Podcasts about interfaces

Reviewing the YouTube podcast “MIT Scientist: Your Brain Evolved to Ignore Aliens — They’re Everywhere”. (Donald Hoffman and Jesse Michels)

BBVA Research destaca que la redistribución del turismo en España continúa avanzando, con un crecimiento más intenso en destinos emergentes y zonas de interior que en los grandes polos tradicionales. Esta tendencia contribuye a desconcentrar los flujos turísticos, impulsar economías locales y reducir la presión sobre los destinos más saturados, consolidando un reparto más equilibrado de la demanda turística.El turismo rural español afronta el verano con buenas perspectivas de ocupación gracias al creciente interés por experiencias en la naturaleza y destinos menos masificados. Sin embargo, el sector muestra preocupación por la futura regulación de las autocaravanas en algunas comunidades autónomas, al considerar que podría afectar a parte de su demanda y a la actividad económica vinculada a este segmento, en un contexto de optimismo moderado para la temporada estival.Aena afronta el verano con una decena de aeropuertos operando cerca de su límite de capacidad debido al fuerte crecimiento de la demanda turística. Instalaciones como Palma de Mallorca, Málaga, Alicante o Valencia registran niveles de tráfico récord, impulsados por el papel de España como destino refugio frente a la incertidumbre internacional, lo que acelera el debate sobre la ampliación de infraestructuras aeroportuarias.El Imserso ha comenzado el envío de cartas a los usuarios inscritos para la temporada 2026-2027, dando inicio al proceso de reserva de plazas para uno de los programas turísticos más relevantes del país. Los beneficiarios recibirán información sobre destinos, fechas y procedimientos de reserva, marcando el arranque de la nueva campaña de viajes para mayores.Canarias ha sido elegida como sede del IV Foro Iberoamericano de Turismo, que se celebrará en 2027 y reunirá a representantes institucionales, empresas y expertos del sector de ambos lados del Atlántico. La designación refuerza el posicionamiento internacional del archipiélago y su papel como punto de encuentro para el debate sobre innovación, sostenibilidad y cooperación turística en el ámbito iberoamericano, consolidando un evento de referencia para el sector.Castellón refuerza su apuesta por el turismo de bienestar con la celebración del festival internacional Satsang Life Experience, un evento centrado en yoga, meditación, crecimiento personal y hábitos saludables. La iniciativa busca atraer visitantes interesados en experiencias transformadoras y posicionar la provincia como referente en el creciente segmento del wellness tourism.

Das Fahrzeug war lange Zeit das Zentrum der Mensch-Maschine-Interaktion. Doch diese Welt verändert sich rasant. Automotive HMIs verlassen das Cockpit, verteilen sich über Smartphones, Tablets, Smartwatches und Cloud-Dienste und werden Teil eines umfassenden digitalen Ökosystems. In dieser Episode des Mensch-Technik Podcasts analysiere ich die nächste Evolutionsstufe der Automotive User Experience: Liquid HMIs, vernetzte Ökosysteme und die digitale Identität des Nutzers. Warum verlieren Interfaces ihre festen Grenzen? Weshalb wird das Fahrzeug zunehmend zu einem Knotenpunkt innerhalb eines größeren digitalen Netzwerks? Und warum kämpfen OEMs, Apple, Google & Co. letztlich um dieselbe Ressource: die Beziehung zum Nutzer? Die Zukunft des Automotive HMI liegt nicht in mehr Displays oder mehr Features – sondern in der intelligenten Organisation eines digitalen Ökosystems rund um den Menschen.

Dans ce 34e épisode de Pop-Code, nous continuons de parler de jeux-vidéo avec "Scott Pilgrim vs. the World" réalisé par Edgard Wright en 2010. On y parle des caractéristiques qui font du jeu-vidéo un type de média unique, du champ de recherche en game studies, ainsi que des débats qui ont animé ce champ (par exemple, la tension entre narration et éléments ludiques). Tout cela avec une invitée de marque, la professeure en information et communication Fanny Barnabé, qui marque l'histoire en étant la 1ère invitée à oser revenir dans le podcast!Suivez-nous et donnez-vous votre avis sur notre page Instagram (https://www.instagram.com/the_real_pop_coders?igsh=am05dGxxYnhsZTc5) ou suivez-nous sur Linkedin (https://www.linkedin.com/in/anthony-simonofski-26793385/).Pour en savoir plus:Barnabé, F. (2016). Le speedrun: pratique compétitive, ludique ou créative? Trajectoire d'un détournement de jeu vidéo institué en nouveau game. Interfaces numériques, 5(3).Triclot, M. (2017). Philosophie des jeux vidéo. La découverte.Sébastien Genvo et Thibault Philippette (dirs), Introduction aux théories des jeux vidéo, Liège, Presses universitaires de Liège, coll. Jeu/Play/Spiel, 2023Brougère, G. et Savignac, E. (dir.) (2024). Dictionnaire des sciences du jeu. érès.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

This bonus content is a reading from Platypus, the CASTAC Blog. The full post by Debjani Chakraborty can be read at https://blog.castac.org/2026/05/domesticating-affordances-from-surveillance-to-navigation-of-interfaces-how-affordances-are-reappropriated-across-contexts-by-rural-indian-women/. About the post: Affordances across social media platforms… are reappropriated as a tool of surveillance, and become grounds for moral judgement of the user. For women in collectivist settings, like in many rural Indian villages, being online is not neutral. It is evaluated through reappropriation. Time spent on the phone can invite scrutiny: why is she online so often? Who is she talking to? For how long?

Wie hat dir die Folge gefallen?Gut

The FRTR 2026 Spring General Meeting, conducted as two web-based virtual sessions, provides an opportunity to share best technical practices and results of recent technical advances in understanding and characterizing contaminant fate and migration across the GSI. The first virtual session explores current scientific understanding of key hydrological and biogeochemical processes affecting contaminant distribution and transport across a GSI. This session also explores available methods and tools for characterizing contaminant migration across a GSI. The second virtual session provides an overview of recent advances in development of innovative technologies for GSI characterization. The session ends with a discussion of needs for further technological development. To view this archive online or download the slides associated with this seminar, please visit http://www.clu-in.org/conf/tio/FRTRSpring26_052126/

The FRTR 2026 Spring General Meeting, conducted as two web-based virtual sessions, provides an opportunity to share best technical practices and results of recent technical advances in understanding and characterizing contaminant fate and migration across the GSI. The first virtual session explores current scientific understanding of key hydrological and biogeochemical processes affecting contaminant distribution and transport across a GSI. This session also explores available methods and tools for characterizing contaminant migration across a GSI. The second virtual session provides an overview of recent advances in development of innovative technologies for GSI characterization. The session ends with a discussion of needs for further technological development. To view this archive online or download the slides associated with this seminar, please visit http://www.clu-in.org/conf/tio/FRTRSpring26_052126/

The FRTR 2026 Spring General Meeting, conducted as two web-based virtual sessions, provides an opportunity to share best technical practices and results of recent technical advances in understanding and characterizing contaminant fate and migration across the GSI. The first virtual session explores current scientific understanding of key hydrological and biogeochemical processes affecting contaminant distribution and transport across a GSI. This session also explores available methods and tools for characterizing contaminant migration across a GSI. The second virtual session provides an overview of recent advances in development of innovative technologies for GSI characterization. The session ends with a discussion of needs for further technological development. To view this archive online or download the slides associated with this seminar, please visit http://www.clu-in.org/conf/tio/FRTRSpring26_052026/

The FRTR 2026 Spring General Meeting, conducted as two web-based virtual sessions, provides an opportunity to share best technical practices and results of recent technical advances in understanding and characterizing contaminant fate and migration across the GSI. The first virtual session explores current scientific understanding of key hydrological and biogeochemical processes affecting contaminant distribution and transport across a GSI. This session also explores available methods and tools for characterizing contaminant migration across a GSI. The second virtual session provides an overview of recent advances in development of innovative technologies for GSI characterization. The session ends with a discussion of needs for further technological development. To view this archive online or download the slides associated with this seminar, please visit http://www.clu-in.org/conf/tio/FRTRSpring26_052026/

In einer Welt, in der Information fast überall kostenlos ist, entscheidet die Qualität der Quelle über den unternehmerischen Erfolg. Marc Berg, CEO von Statista und ehemaliger Manager bei Klarna und Free Now, steht an der Spitze eines der weltweit erfolgreichsten deutschen Digitalunternehmen. Bei How to Hack spricht er mit Carsten Puschmann darüber, warum Deutschland als Startup-Standort gerade massiv den Anschluss verliert und wie Statista den Wandel von der SEO-getriebenen Datenbank zur globalen AI Intelligence Plattform vollzieht.Marc erklärt, warum Statista seine Daten heute aktiv vor LLMs schützt, weshalb „Embedded Intelligence“ das nächste große Ding nach dem Chat-Hype ist und warum ein moderner CEO heute mehr Operator als reiner Stratege sein muss. Ein Gespräch über die harte Realität des Wirtschaftsstandorts Deutschland, den Mut zur radikalen Umgestaltung von Geschäftsmodellen und die Frage, wer künftig eigentlich das Wissen der Welt kontrolliert.Wir reden über

A grounded discussion on how brain–computer interfaces are moving from research into real clinical use, with a focus on restoring communication and autonomy for ALS patients. Kurt Hagstrom explains the technical constraints, regulatory realities, and why simplicity, scalability, and long-term reliability matter more than hype. SPONSORS: ElevenLabs: Thanks to ElevenLabs (⁠https://elevenlabs.io⁠) for supporting this episode and powering Tim's voice. Synchron: Our sponsor Synchron has received permission to start their third clinical trial on the BCI investigational medical device that received FDA Breakthrough Device Designation in 2020. You or a loved one may be eligible to participate. The Synchron BCI is a fully implanted system designed to allow users to control digital devices using their thoughts. No eye trackers, voice commands or hand movements are required. Since 2018, they have completed 2 clinical trials that met their respective safety goals. This new INTENT trial will further evaluate safety while also exploring how it may support communication, connection, independence, and quality of life.  To find out more about the INTENT clinical trial and learn about the Synchron BCI device visit SynchronBCI.com. SOCIAL: Website: ⁠https://nlupod.com/⁠ X: ⁠https://x.com/nlutimgreen⁠ Facebook: ⁠https://www.facebook.com/NLUpod⁠ Instagram: ⁠https://www.instagram.com/nlupod⁠ PERSONAL: Tackle ALS: ⁠https://www.tackleals.com⁠ Tim Green Books: ⁠https://authortimgreen.com⁠ Tim's New Book - ROCKET ARM: ⁠https://www.amazon.com/dp/0062796895/ Learn more about your ad choices. Visit megaphone.fm/adchoices

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Adeline Atlas 11 X Published AUTHOR Digital Twin: Create Your AI Clone: ⁠https://www.soulreno.com/digital-twin⁠SOS: School of Soul Vault: Full Access ALL SERIES⁠⁠https://www.soulreno.com/joinus-202f0461-ba1e-4ff8-8111-9dee8c726340⁠Instagram: ⁠⁠https://www.instagram.com/soulrenovation/⁠Soul Renovation - BooksSoul Game - ⁠https://tinyurl.com/vay2xdcp⁠Why Play: ⁠⁠https://tinyurl.com/2eh584jf⁠How To Play: ⁠⁠https://tinyurl.com/2ad4msf3⁠Digital Soul: ⁠https://tinyurl.com/3hk29s9x⁠Every Word: ⁠⁠⁠http://tiny.cc/ihrs001⁠Drain Me: ⁠⁠https://tinyurl.com/bde5fnf4⁠The Rabbit Hole: ⁠https://tinyurl.com/3swnmxfj⁠Destiny Swapping: ⁠https://tinyurl.com/35dzpvss⁠Spanish Editions: Every Word: ⁠https://tinyurl.com/ytec7cvc⁠Drain Me: ⁠https://tinyurl.com/3jv4fc5n

What if websites could understand how you feel?In this episode of Future of UX, Patricia talks with Sebastian, Director of UX Design at Virtual Identity, about a concept that could fundamentally change how we design digital experiences: the Empathic Web.Instead of static interfaces that look the same for every user, the empathic web describes a future where digital products can adapt to a user's emotional state in real time. Interfaces, tone of voice, information density, and even entire user journeys could dynamically change depending on whether someone feels stressed, curious, overwhelmed, or motivated.Sebastian shares how advances in AI, behavioral signals, and new interaction models might make this possible and what it means for designers.The conversation also explores the growing role of AI agents in digital products and why designers might soon have to design not only for humans but also for autonomous systems interacting with our products.At the same time, this shift raises important ethical questions. If systems can detect emotions, how should companies use that information responsibly? Where is the line between helpful personalization and manipulation?This episode is a deep dive into the future of UX and how AI could transform the way digital experiences are designed.✨ What the Empathic Web is and why it matters✨ How AI could detect emotions through signals like mouse movement, voice, and interaction patterns✨ Why digital experiences may become dynamic instead of static✨ How designers might start designing for emotional states across the user journey✨ What Agentic UX means and why AI agents will change product design✨ How websites may need to become agent friendly and machine readable✨ Ethical questions around emotional data and manipulation✨ Why learning experiences could be a perfect use case for empathic interfacesSebastian is Director of UX Design at Virtual Identity, a digital agency working across Munich, Freiburg, Vienna, and Porto.Before returning to industry, he spent years in academia as a Professor of Design Management in Berlin, researching the intersection of design, technology, and culture.His current work focuses on the future of UX in an AI driven world, including topics like the Empathic Web, Agentic UX, and AI powered digital experiences.Connect with Sebastian: LinkedInAI for Designers: 5-week Bootcamp

En compganie de son invitée Gladys Diandoki, Jean-François Poulin explore l'idée que les sites web pourraient être remplacés par des interfaces conversationnelles capables de guider directement les utilisateurs, comme un service client personnalisé. Face à des contenus souvent mal structurés et difficilement exploitables, l'enjeu devient moins technologique qu'organisationnel.

Send us Fan MailWhat if we could restore vision, communicate directly with the brain, and even extend human life—not with machines alone, but with living, engineered biology?Dr. Alan Mardinly, Ph.D. is the Chief Scientific Officer and Co-Founder of Science Corp. ( https://science.xyz/ ), a neurotechnology company developing next-generation brain interfaces and biohybrid neural implants aimed at restoring human function.Dr. Mardinly leads the company's biohybrid program, focused on combining genetically engineered cells with advanced optical hardware to create optogenetic therapies for vision restoration and new types of brain-machine interfaces.Dr. Mardinly has spent more than 15 years working at the intersection of neuroscience, genetics, and neural engineering. Dr. Mardinly earned his PhD in Neurobiology from Harvard University, where his research explored how experience and activity-dependent gene programs shape synaptic plasticity in inhibitory neurons—work that helped illuminate how neural circuits maintain stability while adapting to learning and sensory experience.Following Harvard, Dr. Mardinly conducted postdoctoral research at UC Berkeley, where he helped pioneer holographic optogenetics, a technology capable of stimulating specific neurons with extraordinary spatial and temporal precision.Dr. Mardinly later joined Neuralink, where he served as Director of Biology, leading teams focused on advancing brain-machine interface technologies. In 2021, Dr. Mardinly co-founded Science Corp., where he now works to translate cutting-edge neuroscience into therapeutic systems designed to restore vision and build the next generation of neural interfaces.#Neurotechnology  #BrainComputerInterface #Optogenetics #VisionRestoration #Biohybrid #ScienceCorp #Neuralink #FutureOfMedicine #BrainImplants #ArtificialVision #Neuroscience #Biotech #Longevity #LifeSupport #SyntheticBiology #HumanAugmentation #NextGenMedicine #DeepTech #Innovation #FutureOfHealthcareSupport the show

Kloiber, Manfred www.deutschlandfunk.de, Computer und Kommunikation

Making a Scene Presents - A Buyer's Guide to Recording Interfaces The Box That Decides Whether Your Studio Feels Fast or Feels Broken There is a certain kind of gear mistake that musicians make all the time. They obsess over microphones, plugins, monitors, and shiny rack toys, then they treat the recording interface like a boring utility purchase. That is backward. Your interface is the center of the studio. It is the box that decides how your microphone gets into the computer, how your speakers get fed, how your headphones behave, how low your latency feels, how your outboard gear connects, and how easy it will be to grow from a simple home setup into a serious project studio. Pick the right one and the whole room feels smooth. Pick the wrong one and everything becomes friction. http://www.makingascene.org

Fresh off raising a monster $15B, Marc Andreessen has lived through multiple computing platform shifts firsthand, from Mosaic and Netscape to cofounding A16z. In this episode, Marc joins swyx and Alessio in a16z's legendary Sand Hill Road office to argue that AI is not just another hype cycle, but the payoff of an “80-year overnight success”: from neural nets and expert systems to transformers, reasoning models, coding, agents, and recursive self-improvement. He lays out why he thinks this moment is different, why AI is finally escaping the old boom-bust pattern, and why the real bottleneck may be less about models than about the messy institutions, incentives, and social systems that struggle to absorb technological change.This episode was a dream come true for us, and many thanks to Erik Torenberg for the assist in setting this up. Full episode on YouTube!We discuss:* Marc's long view on AI: from the 1980s AI boom and expert systems to AlexNet, transformers, and why he sees today's moment as the culmination of decades of compounding technical progress* Why “this time is different”: the jump from LLMs to reasoning, coding, agents, and recursive self-improvement, and why Marc thinks these breakthroughs make AI real in a way prior cycles were not* AI winters vs. “80-year overnight success”: why the field repeatedly swings between utopianism and doom, and why Marc thinks the underlying researchers were mostly right even when the timelines were wrong* Scaling laws, Moore's Law, and what to build: why he believes AI scaling laws will continue, why the outside world is messier than lab purists assume, and how startups can still create durable value on top of rapidly improving models* The dot-com crash and AI infrastructure risk: Marc's comparison between today's AI capex boom and the fiber/data-center overbuild of 2000, plus why he thinks this cycle is different because the buyers are huge cash-rich incumbents and demand is already here* Why old NVIDIA chips may be getting more valuable: the pace of software progress, chronic capacity shortages, and the idea that even current models are “sandbagged” by supply constraints* Open source, edge inference, and the chip bottleneck: why Marc thinks local models, Apple Silicon, privacy, trust, and economics all point toward a major role for edge AI* American vs. Chinese open source AI: DeepSeek as a “gift to the world,” why open models matter not just because they're free but because they teach the world how things work, and how open source strategies may shift as the market consolidates* Why Pi and OpenClaw matter so much: Marc's claim that the combination of LLM + shell + filesystem + markdown + cron loop is one of the biggest software architecture breakthroughs in decades* Agents as the new “Unix”: how agent state living in files allows portability across models and runtimes, and why self-modifying agents that can extend themselves may redefine what software even is* The future of coding and programming languages: why Marc thinks software becomes abundant, why bots may translate freely across languages, and why “programming language” itself may stop being a salient concept* Browsers, protocols, and human readability: lessons from Mosaic and the web, why text protocols and “view source” mattered, and how similar principles may shape AI-native systems* Real-world OpenClaw use: health dashboards, sleep monitoring, smart homes, rewriting firmware on robot dogs, and why the most aggressive users are discovering both the power and danger of agents first* Proof of human vs. proof of bot: why Marc thinks the internet's bot problem is now unsolvable via detection alone, and why biometric + cryptographic proof of human becomes necessaryTimestamps* 00:00 Marc on AI's “80-Year Overnight Success”* 00:01 A Quick Message From swyx* 01:44 Inside a16z With Marc Andreessen* 02:13 The Truth About a16z's AI Pivot* 03:29 Why This AI Boom Is Not Like 2016* 06:33 Marc on AI Winters, Hype Cycles, and What's Different Now* 10:09 Reasoning, Coding, Agents, and the New AI Breakthroughs* 12:13 What Founders Should Build as Models Keep Improving* 16:33 AI Capex, GPU Shortages, and the Dot-Com Crash Analogy* 24:54 Open Source AI, Edge Inference, and Why It Matters* 33:03 Why OpenClaw and PI Could Change Software Forever* 41:37 Agents, the End of Interfaces, and Software for Bots* 46:47 Do Programming Languages Even Have a Future?* 54:19 AI Agents Need Money: Payments, Crypto, and Stablecoins* 56:59 Proof of Human, Internet Bots, and the Drone Problem* 01:06:12 AI, Management, and the Return of Founder-Led Companies* 01:12:23 Why the Real Economy May Resist AI Longer Than Expected* 01:15:53 Closing ThoughtsTranscriptMarc: Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Having said that, I think what's actually happened is an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years where that was controversial. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right?Which is like, it's an overnight success ‘cause it's like bam, you know, chat GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.If I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough.swyx: Before we get into today's episode, I just have a small message for listeners. Thank you. We will not be able to bring you the ai, engineering, science, and entertainment contents that you so clearly want if you didn't choose to also click in and tune into our content.We've been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribed to us to keep all this sustainable without ads, and we wanna keep it that way. But I just have one favor to ask all of you. The single, most powerful, completely free thing you can do is to click that subscribe button.It's the only thing I'll ever ask of you, and it means absolutely everything to me and my team that works so hard to bring the in space to you each and every week. If you do it, I promise you will never stop working to make the show even better. Now, let's get into it.Alessio: Hey everyone, welcome to the Lidian Space Pockets. This is CIO, founder Kernel Labs, and I'm joined by s Swix, editor of Lidian Space.swyx: Hello. And we're in a 16 Z with a, uh, mark G and welcome.Marc: Yes, yes. A and what, half of 16? Something like that. A one. Exactly,swyx: exactly. Uh, apparently this is the, the final few days in your, your current office.You're moving across the road.Marc: Uh, we're, yeah. We have a, we have some, we have some projects underway, but yeah, this is actually, oh, this is the original. We're in actually the original office. We're in the, we're in the, we're, we're in the whole thing.swyx: It's beautiful. Yeah. Great.Marc: Thank you.swyx: So I have to come out, uh, this is a, you know, I wanted to pick a spicy start in October, 2022.I just made friends with Roone and, uh, I wanted to give him something to sort of be spicy about. And I said, uh. Uh, it'll never not be funny. The A 16 Z was constantly going. The future is where the smart people choose to spend their time and then going deep into crypto and not in ai. And that was in October 22nd, 2022.And Ruen says there was an internal meeting in a 16 Z to reorient around Gen ai. Obviously you have, but was there a meeting? What, what was that?Marc: I mean, I don't, look, I've been doing AI since the late eighties.swyx: Yeah.Marc: So I, I don't know, like all that, as far as I'm concerned, this stuff is all Johnny cum lately.Yeah. You, I mean, look, we've been doing ar entire existence. I mean, we've been doing AI machine learning deep, you know, deeply. We've been doing this stuff way from the beginning. Obviously a AI is just core to computer science. I, I, I actually view them as like quite, uh, quite continuous. Um, you know, Ben and I both have computer science degrees.Um, you know, we, we both, Ben, Ben and I actually both are world enough to remember the actual AI boom in the 1980s. Yeah. There was like a, there was a big AI boom at the time. Um, and there was a, was names like expert systems. Um, and they of like lisp and lisp machines. Uh, I, I coded in lisp. I was coding a lisp in 1989.When that was the, the language of the AI future. Um, yeah. So this is something that we're like completely, you completely comfortable with. I've been doing the whole time and are very enthusiastic aboutswyx: is there a strong, like this time is different because, uh, my closest analog was 20 16 17. It was an AI boom.Mm-hmm. And it petered out very, very quickly. Um, we, it just, it just in terms of investingMarc: sort of, sort of,swyx: yeah. Investment, investment excitement.Marc: Although that's really when the, the, the Nvidia phenomenon really, it was, I would say it was in that period when it was very clear that at, at the time it, the vocabulary was more machine learning, but it, it was very clear at that time that machine learning was hitting some sort of takeoff point.Alessio: Yeah.Marc: Well, and as you guys, you guys have talked about this at length on, on your thing, but, you know, if you really track what happened, I think the real story is, it was, it was the Alex net, uh, basically breakthrough in like 2013. That was the, that was the real knee in the curve. Um, and then it was obviously the transformer breakthrough in 17.Alessio: Yeah.Marc: Um, and then everything that followed. But, but, you know, look, machine learning, you know, there were, you know, look, uh, I mean look, I've been working, you know, I've been working with, uh, one of my, you know, kind of projects working with Facebook since 2004. Um, and on the board since 2007, and of course, you know, they, they started using machine learning very early, um, and, you know, have used it basically, you know, for like 20 years for, you know, content, you know, feed optimization and advertising optimization.And obviously many, you know, financial services. You know, many, many, many companies, many different sectors have been doing this. And so it's like one of these things, it's like, it's not a, it's not a single thing. Like it's, it's like, it's like layers, right? Yeah. Um, and, and the layers arrive at different paces and, but they kind of build up.swyx: Yeah.Marc: Uh, they kind of build up over time and then, and then, yeah. And then look, in retrospect, it was 2017 was kind of the, you know, the key, the key point with the trans transformer and then. And then as you guys know, there was this really weird like four year period where it's like the, the transformer existed and then it was just like,swyx: let's go.Yeah.Marc: Well, but, but it was just, but, but between 2020, but between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chat Botts, but they weren't letting anybody use them.swyx: Yeah.Marc: Right. And then, you know, and then OpenAI developed Chat GT or GPT two, and then they told everybody, this is way too dangerous to deploy.Right. Yeah. You know, we can't possibly let normal people, normal people use this thing. And then you, you guys, I'm sure remember AI Dungeon, um mm-hmm. So the o for, there was like a year where like the only way for a normal person to use GP T three was in, in AI dungeon.Alessio: Yeah.Marc: And so you, you, we would do this, you'd go in there and you'd pretend to play Dungeons and Dragons.In reality, you're just trying to talk to talk to GPT. And so there was this, you know, there was this long, you know, and I, you know, the big, big companies, you know, big companies are cautious and, you know, the big companies were cautious. It, it, by the way, it took open ai. You know, they, they, they talk about this, it took open AI time to actually adjust, you know, kind of re redirect their researchswyx: path.I, I think, uh, let say Rosewood, right? Uh, the, the dinner that founded OpenAI was right there.Marc: Right, right. But that, that dinner would've taken place in 20swyx: 18Marc: 19. The formation of OpenAI Uhhuh as late as 2018.swyx: Uh, uh, sorry. Uh, no, I'm, I'm, I'm, I'm wrong. Probably It should be 20. Yeah. They just celebrated a 10 year anniversary, so it it is 2025.Yeah, so, so 2015?Marc: Yeah. 2015. Yeah. 2015. But then, uh, um, Alec Radford did G PT one in what, probablyswyx: mm-hmm. 17, 18,Marc: yeah. 17, 18. So it, yeah. For, and then, and then they didn't really, and then GPT three was what? 2020? 2020.swyx: 2020.Marc: Because that became copilot immediately. Even open ai, which has been, you know, the leader of, of this thing in the last decade, you know, e even they had to adapt and, and, and lean into the new thing.And so. Um, yeah, I, I think it's just this process of basically sort of wave after wave layer after layer, you know, building on itself. And then you kind of get these catalytic moments where, where the whole thing pops and, and obviously that's what's happening now.swyx: Is it useful to think about will there be any ai, winter?‘cause there's always these patterns. Like, is this, in the summer is something I constantly think about because do I get, do I just like. Just get endlessly hyped and just trust that I will only be early and never wrong or right. Well, are we, will there be a winter?Marc: So there's something about, say the following.There's something about AI that has led to this repeated pattern. Um, and, and, and you guys know this,swyx: it's summer, winter, summer,Marc: winter, summer, winter, summer, winter. And it goes back 80 years. Yeah. 80 years. Uh, so the original neural network paper was 1943. Right. Which is, which is amazing. Uh, that it was, it was far back that long.And then there was you, if you guys have ever talked about this on your show, but there was this, uh, there was a big, uh, there was an a GI conference at Dartmouth University in 1950. 55. 55, yeah. And they got a NSF grant to, uh, for the, all the AI experts at the time to spend the summer together. And they figured if they had 10 weeks together, they could get a GI, uh, at the other end.And they got their, by the way, they got the grant, they got the 10 weeks and then, you know, 1955, you know. No, no. A GI. And like I said, I, I lived through the eighties version of this where there was a big, a big boom and a crash. And so, so there is this thing, and there, there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.Um, and, and it's probably on both sides of like the, the, the boom bus cycle. You, you kind of see that play out. Having said that, I think what's actually happened is like just, and you know, and we now know in retrospect like an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years or that was controversial. And, and we now know that that's the case. And so we, we now, you know, everything we're building on today just sort of derives from the original idea in 1943. And so, so in retrospect, we, we now know that like, these, these guys are right.They, they, you know, they would get the timing wrong and they thought, you know, capabilities would arrive faster, or they were, it could be turned into businesses sooner or whatever, but like, they were fundamentally, the, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing.And, and the, and the payoff from, from, from all their work is happening now. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right? Which is like, it's an overnight success.‘cause it's like bam, you know, chat, GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.Um, and thinking, and look, there were AI researchers who spent their entire lives. They got their PhD. They, they worked for, they've researched for 40 years. They retired in a lot of cases, they passed away and they never actually saw it work.swyx: Yeah. It's all sad.Marc: It is. It is sad. It's sad. Knewswyx: Jeff Hinton was like the last guy.Marc: Yeah. Yeah. Well, there were the guys, uh, was a guy, Alan Newell. I mean, there's tons of John McCarthy. You know, John McCarthy was like one of the inventors in the field. He's one of the guys who organized the Dartmouth Conference and you know, he taught at Stanford for 40 years. Wow. And passed, you know, passed away, I don't know, whatever, 10, 10 years ago or something.Never, never actually go. Got to see it happen. But like, it is amazing in retrospect, like, these guys were incredibly smart and they worked really hard and they were correct. So anyway, so then it's like, okay, you know, say history doesn't repeat, but it rhymes. It's like, okay, does that mean that there's gonna be another, like, you know, basically boom buzz cycle.And I, I will tell you, like, let, like in a sense, like yes, everything goes through cycles and, you know, people get overly enthusiastic and overly depressed and there's, there's a time, there's a timelessness to that. Having said that, there's just no question. Um, so the form, the foremost dangerous words in investing this time are, this time is different.Do you know the 12 most dangerous words investing? No. The four most d foremost dangerous words in investing are this time is different. Yeah. Um, the 12 most dangerous words. And so like, I'll tell you what's different. Like now it's working like, like there's just no, I mean, look, there's just no question.And by the way, I, I'll just give you guys my take. Like L LLMs, like from, from basically the Chad G PT moment through to spring of 25. I think you could still, I think well intention, well, and of. Form skeptics could still say, oh, this is just pattern completion. And oh, these things don't really understand what they're doing.And you know, the hall hallucination rates are way too high. And, you know, this is gonna be great for creative writing and creating, you know, Shakespeare and so sonnets and, you know, as, as rap lyrics or whatever, like, it's gonna be great and all that stuff, but we're not gonna be able to harness this to make this relevant in, you know, coding or in medicine or in law or in, you know, you know, kind of feels that, you know, kind of really, really matter.And I think basically it was the reasoning breakthrough. It, it was oh one and then R one that basically answered that question basically said, oh no, we're gonna be able to actually turn this into something that's gonna work in the real world. And, and then obviously the coding breakthrough over the, over basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.Mm-hmm. Where you're just like, alright, if, if, you know, if Linus Tova is saying that the AI coding is no better than he is like. Like, that's, that's never happened before. That's theswyx: benchmark.Marc: Yeah. That's never happened before. And so now we know that it's, it's gonna sweep through coding and, and then, and then we, we know, you know, we know that if it's gonna work in coding, it's gonna work in everything else.Right. It's just then, because that's, that's like, that's like, that's like the hardest in many ways. That's the hardest example. And how everything else is gonna be a, a derivative of that. And then on top of that, we just got the agent breakthrough, you know, with Open Claw, which is fantastic. Which is amazing and incredibly powerful.And then we just got the, the, um, the auto research, uh, you know, the, the self-improvement. You know, we're now into the self-improvement breakthrough. And so the, so the way I think about it is we've had four fundamental breakthroughs in functionality, l OMS reasoning, uh, agents, um, and then, uh, and, and then now RSI, um, and, and they're all actually working.Um, and so I'm, I'm just, as you like, you can tell I'm jumping outta my shoes. Like, like this is, like this is it like this, this is the culmination of 80 years worth of worth of work, and this is the time it's becoming real.Alessio: Yeah.Marc: I, I'm completely convinced.Alessio: I think the anxiety that people feel is like during the transistor era, yet Mors law, and it's like, all right, we understand why these things are getting better.We understand the physics of it. Yeah. With ai, it's. It's so jagged in like the jumps where like, like you said, it's like in three months you have like this huge jump like, and people are like, well this can keep happening. Right? But then it keeps happening,Marc: it'll keep happening.Alessio: And so like how do you think about also timelines of like what's we're building?I think we always have this question with guests, which is like, you know, should you spend time building harness for a model versus like the next model just gonna do it one shot in the lead space. Right. And how does that inform, like how you think about the shape of the technology? You know, you talk about how it's a new computing platform.If you have a computing platform, then like every six months it like drastically changes in what it looks like. It's hard to build companies on top of it.Marc: Yeah. So, so a couple things. So one is like, look, the, the Moore's law was what we now call a scaling law. Like Moore's Law was a scaling law and for your younger viewers, more Moore's Law was every chip chip chips either get twice as powerful or twice as cheap every, every 18 months.And that, and that and that, you know, that it's gotten more complicated in the last few years. But like that, that was like the 50 year trajectory of, of, of the computer industry. And then, and then by the way, and that's what took the mainframe computer from a $25 million current dollar thing into, you know, the phone in your pocket being, you know, a million times more powerful than that.Like that, you know, for, for 500 bucks. And so that, that was a scaling law. And then, and then, and then key to any scaling law, including Moore's Law and the AI scaling laws is, you know, they're not really laws, right? They're, they're, they're, they're predictions, but when they work, they become self-fulfilling predictions because they, they, they, they, they set a benchmark and, and then the entire industry, right?All the smart people in the industry kind of work to make sure that, that, that actually happens. And so they, they kind of motivate the breakthroughs that are required to, to keep that going. And, and in and in chips, that was a 50 year, that was a 50 year run. Right. And it, it was amazing. And it's still happening in, in some areas of, of chips.I think the same thing is happening with the, the core scaling laws. The core scaling laws. In, in, in ai, you know, they're, they're not really laws, but like they, they are basically. There are predictions and then they're motivating catalysts for the research work that is required to be. And, and, and, and by the way, also the investment, uh, dollars, um, uh, you know, required to basically keep, you know, keep the curves going and, and look, it, it is, it's gonna be complicated and it's gonna be variable and they're, you know, there're gonna be walls that are gonna look like they're fast approaching, and then they're gonna be, you know, engineers are gonna get to work and they're gonna figure out a way to punch through the walls.And obviously that's, you know, that's been happening a lot, you know, and then look, there's gonna be times when it looks like the walls have, you know, the, the, the laws have petered out and then they're gonna, they're gonna pick up again and surge and then, and then, and then it, it appears what's happening to the eyes is there's not multiple, you know, multiple scaling laws.Um, there's multiple areas of improvement. And, and I think, you know, I don't know how many more there are already yet to be discovered, but there are probably some more that we don't know about yet. You know, they, like, for example, there's probably some scaling law around, um, world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet.So that, that, that one will probably kick in at some point here. There's a bunch of really smart people working on that. Um, and so, yeah, I, I think the expectation is that, that, you know, the, the scaling laws generally are gonna continue. Yeah. The, the pace of improvement will continue to move really fast.Um. To your question on like what to build. So, uh, I'm a complete believer the scaling laws are gonna continue. I'm a complete believer the capabilities are gonna keep getting amazing, um, you know, leaps and bounds. Uh, the part where I kind of part ways a little bit with how, what I would describe as the AI purists, um, you know, which is, which I would characterize as like the people who are.In many ways, the smartest people in the field, but also the people who spend their entire life, like at a lab, um, and have, have, I would say, have very little experience in the outside world. Um, the, the, the nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated.Um, and, um, and doesn't, you know, it it 8 billion people making collective decisions on planet Earth is not a simple process of like, just like you see this happening now. It's like a bunch of AI CEOs have this thing, which is just like, well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's these, these obvious set of things that so society to do.Alessio: Mm-hmm.Marc: And then they're like, society's not doing any of those things. Right. And it's like, how can society not, you know, what, whatever their theory is, how can society not see x, y, Z? Mm-hmm. And the answer is, well, society is number one. There's no single society, it's like 8 billion people. And they like all have a voice, and they all have a vote, like at the end of the day of how they, they react to change.And then, you know, it just like, it's just human reality is just really complicated and messy. Um, and, and, and so the specific answer to your question is like, as usual, it depends. Um, you know, it, it depends. Look, pe there's no question people are gonna, like, there's no question they're gonna be companies.It's already happening. There are companies that think that they're building value on top of the models and then they're just gonna get blissed by the, by the next model. There's no question that's happening. But I think there's no question also that just the process of adaptation of any technology into the real and into the real messy world of humanity is, is just going to be messy and complicated.It's, it's not going to be simple and straightforward. It's gonna be messy and complicated. And there are gonna be a lot of companies and a lot of products, um, uh, and in, in fact entire industries that are gonna get built to, to, to basically actually help all of this technology actually reach real people.Alessio: The amount of capital going into these companies, I mean, Dario talked about it on the Door Cash podcast and Door Cash was like, why don't you just buy 10 x more GPUs? And he is like, because I'm gonna go bankrupt if the model doesn't exactly hit the, the performance level. How do you think about that?Also as a risk on, you know, you guys are investors, open AI and thinking machines and world apps. It seems like we're leveraging the scaling loss at a pretty high rate, right? Like how comfortable, I guess, do you feel with the downside scenario, like, and say like things Peter out, you think you can kind of like restructure uh, these build outs and uh, you know, capital investments.Marc: Yeah. So should start by saying, so I live through the.com crash, um, and I can tell you stories for hours about the.com crash and it was horrible. No, it was awful. It was, it was, it was apocalyptic by the way. The, a lot of the.com crash was actually at the time, it was actually a telecom crash. It was a bandwidth crash.Like the, the thing that actually crashed, that wiped out all the money with the tele, the telecom companies.swyx: GlobalMarc: crossing. Global, global, yeah.swyx: I'm from Singapore and they, they laid so much cable o over over our oceans.Marc: Actually there was a scaling law in the.com. Era. And it was literally the, the US Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter.Um, and, and actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth is gonna keep doubling every quarter.Doubling every quarter though is like, you know, grains of chess and the chessboard, like at some point the numbers become extremely large. Right. And, and, and it really, and really what happened was the internet. The internet by the way, continuously kept growing basically since inception. And it's, you know, it's, it's continuously grown.It's never shrunk. And it's grown really fast compared to anything else. Mm-hmm. You know, in, in, in human history. But it wasn't doubling every quarter as of 19 98, 19 99. And so there was this gap in the expectation of what they thought was a scaling law versus reality. And that's actually what caused the.com crash, which was the, it they, they way over companies like global crossing way overbuilt fiber, which is sort of the, and by the way, fiber, telecom equipment, you know, so all the, all the networking gear, you know, and then, and then by the way, the actual physical data centers, like that was the beginning of the, of the, of the data center build and then, and the data center overbuild.And so you had that, but it was, it was literally, I think it was like $2 trillion got wiped out, right? It was like Jesus, it was like a big, it was. And by the way, the other, the other subtlety in it was the internet companies themselves never really had any debt. ‘cause tech, tech companies generally don't run on debt, but the telecom companies run on debt.Physical infrastructure companies run on debt. And so the companies like Global Crossing not just raise a lot of equity, they also raise a lot of debt. So they're highly levered. And so then you just do the thing. It's just like, okay, you have a highly levered thing where you're, you're just over, you're overbuilding capacity.Demand is growing, but not as fast as you hoped. And then boom, bankrupt. Right. And, and then it, and then it's like they say about the hotel industry, which is, it's always the third owner of a hotel that makes money. It has to go bankrupt twice, right? You have to wash out all of the over optimistic exuberance before it gets to actually a stable state.And then it makes money. So by the way, all of those data centers and all of those, all the fiber that they're in use, it's all in use today. Yeah. But 25 years later. But it, it, it took, and actually the elapsed time was, it took 15 years. It took 15 years from 2000 to 2015 to actually fill, fill up all that capacity.The cautionary warning is the, the overbuild can happen. Um, and, and, and, and, you know, you, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital, it's like, wow. It's just, I, I don't know how to invest in these crazy software things. For sure I can put build data centers and for sure I can buy GPUs that I can deploy, you know, compute grids and, and all these things.Um, and so, you know, if you're a pessimist, you could look at this and you could say, wow, this is like really set up to be able to basically replicate, you know, what we went through, what we went through in 2000. Obviously that would be bad. The counter argument, which is the one I I agree with, which is the counter on, on the other side is a couple things.One is the companies that are investing all the, the companies that are investing the money are like the bluest chip of companies. And so back, back, back in the, in the do, like Global Crossing was like a, it was like an entrepreneur. It was like a, a new venture, but like the money that's being deployed now at scale is Microsoft, and, you know, and Amazon and Google, Facebook and Facebook and Nvidia and, you know, these, these, these, and, and now you know, by the way, open ai philanthropic, which are now at like, you know, really serious size, um, you know, as companies with, you know, very serious revenue.These are very large scale companies with like, lots, lots of cash, lots of debt capacity that they've, they've never used. And so th this is institutional in a way that, that really wasn't at the time. And then the other is, at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away.Like so, and you guys know this, like everybody's starved for capacity, everybody's starved for compute capacity and then, you know, all the associated things, memory and, and, and interconnected and everything else. Um, data center space. And so e every dollar right now that's being put into the ground is turning into revenue.And, and it, and in fact, I actually think there's an interesting thing happening, which is because everybody starve for capacity, the models that we actually have that we can use today are inferior versions of what we would have if not for the supply constraints. That's true. Um, if Right pose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful mm-hmm.The models would be much better. ‘cause you would just allocate a lot more money to training and you'd just build better models and they would be better. Um, and so we're, we're actually getting the sandbag version of the technology.swyx: Yeah. No. Everything we use is quantized because the, the labs have to keep the, the full versions,Marc: right?swyx: LikeMarc: we're not even getting the good stuff.swyx: Yeah.Marc: But, but getting the good stuff, it's, it's just, even if technical progress stops. Once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, all, all the things that have to happen in the course of the next five or 10 years.Once it happens, even the current technology is gonna get, gonna get much better. And then as you know, like there's just like a million ways to use this stuff. Like there's just like a million use cases for this. Mm-hmm. Like, it, it, you know, this isn't just sending packets across a, a thing, whatever, and hoping that people find something to do with it.This is just like, oh, we apply intelligence into every domain of human activity. And then it works like incredibly well. Yeah. Um. Here's what I know, here's what I know. Um, in the next three or four year, it's like somewhere between three or four years out, basically everything is selling out. So like the, the entire supply chain is, is, is, is sold out or, or, or selling out.And so there, there's no, like, we're just gonna have like chronic supply shortage for, you know, for years to come. Um, there's going to be a response from the market that's gonna result in an enormous, you know, it's happening now. An enormous flood of investment in a new fab capacity and ev you know, every, everything else to be able to do that, at some point the supply chain constraints will unlock, you know, at least to some degree that will be another accelerant to industry growth when that happens.‘cause the products will get better and everything will get cheaper. Um, and so, so I know that's gonna happen. I know that, you know, the deployments, you know, the, the actual use cases are like really compelling. And then, like I said, you know, with reasoning and agents and so forth, like, I know they're just gonna get like much, much better from here.And so I, I, I know the capabilities are like really real and serious. I also know that the technical progress is not going to stop. It. It, it is excel. It is, is accelerating. Like the, the breakthroughs are are tremendous. I mean, even just month over month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic and there, there are cynics, you can look at 2000, you can find echoes.But I can't even imagine betting it that this is gonna like somehow disappoint and, you know, at least for years to come, I think it would be essentially suicidal to make that bet. Yeah. Um, it was that Michael Burry, uh, uh, that'sswyx: anMarc: interesting guy, huh? We'll pick on a guy. We'll pick, let's pick on one guy.We'll pick. Well ‘cause he did, he he came out with, it was, it was the, heswyx: doesn't mind.Marc: It was the Nvidia short. Right. He came with the Nvidia short. And then if you guys probably talked about this, which is the, the analysis now that like the current models are getting better faster at such a rate that if you are running an Nvidia, if you're running an Nvidia inference chip today, that's three years old, you're making more money on it today than you did three years ago because the pace of improvement of the software is, is faster than the, the, the depreciation cycle, the chip.And then my understanding is Google is running. I don't if they've, I don't know exactly what, uh, these are rumors that I've heard or maybe it's public, but, um, I think Google's running very old TPUs, very profitably. Ference. Yeah. And very profit and very profitably. Yeah. Um, and so, so it actually turns out, as far as I can tell, it's actually the opposite of the Beery thesis is actually.He was actually 180 degrees wrong. It's actually the, the, the, the old Nvidia chips are getting more valuable, which is something that's like literally never happened before. Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable. And that, and again, that's an expression of the just ferocious pace of software progress.Ferocious pace of capability payoff. Yeah. Uh, that you're getting on the other side of this. And so I just, the idea of betting against that, like.swyx: Yeah. Yeah. Well, one ofMarc: my, it seems like an invitation to get your face ripped up.swyx: One of my early hits was like modeling the lifespan of the H 100 and h two hundreds and, and going like, you know, usually they advise like four to seven years and it was, you know, maybe you sort of realistically haircut cut it down to two to three.Yeah. But actually it's going up and not down. Yeah. And, and uh, that's, I mean that's, I think that's the dream. Uh, we are finding utilization and I think utilization solves all problems. Like, you can, you can find use, use cases for even like the poor, like even memory, we're having a shortage. Right. And, and even like the, the shittier versions of, of memory that we do have, we are finding use cases for it.So like That's great.Marc: Yeah.Alessio: How, how important is open source AI and kinda like edge inference in a world in which you have three years of supply crunch. Like, do you think in the, like, you know, if you fast forward like five years, like how do you think about inference, uh, in the data center versus at the edge?Marc: Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge, edge inference is very important for a bunch of reasons. I, I think just practically speaking, if we're just gonna have fundamental construc, supply crunches for the next, I mean, you, you guys know if you just project forward demand over the next three years, right?Yeah. Relative to supply, one of the, its main predictions you can do is what's gonna, what, what's gonna happen to the cost of, of inference in the core, uh, over the next three years? And like, it may rise dramatically, right? Like, so, so what is, and then is, is, you know, like the, the, the big model competition are subsidizing heavily right now.Right? Right. And so, so what's the, what will be the average person's, you know, per day, per month token cost, you know, three years from now to do all the things that they want to do. And I, I don't know, it's gonna. I mean, I have, you guys probably have friends, I have friends today who are paying a thousand dollars a day for open claw, for claw tokens to run open claw.Right? And so, okay. $30,000 a month. Right? And, and by the way, those, those friends have like a thousand more ideas of the things that they want their claw to do, right? Yeah. And so you, you could imagine there, there's like latent demand of up to, I don't know, five or $10,000 a day of, of, of tokens for a fully deployed, you know, per personal agent.Uh, and obviously consumers can't pay that, right? And so, so, but it gives you a sense of the fu of the fu of the future scope of demand, right? And so, so even, even if there's a 10 x improvement in price performance, that still, you know, goes to a hundred dollars a day, which is still way beyond what people can pay.Mm-hmm. So there's just gonna be like. Ferocious to me, by the way. The agent thing, the other interesting thing is I think the agent thing, so up until now, a lot of the constraints of GGPU constraints, I think the agent thing now also translates into CPU constraints. Mm-hmm. Right?swyx: CPU memory.Marc: Yes. CPU memory, right?And so, like the entire chip ecosystem is just gonna get wait,swyx: wait for network constraints, that that will be the killer.Marc: It's all bottleneck potentially for years. And so, so I, I think that Brad, and, and I think it's actually possible, I mean, generally inference costs are gonna keep coming down, but I think the, let's put it this way, the rate of decline, I think may level out here for a bit because of these supply constraints.And then at some point, maybe the lab stops subsidizing so much and that, that, that again, will be, be an issue. And so there's just gonna be so much more demand for inference than, than can be satisfied. Um, you know, kind of with the centralized model. And then, and then, you know, you guys know this, but like all the, just the dramatic, I mean just the dramatic innovations that have happened in the Apple silicon to be able to do, uh, inferences, it's quite amazing the level of effort being put.Like the open source guys are putting incredible effort into getting, you know, this recurring pattern where the big model will never run on a pc, and then six months later mm-hmm. Oh, it runs in a pc, right? It's like amazing. And there's very smart people working on that. So there's all that. And then look, there's also, you know.There's also like other, there's other motivators. There's other motivators which is just like, okay, how much trust are the big centralized model providers? You know, how much trust are they building in the market versus, you know, how much are, you know, at least for, in certain cases with some people, for certain use cases, people being like, well, I'm not willing to just like, turn everything over.So there, there, there's all the trust issues. Um, by the way, there's also just like straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud. You just need like a, a a, a smart local model. There's also performance issues where you want, you know, you want, you know, you're gonna want your doorknob to have an AI model in it.Right. You know, to be able to, you know, do, um, you know, to be able to do access control. Um, obviously like everything with a chip is gonna have an AI model in it. Mm-hmm. And it, a lot of those are gonna be local. Um, and so, yeah. No, like I think, I think you're gonna have ti and then you're gonna, by the way, also wearable devices, you know, you don't wanna do a complete round trip.You want, you know, you, whatever your smart devices are, you want it to be like super low latency. Yeah.swyx: The question, do we care who makes it? Yeah. One of the biggest news this week was the collapse of AI two, the Allen Institute. Mm-hmm. One of the actual American open source model labs. Yeah. Um, and, uh, I'm not that optimistic on, on American open source.Yeah. Like you, you guys invested in MIS trial and MIS trial's doing extremely well outside of China. That's about it.Marc: Yeah. We'll see. We'll see. I look, I, number one, I do think we care. Uh, I do think we, I do think we care who makes it. Um, I would say this, the, the, the, the previous presidential administration wanted to kill it in the us Oh yeah.They wanted to drown in the bathtub. Um, and so they wanted to kill it. So at least we have a government now that actually like, actually wants it wants it to happen. And youswyx: earned to councilMarc: and Yeah. And the new and the P pcast. Yeah. So the, the, you know, this admin for whatever other political issues people have, which are many, you know, this administration has, I think a very enlightened view and in particular an enlightened view on AI and in particular on open source ai.Uh, and so they're very supportive. Um, my read is the Chi. The Chinese have a very, the various Chinese companies have a very specific reason to do open source, which is, they, they, they don't fundamentally, they don't think they can sell commercial, uh, AI outside of China right now. And or at least specifically not, not in the US for a combination of reasons.And so they, they kind of view, I think, open source AI as a bit of a loss leader against basically domestic, uh, you know, paid, paid services. And then kind of an, you know, kind of an ancillary products. You know, they're, they're very excited about it, by the way. I think it's great. I think it's great that they're doing it.Um, you know, I think Deeps seek was like a gift to the world. Um, I think. The great thing about open source, open source, the, the, the impact of open source is felt two ways. One is you, you get the software for free, but the other is you get to learn how it works, right? And so like the paper, the paper, the paper and, and the code, right?And the code. And so, like, for example, I thought this was amazing. So open comes out with L one and it's an amazing technical breakthrough, and it's just like, absolutely fantastic. But of course they don't explain how it works in detail. And then of course they hide the, they hide the reasoning traces, right?And, and then, and then, and then everybody's like, okay, this is great, but like, who's gonna be able to replicate this? Are other people gonna be able to do this? You know, is their secret sauce in there? And then our one comes out and it's just like, there's the code and there's the paper, and now the whole world knows how to do it.And then, you know, three months later, every other AI model is, is adding reasoning. And so, so you get this kind of double, like even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful.So that happens and then, I don't know. We'll, we'll see. You know, there are a bunch of American, you know, open source, you know, ai, uh, model companies. I mean, look, there's gonna be tremendous, you know, there already is. There's, you know, there's gonna be tre there's tremendous competition, uh, among the primary model companies.You know, there's, depending on how you count, there's like four or five, you know, big co model companies now that are, you know, kind of neck and neck, uh, in different ways. Um, uh, you know, and, and, and, um, you know, and then obviously Bo Bo both X and then MetAware involved are, you know, both have huge, you know, huge attempts to, you know, kind of, to kind of leapfrog underway.And then you've got, you know, a whole fleet of startups, new companies, including a whole bunch that we're backing, that are, you know, trying to come out with different approaches. And then you've got whatever it is. I don't know how, how many, how many, like main line foundation model companies are there in China at this point?It's probably six. It'sswyx: five Tigers is what they call it. Yeah. Uh, Quinn is in questionable because there's change in leadership,Marc: right?swyx: Yeah.Marc: But that, does that include, that includes like Moonshot,swyx: yes. Can deep seek, uh, uh, ZI, um, Quinn oh one is in there.Marc: Right. And then, um, and by dance and, and then you see,swyx: ance would be like the next tier ance.They weren't as prominent. They weren't, didn't haveMarc: a leading. Yeah. But they, you at least, you know, ance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and, and so forth. And so, so, so like, look, here, here would be a thing you can anticipate, which is there are not these markets, there are not going to be between the US and China right now, there's like a dozen primary foundation model companies that are like at scale, at, at some level of a critical mass.It's not gonna be a dozen in three years, right? Like, it just because these industries don't bear a dozen, it's, it's gonna be three or you know, there's gonna be three or four big winners or maybe one or two big winners. And so there's gonna be like a whole bunch of those guys that are gonna have to figure out alternate strategies.Um, and I think like open source is one of those strategies. And so I, I think you could see like a whole, i, I, I think the questions like, who's gonna do open source? I think that could change really fast. I, I think that, that, that's a very dynamic thing. I think it's very hard to predict what happens. And, and I think it's very important.swyx: NVIDIA's doing a lot.Marc: Well, I was gonna say. Well, exactly. And then you're got Nvidia and then, and then, you know, just to, again, indu, there's an old thing in business strategy, which is called, uh, commoditize Compliments. Commoditize the compliment. That's right. And so if your Jensen is just kind of obvious, of course, you wanna commoditize the software.Yeah. And he's, and to his enormous credit, he's putting enormous resources behind that. And so maybe it, maybe it's literally Nvidia and I think that would be great.Alessio: Yeah. Uh, narrative violation to European projects, uh, in the, uh, damn.swyx: I'm hosting my, uh, Europe, uh, conference soon. And I got both of them.Alessio: They got us.They got us. MarkMarc: finished. They got us, us. Well, wait a minute. Where was Peter? So where was Steinberger when he did? In AustriaAlessio: was, yeah, yeah, yeah.Marc: He was in what? He was in Vienna. Oh, he was in Vienna. And then where is he now?swyx: Uh, he's moving to sf.Marc: Okay. Okay. Alright. Okay, there we go. And then, yeah, the PI guy, right?The PI guys are European.swyx: Yeah, they're also, they're buddies inAlessio: Australia. Mario's also there. Yeah.Marc: Right. And are they, yeah, they haven't announced yet. Any sort of change changed or have theyAlessio: No, they're, they have a company there.Marc: Okay. Got, okay. Good.Alessio: Good, good,good.Alessio: Um,Marc: yeah, good.swyx: Anyways, I think pie and open cloud very important software things and, and I just wanted you to just go off on what you think.Marc: Yeah. So I think in co the, the combination of the two of them I think is one of the 10 most important softwares. Openswyx: Claw got all the attention, but Right. Talk about pie,Marc: pi pie's, kind of the Yeah. PI's, PI's kind of the architectural breakthrough for those of us who are older. There was this whole thing that was very important in the world of software basically from like 1970 to, I don't know, it still is very important, but like 19, from 1973 to like basically the creation of Linux, which is basically this, this thing used to call like the Unix mindset.Like so, so, ‘cause there were all these different, you know, theories. There are all these different operating systems and mainframes and, and then you know, all these windows and Mac and all these things. And then there was this, but kind of behind it all was this idea of kind of the Unix mindset. And the Unix mindset was this thing where basically you don't have these, like, like in the old days, like, like the operating system that like made the computer industry really work, like in the 1960s mm-hmm.Was this thing called o os 360, which was this big operating system that IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky. It was like a, you know, it was like a giant castle. Um, of software. And, and by the way, it worked really well and they were very successful with it.But like, it was this huge castle in the sky, but it was this thing, it was almost unapproachable, which is like, you had to be kind of inside IBM or very close to IBM. And you had to really understand every aspect, how the system worked. And then the, the Unix sky is originally out of at and t and then out out of Berkeley, um, you know, came out and they said, no, let's have a completely different architecture.And the way architecture's gonna work is we're gonna have, we're gonna have a, a prompt and, and a, and a shell. And then, and then we're gonna, all, all the functionality is gonna be in the form of these discreet modules, and then you're gonna be able to chain the modules together. Mm-hmm. Yeah. And so like the, the, the op, it's almost like the operating, operating system itself is gonna be a programming language.Um, and then that led led to the, the, the sort of centrality of the shell. Um, and then that led to sort of, uh, you know, basically chaining together Unix tools. And then that led to the emergence of these, these scripting languages like Pearl, where you, you could basically kind of very easily do this, and then the shells got more sophisticated and then, and then, and then look like, you know, that, that, that number one, that worked and that, that was the world I grew up in.Like I was, I was a Unix guy. You know, sort of from, call it 1988 to, you know, kind of all, all the way through my work and it worked really well. It, it's in the background, um, you know, nor normal people don't need to, didn't need to necessarily know about it, but like, if you were doing like system architecture, application development, you, you, you knew all about it.Um, and then, you know, it's been in the background ever since. And, you know, look, your Mac still has a Unix shell, you know, kind of in there, and your iPhone still has a Unix shell kind of buried in there somewhere. So they're kind of in there. And then, you know, the Windows shell is kind of a, you know, sort of a weird derivative of that.But, um, you know, but look, the inter, the internet runs on Unix, um, and that smartphones, actually, both iOS and Android are Unix derivatives. And so, you know, kind of Unix did end up winning. But, but anyway, and then we just started taking that for granted. And then, and then so, so basically the, the way I think about what happened with Pie and then with Open Claw is basically what those guys figured out is, I always say the, the great breakthroughs are obvious in retrospect, right?Which is the best kind, the best kind. They weren't obvious at the time or somebody else would've done them already. Um, and so there is a, like a real conceptual leap, but then you look at it sort of the backwards looking and you're just like, oh, of course. Mm-hmm. Like the, the, to me those are always the best breakthroughs.Well, actually language models themselves are like that. It's just like, oh, next token completion. Oh, of course.swyx: Yeah. What other objective mattered?Marc: Yeah, exactly. But, but like it, right. But she's even saying it wasn't obvious until somebody actually did it. Right. And so the conceptual breakthrough is real and deep and powerful and, and very important.And so the way I think about pie and olaw is it's basically marrying the, the language model mindset to the un to the Unix, basically shell prompt mindset. And so it's, it's basically this idea that what, what, so what is an agent, right? And as, as, and as you know, like many smart people who have been trying to figure out what an agent is for, for, for decades, and they've had many architectures to build agents and the whole thing.And it turns out what is an agent. So it turns out what we now know is an agent is the following. It's, so it's a language model. And then above that, it's a ba, it's a bash shell. Um, so it's a, it's a Unix shell, and then it's, and then the agent has access, uh, has access to, to the shell. And, you know, hopeful, hopefully in a sandbox, maybe in, maybe in a sandbox.So it's, it's the model. Um, it's the shell. Um, and then it's a fi, it's a file system. Um, and then the state is stored in files. And then, you know, there's the markdown format for the, you know, for, for the files themselves. And then, and then there's basically what in Unix is called Aron job. There's a loop and then there's a heartbeat for the, there's heartbeat and, and the thing basically Wake Wakes up.Wakes up. So it's basically LLM plus shell, plus file system, plus markdown, plus kron. And it turns out that's an agent. And, and, and every part of that, other than the model is something that we already completely know and understand. And in fact, it turns out that like the latent power of the Unix shell is like extraordinary because basically like all, like, there's just like an, there's just enormous latent power in the shell.There's enormous numbers of Unix commands, there's enormous number of command line interfaces into all kinds of things already in the, you know, your entire, I mean your entire, just to start with, your computer runs on a shell. If you're running a Mac or a, or, or a phone, your computer, your computer's running on a shell, uh, already.And so like the full power of your computer is available at the command line level. Um, and then it turns out it's really easy to expose other functions as a command line interface. And so like this whole idea where we need like MCP and these like product mm-hmm. Fancy protocols, whatever, it's like, no, we don't, we just need like a command, command line thing.So that's the architecture. And then it turns out what is your agent? Your agent has a bunch of files starting a file system. And then there's the thing that just like completely blew my mind when I write my head around it as a result of this, which is like, okay. This means your agent is now actually independent of the model that it's running on.Because you can actually swap out a different LLM underneath your agent and your, your agent will change personality somewhat. ‘cause the model is different, but all of the state stored in the files will be retained.swyx: Yeah. Different instruction set, but you just compiledit.Marc: Right, exactly. And it's all right.It's like right. Swapping out a ship and recompiling, but it's, it's still, it's still your agent with all of its memories. Um, and with all of its capabilities. And then by the way, you can also swap out the shell, uh, so you can move it to a different execution environment that is also, is also a b shell, by the way, you can also switch out the file system, right.Uh, and you can, and you can, and you can swap out the, the, the heartbeat for the, the crown framework, the, the loop that the agent framework itself. And so your agent basically is ba basically at the end of the day, it's just. It's just, its files. Um, and then, and then there's of course it a openswyx: call.Marc: Yeah, it's, it's basically, it's, it's just the files.Um, and then by the way, as a consequence of that, the agent and then the agent itself, it turns out a couple important things. So one is it, it's, it, it can migrate itself, right? And so you're, you can instruct your agent, migrate yourself to a different, uh, runtime environment, migrate yourself to a different file system, migrate yourself to a different, you know, swap out the language model.Your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection. It actually, it actually knows about its own files and it could rewrite its own files. Right. Which by the way, is basically no widely deployed software system in history where the, the, the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself.Like that, that, I mean, there have been toy systems that have had that, but there, there's never been a widely deployed system that has that capability and then that leads you to the capability. That just like completely blew my mind when I wrap my head around it, which is you can tell the agent to add new functions and features to itself and it can do that.Extend yourself. Yeah. Right? Extend, extend yourself. Like extend yourself. Give yourself a new capability. Right? And so, and so literally it's just like you run into somebody at a party and they're like, oh, I have my open claw, do whatever, connect to my eat, sleep bed, and it gives me better advice and sleep.And you go home at night and you tell your claw, or if they're at the party, by the way, you tell your claw, oh, add this capability to yourself. And your claw will say, oh, okay, no problem. And it'll go out on the internet and it'll figure out whatever it needs and then it'll go out to claw code or whatever.It'll write whatever it needs. And then the next thing you know, it has this new capability. And so you don't even have to, like, you can have it upgrade itself without even having to, without having to do anything other than tell it that you want it to do that. And so anyway, so the, the combination of all this is just, I mean, this is just like a massive, incredible, I mean, it's just incredible.Like if I, if I were, if I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. Yeah. And again, pe people are gonna look at it and they already get this response. People are gonna look at it and they're gonna say, oh, well, where's the breakthrough?‘cause these, the, all of these components were already known before. Mm-hmm. But, but this is the key, the key to the breakthrough was by using all these components that were known before, you get all of the underlying capability of that's buried in there. And so all, and so for example, computer use all of a sudden just kind of falls, trivi, trivial.Of course it's gonna be able to use your computer. It has full access to the shell. Right. And then, and then you just, you, you give it access to a browser, and then you've got the computer and the browser and, and often away it goes. And, and then you've got all the abilities of the browser also. Um, yeah.And so, and so the capability unlock here is profound. My friends who are, you know, deepest into this, are having their claw do like a, like, literally like a thousand things in their lives. They have new ideas every day. They're just like constantly throwing new challenges at the thing. And by the way, it's early and, you know, these are, you know, these are prototypes and there are, you know, as you guys know, there's security issues.Yeah. And, and so, you know, there's a bunch of stuff to be ironed out, but the, the unlock of capability is just incredible.swyx: Yeah.Marc: And I, I have absolutely no doubt that everybody in the world is gonna, is gonna have at least, you know, an agent like this, if not an entire family of agents. And w

Today, we're diving into one of the most fascinating and rapidly evolving frontiers: brain-computer interfaces, or BCIs, but from a European perspective. Joining us is Dr. Martin Schüttler, Co-Founder and Chief Technology Officer at CorTec, the first German company at the forefront of neural interface innovation. From restoring lost function to potentially redefining how we interact with machines—and even our own biology—BCIs are no longer science fiction. In this conversation, we'll explore Martin's journey into the BCI community, what sets Germany's approach apart on the global stage, and how neural technology could intersect with the complex human nervous system. We'll also tackle big-picture questions around privacy, policy, and power: Who owns brain data? Can global standards keep up with innovation? And what happens when this technology moves beyond the lab and into everyday life? Join us as we get rebelliously curious. Follow Chrissy Newton: Winner of the Canadian Podcast Awards for Best Science Series. YouTube: https://www.youtube.com/channel/UCM32gjHqMnYl_MOHZetC8Eg  Instagram: https://www.instagram.com/beingchrissynewton/  X: https://twitter.com/chrissynewton?lang=en Facebook: https://www.facebook.com/BeingChrissyNewton  Chrissy Newton's Website: https://chrissynewton.com Top Canadian Science Podcast: https://podcasts.feedspot.com/canadian_science_podcasts/     

This episode breaks down Nervous System–Based Perception (NSBP) and how the body translates complex energetic patterns into identity-based interpretations like plasma, solar, and crystalline interfaces.Through a Quantum Psychosomatics® lens, we separate pattern from personhood—revealing how coherence, energy states, and structural organization becomes the key to understanding how your reality is created. What feels like perception is translation: the nervous system rendering unfamiliar dynamics into something relational, symbolic, and recognizable.This is not about debunking experience. It's about refining how reality gets interpreted through the body.Key Topics: NSBP (Nervous System–Based Perception), perception vs reality, pattern recognition, plasma / solar / crystalline archetypes, energetic translation, coherence vs identity, anthropomorphism, somatic interpretation, frequency-first framework, Quantum Psychosomatics®----Welcome to SuperFreq® — Frequency-First LivingA podcast, Substack publication + channel dedicated to decoding the hidden patterns beneath behavior, identity, and reality itself. Through frequency, form, and field—we explore how to rewire the nervous system, reclaim coherence, and build the next evolution of human architecture.Stay Connected //IG: @superfreq.co // @whoistaliyahSubstack: SUPERFREQ® | Frequency-First Living™ > taliyahverse.substack.comWebsite: taliyahverse.comhttps://taliyahverse.store/shop/welcome-to-frequency-work-free-meditation

Brain-computer interfaces (BCIs) have been allowing humans to control objects with their minds for nearly half a century. But in recent years, thanks partly to advances in AI, the technology has evolved dramatically; wearable and implantable devices are now being used to restore speech and movement to stroke survivors, alleviate depression and treat pain. While companies like Elon Musk's Neuralink grab headlines, a somewhat quieter revolution is happening in Canada, where researchers are using BCI to help a historically underserved population: disabled children. In this episode, we explore BCI's potential to transform medicine, the knotty ethical questions at its core and how the tech might just bring us closer together. Featured in this episode: Dr. Adam Kirton is a professor of pediatrics, radiology and clinical neurosciences at the University of Calgary, where he's also the director of the BCI4Kids program. He is also the director of the Calgary Pediatric Stroke Program and is a practicing pediatric neurologist at the Alberta Children's Hospital. He co-founded, and is the CMO, of Possibility Neurotechnologies Anne Vanhoestenberghe is a professor of active implantable medical devices at King's College London and director of MAISi, a facility for the manufacture of active implants and surgical instruments, housed at St. Thomas' Hospital in London, England. Dion Kelly is a clinical neuroscientist and the co-founder and CEO of Possibility Technologies. Dion and Adam launched the company in 2022 to commercialize their brain-controlled technology, which transforms thoughts into actions.  Stephanie Sonnenberg and her daughter, Claire, live outside of Calgary, Alberta. Claire was one of the first users of Possibility Technologies' BCI device, Think2Switch. Further reading: The past, present and future of brain-computer interfaces We've been connecting brains to computers longer than you'd expect. These three companies are leading the way Adam Kirton's Lindenlauer lecture, Columbia University, November, 2024 Altman's Merge raises $252 million to link brains and computers What it's like to have a brain implant for five years Solve for X is brought to you by MaRS, North America's largest urban innovation hub and a registered charity. MaRS supports startups and accelerates the adoption of high-impact solutions to some of the world's biggest challenges. For more information, visit marsdd.com.

Hello listeners! No new episode this week, but we wanted to take the opportunity to dig into the vault and revisit an old favorite from 2024 with Ben Betts. At the time we recorded this one, Ben had receently published a blog post, in which he wrote that 'the LMS is the first point of entry to learning; the front-of-house of our industry.' While that front-of-house may look a little different now than it did twenty years ago, and despite the oft-repeated claim that the LMS is dying, it remains the default gateway to digital learning in organizations. But are things changing? To answer that question and others, Ben joined us to discuss: the many eras of the LMS, and how we got to where we are now; the forces that have shaped e-learning interfaces over time; how AI and other changes in the tech landscape might usher in a new era. You can read Ben's blog post, 'What's the Next Generation of E-Learning Interfaces?', on his website. For more from Mindtools Kineo, visit mindtools.com or kineo.com. There, you'll also find details of our Learning Management Systems, Content Hub for leaders and managers, and custom learning design service. Like the show? You'll LOVE our newsletter! Subscribe to The L&D Dispatch at lddispatch.com Connect with our speakers    If you'd like to share your thoughts on this episode, connect with us on LinkedIn: Ross Dickie Owen Ferguson Ben Betts

This Week In Startups is made possible by:Caldera + Lab - https://CalderaLab.com/TWISTNorthwest Registered Agent - https://northwestregisteredagent.com/twistLemon - https://Lemon.io/twistToday's show:Computers that read your mind? Jets that are all wings? Continuously tracking your body's vital signs to live longer? We've got all that and more on the show today.First, we sat down with the CEO of Paradromics, Matt Angle, to dig into the progress that his brain-computer interface startup has made. Yes, there are more companies working on BCIs than just Neuralink. Paradromics, in particular, is looking for early clinical trial subjects as it works to bring its technology out of the lab and into the market.If you ever wanted to know more about how BCIs work and how long it will be until we can all benefit from the tech, this is the interview for you.Next, we got JetZero CEO Tom O'Leary on the phone to tell us all about his startup's jet. No, it's not competing with Boom, a startup that wants to build a supersonic passenger airliner. Instead, JetZero is building jets that are mostly wing, allowing them to fly with far greater fuel efficiency. Best of all? The future JetZero planes can fit right into normal airports.Finally, we chatted with Nutrisense CEO Dan Zavorotny. While keeping tabs on your glucose levels may not be the sexiest topic in the world, the data that continuous monitoring can bring is incredibly valuable health information. Mix that signal with coaching, and Nutrisense reckons its combination of software, data, and dieticians can really improve health outcomes for its customers.If you needed a break from pure-play AI news, TWiST has you covered!Timestamps:0:00 Introduction1:47 Paradromics3:26 How do BCIs read your mind?9:58 Lemon - Get 15% off your first 4 weeks of developer time at Lemon.io/twist19:54 Northwest Registered Agent - Get more when you start your business with Northwest. In 10 clicks and 10 minutes, you can form your company and walk away with a real business identity — Learn more at northwestregisteredagent.com/twist27:24 JetZero29:07 The advantages of more wing30:13 Caldera Lab - Whether you're starting fresh or upgrading your routine, Caldera Lab makes skincare simple and effective. Head to CalderaLab.com/TWIST and use TWIST at checkout for 20% off your first order.55:30 NutrisenseSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisGreat TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com

What does it look like to demonstrate uncommon care in the way you design and build an interface?Josh Puckett has has spent nearly two decades designing products like Wealthfront, Dropbox and helping dozens of startups.But recently he released Interface Craft which is a library of everything he's learned about through the years about designing with “uncommon care”

In Folge 5 von AI in Finance sprechen Sascha und Maik über eine besonders aufgeladene KI-Woche. Es geht um den Machtkampf zwischen Anthropic und OpenAI, militärische KI-Nutzung, neue Agentic-Commerce-Ansätze und die Frage, wie sich Interfaces, Datenzugriffe und Zahlungsprozesse gerade verändern.

n dieser Episode der Rechtsbelehrung sprechen wir über das Podcasterdasein und darüber, wie wir Podcasts aufnehmen. Damit es aber nicht einseitig wird, begrüßen wir als Gäste Michael Rohrlich und Marc Oliver Thoma vom Podcast Risikobasierter Ansatz (Apple Podcasts) (YouTube-Kanal), die wiederum von ihrem Podcast „Risikobasierter Ansatz“ erzählen. Wir sprechen darüber, wann eigentlich alles gesagt ist, wie viel Vorbereitung drinsteckt, wie wir Zuständigkeiten aufteilen, und wie wir mit dem inneren Perfektionisten umgehen, wenn eine Aufnahme nicht so geworden ist, wie wir sie uns vorgestellt haben. Rechtsanwalt Michael Rohrlich (LinkedIn) gehört der ”Generation C64” an und ist als Rechtsanwalt mit den Schwerpunkten E-Commerce, Datenschutz und KI sowie Fachautor, als Video-Trainer (u.a. für LinkedIn Learning) und Referent tätig. Marc-Oliver Thoma (LinkedIn) ist ebenfalls “Generation C64” aber noch etwas mehr der Nerd. Er studierte an der Technischen Redaktion an der RWTH-Aachen und ist als Trainer, Berater und Coach für KI, Datenschutz und Digitalisierunge, Video-Trainer für LinkedIn Learning, DSB für KMUs tätig. Entsprechend euren Wünschen widmen wir uns auch ausführlich dem technischen Setup: Software, Hardware, Interfaces, DAWs – es wird nerdig, es fallen viele Namen, und wer schon immer wissen wollte, womit ein mit Liebe produzierter Podcast entsteht, sollte hier besonders genau hinhören. Außerdem, wie gehen wir mit Feedback um, was wünschen wir uns für die Zukunft und wohin geht die Reise, wenn Audio, Video und virtuelle Personas immer stärker zusammenwachsen? Herzlichen Dank an Michael und Marc Oliver für ihren Besuch und wir wünschen Euch viel Spaß beim Zuhören! Zeitmarken 00:00:00 – Intro & Gäste. 00:08:00 – Wie alles begann. 00:12:30 – Wann ist alles gesagt? 00:15:30 – Wie viel Vorbereitung steckt dahinter? 00:19:00 – Von der Idee zum fertigen Podcast: Wer macht was? 00:25:00 – Der versteckte Zeitfresser Podcast. 00:28:00 – Wie perfektionistisch darf (muss?) man sein? 00:30:00 – Podcast als Visitenkarte. 00:33:30 – Alleine podcasten – eine echte Alternative? 00:39:30 – Woher kommen die Ideen? 00:46:00 – Unser Tech-Setup: Software, Hardware & jede Menge Name-Dropping. 00:59:00 – Feedback: Was kommt rein, was nehmen wir mit? 01:03:30 – Was wir uns für die Zukunft wünschen. 01:09:00 – Audio vs. Video: Wo liegen die Unterschiede wirklich? 01:17:00 – Podcasting mit virtuellen Personas – Zukunft oder Gimmick? Der Beitrag Aus der Podcastwerkstatt – Obiter Dictum 18/2 erschien zuerst auf Rechtsbelehrung.

Maono continues to expand its presence in the creator‑focused audio market with new microphones and audio interfaces designed to support flexible recording environments. The company's latest products emphasize hybrid connectivity, simplified monitoring, and durable construction suited for podcasting, streaming, and mobile production. At CES 2026, Maono showcased a lineup that reflects the evolving needs of creators who work across multiple devices and platforms.The company's approach centers on versatility. Many creators record on computers, capture content on mobile devices, and monitor audio through separate systems. Maono's new hardware is designed to bridge these workflows without requiring additional adapters or complex routing.PD200W Hybrid Microphone With Triple ConnectivityThe PD200W hybrid microphone is the centerpiece of Maono's new lineup. It supports simultaneous USB, wireless, and XLR output, allowing the microphone to feed multiple devices at once. This configuration enables creators to record through an audio interface while monitoring through a phone or capturing backup audio on a secondary device. The microphone includes a wireless USB‑C receiver that connects directly to phones or computers, supporting mobile recording without cables.The microphone is built as a dynamic model, eliminating the need for phantom power and supporting use in untreated environments. Its hybrid design allows it to function as a traditional XLR microphone for studio setups while also serving as a portable wireless microphone for on‑the‑go recording. The included table stand supports desktop use, and an optional boom arm provides additional mounting flexibility.P2 Dual‑Input Audio InterfaceMaono also introduced the P2, a dual‑input audio interface designed for creators who need multiple recording paths. The interface includes two XLR inputs with 48‑volt phantom power, supporting both dynamic and condenser microphones. Multiple output options are provided, including left and right monitor outputs, a camera output, a live output, and USB‑C connections for computers and mobile devices. A dedicated USB‑C power port allows the interface to operate without draining a connected phone.The front‑facing controls simplify operation by placing gain knobs, monitoring controls, and headphone volume adjustments within easy reach. This layout mirrors the ergonomics of larger studio mixers, reducing the need to reach around the back of the device during recording sessions.P1 Compact Audio InterfaceThe P1 serves as a smaller companion to the P2, offering a single XLR and quarter‑inch combo input in a compact metal chassis. It is designed for creators who need a portable interface without sacrificing durability. The P1 includes front‑mounted controls similar to the P2, supporting quick adjustments during mobile or desktop recording. Its construction and layout make it suitable for musicians, podcasters, and streamers who want a streamlined setup.Accessories and Build QualityMaono's microphone stands and boom arms complement the new hardware with improved stability and cable management. The boom arm conceals XLR cables within its structure, reducing clutter and preventing sagging during recording. The stands are engineered to maintain position under pressure, supporting consistent microphone placement during long sessions.The company's focus on build quality is reflected in the metal construction of the new interfaces and the reinforced joints of the boom arms. These improvements support long‑term use in both studio and mobile environments.ConclusionMaono's CES 2026 lineup demonstrates a commitment to flexible, creator‑friendly audio tools. The PD200W hybrid microphone enables simultaneous multi‑device recording, while the P2 and P1 interfaces offer expanded connectivity and simplified control. With durable accessories and thoughtful design, Maono continues to support creators who need adaptable equipment for diverse recording scenarios.Maono's product lineup is available on Amazon.Interview by Scott Ertz of F5 Live: Refreshing Technology.Sponsored by: Get $5 to protect your credit card information online with Privacy. Amazon Prime gives you more than just free shipping. Get free music, TV shows, movies, videogames and more. Secure your connection and unlock a faster, safer internet by signing up for PureVPN today.

Maono continues to expand its presence in the creator‑focused audio market with new microphones and audio interfaces designed to support flexible recording environments. The company's latest products emphasize hybrid connectivity, simplified monitoring, and durable construction suited for podcasting, streaming, and mobile production. At CES 2026, Maono showcased a lineup that reflects the evolving needs of creators who work across multiple devices and platforms.The company's approach centers on versatility. Many creators record on computers, capture content on mobile devices, and monitor audio through separate systems. Maono's new hardware is designed to bridge these workflows without requiring additional adapters or complex routing.PD200W Hybrid Microphone With Triple ConnectivityThe PD200W hybrid microphone is the centerpiece of Maono's new lineup. It supports simultaneous USB, wireless, and XLR output, allowing the microphone to feed multiple devices at once. This configuration enables creators to record through an audio interface while monitoring through a phone or capturing backup audio on a secondary device. The microphone includes a wireless USB‑C receiver that connects directly to phones or computers, supporting mobile recording without cables.The microphone is built as a dynamic model, eliminating the need for phantom power and supporting use in untreated environments. Its hybrid design allows it to function as a traditional XLR microphone for studio setups while also serving as a portable wireless microphone for on‑the‑go recording. The included table stand supports desktop use, and an optional boom arm provides additional mounting flexibility.P2 Dual‑Input Audio InterfaceMaono also introduced the P2, a dual‑input audio interface designed for creators who need multiple recording paths. The interface includes two XLR inputs with 48‑volt phantom power, supporting both dynamic and condenser microphones. Multiple output options are provided, including left and right monitor outputs, a camera output, a live output, and USB‑C connections for computers and mobile devices. A dedicated USB‑C power port allows the interface to operate without draining a connected phone.The front‑facing controls simplify operation by placing gain knobs, monitoring controls, and headphone volume adjustments within easy reach. This layout mirrors the ergonomics of larger studio mixers, reducing the need to reach around the back of the device during recording sessions.P1 Compact Audio InterfaceThe P1 serves as a smaller companion to the P2, offering a single XLR and quarter‑inch combo input in a compact metal chassis. It is designed for creators who need a portable interface without sacrificing durability. The P1 includes front‑mounted controls similar to the P2, supporting quick adjustments during mobile or desktop recording. Its construction and layout make it suitable for musicians, podcasters, and streamers who want a streamlined setup.Accessories and Build QualityMaono's microphone stands and boom arms complement the new hardware with improved stability and cable management. The boom arm conceals XLR cables within its structure, reducing clutter and preventing sagging during recording. The stands are engineered to maintain position under pressure, supporting consistent microphone placement during long sessions.The company's focus on build quality is reflected in the metal construction of the new interfaces and the reinforced joints of the boom arms. These improvements support long‑term use in both studio and mobile environments.ConclusionMaono's CES 2026 lineup demonstrates a commitment to flexible, creator‑friendly audio tools. The PD200W hybrid microphone enables simultaneous multi‑device recording, while the P2 and P1 interfaces offer expanded connectivity and simplified control. With durable accessories and thoughtful design, Maono continues to support creators who need adaptable equipment for diverse recording scenarios.Maono's product lineup is available on Amazon.Interview by Scott Ertz of F5 Live: Refreshing Technology.Sponsored by: Get $5 to protect your credit card information online with Privacy. Amazon Prime gives you more than just free shipping. Get free music, TV shows, movies, videogames and more. Secure your connection and unlock a faster, safer internet by signing up for PureVPN today.

Welcome to Episode 422 of the Microsoft Cloud IT Pro Podcast. In this episode, Scott and Ben discuss their growing use of   in their daily workflows, particularly Claude Code, GitHub Copilot CLI, and Gemini CLI. They explore how these command-line interfaces offer powerful ways to interact with local files and MCP servers beyond traditional desktop AI chat interfaces. They share how they are using these tools in their day-to-day roles to perform different tasks and accelerate their workflows. Your support makes this show possible! Please consider becoming a premium member for access to live shows and more. Check out our membership options. Show Notes Claude Code overview Using Claude in PowerPoint Create custom subagents Microsoft Work IQ CLI (Public Preview) https://github.com/obra/superpowers How to Use Claude Code: A Guide to Slash Commands, Agents, Skills, and Plug-ins Gemimi CLI overview Github Copilot overview About the sponsors Would you like to become the irreplaceable Microsoft 365 resource for your organization? Let us know!

I am going to be honest right from the start. This is a rant. A friendly one. But still a rant. I'm a geek at heart. I love shiny tools, clever plugins, little bits of tech that make my marketing life easier. But lately it feels like some of my favorites have decided to test me. Really test me. It started with my email marketing platform quietly turning off a third-party API I relied on. Their decision, fine. But maybe a heads up would have been nice. Instead, I spent four months wondering why new subscribers were suspiciously quiet. Turns out, the connection was dead. And I only discovered it while building a completely unrelated page on my website. When I reached out to support, they casually mentioned they don't use that interface anymore. Terrific. Then my chatbot decided to hallucinate. I asked it to summarize an interview and create a teaser. Simple request. Except it thanked a guest who wasn't even in the conversation. Not even close. I have no idea where it found that name. Apparently, creativity is a little too free these days. And just when I thought I had hit my quota for weird tech behavior, the tool I use to make reels took a detour. This is the tool I trust to pull clean little snippets and generate accurate captions. Instead, it rewrote my perfectly articulate guest into something that sounded like bro speak. She deserved better. I deserved better. The whole episode deserved better. So yes, this is a rant. But it is also a reminder. No matter how good a tool is, no matter how long you have trusted it, you still need to double check. Tools change without warning. Interfaces break. Technology goes off the rails. And if you are not paying attention, your marketing can end up in a ditch you did not see coming. Takeaways Check your tools regularly. Even the ones you think are rock solid. A quick test can save months of missing data or embarrassing surprises. • Never hand over your voice completely. AI is helpful, but it is not infallible. Review everything before it goes into the world with your name on it. Because in marketing, the only thing worse than tech that fails is not noticing it failed. More than a Few Words - Marketing Conversation  A bite-sized marketing podcast that cuts through the noise and delivers actionable ideas, with no fluff and no jargon.

In Folge 4 von AI in Finance geben Sascha und Maik mal wieder ein News-Update. Kein Gast, dafür ein dichter Überblick über Marktbewegungen, neue Interfaces, Sicherheitsfragen und Regulierung. Der rote Faden: KI verschiebt sich vom Experimentierfeld zum strategischen Fundament. Technologisch, wirtschaftlich und politisch.

Have you noticed how AI Search is changing organic traffic? Search looks different today, and many solo business owners are wondering if blogging and articles are still effective.In this podcast episode, I break down what AI summaries mean for bloggers, newsletter writers, and solopreneurs who rely on organic traffic.I'll explain why clarity, structure, and thoughtful writing still matter, and how AI evaluates content for summaries.There's a strong case to return to strong writing fundamentals rather than shortcuts or hacks. What You'll Discover in This EpisodeHow AI search summaries are changing user behaviorThe difference between content delivery and content discoveryWhat AI looks for when referencing content in summariesWhy structured, well-written content still mattersWhy strong writing fundamentals are becoming more importantSearch will continue to evolve. Tools will improve. Interfaces will shift. But thoughtful, well-crafted content will always matter. If you stay grounded in clarity, structure your thinking well, and write with real people in mind, you will not be left behind. Take a breath. Go deeper instead of faster. Serve your audience with care. The fundamentals are not disappearing; they are becoming more important.  Free ResourceWrite Better Content with the Support of a Trained AI Editing AssistantAI Editing Guide for Writers Who Value CraftCLICK HERE to grab my free resource.https://learn.marisashadrick.com/ai-editing Skip Hours of Prompt Trial & Error with ChatGPTWhether you're writing, planning, analyzing, or brainstorming, my C.O.N.T.E.X.T. ™ method transforms ChatGPT into a consistent marketing assistant. No steep learning curve.Free Download!https://marisashadrick.com/prompts If you're ready to grow with effective marketing that actually feels manageable, here's your next move.Inside AI Lab for Solopreneurs, get Custom GPTs, templates, and coaching to grow your business. Visit: https://marisashadrick.com/communityListen to the "Amplify Your Authority" Podcast! Click Here! Rate & Review: If you enjoyed this episode, please take a moment to leave a 5-star rating and review on Apple Podcasts. Tip: Answer these questions inside of ChatGPT (free or paid) and have AI craft your review! How did you discover this podcast? What's your biggest takeaway from this episode? How has this podcast helped your current journey? Thanks so much for taking a few minutes to craft a review!

Hoje o papo é sobre acessibilidade! Neste episódio, mergulhamos em uma conversa inspiradora sobre o Colibri e o Conversia, duas plataformas que vêm devolvendo a autonomia e até mesmo a voz a pessoas com impedimentos motores e de fala. Vem ver quem participou desse papo! André David, o host que tem um lado que quase não existe Vinny Neves, Líder de Front-End na Alura Adriano Assis, CEO e fundador da Colibri Interfaces Links: Apresentação do Colibri no Shark Tank Brasil Colibrino Face Mesh Projeto Gameface Conversia Garanta até 30% de desconto para estudar por até dois anos na Alura antes do preço subir! TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

Charlotte Henry and Chuck Joiner return after a holiday break to discuss Chuck's CES impressions, evolving TV hardware, and the viewing experience around Apple TV. They discuss smart TV interfaces, discoverability challenges, and why software now matters as much (if not more) as display quality. They look at both upcoming and canceled Apple TV shows, and pick some favorites among both new and returning series.  Show Notes: Chapters: 00:00 Welcome and CES recap00:53 CES atmosphere and TV trends02:28 Choosing a TV in 202605:19 Apple TV app and interface debate09:54 New and upcoming Apple TV shows13:09 Cancellations and content strategy17:47 Shows to watch and recommendations21:09 Anticipated releases in 202628:41 Wrap-up and looking ahead Guests: Charlotte Henry is a media junkie, covering how Apple is not just a revolutionary tech firm, but a revolutionary media firm. She is based in London, writes and broadcasts for various outlets, and is the author of Not Buying It, an examination of fake news. You can find her on her The Addition blog, her podcast, in her The Addition newsletter on substack, and on Twitter, Facebook, Instagram and TikTok. Support:      Become a MacVoices Patron on Patreon     http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:     http://macvoices.com      Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices      Mastodon:     https://mastodon.cloud/@chuckjoiner      Facebook:     http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:     http://www.facebook.com/macvoices/      MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice      LinkedIn:     https://www.linkedin.com/in/chuckjoiner/      Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes     Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss l

Charlotte Henry and Chuck Joiner return after a holiday break to discuss Chuck's CES impressions, evolving TV hardware, and the viewing experience around Apple TV. They discuss smart TV interfaces, discoverability challenges, and why software now matters as much (if not more) as display quality. They look at both upcoming and canceled Apple TV shows, and pick some favorites among both new and returning series.  Show Notes: Chapters: 00:00 Welcome and CES recap 00:53 CES atmosphere and TV trends 02:28 Choosing a TV in 2026 05:19 Apple TV app and interface debate 09:54 New and upcoming Apple TV shows 13:09 Cancellations and content strategy 17:47 Shows to watch and recommendations 21:09 Anticipated releases in 2026 28:41 Wrap-up and looking ahead Guests: Charlotte Henry is a media junkie, covering how Apple is not just a revolutionary tech firm, but a revolutionary media firm. She is based in London, writes and broadcasts for various outlets, and is the author of Not Buying It, an examination of fake news. You can find her on her The Addition blog, her podcast, in her The Addition newsletter on substack, and on Twitter, Facebook, Instagram and TikTok. Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss l

＜目次＞(0:30) About Ramp and Diego Zaks(3:27) Time = Money(5:47) Measuring and reducing time spent(8:00) Assuming good intent(8:58) Disappearing interfaces and chat UI(12:58) Does AI effect Ramp's design philosophy?(14:41) Diego's reason for joining Ramp(15:52) Building velocity at Ramp(19:36) Finding alignment and fuzzy metrics(21:22) Ramp's pod team structure(22:31) Being right 52% of the time failing cheaply(26:01) Quick decision making culture(28:31) Internal transformation with AI(30:25) Designers and Product Managers(32:37) Evolution of Diego's role(34:01) Creative works Diego keeps coming back to(35:46) Counting days at Ramp(36:56) How Diego describes RampRamp | All-in-one financial operations platform designed to save businesses time and money.https://ramp.com/Diego Zaks (@diegozaks)https://x.com/diegozaks＜About Off Topic＞Podcast:Apple - https://apple.co/2UZCQwzSpotify - https://spoti.fi/2JakzKmOff Topic Clubhttps://note.com/offtopic/membershipX - https://twitter.com/OffTopicJP草野ミキ:https://twitter.com/mikikusanohttps://www.instagram.com/mikikusano宮武テツロー: https://twitter.com/tmiyatake1

AI is moving from chat to action.In this episode of Big Ideas 2026, we unpack three shifts shaping what comes next for AI products. The change is not just smarter models, but software itself taking on a new form.You will hear from Marc Andrusko on the move from prompting to execution, Stephanie Zhang on building machine-legible systems, and Sarah Wang on agent layers that turn intent into outcomes.Together, these ideas tell a single story. Interfaces shift from chat to action, design shifts from human-first to agent-readable, and work shifts to agentic execution. AI stops being something you ask, and becomes something that does. Resources:Follow Marc Andrusko on X: https://x.com/mandrusko1Follow Stephanie Zhang on X: https://x.com/steph_zhang  Follow Sarah Wang on X: https://x.com/sarahdingwangRead more all of our 2026 Big IdeasPart 1: https://a16z.com/newsletter/big-ideas-2026-part-1Part 2: https://a16z.com/newsletter/big-ideas-2026-part-2/Part 3: https://a16z.com/newsletter/big-ideas-2026-part-3/ Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ryo Lu spent years watching his designs die in meetings. Then he discovered the tool that lets designers ship code at the speed of thought: Cursor, the company where Ryo is now Head of Design. In this episode, a16z General Partner Jennifer Li sits down with Ryo to discuss why "taste" is the wrong framework for understanding the future, why purposeful apps are "selfish," how System 7 holds secrets about AI interfaces, and the radical bet that one codebase can serve everyone if you design the concepts right instead of the buttons. Timecodes:00:01:45 - Design Becomes Approachable to Everyone00:02:36 - From Years to Minutes: Product Feedback Loops Collapse00:07:54 - "Each role used their own tool...their own lingo"00:13:15 - "If you don't have an opinion, you'll get AI slop"00:17:18 - The Lost Art of Being a Complete Builder00:21:42 - Design Is Not About Aesthetics00:28:57 - User-Centric vs System-Centric Philosophy00:34:00 - AI as Universal Interface, Not Chat Box00:38:42 - "Simplicity is the Biggest Constraint"00:43:42 - "I Don't Sit in Figma All Day Making Mocks"00:46:33 - RyoOS: Building A Personal Operating System00:48:45 - "We've been doing the same thing since 1984" Resources:Follow Ryo Lu on X: https://x.com/ryolu_Follow Jennifer Li on X: https://x.com/JenniferHliFollow Erik Torenberg on X: https://x.com/eriktorenberg Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!  Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of the Fluid Power Forum, we dive into the evolution of electrohydraulic equipment with our guests, Jason Looman and Andy Gray from Scanreco Group. Join us as we explore the role of human-machine interfaces (HMIs) in enhancing operator experience and safety in smart equipment. Discover how everything from operator feedback to AI-driven designs are shaping the future of fluid power technology. Be sure to listen in for insights on how these innovations are transforming the landscape and what it means for industry professionals. Subscribe to the Fluid Power Forum today to never miss an episode. The podcast is available on all of your favorite podcast platforms, including Apple Podcasts, Spotify, and iHeart Radio. Additionally, we're launching Fluid Power Forum Plus, offering premium, members-only content designed just for our listeners. When you sign up on the NFPA website, you'll unlock a host of rewards, ranging from exclusive content to live panels and networking receptions. Connect with our host, Eric Lanke, at elanke@nfpa.com. Connect with our guests, Jason Looman, at jason.looman@scanreco.com, and Andy Gray, at andy.gray@scanreco.com.   Find and share more interesting fluid power technologies and unique applications using #onlyfluidpowercan and follow podcast and other fluid power industry-related updates at @TheNFPA. #FluidPowerForum #HMIs #operatorexperience

They're especially useful for communicating with people who've lost the ability to speak or move their body. At the extreme, one brain-computer interface prototype can decipher imagined sentences, and comes with password protection to avoid revealing private thoughts. To understand more, Mihi is joined by University of Auckland Professor of Psychology Anthony Lambert.

In this episode, Kiv and Tulechki sit down with Ivan Gligorijevic (CEO & Co-founder, mBrainTrain) and Teodor Vakarelsky (Bulgarian Academy of Sciences) to talk about brain-computer interfaces and their work in the VIBraTE EU

Sometimes interfaces mimic physical materials like paper or metal. Anthony and Katie discuss why this might be, what kinds of materials seem most common and how this has shifted over time.Hosts:Anthony Hobday, Generalist Product Designer: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/hobdaydesign⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Katie Langerman, Design Technologist: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/KatieLangerman

What happens when technology starts to read your mind — or even predict your thoughts before you have them? In this episode, we dive deep into the rapidly evolving frontier of brain–computer interfaces, or BCIs — a technology once confined to science fiction that's now redefining what it means to be human.  Joining me today is Florian Solzbacher, Co-Founder and Chief Science Officer of Blackrock Neurotech, one of the world's leading companies in neural interface technology. We'll explore everything from invasive vs. non-invasive BCIs, privacy and "mind data," policy gaps, and the risks of unconscious influence, to the global implications of brain–computer interfaces — including China's growing leadership in the field and past and future funding from government agencies like DARPA. Follow Chrissy Newton: Winner of the Canadian Podcast Awards for Best Science Series. YouTube: https://www.youtube.com/channel/UCM32gjHqMnYl_MOHZetC8Eg  Instagram: https://www.instagram.com/beingchrissynewton/  X: https://twitter.com/chrissynewton?lang=en Facebook: https://www.facebook.com/BeingChrissyNewton  Chrissy Newton's Website: https://chrissynewton.com Top Canadian Science Podcast: https://podcasts.feedspot.com/canadian_science_podcasts/       

Au programme :Samsung et Google ont annoncé le Samsung Galaxy XR, le concurrent du Vision Pro d'AppleOpenAI a lancé ChatGPT Atlas : est-ce qu'intégrer ChatGPT dans le navigateur suffira à concurrencer Chrome ?Le reste de l'actualité avec news et rumeurs : Grokipedia, l'effervescence (selon Guillaume) sur les sujets liés à Apple, Free TV, licenciements chez Amazon…Infos :Animé par Guillaume Vendé (Bluesky).Co-animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok).Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel BejaLe Rendez-vous Tech épisode épisode 638 – ChatGPT Atlas, Samsung Galaxy XR : nouvelles interfaces (mais c'est pas encore ça)---Liens :

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter Edward Chang is a neurosurgeon, scientist, and a pioneering leader in functional neurosurgery and brain-computer interface technology, whose work spans the operating room, the research lab, and the engineering bench to restore speech and movement for patients who have lost these capabilities. In this episode, Edward explains the evolution of modern neurosurgery and its dramatic reduction in collateral damage, the experience of awake brain surgery, real-time mapping to protect critical functions, and the split-second decisions surgeons make. He also discusses breakthroughs in brain-computer interfaces and functional electrical stimulation systems, strategies for improving outcomes in glioblastoma, and his vision for slimmer, safer implants that could turn devastating conditions like ALS, spinal cord injury, and aggressive brain tumors into more manageable chronic illnesses. We discuss: The evolution of neurosurgery and the shift toward minimally invasive techniques [2:30]; Glioblastomas: biology, current treatments, and emerging strategies to overcome its challenges [10:45]; How brain mapping has advanced from preserving function during surgery to revealing how neurons encode language and cognition [16:30]; How awake brain surgery is performed [22:00]; How brain redundancy and plasticity allow some regions to be safely resected, the role of the corpus callosum in epilepsy surgery, and the clinical and philosophical implications of disconnecting the hemispheres [26:15]; How neural engineering may restore lost functions in neurodegenerative disease, how thought mapping varies across individuals, and how sensory decline contributes to cognitive aging [39:15]; Brain–computer interfaces explained: EEG vs. ECoG vs. single-cell electrodes and their trade-offs [48:30]; Edward's clinical trial using ECoG to restore speech to a stroke patient [1:01:00]; How a stroke patient regained speech through brain–computer interfaces: training, AI decoding, and the path to scalable technology [1:10:45]; Using brain-computer interfaces to restore breathing, movement, and broader function in ALS patients [1:28:15]; The 2030 outlook for brain–computer interfaces [1:34:00]; The potential of stem cell and cell-based therapies for regenerating lost brain function [1:38:00]; Edward's vision for how neurosurgery and treatments for glioblastoma, Parkinson's disease, and Alzheimer's disease may evolve by 2040 [1:42:15]; The rare but dangerous risk of vertebral artery dissections from chiropractic neck adjustments and high-velocity movements [1:44:45]; How Harvey Cushing might view modern neurosurgery, and how the field has shifted from damage avoidance to unlocking the brain's functions [1:46:15]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube