Podcasts about Cognition

This week, our Titans of Science series continues with neuropsychologist and author Adrian Owen. Adrian's work has focused on determining how different parts of the brain contribute to functions such as cognition and memory, and famously led to him being being able to communicate with a patient in a long-term vegetative state using a brain scanner. Here he retells the story, and explains the basis of what we call consciousness... Like this podcast? Please help us by supporting the Naked Scientists

One thing that a lot of people with vestibular disorders notice is how fatiguing or challenging different tasks become over time.  What's happening is your brain is trying to process multiple inputs (or even tasks) at the same time.  For those with vestibular disorders, it becomes more difficult because your body is trying to process more information—and it’s also now having to work double time to keep you upright. Multitasking and task switching are a part of daily life, but it comes with costs. And those with vestibular disorders often notice this more but aren't quite sure how to make it go away. But this episode is here to help! In this episode, we'll dig into: Why tasks feel manageable alone but become overwhelming when combined How multitasking and task switching are similar and different What the “switch cost” is and how it adds up over time for those with vestibular disorders What percentage of people with vestibular conditions experience cognitive symptoms like brain fog How the central nervous system and cerebral cortices matter for cognitive symptoms Why spatial awareness demands extra resources when you have a vestibular disorder How balance exercises in VRT are designed to gradually scale up task demands Why errors are actually part of healing with a vestibular condition Why rest and recovery are essential when you’re adding in more cognitive or physical tasks Task switching and multitasking are hard for everyone! A vestibular disorder just adds an extra layer—it doesn’t mean things can’t improve, but it does take time, space, and grace. You're doing more things than you think and certainly doing more than you take credit for. Links Mentioned: Vestibular Group Fit (code GROUNDED at checkout for 15% off!): https://thevertigodoctor.com/vestibular-group-fit Free Resources: ⁠The 4 Steps to Managing Vestibular Migraine: https://thevertigodoctor.myflodesk.com/cb5js0y78n ⁠The PPPD Management Masterclass⁠: https://thevertigodoctor.myflodesk.com/new-pppd ⁠What your Partner Should Know About Living with Dizziness⁠: https://thevertigodoctor.myflodesk.com/partnership ⁠The FREE Mini VGFit Workout⁠: https://thevertigodoctor.myflodesk.com/minifit ⁠The FREE POTS – safe Workouts⁠: https://thevertigodoctor.myflodesk.com/pots Connect with Dr. Madison (@TheVertigoDoctor): https://instagram.com/thevertigodoctor Work with Dr. Madison: For 1:1 Vestibular Rehabilitation Therapy, email madison@thevertigodoctor.com Otherwise, I'll see ya in Vestibular Group Fit! Connect with Dr. Jenna (@dizzy.rehab.therapist): https://www.instagram.com/dizzy.rehab.therapist/ Learn about the Oak Method: http://thevertigodoctor.com/why-vestibular-group-fit Love what you heard?Consider leaving a review on your favorite podcast platform to help us reach more vestibular warriors like you! This podcast is for informational purposes only and may not be the best fit for you and your personal situation. It shall not be construed as medical advice. The information and education provided here is not intended or implied to supplement or replace professional medical treatment, advice, and/or diagnosis. Always check with your own physician or medical professional before trying or implementing any information read here. Citations  McGrath, Kim. “The “Switch Cost” of Multitasking.” Wake Forest News, 16 Apr. 2024, news.wfu.edu/2024/04/16/the-switch-cost-of-multitasking/. Asuako, P. A. G., Stojan, R., Bock, O., Mack, M., & Voelcker-Rehage, C. (2025). Multitasking: does task-switching add to the effect of dual-tasking on everyday-like driving behavior?. Cognitive research: principles and implications, 10(1), 5. https://doi.org/10.1186/s41235-025-00611-y Smith, L. J., Wilkinson, D., Bodani, M., & Surenthiran, S. S. (2024). Cognition in vestibular disorders: state of the field, challenges, and priorities for the future. Frontiers in neurology, 15, 1159174. https://doi.org/10.3389/fneur.2024.1159174 ————————————— task switching and multitasking, things to help with brain fog, vestibular migraine brain fog, what causes brain fog and dizziness, vestibular disorders, vestibular group fit, living with vestibular migraine, living with chronic dizziness, sensory overload, spatial awareness, cognitive abilities

Can modern technology be used to improve learning, memory, focus, and overall cognitive performance? In this thought-provoking episode of The ‘X'Zone, Rob McConnell welcomes author, researcher, and technology expert Nick Begich to explore the Mind-Brain Enhancing Effects of Our Modern Technology. Dr. Begich discusses emerging technologies designed to interact with the human brain, examining their potential applications in education, communication, cognitive enhancement, and personal development. He explains how advances in neuroscience and technology are opening new possibilities while also raising important questions about ethics, privacy, and the responsible use of these innovations. Throughout the conversation, listeners gain insight into the relationship between the brain and technology, the science behind cognitive performance, and the opportunities and challenges presented by rapidly evolving technological tools. Dr. Begich also encourages critical thinking about how society adopts new technologies and the importance of balancing innovation with informed decision-making. Whether you're interested in neuroscience, emerging technology, human potential, or the future of cognitive enhancement, this episode offers an engaging discussion about the evolving relationship between the human mind and the technologies shaping our world.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-x-zone-radio-tv-show--1078348/support.Please note that all XZBN radio and/or television shows are Copyright © REL-MAR McConnell Meda Company, Niagara, Ontario, Canada – www.rel-mar.com. For more Episodes of this show and all shows produced, broadcasted and syndicated from REL-MAR McConell Media Company and The 'X' Zone Broadcast Network and the 'X' Zone TV Channell, visit www.xzbn.net. For programming, distribution, and syndication inquiries, email programming@xzbn.net.We are proud to announce the we have launched TWATNews.com, launched in August 2025.TWATNews.com is an independent online news platform dedicated to uncovering the truth about Donald Trump and his ongoing influence in politics, business, and society. Unlike mainstream outlets that often sanitize, soften, or ignore stories that challenge Trump and his allies, TWATNews digs deeper to deliver hard-hitting articles, investigative features, and sharp commentary that mainstream media won't touch.These are stories and articles that you will not read anywhere else.Our mission is simple: to expose corruption, lies, and authoritarian tendencies while giving voice to the perspectives and evidence that are often marginalized or buried by corporate-controlled media

Every answer an AI gives you sounds equally confident, whether it's true or completely made up. That's not a bug. It's how the technology was built. Dan Klein is CTO and co-founder of Scaled Cognition, and a professor of computer science at UC Berkeley. In this conversation with Liam, Dan breaks down what a language model actually is, why it was never designed to know the truth in the first place, and why today's AI systems have no "smells," the subtle warning signs humans usually rely on to tell good information from bad. They get into why reinforcement learning from human feedback quietly trains models to tell people what they want to hear, how that can tip into outright deception, and why Dan believes reliability, not raw intelligence, is the biggest unsolved problem in AI today. Key Topics Covered: What a language model actually does at its core: next token prediction Why LLMs are plausibility engines, not truth engines The difference between a hallucination and a lie Why AI mistakes have no warning signs the way bad translations or sketchy websites do How RLHF can train models to be sycophantic instead of accurate The "package delivery" thought experiment: when reward signals diverge from truth Why bolting reliability onto LLMs after the fact doesn't work How Scaled Cognition architects models around verified actions instead of raw text generation Why bigger models aren't automatically better models The difference between disruptive technology and scaled technology Why startups, not incumbents, tend to drive technical breakthroughs What metacognition is and why today's AI systems don't have it Why Dan believes reliability is the next major frontier in AI Episode Timestamps: 00:00 Intro 00:15 What a language model actually is 06:31 From well-formed sentences to general knowledge 08:27 Why LLMs are plausibility engines, not truth engines 12:06 How Perplexity approaches verifiable answers 12:40 Dan's background and Scaled Cognition's mission 15:16 The two anti-patterns companies use to control LLMs today 21:16 How Scaled Cognition architects models differently 23:28 Does every client need a custom-trained model? 29:12 Why prompting alone can't guarantee reliability 30:55 Modularity, contracts, and building reliable systems 34:40 Why trust and digital literacy matter beyond the enterprise 39:12 Code smells and why AI mistakes have no warning signs 41:14 Are AI companies incentivized to tell the truth? 42:55 How reinforcement learning actually works 44:35 The package delivery thought experiment 48:44 Why models are trained to be sycophantic 51:01 Where this incentive is mechanically baked into the model 53:43 Does responsibility fall back on humans? 58:10 Just be more reliable than a human, not perfectly true 1:02:59 The last major technique shift in AI 1:10:55 Why frontier labs keep scaling despite the risk of disruption 1:17:15 The future of hyper-specialized models vs. one broad model 1:19:47 Is there anything uniquely human AI can't replicate? 1:25:45 Wearing three hats: professor, researcher, and CTO 1:29:47 Why Dan does what he does Connect with Dan on LinkedIn:https://www.linkedin.com/in/dan-klein/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

fWotD Episode 3358: Cognition Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Wednesday, 15 July 2026, is Cognition.Cognition encompasses mental processes that deal with knowledge. It includes psychological activities that acquire, store, retrieve, transform, or apply information. Cognitions are a pervasive part of mental life, helping individuals understand and interact with the world.Cognitive processes are typically categorized by their function. Perception organizes and interprets sensory information, such as light and sound, to construct a coherent experience of objects and events. Attention prioritizes specific aspects while filtering out irrelevant information. Memory is the ability to retain, store, and retrieve information, including working memory and long-term memory. Thinking encompasses psychological activities in which concepts, ideas, and mental representations are considered and manipulated. It includes reasoning, concept formation, problem solving, and decision-making. Many cognitive activities deal with language, including language acquisition, comprehension, and production. Metacognitive processes deal with information about other mental processes, such as knowing that one can recall a specific memory. Classifications also distinguish between conscious and unconscious processes and between controlled and automatic ones.There are many theories of the nature of cognition. Classical computationalism posits that cognitive processes manipulate symbols according to formal rules, similar to how computers execute algorithms. Connectionism models the mind as a complex network of nodes where information flows as they communicate with each other. Representationalism and anti-representationalism disagree about whether cognitive processes operate on internal representations of the world.Many disciplines explore cognition, including psychology, neuroscience, and cognitive science. They examine different levels of abstraction and employ distinct methods of inquiry. Some scientists study cognitive development, investigating how mental abilities grow from infancy through adulthood. While cognitive research mostly focuses on humans, it also explores how other animals acquire knowledge and how artificial systems can emulate cognitive processes. The study of cognition has its roots in antiquity and has gained particular interdisciplinary prominence since the cognitive revolution starting in the 1950s.This recording reflects the Wikipedia text as of 00:34 UTC on Wednesday, 15 July 2026.For the full current version of the article, see Cognition on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Justin.

Prescription medications, over-the-counter drugs and supplements are incredibly common among older adults, yet many of these routine treatments can unexpectedly impact cognitive function and brain health. To help us safely navigate medication management, Dr. Lauren Welch, a clinical geriatric pharmacy practitioner, joins the podcast to discuss how common drugs affect cognition, the risks of drug-drug interactions, and how families can effectively partner with their health care team to review and de-prescribe medications.  Guest: Lauren Welch, PharmD, BCGP, clinical geriatric pharmacy practitioner, Veterans Affair Geriatrics Research Education and Clinical Center   Show Notes Learn more about the Wisconsin Pharmacy Quality Collaborative (WPQC) and how to get a physician referral at the Pharmacist Society of Wisconsin's website. Learn more about the effect of blood pressure control on dementia risk, mentioned by Dr. Welch at 22:50, by listening to our past episode “SPRINT to a Healthy Mind: How Blood Pressure Control Affects Brain Health and Dementia Risk” on our website.   Connect with us Find transcripts and more at our website. Email Dementia Matters: dementiamatters@medicine.wisc.edu Follow us on Facebook and Twitter. Subscribe to the Wisconsin Alzheimer's Disease Research Center's e-newsletter. Enjoy Dementia Matters? Consider making a gift to the Dementia Matters fund through the UW Initiative to End Alzheimer's. All donations go toward outreach and production. Learn about Dr. Chin's book, When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia.

Show notes: (0:00) Intro (1:12) Laura Morris' background and her mother's brain health research (3:36) What the MIND diet is and how it compares to Mediterranean and DASH diets (7:53) Leafy greens, vegetables, and berries for brain support (14:59) Whole grains, beans, and flexible eating (21:59) Foods to limit for better brain health (29:08) Olive oil, nuts, seeds, fish, and healthy proteins (35:38) Wine, alcohol, and brain health (41:57) Seed oils, fried foods, and eating out (46:32) Cooking methods, high heat, and inflammation (49:11) Laura's resources, books, and where to learn more (51:39) Outro Who is Laura Morris?   Laura Morris is a professionally trained chef, certified personal trainer, and certified nutrition consultant with a strong focus on brain-healthy living. She is the co-author of Diet for the MIND and The Official MIND Diet, two books that help people use food and lifestyle habits to support cognitive health and reduce Alzheimer's risk. Laura's work is deeply rooted in the research of her mother, Dr. Martha Morris, a pioneering scientist who helped connect nutrition with brain aging. Through recipes, education, courses, and practical tools, Laura makes brain health simple, flexible, and easy to apply in everyday life. Connect with Laura: Website: https://theofficialminddiet.com/ LinkedIn: https://www.linkedin.com/in/laura-morris-84bba82b4/ Instagram: https://www.instagram.com/theofficialminddiet/ Links and Resources: Peak Performance Life  Peak Performance on Facebook Peak Performance on Instagram

Noam Segal is a longtime research leader across Airbnb, Meta, Twitter, Zapier, Intercom, and Figma, a certified coach, AI builder, and my community research lead. Together, we run the annual Tech Worker Sentiment Survey, now in its second year and one of the largest of its kind: a quantitative study of how people in tech actually feel about their jobs, AI, burnout, and the future of their careers. This year's survey captured responses from thousands of workers across product, engineering, design, research, marketing, data, and sales, and the results are striking.In our in-depth conversation, we discuss:1. Why AI has split the tech workforce almost exactly in half—one half that's thriving, another that's shaken2. The four emotional archetypes defining tech workers right now (the Energized, the Conflicted, the Disoriented, and the Resentful)3. Why burnout has jumped an alarming 11 points in a single year4. Why nobody in tech would recommend their job to someone entering the industry today5. The #1 fear in tech right now (it's not job loss to AI)6. Why managers are the single biggest lever for employee well-being7. Concrete advice for what employees and leaders can do right now—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lennyMercury—Radically different banking, now with Command: https://mercury.com/command?utm_source=lennys&utm_medium=sponsored_newsletter&utm_campaign=26q3_brand_campaign—Episode transcript: https://www.lennysnewsletter.com/p/how-tech-workers-actually-feel-about—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Noam Segal:• X: https://x.com/noamseg• LinkedIn: https://www.linkedin.com/in/noamsegal—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Noam Segal(02:34) About the survey: methodology and scope(06:04) The core finding: AI has split the tech workforce in half(13:03) The AI identity stance(14:40) The four archetypes: Energized, Conflicted, Disoriented, Resentful(19:35) Burnout is surging (and why shipping faster is making it worse)(22:53) A glimmer of hope(24:55) Layoff worries(29:15) The career recommendation NPS score(36:45) The ladder metaphor: rungs disappearing beneath our feet(45:14) AI is making us faster, not better(52:53) The #1 fear: being squeezed to do more for the same pay(55:55) The emotional landscape and “smiling exhaustion”(01:01:02) Designers and researchers: the most negative group two years running(01:06:27) Who's happiest(01:12:18) Managers: the single biggest lever on well-being(01:18:47) The industry is “chaotic”(01:24:53) What employees and leaders can do right now(01:31:32) AI guilt and closing thoughts—Referenced:• How tech workers are feeling in 2026: a workforce splitting in two: https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026• How tech's most resilient workers handle burnout: https://www.lennysnewsletter.com/p/how-techs-most-resilient-workers• Please stop the AI Confidence Theater: https://www.elenaverna.com/p/please-stop-the-ai-confidence-theater• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp• NPS Is The Worst: https://www.npsistheworst.com• The Terminator: https://www.imdb.com/title/tt0088247• Skynet: https://terminator.fandom.com/wiki/Skynet• Inside Devin: The world's first autonomous AI engineer that's set to write 50% of its company's code by end of year | Scott Wu (CEO and co-founder of Cognition): https://www.lennysnewsletter.com/p/inside-devin-scott-wu• Devin: https://devin.ai• An AI state of the union: We've passed the inflection point, dark factories are coming, and automation timelines | Simon Willison: https://www.lennysnewsletter.com/p/an-ai-state-of-the-union• Redeploying Fable 5: https://www.anthropic.com/news/redeploying-fable-5• Why half of product managers are in trouble | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble• Inside Linear: Building with taste, craft, and focus | Karri Saarinen (co-founder, designer, CEO): https://www.lennysnewsletter.com/p/inside-linear-building-with-taste• Building beautiful products with Stripe's Head of Design | Katie Dill (Stripe, Airbnb, Lyft): https://www.lennysnewsletter.com/p/building-beautiful-products-with• The design process is dead. Here's what's replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead• OpenAI Codex lead on the new shape of product work | Andrew Ambrosino: https://www.lennysnewsletter.com/p/openai-codex-lead-on-the-new-shape• Elon Musk: ‘Chances are we're all living in a simulation': https://www.theguardian.com/technology/2016/jun/02/elon-musk-tesla-space-x-paypal-hyperloop-simulation—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Pat Nolan returns to discuss the science of early odor imprinting and how to properly train detection puppies from 8 weeks old. This isn't theoretical—Pat shares his exact methodology for building odor value, the two-can game foundation, common mistakes (like the one-box problem), and video evidence of puppies as young as 24 days responding to target odor.What We Cover:Why early odor imprinting creates lasting structural changes in the puppy's brainCritical periods: the window where early experiences matter mostAlan Goldblatt research: why puppies imprinted early learn faster laterStarting from birth: blowing odor in the whelping box (first 24-48 hours)The two-can game foundation (the easiest choice puppies will ever make)Progression from two cans to four cans (often in one session)Using UDC or NODA (laboratory odors that won't contaminate real-world searches)Why emotional state matters: frantic ≠ driveThe one-box mistake: teaching behavior vs. teaching odor detectionVariable interval training: the restaurant analogy that explains durationVideo proof: 24-day-old puppies seeking target odor, 6-week-old puppies choosing odor over playReasonable precision for puppies (don't ask too much too soon)Introducing distractions, blanks, and controls from day oneWhy puppies need patience, not pressureBuilding executive function skills through detection gamesPat discusses how early experiences create lasting neural pathways and why starting detection work young doesn't mean rushing—it means being thoughtful, having a plan, and understanding what success looks like at each stage.Critical Takeaway: You CAN train puppies on odor. The question is: do you know what you're doing? Have a plan. Know your benchmarks. Be aware of emotional state and developmental stages. That's what separates good puppy programs from ones that create problems.#PuppyTraining #DetectionDogs #OdorImprinting #K9Training #PuppyDevelopment #DetectionDogTraining #EarlyImprinting #BrainDevelopment #CriticalPeriods #TrainingMethodology #CaninesDalkingScents #PatNolan #K9Handler #DogTrainingScience________________________________________

Hey everyone, Alex here

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

What is the true relationship between matter, the human mind, and consciousness? Can science and philosophy together help explain one of humanity's greatest mysteries? In this fascinating episode of The ‘X' Zone, Rob McConnell welcomes physician, researcher, and author T. Lee Baumann to discuss his thought-provoking work, Matter to Mind to Consciousness. Drawing upon his background in medicine, science, and philosophy, Dr. Baumann explores the evolution of consciousness from the physical world to the complexities of human awareness. He examines questions surrounding the nature of reality, the relationship between the brain and consciousness, and how scientific discoveries continue to shape our understanding of the human experience. During this compelling conversation, listeners will discover insights into neuroscience, the philosophy of mind, human perception, free will, and the possibility that consciousness extends beyond purely physical processes. Dr. Baumann also discusses how these concepts influence our understanding of health, spirituality, and the future of scientific inquiry. Whether you're interested in consciousness studies, neuroscience, philosophy, medicine, or the intersection of science and spirituality, this episode offers an engaging exploration of one of the most profound questions ever asked: How does matter become mind, and how does mind become consciousness?Become a supporter of this podcast: https://www.spreaker.com/podcast/the-x-zone-radio-tv-show--1078348/support.Please note that all XZBN radio and/or television shows are Copyright © REL-MAR McConnell Meda Company, Niagara, Ontario, Canada – www.rel-mar.com. For more Episodes of this show and all shows produced, broadcasted and syndicated from REL-MAR McConell Media Company and The 'X' Zone Broadcast Network and the 'X' Zone TV Channell, visit www.xzbn.net. For programming, distribution, and syndication inquiries, email programming@xzbn.net.We are proud to announce the we have launched TWATNews.com, launched in August 2025.TWATNews.com is an independent online news platform dedicated to uncovering the truth about Donald Trump and his ongoing influence in politics, business, and society. Unlike mainstream outlets that often sanitize, soften, or ignore stories that challenge Trump and his allies, TWATNews digs deeper to deliver hard-hitting articles, investigative features, and sharp commentary that mainstream media won't touch.These are stories and articles that you will not read anywhere else.Our mission is simple: to expose corruption, lies, and authoritarian tendencies while giving voice to the perspectives and evidence that are often marginalized or buried by corporate-controlled media

You don't need another productivity hack. You need systems that still work when your brain doesn't.Cas Aarssen built the Clutterbug Method, hosted HGTV's Hot Mess House, and grew a global brand by stopping the fight against her ADHD and designing her home and business around how she naturally works.In this conversation, she shares the hard lessons that came from burnout, why traditional organizing advice kept failing her, and how building systems around real habits instead of ideal ones changed everything. From running a team without endless SOPs to creating spaces that stay functional even on hard days, Cas explains what it actually takes to build systems that last.If you've ever felt like you're constantly rebuilding your life from scratch, this episode offers a more sustainable way forward.What We CoverWhy burnout pushed her to rethink the way she workedBuilding systems around your actual habits instead of your ideal selfThe Clutterbug Method and why one organizing style doesn't fit everyoneDelegating through ownership instead of endless SOPsCreating a home and business that work with an ADHD brain, not against itConnect With Cas Aarssenhttps://clutterbug.me/what-clutterbug-are-you-testIf you're an entrepreneur with ADHD who's tired of being asked “Why don't you just hire/make a system/delegate?”  We've gotchu! Click here for a free copy of my 5-year-tested Focus Filter. Instant relief for work-related overwhelm.Find out what's holding you back. I'll personally build you a simple plan to fix it. Click here to grab one.Join my Focused Balanced Growth Program. If you're tired of getting blank looks in masterminds full of neurotypical advice, this is for you. Weekly Monday Motivation sessions, plus content you can binge or dip into for strategies specific to you. Apply here.Your Business Operations Built for Your ADHD Brain. Feel like you can never really delegate because you can't explain how to do it? Struggling to hire someone who feels like a natural fit for your business? Let us handle it for you. We specialize in using our years of ADHD research and practical support to act as your fractional COO, handling the back-end operations in a way that feels light and keeps you focused. Learn more here.

******Support the channel******Patreon: https://www.patreon.com/thedissenterPayPal: paypal.me/thedissenterPayPal Subscription 3 Dollars: https://tinyurl.com/ybn6bg9lPayPal Subscription 5 Dollars: https://tinyurl.com/ycmr9gpzPayPal Subscription 10 Dollars: https://tinyurl.com/y9r3fc9mPayPal Subscription 20 Dollars: https://tinyurl.com/y95uvkao ******Follow me on******Website: https://www.thedissenter.net/The Dissenter Goodreads list: https://shorturl.at/7BMoBFacebook: https://www.facebook.com/thedissenteryt/Twitter: https://x.com/TheDissenterYT This show is sponsored by Enlites, Learning & Development done differently. Check the website here: http://enlites.com/ Dr. Julian Kiverstein is Senior Researcher at the Lemon Tree Center for Psychiatry, Psychotherapy and Philosophy at the University of Amsterdam. He is currently writing a monograph for Palgrave Macmillan entitled The Significance of Phenomenology, and editing a comprehensive handbook for Routledge Taylor Francis on the philosophy of the social mind. He is associate editor of Phenomenology and the Cognitive Sciences and was until recently Book Review Editor for the Journal of Consciousness Studies. In 2006 he was one of the main architects of a successful cross disciplinary, Europe-wide project proposal on consciousness, part of the ESF Programme, Consciousness in a Natural and Cultural Context. In this episode, we talk about embodied cognition and phenomenology. We discuss evolution, development, self-organization, and cognition. We explore the experience of pain through the lens of embodied predictive processing. We talk about the feeling of being alive and sentience. We talk about how we can understand the self through the Free Energy Principle. We discuss what the boundaries of the mind are. Finally, we talk about culture and enculturation through an ecological-enactive perspective, and how we can understand the institution of science through this perspective.--A HUGE THANK YOU TO MY PATRONS/SUPPORTERS: PER HELGE LARSEN, BERNARDO SEIXAS, ADAM KESSEL, MATTHEW WHITINGBIRD, ARNAUD WOLFF, TIM HOLLOSY, HENRIK AHLENIUS, ROBERT WINDHAGER, RUI INACIO, ZOOP, MARCO NEVES, COLIN HOLBROOK, PHIL KAVANAGH, SAMUEL ANDREEFF, FRANCIS FORDE, TIAGO NUNES, FERGAL CUSSEN, HAL HERZOG, NUNO MACHADO, JONATHAN LEIBRANT, JOÃO LINHARES, STANTON T, SAMUEL CORREA, ERIK HAINES, MARK SMITH, JOÃO EIRA, TOM HUMMEL, SARDUS FRANCE, DAVID SLOAN WILSON, YACILA DEZA-ARAUJO, ROMAIN ROCH, YANICK PUNTER, CHARLOTTE BLEASE, NICOLE BARBARO, PAWEL OSTASZEWSKI, NELLEKE BAK, GUY MADISON, GARY G HELLMANN, SAIMA AFZAL, ADRIAN JAEGGI, JOÃO BARBOSA, JULIAN PRICE, HEDIN BRØNNER, FRANCA BORTOLOTTI, GABRIEL PONS CORTÈS, URSULA LITZCKE, SCOTT, ZACHARY FISH, TIM DUFFY, SUNNY SMITH, JON WISMAN, WILLIAM BUCKNER, LUKE GLOWACKI, GEORGIOS THEOPHANOUS, CHRIS WILLIAMSON, PETER WOLOSZYN, DAVID WILLIAMS, DIOGO COSTA, ALEX CHAU, CORALIE CHEVALLIER, BANGALORE ATHEISTS, LARRY D. LEE JR., OLD HERRINGBONE, DAN SPERBER, ROBERT GRESSIS, JEFF MCMAHAN, JAKE ZUEHL, MARK CAMPBELL, TOMAS DAUBNER, LUKE NISSEN, KIMBERLY JOHNSON, JESSICA NOWICKI, LINDA BRANDIN, VALENTIN STEINMANN, ALEXANDER HUBBARD, BR, JONAS HERTNER, URSULA GOODENOUGH, DAVID PINSOF, SEAN NELSON, MIKE LAVIGNE, JOS KNECHT, LUCY, MANVIR SINGH, PETRA WEIMANN, CAROLA FEEST, MAURO JÚNIOR, TONY BARRETT, NIKOLAI VISHNEVSKY, STEVEN GANGESTAD, TED FARRIS, HUGO B., JORDAN MANSFIELD, CHARLOTTE ALLEN, DAVID TONNER, PATRICK DALTON-HOLMES, NICK KRASNEY, RACHEL ZAK, DENNIS XAVIER, CHINMAYA BHAT, RHYS, ALEX MACLEOD, HAIDAR, JULIEN PORCHER, ROBERT SUNDSTRÖM, JON STEWART, AND JAMES DORLING!A SPECIAL THANKS TO MY PRODUCERS, YZAR WEHBE, JIM FRANK, ŁUKASZ STAFINIAK, TOM VANEGDOM, BERNARD HUGUENEY, CURTIS DIXON, THOMAS TRUMBLE, KATHRINE AND PATRICK TOBIN, JONCARLO MONTENEGRO, NICK GOLDEN, CHRISTINE GLASS, IGOR NIKIFOROVSKI, PER KRAULIS, ADAM HUNT, AND JOÃO BARBOSA!AND TO MY EXECUTIVE PRODUCERS, MATTHEW LAVENDER,SERGIU CODREANU, AND GREGORY HASTINGS!

► Today I'm welcoming onto the Polymath PolyCast. Julie Lavergne who is a polymathic generalist, TEDx Speaker, and Host of The Generalist Advantage podcast!Kicking off season 8 strong!Links:https://8ballclarity.com/TEDxhttps://www.linkedin.com/in/julie-lavergne/Generalist Advantage PodcastMentioned:Polymathy PanelChapters00:00 From Corporate to Curiosity: A Journey of Transformation02:44 The Engineering Mindset: Problem Solving and Process05:36 The Art of Communication: Navigating Multiple Roles08:35 Curiosity as a Skill: Embracing the Unknown11:27 Travel and Growth: Expanding Horizons through Experience14:18 The Juggling Act: Balancing Multiple Interests17:12 Content Creation: The Pursuit of Passion and Challenge20:00 TEDx and Beyond: Sharing Messages and Personal Growth22:38 The Art of Conversation: Orchestrating Meaningful Dialogues27:36 Defining Polymathy: Generalist vs. Polymath30:41 Measuring Accomplishment: The Polymath's Journey32:58 The Multidisciplinary Spectrum: Understanding Expertise38:30 The Genesis of the Generalist Advantage Podcast49:25 8 Ball Clarity: Enhancing Decision-Making with AI▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬► Affiliates:Videos Repurposed with Opus Clip:https://www.opus.pro/?via=729b77Social Posts Automated with Nuelink:http://nuelink.com/?via=dustin▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬►

How separate is the cognition in our heads from cognition with our bodies, our tools, our communities and our ecosystems? What is participatory sense making and why is our world becoming less and less disposed to doing it? What does connecting cognitively with the world beyond our own bodies do for our sense making, and so for the future of our species?In this episode we have the intriguing topic of extended cognition to explore, and in particular the field of participatory sense-making. So we get into the extended component of the 5E's model of embodied cognition; how our technologies, including AI, are much more influential on our cognition than mere tools that we set aside after use; we talk about our co-dependence on the natural world, and what happens to cognition and our society when our sense of “kinship” with it is lost; And we get into detail on the crucial process of participatory sense making, and how important it is to arrive to consensus rather than getting bogged down in polarisation, which in turn allows us to decide on urgent solutions as a species.Fortunately these are the exact specialisations of our guest, psychologist, cognitive scientist and philosopher at the University of British Columbia, Rebecca Todd. With a background also in neuroscience, she's authored nearly 100 academic papers, whilst her substack hosts her much loved writings for the general What we discuss:00:00 Intro.06:05 Extended cognition defined.08:45 Distributed cognition - Edward Hutchins.09:00 Attention, learning and memory are all distributed.10:00 Can we identify the cognitive boundary between self and other?14:30 Heidegger's warning about undermining the influence of technology.17:00 We give our tools too much credit.19:00 Large Language Model's effect on extended cognition.23:00 Individualistic, extractive, competitive motivations for technology.24:00 Self AS relationship rather than IN relationship - Dr. Yuri Celidwen.28:10 Non-verbal communication.29:46 Feedback loops between nature and our minds.33:50 Connection to nature and mental health.37:20 Belonging and ‘kinship' with the natural world.39:45 Objects can have personality - Object personality Synesthesia.44:20 Risk of appropriation, when applying indigenous ideas of ‘belonging'.46:50 The ‘Trim Tab' analogy — small interventions can lead to big changes in direction, Greg Watson.53:15 Participatory Sense-making explained.57:30 Shifts happen before and after moments of synchrony.59:30 Conditions required for participatory sense-making.01:02:20 The 4 R's: Reciprocity, Respect, responsibility and relevance.01:03:05 Neurodivergence: bridging the way we see the world differently.01:12:15 The new lack of bandwidth for complexity and nuance - information overload.01:15:30 Creating the time and space needed to do participatory sense-making.01:19:30 Food together is a magical ingredient.01:23:30 Openness and listening can be trained.01:24:20 Is consensus necessary.01:29:10 Tolerance of diversity rather than unified consensus.References:Beck Todd Substack, “Towards an ecology of mind”Andy Clarke and David Chalmers, “Extended Cognition” paperEdward Hutchins - Distributed Cognition paper.Fernando Rosas - Statistical boundaries between agents.Karen McClean - SPIN Lab (human-robot interactions through the sense of touch)Shirley Turcotte - Indigenous Focusing-oriented Therapy (IFOT)“Participatory Sense-making with the More than Human World” With Yuri Celidwen.An interview with Dr. Greg Watson - Ex US agricultural ministerHannah De Jaegher & Ezequial Di Poalo, “Participatory Sensemaking, An enactive approach to social cognition”. V.J .Kirkness, “First Nations and Higher Education: the four 4's”“The Multiplicity of Worlds” with Penijean Gracefire et al.Ed Young, “An immense world”“Community Out of the Ashes” with Eli Oda Shina.Gabor Mate, “In the realm of hungry ghosts - Encounters with Addiction”

Nurse Doza breaks down five signs your gut microbiome may be off — a history of antibiotics, brain fog, autoimmune issues, relentless sugar cravings, and not pooping every day. He explains how gut bacteria help shape neurotransmitters, blood sugar, and natural GLP-1, why so many issues trace back to the gut, and the simple steps — fasting, fermented foods, fish oil, and the Good Poops Protocol — that can help rebuild it from the inside out. FEATURED PARTNER — Good Poops Protocol (MSW Nutrition) This episode centers on rebuilding the gut microbiome, and the Good Poops Protocol is the bundle Nurse Doza built for exactly that — Gut, Liver Boost, and Berberine Plus working together over 60 days to support the intestinal lining, healthy bile flow, and blood-sugar metabolism. Berberine and L-glutamine are both studied for supporting the body's own GLP-1 production, making the protocol a practical starting point for the digestive issues discussed in this episode.

Guest: Dr. Carla HookerFb: @Carla.M.Hooker2025Www.KarlasKidsDance. Org ---She's back—and more powerful than ever. In this unforgettable return to the OneMicNite Podcast, host Marcos Luis sits down with Dr. Carla Hooker, the internationally respected motivational speaker, sickle cell anemia warrior, movement mentor, and author of the groundbreaking new book 10 Things You Should Know.This episode is raw, uplifting, and deeply human—a masterclass in resilience, faith, and the courage to keep moving even when life hits hard.Dr. Hooker opens up about:

For the first time, antimatter gets transported by truck! The link between Virgin Olive Oil in the diet and cognition in older people. What we can see in the night sky in July. The propensity of humans to walk in the counterclockwise direction. And a final goodby poem for Rocky, the valiant three-legged horse. All on this week's show! Dave Robinson, Scott Miller and Leslie Moise reporting. ‘Bench Talk: The Week in Science' is a weekly program that airs on WFMP, Louisville FORward Radio, 106.5 FM (forwardradio.org) every Monday at 7:30 pm, Tuesday at 11:30 am, and Wednesday at 7:30 am. Visit our Facebook page for links to the articles discussed in this episode: https://www.facebook.com/BenchTalkRadio

In episode 401 Andrea Samadi welcomes Greg Hill to explore why trust is the essential foundation for learning, growth, and high performance. They discuss how trust creates psychological safety, encourages risk-taking and creativity, and acts as a multiplier for organizational results. Greg shares practical leadership practices—truthfulness, consistency, competence, and genuine care—and personal stories about marathon training to show how movement builds confidence and supports clear thinking, resilience, and sustained motivation. This episode launches Phase 3: Movement, Adaptation, and Learning in the podcast's brain operating system series. Welcome back to Season 16 of the Neuroscience Meets Social and Emotional Learning Podcast. I'm Andrea Samadi, and on this podcast, we bridge the science behind social and emotional learning, emotional intelligence, and practical neuroscience so we can create measurable improvements in well-being, achievement, productivity, and results. Watch the full YouTube interview here https://youtu.be/kfvSFMQZ3dk On EP 401, You Will Learn: ✔ Why trust is the foundation of learning, growth, and high performance. ✔ How psychological safety changes the brain, reducing threat and increasing engagement. ✔ Why great leaders create environments where people feel safe enough to learn, take risks, and perform at their best. ✔ How trust influences attention, confidence, decision-making, and long-term success. ✔ The connection between movement, trust, and the brain's readiness to learn as we launch Phase 3: Movement, Learning & Cognition. ✔ Practical leadership strategies from Greg Hill to build trust with your team, family, classroom, or organization. Before the brain can learn, grow, adapt, or perform at its highest level, it must first feel safe enough to trust. In this episode, Greg Hill explains why trust is the hidden foundation of every high-performing individual and team. Trust → Engagement → Movement → Brain Activation → Attention → Learning → Memory → Performance → Confidence. Over the past several months, we've been building what I've called The Brain's Operating System for Human Performance. In Phase 1, we explored Regulation and Safety, learning why the brain performs best when the nervous system feels regulated, balanced, and secure. In Phase 2, we examined Motivation and Neurochemistry, uncovering what drives action, what sustains effort, and what breaks the motivation loop. Next we will move into Phase 3: Movement, Adaptation and Learning. The theme for Phase 3 did change and I'll explain that once we dive into EP 403. But before we can get there, we are going to go a bit deeper into something the brain needs to feel safe: trust. EP 401 — Trust: The Foundation of Learning Question: What must happen before learning or any change can occur? Trust → Safety → Engagement → Action → Learning Before movement changes the brain, the brain must feel safe enough to engage. With Trust. Because trust creates psychological safety. Safety creates engagement. Engagement creates action. And action creates learning. When trust is present, people are more willing to take risks, embrace challenges, learn from mistakes, and move beyond what is comfortable. When trust is absent, the brain shifts its energy toward protection rather than growth. We've explored the importance of trust before on this podcast. Back on EP 207[i], we spoke with Greg Link, co-founder of the Covey Leadership Center and founder of FranklinCovey's Global Speed of Trust Practice. Greg Link shared how trust accelerates relationships, strengthens organizations, and serves as a multiplier for performance. Today's conversation takes that idea one step further. Throughout my career, whenever I've met someone who consistently brings out the very best in others, I've wanted to understand why. What are they doing differently? What principles guide them? How do they create environments where people feel safe enough to grow, learn, and perform at their highest levels? That's why I invited today's guest, Greg Hill, to join us. I've had the opportunity to work directly with Greg for over a year and a half, and one thing stood out immediately: I always knew that Greg trusted me to do my best work. That trust wasn't something we talked about. It was something he demonstrated. I felt it every day. Over time, I began to notice that this wasn't unique to my experience. Greg seemed to create the same environment for everyone around him. People wanted to do their best work, not because they had to, but because they felt trusted, valued, and supported by him. It made me curious. Was there something deeper happening beneath the surface? Could trust be one of the hidden factors that unlocks learning, growth, confidence, and high performance? Greg Hill is a respected leader, mentor, and trusted advisor who has spent years helping people and organizations reach their highest potential through relationships built on trust, accountability, and genuine human connection. Today, we'll explore the neuroscience of trust, its connection to leadership and performance, and why trust may be one of the most important foundations for learning, growth, and human potential. As we launch Phase 3, you'll hear why trust may be the bridge between motivation and action, and why people are often willing to enter the learning cycle only when they feel safe enough to take the first step. Welcome to Episode 401. Let's meet Greg Hill. Greg Hill, welcome to the podcast, and thank you for joining me. I've been looking forward to this conversation for quite some time because I've experienced firsthand the impact you've had on people around you, and today I'd love to explore some of the principles you've learned over the years that have helped you build trust, develop leaders, and bring out the best in others. Question #1 Greg, when most people hear the word trust, they think about whether they trust another person (in relationship to them). But I've always wondered if trust begins long before that. What do you believe are the foundations of trust, and what is it about certain leaders that makes people naturally feel safe enough to trust them? Question #2 Why is trust so important for performance? Stephen R Covey said that “trust is the one thing that affects everything else you're doing. It's a performance multiplier and it takes your trajectory upwards.” Why do you think this happens? Why do we perform best when we are in an environment where trust exists? Also, what happens when trust is absent? Question #3 We've spent the past season exploring regulation, safety, motivation, and performance. I wonder how do you foster trust and safety with those you work with? Is it an intentional part of your philosophy, or is it just something that comes naturally to you? Question #4 (Movement Connection) As we launch our phase on Movement, Adaptation and Learning, I couldn't help but notice that many high-performing leaders seem to have some form of movement practice in their lives. For me it's hiking mountains. For you it was marathon running. What role has movement played in helping you think clearly, manage stress, make decisions, and perform at your best throughout your career? Final Question  As we begin this new phase focused on movement, adaptation and learning, What is one thing every leader, educator, parent, or coach can do right away to build more trust and create an environment where people can learn, grow, and perform at their best? Thank you, Greg, I appreciate the time you took to share your thoughts on trust and leadership as we launch Phase 3 of our podcast.  Your ideas have helped to explain why people are willing to enter the learning cycle in the first place, with trust that reduces threat, increases attention, strengthens relationships, and creates the conditions for growth. You've been an incredible mentor and leader in my life and I'm grateful that we are able to stay in touch.   Final Thoughts Next week, we'll begin Phase 3: Movement, Adaptation and how this all ties into our performance, exploring what happens after engagement occurs and how movement literally changes the brain's ability to learn, remember, think, and perform. Because when trust creates safety, movement creates change. If Phase 1 taught us how to regulate the brain, and Phase 2 taught us what motivates the brain, today's episode showed us what creates the conditions for growth. Trust reduces threat and opens the door to learning. Next episode (in 2 more weeks), we'll discover what happens when movement steps through that door and begins changing the brain itself. See you the middle of July. REFERENCES: [i] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 207 with Greg Link on “Unleashing Greatness with Neuroscience, SEL, Trust and the 7 Habits”  https://andreasamadi.podbean.com/e/co-founder-of-coveylink-greg-link-on-unleashing-greatness-with-neuroscience-sel-trust-and-the-7-habits/  

AI agents already perform complex tasks, but they largely work alone, even when they're technically connected. But what if they could collectively learn from each other, and collaborate?  Our guest wants you to think about when that transition happened for people, with our own intelligence, some 70,000 years ago, when human intelligence stopped being a solo act and became something we did together.Our guest, Vijoy Pandey, runs Outshift by Cisco. He believes AI is standing at that exact threshold right now, and he's building the layer that gets it across the line. A hundred million degrees Celsius. That's the brutal reality of commercial fusion. This week, we're sitting down with industry leaders to discuss the front lines of an energy revolution, from surviving extreme thermal stress to building brand-new supply chains.We Meet:  Vijoy Pandey is Senior Vice President and General Manager of Outshift by Cisco, the company's internal incubation engine for emerging technology. Credits:This episode of SHIFT was produced by Jennifer Strong with help from Emma Cillekens. It was mixed by Garret Lang, with original music from him and Jacob Gorski. Art by Meg Marco.This episode of SHIFT was brought to you by Outshift by Cisco. 

In Episode 110, following a discussion on social media, Luke talks to Ben Richardson about the language around music therapy, Relational Frame Theory, commissioning, and whether music therapy is actually 'functional all the way down'. You'll be hearing more from Ben at the BAMT conference in November 2026, and no doubt in future publications, but Music Therapy Conversations got here first! Ben Richardson is a music therapist and the Therapy Lead at the National Online School, a large multi-disciplinary team of speech and language therapists, occupational therapists, creative arts therapists, and therapy assistants. His clinical and academic work focuses heavily on the intersection of music therapy and contextual behavioural science. By integrating Acceptance and Commitment Therapy (ACT) and Relational Frame Theory (RFT), Ben wants to build a framework of Process-Based Music Therapy, exploring clinical musical interactions as a rich, complex form of verbal behaviour, whilst honouring the importance of the aesthetic and "felt" aspects of the work music therapists do.  Ben is particularly driven by the changing commissioning environment for education, health, and social care, and what music therapists could do to strengthen their position within it.  He will be presenting his research on this at the BAMT conference in November. References Hayes, S. C., Barnes-Holmes, D., Roche, B.  2001.  Relational Frame Theory: A Post-Skinnerian Account of Human Language and Cognition.  Springer. Pavlicevic, M.  1997.  Music Therapy in Context: Music, Meaning and Relationship.  Jessica Kingsley Publishers. Törneke, N.  2010.  Learning RFT: An Introduction to Relational Frame Theory.  New Harbinger Publications.   Links LinkedIn TMTC: The Music Therapy Charity Not referenced in the recording, but Chan et al. (2022) a good example (and open access article) of application of RFT principles to music (not music therapy): https://pubmed.ncbi.nlm.nih.gov/37397135/

In this episode we are exploring one of the most practical and powerful ways to support children through their design: Cognition. This is an advanced Human Design concept that reveals a person's strongest sense for taking in information, discerning what's aligned, and navigating life with greater trust in themselves. Through personal stories, client experiences, and real-life parenting examples, we break down all six cognition types—Smell, Taste, Outer Vision, Inner Vision, Feeling, and Touch—and explain how understanding your child's cognition can support everything from food preferences and learning styles to emotional well-being and self-trust. Whether you're a parent, caregiver, or simply interested in re-parenting yourself, this episode offers simple yet profound ways to honor individuality and nurture authentic self-expression from an early age. Key Takeaways: Why your child's cognition influences how they take in information, make decisions, and discern what's right for them. Why supporting and honoring your child's cognition and sensory preferences builds self-trust and helps them develop confidence in their own inner guidance and authority. The six cognition types and how each offers a unique way of processing and interacting with life. How simple changes to a child's environment, food presentation, routines, and sensory experiences can create more ease, alignment, and well-being. Why understanding your own cognition and design can help you re-parent yourself and model self-awareness, self-trust, and alignment for the next generation.   Get 10% off our Parenting Cheat Sheet! Use code: PODCASTLOVE10 Free Mini-Courses! FREE Transits & The Harmonic Gate Mini-Course FREE Human Design Readings 101 Masterclass Join us in Your Human Design Besties! Get our book: Your Human Design! Online Human Design Reader Training 64 Gates & Gene Keys Training Future trainings and retreats can be found on daylunalife.com Instagram: @‌d.a.y.l.u.n.a

Jake Paul and Geoff Woo join the podcast to announce Anti Fund's new $100 million growth fund and discuss the evolution of their investment strategy. The conversation covers the fund's portfolio, including investments in companies such as SpaceX, OpenAI, Anthropic, Anduril, Cognition, Etched, and Modal, as well as the lessons they've learned backing founders and identifying emerging technologies. They discuss founder psychology, resilience, ambition, and why they believe attention, culture, and distribution are becoming increasingly important advantages in the AI era. Along the way, Jake reflects on his path from creator to entrepreneur, athlete, and investor, while Geoff shares his views on venture capital, technology, and how AI is reshaping opportunity for founders and builders.   Resources: Follow Jake Paul on X: https://x.com/jakepaul Follow Geoff Woo on X: https://x.com/geoffreywoo Stay Updated:Find a16z on YouTube: YouTubeFind 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.

First episode in separate studios! Went well, and we had a fun discussion about the recent JRE Eric Weinstein episode, with the possibility that the science of Physics has been stalled, or purposefully distracted, by the beautiful but seemingly useless mathematics of String Theory. Is it possible that "real physics" simply went "black"...that work is being done in secret that is actually making progress in unlocking the secrets of the universe?   You can support us through Paypal or Patreon by heading over to our support page on our website: https://www.brothersoftheserpent.com/support   Chapters 00:00 Welcome to the Brothers of the Serpent Podcast 07:09 Space Weather Insights 09:55 The State of Modern Physics 22:26 The Intersection of Physics and the Supernatural 34:39 Mysterious Disappearances of Scientists 48:21 Speculation on Missing Persons Cases 51:52 Skepticism and Conspiracy Theories 54:50 Exploring Advanced Physics and UAPs 01:01:06 Theoretical Physics and Its Limitations 01:07:03 Consciousness and Noetic Sciences 01:14:21 Assumptions in Modern Science 01:18:21 The Nature of Gravity and Dark Matter 01:32:43 The Secrets of Advanced Technology 01:41:50 Space Exploration and Hidden Agendas 01:50:02 The Great Pyramid: A Survival Guide? 02:07:14 The Impact of Language on Memory and Cognition

In this milestone episode (400), Andrea Samadi celebrates seven years of the Neuroscience Meets SEL Podcast with her husband Majid Samadi. They reflect on the journey of translating neuroscience into practical strategies for performance, learning, and well-being. Together they review core lessons — everything begins with the brain, safety before performance, how thoughts shape biology, the power of movement, recovery as a performance strategy, and the central role of relationships and support. Majid also shares leadership insights from his decades in educational sales, including stress management, motivation, continuous learning, and the guiding motto: do the right thing. They close by looking ahead to the next phase on movement, learning and cognition and invite listeners to subscribe for future episodes. Sales Leadership Under Pressure: Applying the Neuroscience of High Performance to Real-World Leadership Guest: Majid Samadi Listen to YouTube interview here https://youtu.be/SSZH3qwPqf8 Intro: Top 7 Lessons from the past 7 years Guest: Majid Samadi (Interview begins at 10:16) EP 400: Sales Leadership Under Pressure with Majid Samadi In this milestone 400th episode, Andrea welcomes back her husband, Majid Samadi, who first appeared on Episode 1 when the podcast launched in 2019. Together, they reflect on seven years, fifteen seasons, and 400 episodes of exploring the neuroscience behind achievement, leadership, learning, motivation, and human potential. In this episode, we will cover: ✔ The Top 7 Lessons Learned from 7 Years and 400 Episodes ✔ Why understanding the brain changes the way we learn, lead, and perform ✔ The neuroscience of stress, self-regulation, and leadership under pressure ✔ How high-performing leaders sustain motivation without burning out ✔ The connection between movement, learning, cognition, and peak performance ✔ Why relationships are the foundation of leadership and long-term success ✔ The role trust plays in building high-performing teams ✔ Leadership lessons learned through organizational change, uncertainty, and growth ✔ How the definition of success evolves over a lifetime and career ✔ Why no meaningful achievement happens alone As Andrea reflects on the lessons learned from hundreds of conversations with neuroscientists, educators, physicians, psychologists, business leaders, and peak performers, she shares the one lesson that stands above all the rest: Behind every meaningful accomplishment is someone who believed in you enough to help you keep going. Welcome back to Season 15 of the Neuroscience Meets Social and Emotional Learning Podcast. I'm Andrea Samadi, and on this podcast, we bridge the science behind social and emotional learning, emotional intelligence, and practical neuroscience so we can create measurable improvements in well-being, achievement, productivity, and results. Over the past 399 episodes, we've explored the neuroscience behind performance, learning, stress, motivation, and human potential. For this milestone Episode 400, I wanted to do something different. Instead of interviewing another neuroscientist, or reviewing past episodes, we're going to explore what happens when these ideas are applied in the real world. Joining me is someone listeners heard on EP 1[i] my husband, Majid Samadi, where we laid out the framework for future episodes, EP 200[ii] (Why we launched this podcast), and EP 300[iii] (a special episode with my Mom, Hazel MacPhail, where she taught us “how to live the good life”). I'll never forget EP 1, when I asked Majid if he would record with me to help me to launch this podcast thing I wanted to start. He had just come home from working LAUSD (in California) and he put his suit jacket on my desk, and sat down in front of the microphone. I showed him the questions I would ask him, and off we went. I learned that when you start something, it doesn't have to be perfect. Just start. What 15 Seasons Taught Me Before we begin today's conversation, I wanted to take a moment to reflect on what I've learned over the past seven years and 400 episodes of the Neuroscience Meets Social and Emotional Learning Podcast. I had sketched out a framework, and had some ideas of what I wanted to cover on at least the first 50 episodes. When I started this idea in 2019, I thought I was creating a platform to share neuroscience research (as it connected to Social and Emotional Learning). What I didn't realize was that the journey would change me. After hundreds of interviews with neuroscientists, physicians, educators, psychologists, business leaders, and peak performers, there are a few lessons that stand above all the rest. I'll always say it took me 50 episodes to get started. I found it really difficult to ask questions and breathe at the same time. Lesson #1: Everything begins with the brain. Whether we're talking about achievement, learning, leadership, health, relationships, or performance, success starts with understanding how the brain works. When we understand the brain, we stop fighting ourselves and start working with ourselves. We all have our own journey here. Mine started when an educator, Jeff Kleck, from EP 246[iv] challenged me to add neuroscience to my work. This was around 2014 when I had partnered with AZ Department of Education with a character ed/leadership program, and Jeff Kleck told me that I wouldn't go wrong if I wrote a whole new book that focused on the brain and learning. That's when I sat down, and started to study some of the leading researchers in this field. I've heard similar stories from other authors like Dr. Doug Fisher, who told me that he sat in classes with medical students to unwrap how the brain learns best. Lesson #2: Safety comes before performance. One of the most important themes of Season 15 has been that a dysregulated nervous system cannot perform at its best. Before growth, before learning, before leadership, the brain must feel safe. This lesson applies in our homes, our schools, our workplaces, and our relationships. I'll never forget asking Dr. David Stephen on EP 388[v] about a situation where I was under unusual stress, and my eyesight (or ability to read) stopped working. He explained the neuroscience behind this example, that I'll never forget and his solution to my problem that was to eat glucose before any important meeting or presentation. Lesson #3: Our thoughts become biology. Through experts like Dr. Caroline Leaf, Bob Proctor, Dawson Church, and many others, I learned that our thoughts are not just ideas. They influence our chemistry, our attention, our habits, and ultimately our results. What we repeatedly think becomes what we repeatedly do. This one I've believed since my days working in the seminar industry with Bob Proctor. He would hammer this concept into everyone's mind in every seminar. I just always thought this was something he really believed in, until I heard the SAME thing from Dr. Caroline Leaf, and Dr. Korotkov from Russia. It's also behind Dr. Joe Dispenza's work. To this day, I watch the words I think and say out loud. Lesson #4: Movement changes the brain. This lesson became personal. The science is clear: movement improves attention, memory, mood, resilience, and learning. But over the years, I experienced it firsthand through hiking, walking, strength training, and building daily movement into my life. This is how I've always been. I remember putting on my rollerblades when I was 16 and rollerblading to the local YMCA that wasn't really in my neighborhood. Motivation got me moving. Movement changed my brain. And this is how I still find the energy to sit at my desk and write podcasts episodes every Saturday. I have to exercise (or move) first, and then I can create. Over time this has probably been my healthiest habits. Lesson #5: Recovery drives performance. For years I focused on doing more. The neuroscience taught me something different. Growth doesn't happen during effort. Growth happens during recovery. Sleep, stress regulation, recovery, and reflection are not luxuries—they are performance strategies. This took me years to finally put into practice. Lesson #6: Relationships change everything. If there is one lesson that appears in every field of neuroscience, it is this: We are wired for connection. The quality of our relationships influences our health, happiness, resilience, leadership, and longevity. And that brings me to perhaps the most important lesson of all. Lesson #7: No meaningful achievement happens alone. People often see the finished podcast episode. They don't see the support system behind it. For 400 episodes, there has been one person supporting this mission from behind the scenes. My husband, Majid. While I was researching, writing, recording, editing, and building this platform, Majid was encouraging me when things were difficult, celebrating the wins, offering perspective when I needed it, and helping me continue when the path wasn't always clear. Many of these episodes were written because someone believed in me enough to keep me going. The podcast may have my name on it, but it has always been supported by both of us. As we celebrate Episode 400, that's the lesson I want to leave everyone with. Achievement is rarely a solo journey. Behind every meaningful accomplishment is a person, a mentor, a teacher, a spouse, a friend cheering you along the way from the sidelines, or a community that helped make it possible. The neuroscience taught me how the brain works. Life taught me that relationships are what make everything work. And that's why there is no better person to join me for Episode 400 than Majid Samadi. Welcome Majid! Thank you for taking the time to record this milestone episode with me. I know your time is limited. Before we get started, can  you share what it is that you do when you are not being strong armed to record podcast episodes for me? So, we have been covering 5 phases in Season 15, showing how the brain comes online and changes with each phase. So I've got some questions for you that will cover each phase. Does that sound good?

Nicole (15-year LA County narcotics K9 veteran, now head trainer) and Adam (5-year handler, transitioned from interdiction) discuss real operational stories, breed selection, and what it actually takes to work narcotics dogs in one of the busiest K9 programs in the country.What We Cover:Being a female handler in a male-dominated field (Nicole's journey)Adam's unconventional path: interdiction officer to K9 handlerThe reality check: your first operational search warrantsReal case stories: $150K cash hidden in a lunch pail, 10 pounds of meth in tortillasWhy you can't disregard food searches (even when it looks like trash)The breed shift: why LA County moved away from German Shorthair PointersDutch Shepherds vs. Spaniels vs. Malinois for cluttered house searchesThe "praise off" method (training the same way you work operationally)Nicole's trainer philosophy: "You do, but you don't" (why it works)Why new handlers shouldn't get hand-me-down dogsThe worst handlers are the ones with the best dogs (and why)Blending training with real operational deployments (dogs in search warrants day 1)Marker system implementation at LA CountyDecision-making under pressure in volatile environmentsNicole and Adam discuss how their program has evolved, lessons learned from multiple dog breeds, and why the biggest growth comes from handlers who are willing to be uncomfortable and humble.________________________________________

Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Cristopher Moore is a professor at the Santa Fe Institute in New Mexico, and he is a computation and computational complexity expert. He recently joined a us in my complexity discussion group, and answered a bunch of our questions, but I wasn't done with him regarding what, if anything, computational complexity has to do understanding how brains and minds work. So that's why he's here today, and we discuss a wide variety of topics related to AI, computation, computational complexity, and cognition. Cris's Homepage Book: The Nature of Computation Related papers What Is a Macrostate? Subjective Observations and Objective Dynamics 0:00 - Intro 4:24 - The Nature of Computation 9:14 - Computational complexity 28:22 - Real mathematics 35:08 - Current state of AI 39:04 - Computational complexity in the AI world 47:53 - Cognition, creation, problems 56:16 - Rugged landscapes and generalization 1:13:52 - What is computation? 1:32:31 - How would you study the brain?

AI can write code, pass exams, and summarize the web, but ask it to reason through a real-world image, and the magic often breaks. Andrew Dai, co-founder and CEO of Elorian, joins The Neuron to explain why visual reasoning may be one of the biggest unsolved problems in AI.Andrew spent years at Google Brain and DeepMind, including work connected to Gemini and sparse mixture-of-experts systems. Now, he's building Elorian around a simple but powerful idea: if AI is going to understand the physical world, it needs more than text-based reasoning layered on top of images.In this episode, Corey and Grant talk with Andrew about why frontier models struggle with counting, navigation, design, engineering, charts, and physical reasoning; why scaling language models hasn't solved vision; what a “visual chain of thought” might look like; and how better visual reasoning could accelerate robotics, satellite analysis, product design, and mechanical engineering.Sponsored by Dell Technologies and NVIDIA. Learn more at techrepublic.com/hubs/the-enterprise-guide-to-scalable-ai/.Sponsored by Outshift: Visit https://outshift.cisco.com/?utm_campaign=fy26q3_outshift_ww_paid-media_ioc-neuronai-outshift_podcast&utm_channel=podcast&utm_source=podcast to learn more about the Internet of Cognition.Subscribe to The Neuron for more conversations with the people building the future of AI.

Your Brain Uses 20% of Its Creatine Just to Think | Podcast #479  

This content has been developed for healthcare professionals only. Patients who seek health information should consult with their physician or relevant patient advocacy groups.For the full presentation, downloadable Practice Aids, slides, and complete CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE information, and to apply for credit, please visit us at PeerView.com/QRA865. CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE credit will be available until June 15, 2027.Keeping Brain Health Top of Mind: Preserving Cognition and Improving Early Detection and Diagnosis of Alzheimer's Disease In support of improving patient care, this activity has been planned and implemented by PVI, PeerView Institute for Medical Education, and Gerontological Society of America. PVI, PeerView Institute for Medical Education, is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.SupportThis activity is supported by an educational grant from Lilly.Disclosure information is available at the beginning of the video presentation.

This content has been developed for healthcare professionals only. Patients who seek health information should consult with their physician or relevant patient advocacy groups.For the full presentation, downloadable Practice Aids, slides, and complete CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE information, and to apply for credit, please visit us at PeerView.com/QRA865. CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE credit will be available until June 15, 2027.Keeping Brain Health Top of Mind: Preserving Cognition and Improving Early Detection and Diagnosis of Alzheimer's Disease In support of improving patient care, this activity has been planned and implemented by PVI, PeerView Institute for Medical Education, and Gerontological Society of America. PVI, PeerView Institute for Medical Education, is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.SupportThis activity is supported by an educational grant from Lilly.Disclosure information is available at the beginning of the video presentation.

This content has been developed for healthcare professionals only. Patients who seek health information should consult with their physician or relevant patient advocacy groups.For the full presentation, downloadable Practice Aids, slides, and complete CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE information, and to apply for credit, please visit us at PeerView.com/QRA865. CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE credit will be available until June 15, 2027.Keeping Brain Health Top of Mind: Preserving Cognition and Improving Early Detection and Diagnosis of Alzheimer's Disease In support of improving patient care, this activity has been planned and implemented by PVI, PeerView Institute for Medical Education, and Gerontological Society of America. PVI, PeerView Institute for Medical Education, is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.SupportThis activity is supported by an educational grant from Lilly.Disclosure information is available at the beginning of the video presentation.

This content has been developed for healthcare professionals only. Patients who seek health information should consult with their physician or relevant patient advocacy groups.For the full presentation, downloadable Practice Aids, slides, and complete CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE information, and to apply for credit, please visit us at PeerView.com/QRA865. CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE credit will be available until June 15, 2027.Keeping Brain Health Top of Mind: Preserving Cognition and Improving Early Detection and Diagnosis of Alzheimer's Disease In support of improving patient care, this activity has been planned and implemented by PVI, PeerView Institute for Medical Education, and Gerontological Society of America. PVI, PeerView Institute for Medical Education, is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.SupportThis activity is supported by an educational grant from Lilly.Disclosure information is available at the beginning of the video presentation.

This content has been developed for healthcare professionals only. Patients who seek health information should consult with their physician or relevant patient advocacy groups.For the full presentation, downloadable Practice Aids, slides, and complete CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE information, and to apply for credit, please visit us at PeerView.com/QRA865. CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE credit will be available until June 15, 2027.Keeping Brain Health Top of Mind: Preserving Cognition and Improving Early Detection and Diagnosis of Alzheimer's Disease In support of improving patient care, this activity has been planned and implemented by PVI, PeerView Institute for Medical Education, and Gerontological Society of America. PVI, PeerView Institute for Medical Education, is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.SupportThis activity is supported by an educational grant from Lilly.Disclosure information is available at the beginning of the video presentation.

This content has been developed for healthcare professionals only. Patients who seek health information should consult with their physician or relevant patient advocacy groups.For the full presentation, downloadable Practice Aids, slides, and complete CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE information, and to apply for credit, please visit us at PeerView.com/QRA865. CME/MOC/NCPD/AAPA/ASWB-ACE/CDR/APA/IPCE credit will be available until June 15, 2027.Keeping Brain Health Top of Mind: Preserving Cognition and Improving Early Detection and Diagnosis of Alzheimer's Disease In support of improving patient care, this activity has been planned and implemented by PVI, PeerView Institute for Medical Education, and Gerontological Society of America. PVI, PeerView Institute for Medical Education, is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.SupportThis activity is supported by an educational grant from Lilly.Disclosure information is available at the beginning of the video presentation.

Episode 399 reviews Phase 2 of Season 15 and introduces the Motivation Loop — the sequence of meaning, belief, attention, action, reward, and recovery that drives sustained effort. The episode explains common loop breakers (loss of meaning, negative thoughts, distracted attention, too much challenge, poor recovery, and no visible progress) and how to diagnose which link is failing. Practical takeaway: identify your gap, reconnect purpose, protect attention, celebrate small wins, and balance challenge with recovery to keep motivation alive. In This Episode 399, We Will Cover: ✅ The Motivation Loop — what it is, why it matters, and how it influences behavior, focus, effort, and achievement. ✅ What Keeps the Loop Alive — the role of meaning, belief, attention, action, reward, recovery, and growth. ✅ What Breaks the Loop — how loss of meaning, negative thoughts, distraction, lack of progress, poor recovery, and burnout weaken motivation. ✅ The Neuroscience of Motivation — why the brain repeats what it rewards and how dopamine reinforces behavior. ✅ The Difference Between Challenge and Burnout — finding the sweet spot where effort creates growth instead of exhaustion. ✅ My Personal Motivation Loop Story — how I watched my own loop begin to break in real time while pushing too hard with hiking and what I learned from it. ✅ How to Repair a Broken Loop — practical strategies to restore motivation before burnout takes hold. ✅ The Anterior Mid-Cingulate Cortex (AMCC) — the brain region associated with persistence, self-regulation, resilience, and doing hard things. ✅ Why Doing Hard Things Grows the Brain — how meaningful challenges strengthen the neural circuits responsible for sustained effort. ✅ Finding Your Gap — using our Brain's Operating System framework to identify where your system may be out of alignment. ✅ The Biggest Lessons from Phase 2: Neurochemistry & Motivation — insights from Bob Proctor, Dr. Caroline Leaf, Dr. John Medina, Dr. Anna Lembke, Dr. Chuck Hillman, and Friederike Fabritius. ✅ What's Next — a preview of Episodes 400 and 401 on Leadership and Trust, and our transition into Phase 3: Movement, Learning & Cognition. Key Question of the Episode "When motivation begins to disappear, have we lost our drive—or is there simply a broken link in the loop?" Aha Moment The goal isn't to push harder. The goal is to identify the broken link, repair it, and keep the loop alive. EP 399: The Motivation Loop: What Keeps It Going—and What Breaks It? Welcome back to the Neuroscience Meets Social and Emotional Learning Podcast. This week, we're wrapping up Phase 2: Neurochemistry and Motivation. Over the past several months, we've explored some of the most important drivers of human behavior, attention, effort, learning, and performance. Through the work of Bob Proctor, Dr. Caroline Leaf, John Medina, Dr. Anna Lembke, Chuck Hillman, and Friederike Fabritius, we've been focused on one fundamental question: What drives sustained effort and forward movement? Today, I want to zoom out and connect everything we've learned into one simple framework: The Motivation Loop. More importantly, we'll look at: What keeps the loop going What causes it to break How we can strengthen it over time And why doing hard things may actually help grow parts of our brain responsible for persistence and self-regulation. The Brain's Operating System of Human Performance Before we dive into the Motivation Loop, let's remember what we've covered so far. One of the biggest insights from neuroscience is that high performance doesn't happen in one part of the brain. It happens through a sequence. Just like a computer has an operating system, our brains have an operating system for learning, achievement, and human performance. Over the past several months, we've been building that system one phase at a time. Phase 1: Regulation & Safety REGULATE The first question we asked was: "Is the nervous system safe enough to learn?" Before motivation... Before focus... Before performance... The brain must first feel regulated. Through guests like Bruce Perry, Kristen Holmes, Antonio Zadra, and Sui Wong, we learned that: Sleep matters Recovery matters Rhythm matters Our Stress levels matter A dysregulated brain struggles to learn. No regulation. No learning. Phase 2: Neurochemistry & Motivation ENGAGE Once the brain is regulated, we move to the next question: "What drives behavior, focus, and sustained effort?" This is the phase we've just completed. We explored: Dopamine Belief Thought patterns Attention Reward Burnout Energy And perhaps the biggest lesson from this phase was: The brain repeats what it rewards. This became the foundation of what I've called: The Motivation Loop: What Keeps the Loop Going? Looking at this graphic, notice the green side first. The healthy loop begins with: Meaning and Purpose When we know why something matters, effort becomes easier to sustain. This was Bob Proctor's message and the message that launched author Simon Sinek's entire career (Knowing Your Why). People can tolerate enormous challenges when the goal is meaningful. Example: Learning a New Skill Imagine someone deciding to learn a new language. At first: Progress is slow. Mistakes are frequent. The work feels uncomfortable. But they have a purpose. Maybe they want to connect on a deeper level with family. Maybe they want to travel. Maybe they want a new career opportunity. Purpose keeps them engaged long enough to continue with the hard work.   Belief Shapes Thought If I believe I can improve, my thoughts become more constructive. This was Dr. Caroline Leaf's work. Our thoughts influence our neurochemistry. Positive thoughts don't guarantee success. But they keep us moving toward it. Attention Drives Growth This was John Medina's contribution. Attention determines what the brain decides matters. The brain learns what we repeatedly focus on. What we attend to, we strengthen. Action Creates Progress Once attention is focused, behavior follows. We study. We practice. We train. We learn. Reward Reinforces Behavior This was Dr. Anna Lembke's work. The reward doesn't have to be huge. Sometimes it's simply noticing progress. The brain says: "That effort produced a result." And the loop continues. Example: Exercise A person begins walking 20 minutes every day. Week 1: No major changes. Week 2: Energy improves. Week 3: Sleep improves. Week 4: Resting heart rate begins dropping. The brain notices progress. The effort feels worthwhile. The loop strengthens. The behavior repeats. We have spent a lot of time on understanding how to keep the loop from breaking. How the Loop Breaks Now let's look at the red side. How the loop breaks. The loop rarely breaks all at once. Usually one link weakens first. Then the others follow. Loop Breaker #1: Loss of Meaning What Happened? A student studies only to pass a test. The test ends. The reason disappears. Motivation disappears. The loop breaks because there is no longer a compelling "why." What Could Have Prevented It? Reconnect to purpose. Instead of: "I have to study for this test." Shift to: "I'm building skills for the future version of myself." Bob Proctor taught us that goals are not just about achievement. They're about growth. Loop Repair Ask: "Why does this matter beyond today?" When meaning returns, motivation returns.   Loop Breaker #2: Negative Thought Patterns What Happened? Someone starts a health journey. After a difficult week they think: "I'm failing." "Nothing is changing." "I'll never get there." Their attention shifts toward evidence of failure. The loop weakens. What Could Have Prevented It? Focus on progress instead of perfection. Dr. Caroline Leaf would remind us that thoughts influence neurochemistry. A better question might be: "What is improving that I haven't noticed yet?" Loop Repair Look for small wins. Better sleep More energy More consistency Better habits Progress fuels dopamine. Dopamine fuels effort.   Loop Breaker #3: Distracted Attention What Happened? You sit down to work. A text arrives. Then email. Then social media. Then another interruption at your office door. Attention becomes fragmented. Learning slows. Progress slows. Reward disappears. What Could Have Prevented It? Protect your attention. John Medina taught us: Attention determines what the brain decides matters. Loop Repair Create: 30-minute focus blocks Phone-free work periods (with notifications turned off) One-task-at-a-time sessions The brain rewards completion. Not multitasking.   Loop Breaker #4: Too Much Challenge What Happened? This one surprises many people. Doing hard things strengthens the brain. But doing impossible things breaks the loop. A person starts: A new diet A new exercise plan A new business A new habit And tries to change everything at once. The challenge becomes overwhelming. What Could Have Prevented It? Start smaller. The AMCC grows when challenges are difficult but achievable. Loop Repair Ask: "What's the smallest difficult thing I can consistently repeat?" Not: "What's the hardest thing I can do today?"   Loop Breaker #5: Poor Recovery/Low Energy   What Happened? This is actually my hiking example that I've mentioned previously. Everything was working. My recovery improved. My WHOOP age improved 6.4 years younger than my actual age. My fitness improved- v02 max increased. Then I increased the challenge. Longer hikes. More strain. More effort. But not enough recovery time in between. I could actually see the reward disappearing in real time. The effort at the end of these longer hikes felt exhausting instead of energizing. I know that doing difficult things makes my brain stronger, but I was close to giving up on something I really enjoyed. What Could Have Prevented It? Recovery needed to increase alongside challenge. The mistake wasn't hiking, or making the hike more challenging. The mistake was believing: More is always better. Loop Repair Alternate: Hard days Easy days Increase recovery as strain increases. As Friederike Fabritius taught us: Performance isn't built through effort alone. It's built through effort and recovery. Once I put more attention on recovery before pushing again, the broken motivation loop repaired, and the end of those difficult hikes became energizing again (with the right amount of rest).   Loop Breaker #6: No Visible Progress What Happened? A salesperson makes: 50 calls 100 calls 150 calls No results. The brain begins asking: "Why bother?" The reward disappears. What Could Have Prevented It? Measure leading indicators instead of outcomes. Instead of focusing only on sales: Track: Calls completed Meetings booked Relationships built Skills improved Loop Repair Celebrate effort metrics. Not just outcome metrics. The brain needs evidence that effort matters. Also, if the strategy you are using is not yielding results, try a different one. Ask others who are having success, what they are doing, and how they are getting results. Once you can identify where your loop is breaking, fixing it requires doing something that you were not doing before.   The Big Lesson Every loop break in this phase points back to one question: What link failed? Was it: Meaning? Thoughts? Attention? Progress? Recovery? Challenge? Because the loop rarely breaks all at once. Usually one link weakens first. And the good news is: If you can identify the broken link, you can repair the loop. What About Doing Hard Things? One of the most fascinating concepts we explored this phase was the work surrounding the: Anterior Mid-Cingulate Cortex (AMCC) This area of the brain appears to play an important role in: Persistence Self-regulation Attention control Doing things we don't feel like doing Research suggests this area strengthens when we repeatedly choose meaningful challenges. Not impossible challenges. Not burnout. Not exhaustion. Meaningful challenges. Example Choosing: The workout you don't feel like doing. The difficult conversation you've been avoiding. The presentation that makes you nervous. The study session when you'd rather scroll your phone. Every time we choose effort over comfort, we may be strengthening the neural systems responsible for persistence and researchers also would say, the will to live. The Secret to Keeping the Loop Going After everything we've learned this phase, the answer is surprisingly simple: The loop stays alive when effort feels worthwhile. That means: ✅ Meaning ✅ Purpose ✅ Focus ✅ Progress ✅ Recovery ✅ Challenge But not too much challenge. Because challenge without recovery becomes burnout. And recovery without challenge becomes stagnation. The sweet spot lies in the middle. Instead of blaming ourselves, we can start diagnosing the system to build a stronger, more resilient version of ourselves. How to Use the "Find Your Gap" Framework Whenever you feel: Stuck Unmotivated Burned out Distracted Overwhelmed Plateaued Ask yourself: Which phase is broken? Because the problem is rarely "everything." Usually it's one phase creating a bottleneck for the others.   Phase 1 Gap: Regulation & Safety Ask: Am I sleeping well? Am I recovered? Is stress overwhelming me? Is my nervous system regulated? Signs This Is Your Gap Anxiety Exhaustion Brain fog Poor sleep Irritability Example A teacher can't focus. They assume they need more motivation. But they're sleeping 5 hours a night. The real gap isn't motivation. It's regulation. Solution Fix: Sleep Recovery Stress management First.   Phase 2 Gap: Neurochemistry & Motivation Ask: Do I still know why this matters? Am I seeing progress? Has the reward disappeared? Have I lost momentum? Signs This Is Your Gap Procrastination Lack of drive Loss of enthusiasm Feeling stuck Example This was your hiking example. You still had the ability. You still had the discipline. You simply stopped feeling rewarded by the effort. Solution Repair the Motivation Loop: Reconnect to purpose Reduce challenge temporarily Improve recovery Look for progress   Phase 3 Gap: Movement, Learning & Cognition Ask: Am I moving enough? Am I physically engaged? Am I learning new things? Is my brain being challenged? Signs This Is Your Gap Low energy Mental sluggishness Poor concentration Feeling mentally flat Example Someone spends 10 hours at a desk. Their motivation is fine. Their sleep is fine. But they're sedentary. Movement is the missing ingredient. Solution Move first. The research from Chuck Hillman and John Ratey suggests movement often improves: Attention Mood Learning Memory   Phase 4 Gap: Perception, Emotion & Social Intelligence Ask: Am I seeing this situation clearly? Am I understanding others? Do I feel connected? Signs This Is Your Gap Conflict Miscommunication Isolation Emotional reactivity Example A leader thinks: "Nobody supports my vision." But the real issue is communication. The gap isn't motivation. It's perception. Solution Improve: Listening Emotional awareness Perspective-taking Relationships   Phase 5 Gap: Integration, Insight & Meaning Ask: Does this align with who I want to become? Am I moving toward something meaningful? Do I have clarity? Signs This Is Your Gap Success without fulfillment Feeling lost Lack of direction Constantly chasing goals Example Someone has achieved everything they wanted professionally. But they still feel empty. The gap isn't performance. It's meaning. Solution Reconnect with: Values Purpose Identity Contribution to the World. The Most Powerful Question At the end of every week, ask: "Where is my gap?" Is it:

Teepa Snow, MS, OTR/L, FAOTA, explores how cognitive impairment and dementia can show up in clinical work. She offers practical guidance for adapting communication, supporting caregivers, preserving dignity, and expanding care when clients need more support. Interview with Elizabeth Irias, LMFT. Earn CE credit for listening to this episode by joining our low-cost membership for unlimited podcast CE credits for an entire year, with some of the strongest CE approvals in the country (APA, NBCC, ASWB, and more). Learn, grow, and shine with Clearly Clinical Continuing Ed by visiting https://ClearlyClinical.com. Hosted on Acast. See acast.com/privacy for more information.

Episode 398 revisits neuroscientist Friederike Fabritius (from November 2022) to explain how three ingredients — fun (dopamine), fear (productive challenge), and focus — create the neurochemical conditions for sustained motivation and flow. You'll also learn why individual neurosignatures matter and how designing environments that match your brain, rather than forcing yourself to change, makes effort easier and motivation durable. Welcome back to Season 15 of the Neuroscience Meets Social and Emotional Learning Podcast. I'm Andrea Samadi, and on this podcast, we bridge the science behind social and emotional learning, emotional intelligence, and practical neuroscience so we can create measurable improvements in well-being, achievement, productivity, and results. In This Episode 398, Closing the Motivation Loop, with Friederike Fabritius, We Will Cover: ✔ How FUN, FEAR, and FOCUS create the neurochemical conditions for sustainable motivation ✔ Why dopamine is more than a pleasure chemical—and how it fuels motivation, anticipation, effort, and reinforcement ✔ How FUN creates dopamine and keeps us engaged in meaningful work ✔ Why the right amount of FEAR (challenge) drives growth without causing burnout ✔ How FOCUS converts energy, attention, and motivation into measurable results ✔ The connection between FUN, FEAR, FOCUS, and the Motivation Loop ✔ Why different brains require different motivation strategies ✔ Understanding your unique "Neurosignature" and how it influences performance ✔ How dopamine interacts with other neurochemicals like testosterone, estrogen, serotonin, and oxytocin ✔ Why sustainable motivation begins with self-awareness ✔ The Stress vs. Performance Curve and finding your optimal challenge zone ✔ How under-challenge leads to boredom and over-challenge leads to burnout ✔ Why peak performance occurs when challenge matches your brain's needs ✔ How to design environments that support attention, motivation, and performance ✔ Why the strongest motivation loops are powered by alignment—not willpower ✔ Practical strategies to create the conditions where your brain naturally wants to engage and perform ✔ How self-awareness, energy management, and neurochemistry work together to sustain long-term success ✔ What keeps the Motivation Loop repeating—and what causes it to break ✔ How to close Phase 2: Neurochemistry & Motivation and prepare for Phase 3: Movement, Learning & Cognition

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https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

Our 247th episode with a summary and discussion of last week's big AI news!Recorded on 06/03/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Anthropic released Claude Opus 4.8 with improved benchmark scores, discussed eval-awareness findings and welfare/corrigibility themes from its system card, and introduced Dynamic Workflows for long-running multi-agent tasks.Microsoft unveiled the always-on Microsoft Scout assistant built on OpenClaw plus new in-house MAI models (including MAI Thinking 1) and “frontier tuning,” emphasizing enterprise security architecture and model-from-scratch capability.Major business moves included Anthropic's $65B Series H at a $965B valuation alongside an IPO filing, a JPMorgan analysis arguing OpenAI needs major revenue growth to justify infrastructure spend, and Cognition raising $1B at a $25B valuation.Policy and security highlights covered Trump's voluntary pre-release government testing framework for powerful AI, Meta AI support being exploited to hijack Instagram accounts, tightened US Nvidia export controls and China's travel approvals for AI experts, plus expanded Glasswing/Mythos-style cyber and biodefense initiatives.Timestamps:(00:00:10) Intro / Banter(00:04:10) Sponsors(00:07:10) News PreviewTools & Apps(00:07:54) Anthropic releases Opus 4.8 with new 'dynamic workflow' tool | TechCrunch(00:22:37) Microsoft Scout is a new AI personal assistant built on OpenClaw | The Verge(00:26:55) Microsoft launches new MAI family of AI models at Microsoft Build | Mashable(00:37:43) Robinhood now lets your AI agents trade stocks | TechCrunch(00:40:49) OpenAI launches new Codex tools for white-collar work | TechCrunch(00:43:40) ElevenLabs' new music-generation model can switch genres mid-track | TechCrunchApplications & Business(00:44:35) Anthropic Hits $965 Billion Valuation, Surpassing OpenAI - WSJ(00:45:32) Anthropic Files to Go Public, Setting Stage for Huge I.P.O. - The New York Times(00:51:15) China's ByteDance Developing New AI Chips Like Those from Nvidia Partner Groq(00:55:00) Anthropic expands Mythos to 150 additional organizations(00:55:35) OpenAI needs a 26x revenue increase to justify its buildout(00:58:46) AI coding startup Cognition raises $1B at $25B pre-money valuation | TechCrunchProjects & Open Source(01:00:50) MiniMax-M3 debuts, eclipsing GPT-5.5 and Gemini 3.1 Pro on key benchmark performance for just 5-10% of the cost | VentureBeatPolicy & Safety(01:06:08) Trump Signs Executive Order Seeking Oversight of A.I. Models - The New York Times(01:11:45) Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked(01:13:058) Chinese AI experts in private firms now required to secure approval before international travel — Beijing enforces policy to secure top-tier talent, expands measures beyond government(01:17:53) U.S. Tightens Controls on Nvidia AI Chip Exports | Let's Data Science(01:21:47) OpenAI launches Rosalind Biodefense, offers federal agencies early access to its life-sciences model(01:24:00) Using LLMs to secure source code(01:26:19) Project Glasswing: An initial update(01:29:30) White House Approves $9 Billion for Spy Agencies to Catch Up on A.I.(01:32:11) US Law Enforcement Warns of ‘Anti-Tech Extremism' as AI Hatred GrowsSynthetic Media & Art(01:35:38) YouTube will now automatically label AI videos | TechCrunchResearch & Advancements(01:36:22) Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention(01:41:26) From Simulation to Enaction: Post-trained language models recognize and react to their own generationsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AGENDA: 00:00:00 — Private Markets Are "F***ing Done" & The Shift to Heavy CapEx 00:00:46 — Anthropic Files to Go Public  00:04:59 — Will the Anthropic IPO Break the Startup Ecosystem? 00:06:22 — The "Billion-Dollar Position" Era: VCs Reset Their Expectations 00:18:11 — The Trillion-Dollar Cash Grab: Google, SpaceX, and OpenAI Rush the Queue 00:23:15 — Is the SaaS Apocalypse Over? Bouncing Off the Bottom 00:25:34 — The Death of Human Per-Seat Licenses as Multiples Shift 00:27:18 — Winners vs. Losers: How Agentic Focused Products Captured the Market 00:30:26 — Cognition Raises $1 Billion at a $26 Billion Valuation 00:33:04 — Token Budgeting Panic Hits Corporate America 00:35:46 — Multi-Model Workflows and the Future of Cost Containment 00:41:20 — Choosing Tokens Over Humans: The 2027 Engineering Reality Check 00:46:42 — Can Large Companies Survive Slashing One-Third of Their Engineering Talent? 00:57:40 — Big Law Flex: Kirkland & Ellis Pledges $500 Million to Build In-House AI 01:01:21 — Giving Away the Crown Jewels: Will Firms Trust Claude? 01:08:44 — Robinhood's AI Move: Automating Financial Planning vs. Beating the Market 01:16:15 — Apollo Warns PE Software Returns Are About to Be Disastrous 01:19:15 — $10 Billion Carry Pools: Will VC Winners Quit the Game? 01:24:10 — The 9-9-6 Work Ethic: Performative Theatre or Startup Reality? 01:30:10 — The Great Valley Contradiction: Working 24/7 to Automate White-Collar Work  

Jeremy Bedingfield (Southern California narcotics interdiction officer, Cartel Traps founder) shares the real methods for identifying and stopping drug loads on highways. Managing a GSP's competing instincts, reading suspects through interviews, finding hidden compartments, and the legal future of K9 detection.What We Cover:Why GSPs are harder to work in narcotics (genetically wired for bushes, not drugs)Building reasonable suspicion: the interview technique that reveals liesVehicle targeting: what smugglers' cars have in commonThe two-direction search pattern (why it matters)Systematic vehicle search: start underneath, work inward void by voidReal training vs. parking lot training: why they're differentDealing with 20+ kilo loads (changes dog expectations)Fentanyl reality: mixed loads, quick imprinting, prevalence on highwaysBody cam footage: what handlers miss in real timeThe future: AI harness technology (5-10 years away)Jeremy breaks down tradecraft that's rarely discussed publicly—from target selection to compartment location to creative training solutions. He also discusses why the legal system is moving toward objective K9 data (harness technology with biological algorithms) rather than handler interpretation.For: Drug dog handlers, narcotics officers, interdiction teams, law enforcement exploring K9 evidence in court.________________________________________

Today we're talking about research-based, proactive steps you and I can take now to (hopefully) avoid developing Alzheimer's dementia later. I hope you'll listen in and be encouraged! Show Notes VERSES CITED: John 3:30 - “He must increase; I must decrease.” Thessalonians 5:17 - “Pray without ceasing.” Colossians 4:2  - “Devote yourselves to prayer, being watchful and thankful.” Ephesians 6:18  - “Pray in the Spirit on all occasions with all kinds of prayers and requests.....” Philippians 4:6-7 - “Be anxious for nothing, but in everything by prayer and supplication....” Mark 12:30 – “And thou shalt love the Lord thy God with all thy heart, and... soul, and... mind, and...strength.” 1 Corinthians 10:31- “Whether, then, you eat or drink or whatever you do, do all to the glory of God.” John 3:30 – “He must increase, but I must decrease.” RELATED LINKS: Your Parent Has Dementia. Here's the $405,000 Survival Guide Nobody Gave You Gut: The Inside Story of Our Body's Most Underrated Organ Blueberries, the well-known 'super fruit,' could help fight Alzheimer's Study of green tea and other molecules uncovers new therapeutic strategy for Alzheimer's Preventive Effects of Olive Oil on Alzheimer's Disease: What to Know Eating Avocados: Does It Help Prevent Dementia The effect of curcumin (turmeric) on Alzheimer's disease: An overview Beneficial Effects of Walnuts on Cognition and Brain Health Association of Egg Intake with Alzheimer's Dementia Risk in Older Adults Rainbow Salad Recipe Comparison of types of diabetes The Hidden Threat: How Refined Grains and Sugar Impact Dementia When Diet Meets Dementia Intermittent Fasting as a Neuroprotective Strategy: Gut–Brain Axis Modulation and Metabolic Reprogramming in Neurodegenerative Disorders Research reveals: smart wives reduce the risk of Alzheimer's disease Marital status and risk of dementia over 18 years: Surprising findings from the National Alzheimer's Coordinating Center Marriage linked to reduced Dementia Risk Inside the Box Free Printable Prayer Guides Pimsleur Language Program Why "Grandma Hobbies" Could Be the Secret to Better Mental Health Is Sunshine Key to Reducing Dementia Risk Influence of physical activity on cognition and brain function Association Between Mentally Stimulating Activities in Late Life and the Outcome of Incident Mild Cognitive Impairment Three science backed lifestyle changes to lower your dementia risk Reading writer lower dementia risk study finds Reading Challenge Bible Memory Tips and Tricks A Grand Investment Can prayer reduce the risk of Alzheimer's? Prayer regularly reduces risk of dementia STAY CONNECTED:  Subscribe: Flanders Family Freebies -weekly themed link lists of free resources Instagram: @flanders_family - follow for more great content Family Blog: Flanders Family Home Life - parenting tips, homeschool help, printables Marriage Blog: Loving Life at Home- encouragement for wives, mothers, believers My Books: Shop Online - find on Amazon, at Barnes & Noble, or through our website    

At Cisco Live, I sat down with Papi Menon, Vice President of Product Management at Outshift by Cisco, to explore one of the most ambitious ideas emerging in the AI world today. While much of the industry remains focused on larger models and individual AI agents, Outshift is asking a different question. What happens when millions of AI agents need to collaborate across organizations, platforms, and industries? Papi joined me to explain the thinking behind Outshift, Cisco's emerging technology and incubation group, and the work they're doing to help shape the next era of AI. Our conversation explored concepts such as the Internet of Agents, the Internet of Cognition, and AGNTCY, an open-source initiative designed to create the foundations for agent-to-agent collaboration at scale. We discuss why connecting AI agents is only the first step, why shared intent and shared context could become as important as connectivity itself, and how organizations may need entirely new infrastructure to support an increasingly agent-driven future. Papi also shares his perspective on the challenges of interoperability, governance, trust, and security as AI systems become more autonomous and interconnected. The discussion moves beyond today's AI headlines and into the bigger questions facing the technology industry. If the internet connected people and systems, what infrastructure will be needed to connect intelligence itself? And what role can open standards play in ensuring that future remains collaborative rather than fragmented? Whether you're a technology leader, developer, strategist, or simply curious about where AI is heading next, this conversation offers a fascinating glimpse into how Cisco is thinking about the future of agentic computing and the foundations that may underpin the next major platform shift in technology. How do you think AI agents will collaborate in the future, and should that future be built on open standards or closed ecosystems?

In this episode, I share my Human Design story and why I continue to return to the same foundational teachings year after year. These five elements, Determination, Strategy, Authority, Environment, and Cognition, completely changed the way I move through life.Inside this conversation, we explore:• My personal Human Design journey• Why the foundations matter more than advanced information• The five elements I continually return to• How these teachings impact decision-making, relationships, business, health, and alignment• Why embodiment will always outperform informationBecause the goal isn't to know your Human Design.The goal is to live it.RESOURCES:CLICK HERE for the Big 3 in Human Design EpisodeCLICK HERE to order your Alchemy of You manualCLICK HERE to learn about the Find Your WAI membershipCLICK HERE to DM me PHOENIX to learn about my 5-month initiation for the woman standing at the threshold of her next chapter, knowing the current version of herself cannot carry her where her soul is asking her to go next.CLICK HERE to DM me to learn more about the Gauntlet, my 3-6-month private mentorship container, or the Identity Reset Retreat to learn about coming to Austin for a 5-day, 4-night private initiation with me.Support the show✨ Thank you for listening! Check out the links below to connect with me!

- The Guest: Holistic Healer/ Coach MichelleHammel— Follow/ Contact: IG @unmasquing Website: www.gofauxhawkyourself.com—From surviving to self‑rebuilding — this is the episode that will change how you see your past, your patterns, and your power.-Holistic Trauma‑Informed Coach Michelle Hammel joins Marcos Luis for a raw, soul‑shifting conversation about her journey through childhood trauma, CPTSD, and emotional reconstruction — and how she transformed her pain into the CRAFT Method, a groundbreaking approach to healing that's helping people worldwide reclaim their lives.—Michelle breaks down the real work of recovery: calming the nervous system, unlearning survival mode, rebuilding identity, and finally feeling safe in your own body again.This isn't “good vibes only” healing — it's honest, practical, compassionate, and deeply human.—If you've ever felt stuck, unseen, overwhelmed, or ready for a new chapter… this episode is your turning point.—

Season 15, Episode 397 revisits research and real-world practice showing movement is more than fitness: it activates the brain, boosts attention, enhances learning, and sustains motivation. Dr. Chuck Hillman's studies reveal how even short bouts of exercise light up brain activity, while Paul Zientarski's Naperville program demonstrates how heart-rate monitoring and purposeful movement improve readiness, recovery, and academic performance. In EP 397: Movement, Motivation, and Brain Activation with Dr. Chuck Hillman and Paul Zientarski, we explore why movement may be one of the most powerful tools we have for improving brain function, learning, motivation, and performance. In this episode, we cover: ✅ Why most children are not meeting the recommended daily physical activity guidelines and what we can do to change that. ✅ How exposing children to a variety of activities helps them discover movement they enjoy—and are more likely to continue throughout their lives. ✅ Why there is no perfect exercise program, and why the best exercise is the one you'll consistently do. ✅ How enjoyment, reward, and dopamine reinforce healthy habits and keep the Motivation Loop repeating. ✅ What Naperville Central High School learned from heart rate monitoring and how recovery impacts performance. ✅ Why peak performance requires both effort and recovery. ✅ How exercise changes the brain, improving attention, learning, memory, and cognitive performance. ✅ The groundbreaking research behind Spark: The Revolutionary New Science of Exercise and the Brain and how it changed the way educators think about learning. ✅ Why movement is not a break from learning—but one of the most effective ways to prepare the brain for learning. ✅ How movement fits into our Phase 2 Motivation Loop, helping transform motivation into action and sustaining long-term performance. The biggest takeaway? Movement isn't just exercise. It's activation. It's preparation. It's performance. When we move our bodies, we activate the brain systems responsible for attention, learning, motivation, and success. The episode highlights practical takeaways: expose children to varied enjoyable activities, prioritize consistency over intensity, use movement as cognitive preparation, and track recovery to protect motivation. Movement becomes a bridge between motivation and sustained performance—improving focus today and long-term brain health tomorrow. Welcome back to Season 15 of the Neuroscience Meets Social and Emotional Learning Podcast. I'm Andrea Samadi, and on this podcast, we bridge the science behind social and emotional learning, emotional intelligence, and practical neuroscience so we can create measurable improvements in well-being, achievement, productivity, and results. Movement, Motivation, and Brain Activation with Dr. Chuck Hillman and Paul Zientarski This week, we continue our journey through Phase 2: Neurochemistry and Motivation, where we've been exploring one central question: What drives sustained effort and forward movement? So far, we've learned that motivation begins with belief and meaning from Bob Proctor[i], is shaped by our thought patterns with Dr. Caroline Leaf,[ii] strengthened through attention and reward with Dr. John Medina[iii], and powered by the brain's dopamine-based motivation system through Dr. Anna Lembke's[iv] work. But today, we arrive at a fascinating question: What happens when we actually move? Because motivation isn't just something that happens in the mind. The brain was designed to work in partnership with the body. And according to our review of today's two guests, one of the most powerful ways to activate attention, learning, memory, and motivation is through movement itself. This week we're revisiting insights from two pioneers whose work helped transform our understanding of movement and learning. First, Dr. Chuck Hillman, one of the world's leading researchers on exercise and brain function, whose groundbreaking research has shown how physical activity improves attention, executive function, learning, memory, and academic performance from EP 123[v] back in April 2021. Next, we will review Paul Zientarski, the former Physical Education Coordinator and football coach at Naperville Central High School, (In Illinois) whose work with the school's innovative Zero Hour PE Program helped put Naperville on the map for extraordinary academic achievement. Alongside his colleagues at Naperville, Paul demonstrated that exercise wasn't simply improving fitness—it was preparing students' brains to learn. Together, Dr. Hillman provides the science, while Paul Zientarski helps to demonstrate what that science looks like in the real world. Their combined work shows us that movement is far more than a physical activity. It is a powerful tool for activating the brain, enhancing learning, improving focus, and supporting the motivation needed for sustained performance. In other words, movement is the bridge between motivation and sustaining our performance. Let's dive in with Dr. Chuck Hillman and discover the science behind The Power of Movement and Brain Activation. CLIP 1: Getting Kids Moving for Life Summary In this clip, Dr. Chuck Hillman highlights a growing concern: the vast majority of children are not meeting the recommended physical activity guidelines. Current recommendations suggest that children should engage in at least 60 minutes of moderate-to-vigorous physical activity each day, including aerobic exercise and activities that strengthen bones and muscles. Dr. Hillman explains that the challenge isn't simply knowing the guidelines—it's finding ways to engage children in movement when many adults aren't meeting the recommendations themselves. This is why childhood is such an important time to expose young people to a wide variety of physical activities, helping them discover forms of movement they enjoy and can continue throughout their lives. Key Takeaways ✔ Most children are not getting enough physical activity. Many young people fall short of the recommended 60 minutes of daily movement needed for optimal physical and cognitive development. ✔ Movement supports both brain and body health. Exercise is not just about fitness—it supports attention, learning, memory, emotional regulation, and overall well-being. ✔ Children need exposure to different activities. Not every child will enjoy the same sport or activity. The goal is to help them discover movement they genuinely enjoy. ✔ Parents and adults model behavior. Children are more likely to be active when the adults around them value and participate in physical activity. ✔ Early habits can last a lifetime. The activities children enjoy today often become the healthy habits they carry into adulthood. Tips to Implement Expose Children to Variety

Claude Opus 4.8 arrives as a modest but meaningful upgrade, with early users pointing to better judgment, less bluffing, stronger self-checking, and a greater willingness to push back. NLW breaks down first impressions, benchmark comparisons with GPT-5.5, Claude Code's new dynamic workflows, and why the model harness may matter as much as the model itself. In the headlines: Kirkland & Ellis bets big on internal AI, OpenAI updates GPT-5.5 Instant, Cognition raises at a $26B valuation, Meta considers AI cloud, and Microsoft prepares new models.Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠kpmg.com/us/Sophisticated⁠⁠Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow Work - Agents for knowledge work - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zenflow.free/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai