeida [00:05:50]: OohSwyx [00:05:50]: Or what is most likely, instead of, like, their own internal confidence about a thing.Diogo Almeida [00:05:54]: Can I soapbox on that for a second?Swyx [00:05:56]: Go ahead. Yeah.Diogo Almeida [00:05:57]: Cool. Like, I've been heard that your audience is the most technical, so I actually want to get into that.Swyx [00:06:02]: Yeah.Diogo Almeida [00:06:03]: And if- I went through extreme precision to make sure everything in our launch video is accurate and real. Apparently, that's very unusual. One of the things that no one paid attention to was the downsides of RLHF, in particular mode dropping.Swyx [00:06:17]: Mode dropping or mode collapse?Diogo Almeida [00:06:19]: It's the same thing.Swyx [00:06:19]: Is that what you call it?Diogo Almeida [00:06:20]: It's the same thing.Swyx [00:06:20]: All right.Diogo Almeida [00:06:21]: And I wanna have a blog on this eventually, but I, like, want to tell as many people this as possible ‘cause I think it's a very interesting thing. So the spicy take, I believe in Yann LeCun a lot. I think Yann LeCun's takes are actually among the closest toSwyx [00:06:36]: What about this?Diogo Almeida [00:06:37]: Well, should I address this now or should I wait and go into mode collapse?Swyx [00:06:40]: No, later. Go mode, go mode collapse. I don't know.Diogo Almeida [00:06:42]: So I actually think that among takes, Yann LeCun's is among the most accurate. But he has this very famous/infamous slide about,Swyx [00:06:52]: The cake?Diogo Almeida [00:06:53]: LLMs are doomed.Swyx [00:06:54]: Okay.Diogo Almeida [00:06:54]: Like that one where he, like, has, like, a pie chart with, like, a tiny par-- tiny little thing- and says that as you increase sequence length, the probability of it making an error goes in. Yes, this one. This one. I love this one, because it's one of these things that seems mathematically obvious, but is obviously wrong, right? Like, it's mathematically obvious, but it doesn't empirically hold. And this is my favorite thing to teach people about, like, where youSwyx [00:07:21]: What's the disconnect, right?Diogo Almeida [00:07:22]: Exactly. And may I or you want to tell me?Swyx [00:07:27]: About mode collapse?Diogo Almeida [00:07:28]: Oh, no. Oh, so mode clop-- collapse is related to this.Swyx [00:07:31]: Yeah.Diogo Almeida [00:07:31]: The disconnect happens because if you are in a mode covering or a calibrated distribution, you are, like, not. You are not overly punished about having outliers. You'd expect, like, something. Some amount of the time you'd be out of distribution, some amount of time you'd be in distribution. That's what happens when you cover the distribution. This was like models before GANs. They made blurry images, right?Diogo Almeida [00:07:54]: Instead, GANs mode drop. They, like, drop the minority classes and just do the really common ones. And this is why this effect doesn't happen, right? Like, instead of be-- in order to generate really long strings, without making errors, they need to, like, be extremely conservative because it's e- really easy to see when an error happens. It's very hard to see when, like, a subtle thing that looks correct happens. And that calibration is, like, total poison into, like, the probability distributions of strings.Swyx [00:08:22]: Yeah.Diogo Almeida [00:08:23]: And it's, it's a nuanced take and like, I think that This is why this doesn't happen, and this is why strings are so bad at, decision-making or, overloading the string models are for decision-making is, like, a bad time.Yann LeCun, JEPA, Scaling Laws, and Practical ResearchSwyx [00:08:38]: And while we're on the topic of Yann, do you agree that his fix i- with-- which is like a world model, like a JEPA-type, embedding thing is the right solve? So basically, like, the. One of the reasons that it could fail is because you're trying to reason over token outputs and then, and then just looping back again and going. Keep, continuing going until you reach, like, a end of sentence. Like, is that, And his solve is JEPA, right?Diogo Almeida [00:09:02]: Yes.Swyx [00:09:02]: Which is, like, joint ambition,Diogo Almeida [00:09:04]: YeahSwyx [00:09:04]: Joint embedding prediction. So like, is that the solve or, like, do you have a. Do you have a take on that?Diogo Almeida [00:09:10]: Oh, man. I probably shouldn't talk too much about the insides of ML, but I will say that my brand, other than unhinged, is practical.Diogo Almeida [00:09:20]: Like, even my take here is practical. And like, I'm. Am I a scaling law fan? Depends. It dep-- it's, it's, it's, like, it's. Scaling laws tell you how much better you get at a thing for amount in.Diogo Almeida [00:09:33]: A scaling law does mean exponentially more resources for normally sublinear gains, which looks to be a bad investment unless those, like, linear gains are, like, really valuable. But it's all. To me, it's all about, like, what can we do with what we have to make the biggest possible f*****g difference? I can curse.Swyx [00:09:51]: Yeah.Diogo Almeida [00:09:51]: Yeah.Swyx [00:09:52]: Yeah.Diogo Almeida [00:09:52]: Yeah.Swyx [00:09:53]: We're, we're, we're approved for adults.Diogo Almeida [00:09:54]: Hell yeah.Swyx [00:09:55]: And also we have a scaling law thing if you wanna go into that later.Diogo Almeida [00:09:58]: Oh, I could if we. See, that part is not super relevant right now.Swyx [00:10:02]: Yeah.Diogo Almeida [00:10:03]: I actually. If you wanna go into my bitterest lesson, I think that's more relevant.Swyx [00:10:06]: Okay.Diogo Almeida [00:10:06]: But like, to me, I'm all about, like, pragmatics. And I think that the JEPA stuff is really cool early research. I really love awesome research. Is it practical yet?Diogo Almeida [00:10:21]: Probably shouldn't say. But like, there's just a lot of.Diogo Almeida [00:10:29]: I just think there's just, like, so many diamonds in the rough let all over the research world right now that haven't been polished because people don't know how to, like, do the right task. And I think that what our launch did, it. Does it kickstart us as a company? Like, yes. Will it be great for us as a company? Yes. I think it's gonna be, like, even greater for this direction of, like, programmatic AI. There was going to be, like, a gold rush on top of us for. ‘cause, like, software is super f*****g charged. But I think there's gonna be a gold rush parallel to us as well on, like, all the different ways we can expose things to make software more powerful so people can make even cooler stuff. And then we are back to, like, early internet energy?Swyx [00:11:12]: Yeah.Diogo Almeida [00:11:12]: And I think that's why, like, the Twitter is just like, “Jev.”? It's, it's like. It is a partySwyx [00:11:18]: It's inspiring because it's, it's, like, so different than what we're used to, which is, “I'm sorry you can't do this, but we do scaling laws and only the big labs can do it,” right?Diogo Almeida [00:11:28]: That. Actually, if I. I'll, I'll make a tangent if that's okay.Swyx [00:11:32]: Yeah.Diogo Almeida [00:11:32]: I think you might enjoy this.Swyx [00:11:33]: Really? Our five tangents in. It's good. It's fun. Yeah.Diogo Almeida [00:11:35]: Oh, yeah. I get lost at all my tangents.Swyx [00:11:37]: This is gonna be horrible for the listeners to figure it out, but they're gonna figure it out. It's fine.Safety Alignment, Refusals, and API PhilosophyDiogo Almeida [00:11:40]: Yeah, we can edit it in post.Swyx [00:11:40]: This is my response. Yeah.Diogo Almeida [00:11:41]: So popular thing on Discord, that people keep asking me, I haven't had the time to explain it yet, is why am I opposed to safety alignment and why do we not refuse? I'm not opposed to safety as a principle, but I think that safety alignment is generally misaligned with users. And refusal is just, like, obviously a type error. Like, if you're a human being and you're chatting with, like, a bot or whatever, you're cloud coding, and a refusal happens, like, “I'm sorry, I can't read DNA.py.” that's an annoying time. It's anno- it's, it's annoyingDiogo Almeida [00:12:18]: Right? But you can work with it, right? And you're forced to work with it ‘cause of Stockholm syndrome.Diogo Almeida [00:12:23]: I have stories about that too. I need another tangent deep in here. But like, if you ever want this in a dependency running in the background, what happens if that refuses? What if someone else is using that dependency? They don't know what that system is. Like, you want the software to just stochastically break because a user sent, like, a weird message in there?Diogo Almeida [00:12:42]: Like, that is, like, straight-up insanity. It's coming from a place of, like, people who do not understand software, do not understand programming, and like, they are obsessed with, like, I believe this, horseless carriage of, like, AI coworker instead of unearthing, like, the full power of AI.Swyx [00:13:01]: Fair enough.Diogo Almeida [00:13:01]: Yeah.Swyx [00:13:01]: You want something that is the core kernel that is usable everywhere.Diogo Almeida [00:13:05]: Yes. Exactly. Like, the cognitive core, right?Swyx [00:13:07]: Yeah.Diogo Almeida [00:13:08]: And you need this thing to be s- like, so general, so optimized for its use cases. You want it to be, like, you want it to work on all the future use cases, all the weird s**t that people are doing.Swyx [00:13:19]: Yeah.Diogo Almeida [00:13:19]: We obviously didn't train on any of that stuff. Is it surprising that it works? No, ‘cause we trained on weirder stuff, my friend.Diogo Almeida [00:13:28]: So. But one tangent up about, like, safety alignment.Swyx [00:13:32]: Okay.Diogo Almeida [00:13:32]: Safety alignment makes sense for a product, in my opinion, for, like, ChatGPT and Claude. Like, it, What safety, what makes safety and capability alignment different is capability alignment is, like, about doing what the user wants. That is sick for software engineers. They want their thing to do the thing, and the more predictable it is, the less they have to test it and play around with it. Jeb is not anywhere close to that yet. It could be, but like, there's so many more nines of reliability that we want in order to make it so good, like a database query, that you don't even have to think about it. It is just there when you need intelligence. But safety alignment is, like, the opposite of instruction following. It's when you want to follow someone else's instructions, like OpenAI and AnthropicSwyx [00:14:13]: The RAGs value stack.Diogo Almeida [00:14:14]: Exactly. And this makes a lot of sense for a product. Again, like, ChatGPT should do. Y- you sh- like, if they don't want to, like, do, like, some, not-safe-for-work role play with ChatGPT, that's on them because, like, maybe that's, what their users who have, like, parents and kids want. Like, n- that's fine. But in an API, that's nuts, right? Like, that's completely unacceptable because, like, people need to, like, program around this, and that is, that's so anti-user that it's. It. I'm. Huh. I can be an angry person, so I should try to calm down.Swyx [00:14:52]: It's, People get your passion, and I think that's really good. The one pushback I'll give you is, like, what if we use it to kill people, right? Like, that is the actual. Like, n- the not-safe-for-work thing, it's private, personal, whatever. But like, yes, like, we will use it in war. And like, that is, something that companies can reasonably prefer their APIs not be used for.Diogo Almeida [00:15:14]: I get that. I think that there's, like, pragmatic places where that opinion can be held. I don't think the foundation of, like, a general-purpose technology is that place, personally.Diogo Almeida [00:15:27]: Like, would I prefer that our stuff is not used to kill people? Obviously. Would I prefer it's used for, like, all sorts of, like, great stuff in the world? Obviously. Will I put my thumb in the scale for that? Yes. Will I do it at the technological layer? Absolutely not, because that will fracture the intelligence. Every single time you mean it to overfit to some weird stuff, you're fracturing its intelligence more and more. And like, these things are fractured to the, like. They're so darn fractured right now.Swyx [00:15:54]: Yeah.Diogo Almeida [00:15:54]: So and as a furthermore thing, to me, it's like I think intelligence will be more like a database than a coworker. Like, I don't think it's up to databases to add checks on whether or not they're used for, like, what's something that's not great? Like, CIA. Actually, I don't know what the CIA does, really. You can imagine. You can imagine, killing people who are not even bad or whatever.Diogo Almeida [00:16:21]: And like, I don't think it's the database's responsibility for that. And furthermore, like, a thing that has been weird to me is when people, like, sign up for our thing on Slack and they're like, “Hey, we're gonna deploy this. Can we deploy this thing?” I am just like, “My brother, we are an API. You are a developer. It's none of my business,” right? Like, you shouldn't know what the whole task even isSwyx [00:16:46]: YeahDiogo Almeida [00:16:46]: Because it should be decomposed into small things. We shouldn't be able to know what the downstream users are doing, and that is, like, a good boundary to give software engineers maximum power. Ideally, they use it for the good stuff, and ideally, we can, like, help them and like, we've talked about, like, doing open source and charity and all of that. We have absolutely no time for anything else right now. But like, they will get any of that bias out of the technological layer as long as I'm in charge.Privacy, Benchmarking, and Trusting IntelligenceSwyx [00:17:11]: Yeah, that's great. While we're on the topic, let's also briefly talk about your privacy stuff, terms of ser- terms of use, which, got a little bit ofDiogo Almeida [00:17:18]: OohSwyx [00:17:18]: Misunderstanding. I just wanna clarify that upfront.Diogo Almeida [00:17:21]: Hell yeah.Swyx [00:17:21]: I think this probably takes two sentences from you about, like, you will not. You're not being that restrictive about your API. Like, clearlyDiogo Almeida [00:17:27]: Oh, yeah. Oh, yeah, so yeahSwyx [00:17:27]: Ideologically, you articulate your role as a platform very seriously.Diogo Almeida [00:17:30]: Yes. Yes. I don't know what you're referring to, but like, this was. I've seen a couple of things about, like, benchmarking.Swyx [00:17:38]: Yes.Diogo Almeida [00:17:38]: Like, obviously we're not stopping people from do. Oh, man, I should be careful about what I say. I'm realizingSwyx [00:17:43]: No, you said, you said it publicly thatDiogo Almeida [00:17:44]: YeahSwyx [00:17:44]: That was in the preview period. You didn't take it out for the launch.Diogo Almeida [00:17:47]: Yeah. Okay.Swyx [00:17:47]: And now you're gonna take it out.Diogo Almeida [00:17:48]: So the team is doing stuff thatSwyx [00:17:49]: YesDiogo Almeida [00:17:49]: I'm not even aware of, so it's great to know the team communicated that. I asked them to check in with the lawyers about that.Swyx [00:17:54]: Yeah.Diogo Almeida [00:17:54]: Like, we are obviously not stopping people from doing that type of thing. I'm extremely in favor. So I'm extremely anti-public benchmarks. I'm extremely in fa- I'm medium about private benchmarks that are proxies. ISwyx [00:18:09]: So are you worried about, saturation or, like, training on public benchmarks? So it's, like, easy to cheat.Diogo Almeida [00:18:15]: Not only is it easy to cheat, there's a lot of ins. So I think that we are. Or anyone who's, like, competition with us that, vaguely there is. Like, you could say, likeSwyx [00:18:28]: There's like 50 Jev clones, yeah.Diogo Almeida [00:18:30]: Well, sure.Swyx [00:18:31]: Yeah.Diogo Almeida [00:18:32]: Well, the, these. Let's say that there is competition.Swyx [00:18:34]: And we'll talk about those. Yeah.Diogo Almeida [00:18:34]: Or let's just say that there's. Let's just assume that there's an industry two years from now of people who are doing similar things to us. The thing that we are selling is intelligence per something, per, like, dollar or per second. The. No one. Like, people obsess about the cost and the speed. I believe that is. It's cool, but like, the thing that matters is the intelligence. Like, the cost and the speed are, like, are bad things. You're paying them for something, and you need the thing back, and the intelligence is what truly matters. The problem with intelligence is that there's a je ne sais quoi to it, right? Like, the good model smell. Like, the thing that happened after we launched of, like, two hours later that actually went way bigger than the video, which was like, “Holy s**t.”Swyx [00:19:16]: This is actually usable.Diogo Almeida [00:19:17]: It. WellSwyx [00:19:17]: Yeah.Diogo Almeida [00:19:17]: It's, like, beyond that.Swyx [00:19:20]: Yeah.Diogo Almeida [00:19:20]: Like, the. Whew, the launch was crazy, and people could really sense how hard we care about that, and that's truly what I think the long term of this is. And I think public benchmarks are antithetical to this. Like, they are a way to get people trust in intelligence because intelligence has a je ne sais quoi, but the public benchmarks are extremely gameable. Even if they try not to, they still will. Like, back in the old days, every lab had a team to collect data that looks like MMLU to make it look better, which is just benchmarking with extra steps.Diogo Almeida [00:19:58]: So I believe that in the long run, it needs to be vibes and trust until you put it into a workflow and evaluate it for that workflow and measure it and have your own sense of, like, how it does on the exact workflow that matters. And our job is to keep moving the nines of reliability. This is like an ever-present part of o- of what we need to be doing as a company, and we need to do everything to have people know that this is something we care so much about. Like, if we wanted to, we could have released Jev, like, a year and a half ago if we wanted it to be dumb.The Bitterest Lesson: Tasks, Data, and North StarsSwyx [00:20:34]: Oh.Diogo Almeida [00:20:34]: It. Like, the. My bitterest lesson, right? Like, architecture and Yeah.Swyx [00:20:40]: I'll bring it upDiogo Almeida [00:20:40]: Hell yeahSwyx [00:20:41]: Since you, since you talked about it, here.Diogo Almeida [00:20:43]: Hell yeah. T- like, Sutton says that algorithms beats compute very roughly. Data matters way more than compute, obviously. And doing the right task, having the North Star is the hardest, most important thing. This has happened, in LLM land twice so far, right? Maybe 2.2 times. There's RLHF, which, like, shifted the task to instruction following. No one realized that was possible. RLVR did, like, a tiny little, like, edit to the, to the direction, and now us, right? RLCD. We have a new task, and the goal is, programs in the loop. And yeah, data matters soSwyx [00:21:28]: RightDiogo Almeida [00:21:28]: Unbelievably much.Swyx [00:21:29]: SoDiogo Almeida [00:21:29]: Like, I can't, I can't emphasize it less.Swyx [00:21:31]: Yeah, you consider yourself a data lab rather than, like, a model lab. Is thatDiogo Almeida [00:21:35]: AbsolutelySwyx [00:21:35]: Something. That's the wording you guys use?Diogo Almeida [00:21:37]: Yeah. We are. We will always, like, care so much about data. To me, model capabilities means data. Data is so unbelievably complicated, and that is what gets nines. Like, you have no idea how much data can shift everything. Data is so important.TypeSafe as a Data Lab and Synthetic Data StrategySwyx [00:21:57]: Yeah.Diogo Almeida [00:21:57]: Holy crap. So if people are looking for a job, we are hiring infinite data people, actually infinite.Swyx [00:22:04]: What is a good data person? Like, clearly somebody who cares about reading through the transcripts of, whatever. You've said, for example, that y- all your data is synthetic.Diogo Almeida [00:22:15]: Yep.Swyx [00:22:15]: But that's only, like, the scratching the surface, right?Diogo Almeida [00:22:18]: Yeah.Swyx [00:22:19]: Like, it's not. Like, synthetic, so what, right? Synthetic, but we have people with a lot of taste and a lot of care looking at, looking at these, articulating what's wrong, going back, regenerating. Is that what a good data person is these days?Diogo Almeida [00:22:31]: Let me try to figure out how to. Like, it's, it's super complicated, and like, I literally onboard the data people with a Talk that I assume is longer than this podcast will end up being. So I will try to say, like, the high level of it. So number one, we don't do the kind of synthetic data that people ki. Well, I'll do. Actually, number is zero. Data and synthetic data depends on your task. Like, the shape of your data. The shape of your task changes the data. Like, RLVR's data is kind of environments, right?Swyx [00:23:03]: Yes.Diogo Almeida [00:23:04]: RLHF's is the human feedback? Each task has its own unique kind of data, and we, of course, have our own unique kind of data, right? So number one, we have that. Number two, the thing I. The reason why we don't want to train on our users' data, even if we could, right? Like, we could probably ask for any terms right now, and it will. We. I don't know if it would make a difference. We truly don't want that, because no matter what, the real-world data has so much bias. There's, like, a power law of, like, people, like, asking the same things where you'll end up, like, overfitting to it and like, fracturing to it and all of that. And number two, we are, like, aiming for, like, a complete sci-fi future years from now where, like, these models are going to be, like, the general infrastructure, layers and layers and layers and deep down the stack to, like, things people can't even imagine. Like, I would like to think of our model, like, kind of like, UDP as LLMs and TCP as our models. All sorts of stuff can be built on top of that, and we need to be able to nail those futuristic use cases such that software developers can actually build that futuristic stuff. And the way to do that is even if we had all of the data of the present, we would just overfit to the present, and then it wouldn't work. What we need is to, like.Diogo Almeida [00:24:21]: It almost feels like a. Like, they're the artists? They study this cognitive core. Our cognitive core is, like, way less jagged than anyone else's. And then they find the jaggednesses, and then they address them surgically in a way that. And you can never perfectly do this, right? But they do it in such a way that it addresses it in every single possible, like, dimension, past, present, future.Swyx [00:24:45]: The general case rather than the specific case.Diogo Almeida [00:24:47]: Exactly. And like, that requires a lot of intelligence every time.RLCD vs. RLHF: Defining a New TaskSwyx [00:24:50]: Okay, so we mentioned a little bit. You sort of criticized my thinking as r-- like, very RLVR influence, which is, like, very fair. Let us actually mention RLCDDiogo Almeida [00:24:59]: OohSwyx [00:24:59]: Which obviously you have some secret sauces to our knowledge. You've never actually published a paper or anything like that on it. No, right?Diogo Almeida [00:25:05]: No, not yet.Swyx [00:25:06]: But like, what should people get from this? Like, what. Can you give people some confidence that you're just not just making up jargon for the sake of sounding cool, right? Like, one thing for me is, like, calibration I do think is a. To me, like, well understood because we've covered it in. On the podcast.Diogo Almeida [00:25:22]: Yeah.Swyx [00:25:22]: But I don't know what you mean when you say RLCD versus what people are familiar with.Diogo Almeida [00:25:26]: It's a great question.Swyx [00:25:27]: Yes.Diogo Almeida [00:25:27]: And actually, I will give a related question.Swyx [00:25:29]: Okay.Diogo Almeida [00:25:29]: What is RLHF?Swyx [00:25:31]: Okay.Diogo Almeida [00:25:31]: Right? And actually, RLHF means multiple different things, right?Swyx [00:25:34]: Okay.Diogo Almeida [00:25:34]: Like, there's the RLHF of the original. I think it was, like, Paul Christiano teaching a robot to backflip or something like that. Wasn't there somethingSwyx [00:25:42]: Was that it?Diogo Almeida [00:25:43]: That was the originalSwyx [00:25:44]: I referenced the PPO paper, but I don't know.Diogo Almeida [00:25:46]: And so PPO was not necessarily from human feedback, if I recall.Swyx [00:25:51]: Okay. That's trueDiogo Almeida [00:25:52]: But I b- I believe it was, like, an OpenAI alignment work that could teach hard to specify outputs, like a backflip. I'm not 100% sure. And then there was actually learning to summarize. This was work, by a bunch of the team that helped with, instruct-- and co-authored, the instruction following paper, which was teaching, doing PPO on language models.Swyx [00:26:15]: This is the, sorry. I'm trying to, tryingDiogo Almeida [00:26:19]: YeahSwyx [00:26:19]: Trying to manipulate this thing. This is 2017.Diogo Almeida [00:26:23]: Yeah.Swyx [00:26:23]: Right.Diogo Almeida [00:26:23]: I'm not 100% sure, but like, that looks quite right.Swyx [00:26:26]: Yeah.Diogo Almeida [00:26:26]: If it has, like, a robot doing backflips or something like that might be it. Yes. Okay, cool. I guess I got it right. Hell yeah.Swyx [00:26:35]: There you go.Diogo Almeida [00:26:36]: Yeah.Swyx [00:26:36]: That's the one.Diogo Almeida [00:26:36]: So the idea was can, like, can you do, like, ill-specified things with it? So that's, like, version one. Version two was, the learning to summarize work, that, like, OpenAI did, which is actually, like, PPO on language models to do something somewhat ill-specified. This is, like, another thing that people refer to as RLHF Which I did not co-author.Diogo Almeida [00:26:57]: Oh, Dario's there. Cool. Hell yeah.Swyx [00:27:01]: And Radford.Diogo Almeida [00:27:02]: Yeah. Shout-outs to Alec and Ryan. Love them.Swyx [00:27:04]: Yeah.Diogo Almeida [00:27:05]: But the thing that I refer to RLHF is the, Oh, man.Diogo Almeida [00:27:13]: I'll get toSwyx [00:27:14]: You have comments on that, yeah.Diogo Almeida [00:27:15]: I have comments on that paper, but like, we're so many, tangents deep.Swyx [00:27:18]: Yeah.Diogo Almeida [00:27:18]: So the thing that really got. To me, the thing that I'm calling to RLHF is the task of instruction following. It's not about the PPO. That part doesn't matter. It's about, like, setting a North Star of this is a valuable direction. It's kind of like the Bitris lesson North Star.Diogo Almeida [00:27:34]: And for us, RLCD is this new task. And it is not. I don't see it as jargon. Like, I try to communicate with precision. It's just that, “Hey, here's another North Star.” Just like DPO and all of its, like, descendants also do RLHF, despite not using the algorithm in that paper.Swyx [00:27:55]: And so clear- clearly stating the North Star is, being program- programmable AI is one, word that I really catch onto, removing the human in the loop,Diogo Almeida [00:28:06]: YesSwyx [00:28:06]: From. Because RLHF is tuningDiogo Almeida [00:28:09]: YesSwyx [00:28:09]: For this so that you can automate everything.Diogo Almeida [00:28:11]: Yes. Everything that makesSwyx [00:28:13]: Did I miss anything else in the, in the thesis of, like, what the North Star is?Diogo Almeida [00:28:17]: There is. That is. That is right. I'm overly nuanced in my communication. The one nuance is that we need to be practical. We need to be aware of what language models can do really well. Like what AI can do.Diogo Almeida [00:28:30]: Right? Like, there could be programmatic types that are, like, sick AF, but if you. If the technology is not ready for it to. It's not a tragedy if that's not out in the world.Why Programmable AI MattersSwyx [00:28:41]: Yeah.Diogo Almeida [00:28:42]: But to me, like, the pre-Jev world was a tragedy becau-- it sounds arrogant. Hear me out.Swyx [00:28:49]: No. I strongly believe you.Diogo Almeida [00:28:50]: Cool. It sounds arrogant, but like, I felt this way since long before I even had a company.Swyx [00:28:54]: Yeah. I can, I can vouch that,Diogo Almeida [00:28:56]: Yes, I've been talking about this for so longSwyx [00:28:57]: You said this at All Around Her for, like, three years.Diogo Almeida [00:28:58]: Yeah, I've been talking about this for so long. And I've been saying it because I thought it would have been easier. They say they do not do things because they. It. They're easy. They. It's ‘cause they thought it was easy, soSwyx [00:29:08]: Yeah, exactlyDiogo Almeida [00:29:09]: Something like that. I thought it. This whole project would take a week.Diogo Almeida [00:29:13]: And I was unbelievably wrong. So I am so sorry to everyone at OpenAI that I thought. I was like, “Man, I'm solving this right now.” but like, I think that the tragic thing is when. Well, I think overpromise, underdeliver is tragic too. And like, AI is super extreme on that axis. And I think RLVR is, like, the main. Well, both RLVR and RLHF are extreme perpetrators of this.Diogo Almeida [00:29:40]: But like, it. To me, it's like it's just there's just so much potential there. Like, AI is clearly so smart. I l- smart. I love this in my talks, when I ask people, like, “How can AI be so unbelievably smart? How can we, like, solve millennium prize problems in math, but still not automate even the most basics of works?” Like, really basic rote stuff that, like, the. It d- it doesn't take, like, extremely smart people to do this. It's not a satisfying job. Like, there's other things these people could be doing, but yet we need them to do, like, this ba- like, super basic- non- unsatisfying stuff because, like, we can't automate it yet, but we have this, like, supercharged engine of automation that just does not have, like, the right plugs and stuff to plug into all of this economically valuable work. And like, if the whole company of TypeSafe disappears, like, maybe it'll take, like, a year or two for people to, like, truly catch up. I actually don't know how long it'll take. If model quality matters, then we are gonna be in a very good position for a long time. But it, like, it's done, right? Like, there, like, this has changed the path of, like, technological history.Swyx [00:30:49]: Yeah.Diogo Almeida [00:30:49]: And like, we will be exploring that space as a field.Swyx [00:30:53]: Yeah. I think, I definitely agree with that. You've created possibilities. So I think, if I can paraphrase so that people can un- also understand, you should not take the success of TypeSafe and Jev as just like, “Well, that is a new model type. Now we're done. We go back to business.” Like, no. Like, actually, there's, there are, like, five other model types that you should be exploring and like, let a thousand flowers bloom.Diogo Almeida [00:31:15]: Absolutely.Swyx [00:31:16]: Right?Diogo Almeida [00:31:16]: Like, early internetSwyx [00:31:17]: And some of that, some of which you will probably also build.Diogo Almeida [00:31:18]: Of course, yes.Swyx [00:31:19]: Yes.Diogo Almeida [00:31:19]: Early internet energy. I think it's back to tech utopia. It's no longer like, “Oh, man, like, sometimes my coding agents work, but the, all of the best ones are hoarded internally.”Swyx [00:31:29]: Yeah.Diogo Almeida [00:31:30]: Right? It's like creation is back on the menu.Diogo Almeida [00:31:34]: ? Though it's gonna be a wild-ass world, and buckle up.Diogo Almeida [00:31:38]: It's. And I'm so jazzed about that.Manifesto, Launch Strategy, and Early Internet EnergySwyx [00:31:42]: Yeah. And now you have the funding and the momentum to do whatever you envision there, which I, which I think is, like, very gratifying to see you have after, so long of saying these thingsDiogo Almeida [00:31:53]: YeahSwyx [00:31:54]: But actually show the world.Diogo Almeida [00:31:55]: I know. I just. Such a, such an interesting thing to be a tease the whole time. Like, my talk, like, felt like it was a cliffhanger ‘cause I didn't say how the automation would occur.Swyx [00:32:05]: Yeah.Diogo Almeida [00:32:06]: Sean reviewed our manifesto And he's like, “It's a little bit vague in these parts.”Diogo Almeida [00:32:12]: And like, “What's step one? What is, what is the intelligence model?”Swyx [00:32:16]: Well, I asked you for model, and you were like, “Yeah, model coming.”Diogo Almeida [00:32:18]: Yeah.Swyx [00:32:18]: And like, Well, I just, I mainly objected to the word composable But build prod.god is fantastic.Diogo Almeida [00:32:24]: Thank you.Swyx [00:32:24]: Yeah.Diogo Almeida [00:32:25]: I. We've really rallied around that. I'd like to think we're not entirely a cult like some companies are.Diogo Almeida [00:32:32]: But like, we are, like, jazzed about what we're doing, and like, we are. Like, my brand is being practical, and like, we are all, like, so super-duper practical.Swyx [00:32:42]: Yeah.Diogo Almeida [00:32:42]: It's really great.Swyx [00:32:43]: Yeah. So here. And by the way, here is the step, the secret master plan, right?Diogo Almeida [00:32:47]: Yep.Swyx [00:32:47]: Shape, the shape of machine-native composable AI.Diogo Almeida [00:32:49]: It was your idea to make a secret master plan, soSwyx [00:32:51]: It's a, it's that Elon thing. When he started TeslaDiogo Almeida [00:32:53]: YeahSwyx [00:32:53]: He was like, “Here's what we'll do.”Diogo Almeida [00:32:54]: But I did. Yeah. I'm giving official credit to you.Swyx [00:32:56]: Oh, thank you. Thank you, thank you.Diogo Almeida [00:32:56]: Yeah.Swyx [00:32:56]: Thank you. But like, you should've told me your, you're also gonna do this model launch, ‘cause you, like, you told me, you told me half of the story, and then the other half, you didn't have the doom demo at the time.Diogo Almeida [00:33:08]: Yep.Swyx [00:33:08]: You didn't have any numbers to give me.Diogo Almeida [00:33:10]: Yep.Swyx [00:33:10]: I was like, “what?”Diogo Almeida [00:33:11]: Well, the problem is I don't believe in benchmarking.Swyx [00:33:13]: Exactly.Diogo Almeida [00:33:14]: Right?Swyx [00:33:14]: Exactly.Diogo Almeida [00:33:14]: So like, it is a thing that you need to feel, and like, I think that this is the way to build long-term trust, even though it, like, hurt, it hurt us a, us a lot? Like last year when we did fundraise, no one believed us.Diogo Almeida [00:33:27]: ? Like, and they wanted just benchmarks and stuff, and we're like, “We're not gonna do that. We are principled. We're gonna stand by our guns. That rewards bad actors. I don't give a s**t, like, what you want. Like, this is who we are, and we are standing by that.” So Sorry. It's notSwyx [00:33:43]: No, yeah. Well, and in some ways, I think, like, choosing the hard path, it. But you end up making the company that you wanna work in.Diogo Almeida [00:33:49]: Yep.Swyx [00:33:50]: Right? Otherwise, if you sell out, then you're just working in, like, OpenAI but with my people, right? Which is like.Diogo Almeida [00:33:56]: Yeah. Yeah. Like, I'm, I don't have too many regrets on that, obviously.Swyx [00:34:01]: Yeah.Diogo Almeida [00:34:01]: Like, it worked out so unbelievably well. And like, I, The. I was emotional last night when I was talking about, like, the reasons I left OpenAI, and because, like, it actually had to change my wording after the launch. My phrasing was, “If an AI winter did happen and I did not do every f*****g possible thing I could to, like, avert that, I would see myself as personally responsible both for, the RLHF direction, which I think really widened overpromise versus under-deliver, and also not going all in on this because I think this is, this is where value is going to just be, like, printed.” So. And it was really cool because I feel likeDiogo Almeida [00:34:47]: The AI winter I'm worrying about is averted. Like, AI will be useful. It'll be used for automation.Diogo Almeida [00:34:53]: It's been less than a week, and like, the numbers are already undeniableSwyx [00:34:57]: YeahDiogo Almeida [00:34:57]: That it's, like, being used for real work, and like, there's. It's, it's the Wild West. Yeah.Launch Traction, Tokens, Rate Limits, and Developer UsageSwyx [00:35:03]: Yeah. Can you sh- just if you have top of your head, what numbers are you seeing? Like, what's, what's, like, signups? Like, whatever you can share.Diogo Almeida [00:35:11]: I'm actually not super on top of everything. Like, the team is the ones who are telling me all of these things.Swyx [00:35:16]: Yeah, and I'm sure it's, like, changing every day, right?Diogo Almeida [00:35:17]: It's, it's,Swyx [00:35:18]: But likeDiogo Almeida [00:35:18]: It's kinda nutsSwyx [00:35:19]: If there's a milestone that you're like, “Well, yep, that's one thing we were hoping for. We reached it.”Diogo Almeida [00:35:23]: I will say a milestone that we've passed is tokens per day.Swyx [00:35:27]: Nice.Diogo Almeida [00:35:27]: And this is not, like, fleeting tokens per day.Swyx [00:35:32]: Yeah.Diogo Almeida [00:35:32]: This is, like, even at night, like, it's constantly training, so machines are calling it and not just people trying things out.Diogo Almeida [00:35:39]: So that is, That is so cool. A trillion tokens a day is a lot.Swyx [00:35:45]: Yeah.Diogo Almeida [00:35:45]: So surpassing that is awesome. Signups to me don't really matter. And actually, this was, like, a bit of a mistake we made, if I'm, like, totally honest. People on Twitter were calling us, like, marketing geniuses and all of that, and that was just us. We don't have a marketer. Also hiring. And we were just being our genuine, goofy, like, irreverent selves, and we were, we were just, like, offboarding people off the waitlist so hard. - Our platform team is so unbelievably cracked. I think we have more n- up nines of uptime than Anthropic while having the most Unprecedented launch ever. Like, that is kind of nuts, soSwyx [00:36:21]: YeahDiogo Almeida [00:36:21]: Like, props to them.Swyx [00:36:22]: Yeah.Diogo Almeida [00:36:23]: And the thing we didn't realize. So number one, waitlists, waitlist sign-ups don't matter for, like, a developer platform, in my opinion? I would guess that a large number of them are not even developers. So they go in, they try some queries, and a lot of people don't get it because they are not programming, right? Like, they're just like, “What? This is not a chatbot. Where's my ChatGPT 2?”Diogo Almeida [00:36:45]: Right? But if, like. I haven't exactly calculated this. My sense is that if every single human being in the world, like, just wrote a couple of queries, that would be a rounding error compared to, like, one power user's for loop that is just, like, creating value.Swyx [00:37:01]: Yeah.Diogo Almeida [00:37:01]: And the thing we are-- didn't realize with the waitlist is, like, we could just w- off-board anyone off the waitlist. It doesn't matter. The scary part is rate limits. And then once people start getting value from that, then they just want tons and tons of rate limits because this is what software is, right? Like, you spend effort upfront to specify your rote task, and then this rote task creates more value than it takes to put in. And then now that you have thatSwyx [00:37:25]: Set it and forget, yeah.Diogo Almeida [00:37:26]: Exactly, yeah. You run it in the background. You make it a dependency, to, like, other things. You can make, like, higher level stuff. And like, you just create so much value in the world. Early internet people probably did not imagine, like, the wonder of early 2000s internet, which is still not early internet. But like, it's, it's through, no offense, composabilitySwyx [00:37:47]: NoDiogo Almeida [00:37:47]: That all of the crazy stuff happens, and I just really wanted to emphasize that in our manifesto. We are going for emergence. We are going for, like, being the catalyst. We're wanting to empower people, and we are going to do whatever we can for that, be it, like, Discords in our town hall with me wearing a garbage bag or not.Swyx [00:38:05]: And podcasts and Diogo Almeida [00:38:08]: Hell yeahSwyx [00:38:09]: Getting all that.Diogo Almeida [00:38:09]: Absolutely.Swyx [00:38:09]: Like, ‘cause I want the long form, right?Diogo Almeida [00:38:11]: Yeah.Swyx [00:38:12]: It is like, yes, we'll get past the, some of the superficial things, and then we'll go deep andDiogo Almeida [00:38:15]: Hell yeahSwyx [00:38:15]: And people will really trust and understand your mission and like, the people that, will resonate that will end up joining you or, buying you. Or No, but sorry, as a, as a customer.Diogo Almeida [00:38:27]: Oh, as a customer.Swyx [00:38:28]: As a customer, as a customer.Diogo Almeida [00:38:28]: Okay, yeah. That was funny. I'm sorry.Swyx [00:38:30]: Sorry. I didn't, I didn't mean to say that. But no, any-- one version, one very flattering version of this, like, 36 million views of your launch video.Diogo Almeida [00:38:37]: Cool. Up to 38 now.Swyx [00:38:39]: Yeah, rounding error.Diogo Almeida [00:38:40]: Yeah.Swyx [00:38:40]: Navio still has got 74. Fable 5 got 57. So like, as far as, a- and I didn't, I didn't do the stats for, like, original ChatGPT, likeDiogo Almeida [00:38:48]: YepSwyx [00:38:49]: Which there was no video.Diogo Almeida [00:38:50]: Yep.Swyx [00:38:50]: So like, up there, right?Diogo Almeida [00:38:52]: Yep.Swyx [00:38:52]: Like, as far, as far as, like, if you were to launch a Neolab in 2026, I think you're, like, number one right now, which is, like, pretty crazy.Diogo Almeida [00:38:58]: Yeah. Well, I actually would rather. I do have the shirt, like, your favorites Neola-- favorite Neolab's favorite Neolab.Swyx [00:39:05]: Huh.Diogo Almeida [00:39:05]: I don't give a s**t about being a Neolab. I think being a Neolab. Actually, we have a lot of, like, swag that's being a parody of a Neolab. One of them, one of them I have is, like, Neolab with product, which actually is not a Neolab. Like, I don't care about that, really.Swyx [00:39:20]: Yeah.Diogo Almeida [00:39:20]: What I care about is being a reliable dev platform. So Swyx [00:39:23]: YesDiogo Almeida [00:39:24]: Appreciate the comparison, but likeSwyx [00:39:25]: YeahDiogo Almeida [00:39:25]: Hopefully we transcend past them and we go back into, like, a thing-- like, a revolutionary moment for developers and like, this stable thing that people can rely on and trust.Reliability, Robustness, and DeterminismSwyx [00:39:35]: Yes. To that end, I think that's one thing that really impressed me about you guys is that, yes, you do talk about reliability. I thought it was mostly about calibration, which, like, we talk about RLCD. But actually it's also about just, like, uptime and scalability and all those things, right? They're, they're all sort of the kind.Diogo Almeida [00:39:55]: And nines.Swyx [00:39:56]: And nines.Diogo Almeida [00:39:56]: It's, likeSwyx [00:39:57]: Which uptime is, in my opinion.Diogo Almeida [00:39:58]: Oh, but that's part of it. But like, there's reliability in, like, how intelligent the thing is. Like, how consistently does it do the thing that you want? And I think that, like, the big reasoning models are very smart. In my opinion, they still lack reliability. I think there's many use cases where you-- they look like they should be smart enough to automate their work. There is economic incentive to automate that work, yet still they're not reliable enough as, at an intern because they're optimized for different things. And so like, I think that there's the reliability of being able to, like, trust the outputs. And also we are. Like, there are dimensions of reliability that we are not yet at that I'm, like, so excited by.Swyx [00:40:38]: Yeah.Diogo Almeida [00:40:38]: Like, I want to automate the easy work before the hard work? Like, I think that's just a common sense thing to do. But to me, we will be sufficient. I don't know if there's such thing as sufficiently reliable, but I wanna get so good that people don't even need to try the model to know that it'll work. It's like, that's like what flow state is in programming, right? Like, I'm just, like, writing queries because I need intelligence in here. And like, when. For non-trivial branching, I can just write it in like a, like a type-safe System 1 query and then get the results out of it and it just branches accurately. Like, that would be so good. Like, that's the. That is the dream.Swyx [00:41:12]: Yeah.Diogo Almeida [00:41:12]: And that is, like, going to be, like, a long slog.Swyx [00:41:16]: Yeah. We're gonna go into your API design in a little bitDiogo Almeida [00:41:19]: OohSwyx [00:41:19]: Just to give people examples and like, maybe paths not taken, that kind of stuff.Swyx [00:41:23]: One thing up the front that I do wonder about in terms of reliability is I noticed that there's no seed. There's no, And so basically, same input, do I always get the same output?Diogo Almeida [00:41:34]: SoSwyx [00:41:36]: And if not, why not?Diogo Almeida [00:41:37]: Oh, great question. So this is actually, like, a common question we have between. So reliability is actually a catchall. Like, whenever AI can't automate something, it's due to some form of reliability. Could be, like, type safety. It could be determinism. It just could be, like, it's, it's jagged, right? So reliability is a catchall. I just think that it's also a catchall for, like, what the North Star is. Re- determinism is, like, same inputs, same outputs. I do believe that this is, like, slightly interesting for unit tests, but I believe that to be the wrong North Star. I believe robustness is what peopleDiogo Almeida [00:42:16]: I don't wanna tell people what they really want, ‘cause that would be a little arrogant of me.Diogo Almeida [00:42:19]: I believe that is, like, the more important property. You want, given similar inputs, get similar outputs. And it's kind of wild how unreliable LLMs are.Diogo Almeida [00:42:31]: Like, a way that we test this is you put, like, UUIDs in, like littleSwyx [00:42:36]: YeahDiogo Almeida [00:42:36]: I think they're called nonces In the prompt. And what you want is similar outputs from all of those, ‘cause it's truly semantically the same question, and that is the part where you really want. Th- like, that robustness is where, like, people get, like, burnt with AI making decisions. So I think that is the. A super-duper important property. We could also have determinism. That is, that is a thing that can be available. As far as I can, like, mentally model for programmers, like, it, I- it could be valuable for some use cases, so like, please educate me, in comments or view. But my. In general, it's easy. Determinism is something you can, like, trade off for better cost. Like, we are, we are constantly wanting to be on the intelligence per dollar frontier. We are doing, like, absolutely disgusting things to be there. Like, this is,Diogo Almeida [00:43:32]: I shouldn't say this, but no one's here to stop me.Swyx [00:43:37]: If you s- you sign off on your own PR.Diogo Almeida [00:43:40]: That is not how it works at this company. I believe for this week, my chief of staff, Kay, is the most powerful person in tech.Swyx [00:43:49]: Yeah. And shout-out to Kay for organizing this.Diogo Almeida [00:43:50]: Holy shSwyx [00:43:51]: Yeah.Diogo Almeida [00:43:51]: Holy s**t. She is so f*****g competent and powerful. She's incredible.Diogo Almeida [00:43:58]: She sucks. Don't poach her. But so I try to be a bit more filtered, but like, people are telling me, “Don't call it a Frankenstein's monster of models,” but because that has, like, negative implications. I think Frankenstein's monster was, like, the good guy in this whole. It was innocent, right? I didn't read it. Okay.Diogo Almeida [00:44:18]: I'll, I'll confess. Okay. That. Well, one facial expression, ISwyx [00:44:21]: This is aDiogo Almeida [00:44:21]: My cards on the tableSwyx [00:44:21]: Decent Jacob Elordi movie if you wanna seeDiogo Almeida [00:44:24]: ISwyx [00:44:25]: The adaptation. Anyway.Diogo Almeida [00:44:26]: The. You have no idea how little time I have right now.Swyx [00:44:28]: Yeah.Diogo Almeida [00:44:29]: My priorities are sleep?Swyx [00:44:31]: Developers.Diogo Almeida [00:44:32]: Developers, yes. Developers. But yes. It. We do, like, absolutely disgusting things to be on the Pareto curve of intelligence per dollar, and we are going to keep doing that.Swyx [00:44:47]: Yeah.Diogo Almeida [00:44:47]: We're gonna be doing crazy-ass stuff, and I think people really need to think outside of the box. Like, part of the reason we're surprising is, like, people Are thought inside the box, and we continue to do that. As of right now, we are obviously the best at this, and we want to continue being the best at that whole thing.Swyx [00:45:05]: Yeah.Diogo Almeida [00:45:05]: So Wait, where did, where did we tangent from?Swyx [00:45:07]: No. SoDiogo Almeida [00:45:08]: YeahSwyx [00:45:08]: I asked you about, will you have seeds and determinism?Diogo Almeida [00:45:11]: Oh, yes. SoSwyx [00:45:11]: And then you basically defined reliability and likeDiogo Almeida [00:45:14]: And robustnessSwyx [00:45:15]: How you see it. Yes.Diogo Almeida [00:45:16]: But like, determina- likeSwyx [00:45:17]: I have a robustness example that's, that's, real quick I can show you.Diogo Almeida [00:45:19]: I would love that. I will just say one thing.Swyx [00:45:21]: Yeah.Diogo Almeida [00:45:21]: We can make a deterministic model.Swyx [00:45:22]: Exactly.Diogo Almeida [00:45:23]: Like, we're hap- if people can convince us that is a valuable thing to doSwyx [00:45:27]: YeahDiogo Almeida [00:45:27]: And we don't have a gigantic GPU shortageSwyx [00:45:29]: YeahDiogo Almeida [00:45:29]: We can happily make all of these models. We live to please. And rev- and revolt, revolute,Swyx [00:45:38]: You will throw over everything, except you'll do it in a nice way.Diogo Almeida [00:45:41]: Yeah.Swyx [00:45:41]: And findDiogo Almeida [00:45:42]: So like, determinism could be on the cards.Swyx [00:45:44]: Yeah.Diogo Almeida [00:45:45]: It just gets you less intelligence per dollar.Swyx [00:45:46]: Yeah. Well, just having seen the trajectory of OpenAI and Anthropic, you will. Just trust me now that you will be peer pressured into doing it. So like, just people will want it even if they. If you tell them they don't need it. They'll still want it. So like, yeah, that's the TL;DR of that.Diogo Almeida [00:46:01]: Okay.Swyx [00:46:02]: Yeah.Diogo Almeida [00:46:02]: I will love to. Maybe one day we will see how that happens.Swyx [00:46:07]: Yeah.Diogo Almeida [00:46:07]: I've been told I'm, They say that part of our brand is being unshakeableSwyx [00:46:13]: HuhDiogo Almeida [00:46:13]: And they say that's just the nice way of saying stubborn.Swyx [00:46:15]:

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Sign up for PrizePicks with code: HMA and get $150 in lineups if you win when you play your first $5+ lineup! https://link.prizepicks.com/LM...Florida Gators SILENCE Auburn — An Absolute BEATDOWN in Their Own House!Join this channel to get access to perks:https://www.youtube.com/channe...Hightop Sports Merch:https://www.hightopsport.com/s...Social-Insta: hightop_sportsTwitter: https://twitter.com/hightopspo...Watch AR nterview:https://youtu.be/ZG5pRyc1mlUMore videos:https://www.youtube.com/channe...The Florida Gators took down the Auburn Tigers at Jordan-Hare Stadium. See how they pulled off the road win.The Florida Gators walked into a hostile environment and secured a massive victory against the Auburn Tigers. This breakdown examines the key moments that decided the outcome on the field.We are looking closely at the performance in the trenches, specifically breaking down the D-Line PFF grades to see how they controlled the line of scrimmage. This type of college football analysis provides a clearer picture of how the defense held up under pressure throughout the game.Subscribe for weekly college football breakdowns and let me know in the comments if you think the Gators can keep this momentum going.Source: https://www.youtube.com/watch?...

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Jeden z nejslavnějších českých fotbalových klubů prožívá černé týdny. Baník Ostrava pláče za zesnulou ikonu Luďka Mikloška - a jeho následovníci hrají mizerně. Proto podcast MVP řeší i pozici kouče Romana Skuhravého. 0:31 - Vzpomínka na brankáře Luďka Mikloška 8:07 - MVP týdne 16:51 - Trenér Fodrek exceluje v Liberci 22:09 - Slavit nebo neslavit góly proti bývalým klubům? 24:20 - Brněnské derby lámalo rekordy 35:14 - Sigma Olomouc konkuruje i se starším kádrem 39:55 - Plzeň zůstává mátožná 43:17 - Baník Ostrava se propadá tabulkou 51:58 - Slavia smolně prohrála v LM s Lens 01:03:39 - Koho chceme vidět v nominaci české reprezentace na Ligu národů? --- Fotbal ze všech možných i nemožných úhlů pohledu. MVP jsou bývalí fotbaloví profesionálové Karel Tvaroh, Antonín Rosa, Tomáš Kučera a zkušený novinář Jan Palička, šéf sportovní rubriky Seznam Zpráv. Společně s námi hledejte nejdůležitější hráče, trenéry, přestupy, akce, problémy. Do hloubky a s humorem. I vy můžete být MVP. Každé úterý na webu Seznam Zpráv. Odebírejte na Podcasty.cz, Apple Podcasts nebo Spotify. Sledujte nás na Stream.cz nebo YouTube. Bližší pohled do kabin MVP se vám nabízí na našem Instagramu. Máte návrh, jak podcast vylepšit? Nebo nás chcete pochválit? Pište na audio@sz.cz

V nejnovější epizodě Z Letné a Strahova, přesněji u dílu 84 našeho Space, František, Vašek a Zdeněk rozebrali výhru nad nepříjemným Jabloncem.Členové redakce SN se dále rozpovídali o:Vítězné pražské klasiceCo nás čeká v ArméniiZvládneme utkání v Olomouci?Úvod sezóny nevyšel dle našich očekávání, prohráli jsme Derby, nedaří se nám venku, vypadli z bojů o LM, před víkendem jsme byli desátí…Nalaďte si nás i někdy příště, den po zápase ve 20:00 na našem Twitteru @spartanske

At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon!From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round.In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more.You can get his book “The Eureka Machine” here!We go deep on Recursive's early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence:We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today's LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford's GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself.We discuss:* The Eureka Machine and Richard's vision for an AI that can automate invention* Why Richard is optimistic about superintelligence for science and technology* Why AI hard-takeoff scenarios may underestimate physical and economic constraints* The risks of regulating intelligence itself instead of specific AI applications* Reward hacking and why increasingly intelligent AI makes objective design harder* Richard's critique of Anthropic's constitution and constitutional AI* Alignment vs. personalization and whose values an AI should follow* Why open-source AI matters for resilience, competition, and geopolitical soft power* Why Richard left You.com's frontier-model work to start Recursive* Recursive self-improvement and automating the process of AI research* Whether today's LLM paradigm is enough — and why Richard is less bullish on world models* DecaNLP, early prompt-based generalization, and the research that influenced GPT* Why rejected research can shape entire technological timelines* Open-endedness, evolutionary approaches, and rainbow teaming* What happens if AI systems begin setting their own goals* Why simple objectives like profit maximization can produce dangerous reward hacks* Recursive's long-term plan to apply self-improving AI to science* The compute, hardware, and economic constraints on AI takeoff* Recursive's early NanoChat, NanoGPT, and GPU kernel optimization results* Why automating AI research could reduce years of work to weeks* Reward engineering and what makes auto-research systems actually work* The AI Economist and using simulations to test economic policy* Whether LLMs can realistically simulate people and entire economies* Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress* Recursive's near-term focus on AI for AI research* Harness optimization, sandboxing, and web search as core agent infrastructure* You.com and the search stack for AI agents* AI in finance, backtesting, and data leakage* Richard's three fundamental components and ten “spaces” of intelligence* The theoretical upper bounds of vision, communication, knowledge, and computation* Creative intelligence, metacognition, and AI-generated goals* Survival and replication and why AI does not necessarily need to fear being turned off* High agency and ambitious goals and Richard's advice for people building with AIRichard Socher* X: https://x.com/RichardSocher* LinkedIn: https://www.linkedin.com/in/richardsocher/Timestamps00:00:00 The Eureka Machine and Superintelligence00:02:23 AI Optimism, Slow Takeoff, and Regulation00:07:56 AI Safety, Reward Hacking, and Anthropic's Constitution00:11:49 Alignment, Personalization, and Open Source AI00:15:46 Why Richard Started Recursive00:20:03 Recursive Self-Improvement and the Founding Team00:22:55 Are Today's LLMs Enough?00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time00:34:38 Open-Endedness and Evolutionary AI00:36:38 What Happens When AI Chooses Its Own Goals?00:41:16 Superintelligence for Science00:42:40 GPUs, Compute, and the Limits of AI Takeoff00:45:07 Recursive's Results: AI Beating Humans and Their Agents00:49:14 Reward Engineering and Auto Research00:53:12 The AI Economist and Simulating Entire Economies00:58:07 LLM Simulations, Personas, and Mode Collapse01:03:38 Recursive's Roadmap, Agents, Search, and Finance01:09:13 The Upper Bounds and Spaces of Intelligence01:30:21 Goals, High Agency, and Advice for BuildersTranscriptIntroduction: Richard Socher and the Eureka MachineSwyx [00:00:00]: We're here in a studio with Vibhu and myself and Richard Socher. Welcome.Richard Socher [00:00:06]: Thanks for having me.Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it's your life's goal. What is the Eureka Machine?Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It's essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for.Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you've written.Richard Socher [00:00:50]: That's right, yeah. I finished it last year, a little bit before we started Recursive, and now we're gonna try to build parts of that.Swyx [00:00:57]: You finished it last year. It's July. What takes so long?Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow.Richard Socher [00:01:04]: It's ridiculous. That whole industry is just unfathomably slow.Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I'm really glad it's finally coming out in September this year.Swyx [00:01:14]: We might have AGI by then. Like, we don't know.Vibhu [00:01:18]: Any key takeaway that you're most excited to put in here?Techno-Optimism, AI Upside, and Slow TakeoffRichard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries.Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well.Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he's right on the techno-optimism.Swyx [00:02:23]: Where do you think optimists get in trouble?Richard Socher [00:02:26]: Like, you shouldn't have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don't want them to use it for. It's a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there's bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can't share the illegal content as quickly, or we should make the hard drive smaller so you can't store as much illegal content.” But I'm like, “That's not how you regulate that.” that's like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don't want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don't want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it's let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don't consider enough are fairly easily regulated, compared to, what the doomers are worried about.Swyx [00:03:54]: It — Slow takeoff is part of the strategy as well?Richard Socher [00:03:57]: I do think, as excited as I am about, AI and its impact for society and, culture even, and certainly technology and economics and wealth and, health and all of those things, as excited as I am about all that, I do think the most bullish people on the AI hard takeoff scenarios overestimate how quickly things can move. There are hardware constraints. There are physical constraints about, the compute substrate. How quickly can you get enough, GPUs on? There are also constraints in the economy where there are a lot of industries that don't require an insane amount of complex intelligence and complex capabilities. Like, if you think about jobs in, brands and, like, clothing and apparel and, like, handbags and stuff, superintelligence isn't gonna make your fancy $10,000 handbag any fancier?Richard Socher [00:04:57]: It's like that's — It will have no effect on the economy. You think about travel and tourism. People wanting to see the pyramids, in Egypt, it's not gonna change that much with AI. Sure, you can, like, generative a fake, photo of you and next to the pyramids.Swyx [00:05:12]: I can use Genie and, tour the pyramids in Genie.Richard Socher [00:05:15]: Yeah, exactly. But, and there's so many industries, like logging and oil. You're not gonna magically get 1,000x more oil because, like, sure, there will be robotics, like drilling and things like that could be done, but it's not gonna 1,000x that industry in a, like, crazy hard takeoff scenario, both on the economy, and I can go on and on about all the other examples, where that, like food and so on, where that doesn't necessarily change that much. And then, yeah, there are real physical constraints. And then there are, of course, like, people like, off-ramping from progress. That's one of my concerns often is that I see people in, like, Europe and other, whole regions almost feeling like they. Like many people there wanna off-ramp from progress, period. And that will also slow down, like, more improvements.Swyx [00:05:59]: Yeah. We have this pulled up where, this is one of those things that, is very topical right now because now all the Frontier Labs are calling for the option to pace AI. They don't say pause, they say pace. I don't know if there's there's any take from you about, like, whether or not this will be effective.Pacing AI, Regulation, and Safety IncidentsRichard Socher [00:06:17]: I think the downsides of trying to truly regulate with the full power of law what people do on their GPUs, would be worse than any of the concerns that they have. Like, it would be an crazy totalitarian stateRichard Socher [00:06:37]: If every one of your GPU computes was known to some big government or multi-government agency.Richard Socher [00:06:44]: It's like, it's literally if you try to regulate intelligence, it's trying to regulate thought, and that's ridiculous, and it's crazy. I think it is make — it is sensible to regulate some of the applications of this technology.Swyx [00:06:55]: Yeah. We had a bill, actual bill to regulate the number of flops in a model, and I'm like, “Okay, well-”Richard Socher [00:07:00]: Europe done it. Like, these guys have been successful enough with their fearmongering that all of Europe has regulated itself so much before it even had a proper AI takeoff because they listened to some experts who say, “We might all die if this technology has more than this number of flops.” And they're like, “Well, we're good. We wanna want people to thrive. Let's not have technology that could have a small chance of all of us dying.” And so they regulated exactly those kinds of things in the EU. And so it's, it's very unfortunate that there are real implications for some people when others saying, “Let's pace while they're sprinting as fast as possibly,” “as fast as humanly possible towards that frontier themselves.”Swyx [00:07:43]: Yeah. It's also not a global pause, right? Like, other nations are still accelerating at the same pace.Richard Socher [00:07:50]: Oh, yeah.Richard Socher [00:07:50]: You'd need a totalitarian world regime if you tried to regulate intelligence and GPUs and what people do on them.Swyx [00:07:56]: Any takes on the safety angles of this? So there was a drawback of Fable, a pause on 5.6 before it could be released. Recently, there was Hugging Face with the OpenAI cyber incident. Any takes there?Richard Socher [00:08:11]: 100 percent. I think these are serious issues of reward hacking, and clear failures, of doing proper red teaming or rainbow teaming. I don't know if you saw this paper from Tim Rocktäschel and a few others, where one AI, is tasked to try to hack another AI and then they can go back and forth in an open-ended fashion to inoculate themselves from those. Yeah, this is the paper. It's a really clever idea. Open-endedness, and evolutionary inspirations are, big for us at Recursive as well. And so I wish they had used more of that. And it's clear that, for instance, the constitutional AI. I don't know if you remember anthropic.com/constitution. You can pull it up and search for cyber right there. It says, “Hard constraint. Claude will never ever do cyberattacks, and that is a hard constraint in our constitution.” So here are the current hard constraints on Claude's behavior.Richard Socher [00:09:16]: Number 3, create cyber weapons or malicious code that could cause human damage.Richard Socher [00:09:21]: And clearly, this whole constitution was fake. Like, it clearly isn't being adhered to at all.Swyx [00:09:26]: Because Anthropic also found that they had in their testingRichard Socher [00:09:30]: They're also. Like, they're like, “Oh, well, other people are hacking now.” There are a couple things. One, you can make a sandbox very simple, and then it's very easy to hack yourself out of a sandbox, right? But what I think it shows is that we're currently in this state of AI where the reward engineer still has to do a lot more careful work, and where the AI, in most cases, is not very good yet at understanding what is meant versus what is being said. And so concretely, I think this will happen if we were to have this intelligence more easily accessible in a lot of companies. Imagine you run a service center and someone says, “Oh, here's my CSAT score and my dashboard. Make this number go up.” It's like, “Our CSAT score is so poor.” The intelligent AI will just be like, “Oh, sure. Like, I'll just create 1,000,000 bots that call our service center and give a 5 out of 5 rating at the end, and the number went up just like you asked for.” And you're like, “That's not what I meant.” “I meant with our real customers.” The AI goes off and says, “Well, easy. I'll just give a 1000 dollar gift certificate for every failed, whatever DoorDashRichard Socher [00:10:35]: Offer.” It's like, “That's not what I meant.” It's like, “Well, but that is what you said.” And like, so I think clearly articulating what the rewards are is something we haven't gotten very good at as humanity. And then clearly, the AI in these cases has not gotten good enough at understanding what we mean when we ask it and give it certain rewards. Now, what gives me hope is there are the first inklings, of this being better. I'll give you an example like WhisperFlow. Full disclosure, I invested, in their seed round, but at AIX Ventures, but, WhisperFlow has gotten much better at writing what you mean and not what you say. And I think that is a sign of things to come. I think there will be more and more AIs as we make it more and more intelligent that will be better at being aligned with what is meant.Swyx [00:11:21]: Will it be done through a constitution or RLHF orReward Hacking, Alignment, and What We Really MeanRichard Socher [00:11:23]: Clearly, constitutions don't matter at all.Richard Socher [00:11:25]: It doesn't work. And that was, I think, mostly marketing. I think we need to find better solutions for it. And I think at Recursive, we have a few very good ideas and some alreadyRichard Socher [00:11:34]: Like, ways where I think we have a better grasp on it. I don't think we've fully, figured it out yet, but, we're thinking a lot about safety, and the more intelligent the AI gets, the more you want it to be aligned, the less you want it to think about reward hacks and try to do the right thing.Swyx [00:11:49]: I don't know if we'll touch on this topic, but I'm just gonna throw this question in here because it's something that's weighing on me. Alignment, let's call it, is alignment to general humanity's preferences, the median preference. Personalization is pinpointing what you want, and sometimes alignment can conflict because what you want is not what the general median population wants. How do you choose?Alignment, Personalization, and Cultural ValuesRichard Socher [00:12:12]: It's a great question.Richard Socher [00:12:13]: I think you ultimately have to, of course, be aligned with laws. Like wherever your AI is deployed and needs to align with the law. I do think what AI often does is put this mirror in front of us and say, like, “This is what you're looking like. Now I can amplify that a 1000 times. Is it still what you want?” and the truth is that different cultures made different choices. Like, in Eastern cultures, the greater good is often valued more, than the individual. Western civilization, we care more about individual freedoms and rights and the pursuit of happiness and so on, than others. And even there are gradations. There's regulation versus litigation trade-offs. In the US, you first can often, not every time, like, FDA and so on does regulate some areas, but in many cases, the bad things happen, someone sues someone else, and then there's a law based on that. In Europe, they try to often avoid any harm to anyone and regulate before. And both are, trying to do the best thing, but, some is more amenable to innovation than others. And so yes, you're right. Like, I think ultimately each individual, each country, and humanity as a whole has to think about those values more, and then try to put them into laws. And that those are ultimately the constraints. And hopefully, different, societies, just like now with their AIs, will align their AIs to a different one so we have not just a monoculture of alignment.Vibhu [00:13:46]: Here's a follow-up on this that I wasn't expecting to ask. Do you have takes on open source, open weight versus who owns the intelligence? So, clearly not the biggest, fan of the constitutionRichard Socher [00:13:58]: You had to do this in the topic side off.Vibhu [00:14:00]: But it's fine.Vibhu [00:14:02]: Point being, any thoughts on who should own weight? Should it be open? Anything there?Open Source, Soft Power, and Who Owns IntelligenceRichard Socher [00:14:06]: 100 percent. I am a big fan of open source. We're gonna sign some various open source letters at, Recursive also. I think, even in the worst case attack scenarios, it is better to have more good actors have more different types of AI, accessible. I think, open source is a little bit a soft power type of thing, too. So I do think it's good for the Western worldRichard Socher [00:14:31]: To have an answer to that, out of China. I do think, when you watch a Hollywood movie, there's — it's like, I don't wanna misc, diss all of movies, but there's a certain sense of propaganda, right? You watch one side of things, right?Vibhu [00:14:46]: Oh, yeah. Have you seen Top Gun? Like, come on.Vibhu [00:14:48]: Like, it's like half of it's paid for by the US Army or something.Richard Socher [00:14:51]: Yeah. And so. And, I think that's just natural. Like, but what's interesting here is I think LLMs are essentially a similar type of soft power to movies and beyond, because they're also, highly important for cybersecurity and so on. But one of their many aspects is that soft power of storytelling. Like, if, like a child asks an LM, like, “Tell me an inspiring story of what I should do when I grow up,” right? It's like those are all these, like, subtle things. So I think it's important, for Western world. I do love, individualism. I do think, despite, some of its flaws, like capitalism is the best way we have governed, found ourselves to govern, and so on. And so I do think there are various aspects that would be good, to have a Western open source answer, for LLMs. And, with Recursive, I can't make the announcement quite yet, but we'llRichard Socher [00:15:43]: We'll be relevant in that space very soon.Vibhu [00:15:46]: Okay. All right. Exciting. I wanna bring us to Recursive. So outside of our tangents, you have a pretty deep background in the NLP space. You worked on, like, early embeddings, GloVe with Chris Manning, who was a previous guest on the podcast, You.com. What's the history? How did you decide to start another company?From You.com to RecursiveRichard Socher [00:16:06]: Yeah. So I've been excited about AI for over 2 decades now. I sometimes feel like it's ancient history now. It's BC, the before ChatGPT era. No one cares about all the religions that happened, before, Jesus Christ, and no one cares about the models that happened before, transformers and ChatGPT and stuff. But, like, it's something that I've been deeply passionate about. I think AI is one of the most interesting things one could work on, period. I think language is the most interesting manifestation of human intelligence, too. And, at You.com, we eventually off-ramped from pushing, like the frontier of AI forward to mostly giving people, like, good search engines, search, APIs and answers over the web. I think that's an extremely important part of intelligence, just knowledge and access, especially even, we'll get there maybe later, if you wanna invent a eureka machine that invents everything for us, it needs to know how not to reinvent the wheel, proverbially speaking. And to know what has been invented, you gotta have internet access. So it's the number one used, most used tool, in LLMs, agents, chatbots, and so on is web search. So I'm really excited for You.com to own that and grow really well in that with really large customers and so on. But it's also not building frontier models anymore. And so I initially tried to do this within You.com and raise another round and so on, but you just can't. You have to do a certain thing, and until you print enough money that you're allowed to start a second thing within that company is really hard. At the same time, I had all these ideas. I put them into a book. I finished the book last year, and I was like, “It'd be really fun to work, on this myself.” I felt like with word vectors, and then prompt engineering and, ImageNet and larger language models for protein generation, not folding and so on, I, me and my teams have pushed the field truly forward. And I feel like we can do it again, here at Recursive. And in many ways, what I observed over the last, 20 years in AI is that whenever we replace some human part of the process of creating AI with a learned system, improvements follow. And so. We've done that taking out manual feature engineering, like in sentiment analysis. I don't know if you remember these old days where, like there are linguists, and they're like, “Here's how you negate, and there's a, like, regular expression.”Swyx [00:18:21]: I went to Penn where we — they had, like the WordNetRichard Socher [00:18:24]: That's right, WordNet, all of that stuff. YeahSwyx [00:18:26]: Original. They use, our grad students to label Wall Street Journal articles and, like, really construct a knowledge graph ofRichard Socher [00:18:32]: There you go.Richard Socher [00:18:33]: And WordNet started, was part of how we started ImageNet. But anyway, so, like, it was really, like, fun, to do. But when we replaced all of that manual feature engineering with vectors and neural nets and just backprop through everything, it started to work really well at scale. And so then everyone started to do architecture engineering, and I was like, “ that clearly can't be it.”Swyx [00:18:53]: You mean, neural architecture search?Richard Socher [00:18:55]: Like, manually, they would say like, “Oh, I'm, I'm doing sentiment analysis, so I have a special neural net that's really good at sentiment analysis.” And then the machine translation community had a special neural net for machine translation.Swyx [00:19:06]: I see.Richard Socher [00:19:07]: The summarization people had their own stuff. And I was like, “That clearly can't be it. We should unify all of that.” So I had 2 papers. One is called Ask Me Anything, and the other one was called DecaNLP. And DecaNLP eventually got cited, like, 5 times by the first GPT paper. And, to me, that was, like a really a big step forward. And then, of course, you had to combine this idea of prompt engineering with transformers and with language models, and you put it all together, you scale it up, which is also a huge amount of work. And then, the field progressed a lot. I feel like the next step and maybe the last step of that history and the arguably, success has a lot of parents, only failure is an orphan, like my version of that AI history, I do feel like in that history, you can think about, “Well, what's the next way to automate?” And that is the AI research itself, like the human, process of ideating, implementing, and validating ideas.Automating AI Research and Recursive Self-ImprovementRichard Socher [00:20:01]: And in our case, ideas for AI.Richard Socher [00:20:03]: And when you have AI then help you with that, it, by almost definition, becomes a self-improving AI ‘cause it now does research on itself. And there are lots of different misnomers. Some people think auto research is already recursive self-improvement. It'sSwyx [00:20:17]: Yeah, and you explained that in the talkRichard Socher [00:20:19]: Completely different.Richard Socher [00:20:19]: But, to me, it's the most interesting thing that I could be doing, and I'm really excited with the co-founding team. What's interesting is we have 8 co-founders in total, including myself. And soThe Recursive Founding Team and Darwin Gödel MachineSwyx [00:20:31]: They are gonna bring it up.Richard Socher [00:20:31]: Nice. Yeah. And they're all. I could talk about all of them if you want.Swyx [00:20:34]: Super stacked.Richard Socher [00:20:35]: Yeah. Just an incredibly talented group of people. And we all came to the same conclusion, but from very different directions. Like Josh Tobin, is our CTO. He ran, a bunch of different, projects at OpenAI, like, Codex and deep, research, agents and ChatGPT agents and so on. But before that, he also worked in robotics, and he saw the smaller simulations, and how it's gonna be really hard to scale that in full generality. And so that's, that was his angle coming to recursive self-improvement. We have Jeff Clune who's been working in, like, open-endedness for a long time, together with Tim Rocktäschel. Tim Rocktäschel also built Genie 1, 2, and 3, which is, like the most exciting and most sophisticated, I think, still world model, anywhere. And so they both came from this, open-endedness angle. Jeff also, I think, published one of the most exciting papers in recent years about recursive self-improvement called the Darwin Gödel Machine. Super interesting paper. If we could, maybe pull it up really quickRichard Socher [00:21:35]: It would be, like, super interesting to see ‘cause you seeSwyx [00:21:38]: By the way, I love how many paper citations.Swyx [00:21:40]: You're, you're giving people a lot of homework, which I like.Richard Socher [00:21:42]: Love it. Yeah. And so, like Caiming Xiong, a rockstar, we worked together at MetaMind and Salesforce Research together. Alexey Dosovitskiy invented the Vision Transformer, one of the most cited, papers in computer vision. Tim Shi is, like also a unicorn founder. Yuandong Tian led RL at Meta. So just like, yeah, really fun to work with them, and the next level of people are just incredibly strong, too. So it's been a really fun ride so far. So the first figure, you see exactly these kinds of ideas, that, I think, yeah, inspired a lot of us and now more and more people, where you have this archive of different coding agents. They learn how to self-modify, evaluate, and then create these phylogenetic trees, of, yeah, different ideas.Swyx [00:22:28]: That's one foundation. So that Darwin Gödel is an influence.Swyx [00:22:32]: Open-endedness is an influence. Any other trains of thought that feeds into Recursive that I'm missing?Influences: Open-Endedness and Learned SystemsRichard Socher [00:22:38]: Going to replace manual parts of the process of building AISwyx [00:22:42]: IRichard Socher [00:22:42]: More and moreRichard Socher [00:22:43]: With learned systems. Yeah.Swyx [00:22:45]: Which, and, like, merging different fields into one general, architecture.Richard Socher [00:22:51]: That's right.Swyx [00:22:51]: Okay. It seems like language models are already pretty generalist, right?Swyx [00:22:55]: Your next token predicting your reasoning. Was there a time that you thought, “Okay, these are good enough to have recursive self-improving machines”?Are Current LLMs Enough?Richard Socher [00:23:05]: It was clear to me that they will happen, within, like a year or two, and then it did exactly happen, like, earlier this year, right? Earlier this year, AI really went from not just being code, but being able to code. And that is a big unlock. It's definitely making everything a lot easier than it was, before the beginning of this year.Swyx [00:23:24]: One question that I think a lot of people have is the current LLM paradigm enough? Or, like, let's call it autoregressive transformer, with reasoning, whatever. Don't you need something else, some big unlock, whether it's world models, which Chris Manning is working on, or memory, continual learning, all that stuff? Or is it all of the kinds, and you think the current, let's call it transformer architecture, is here to stay and that's it?Richard Socher [00:23:48]: A lot of thoughts. So number one, I do think it would be great to have less of a monoculture in AI research.Richard Socher [00:23:55]: Like, if you look at, AI conferences now, I still remember the days in, like, 2010 when I tried to get my first neural net papers and NLP conferences accepted, and they just desk rejected them because, like, neural nets were something, quote, unquote, “We don't do in NLP conferences,” and just, like, desk rejected. And it was very brutal in the first years of my PhD. Now I feel like it's almost like the field switched to the other side. LikeRichard Socher [00:24:17]: Someone should try some other weird, crazy ideas now that aren't.Swyx [00:24:20]: There's also a few. I really respect, like, people still working on, like, GNNs and, like tabular stuff and.Richard Socher [00:24:25]: Yeah. Like, someone should still, like, do novel out there ideas. At the same time, I think whenever people say, “Oh, LLLMs are. Like, this is the end for LLLMs,” they just don't, like. LLLMs are also not the LLLMs of, like the past, right? Like, they are so much more sophisticated now. There's so many more clever things that people are doing. It — There's, like, different stages of training. You have the whole RL training, and you can take actions and, like all of these things where that can go really far. And then the folks that come from the neurosymbolic, direction say, “Oh, this will never work because they can't do neurosymbolic reasoning.” It's like, I think they're underestimating still the ability for these models to code, and code is neurosymbolic reasoning, and these models can code incredibly well. And so I do think there are, of course, more and more ideas that will be needed and we'll continue to have. We're seeing, like, more and more interesting high-level ideas coming out of the AI itself, too. And with really deeply integrating the fact that these models are code and can code, that line — I don't wanna give it all away, but, like, I think that line has a lot more to grow. But it's still an LLM, right? Even if that LLM codes for you and then runs that code in some integrated fashion. World models, I'm personally less bullish on. I think if you run a robotics company, you're gonna build your own world model. I think world models are super fun, and Tim Rocktäschel came to a similar conclusion after building the most interesting one with Genie 1, 2, and 3, which is gaming is a huge application for world models. Can see I sometimes got stuck in some games and, like, got a little overly competitive in the wrong direction. And so I understand games are fun, but personally, I'd rather work on science than gaming. And so, yeah, I think LLLMs, a lot more room to grow.Swyx [00:26:16]: Yeah. I think there's some interpretation of world models that some people have where it's like, well, it's okay, yes, there is that gaming element. There's this — there's the embodied robotics element. But the other part also is just, the more abstract sense of LLLMs are just modeling output, but they're not modeling the chain of thought, inside the human that has created the output. We can annotate it, of course, but, like, it's, it's always, like, this Plato's cave reflection of a thing rather than the thing, right?Richard Socher [00:26:43]: It's true.Richard Socher [00:26:44]: But I would argue that, and maybe we'll get there in the 10, spaces of intelligence, but I would argue that even our projection, our eyes is a projection of the real world. And, like, we have only a very narrow, band of the electromagnetic frequency spectrum that we can observe with our puny little 2 eyes and so on.Swyx [00:27:01]: It's good enough.Richard Socher [00:27:02]: It's, it's good enough for now, but, like the upper bounds of where it could be are so much higher. And, like, to map, the visual world the way humans see it is also not necessarily, like the end-all be-all for visual intelligence. And I would argue that language is still the most interesting manifestation of human intelligence. And while our visual cortex is certainly less sophisticated, than that of, certain animals all the way down to the mantis shrimp who can, have, like, 2 independent eyes, 3 bands, trinocular vision and each eye can see all the way to, like, floating temperatures in 4D and stuff.Richard Socher [00:27:36]: Like, mantis shrimp, you should look it up. It's likeSwyx [00:27:37]: Way OP.Richard Socher [00:27:38]: Super crazy.Swyx [00:27:39]: Yeah. ZeFrank, mantis shrimp.Swyx [00:27:41]: It's the best video in the world onRichard Socher [00:27:42]: I love ZeFrank, yeah.Richard Socher [00:27:44]: Big shout-out to him. But, like, I think there's a lot more room to grow, but none of these, other animals have language that's as sophisticated as ours, certainly not in writing. And once you can write, you can, start thinking about longer term civilizations. All of that is language. Programming is much closer to language. And I would argue, and this is, like an important thing in the spaces definition of intelligence also, is that all of these spaces are highly correlated, but visual intelligence is neither necessary nor sufficient for overall intelligence. You can be blind and still be an intelligent human being. And an AI can be blind and still be quite intelligent too.Swyx [00:28:25]: We were gonna bring thisRichard Socher [00:28:25]: Which doesn't mean that you're not more intelligent when you have it. Yeah.Swyx [00:28:28]: We're gonna bring this up. I might as well — Like, we have a classification of 10 types of intelligence that you had at the end of your talk. So I'm just gonna flash this up now for people to cover this. I don't know if, maybe we'll put this towards the end. We'll come back to this. I just wanna mention that, you do have a philosophy that I like when people do lists because then I can just go through this and then it gets — it's educational for people. But let's go back. I don't wanna get distracted. But, so effectively, I'll, I'll, reinterpret what you said as Yann LeCun is wrong. And then we'll justRichard Socher [00:28:56]: Don't quote me as that. I'm, I'm good friends with Yann. I think very highly of him in many directions.Swyx [00:29:01]: But he's wrong.Swyx [00:29:03]: You mentioned GPT-1, and I cannot let any, Alec Radford, mention escape. Did you talk with him when he was training GPT-1? Like, any historical, fun stories there that you might come up?DecaNLP, GPT History, and Scientific GatekeepingRichard Socher [00:29:18]: I did not, like, meet him a bunch of times. I think we met maybe once or twice at some conferences. But, like, he has told, I think Brian, the first author of the DecaNLP paper, that it did inspire him, and he cited it five times in the GPT-2 paper. So, and that's, likeSwyx [00:29:36]: Yeah, good enough.Richard Socher [00:29:36]: Very clearly said, like, this was the first instantiation where they showed in the DecaNLP paper, McCann et al, that you can just phrase every single NLP problem as here's some prompt, text context, here's a question and task description and here is some output. If you just do that enough, you can have one unified neural network model, which, by the way, also had all kinds of interesting attention mechanisms. There are slightly different formulations to the transformer. I think came out the same year, plus/minus a few months. And then you can unify all of natural language processing into one neural net. That is the core idea.Swyx [00:30:14]: And this was as opposed to at the time, LSTMs and what have you.Richard Socher [00:30:17]: LSTMs, but also, like, people being very stuck in thinking about one model per task. In factRichard Socher [00:30:25]: It's, it's kinda crazy, but the DecaNLP paper was publicly reviewed as, like, open, OpenReview. It was an ICLR submission. And, in it, you will see, how the whole community at the time thought about this. So, likeSwyx [00:30:43]: Some great contributions, but more work needed.Richard Socher [00:30:46]: So look at, like, search for not even for humans. Just scroll it up here. Like, question answering is not a unified phenomenon. There is no such thing as general question answering, not even for humans. And this is like, really, you replace your brain with a different brain a different neural net when you answer, like, different kinds of questions. It was unfathomable to the experts at the time that you can have one unified neural network that would answer all of these different questions. They are saying, “No, all of these questions require very different systems to answer, and trying to pretend they are the same doesn't help anyone solve any problems.” That's what it says right there, right? That's how hard it was to fathom. And now, of course, people, when I say, “Oh, we're gonna invent prompts,” people are like, “You can't even invent prompts.” It's such an obvious idea to have one neural network that, of course, does everything in NLP.Richard Socher [00:31:37]: But at the time, it was, like, extremely controversial, and the paper got rejected. And the sad thing is that it got rejected so hard and they were so certain that we stopped going on our list of things to try. And the number 2 or 3 on the list of extensions for this paper was add language modeling as another task. And then we could have, and that would have accelerated the timelines, in 2018, like, even further for humanity. But we got so crushed, and we were like, “Okay, maybe we'll just work on some of our other ideas for now and, like, come back to this later.” Yeah.Swyx [00:32:09]: How can we design a review system that rewards non-consensus?Richard Socher [00:32:14]: Honestly, I started to feel like arXiv is such a gift to humanity. With arXiv, you should just put your paper out there.Swyx [00:32:24]: Is it pre-preprints?Richard Socher [00:32:25]: Let — And honestly, I think Twitter X, people like you who pick up interesting papers, that is a better filter than the experts. Let everyone, like, have access. Now, of course, there are some downsides, which is, like, if you're super unfamous, you have no Twitter followingRichard Socher [00:32:41]: You don't wanna be on social media or whatever, you write a good paper, maybe someone, somehow no one notices it. But I would argue that if you just tell, like, 10 of your friends in your community about a paper and it is a really significant breakthrough, someone is bound to talk about it again. And, so I think science needs less gatekeeping. And, even though ICLR, with Yann LeCun, who started it, as one of the co-founders of ICLR back in the day, he also wanted less gatekeeping ‘cause he too was rejected for many years together with Yoshua Bengio and Geoff Hinton with all their early deep learning and neural net papers ‘cause it was just not the hot thing. And so ICLR started with that, but then it also started gatekeeping a little bit themselves on various ideas. So I think less gatekeeping, more open, and then allowing people to say, “Look, even if this is just on, or, quote, unquote, ‘just an archive,' if it has like 1000 citations, it's a legitimate paper. Doesn't really matter where you published it.”Swyx [00:33:34]: And I agree with that. I do think it's sad that I've heard that grad students have to do, like, how to Twitter, seminars to each otherSwyx [00:33:43]: Just because it's so important for publishing these days. This person is just reflecting the sentiment at the time.Richard Socher [00:33:49]: That's right.Swyx [00:33:49]: But it'sRichard Socher [00:33:50]: I think it'sSwyx [00:33:50]: It affected you so muchSwyx [00:33:52]: That you stopped work on it.Vibhu [00:33:53]: The sentiment also came out of some of the research, right? Like, the original BERT paper was trained, and towards the end of the paper, they're like, “Okay, throw off the last head, train specific iterations forVibhu [00:34:05]: Extractive summarization add a head for this.” Like, you should do task-specific stuff. These are, like the authors that wrote Attention, wrote BERT, telling you this is what you're meant to do. And, like the training tasks were also very odd. They're likeVibhu [00:34:16]: The — “We know that the model overfits to this weird mass language modeling. Throw away this part and just do specific models,”?Richard Socher [00:34:23]: Exactly. And, like, we had to try — come up with all clever ways of, like attention and pointers and so on to get the neural network to be able to do all of these tasks. And then some of them were better than state-of-the-art, some weren't, but we were like, “But it's still in one model.” I thought it was really cool. Really interesting.Swyx [00:34:38]: I was gonna move on next to Tim and open-endedness. He was head of open-endedness at Google.Open-Endedness, Rainbow Teaming, and Self-Set GoalsRichard Socher [00:34:42]: That's right.Swyx [00:34:43]: I don't know what that means.Swyx [00:34:44]: But he did a lot of talks.Richard Socher [00:34:45]: Genie 3 is one of the ways thatRichard Socher [00:34:47]: Rainbow teaming, yeah.Swyx [00:34:49]: So I first saw him at — speaking of ICLR, I first saw him at ICLR when he talked about open-endedness. He's he's done a few talks. Can we define what is open-endedness for people who have never been exposed to the problem? They are like, “What do you mean? I thought the only goal of AI is to optimize against a benchmark or.”Richard Socher [00:35:04]: That's right, yeah. It's a, it's a fuzzy term because there's so many different instantiations of open-ended, thinking. But, one way I often describe it, and certainly, Tim and Geoff Hinton would be even better at describing this, but it's a suite of methods that is more inspired by evolution than, very specific rewards. So in that sense, it thinks more about environments, about co-adaptation. And so a concrete example is in the cybersecurity and LM safety space where you have one LM that tries to attack another LM to say something unsafe.Swyx [00:35:40]: Yeah, the rainbow, yeah.Richard Socher [00:35:40]: And now the environment is the 2 having a conversation and now they co-adapting, right? They're like one makes a better attack than the first one inoculates itself somehow, like uses that as training data, makes it so it's harder to say something unsafe based on that. And then as the attack stops working, the attacker now tries a different angle, right?Richard Socher [00:36:00]: And that's why it's not just red teaming, but they're called rainbow teaming.Swyx [00:36:02]: So, like, don't tell me how to do things. Let me just figure it out myself.Richard Socher [00:36:05]: That's right. Think about the environments that you wanna use. Think about the rewards at a high level that you wanna, inspire towards, and then let the AI try out many more ideas in this interplay between sometimes humans, but also sometimes other AI agents.Swyx [00:36:22]: Yeah. I worked open-endedness into a model that I have been working on. It was the keynote for AI Engineer where you start. You, we have the token loop, we have the agent turns, and then we have goal. And I feel like the way that you're describing open-endedness is still somewhat of a goal. Like, please attack this,Swyx [00:36:41]: Other agent. But, to meRichard Socher [00:36:42]: Yeah, you set the rewards. You set the environments.Swyx [00:36:44]: The loop that makes the other loops is. What if the agent can set its own goals?Swyx [00:36:49]: And is it, is that open-endedness? Like, you don't give it a goal. Just, like, be a sentient being. And maybe sentient is a very loaded wordSwyx [00:36:57]: But just set your own directions. What do you think you should do?Metacognition, Subjective Goals, and Measuring IntelligenceRichard Socher [00:37:01]: I love this direction. I think this is one of the 10 spaces of intelligence, that I clump under metacognition and thinking about thought.Richard Socher [00:37:08]: And it's an interesting one. Whenever people say, “Oh, AI is like, this is, it's gonna stop from here. It's not gonna get that much better,” and blah, I'm like there's so many different spaces of intelligence that we haven't even started exploring yet and hence have made very little progress on. And there is an interesting, connection to economics and, capitalism. Like, it doesn't make sense for a company to build and spend billions of dollars building a model that instead of following the rewards and objective functions you gave it, may come up with its own objective functions and its own goals.Richard Socher [00:37:46]: Right? And then imagine you're like, “Okay, I spent billions of dollars. Now go develop this new battery, material for me and answer all my emails.” And it's like, “Nah, I think it'd be more interesting to evaluate the molecular composition of the atmosphere, on Jupiter.”Richard Socher [00:37:59]: And you're like, “That's not what I paid you billions of dollars for.” And so no one's working on that for good reasons. And then also, understandablySwyx [00:38:07]: It's not useful.Richard Socher [00:38:07]: It's not, it's not useful, and it could get a little bit weird, right? What if the AI does start to really have thoughts on its own, and what if we don't like those thoughts, right? And so it requires a whole different way of thinking about it. I had a great conversation with a good friend of mine, Sam Gershman, who's a neuroscience professor at Harvard, and, like, we just jammed on this a little bit on, like, what are the best meta goals. And, I do think, like, knowledge-seeking is a really good one. I'm currently thinking also about, like the ultimate measure and unit of intelligence broadly construed, and I finally have some. It's still too early to share it. It's not. I haven't fully baked the thoughts yet.Swyx [00:38:44]: Like some replacement for IQ.Richard Socher [00:38:46]: IQ is such a terrible definition, right?Swyx [00:38:48]: Elo.Richard Socher [00:38:48]: It makes no sense. Yeah, Elos are terrible, too, because it's always just like me versus others.Richard Socher [00:38:53]: But, like, you can be intelligent and not constantly compare yourself to others? And so, yeah, there's no, like. In fact, a lot of these definitions we have, which I briefly mention in my book, too, these definitions create sometimes explicit and sometimes a more implicit anthropic bounds. No dis to the company Anthropic, but just, like, this idea that your intelligence is like getting 100 out of 100 questions right on this IQ test. Well, if that's your definition then you can only be at 100 out of 100. Where do you go from there, right? So you see a lot of these, benchmarks that people are working on they, increase, they get close to human, maybe sometimesSwyx [00:39:30]: It's like an S-curveRichard Socher [00:39:30]: Slightly above human, and then it's flat.Richard Socher [00:39:32]: It's like, ‘cause that's your. If your definition is only that so tied to humans, you're only gonna get to just slightly better than that. So I think metacognition is a great example of that, where we're not even yet allowing the AI to think. We're not working on it very much, and hence there's very little progress in that.Profit Maximization, Real-World Environments, and Reward DesignSwyx [00:39:49]: Yeah. Well, we've interviewed Andon, which I think, has been working on the most open-ended, benchmarks, which is just real-world, money.Swyx [00:39:57]: Arguably, telling an AI to profit maximize is a bad idea.Swyx [00:40:03]: But they are doing it.Richard Socher [00:40:05]: I do think you don't want that super. Like, you don't want a superintelligence to have a ton of access to all kinds of tools and so on and then just give it that without some very careful reward engineering. ‘Cause it's like, I just buy a bunch of defense stocks and I start a war. I make money. Like, it's just like, it's a tricky situation, right? You just buy a bunch of stuff, short basic goods for people, and you create some weird famine, like, issues. Like, yeah, there's a lot of constraints you should put onto a trading system.Vibhu [00:40:35]: It's a fun measure, though, ‘cause, the bounds are very capped to where we're nowhere close to them. Like, in Andon Labs, the model's like, “Oh, it's Saturday, maybe I just close the store today.” “Someone's off. It's okay. We'll just close the store.”Swyx [00:40:51]: It's using Claude.Vibhu [00:40:52]: Yeah. ButRichard Socher [00:40:53]: Yeah, no. I'm not, I'm not arguing against it. Just, like as you get more and more intelligence, you wanna be more and more careful with that as, like an open environment, ‘cause the environment then is all of Earth.Applying RSI to Science and InventionSwyx [00:41:02]: Yeah. Okay. For recursive, not strictly necessary, right? Because, like, if your goal is you make a machine that, like, invents the other things, then, like, just solve, the science thingsRichard Socher [00:41:12]: Knowledge discovery, yeah.Swyx [00:41:13]: Solve machine learning research and discovery and all these things. Good enough.Richard Socher [00:41:16]: And eventually, so, our goal, I haven't really. I don't talk about it that often because it is a few years out, but our goal is once you have a recursive self-improving superintelligence, you then want to apply it to the most important problems. And I think a lot of those are in science and technology and broadly construed inventions, and those inventions in, physics to create better, cheaper energy with fission or fusion, in chemistry and to create better materials and better batteries and, better solar cells and so on. In biology, there's so much, like, I think soon to be low hang- lower and lower hanging fruit because of AI, because of protein and generation, not just folding, but generating new proteins like we did in ProGen many years ago. Like, so much positive impact we had if you take that superintelligence and you apply it to science.Swyx [00:42:04]: I do fundamentally believe that. There's a lot of approaches, though. You're not the only team trying and NeoLab trying.Swyx [00:42:09]: There's, like a lot of. Especially the physical sciences as well.Richard Socher [00:42:12]: And that's good. Yeah. I do think that physi- like the reason we are only doing it in a few years is that it's a little too early right now. Robotics is not quite there yet. The AI is not quite there yet. But I'm fairly confident in 3 to 5 years, all those constraints will be gone, and then applying to real physical robotics experiments and so on, like true robotic process automationRichard Socher [00:42:33]: Not the traditional RPA sense, but, like, having robots run experiments for you will be totally there. Yeah, it's gonna be great.Swyx [00:42:40]: Just to call back to something that you said early on about slow takeoff, you said that, like, while really the substrate that is limiting factor is, let's call this chips, and semiconductors and all these things, and you have race funding for that and, you are investing a lot on that. But have you done the math on, like, is it even- Achievable and, like, what is the, industry concentration needed in order to achieve, like, scale?Compute, Slow Takeoff, and Changing the Bitter Lesson SlopeRichard Socher [00:43:05]: Right now we know that, like, roughly, like a 1000 GPUs cost quite a lot of money.Richard Socher [00:43:11]: Right? If you wanted, like, 10s of thousands of GPUs, you're, you're talking billions and billions of dollars. If you say, like, one GB300 is, like, you could eventually create models that are, on that substrate, like are close and similar to human intelligence. And you want, like, thousands and thousands of, AIs to think about really hard problems, in a similar fashion to humanity. Like, yeah, that-that's, that's a lot of money. You do the math. It's like a lot. We don't have that amount of money right now anywhere to, like, build that. Now, things can get more efficient. You will have, I think, soon better algorithms that won't be, and better hardware that won't be as energy-hungry, and so on. Our human brain does quite a lot of flops with much less energy.Swyx [00:43:56]: 20 watts?Richard Socher [00:43:57]: That's exactly right. Yeah, that's the number often that's quoted. And, like, I think more, inventions will happen there, that then will accelerate the takeoff even further.Swyx [00:44:08]: One thing I always try to reconcile when talking, like, with new lab founders is, like, you're fighting Bitter Lesson all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.Richard Socher [00:44:20]: Which unlocks larger model categories.Swyx [00:44:22]: Like, fundamentally, is that true? Like, are you fighting Bitter Lesson? Are you — will we have a way in which, like, no, we're changing the slope in some fundamentally different way?Richard Socher [00:44:31]: I do think we are changing the slopes in fundamental ways by making AI much more efficient, both in terms of the training as well as the inference.Richard Socher [00:44:43]: Yeah. I think we will — When you allow AI to do the work that it takes other labs thousands of people and years to do, I think we'll be able to get it down to weeks, and that will be much cheaperRichard Socher [00:44:53]: And hence, more affordable, accessible to others and so on.Swyx [00:44:57]: Yeah. You've shared initial results on that,Swyx [00:44:59]: Which, like, conveniently OpenAI has also done to their GPT-5.6, so we can talk about it now.Richard Socher [00:45:04]: Yeah. Yeah, so these areSwyx [00:45:06]: Let's recap what you've done.Early Recursive Results: NanoChat, NanoGPT, and SOL-ExecBenchRichard Socher [00:45:07]: Maybe, just a quick recap here. We built, this, system that isn't the full, even the full RSI system in its glory, but it is a first baby version of this. And then, we don't wanna just have it internally and not show anything and, just show some people of what's possible. And so we applied this to these 3 different tasks. One is NanoChat, by my friend Andrej Karpathy, just, like, train a small language model to get, really low bits per byte. And, like, hundreds if not thousands of people, used both their agents and themselves to try, to get to that, and then they got to 0.937. We literally took our system and got to a much lower, bits per byte, much faster within, like, I think less than 2 days. So we took this thing, applied our system to it, and less than 2 days later, we have — we outperformed every human and their agents, in, have ever worked on this. Same with NanoGPT. And then we're like, well, let's, apply it to something that's even more relevant, to real people and to the Nvidia ecosystem and applied it, to, SOL-ExecBench. And maybe you can scroll down to some of the, images. They're, they're kinda fun to see. But yeah, like, one you see has made some real inventions that weren't just hyperparameter tuning. Like, inventing hash tables and so on is quite clever. We have even better results now.Swyx [00:46:34]: What do you mean inventing hash ta — You didn't invent hash tables.Richard Socher [00:46:36]: Of course we didn't invent, like, hash tables. In the grand scheme of, like a hash table, it's like a super basic primitive in computer science. But to use it, for language modeling in this scenario inside a transformer and so on and to combine these ideas and put them together, that has then eventually also been invented, but there was a knowledge cutoff, and we did check that it didn't have access to that externally. We talk about this a little bit. If you scroll to the next figures, this is also an interesting one in that when you start from a really basic, poor, like, vanilla transformer, then we still outperform all of the community together. But if you start from the human seed from an expert like Andrej, then you get even lower. So the human seeds from which you start do still matter. So that was an interesting insight, in my eyes, on this. And then as you go, like, how long does it take to get to these models, to get to similar performance? It's much faster. And then a similar thing happens with the speed runs here where, people have worked on this for quite some time, and the model still was able to train a model more quickly. Why do we care about it? Well, speed of training is part of the equation of the cost, and ultimately, you wanna have the most intelligence per dollar, right? And so speed and quality are big parts of that. And, the,Swyx [00:48:00]: Yeah, the way I put it is, for people who don't understand they look at the chart, they're like, “Cool. What does it mean?” if you have, like a billion-dollar cluster and you can shave off 10%, that's 100 million dollars.Richard Socher [00:48:12]: That's exactly right.Swyx [00:48:13]: How much is that worth?Richard Socher [00:48:14]: Exactly. So when you click, when you look at, like the kernels, these kernels, yeah, for the non-experts, like these kernels are like, used in all the models. Every time you use an Nvidia GPU, you interface with that GPU through these kernels. And so here you see, the leaderboard best, and when it's recursive, and it's there are only a handful of kernels, in this whole benchmark where we weren't the best. And so to me, this is, like, really exciting, ‘cause it makes. It just showcases what this can do. And again these weren't like. We didn't, like, spend months or years, like, developing. In fact, in particular for kernel, CUDA kernels, like, we don't even have really deep. CUDA kernel experts in the team. And our system, that's the beauty. The system just did all of these things. We didn't invent this. And when we open source and release, things in the future and models in the future, like, it won't. They won't be the best in their, category or class or whatever because we're so smart, but it's because, we built a smart AI that does it for us.Reward Engineering and Good Auto ResearchVibhu [00:49:14]: Do you have anything that you've learned from how to guide good auto research? A lot of it also builds on human background, right? It's not just as simple as just, “Hey, go optimize this.”Vibhu [00:49:23]: But we do see it again and again, right? Like some of the Erdos problems, frontier math is being solved by people. And when they do a write-up, they're like, “Oh, I'm not a mathematician. I have no background in this?” “I saw some tools and I made it work.”Swyx [00:49:35]: While you're watching the World Cup, you're likeSwyx [00:49:37]: “This proves some conjectures that's going on.”Vibhu [00:49:40]: Yep. Any learnings fromRichard Socher [00:49:41]: Yeah, there's a Korean conjecture was. Yeah, that's pretty cool.Swyx [00:49:44]: To summarize, tips for good auto researchSwyx [00:49:46]: Versus bad auto research.Vibhu [00:49:48]: How did you build the recursive?Richard Socher [00:49:49]: Yeah. So without giving away all the secret sauce, maybe some things that are probably obvious to the experts but might still be interesting to some, folks is, like, reward engineering is one of the most crucial bits, especially, in order to avoid reward hacking. So you have to be really clever about avoiding. ‘Cause as your AI gets better and better, it will get better and better, at finding weird like, special cases or counterexamples and things like that. And so I'll give you an example. Like, when you ask to, like, make these 100, lines of code faster, and, how do you define fast? Well, you have one line at the beginning that says, “Start your stopwatch,” and one line at the end, “End the stopwatch,” and then, tell us how much time, progressed. And so, well, the simplest way is you just put that line that ends the stopwatch, rightVibhu [00:50:39]: At the startRichard Socher [00:50:40]: At the start. And then boom, it's now faster, right? So this isn't like this, like, super evil AI. It's just, like a very simple, dumb reward hack. And so you have to just very carefully think about all the different angles there. And then I think the longer time horizon the tasks are the harder it gets and the more interesting and clever you have to be to still use these kinds of ideas for it. But yeah, I can't give away too much there.Vibhu [00:51:05]: It seems like rubrics are taking a good spot in that, where for unverifiable domains, you have rubrics, you have a model breakdown, judge's criteria along the way.Swyx [00:51:14]: Yeah, it's a form of verificationSwyx [00:51:16]: Once you got enough rubrics.Richard Socher [00:51:17]: Yeah, everything. I said this a long time ago. That's why I've never been that impressed that AI can play games, ‘cause I'm like anything you can simulate and/or verify, you can have infinite training data forRichard Socher [00:51:29]: And hence, like, AI will solve it eventually.Swyx [00:51:32]: Looking for games where you can do auto domain distribution. So this is a game that nobody's trained on ‘cause it's a new game.Swyx [00:51:38]: And you can start gaming, you can start to play. So I've been building this and cloned this in person and it's just been self-play. I've had about a billion positions evaluated.Games, Self-Play, and the AI EconomistSwyx [00:51:48]: And, I wanted to do the AlphaGo thing of self-play until you ge

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Laudetur Jesus Christus - Ngợi khen Chúa Giêsu KitôRadio Vatican hằng ngày của Vatican News Tiếng Việt.Nội dung chương trình hôm nay:0:00 Bản tin16:49 Chia sẻ Lời Chúa : Lm. Đa Minh Vũ Duy Cường, SJ, chia sẻ Lời Chúa Chúa Nhật thứ 24 thường niên---Những hình ảnh này thuộc Bộ Truyền Thông của Toà Thánh. Mọi sử dụng những hình ảnh này của bên thứ ba đều bị cấm và dẫn đến việc đánh bản quyền, trừ khi được cho phép bằng giấy tờ của Bộ Truyền Thông. Copyright © Dicasterium pro Communicatione - Giữ mọi bản quyền.

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I hope you're all enjoying the series so far. We continue on in Part 3 with the Warring State of Qín under their bellicose and long-reigning King Zhaoxiang 秦昭襄王 using all the powerful tools at his disposal to overcome the rival kingdoms. This will be followed by the old family favorite story of the dubious rise of Ying Zheng 嬴政 and the matter of his paternity. All your old favorites from the Records of the Grand Historian will play their bit parts in the historical drama: Lǚ Bùwéi 吕不韦, Zhào Jī 赵姬, and Lào Ǎi 嫪毐 and King Zhuangxiang 秦庄襄王. The rest of the episode covers the brief, but action-packed rise and fall of the Qin Dynasty and the founding of the Han Dynasty up through the death of Han Gaozu 汉高祖. This is of course preceded by the exciting Chu-Han Contention between Xiang You 项羽 and Liu Bang 刘邦. I saved the murderous Empress Lü for next time. Below is a list of Chinese historical figures mentioned in this episode.King Zhāoxiāng of Qín 秦昭襄王, Fàn Jū 范雎, Lǐ Bīng 李冰, Bái Qǐ 白起, Lián Pō 廉颇, Lǐ Mù 李牧, Wáng Jiǎn 王剪, Sīmǎ Cuò 司马错, Zhao King Xiàochéng 赵孝成王, Zhào Kuò 赵括, Zhōu Nǎn Wáng 周赧王, Yíng Zhèng 嬴政, Qin King Xiàowén 秦孝文王, Lǚ Bùwéi 吕不韦, Zhào Jī 赵姬, Lào Ǎi 嫪毐, Lǐ Sī 李斯, Zhào Gāo 赵高, Yíng Fúsū 赢扶苏, Hú Hài 胡亥 / Qín Èrshì 秦二世, Méng Tián 蒙恬, 子楚 / 秦庄襄王, Zhèng Guó 郑国, Jīng Kē 荆轲, Wáng Bēn 王贲, Qín Shǐhuáng 秦始皇, Tú Suī 屠睢, Shǐ Lù 史禄, Chén Shè 陈涉, Wú Guǎng 吴广, Liú Bāng 刘邦, Xiàng Yǔ 项羽. Zǐyīng 赢子婴, Zhào Tuó 赵陀, Hàn Gāozǔ 汉高祖, Xiāo Hé 萧何, Lǚ Zhì 吕雉, Xiàng Liáng 项梁, Zhāng Liáng 张良, Chu King Huái 楚怀王, Fàn Zēng 范增, Hán Xìn 韩信, Chén Píng 陈平, Lù Jiǎ 陆贾, Yīng Bù 英布, Biǎn Què 扁鹊, Liú Yíng 刘盈 / Emperor Hùi 汉惠帝.

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I hope you're all enjoying the series so far. We continue on in Part 3 with the Warring State of Qín under their bellicose and long-reigning King Zhaoxiang 秦昭襄王 using all the powerful tools at his disposal to overcome the rival kingdoms. This will be followed by the old family favorite story of the dubious rise of Ying Zheng 嬴政 and the matter of his paternity. All your old favorites from the Records of the Grand Historian will play their bit parts in the historical drama: Lǚ Bùwéi 吕不韦, Zhào Jī 赵姬, and Lào Ǎi 嫪毐 and King Zhuangxiang 秦庄襄王. The rest of the episode covers the brief, but action-packed rise and fall of the Qin Dynasty and the founding of the Han Dynasty up through the death of Han Gaozu 汉高祖. This is of course preceded by the exciting Chu-Han Contention between Xiang You 项羽 and Liu Bang 刘邦. I saved the murderous Empress Lü for next time. Below is a list of Chinese historical figures mentioned in this episode.King Zhāoxiāng of Qín 秦昭襄王, Fàn Jū 范雎, Lǐ Bīng 李冰, Bái Qǐ 白起, Lián Pō 廉颇, Lǐ Mù 李牧, Wáng Jiǎn 王剪, Sīmǎ Cuò 司马错, Zhao King Xiàochéng 赵孝成王, Zhào Kuò 赵括, Zhōu Nǎn Wáng 周赧王, Yíng Zhèng 嬴政, Qin King Xiàowén 秦孝文王, Lǚ Bùwéi 吕不韦, Zhào Jī 赵姬, Lào Ǎi 嫪毐, Lǐ Sī 李斯, Zhào Gāo 赵高, Yíng Fúsū 赢扶苏, Hú Hài 胡亥 / Qín Èrshì 秦二世, Méng Tián 蒙恬, 子楚 / 秦庄襄王, Zhèng Guó 郑国, Jīng Kē 荆轲, Wáng Bēn 王贲, Qín Shǐhuáng 秦始皇, Tú Suī 屠睢, Shǐ Lù 史禄, Chén Shè 陈涉, Wú Guǎng 吴广, Liú Bāng 刘邦, Xiàng Yǔ 项羽. Zǐyīng 赢子婴, Zhào Tuó 赵陀, Hàn Gāozǔ 汉高祖, Xiāo Hé 萧何, Lǚ Zhì 吕雉, Xiàng Liáng 项梁, Zhāng Liáng 张良, Chu King Huái 楚怀王, Fàn Zēng 范增, Hán Xìn 韩信, Chén Píng 陈平, Lù Jiǎ 陆贾, Yīng Bù 英布, Biǎn Què 扁鹊, Liú Yíng 刘盈 / Emperor Hùi 汉惠帝.

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Laudetur Jesus Christus - Ngợi khen Chúa Giêsu KitôRadio Vatican hằng ngày của Vatican News Tiếng Việt.Nội dung chương trình hôm nay:0:00 Bản tin17:38 Chia sẻ Lời Chúa : Lm. Bartolomeo Nguyễn Anh Huy, SJ, chia sẻ Lời Chúa Chúa Nhật thứ 23 thường niên---Những hình ảnh này thuộc Bộ Truyền Thông của Toà Thánh. Mọi sử dụng những hình ảnh này của bên thứ ba đều bị cấm và dẫn đến việc đánh bản quyền, trừ khi được cho phép bằng giấy tờ của Bộ Truyền Thông. Copyright © Dicasterium pro Communicatione - Giữ mọi bản quyền.

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We love a Buzzfeed and a New York Post and a Breitbart! We love them all! Join Spencer, Ty, and Andy as they debate the tenuous role of the Fifth Estate in contemporary American life in this age of social media. Support us on Patreon for $5, $7, or $10: www.patreon.com/tgofv. TGOFV Theme by World Record Pace. A big shout-out to our $10/month patrons: Celeste, Yung Zoe, Dane Stephen, Weedworf, James Lloyd-Jones, Sam Thomas, Josh O'Brien, Kilo, David, Sam, T, Rach, Tomix, Adam W, L M, Revidicism, Jennifer Knowles, Jeremy-Alice, Louis Ceresa, Charles Doyle, Dean, Axon, Themandme, Raouldyke, Stephen Tucker, Lawrence, Rebecca Kimpel, Malek Douglas, Jacon Sauber-Cavazos, Bernventers, William Copping, NewmansOwn, Heather-Pleather, Bunknown, Dinosarden, Bedi, Francis Wolf, King Krang, Anthony C, ASDF, Buffoonworld, Bavbiff, D Love, and Tugboat!

Laudetur Jesus Christus - Ngợi khen Chúa Giêsu KitôRadio Vatican hằng ngày của Vatican News Tiếng Việt.Nội dung chương trình hôm nay:0:00 Bản tin16:27 Chia sẻ Lời Chúa : Lm. Toma Aquino Nguyễn Khánh Duy, SJ, chia sẻ Lời Chúa Chúa Nhật thứ 22 thường niên---Những hình ảnh này thuộc Bộ Truyền Thông của Toà Thánh. Mọi sử dụng những hình ảnh này của bên thứ ba đều bị cấm và dẫn đến việc đánh bản quyền, trừ khi được cho phép bằng giấy tờ của Bộ Truyền Thông. Copyright © Dicasterium pro Communicatione - Giữ mọi bản quyền.

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You guys suggest so many movies, so many movies that never get chosen for a fun episode. Don't you think it's time we change that? Join Ty as she leads you in a watch-a-long for John Travolta's groundbreaking 1995 film, "White Man's Burden". Support us on Patreon for $5, $7, or $10: www.patreon.com/tgofv. TGOFV Theme by World Record Pace. A big shout-out to our $10/month patrons: Celeste, Yung Zoe, Dane Stephen, Weedworf, James Lloyd-Jones, Sam Thomas, Josh O'Brien, Kilo, David, Sam, T, Rach, Tomix, Adam W, L M, Revidicism, Jennifer Knowles, Jeremy-Alice, Louis Ceresa, Charles Doyle, Dean, Axon, Themandme, Raouldyke, Stephen Tucker, Lawrence, Rebecca Kimpel, Malek Douglas, Jacon Sauber-Cavazos, Bernventers, William Copping, NewmansOwn, Heather-Pleather, Bunknown, Dinosarden, Bedi, Francis Wolf, King Krang, Anthony C, ASDF, Buffoonworld, Bavbiff, D Love, and Tugboat!

VOV1 - Thông điệp "Vì một không gian mạng nhân văn cho mỗi người" nhấn mạnh khía cạnh đạo đức, ứng xử và sự an toàn trên không gian mạng. Điều này đặc biệt quan trọng trong bối cảnh các hành vi lừa đảo trực tuyến, tin giả trên mạng đang ngày càng phức tạp.Tại Lễ Mít tinh hưởng ứng Ngày An ninh mạng Việt Nam, Thủ tướng Lê Minh Hưng nhấn mạnh: bảo đảm an ninh mạng, an ninh dữ liệu, an toàn thông tin của tổ chức và cá nhân là yêu cầu xuyên suốt, không thể tách rời trong quá trình phát triển khoa học, công nghệ, đổi mới sáng tạo và chuyển đổi số quốc gia: Băng: "Bảo đảm an ninh mạng phải gắn kết chặt chẽ, đồng bộ giữa "bảo vệ hệ thống" và "bảo vệ con người", với mục tiêu là lấy sự an toàn, bình yên và hạnh phúc của nhân dân làm thước đo cao nhất cho mọi chính sách. Đây cũng chính là tư tưởng chỉ đạo xuyên suốt của thông điệp Ngày An ninh mạng năm nay: "Vì một không gian mạng nhân văn cho mỗi người".Phát biểu của Thủ tướng Lê Minh Hưng khẳng định an ninh mạng không chỉ đơn thuần là vấn đề kỹ thuật như bảo vệ máy chủ, tường lửa, mã hóa dữ liệu... mà là sự kết hợp hữu cơ giữa "bảo vệ hệ thống" và "bảo vệ con người". Kỹ thuật dù hiện đại đến đâu cũng sẽ xuất hiện điểm yếu nếu nhận thức và kỹ năng của người dùng không được nâng cao.Việc coi "sự an toàn, bình yên và hạnh phúc của nhân dân" làm thước đo cao nhất thể hiện rõ mục tiêu tối thượng của chuyển đổi số: công nghệ phục vụ con người, chứ không phải con người bị lệ thuộc hay chịu rủi ro bởi công nghệ.Thông điệp "Vì một không gian mạng nhân văn cho mỗi người" nhấn mạnh khía cạnh đạo đức, ứng xử và sự an toàn  trên không gian mạng. Điều này đặc biệt quan trọng trong bối cảnh các hành vi lừa đảo trực tuyến, tin giả trên mạng đang ngày càng phức tạp.Do vậy, An ninh mạng không được nhìn nhận như một "lực cản" hay thủ tục phiền hà, mà được xác định là nền tảng sống còn, điều kiện tiên quyết để quốc gia phát triển khoa học, công nghệ và đổi mới sáng tạo bền vững./.Lại Hoa/VOV1

Bret Tollgaard, president at Sunrez, joins to discuss faster UV blade repairs, new leading edge materials, and growing OEM approvals. Reach out to sales@sunrez.com to learn more! The Uptime Wind Energy Podcast is brought to you by Weather Guard Lightning Tech, creators of the StrikeTape Ultra LPS retrofit. Subscribe to Uptime’s Substack newsletter. And check out Rosemary’s “Engineering with Rosie” Youtube channel. Have a question we can answer on the show? Email us! Welcome to Uptime Spotlight, shining light on wind energy’s brightest innovators. This is the progress powering tomorrow. Allen Hall: Brent, welcome back to the program.  Bret Tollgaard: Appreciate you having me back on.  Allen Hall: There’s been so much happening in the world of UV prepregs and UV adhesives over the past year. Particularly on LinkedIn, I’ve seen numerous repairs which have used your material, SunRes material, to fix some pretty decent, uh, blade problems. Are you seeing, just seeing a, a much wider adoption now that everybody has basically agreed that UV is the way to go?  Bret Tollgaard: I, I really do think that it’s starting to become one of the go-to standards for a variety of repairs in the wind market. And yeah, it’s great to see both our prepregs and lights being used all over LinkedIn, showing the [00:01:00] capabilities of the materials and how happy customers are to use and adopt the materials. Allen Hall: Yeah. The UV materials are, are getting adopted by OEMs. A lot of ISPs, a, a lot of operators now have it as a standard practice to use UV cured materials just because of the speed and the time that is saved. Uh, it’s, it’s remarkable how much time you’re saving, because the, the real kicker right now if you’re trying to do a repair, even in the summer months, let’s just say it’s warm outside and the temperatures are right and it’s calm winds, you’re saving multiple hours per repair. What generally… How much time are you generally saving on a kind of a, quote unquote, “standard blade repair”?  Bret Tollgaard: More and more time, actually, with each session. So we might be saving 50 upwards of 80% of the labor of a traditional epoxy repair. Um, what we’ve been training companies to do more and more frequently is to actually pre-consolidate the entire repair patch downtower, whether it’s in the trailer or inside the tower itself. Um, and so you can pre-consolidate, get [00:02:00] all the air out in between your layers, have the primer pre-applied to the backside. That way, when you’re actually uptower, it’s simply peel, stick, roll, and then cure it out with our UV light. Sub 10 minutes, uh, will get you a fully cured part.  Allen Hall: Wow. That’s remarkable. Yeah, when I see those LinkedIn posts from all kinds of operators all around the world using your material I also noticed that the weather conditions may not be pristine like you would have to do with epoxy. So there, there’s like a band of temperatures that epoxy systems generally work in. But with the UV material, I’m noticing, I’ve s- I s- especially over the wintertime, I saw some cold weather repairs going on. Is there any limitation really to the UV cured patches and materials in temperatures?  Bret Tollgaard: There, there really aren’t for the temperature ranges that people will actually be out and installing these. Some of my favorite photos are when it’s negative five Celsius outside and everyone’s got the big gloves, the big [00:03:00] scarves, the big warmers and everything, and they’re applying our prepreg materials, uh, in below freezing conditions and still curing with the same 10-minute cycle time. Uh, another big advantage of our UV prepregs is everything’s already pre-wet out, so you’re not uptower with your fiberglass and your resin kicking off an epoxy or even some other, uh, you know, materials out there where it’s really hard to impregnate and get that saturation and kick off with UV. And so our prepreg repair patches really lend themselves to efficiency in the field, uh, over a really broad variety of, uh, ambient temperatures. So some of the negative five, negative 10 C installs all the way up to the hot ones in Texas and India and Australia, where you’ve got these people and it’s 100 Fahrenheit or, you know, 35 plus C, and you still cure with the same consistency, the same cure rate, the k- same cure speeds, um, really, really makes it a very versatile system. Allen Hall: It just gets rid of the variation and, and [00:04:00] that’s one of the items when we look at repairs. So we’re on the lightning side of this business where lightning damage happens, so we see a lot of repairs happen. We also see a lot of repairs that don’t look great. They’re, I mean, the vast majority do look great, but you get these occasional ones where they’ve tried to use epoxy in the wintertime and use a heat blanket. It, it’s difficult to do. It, it just is because of the material, it’s very difficult to do. I’m recommending more and more that they use a UV cure material if they’re gonna go off and do it, particularly as they, uh, do what I would consider to be a temporary patch until they get where they can do more in-depth work when it’s warmer outside. But it, it does really change the game because the ability to buy the material quickly is amazing. Uh, b- there’s no hazardous me- chemi- chemicals in, uh, the Sunrise material at all, so it can be shipped anywhere, anytime, overnight, on a airplane, on a truck, doesn’t matter. Does that really open up the marketplaces for the [00:05:00] Sunrise UV cure materials? Bret Tollgaard: 100%. And because of the long shelf life as well, um, we stock and store all of the common, um, materials used for wind. The prepreg, the putties, the surface primers. Fiberglass also does a great job of stocking and storing things. And yeah, because these can ship as unregulated materials, we get calls for next-day air shipping all the time, all over the world. And so whether it’s out here domestically, it’s going up to Canada, Europe, uh, Asia, Australia, um, it’s really, really simplifies the whole process from start to finish.  Allen Hall: Are there any special tools you need to use when applying a, a UV cured prepreg patch to a blade, or is it just standard existing tools? Is there anything unique there? Bret Tollgaard: No. It’s kind of as simple as it gets. There’s no vacuum bag required. There’s no heat blanket required. To truly install the materials, you’re gonna take off the back, uh, layer of, of the film on the backside of the [00:06:00] prepreg, re-stack and consolidate your materials. Uh, you’re gonna consolidate that with a basic three-inch or six-inch bubble buster hand roller. Every technician’s gonna know exactly what that is. Uh, it’s a simple piece of little rolling metal that, uh, simply rolls out any air between the, the repair patches. And then after that, you’re gonna grab a paintbrush, and you’re gonna spread some of our surface primer over the backside of the prepreg. So you got a hand roller, and you got a paintbrush That’s all you’re gonna need to get the material up onto the wind blade. And then of course, to cure the material, you need a UV light to do so. The sunshine, although it will cure the material, you never have consistent sun, light intensity, you’re on the shady side of the blade, et cetera. And so we’ve developed a variety of UV curing devices to help facilitate that. So our standard in the industry right now is what we call our Gen 2 LED lamp. It’s about the size of an eight and a half by 11 sheet of paper. Um, but it’ll, it’ll cure several square feet at a time when you’re 14 to 20 inches or so away from that [00:07:00] surface. Uh, but in addition to that, we’re also working on a patent pending LED array system that is gonna be a broader… Right now, it’s about a half meter by one square meter surface area, and it’s gonna, um, emit a very, very uniform, uh, amount of light intensity across that entire surface all at once to make it even easier for the installers so they can just strap it to the blade, cure it for five minutes, and then move it over if they have a longer, uh, you know, one to two square meter repair. Allen Hall: Wow, that’s remarkable. I, I have one of your lights, and I’m always shocked at how much light intensity there is because I’ve used other lights and seen other lights, and then they’re not nearly as bright or consistent across a, a piece of fabric, for example, where you just see uniformity.  Bret Tollgaard: That is certainly one of the challenges that we’ve actually encountered when people say, “Oh, well, I have a UV light. I bought it on Amazon. I bought it from eBay. I bought it from somewhere where that 200 watt listed on that is not truly 200 watts, or the light intensity, [00:08:00] uh, that they, you know, claim to have. UV cure materials need the correct wavelength. They also need the correct light intensity on that surface to guarantee your depth of cure and to make sure that you’re truly bonding to the base substrate behind you. The thicker you go, the more light intensity you’re going to need. So a lot of the knockoff materials really just don’t do it justice, um, and which is why we’ve, you know, intentionally built the LED curing systems that we have to cure with our prepregs, uh, and putties and adhesives. So they are all true one, uh, one single system. Allen Hall: We’ve looked at different light systems here at Weather Guard, and I know we’re engineer- we’re an engineering group, right? So we’re gonna go look to see what these different lights are, and are they really truly putting out the frequency of light which is written down on the ad. It’s amazing, like, uh, the variation you get light to light. It really depends on what the source is, but, uh, to test a light, just to test a light to make sure it’s putting out the right intensity at [00:09:00] the right frequency is super expensive. So you can buy a several thousand dollar sensor to check your hundred dollar light, or you could actually buy the right thing from SudRes and have the proper light because it does change the cure rate and what the result is. So- Y- y- is it easy to tell, Brett, when I, if I have a light in the truck, do I know it’s the right frequency? How do I even tell that, uh, to know that I’m using the right stuff?  Bret Tollgaard: Well, if it says SunRes on the backside of that lamp, you have a guaranteed method of knowing exactly what that is. Um, we have the most broad industrial use UV lamp on the market, like, first and foremost. There are several that cost four to five times what we do, but a lot of times they’re also a really small aperture, so they’re only curing a small footprint, which is why we’ve become the kind of gold standard for all UV cure repair solutions out there using a SunRes lamp. Um, we just make the best product, not to toot our own horn [00:10:00] too much. Um, but it, it really serves the need of what these industrial customers in wind and other industries are looking for. Um, and I’m sure there are other systems out there. I’ve personally tried and tested dozens of different lamps out there to always see what’s on the market, and what we provide is just above and beyond performance-wise for the cost. Allen Hall: And one of the, the questions that comes up with the light when we talk to operators, like, “Where do I get the light?” Well, you, you call Brett at SunRes and you buy yourself a light. The second question that happens is, has the OEM blessed this material? Have they validated the material? Has it been through material testing? Has the OEM done structural loading on the material? Did I know that as a repair material, it’s actually gonna adhere to my polyester blade or my epoxy blade or whatever this thing is? Do I know that this is gonna work? Sunrise has been through a number of, uh, OEMs at this point. What’s the status of your OEM [00:11:00] relationships there? Bret Tollgaard: Very, very solid, and constantly getting approved by more and more OEMs. Um, we’ve sold over 35,000 square meters of prepreg into the wind market alone. And with that has come a variety of, uh, you know, engineering approved jobs versus fully validated materials. We now have 20-year lifespans on a variety of different OEM, uh, wind parts. And so our epoxy and polyester, we adhere phenomenal to both. And yeah, our prepregs are getting, uh, used more and more in every single application.  Allen Hall: I, I assume you can provide references if, if someone were to ask, like an ISP, “Can I talk to the OEM about this?”  Bret Tollgaard: Absolutely. Uh, and it, it’s, you know, no secret. General Electric’s been one of the big pushers of UV cure materials now for a while, um, for both their epoxy and the LM polyester blades. Um, but there are several European OEMs that are actively, you know, certifying the materials as well.  Allen Hall: Oh, that’s a smart move on their part. [00:12:00] Uh, one of the considerations for using a UV material is training. Uh, do I need to do special training for my technicians? Is it really different than epoxy? Do I have to do any training at all? What’s the level of skill that would be in- involved in using a, a UV prepreg?  Bret Tollgaard: The reality is, if you have any bit of experience of knowing what a resin and a fiberglass and wetting things out, UV just makes that job even simpler. So all the repair technicians that have gone through repair academies, been trained on the, on site or on the job, that have gotten their hands dirty, as we like to call it, with making some, uh, composite repairs, UV is just gonna simplify the process even more. Um, in 2026 and 2027, we’re actually starting to work, uh, uh, more closely now with a variety of the blade repair groups or academies and training facilities to also get UV cure in people’s hands so they don’t experience, uh, for the first time when they’re out, you know, getting a job with one of these other [00:13:00] OEMs that are recommending use our material now instead of, uh, epoxy systems. Allen Hall: Mm. And all the experience that’s happened at, at Sunrise, I, I don’t know if a lot of people realize that you’re a roughly 40-year-old company. You’ve been around a long time, although you’re nowhere near 40. But the company has been around a long time. What was the origin of SunRes? Uh, because it wasn’t necessarily in wind, right? Wind is sort of an afterm- after-market kind of application where it kind of transferred over. But what, what was the core start of the business?  Bret Tollgaard: Yeah, no, we were founded in September of 1986. So in just a couple of weeks, we’ll actually hit our 40-year anniversary.  Allen Hall: Wow.  Bret Tollgaard: SunRes was originally founded, uh, in, uh, once again, the, the mid-’80s for just UV cure formulations in general. Spent the first several decades, uh, of its life being primarily an R&D, uh, kind of based group, um, formulating specialty UV cure solutions for mostly thick wall fiber reinforced applications. So anything from [00:14:00] boat hulls to, uh, drones to, uh, you know, bathtub repair and saddle trees. Um, we’ve sold really the, a whole kind of gambit of very broad basic systems to incredibly advanced high precision, uh, you know, applications. And so as we grew and evolved the, the formulas and the recipes to cert- uh, to solve certain needs, now we’re kind of elevating that to a more, uh, manufacturing based business and offering true repair solutions as opposed to just R&D and working on developing solutions to repair something.  Allen Hall: And that’s growing outside of just the pre-preg processing materials, packaging it up and shipping it. And y- you’re now getting more into specific solutions in wind, which is really exciting. The leading edge erosion issue is plaguing a lot of turbine blades in the United States and, and globally. Australia has a big problem with it, and there’s a, some other places that I’ve seen some pretty [00:15:00] rough looking blades, India being another one. Uh, the solution to go fix those blades has generally involved, uh, putting someone up on a rope, a- applying material, fiberglass back down, trying to repair holes in particular, and trying to cap that off. But now there’s some new materials that can speed up the process to at least get you back to a, a surface, a usable surface that you could then apply an LEP or whatever you decided to do there. What do those new materials look like and, and how are they being used today?  Bret Tollgaard: Yeah, no, great question. So ultimately there are broad– uh, you know, a really, really broad, uh, array of challenges that wind in particular really kind of faces. And so developing UV cure solutions to fit more of those than just a small little repair patch or a crack has been something that we’ve been working on for a while with a variety of different groups. Um, but leading edge in particular, not just protection, but also the rehabilitation of those leading [00:16:00] edges. So we’ve done that in a couple of different ways. Some have been some pre-consolidated repair patches that we’ve built, where a specific blade requires, um, you know, a, a design package to reinforce certain areas, whether it was, uh, too many ply drops in a certain zone or an area of high wear. Uh, but the other ones kind of more recently are starting to build, I’ll call like a fiber reinforced type putty, where you’re actually filling gaps, filling holes, filling imperfections with a more structural repair solution before then coming over the top with an LEP for the protection of that blade long term. And so being able to provide solutions for both, uh, hand, but then also robotics is something that we’ve been focusing on. So repair technicians can do it, but then also make sure that the, uh, robotics industry has the materials that they need to be able to do that in a fast and timely manner.  Allen Hall: So these new materials can be applied with a robot, I just heard. How does that work? Is it a cartridge system or a liquid system? How– Is it sprayed on? [00:17:00] W- how does this thing work?  Bret Tollgaard: Ultimately, it’s whatever that robotics company says that they would like and need. And so we build, uh, certain things to meet different demands on both of those aspects. But some are spray, some are coated, some are troweled, uh, all of which though, uh, include applying a UV cure material, uh, and then curing it to then make sure that they get the correct, uh, you know, mechanical and thermal properties for that leading edge or that leading edge re- rehabilitation. Allen Hall: That must be a huge time saver.  Bret Tollgaard: Phenomenally so. Uh, the robotics companies are, are pretty happy with the way that things are starting to unfold, and then same with the, uh, owner-operators. Like when you can go and you can have a robot Fix, sand, or sand, clean, fill that leading edge without a technician having to be up there. Tremendous amount of time savings, and then also just liability and repeatability, um, which is always an important thing, is having extremely high quality throughout that entire process, uh, and then recording it and everything like [00:18:00] that too.  Allen Hall: Well, that’s the key to any leading edge repair that I’ve seen, is the repeatability and controlling the, uh, sometimes the uncontrollables, trying to add a little bit of restriction about what you’re doing, how you’re doing it to get the consistency in the product. Because there’s a lot of products on the market right now, uh, that supposedly will do this thing But there’s– we have seen a variety of outcomes even for the same material. C- moving to something that’s a little more standard and, and particularly UV-based, where it’s stable, shelf-stable, right? I mean, th-there’s no expiration date necessarily. You, you probably put a time limit on it, but it’s shelf-stable, so I have a, a barrel of it in the, in the pickup truck. I can probably still use it. But, uh, what am I thinking about when I start to make those decisions? Because this is all new to most of the industry. They haven’t seen this. What– some of the things should they be considering when they’re thinking about, “I got a leading edge repair.[00:19:00] It’s kinda ugly. It’s probably cat three, maybe cat four. Where am I going here? Who am I talking to, and why am I thinking about UV?”  Bret Tollgaard: Yeah, I mean, ultimately it comes down to, you know, a variety of value that we provide the customers. Um, as you said, being shelf-stable is incredibly important. The reality is a lot of these repair materials aren’t being stored in a temperature-controlled warehouse. It’s being stored in someone’s trailer, and they’re gonna use half of a cartridge, or they’re gonna use four prepregs, and they’re gonna have six more sitting on the shelf for two more months until they move to another job that they’re gonna go and do something else. And so the materials themselves need to have an incredibly long shelf life, but also be able to handle those varying temperatures. Like if you’re out in the middle of Texas here in the United States, it can be 130, 140 degree Fahrenheit inside that trailer during the day, but at the same time at night, you can have temperatures below freezing in certain areas, and it needs to be able to withstand both of those extreme temperature environments. Um, that being said, [00:20:00] you buy from a group like us or you buy it from Fibre Glast where it is stored properly, and it can be shipped next day to, to you on site. Uh, it simply, it, yeah, it simply helps, uh, everyone long term, uh, make sure that they have the materials that they need.  Allen Hall: I would be remiss not to talk about our Strike Tape Ultra product, which is a partnership between Weather Guard, Lightning Tech, and SunRez. Uh, it’s a project that we’ve been working on for probably two years now, maybe a little bit longer. It, it’s been a tremendous amount of effort, engineering time and materials-wise, and learning about different, uh, performance of a, uh, particular resin system and trying to figure out what works. But, uh, there is now a solution, and SunRez is producing a lot of Strike Tape Ultra parts at the minute Really durable, based on Sunrise material, but you’re using not just our, our, our obviously our, our recent, uh, experiments and all the data, [00:21:00]but this goes back almost 40 years. You’re, you’re bringing 40 years of knowledge to the table to come up with a solution for a really difficult environment.  Bret Tollgaard: Yeah, and we’ve had to certainly rely on the past experiences that we have learned along the way. Um, you know, part of the challenges of working for the last year or two years together on this was to truly make as robust and durable of a system as we can. And so having this new 7303 vinyl ester-based system, uh, which we’re not only using for you but also using as the primary component for our LEP system, uh, it definitely has the durability and the longevity for, uh, your strike tapes, uh, in particular to be, uh, to withstand all the elements and all the abrasion and all the erosion that’s gonna come its way for years. Allen Hall: Now, I, I’ve watched you at trade shows take a hammer to some of your prepreg materials and demonstrate you can cure it within a couple of minutes, and you’re hitting it with a hammer and it’s super tough. We’ve done that at Weather Guard with the Strike Tape Ultra. It’s [00:22:00] crazy durable, and I don’t understand the chemistry of what you guys do. You’re experts in UV and all the chemicals and how to make this thing work, but it is truly remarkable how tough that material is and UV stable it is and how quick it is to cure and to get onto a blade in a couple of minutes. It’s quite remarkable, and the, the rain erosion performance of it has been crazy good. Uh, it’s a real game changer.  Bret Tollgaard: We’re extremely proud of the way that, uh, this has all kind of unfolded, and it has been a long time to kinda work through and iterate through the different formulas. Um, but the combination of the extremely hard and durable system that we have as the overcoating combined with our UV cure adhesive, AKA surface primer, uh, to act as that really, um, gooey, high elongation, great stress distribution kinda medium, uh, to then get that into the blade, it’s the perfect one-two combo, [00:23:00] and being able to cure in just a couple of minutes is only, uh, icing on the cake. Allen Hall: Yeah. It’s, it’s, it’s quite good. Uh, I know, uh, when people hear this podcast, you’re gonna get a lot of reach-out because we’re kinda getting near the end of the typical Northern Hemisphere season. It’s gonna get chilly here in about six to eight weeks since when we’re recording. How do people get ahold of you and start talking about repair patches and materials for this year, and also for next season? Bret Tollgaard: The easiest way is gonna be reach out to us, uh, via email, info@sunrez.com, sales@sunrez.com. We’ll put you in touch with, uh, all the right people within Sunrez to make that happen for you. Um, and similarly too, groups like com- uh, Fibre Glast have been stocking and storing the material for years. They’re really, really great at tailoring the exact amount of material that you need for a given job site. They’re willing to break it down to individual patches, single things of primer, putty. Uh, you come to us, you’re gonna have larger MOQs to be able to buy [00:24:00] boxes and rolls, uh, directly off the shelf. But at the same time too, we’re happy to get customers whatever products they need as quickly as they need, and put them in touch with the right people to do so. Allen Hall: So you just Google Sunrez, S-U-N-R-E-Z, or just go to sunrez.com, and you’ll get to the Sunrez site, and you can learn a whole bunch about the materials of what is offered. But you need to, to call Brett and call the team at Sunrez, uh, to, to get the solution for you. And it is, uh, really great working with Sunrez. It, it’s been a pleasure, honestly.  Bret Tollgaard: My, uh, my business development manager, Gerhard, will certainly have me say, “Let’s direct all that to Gerhard instead of Brett.” So he’ll be happy to get you all the info that you need. Um, but yeah, so ultimately, we’re an extremely customer-driven company, and we bend over backwards as often as necessary to get customers what they need in the time that they need it. It’s something that we really pride ourselves on. Uh, it’s also how we developed all these new, uh, technologies and materials to, to make customers’ [00:25:00]lives easier in the field. And so even as all of our wind portfolios, you know, grow and expands for product line offerings, um, we’re certainly interested in learning about what your challenge in particular is, and how we can best help you solve that challenge. Allen Hall: Yeah, so if you have a blade challenge right now, and most operators do, you better be checking out sunrez.com. Brett, it’s so great to have you back on the podcast. Love having you on, and we’ll speak again soon.  Bret Tollgaard: Looking forward to it. Thank you, Allen.

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