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In this episode Andrea Samadi explores how movement and sleep work together in a "brain operating system" for human performance, focusing on restorative sleep (deep + REM), personal WHOOP data, and the trade-offs created by early-morning exercise. She shares four lessons and a simple experiment to help listeners protect REM and deep sleep while maintaining an active life, emphasizing weekly rhythms, small changes, and tracking patterns rather than chasing perfect numbers. SEASON 16 | BONUS EPISODE 4 RESTORATIVE SLEEP Where Adaptation Happens Why deep sleep restores the body, REM helps integrate experience, and healthy habits must work together. ON BONUS EPISODE 4, YOU'LL LEARN: ✔ What restorative sleep really measures ✔ How deep sleep and REM support different forms of recovery ✔ Why REM percentage and duration tell different stories ✔ What my six-month sleep data revealed ✔ The hidden tradeoff behind my 4:00 AM hiking routine ✔ What happened when I slept just 21 minutes longer ✔ Why healthy habits can sometimes compete ✔ How to protect sleep without giving up movement ✔ 4 Lessons and a simple experiment for discovering your own best rhythm Episode Introduction Welcome back to the Neuroscience Meets Social and Emotional Learning Podcast, where we bridge neuroscience, social and emotional learning, and human performance to create measurable improvements in well-being, achievement, leadership, productivity and results. I'm Andrea Samadi, and throughout Season 16 we have been building what I call the Brain's Operating System for Human Performance—a neuroscience-based framework for understanding how the systems of the brain and body work together to influence how we learn, adapt, connect, lead and ultimately perform. We are currently in Phase 3: Movement, Learning and Cognition. Movement is the foundation of this phase because it affects far more than our muscles. When we move, we activate processes throughout the brain and body. Movement supports neurogenesis—the development of new neurons—particularly in areas involved in learning and memory. It strengthens connections between neurons through synaptic plasticity. We covered neurogenesis on EP 141[i] if you want to revisit that episode, if you are as curious as I am about how to regrow our brain cells. Movement that we are learning in this phase, also influences brain chemicals such as dopamine, serotonin, norepinephrine and acetylcholine, which affect motivation, mood, attention and learning. Movement can also strengthen our resilience by helping us manage stress and become more emotionally flexible. It builds sleep pressure, (the longer we stay awake, the more adenosine accumulates that increases our need or “pressure” to rest) which can support deeper, more restorative sleep. And over time, these changes may contribute to greater creativity, insight, endurance and human potential. But movement only creates the stimulus. The benefits don't come from movement alone. They come from the way the brain and body respond to that movement—and that response is called adaptation. Movement changes the brain. Adaptation changes the body. Recovery is what connects the effort we make today with the strength, resilience and capacity we hope to build tomorrow. Without adequate recovery, we can receive the stimulus without fully receiving the benefit. That is why sleep belongs inside the Movement Loop. Sleep is not separate from movement, learning or performance. It is one of the primary places where the brain and body respond to what we have asked them to do. Throughout Season 16, I've been sharing some of my own health and performance data as a living case study—not because everyone should try to reproduce my numbers, but because our individual trends can help us understand how neuroscience shows up in everyday life. I thought that if I was curious about understanding my numbers—and using them to improve my health, well-being and productivity—then other people might benefit from what I've learned along the way. My data has helped me see that movement, recovery and performance cannot be understood in isolation. They work together as a system: Movement creates the stimulus. Recovery creates the conditions for adaptation. Restorative sleep is one of the places where that adaptation unfolds. And this brings us to one of the most important recovery metrics I've learned to follow: Restorative sleep. We often celebrate what happens while we're awake: Our productivity. Our focus. Our learning. Our movement. Our performance. But much of the adaptation supporting those abilities happens while we sleep. Deep sleep supports physical restoration. REM sleep supports emotional processing, memory integration and cognitive flexibility. Together, these stages help convert the demands of one day into greater capacity for the next. This episode began with one honest question about my own routine: THE QUESTION: What am I gaining by waking at 4:00 AM to hike—and what might I be giving up by not sleeping longer? This is not simply an episode about sleeping more. It is about creating a rhythm in which two essential health behaviors—sleep and exercise—support one another instead of competing for the same limited hours. You will see my data exactly as it is. Sleep is one of the weaker links in my current health routine, which makes it a useful place to learn. The goal is not to judge the data. The goal is to let the data ask a better question. Lesson 1: Restorative Sleep Contains Two Stories WHOOP (the wearable device that I use to measure sleep) defines restorative sleep as the combined time spent in deep sleep, also called slow-wave sleep, and REM sleep. Combining the two creates a useful overview, but separating them reveals two different parts of the story. Deep Sleep or Slow-Wave Sleep: Physical Restoration Deep sleep is concentrated more heavily in the earlier part of the night and supports processes associated with physical restoration. It is relevant after hiking, Zone 2 exercise, strength training and other demanding activity because sleep is part of how the body responds to the strain we create. During deep sleep: Tissue repair is supported. Growth hormone release increases. Immune function is supported. Energy is restored. The body adapts to physical strain. REM Sleep: Integration REM stands for rapid eye movement sleep and is known as the “mentally restorative” stage of sleep. This is where most dreams occur, and short-term memories are converted into long-term memories. During REM, the brain is highly active while most voluntary muscles remain temporarily inhibited. I'll never forget the part of our interview with Dr. Jaland Balal[ii], when he explained that this feature is what keeps us safe from moving around too much while we are sleeping, keeping our sleeping partners safe. REM has been associated with: Emotional processing Memory integration Aspects of learning Cognitive flexibility Creative associations Connecting new information with previous experiences Memory is supported across several sleep stages—not by REM alone. REM helps the brain process, connect and integrate. REM periods also tend to become longer as the night progresses. That means shortening the end of the sleep window may disproportionately reduce the opportunity for the longer, more REM-rich cycles that occur toward morning. Deep sleep helps restore the body. REM helps integrate the brain. We need both. That second sentence is personally meaningful because I have logged my dreams for years. A dream is not a diagnosis or a literal solution, and dreams can occur outside REM. But a dream log gives me a qualitative record of recurring emotions, images, relationships, problems and emerging ideas that my mind may still be processing. Since I enjoy learning, interviewing, writing, teaching and connecting ideas across neuroscience and social-emotional learning, REM matters for more than dream recall (which by itself is fascinating and something we will look deeper at in Phase 5 with Integration and Meaning). Lesson 2: My Data Revealed a Duration Gap When I looked at my WHOOP trends, the total restorative-sleep number was only the beginning. Metric My average Six-month restorative sleep 2 hr 43 min Lifetime REM 1 hr 37 min Last 90 days REM 1 hr 18 min Last 30 days REM 1 hr 20 min Recent REM percentage About 20% When I looked at six months of WHOOP data, my average restorative sleep was: 2 hours and 43 minutes per night. That number gave me the total time spent in deep sleep and REM—but it did not tell me how those two stages were distributed. When I separated them, another story emerged. My lifetime REM average was approximately: 1 hour and 37 minutes per night. But over my more recent periods, that changed: Last 90 days: approximately 1 hour and 18 minutes Last 30 days: approximately 1 hour and 20 minutes Recent REM percentage: approximately 20% of my total sleep Twenty percent is within a commonly reported adult range. But compared with my own lifetime average, my recent REM duration was approximately 17–19 minutes lower per night. That distinction matters. My REM percentage may appear healthy while my total REM duration is still lower than my personal historical average. So my real question became: Not “Is my REM normal?” but “What conditions help my brain produce more of its own normal REM?” This is the value of tracking. THE DATA LESSON: A population range provides context. Your baseline provides the story. Your trend gives you a question to test. Lesson 3: The 4:00 AM Tradeoff I love hiking early. It gives me cardiovascular conditioning, time outside, morning light, mental clarity and consistency. In Arizona, it also helps me finish before extreme heat heats around 8am. But if I go to bed at 8:30 PM and wake at 4:00 AM, I create a maximum sleep opportunity of seven and a half hours. That is time in bed—not necessarily time asleep. It does not include the time required to fall asleep or periods of wakefulness during the night. If I remain in bed until 6:00 AM, I create two additional hours of sleep opportunity. Those two hours would not equal two hours of REM. They would contain a mixture of sleep stages and perhaps brief wakefulness. But because REM episodes generally lengthen later in the night, the added opportunity may protect some of the more REM-rich portion of sleep. If you want to understand sleep cycles, I highly recommend taking Dr. Mathew Walker's Masterclass[iii] The Science of Better Sleep. What the Early 4am Wake Gives Me Early movement and consistency Cardiovascular conditioning Time outdoors and morning light The mental and emotional benefits of hiking What Might it Cost Me Up to two hours of total sleep opportunity (lost) Part of one or more later sleep cycles (lost) Greater opportunity for REM and dream recall (important to me) Time for memory and emotional processing across sleep (important to me) The accurate interpretation is not that I lose two hours of REM. It is that I may give up two hours of sleep opportunity containing some of the night's more REM-rich cycles. That led to my central realization: healthy behaviors (like my early wake to hike) can compete when their timing is not coordinated. MY AHA MOMENT: Am I creating better sleep through movement—or repeatedly borrowing from the final part of sleep to make that movement happen? My wearable cannot prove that early wake times caused the change. Consumer wearables estimate sleep stages; they do not measure them with clinical polysomnography. But the pattern gives me a reasonable hypothesis to test. That is what self-tracking should do: not make us anxious about a number, but help us ask a better question. A Real-Time Clue: What 21 More Minutes Revealed While preparing this episode, my data gave me a real-time example of the tradeoff. On Thursday, August 27, I woke at 4:21 AM instead of 4:00 AM. That night I recorded three hours and two minutes of restorative sleep—the highest total of the week. It included one hour and seven minutes of deep sleep and one hour and 55 minutes of REM. That REM duration was approximately 35 minutes above my recent 30-day average. My graph in the show notes showed a substantial REM period close to the end of the sleep window. Twenty-one additional minutes cannot explain a 35-minute difference, and one night cannot establish cause. Sleep varies from night to night. But the observation supports the hypothesis that a 4:00 AM alarm may sometimes interrupt a REM period already underway. I also remembered my dream and added it to my dream log. That gave me another kind of data: not just how long I slept, but what my mind may have been processing, integrating or connecting. One night is a clue—not proof. Watch the trend. The lesson is not that 4:21 AM is a magical wake time. The lesson is that the final portion of sleep may be more valuable than its length makes it appear. A small extension may sometimes allow the brain to finish a cycle that an earlier alarm (or your natural body's alarm) would interrupt. Lesson 4: Build a Rhythm, Not a Perfect Day The remedy I've concluded is not to stop hiking, (I would be miserable) and it is not to maximize sleep or exercise in isolation (I wouldn't feel productive sleeping in longer). The solution is to create a weekly rhythm in which movement provides the stimulus and sleep protects the opportunity for adaptation. For me, that rhythm can include two kinds of days: 1. Early-Hike Days (twice a week) When a 4:00 AM wake time is necessary, (and possible). I can treat bedtime as part of the training plan. If family responsibilities the night before make a very early bedtime unrealistic, I can recognize that constraint and adapt. But it is important that I get to bed by 8:30pm if I want to wake up 4am. 2. Sleep-Protected Mornings On selected non-hiking days, I can remain asleep until approximately 5:00 or 6:00 AM and move later. These mornings create more opportunity for later sleep cycles and give me a comparison condition for my experiment. What happens if I stay in bed longer? The goal is not a perfect day. The goal is a sustainable week. The Movement Loop is not: Move → Move More → Keep Pushing. It is: Movement → Recovery → Adaptation → Performance → Greater Capacity. For you, the listener, are you giving yourself enough time to recover? To Adapt? To Increase Performance? That leads to greater capacity? Or, did you notice, like me, that there was a trade off with your schedule? Tips to Implement: Run Your Own Restorative-Sleep Experiment You do not need my wake time, my REM number or a WHOOP device to learn from your own pattern. Use this five-step experiment for two to four weeks. Establish your baseline. Record your usual bedtime, wake time, estimated sleep duration, morning energy and—if available—deep and REM sleep for at least seven nights. The Whoop device rewards users who go to bed, and wake at the same time, calling it sleep consistency. Choose one change. Add 20–60 minutes of sleep opportunity on selected mornings, or move bedtime earlier before early-training days. Avoid changing every variable at once. Stanford Professor, Dr. Andrew Huberman[iv] suggests that if you can find a way to add heat to your sleeping environment in the final 2 hours of your sleep, it can increase your REM sleep. Sleeping just 21 more minutes for me made a notable difference. Protect movement differently. On sleep-protected mornings, move later, shorten the session or choose a lower-intensity option instead of skipping movement entirely. This was the game changer for me. I opted for a walk on sleep protected days. Track a small set of outcomes. Use total sleep, restorative sleep or dream recall if available, morning clarity, afternoon energy and exercise quality. Optional wearable metrics include recovery, HRV and resting heart rate. This is where you have to notice what you see from this ONE change. It might be obvious like mine—I noticed that extra time led to a dream that I could recall. Review the pattern—not the best night. Compare at least one or two weeks of early mornings with sleep-protected mornings. Look for a repeatable combination that supports both recovery and movement. The patterns will reveal whether the ONE thing you changed made a difference for you. If you do use a wearable, remember that sleep stages are estimates. Use the device to compare patterns under similar conditions, not to diagnose a sleep disorder or chase a perfect stage score. KEY QUESTION: Can I preserve the benefits of movement without repeatedly borrowing the time my brain and body need for recovery? A Bigger Experiment Is Still Underway I have also removed one significant sleep disruptor and am tracking what happens to my REM, sleep stress, HRV, resting heart rate and recovery. I do not want to draw conclusions too early. I want enough data to distinguish a temporary response from a genuine physiological shift, so I will return to that experiment in a future episode. For now, the question is narrower: how can I protect restorative sleep while continuing to live an active life? Review To review and conclude this week's BONUS EP 4, let's bring the four lessons—and the data—together. Lesson 1: Restorative Sleep Contains Two Stories Restorative sleep combines two important stages: Deep sleep and REM sleep. Deep sleep supports physical restoration and adaptation. REM supports emotional processing, memory integration, cognitive flexibility and creative association. We need both. Every morning, I look at my restorative-sleep total and hope it is closer to three hours than two. But this episode taught me not to stop at that combined number. The total gives me the overview. Deep sleep and REM tell me how that restoration was distributed. And REM is personally meaningful to me for another reason: I have recorded my dreams for years. Dream recall does not provide a literal interpretation of everything happening in my life. But it gives me a qualitative record of the emotions, experiences, problems and ideas my mind may still be processing. So Lesson 1 is: Restorative sleep is not one number. It contains the story of how the body restores—and how the brain integrates. Lesson 2: My Data Revealed a Duration Gap Over the past six months, my average restorative sleep has been two hours and 43 minutes. My recent REM has represented approximately 20% of my total sleep—a percentage that may appear reasonable. But when I looked at duration rather than percentage, I discovered that my recent REM was approximately 17–19 minutes below my lifetime average. REM percentage and REM duration answer different questions. Percentage tells me how my sleep was distributed. Duration tells me how much actual time my brain spent in that stage. That distinction matters because someone can have a reasonable REM percentage but still receive less total REM when the overall sleep window is shortened. So Lesson 2 is: A percentage can look healthy while duration still reveals a gap. This is why personal baselines are so valuable. Our trends help us identify the question we need to investigate. Lesson 3: The 4:00 AM Wake Time Creates a Tradeoff My data led me to examine my 4:00 AM wake time. Waking at 4:00 does not mean I am losing two hours of REM. It means I may be giving up two hours of total sleep opportunity—time that could contain approximately one to one-and-a-half later, more REM-rich sleep cycles. Then, while I was preparing this episode, I received an early clue. When I slept just 21 minutes longer—waking at 4:21 instead of 4:00—my restorative sleep reached three hours and two minutes. That included one hour and seven minutes of deep sleep and one hour and 55 minutes of REM—approximately 35 minutes more REM than my recent 30-day average. I also remembered my dream and added it to my dream log. Twenty-one additional minutes cannot explain the entire 35-minute difference, and one night does not prove causation. But the graph showed a substantial REM period close to the end of my sleep window. That gave me a clue worth investigating: My 4:00 AM wake time may sometimes interrupt a REM period that is already underway. So Lesson 3 is: The final portion of sleep may be more valuable than its length makes it appear. This does not mean that 4:21 is a magical wake time. It means that a small extension may sometimes allow the brain to finish a cycle that an earlier wake time would interrupt. Dr. Huberman, and Dr. Holmes offered tips to stay in bed longer, to capture REM rich sleep time. Lesson 4: Build a Rhythm, Not a Perfect Day My early-morning hikes provide cardiovascular conditioning, time outside, morning light, mental clarity and emotional regulation. They support my health. They also make me happy. The remedy is not to stop exercising and become miserable. The remedy is to protect sleep as well as I realistically can and arrange movement around a more complete sleep window. That might mean saving selected mornings for longer sleep. It might mean moving later on non-hiking days. It might mean moving bedtime earlier when possible. And it might mean shortening a workout when sleep has been limited. The goal is not to maximize sleep or exercise in isolation. It is to create a sustainable weekly rhythm in which both can happen. So Lesson 4 is: The best routine is not the one that produces a perfect number. It is the one that allows the whole system to work. Now let me ask you: Do you know how much restorative sleep you receive? Do you look beyond the total and examine both deep sleep and REM? Are you tracking percentages—or actual duration? Does your schedule give you enough sleep opportunity? And could one healthy behavior in your life be unintentionally competing with another? My restorative-sleep average gave me the overview. My REM duration revealed the tension. My 4:00 AM wake time created the hypothesis. My additional 21 minutes gave me an early clue. My next step is to test a better rhythm. That is what measurement is supposed to do. Not judge us. Not pressure us to produce a perfect number. Guide us toward a better decision. I hope this deeper look at restorative sleep encourages you to identify one small experiment of your own—not to chase a perfect number, but to discover the rhythm that helps you learn, adapt and perform at your best. We'll see you next time as we return to Dr. John Medina's work—not to repeat what we have already learned about attention, but to answer the next question in the Movement Loop: once movement activates the brain, how does attention determine what becomes learning? From there, we'll revisit Jason Wittrock's work through a new lens: how metabolic health, nutrition and energy availability help the brain and body sustain movement, recovery and performance. Resources and Episode Pathway Phase 1: Regulation & Safety The Foundation Core Question: Is the nervous system safe enough to learn? Everything begins with regulation. Before we can focus, learn, lead, or perform, the brain first asks one fundamental question: Am I safe? Throughout Phase 1, our guests showed us that regulation isn't simply about reducing stress—it's about creating the biological conditions that allow the brain to learn, adapt, and thrive. Together we explored: Baland Jalal – how sleep, curiosity, imagination, and creativity prepare the brain for learning. https://andreasamadi.podbean.com/e/hypnagogic-genius-capture-your-best-ideas-at-the-edge-of-sleep/ Dr. Bruce Perry – why regulation, rhythm, and relationships form the foundation of every healthy nervous system. https://andreasamadi.podbean.com/e/safety-first-why-a-regulated-brain-is-the-key-to-learning/ Dr. Sui Wong – how lifestyle medicine and autonomic balance build lifelong brain resilience. https://andreasamadi.podbean.com/e/your-eyes-the-brain-s-early-warning-system/ Rohan Dixit – how heart rate variability gives us real-time feedback on our ability to regulate stress. https://andreasamadi.podbean.com/e/breathe-to-reset-how-hrv-tech-reveals-hidden-stress/ Dr. Kristen Holmes – how recovery metrics reveal our physiological readiness to perform. https://andreasamadi.podbean.com/e/kristen-holmes-from-whoopcom-on-unlocking-a-better-you-measuring-sleep-recovery-and-strain/ Dr. Antonio Zadra – how sleep and dreaming consolidate memories, regulate emotions, and generate insight. https://andreasamadi.podbean.com/e/when-brains-dream-how-sleep-integrates-emotion-insight-and-creativity/ Together these conversations taught us that sleep and stress regulation aren't optional—they're the operating system that allows every higher brain function to work. Phase 2: Motivation & Neurochemistry The Direction Core Question: What moves us into action? Once the brain feels safe, it becomes ready to pursue goals. In Phase 2, we explored the internal chemistry that transforms intention into action. Our experts helped us understand that sustainable motivation isn't about willpower—it's about aligning our beliefs, thoughts, attention, energy, and movement. Together we discovered: Bob Proctor — our beliefs determine the direction of our lives. https://andreasamadi.podbean.com/e/belief-first-the-neuroscience-of-motivation/ Dr. Carolyn Leaf — our thinking literally changes our brain chemistry. https://andreasamadi.podbean.com/e/thoughts-as-biology-how-your-mind-shapes-neurochemistry/ Dr. John Medina — attention determines what the brain encodes and remembers. https://andreasamadi.podbean.com/e/theory-of-mind-the-missing-link-between-attention-reward-and-motivation Dr. Anna Lembke- Dopamine, Motivation and Why the Brain Repeats Behavior https://andreasamadi.podbean.com/e/dopamine-nation-the-pleasure%e2%80%93pain-balance-that-drives-motivation/ Dr. Friederike Fabritius — managing our energy allows high performance to become sustainable. https://andreasamadi.podbean.com/e/fun-fear-focus-closing-the-motivation-loop/ Dr. Chuck Hillman & Paul Zientarski — movement activates the brain, preparing it to learn. https://andreasamadi.podbean.com/e/move-to-learn-how-movement-activates-the-brain-and-fuels-motivation/ By the end of Phase 2, we introduced what became The Motivation Loop, showing how beliefs influence thoughts, thoughts influence actions, actions create results, and results reinforce future beliefs. Phase 3: Movement, Learning & Human Performance The Transformation Core Question: How does movement change the brain—and how does recovery transform that change into performance? Now we're taking the next step. Phase 3 builds on everything we've learned so far. If Phase 1 created a regulated nervous system... If Phase 2 created motivation and direction... Phase 3 explains how the brain and body actually become stronger. Together we've explored this process through conversations with: Dr. Chuck Hillman & Paul Zientarski — why movement activates the brain before learning. https://andreasamadi.podbean.com/e/movement-first-how-a-20%e2%80%91minute-walk-lights-up-the-brain/ Dr. John Ratey — how exercise builds a healthier, younger brain. https://andreasamadi.podbean.com/e/movement-matters-how-every-move-rewires-the-brain/ Dr. Kristen Holmes — why recovery determines adaptation and readiness. https://andreasamadi.podbean.com/e/movement-isnt-enough-how-recovery-drives-real-adaptation Dr. John Medina — how attention transforms movement into lasting learning. Jason Whitrock — how metabolism and cellular energy fuel long-term performance. WHERE WE ARE GOING NEXT Phase 4 — Connection, Emotion & Social Intelligence The Human System Core Question: How do we thrive with other people? The brain didn't evolve in isolation—it evolved through relationships. In Phase 4, we'll explore emotional intelligence, empathy, communication, trust, leadership, and psychological safety to understand how our relationships shape learning, well-being, and performance. Phase 5 — Integration & Human Performance The Complete System Core Question: How do all the systems work together? In our final phase, we'll bring everything together—regulation, motivation, movement, emotion, relationships, learning, and recovery—into one integrated framework. We'll discover how these systems work together to create measurable improvements in our well-being, achievement, leadership, productivity, and results. Selected Sleep References “What Is Restorative Sleep?” https://www.whoop.com/us/en/thelocker/what-is-restorative-sleep/ Patel AK, Reddy V, Shumway KR, Araujo JF. “Physiology, Sleep Stages.” StatPearls/NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK526132/ Goldstein AN, Walker MP. “The Role of Sleep in Emotional Brain Function.” Annual Review of Clinical Psychology. https://pmc.ncbi.nlm.nih.gov/articles/PMC4286245/ Paller KA, Creery JD, Schechtman E. “Memory and Sleep: How Sleep Cognition Can Change the Waking Mind for the Better.” Annual Review of Psychology. https://pmc.ncbi.nlm.nih.gov/articles/PMC7983127/ REFERENCES: [i]Neuroscience Meets Social and Emotional Learning Podcast EPISODE 141 https://andreasamadi.podbean.com/e/brain-fact-friday-on-neurogenesis-what-hurts-or-helps-your-brain-cells/ [ii]Neuroscience Meets Social and Emotional Learning Podcast EPISODE 384 Review of our Interview with Dr. Baland Jalal https://andreasamadi.podbean.com/e/hypnagogic-genius-capture-your-best-ideas-at-the-edge-of-sleep/ [iii] www.masterclass.com Mathew Walker The Science of a Better Sleep [iv] https://www.instagram.com/p/DbO7dKLO-c8/?hl=en Dr. Andrew Huberman on ways to increase REM sleep.
The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch
Eno Reyes is the co-founder and CTO of Factory, the agent-native software development platform building autonomous "Droids" for enterprise engineering teams. Factory has raised $220 million, most recently a $150 million Series C at a $1.5 billion valuation, from investors including Khosla Ventures, Sequoia Capital, 20VC, NEA, Blackstone, Insight Partners and Nvidia. Before founding Factory, Eno worked as a machine-learning engineer at Hugging Face, training, optimizing and deploying large language models for enterprise customers. AGENDA: 00:00 Are We Underestimating AI by an Order of Magnitude? 06:35 Why Can the Smartest AI Model Be the Cheapest? 18:51 Is Anthropic's Coding Business Really Worth $2 Trillion? 33:41 Will Continuous-Learning Models Help or Hurt Factory? 40:43 Will 80–90% of Neo-Labs Die in the Next 18 Months? 44:33 Should American Enterprises Work With Open-Source Chinese Models? 55:42 Must AI Founders Radically Rethink What a Great Outcome Looks Like? 1:04:30 Do Pedigree and Credentials Still Matter in AI Hiring? 1:19:17 Which Is the Biggest Threat: Claude Code, Codex, Cognition or Cursor? 1:24:20 What Seems Crazy Today but Will Be Obvious in Five Years?
Jordana Gamble (former Canada Border Services Agency handler, Food/Plant/Animal canine handler) reveals the world of biosecurity and agricultural detection dogs—the discipline that gets zero love but produces the most finds. Working at Toronto Pearson Airport and international postal facilities, Jordana discusses the challenges, the rivalry with drug dog handlers, and why this is actually the BEST detection dog career.What We Cover:The path to becoming an ag/biosecurity handler: CBSA selection, 10-week academy, dog pairingDrug dog dropouts make excellent ag dogs (motivation differences: food vs. toys)Postal mode detection: endless boxes, short searches, keeping dogs engaged all dayPassenger mode detection: baggage carousels, fluid environment, longer search timesTraining aids: grocery store meat, smoked products, cured items, real food variationThe generalization nightmare: raw vs. cured vs. smoked meats confuse dogsFalse positives: baked goods with apple filling (legal but dog alerts)Real finds: grass cutters, dried monkeys, Iberian ham, endangered species, live animal smugglingThe rivalry: "Apple Dogs" vs. "Sausage Police" vs. Drug HandlersThreat response: African swine flu prevention, bird flu concerns, economic protectionDaily find rates: 10+ interceptions per day (vs. drug handlers going weeks with nothing)Reading your dog after thousands of finds: knowing fish smell vs. rotten ham vs. pork flossFinal response problems: dogs going active on bags (markers saved training)The "dog brain" override: when smell/frustration overrides trained behaviorWhy postal is physically brutal but rewardingHandler advice: never turn down an ag dog positionCareer satisfaction: dogs see work as playground, not just trainingWhy agricultural detection protects economies and industriesThe hidden importance of biosecurity workJordana worked both postal (conveyor lines, thousands of parcels) and passenger (airport baggage) modes. She shares how target-rich environments accelerate handler development—you get hundreds of finds to learn from, versus drug dogs going weeks without positive reinforcement. She also discusses the competitive dynamic with her drug handler husband, who now wishes he had her find rate.Critical Takeaway: "Detection work is detection work. Whether you're looking for drugs, firearms, currency, or food—it's all the same. Don't be too proud for the ag dog."________________________________________
Armin Winkler (renowned detection dog trainer, international expert) and Cameron Ford break down why current K9 certification standards don't actually validate operational readiness. They discuss why training records matter more than certifications in court, why everyone trains for the test instead of the job, and what real operational readiness actually looks like.What We Cover:Certification is department CYA, not a measure of reliabilityDetection dogs should have OPERATIONAL standards, not minimum standardsTraining records with proper documentation beat certifications in court every timeWhy observers/supervisors signing off on training matters more than a piece of paperRunning multiple dogs in same area: nasal secretions and saliva contaminate everythingThe "cheat code" dogs use when training mimics certificationHow evaluators inadvertently give away test locations through nonverbal communicationWhy environmental soundness testing is critical (traffic, noise, distractions)The difference between ORT (odor recognition test) and operational readinessLottery-based scenario testing instead of rote certification patternsHandler decision-making: the most untested aspect of any certificationWhy SEAL Teams didn't certify but had real resultsTraining for certification vs. training for the street (these are NOT the same)Real-world deployment: rain, night, roadside traffic stopsHide placement strategy: hide like a criminal, not in filing cabinetsThe handler's job: watch the DOG, not the room ("the nose nose")The avalanche dog that found both victims but failed certificationWhy handlers don't see their dogs during searches (looking for hide locations instead)Apprehension dog certification problems (false sits, stress on one-day tests)Free air sniff legality: roadside delays = searches, not free sniffsInternational perspectives: Norway military working dogs, customs operationsThe problem with "master trainer" guru cultureSupervision and training records as alternative to certificationScenario-based training in real conditions (using cooperating officers)Why some agencies (LAPD) use OJT/FTO model instead of certification gatesThe risk of certification standards creating liability instead of protectionArmin draws on decades of experience working detection dogs internationally, including military operations, customs, search and rescue, and avalanche response. He challenges the industry's comfort with certification-based standards and advocates for documentation, supervision, and scenario-based operational testing.Critical Takeaway: "If you're afraid of rewarding the wrong thing during testing, you're not ready to test. You have a bigger problem than that moment."________________________________________
Episode 4210 │ August 25, 2026 Anthropic admitted degrading their own AI's output quality to comply with EU watermarking law. Mike Adams says that changed the entire race. WHAT THIS EPISODE COVERS Scott Kesterson sits down with Mike Adams for their first conversation in a year, opening with the oracle versus librarian framework both have independently arrived at — AI as a tool for research and task execution, never as a divine verdict-giver — and using the Chinese farmer Wu's destroyed sesame crop as the case study for why blaming AI rather than human accountability misdiagnoses every failure. Adams delivers the most significant claim of the interview: Anthropic publicly admitted to watermarking all model outputs under EU law by statistically biasing token selection, a technical concession that provably reduces output quality, while China's DeepSeek, Qwen, and other open-published models have pulled ahead of the US frontier labs specifically because Chinese companies shared their research openly instead of hoarding it as classified defense technology. The conversation closes on the sovereign computing model both are independently building — desktop AI on AMD's Strix Halo chip, open-weight models run locally on solar power, and a coming economy where owning hardware and generating your own energy matters more than money, because decentralized cognition is, in Adams's words, the single greatest threat to globalist control of what people are allowed to know. KEY QUESTIONS ADDRESSED What did Anthropic publicly admit about watermarking their AI model outputs to comply with EU law — and why does Mike Adams argue this technical concession provably reduces the quality of every response, even for US domestic users who never consented to it? How did China overtake the US in the AI race — and why does Adams argue that China's strategy of openly publishing research papers and sharing innovations like sparse attention mechanisms and KV cache compression across companies proved more effective than the secrecy-driven US model treating AI as classified defense technology? What is the sovereign computing model Scott and Mike Adams are both building — and why does running open-weight AI models locally on solar-powered hardware represent, in Adams's words, decentralized augmented cognition and the single greatest threat to globalist controllers who have always sought to limit public access to knowledge? ABOUT BARDSFM BardsFM is a daily independent podcast covering faith, liberty, history, and information warfare. Hosted by Scott Kesterson — combat veteran, documentary filmmaker, and rancher. Over 4,100 episodes and 50 million lifetime downloads. New episodes every weekday. bards.fm This episode was researched and produced under the Spatial Terra Intelligence Methodology (STIM v5) — the analytical framework built by Scott Kesterson — with AI-assisted research synthesis at a 70/30 human/AI authorship ratio, fully disclosed. All analysis, conclusions, and editorial judgments are those of Scott Kesterson. BardsFM's archive includes hundreds of episodes on prayer, scripture, and walking the Way of Christ — available free in the full episode catalog. DOWNLOADS Citizen's Guide - Community Organizing Against Data Centers: click here Citizen's Guide - Auditing Automatic License Plate Readers: click here Citizen's Guide - Auditing Your State's Driver License Data: click here AFFILIATE LINKS Bards Nation Health Store: www.bardsnationhealth.com MYPillow promo code: BARDS >> Go to https://www.mypillow.com/bards and use the promo code BARDS or... Call 1-800-975-2939. EMPShield protect your vehicles and home. Promo code BARDS: Click here Treadlite Broadforks...best garden tool EVER. Promo code BARDS26: TreadliteBroadforks.com EnviroKlenz Air Purification, promo code BARDS to save 10%: www.enviroklenz.com Morning Intro Music Provided by Brian Kahanek: www.briankahanek.com Founders Bible 20% discount code: BARDS >>> TheFoundersBible.com Windblown Media 20% Discount with promo code BARDS: windblownmedia.com White Oak Pastures Grassfed Meats, Get $20 off any order $150 or more. Promo Code BARDS: www.whiteoakpastures.com/BARDS Mission Darkness Faraday Bags and RF Shielding. Promo code BARDS: Click here DONATIONS: If you wish to support this podcast directly you can donate here... DONATE: Click here MAILING ADDRESS: Xpedition Cafe, LLC Attn. Scott Kesterson 591 E Central Ave, #740 Sutherlin, OR 97479
Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Substackhttps://substack.com/@theoccultrejects?r=7auau0&utm_campaign=profile&utm_medium=profile-pageCash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsBibliographyRepetition, Fluency, and BeliefBacon, Frederick T. “Credibility of Repeated Statements: Memory for Trivia.” Journal of Experimental Psychology: Human Learning and Memory 5, no. 3 (1979): 241–252.Begg, Ian Maynard, Ann Anas, and Suzanne Farinacci. “Dissociation of Processes in Belief: Source Recollection, Statement Familiarity, and the Illusion of Truth.” Journal of Experimental Psychology: General 121, no. 4 (1992): 446–458.Dechêne, Alice, Christoph Stahl, Jochim Hansen, and Michaela Wänke. “The Truth About the Truth: A Meta-Analytic Review of the Truth Effect.” Personality and Social Psychology Review 14, no. 2 (2010): 238–257.Fazio, Lisa K., Nadia M. 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On Repeat: How Music Plays the Mind. New York: Oxford University Press, 2014.Marjieh, Raja, Pol van Rijn, Ilia Sucholutsky, Harin Lee, Thomas L. Griffiths, and Nori Jacoby. “A Rational Analysis of the Speech-to-Song Illusion.” Preprint, 2024.Vickhoff, Björn, Helge Malmgren, Rickard Åström, Gunnar Nyberg, Seth-Reino Ekström, Mathias Engwall, Johan Snygg, Michael Nilsson, and Rebecka Jörnsten. “Music Structure Determines Heart Rate Variability of Singers.” Frontiers in Psychology 4 (2013): 334.Breath, Vocalization, and Autonomic PhysiologyBernardi, Luciano, Peter Sleight, Gianfranco Bandinelli, Simone Cencetti, Lino Fattorini, Jacek Wdowczyc-Szulc, and Andrea Lagi. “Effect of Rosary Prayer and Yoga Mantras on Autonomic Cardiovascular Rhythms: Comparative Study.” BMJ 323, no. 7327 (2001): 1446–1449.Lehrer, Paul M., and Richard Gevirtz. “Heart Rate Variability Biofeedback: How and Why Does It Work?” Frontiers in Psychology 5 (2014): 756.Shaffer, Fred, and J. P. 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Gangadhar. “Neurohemodynamic Correlates of ‘OM' Chanting: A Pilot Functional Magnetic Resonance Imaging Study.” International Journal of Yoga 4, no. 1 (2011): 3–6.Travis, Fred, and Jonathan Shear. “Focused Attention, Open Monitoring and Automatic Self-Transcending: Categories to Organize Meditations from Vedic, Buddhist and Chinese Traditions.” Consciousness and Cognition 19, no. 4 (2010): 1110–1118.Ritual, Attention, and Embodied PracticeBell, Catherine. Ritual Theory, Ritual Practice. New York: Oxford University Press, 1992.Hobson, Nicholas M., Juliana Schroeder, Jane L. Risen, Dimitris Xygalatas, and Michael Inzlicht. “The Psychology of Rituals: An Integrative Review and Process-Based Framework.” Personality and Social Psychology Review 22, no. 3 (2018): 260–284.McCauley, Robert N., and E. Thomas Lawson. Bringing Ritual to Mind: Psychological Foundations of Cultural Forms. Cambridge: Cambridge University Press, 2002.Rappaport, Roy A. Ritual and Religion in the Making of Humanity. Cambridge: Cambridge University Press, 1999.Xygalatas, Dimitris. Ritual: How Seemingly Senseless Acts Make Life Worth Living. New York: Little, Brown Spark, 2022.Also want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball.
Andrea Samadi shares her personal resting heart rate data to show why measuring over time matters more than fixating on a single score. She explains how trends reveal stress, recovery, and adaptation, and why comparing yourself to your own baseline is the most useful approach. Learn practical steps to measure consistently, contextualize changes, experiment with one behavior, and use your physiological data to make intentional improvements in sleep, recovery, and performance. BONUS EPISODE 3: ON THIS EPISODE, WE'LL COVER ✔ What resting heart rate can reveal about stress, recovery, and adaptation over time ✔ The story hidden in my own data — from a high of 57 bpm in February to 53 bpm in August ✔ Why the six-month trend matters — and what my 2% lower average may be telling me ✔ How stress and training load can show up in your physiology before you consciously recognize it ✔ Why recovery and nervous-system regulation matter for building a more resilient baseline ✔ How to find your own RHR baseline instead of comparing yourself with someone else ✔ How to look for deviations and patterns across weeks and months—not individual days ✔ How to connect RHR with sleep, HRV, recovery, training, stress, and life events ✔ How to run your own personal experiment: change one variable, measure, and see what happens ✔ The bigger lesson: What gets measured over time can reveal a story you couldn't see day to day. Welcome back to the Neuroscience Meets Social and Emotional Learning Podcast, where we bridge neuroscience, social and emotional learning, and human performance so we can create measurable improvements in our well-being, achievement, leadership, productivity, and results. I'm Andrea Samadi, and if you've been following along through Season 16, you'll know that we've been building what I call The Brain's Operating System for Human Performance—a neuroscience-based framework designed to help us understand how the different systems of the brain and body work together to influence how we learn, adapt, connect, lead, and ultimately how we perform. We are currently in Phase 3 where we are covering Movement, Learning and Cognition. If you just finished Episode 405[i] with Dr. Kristen Holmes, you know we've been looking at recovery as an essential part of our Movement Loop. Movement → Brain Activation → Attention → Learning → Recovery → Adaptation → Performance → Greater Capacity. And Kristen's work has reinforced something I've learned after years of tracking my own physiology: What gets measured gets improved. But this week, I wanted to dive into my own personal data to show us something I think is easy to miss: Sometimes what gets measured over time reveals a story you simply couldn't see day to day. When we're looking at a wearable every morning, it's easy to become focused on today's number. Today's recovery score. Today's HRV. Today's resting heart rate. Was it higher? Was it lower? But one number—or even one week—rarely tells us very much about the direction we're actually moving. It's when we begin to zoom out that patterns emerge. And that's exactly what happened when I looked more closely at my resting heart rate this year. What initially looked like a collection of numbers began to tell a much bigger story about stress, recovery, consistency, and adaptation. So my hope for this episode isn't that you compare your resting heart rate to mine. Your numbers will be different. My hope is that this episode encourages you to become curious about your own data. Maybe you track sleep. Maybe it's HRV. Resting heart rate. Recovery. Exercise. Blood pressure. Steps. Or even something as simple as your energy, mood, or ability to focus. Take one metric and zoom out. Look at three months. Six months. Maybe even a year. And instead of asking, “Is today's number good or bad?” Ask a different question: “What is the trend that I'm seeing here, trying to tell me?” Is something improving? Is something slowly moving in the wrong direction? Does your physiology change during periods of higher stress? (like you will see mine did). Can you see what happens when you sleep more consistently, exercise differently, recover better, or change one habit? Because sometimes the most valuable insight isn't found in today's score. It's hidden in the pattern. And once you can see the pattern, you have information you can actually use. That's really the purpose of sharing my personal data with you today. Not to say, “Here's what worked for me, so you should do exactly the same thing.” But to show you what can happen when we become students of our own physiology. We measure. Find the tool that works best for you. We notice. Patterns…ups and downs. We experiment. We become intentional with our behaviors to change/improve the numbers. We adjust and try something new/different. And then we measure again. Because ultimately, what gets measured gets improved—but what gets measured over time can reveal the story that you tell you more than just a bunch of numbers. And sometimes that story shows us exactly where our next opportunity for change might be. And that's exactly what happened with my resting heart rate. Earlier this year, I watched my monthly average climb to 56...and then 57 beats per minute. You can see my data in the show notes. Sept and October were even, at 54, and then something happened in November that took my RHR up, and it kept climbing. I know what was happening in those months to cause the increase. And I went all out to protect my recovery. I worked on rest, hydration, extra exercise, and recovery sessions. Then it began coming down. in March 54 in April 53 The 56 in May isn't the most accurate representation of what was happening with my data, as we travelled internationally and there were 5 days my whoop didn't calculate my score correctly (something to do with the change in time zones) so I'm not going to say that I was stressed out during a relaxing vacation. But something happened that caught my attention. You can see it with the graphics in the show notes. My newest six-month WHOOP average dropped from 55 to 54 beats per minute—2% lower than the previous six months. This NEW result was highlighted in GREEN for me to notice the improvement. For the first time in this story, the baseline itself had moved. And that's what we're going to dive into today. Because this episode isn't really about whether 53 is a good resting heart rate. It's about something much more useful: Do you know your baseline with YOUR RHR—and do you know which direction it's moving? Even better, if you can forsee that it will be moving up, like I knew it was going to in January/February, do you have strategies in place to buffer the stress, and keep this number as close as you can to your baseline? Let's dive into this bonus episode. The Story Behind My Resting Heart Rate: Do You Know Yours? There's a number I've been watching for the past year. I watched it raise higher (when I'm stressed) and go lower when I'm doing all the right things to rest and recover. When I zoom out and look at this number over the past year, it tells a fascinating story about my fitness, recovery, stress, and the way my body is adapting. That number is my resting heart rate. And today I want to ask you: Do you know yours? Because one of the biggest lessons I've learned from nearly five years of tracking my physiology is something we've talked about throughout this podcast: What gets measured gets improved. But I think I need to add something to that. What gets measured over time reveals the story. And my newest data makes that clearer than ever. PART 1 — WHAT IS RESTING HEART RATE? Resting heart rate, or RHR, is simply the number of times your heart beats per minute while your body is at rest. Normal Ranges and Fitness[ii] Average adults: 60 to 100 bpm. Active adults: 55 to 85 bpm. Athletes: 40 to 60 bpm, because their heart muscle is strong and pumps blood with ease For adults, resting heart rate varies considerably between individuals. Fitness, genetics, age, sleep, stress, illness, medications, hydration, alcohol, temperature and training load can all influence it. So I'm not interested in comparing my resting heart rate with someone else's. I'm interested in comparing me with me. That's where wearable data becomes useful. Instead of asking: “Is 53 good?” I'm asking: “What is happening to my baseline over time?” And that distinction matters. PART 2 — MY RHR STORY BEGAN WITH A RISE When I first looked at this data, I focused on January and February. My monthly resting heart rate had climbed: January: 56 bpm. February: 57 bpm. Those numbers weren't alarming. I was experiencing higher levels of stress than usual at that time, and I was even prepared for the stress. But relative to my baseline, (55 bpm) the rise caught my attention. And that's an important distinction. A wearable isn't diagnosing why something happened. It can't look at 57 and tell me: “Andrea, this is stress.” But it can show me that something has changed. Then I can look at the rest of my life. How am I sleeping? How am I training? What's my stress level? Am I recovering? What has changed? That's when data becomes feedback. I was ready to implement some stress reduction strategies to get my RHR and nervous system back to its baseline. PART 3 — THEN THE PATTERN CHANGED After February, here's what happened: March: 54. April: 53. May: 56 (International Trip impacted this data) June: 53. July: 55. August: 53. And I love this part of the story because it wasn't a perfectly straight line. My RHR didn't simply go: 57...56...55...54...53. Real physiology rarely works like that. There were fluctuations. May went back to 56. July went to 55. But my resting heart rate kept returning to 53. April: 53. June: 53. August: 53. That's when I began wondering whether I was seeing something more meaningful than a few good months. And now I have another piece of evidence. PART 4 — THE NEW SIX-MONTH RESULT My newest WHOOP six-month report covers February 23 through August 21, 2026. And this is where the story gets interesting. My previous six-month average resting heart rate was: 55 bpm. My newest six-month average is: 54 bpm. WHOOP shows that as: 2% lower than the previous six months. One beat per minute doesn't sound dramatic. But I'm not looking for dramatic. I'm looking for adaptation. Something to show me that my body responded to the extra recovery that I was implementing to buffer the higher stress. For the first time in this particular story, the six-month average itself has moved down. And that means I'm no longer looking only at a few isolated low readings. The trend is beginning to move. PART 5 — THE AVERAGE WAS HIDING THE STORY This may be my favorite lesson from this entire experiment. Earlier, when I looked at a different six-month window, my average RHR was 55 bpm. And the previous six months? Also 55. If I had stopped there, I could have concluded: Nothing changed. But underneath that average was a changing pattern. My RHR had risen to 57 in February. This makes sense to me when I glance at the data. Then it repeatedly returned to 53 with my focus being on rest/recovery. And eventually the rolling six-month average moved from 55 to 54. This is why one measurement rarely tells the whole story. Trends matter. And sometimes your body is changing before the average catches up. PART 6 — 57 TO 53 There's another way to look at the data. February was my highest monthly average in this January–August period: 57 bpm. August is: 53 bpm. That's approximately 7% lower than that February high. I want to be careful with this number because I'm comparing two monthly endpoints—not two six-month averages. So I wouldn't call this a 7% improvement in my overall cardiovascular health. But it is another signal inside the larger pattern. What was happening in February? What is currently happening in August? And when multiple signals begin pointing in the same direction, (showing an improvement) I'm paying attention. PART 7 — WHAT CHANGED? This is where I think self-experimentation becomes powerful. I didn't respond to the higher numbers by simply exercising harder. I've spent this year becoming much more intentional about recovery and regulation. Sleep. Daily movement. Zone 2 work. Meditation. Hydration. Infrared sauna. Red-light therapy. Paying attention to training strain. And increasingly, listening to what my physiology is telling me instead of automatically pushing through fatigue. I've also been experimenting with removing alcohol at different times this year, and the changes I've seen in recovery, HRV, sleep stress and resting heart rate have given me another variable to continue watching. I can't take this dataset and say: “This one behavior caused my RHR to fall.” There are too many variables. But I can say: The environment surrounding my physiology changed—and my trend changed with it. That's worth paying attention to. PART 8 — WHY DOES A LOWER RHR MATTER? In many people, particularly when associated with aerobic fitness, a lower resting heart rate can reflect a heart that is able to pump blood efficiently with fewer beats at rest. Training can increase stroke volume—the amount of blood pumped with each heartbeat. That means the heart may not need to beat as frequently to deliver the same cardiac output at rest. Autonomic regulation also plays a role. The sympathetic nervous system helps mobilize us for action. The parasympathetic nervous system supports rest-and-recovery processes. Resting heart rate reflects influences from both, along with many other physiological factors. That's why I don't view RHR as a standalone score. I view it as one piece of my physiological dashboard. RHR. HRV. Sleep. Recovery. Training load. And, most importantly: How I actually feel. PART 9 — THIS CONNECTS DIRECTLY TO THE MOVEMENT LOOP Remember the Movement Loop we've been building in Phase 3: Movement → Brain Activation → Attention → Learning → Recovery → Adaptation → Performance → Greater Capacity. My RHR data brings us directly back to the part of the loop I don't want us to overlook: Recovery → Adaptation. Exercise provides the stimulus. But we don't become fitter during the stimulus itself. The body needs an opportunity to respond to that stimulus. To repair. To replenish. To adapt. And eventually, that adaptation may become visible in our trends. That's why recovery isn't the opposite of performance. Recovery is part of the process that makes performance possible. PART 10 — THE BIGGER LESSON This episode isn't really about whether your resting heart rate should be 53. Mine doesn't need to be yours. Your number might be completely different. The question is: Do you know your baseline? And more importantly: Do you know which direction it's moving? What increases it? What decreases it? Because your physiology is constantly responding to the way you're living. Training. Sleeping. Recovering. Working. Traveling. Eating. Drinking. Managing stress. And sometimes those changes are happening quietly beneath the surface. Until you measure them. PART 11 — HOW TO IMPLEMENT THIS IN YOUR OWN LIFE So now I want to turn this over to you. I've shown you my resting heart rate story. What's yours? And you don't need five years of WHOOP data to begin looking for it. You just need enough information to establish your baseline, and then you need to become curious about what causes that baseline to change. Here's how I would start. STEP 1 — FIND A WAY TO MEASURE YOUR RESTING HEART RATE CONSISTENTLY First, find the measurement tool that works for you. Maybe that's WHOOP. Maybe it's an Apple Watch, Garmin, Oura Ring, Fitbit, or another wearable. Or maybe you're tracking your resting heart rate another way. The specific device isn't the most important part of this experiment. You can even just count your pulse beats that you get in one minute while resting. Just stay consistent as you measure. If you're going to look for a trend, you want to compare measurements collected under reasonably consistent conditions. We're not trying to find the lowest number we can possibly produce. We're trying to understand our normal pattern. STEP 2 — LEARN YOUR BASELINE Next, don't start by asking: “Is my resting heart rate good?” Start with: “What is normal for me?” Look at your data over time. If you have it, look at your last month. Then three months. Six months. And eventually a year. What number—or range—does your RHR tend to return to? For me, I knew that my baseline was somewhere around the mid-50s. That's why 56 and 57 caught my attention. Those numbers weren't necessarily concerning when compared with somebody else's resting heart rate. Someone else I know has a RHR of 80 and that's in the normal range. The higher scores mattered to me, because they represented a change from my baseline. That's the first lesson: Compare yourself with yourself. STEP 3 — LOOK FOR THE DEVIATIONS Once you understand your baseline, look for the places where your data moves away from it. Don't immediately label the change as good or bad. Just become curious. When did your RHR rise? When did it fall? Was it one unusual night? Several days? Several weeks? Or did your monthly average actually begin to move? This is where we stop looking at individual dots and begin looking for a pattern. STEP 4 — PUT YOUR LIFE NEXT TO YOUR DATA Now comes the part that I think is the most interesting. Take the RHR graph and mentally place your life beside it. Ask yourself: What was happening here? Was work unusually stressful? Were you traveling? Were you sleeping less? Were you sick? Had your training load increased? Were you drinking more—or less—alcohol? Had you changed your exercise routine? Were you recovering differently? What was happening emotionally? What was happening physically? This was important in interpreting my own data. When I saw my RHR increase in January and February, I knew what was happening in my life during those months. And when I saw May increase, I also had context: I had been traveling internationally, and I knew there were issues with several days of my wearable data. Context changes interpretation. A graph can show you what changed. Your life can help you investigate why. STEP 5 — LOOK AT MORE THAN ONE METRIC Here's another important point. Don't interpret resting heart rate completely by itself. Put it next to other information. What was happening with your HRV? Your sleep? Your recovery? Your training load? Your energy? Your stress? And most importantly: How did you feel? One metric gives us a clue. Multiple signals moving together can give us a much richer picture. Think of it as your own physiological dashboard. You're looking for relationships between the signals rather than allowing one number to define whether you're doing well. STEP 6 — CHOOSE ONE VARIABLE TO EXPERIMENT WITH Now we move from observation to action. Ask: “What is one behavior I could change consistently and then watch what happens?” Maybe it's getting to bed earlier. Maybe it's improving hydration. Adding more Zone 2 exercise. Building recovery days into your training. Meditating. Walking. Changing your alcohol intake. Or simply protecting more downtime during a stressful period. Choose something that's meaningful and realistic for you. Then give it time. This is important because we're not trying to manipulate tomorrow morning's score. We're trying to see whether consistent behavior begins to influence a trend. STEP 7 — MEASURE AGAIN And now we come back to the beginning. Measure. Notice. Experiment. Adjust. And measure again. You might even keep a very simple note beside your data: What changed this month? You could write: Higher workload. Travel. Better sleep. Started Zone 2. Stopped drinking alcohol. Illness. Vacation. More recovery. Over time, you begin creating something much more useful than a collection of numbers. You're creating a map of how your body responds to your life. DISCOVER YOUR RHR STORY So here's the experiment I want to leave you with. Pull up your resting heart rate data. And ask yourself these five questions: What is my baseline? When does my RHR move above or below that baseline? What was happening in my life when it changed? What other physiological signals moved with it? What is one behavior I could intentionally change and measure over time? Then zoom out. Don't judge yourself by tomorrow morning. Look for the story. Because the goal isn't to achieve somebody else's resting heart rate. The goal is to understand your own physiology well enough to recognize when something changes. And then have strategies available to respond. That's where measurement becomes useful. It moves us from simply collecting information... to using information to make better decisions. REVIEW — WHAT DID WE LEARN? Before we close, let's pull everything together. We started with one simple metric: resting heart rate. We learned that RHR tells us how many times our heart beats per minute while we're at rest—but the number becomes much more useful when we stop comparing ourselves with other people and begin comparing ourselves with ourselves. Then we looked at my own story. My RHR rose to: 56 bpm in January. And then: 57 bpm in February. Because I understood what was happening in my life at the time, those increases gave me information I could use. Rather than simply pushing harder, I became increasingly intentional about recovery. Then the pattern began to change. My RHR repeatedly returned toward 53. And eventually something even more interesting happened. My newest six-month average moved from: 55 bpm → 54 bpm. WHOOP identified that as: 2% lower than my previous six-month average. One beat per minute. Not dramatic. But that's exactly the point. We're not looking for dramatic. We're looking for patterns. We're looking for evidence that the system may be adapting over time. And that's why my biggest lesson from this experiment isn't: “My resting heart rate is 53.” It's: “I understand my resting heart rate better than I did before.” I know my baseline. I know some of the circumstances that push it higher. I'm beginning to understand some of the behaviors associated with it moving lower. And most importantly, I know to keep watching the trend. CONCLUSION — BECOME A STUDENT OF YOUR OWN PHYSIOLOGY This brings us back to something Dr. Kristen Holmes reinforced in Episode 405: What gets measured gets improved. But after looking at this data, I want to expand that idea. What gets measured gives us information. What gets measured consistently reveals patterns. And what gets measured over time can reveal a story. That's the opportunity I hope you take away from this episode. Become curious about your own physiology. Not obsessed with it. Not controlled by a score. Curious. Learn your baseline. Notice when something changes. Put that change into the context of your life. Experiment thoughtfully. And then zoom back out and see what happened. Because ultimately, we're not collecting data just to collect more data. We're trying to turn: Data → Awareness → Action → Adaptation. And that brings us right back to our Movement Loop: Movement → Brain Activation → Attention → Learning → Recovery → Adaptation → Performance → Greater Capacity. Your resting heart rate is only one metric. But it gives us a perfect example of what adaptation can look like when we stop staring at today's number and start paying attention to the larger pattern. So sometime after listening to this episode, open your own data. Go back three months. Six months. A year if you can. And ask yourself: “What story has my body been trying to tell me?” You might be surprised by what you find. COMING NEXT — BONUS EPISODE 4 And there's one part of my own physiological dashboard that I deliberately haven't explored deeply today. Sleep. Because recovery doesn't end when we stop exercising. Some of the most important recovery processes happen while we're asleep. So before we return to Dr. John Medina and move deeper into attention, learning, and cognition, I want to stay with the recovery side of our Movement Loop for one more bonus episode. In Bonus Episode 4, we're going to investigate restorative sleep. What exactly is restorative sleep? How can we measure it? What can our sleep data tell us? And perhaps most importantly: How can we use that information to improve the way we recover, adapt, and perform? We'll apply the same approach we used today. Not just: “How did I sleep last night?” But: “What story is my sleep telling me over time?” Because movement creates the stimulus. Recovery creates the opportunity to adapt. And sleep may be one of the most important places where that recovery takes place. I'll see you next time for Bonus Episode 4: The Story Behind My Restorative Sleep. REFERENCES [i]Neuroscience Meets Social and Emotional Learning Podcast EPISODE 405 “Movement Isn't Enough: How Recovery Drives Adaptation” https://andreasamadi.podbean.com/e/movement-isnt-enough-how-recovery-drives-real-adaptation/ [ii] Health Direct “What is RHR?” https://www.healthdirect.gov.au/resting-heart-rate
-OneMicNite Podcast with Marcos LuisFeaturing Guest Co‑Host: #oneMicNiteTalk--Shane Mark Tull LCSW-RBook: "The Mental Health Pandemic" Available on AmazonIf you need help or feeling suicidal there are people you can call..*** MentalHealthAmerica.org (MHA)1-800-273-TALK (8255)--support groups--therapists--⭐ Episode SummaryIn this urgent and emotionally charged episode of OneMicNite Podcast, host Marcos Luis sits down with clinical psychotherapist Shane M. Tull, LCSW‑R to break down the known facts surrounding the Nolan Wells case — a case that has shaken communities, raised national questions, and exposed deep fault lines in America's racial and justice systems.---Together, Marcos and Shane explore the psychological, cultural, and systemic layers behind Nolan's disappearance and death. They examine how racial dynamics, implicit bias, and the long‑standing fear imposed on Black men in America shape both public perception and investigative outcomes. The conversation also confronts the possibility of a community‑level cover‑up, the silence of key witnesses, and whether federal agents should now intervene to ensure transparency and accountability.--This episode blends clinical insight, social analysis, and community truth‑telling, offering listeners a grounded, compassionate, and unflinching look at a case that continues to demand answers.
Stripe schreibt seinen Investoren, die Singularität habe am 1. Januar begonnen, und bestätigt im selben Brief den Kauf von OpenRouter. Danach geht es eine Stufe tiefer in die Nahrungskette der Absatzfinanzierung: Broadcom nimmt bis zu hundert Milliarden Dollar Schulden auf, um Anthropic den Kauf von Chips zu ermöglichen, die Broadcom selbst entworfen hat. In Ohio warnt das Wahlkampfkomitee der Republikaner die KI-Konzerne, dass Rechenzentren die Partei einen Senatssitz kosten. Ab Montag läuft ChatGPT-Werbung auch in Deutschland, und es geht um die Frage, ob eine Suchmaschine mit Werbung überhaupt glaubwürdig bleiben kann. Bei den Quartalszahlen liegt Anthropic vor OpenAI, in den ersten Wochen des laufenden Quartals dreht sich das Momentum aber wieder. Anthropic könnte in den nächsten Tagen den Börsenprospekt vorlegen. Dazu Drohnen von Amazon in bis zu fünfhundert Städten, ein Cybercab ohne Lenkrad, eine Kamera im AirPod und eigene Chips von Waymo. Bei den Zahlen bremst Klarna wegen Deutschland, und die Doping-Olympiade macht achtzehn Millionen Umsatz bei sechsundneunzig Millionen Kosten. Am Ende eine längere Rechnung darüber, warum Zara mit Innenstadtläden mehr verdient als Shein online. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf doppelgaenger.io/werbung. Vielen Dank! Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Stripe und die Singularität (00:08:21) VC-Konzentration (00:11:16) Broadcom bürgt für Anthropic (00:13:24) Rechenzentren im Wahlkampf (00:20:19) Die Kelce-Kampagne (00:23:18) ChatGPT-Werbung in Europa (00:38:05) OpenAI gegen Anthropic (00:51:45) Amazon-Drohnen (00:57:05) Tesla Cybercab (00:58:51) AirPods mit Kamera (01:03:35) Waymo baut Chips (01:06:17) Der günstige Waymo (01:08:42) SpaceX wollte Cognition (01:12:45) Klarna (01:15:52) Enhanced Games (01:19:28) Ramp Router (01:22:08) Personio (01:23:09) LinkedIn-Slop (01:25:04) Unitree (01:26:51) Shein gegen Inditex (01:37:52) Apotheken und E-Commerce (01:42:50) Neom (01:43:32) LandSpace landet (01:45:09) Spirit-Flugbegleiter (01:46:12) Metas Suchtprozess Shownotes Stripe schreibt Investoren, die Singularität habe begonnen - axios.com Venture Capital war noch nie so konzentriert - profgmedia.com Broadcom sucht mehr als 60 Mrd. für den nächsten KI-Schuldendeal - bloomberg.com Das GOP-Memo zum Rechenzentrums-Risiko in Ohio - axios.com Die Kelce-Brüder sammeln Kühlmittel für Rechenzentren - thoughtcatalog.com ChatGPT Ads kommt in 31 europäische Märkte - openai.com OpenAI baut Vertriebsteams für Werbung in Europa auf - digiday.com OpenAIs Q2-Umsatz wächst langsamer als der von Anthropic - wsj.com Friar: OpenAI wird 2027 eine Public Company - cnbc.com Anthropic will die Größe des SpaceX-IPO erreichen oder übertreffen - bloomberg.com Amazon weitet die Drohnenlieferung aus - bloomberg.com Teslas Cybercab startet mit Mitarbeitern als Fahrgästen - thenextweb.com Hinweise auf AirPods mit Kamera in macOS 26.7 - macrumors.com Waymo hat einen eigenen Chip für seine Robotaxis gebaut - bloomberg.com Das günstigere Waymo-Robotaxi ist in drei Städten für alle offen - techcrunch.com SpaceX wollte Cognition kaufen - bloomberg.com Klarna senkt die Prognose und sucht einen neuen CFO - wsj.com Enhanced Games mit 62 Mio. Verlust im zweiten Quartal - themirror.com Ramp startet einen eigenen KI-Modell-Router - techcrunch.com Personio zeigt KI-Ausgaben neben den Vergütungsdaten - linkedin.com Unitree legt im Shanghaier Debüt um mehrere Hundert Prozent zu - cnbc.com Shein peilt den Handelsstart am 28. August an - reuters.com Neom: The Line wird zum KI-Rechenzentrum - golem.de China landet erstmals eine Trägerrakete auf dem Boden - qz.com Flugbegleiter wehren sich gegen Googles Datenkauf - wsj.com Der erste Zeuge im Meta-Prozess in Oakland - thenextweb.com
Dr. Robert Lustig is a neuroendocrinologist, pediatrician, and bestselling author who has spent 45 years watching children get sicker despite his best efforts to stop it. His work connects the dots between ultra-processed food, mitochondrial dysfunction, dopamine hijacking, and the chronic stress epidemic in a way that is impossible to dismiss. In this conversation, we explore what he calls the hostage brain, what happens to a nervous system under sustained biochemical assault, and why so much of what we call personal failure is actually a systems problem.What We Dive Into:1. When chronic pain, emotional or physical, goes unresolved, the brain reaches for anything that spikes dopamine to quiet it. Over time, tolerance builds and neurons die. Lustig's point is that this is not a personal weakness. The food, the screens, and the stress have conspired to put nearly all of us in some version of this state.2. Dopamine drives wanting. Serotonin drives contentment. The loneliness epidemic is not simply a social problem but a neurochemical one. Cortisol reduces serotonin, dopamine antagonizes it, and ultra-processed food depletes the tryptophan needed to make it. 3. Lustig draws a clear line between primary needs, love, sleep, real food, and secondary desires, status, power, accumulation. Meeting the basics first is both the simplest and most radical act available.Know Thyself, but not by yourself. A guided space to return home to yourself.https://www.knowthyselfcollective.com✨THANK YOU TO OUR SPONSORS:https://www.mudwtr.com/knowthyselfUse code KNOWTHYSELF for up to 43% off sitewidehttps://www.oneskin.co/KNOWTHYSELF Use code KNOWTHYSELF for 15% off OneSkin___________00:00 Intro02:02 What Is Hostage Brain?04:47 The Loss of Stress Resilience09:15 Dopamine, Tolerance, and the Addicted Brain11:26 The Five Causes of Hijacked Biology13:08 The Amygdala: Fear Center, Air Traffic Controller, Buckshot16:07 The Four Brakes on the Amygdala and Why They're Failing22:13 From Real Food to Ultra-Processed Food: The Five Changes23:51 Sugar: The Big Kahuna29:57 Mitochondria, Body Temperature, and the Energy Crisis32:33 The Corporate Machine: Big Food, Big Pharma, and the Atlas Network36:26 Does Big Food Collude with Big Pharma?40:51 Children Getting Sicker: The Systemic Failure46:10 "Personal Responsibility" Is a Tobacco Industry Invention51:44 Freedom of Choice Is Really Freedom to Blame53:11 Loneliness vs. Solitude: The Serotonin Distinction56:52 You Can't Love with an Inflamed Brain57:25 The Gut-Brain Axis and the Light of Tryptophan1:03:13 Orange vs. Orange Juice: Why Fiber Is the Point1:07:44 BioLumen and Making Ultra-Processed Food Healthy1:16:14 Insulin, the Three Fat Depots, and Thin Outside Fat Inside1:25:10 How to Reduce Liver Fat1:26:19 Sugar, ADHD, and the Prefrontal Cortex1:26:41 Practical Steps: Phones, Plastics, and Environmental Toxins1:33:18 The Four C's: Connect, Contribute, Cope, Cook1:41:29 Community Questions: ATP, Cognition, and the Biological Basis of Consciousness1:45:27 What Gives Dr. Lustig Hope1:47:38 Closing Message: Tend to Yourself First___________✨MORE FROM DR. ROBERT LUSTIG↳Facebook: https://www.facebook.com/DrRobertLustig/↳Metabolical Book: https://metabolical.com↳https://robertlustig.comSTUDIES MENTIONED:↳https://pubmed.ncbi.nlm.nih.gov/41880620/↳https://supreme.justia.com/cases/federal/us/505/504/↳https://www.sciencedirect.com/science/article/abs/pii/S0306987726001544
There is perhaps no figure more emblematic of the paranormal than the psychic. Able to predict the future, see into the past, and even communicate with the dead, the psychic’s (supposed) awesome gifts are matched only by his or her ability to withstand skepticism and ridicule. But are our misgivings towards these intuitives justified? Is it merely smoke and mirrors which they’ve learned to master, or are they, in fact, possessed of powers beyond our comprehension? This hour, we speak with believers, skeptics, and self-proclaimed psychics to find out. GUESTS: Daryl Bem: Emeritus professor of psychology at Cornell University and the author of Feeling the Future: Experimental Evidence for Anomalous Retroactive Influences on Cognition and Affect Allison Dubois: A psychic medium and profiler and the author of several books, including Into the Dark: How the Dead Help Us Heal Benjamin Radford: Deputy editor for Skeptical Inquirer and co-host of Squaring the Strange Emily Stroia: An ituitive medium, the founder of the Intuitive Soul Academy, and the author of several books, including Psychic Development for Beginners Jenniffer Weigel: An Emmy Award-winning broadcast journalist and the author of Psychics, Healers and Mediums: A Journalist, a Road Trip, and Voices from the Other Side The Colin McEnroe Show is available as a podcast on Apple Podcasts, Spotify, Amazon Music, TuneIn, Listen Notes, or wherever you get your podcasts. Subscribe and never miss an episode! Subscribe to The Noseletter, an email compendium of merriment, secrets, and ancient wisdom brought to you by The Colin McEnroe Show. Join the conversation on Facebook and Twitter. Colin McEnroe, Jonathan McNicol, and Chion Wolf contributed to this show, which originally aired June 28, 2017.Support the show: http://www.wnpr.org/donateSee omnystudio.com/listener for privacy information.
In this episode Andrea Samadi explores Phase 3 of the Brain's Operating System—movement, learning, and cognition—focusing on the recovery and adaptation side of the "movement loop." She revisits Dr. Kristen Holmes' insights on measuring strain, sleep, and recovery to show how movement provides the stimulus while recovery enables lasting adaptation and greater capacity. The episode offers practical steps: identify your stimulus, track recovery, find your weakest link, and use feedback to train smarter—not just harder—so measurement becomes a tool for meaningful change. IN EPISODE 405, WE WILL COVER: ✔ Why recovery is not the absence of work—it is where adaptation occurs → How recovery allows the brain and body to absorb stress and build greater capacity. ✔ Where Dr. Kristen Holmes fits into the Movement Loop → Movement creates the stimulus; recovery determines how effectively we adapt. ✔ Why measuring your data matters → What gets measured can be adjusted, repeated, and improved. ✔ How to identify your personal recovery patterns → Using sleep, HRV, resting heart rate, strain, stress, and daily behaviors to uncover what helps—or limits—your performance. ✔ Why your recovery formula is uniquely yours → The habits that improve one person's readiness may not produce the same results for someone else. ✔ How recovery helps create more predictable performance → When you understand your patterns, you can make better daily decisions instead of leaving your results to chance. ✔ How repeated behaviors become adaptation → Measure → Adjust → Repeat → Recover → Adapt → Build Capacity. ✔ Where the larger journey is taking us → A preview of The Journey of the Mind Workbook and its five-step process: Understand → Reflect → Apply → Measure → Become ✔ What comes next in the Movement Loop → A Bonus Episode applying recovery science to real-world data, followed by Dr. John Medina on how an activated brain turns experience into attention, learning, and lasting change. EP 405 — From Recovery to Adaptation: Revisiting Dr. Kristen Holmes in Phase 3 WELCOME BACK Welcome back to the Neuroscience Meets Social and Emotional Learning Podcast, where we bridge neuroscience, social and emotional learning, and human performance so we can create measurable improvements in our well-being, achievement, leadership, productivity, and results. I'm Andrea Samadi, and if you've been following along through Season 16, you'll know that we've been building what I call The Brain's Operating System for Human Performance—a neuroscience-based framework designed to help us understand how the different systems of the brain and body work together to influence how we learn, adapt, connect, lead, and perform. Our roadmap has five phases: Phase 1 — Regulation & Safety Where we built the foundation by asking: Is the nervous system regulated enough to learn and grow? Phase 2 — Neurochemistry & Motivation Where we explored the systems that drive us to act, persist, and pursue meaningful goals. And now, we are here—Phase 3: Movement, Learning & Cognition. This is where movement becomes the catalyst for learning, adaptation, and performance. The central idea of Phase 3 is that the brain does not change through information alone. It changes through experience. Through movement. Through challenge. Through recovery. And through the repeated process of adapting to what we ask our brain and body to do. I called the framework we are building in this phase: The Movement Loop: Movement (the stimulus) → Brain Activation (the brain responds) → Attention (the brain becomes engaged and ready to learn) → Learning (new information and experience begin changing neural pathways) → Recovery (the brain consolidates and restores) → Adaptation (the brain and body adjust to the repeated challenge) → Performance (the adaptation shows up in what we can do) → Greater Capacity (we become capable of more) Movement is the stimulus. Brain activation is the response. Attention opens the door to learning. Learning creates change. Recovery helps that change stick. Adaptation builds the system. Performance reveals the result. Greater capacity is what we gain. And then we repeat the cycle. In our last episode, EP 404, we revisited the groundbreaking work of Dr. John Ratey, who helped us understand the front end of this loop. Dr. Ratey showed us that: Movement changes the brain. Exercise activates the brain. It supports attention. It prepares us for learning. And it creates the biological conditions that allow the brain to change. But that leaves us with an important question. What happens after we create the stimulus? Because movement alone isn't enough. Training alone isn't enough. And more strain does not automatically equal more improvement. At some point, the brain and body have to recover from what we've asked them to do And that's where today's episode begins with the back end of the movement loop. WHERE THIS JOURNEY IS GOING Before we move into today's episode, I want to zoom out for a moment and share where I believe this entire journey is taking us. Because as we've been building The Brain's Operating System for Human Performance—making sense of the interviews we've gathered over the past seven years and organizing them into five phases we can all understand and apply—I realized we are doing something much bigger than simply reviewing past episodes. I had to stop for a moment and look at what we've built so far. And that's when something became clear: We are mapping a journey. We aren't finished yet—we are only in Phase 3—but as we continue into Phases 4 and 5, I think you'll begin to see the bigger picture. This is about more than learning how the brain works or connecting social and emotional learning skills to our daily lives. It's about asking: What will we actually do with everything we've learned once we've traveled through all five phases? That's what led me to what I'm calling The Journey of the Mind. We could just as easily call it The Journey of the Brain because we now understand how deeply connected the mind and brain are.[i] But for me, Journey of the Mind has a much older meaning. That phrase takes me all the way back to the late 1990s, when I had the opportunity to watch an unpublished video program with that title, created by my mentor, Bob Proctor. I had no idea where that experience would lead me. But it planted a seed. It encouraged me to stop looking outside of myself for every answer... and to begin looking inward. To become curious about my thoughts. My beliefs. My habits. My potential. And ultimately, the relationship between what happens inside of us and the results we create outside of us. That idea influenced my early writing and eventually became part of the path that led me here. And now, all these years later, I can see that the podcast has evolved into its own Journey of the Mind. Only now, we have neuroscience to help us understand what is happening along the way. We have hundreds of experts. Years of research. Stories. Experiments. Data. And our own lived experience. The five phases give us a way to bring all of that together. We began with Regulation and Safety, because before we can grow, the nervous system needs a stable foundation. Then we moved into Neurochemistry and Motivation, where we explored what drives our behavior. Now, in Phase 3, we are looking at Movement, Learning and Cognition—how experience changes the brain and how recovery and adaptation help us build greater capacity. Next, we'll move into Connection, Emotion and Social Intelligence, because none of us develops in isolation. And finally, we'll reach Integration, Meaning and Human Performance, where these systems begin to come together. And when we reach the end of Phase 5, I want to bring the entire journey into something practical. THE JOURNEY OF THE MIND WORKBOOK I'm currently developing a companion workbook for these five phases. The Journey of the Mind Workbook will help us take what we've learned and apply it to our own lives through a simple process: Understand. Reflect. Apply. Measure. Become. Understand the science. Reflect on our own patterns. Apply what we are learning. Measure what changes. And then ask the bigger question: Who are we becoming? Because the goal isn't simply to know more about neuroscience. It's to use what we know. To identify our gaps. Recognize what is working. Build a clearer understanding of how we learn, recover, connect, perform, and grow. And become more intentional about the future we are creating. The workbook is still taking shape, but it's coming. And my hope is that when we reach the end of these five phases, we won't just have more information. We'll have a map. A clearer understanding of our brain. A deeper understanding of ourselves. A way to measure our growth. And a framework for becoming who we are capable of being. Now, let's return to where we are today... Phase 3: Movement, Learning and Cognition. Before we go further, I want to give you a quick roadmap for today's episode. We're going to explore Kristen Holmes' work through 15 short parts, each one helping us move step-by-step through the recovery and adaptation side of the Movement Loop. (The left side of the loop). We'll begin by clarifying the difference between readiness and adaptation. Then we'll look at the stimulus–recovery–adaptation cycle, revisit Kristen's original idea that what gets measured gets improved, and connect that to what I've learned from tracking my own data over the past five years. From there, we'll explore the difference between training harder and training smarter, why movement is information, why the goal is not maximum strain, and how these same principles apply to learning and performance far beyond exercise. We'll also return to one of my favorite ideas from Kristen's original interview: Your competitive advantage is often built in your downtime. And finally, we'll bring everything back to the Movement Loop and ask the question that really matters: Is what I'm doing building more strain—or building greater capacity? So think of these 10 parts as a guided walk through the second half of the loop... from recovery... to adaptation... to performance... and ultimately to greater capacity. PART 1 — WHY ARE WE RETURNING TO DR. KRISTEN HOLMES? When we first interviewed Dr. Kristen Holmes in 2021 for EP 134[ii], she was Vice President of Performance Science at WHOOP. At the time, I had recently turned 50. I purchased WHOOP because I wanted to improve what I knew was my weakest link: sleep. And in that original conversation, Dr. Holmes introduced ideas that I've now carried with me for more than five years. One of them was this: “What gets measured gets improved.” Another was the idea that what we do in our downtime may actually give us our greatest competitive advantage. At the time, I understood this at one level. Sleep matters. Recovery matters. Strain matters. Your habits outside of the workout matter. But after five years of looking at my own data, I understand her message differently. And I think this is one of the most important things about revisiting experts over time. We hear something at one stage of our development... and then years later, we hear the same idea again with an entirely different level of understanding. We revisited this same 2021 interview earlier in this season during Phase 1: Regulation & Safety on EP 390.[iii] So why are we covering this topic again? Because I realized that Dr. Holmes' work sits at a critical intersection inside our Brain's Operating System. In Phase 1, her work helped us understand regulation and readiness. Her message was: Can your system handle what you are asking it to do today? But in Phase 3, the question changes. Now we're asking: Is what I'm doing today actually helping my system adapt? That's very different. In Phase 1, recovery helped us understand whether the system was ready. In Phase 3, recovery helps us understand whether the system is changing. And that's why we're returning to Dr. Kristen Holmes. Not to repeat what we learned. But to take it one level deeper. Just a note here: while I've been using WHOOP to measure my movement, sleep, strain, and recovery, WHOOP is only one tool. There are many other ways to track these patterns—from other wearable devices and apps to simple measures like sleep duration, resting heart rate, exercise intensity, energy levels, and how recovered you feel. The important point isn't the device you use. It's learning to pay attention to your own patterns and using that feedback to make better decisions. PART 2 — READINESS VS. ADAPTATION Now let's return to the Movement Loop we opened this episode with: Movement → Brain Activation → Attention → Learning → Recovery → Adaptation → Performance → Greater Capacity In our last episode, Dr. John Ratey helped us understand the front end of this loop—how movement activates the brain, supports attention, and creates the conditions for learning. Today, Dr. Kristen Holmes helps us move into the recovery and adaptation side of the loop—what happens after we create the stimulus. This is where we ask a different kind of question. In Phase 1, we looked at Kristen's work through the lens of readiness: What is my capacity today? How much strain can I tolerate? How well did I sleep? What does my HRV tell me? How recovered am I? Do I have the capacity to push? Or would backing off today serve me better? That was the Phase 1 question: Am I ready? But now, in Phase 3, we move one step further around the loop. Now we are asking: Am I adapting? Is the movement I'm doing helping me become stronger? More resilient? More efficient? Better able to recover? More capable than I was before? That is the shift from readiness to adaptation. And this is where Dr. Kristen Holmes' work becomes essential to Phase 3. Because movement may provide the stimulus... but recovery is what gives the brain and body the opportunity to respond to that stimulus, adapt, and ultimately build greater capacity. Or would backing off today serve me better? That was our Phase 1 lens. And it was foundational. Because if the nervous system is depleted, overstressed, or under-recovered, everything downstream is affected. But Phase 3 takes us another step forward. Now we are not only asking: Am I ready? We are asking: Am I adapting? ADAPTATION asks: What is changing because of what I repeatedly do? Am I becoming stronger? More resilient? More efficient? Better able to tolerate stress? Better able to recover? More capable than I was before? And this is where I think recovery is frequently misunderstood. Recovery isn't simply what happens when we stop working. Recovery is not the opposite of performance. Recovery is part of the process that creates future performance. That distinction changes everything. Because the goal is not simply to recover enough to get through today. The goal is to recover in a way that allows the brain and body to become better prepared for tomorrow. So I want to give you one sentence to keep in mind throughout this entire episode: Movement provides the stimulus. Recovery allows the adaptation. That's where Kristen Holmes' work enters Phase 3. Dr. Ratey helped us understand why movement activates the brain and creates the stimulus for change. Dr. Holmes helps us to understand what has to happen afterward if that stimulus is going to become something useful. Something the body can integrate. Something the brain can learn from. Something that eventually becomes greater capacity. PART 3 — THE STIMULUS–RECOVERY–ADAPTATION CYCLE Think about exercise for a moment. When you challenge your body, you create stress. That's not necessarily a bad thing. In fact, some level of challenge is exactly what we need. It's called hormesis, right? The challenge is the signal. It tells the system: Something is being asked of me. But the signal alone isn't what we are looking for. What matters is what your body does afterward. You place a demand on the system. Then the body needs time and resources to respond to that demand. We can think of it very simply: STIMULUS → RECOVERY → ADAPTATION → GREATER CAPACITY The stimulus is the challenge. Recovery is the response period. Adaptation is the change. And greater capacity is the result. The purpose of training isn't simply to create more strain. (Which is what I did for years). The purpose is to create a strain that your system can successfully recover from and adapt to. And that idea changed the way I began looking at my own movement. For years, I thought: Harder must be better. Longer must be better. More must be better. If I could hike for three hours, that had to be better than walking for one. If I could push harder, I assumed I would get better results. If I felt exhausted after a workout, I often assumed that meant I had accomplished more. But the data started showing me something different. Sometimes more strain simply created... more strain. And if I wasn't sleeping enough...(because I was repeating the same thing without enough rest) if I wasn't recovering... if I was stacking high strain on top of stress... then I wasn't necessarily building greater capacity. I could actually be interrupting the very adaptation I was trying to create. This is why one of the most important questions in Phase 3 becomes: How much challenge can my system absorb, recover from, and adapt to? Not: How much can I force myself to do? And this takes us directly back to the lesson Kristen Holmes gave us five years ago. Because if we want to know whether the stimulus we're creating is actually working... we need feedback. We need to see the patterns. We need to understand the relationship between what we do and how our system responds. Which brings us to Part 4: PART 4 — WHAT GETS MEASURED GETS IMPROVED In our Phase 1 episode with Dr. Holmes, I shared something that has become very important to me over the past five years. Once you begin measuring your sleep, your recovery, your strain, and your habits... you stop guessing. And when you stop guessing, patterns begin to emerge. For me, some of those patterns took years to understand. I began seeing that: Too much strain + not enough sleep = poor recovery. Over and over again. I knew intuitively that I was pushing too hard. But knowing something and changing behavior are two very different things. The data eventually forced me to look at the pattern honestly. There were times when I was training hard... waking early... cutting sleep short... and then wondering why my recovery didn't improve. I was giving my system a stimulus. But I wasn't giving it enough opportunity to adapt. That distinction matters. Because the goal is not simply: How much can I do? The better question is: How much can I do, recover from, and adapt to? That is a completely different way of approaching human performance. PART 5 — MY UNDERSTANDING OF RECOVERY HAS CHANGED When I first bought WHOOP in 2021, my weakest link was sleep. I knew I needed to improve it. And I did. Not perfectly. But significantly. What I find interesting now is that as one weakness improves, another one becomes easier to see. That's the value of measurement. Your weakest link can change. In 2021, mine was sleep. Today, I can see other areas much more clearly. Strength training is one. Balancing different kinds of movement is another. And this is where I began looking beyond total exercise time. Instead, I started asking: How much time am I spending in lower intensity movement? How much time am I spending at higher intensity? What happens to my recovery when I increase strain? How much sleep do I need after harder training? How quickly do I recover? What habits seem to help me recover faster? What habits tank my recovery? That's a completely different mindset than: Did I work hard today? PART 6 — TRAIN HARDER VS. TRAIN SMARTER One of the biggest lessons I pulled from Kristen Holmes' work was the difference between training harder and training smarter. In our Phase 1 review, I shared one example that really challenged my assumptions. I went on a walk along the canal near my house. Nothing extreme. I was walking my dogs and wearing a weighted vest. And during this much more moderate activity, I was able to reach Zones 4 and 5 for short periods. That surprised me, because those higher-intensity zones could sometimes be difficult for me to reach even during much longer hikes. I noticed the same thing when I started comparing different trails here in Arizona. When I first moved to AZ, Camelback Mountain—especially the Echo Canyon Trail—was my go-to hike. I used to hike it after work and again on the weekends. It was short, steep, and intense—about a 2.5-mile round trip on Echo Canyon, with the Cholla side a bit longer. So when I later moved to another part of the Valley and started hiking Telegraph Pass at South Mountain, I initially thought: This isn't going to give me the same workout. Telegraph Pass felt less intense. It didn't have that same steep, all-out challenge I was used to on Camelback. But once I started measuring my hikes, or learning new trails that were longer, I saw something I didn't expect. The longer, lower-intensity hikes were sometimes producing more overall strain than the shorter, harder hikes. That honestly shocked me. Because I had always assumed that if something felt harder, it must be giving me better results. But the data showed me that wasn't always true. A longer period of sustained effort—even at a lower intensity—could place a greater total load on my system than a shorter burst of very difficult exercise. And that taught me something important: How hard something feels in the moment is not always the same thing as the total load it places on your body. That was one of the moments when measurement changed the way I thought about exercise. I stopped asking only: How hard did that feel? And started asking: How did my body actually respond? And this is where data becomes useful. Not because the number knows everything. But because the number can reveal something we might otherwise miss. It can become feedback. And feedback helps us learn. PART 7 — MOVEMENT IS INFORMATION Movement is not just calories burned. I hardly ever think about calories anymore. There was a time when I could tell you almost exactly how much effort it took to burn off certain foods. But I think about movement very differently today. Movement is not punishment for what we ate. Movement is not simply exercise. Movement is information. Every time we move, we send signals to the brain and body about the demands being placed on the system. How much strength do I need? How much endurance? How much balance, coordination, speed, or cardiovascular capacity? And when we repeatedly expose ourselves to manageable challenge, the system begins to learn from those signals. It responds. It adjusts. And, with adequate recovery, it adapts. So movement isn't just something we do to burn energy. Movement is one of the ways we teach the brain and body what we want them to become capable of doing. PART 8 — THE GOAL IS NOT MAXIMUM STRAIN This brings me to what may be the biggest takeaway from today's episode. The goal is not maximum strain. The goal is the right amount of strain for your current capacity. Because too little challenge may not create enough stimulus. But too much challenge without adequate recovery can work against the very adaptation we're trying to create. And this is where Kristen Holmes' message about strain and recovery becomes so powerful. In Phase 1, we used recovery to ask: Should I push today? In Phase 3, we can use recovery to ask: Is my current training producing the adaptation I want? That is a much bigger question. And it applies far beyond exercise. PART 9 — THE SAME PRINCIPLE APPLIES TO LEARNING Think about learning. We don't learn best by forcing information into the brain endlessly without rest. We need exposure. Attention. Practice. Then space. Sleep. Recovery. Integration. And then we return to the material again. Spaced Repetition. The brain changes through repeated cycles of challenge and recovery. So while we're talking today about Kristen Holmes, recovery, movement, and strain... the same architecture appears in learning. It appears in work. It appears in leadership. It appears in stress management. It appears almost anywhere humans are trying to grow. Challenge alone doesn't guarantee growth. Challenge + recovery + repetition creates the conditions for adaptation. PART 10 — YOUR COMPETITIVE ADVANTAGE IS STILL BUILT IN YOUR DOWNTIME One of my favorite ideas from my original conversation with Kristen was something we revisited in Phase 1. Listen to Dr. Holmes' CLIP here. She talked about the importance of what we do in our downtime. That what happens outside of the performance itself may give us our competitive advantage. Five years later, I think I finally understand how deep that idea goes. Your workout may last an hour. Your hike may last two hours. Your presentation may last 45 minutes. Your game may last an afternoon. But adaptation is happening across the rest of the day. What are you doing with the other hours? Are you sleeping? Hydrating? Fueling? Managing stress? Allowing the nervous system to come back down? Giving the body what it needs to repair? That downtime isn't empty time. It's part of the training. PART 11 — RECOVERY IS AN ACTIVE PROCESS This is another mindset shift that helped me. I used to think of recovery as: Doing nothing. Resting. Taking a day off. But recovery can actually be very intentional. For me, over the past several years, that has included things like: prioritizing sleep hydration meditation breathing spending time outside lower-intensity movement infrared sauna red light therapy And again, the point isn't that everyone should copy my routine. The important question is: What actually helps your system recover? That takes us back to one of the central ideas of our recent Bonus Episodes. PART 12 — FIND YOUR GAP In Bonus Episode 2[iv], we introduced the idea of finding your gap. Instead of copying someone else's recipe for high performance, identify the variable that is currently limiting you. Maybe your gap is sleep. Maybe it's hydration. Maybe it's high stress. Maybe it's too much intensity. Maybe it's not enough movement. Maybe it's inconsistent recovery habits. Maybe it's strength. The point is that your gap is personal. And it can change. This is one of the most useful things measurement can give us. It can show us: Where is the breakdown in my loop? Let's return to the Movement Loop. Movement → Brain Activation → Attention → Learning → Recovery → Adaptation → Performance → Greater Capacity Where is your gap? Are you not moving enough? Are you moving but not recovering? Are you recovering but not challenging yourself enough to create adaptation? Are you pushing constantly without allowing your system to consolidate the gains? If you can locate your gap, you know where to begin. PART 13 — THE MOVEMENT LOOP THROUGH DR. KRISTEN HOLMES Let's walk through the entire loop now through the lens of Dr. Ratey and Dr. Holmes' work. MOVEMENT We create a stimulus. We challenge the brain and body. BRAIN ACTIVATION Movement changes our physiological and neurological state. This is where Dr. Ratey's work helps us understand why movement prepares the brain. ATTENTION An activated brain is better positioned to engage. LEARNING The brain receives information and experience. But learning isn't finished simply because we experienced something. RECOVERY Now the system needs space. This is where Kristen Holmes enters the loop. How well did we sleep? How much stress are we carrying? What was our total strain? How prepared is the system to absorb what we've done? ADAPTATION This is where the system changes. Over time, repeated cycles of challenge and recovery create new capacity. PERFORMANCE We begin to see the result. We move differently. Think differently. Learn more efficiently. Handle greater demands. Recover faster. GREATER CAPACITY And this is what makes the loop a loop. Because now tomorrow's starting point may be different from yesterday's. The system has adapted. So we can begin again. The WHOOP wearable device may have started with elite athletes, but optimizing performance isn't only for elite athletes. Any of us can measure our patterns, learn from the feedback, and use that information to build greater capacity. PART 14 — THIS CHANGES HOW I THINK ABOUT PERFORMANCE For most of my life, I thought performance was primarily about effort. Work harder. Try harder. Push harder. And effort still matters. But effort without adaptation can become a dead end. What I'm learning through this phase is that performance is much more dynamic. You need the stimulus. You need the challenge. You need the work. But you also need recovery. Because recovery is what allows the system to absorb the work. And that leads me to an important sentence I want us to remember: The goal isn't to prove how much stress you can tolerate. The goal is to become capable of more over time. That's adaptation. PART 15 — FIVE YEARS LATER, DR. HOLMES' MESSAGE MEANS SOMETHING DIFFERENT When we first interviewed Kristen Holmes in 2021, the idea that what gets measured gets improved resonated with me immediately. That idea is partly why I bought a wearable device in the first place. I wanted data. I wanted answers. I wanted to improve sleep. But five years later, I think the lesson is bigger than measurement. Measurement itself isn't the goal. The goal is feedback. And feedback only becomes useful when we do something with it. Measure. Write it down somewhere. Observe what's happening month to month and year to year. Adjust. Recover. Adapt. Repeat. Measurement closes the learning/performance loop. That's why Dr. Holmes' work was worth revisiting here in Phase 3. Not simply because she studies recovery. But because measurement gives us information about whether the behaviors we repeat are actually creating the changes we want. PRACTICAL APPLICATION — HOW TO USE THIS THIS WEEK So let's make this practical. You do not need WHOOP. You do not need a wearable. You don't need complicated equipment. You simply need to start paying attention. Identify your current stimulus. What are you asking your body and brain to adapt to right now? Maybe it's: exercise strength training a new learning goal a demanding work schedule stress adapting to intensive heat in your new sauna Know what you're asking the system to do. Identify your recovery. Ask: How am I sleeping? How am I managing stress? Am I giving myself enough downtime? How do I feel the day after harder efforts? What patterns do I notice? Watch the relationship between effort and recovery. Does more effort always give you better results? Or is there a point where more becomes counterproductive? Find your gap. What is your weakest link right now? Don't try to fix everything. Start there. Look for evidence of adaptation. Ask yourself: Am I becoming more capable? Can I handle something today that used to be difficult? Am I recovering faster? Am I stronger? More focused? More resilient? If the answer is yes, your loop is working. REVIEW AND CONCLUSION To review and conclude this week's episode 405, let's bring this episode together. We brought Dr. Kristen Holmes back into our roadmap intentionally. In Phase 1: Regulation and Safety, Dr. Holmes helped us understand recovery as readiness. Sleep. HRV. Strain. Stress. Recovery. The question was: Is the system prepared for today? Today, in Phase 3: Movement, Learning and Cognition, we've looked at those same ideas through a different lens. Now the question is: Is the system adapting for tomorrow? That's the progression. Dr. John Ratey helped us understand that movement provides the brain with a powerful stimulus. Kristen Holmes helps us understand that the story doesn't end with the stimulus. We need recovery. Because: Movement provides the stimulus. Recovery allows the adaptation. Adaptation improves performance. Performance builds greater capacity. And then the loop begins again. ONE QUESTION TO TAKE WITH YOU Before you move on with your day, I want you to ask yourself one question: Am I creating more strain…or am I creating more capacity? They are not the same thing. You can push harder without improving. You can work longer without adapting. You can accumulate strain without building capacity. The goal is not simply to do more. The goal is to create a system where you can become more. FINAL TAKEAWAY And this brings us right back to Kristen Holmes's message from 2021: What gets measured gets improved. But today, I would add something to it: What gets measured can be adjusted. What gets adjusted can be repeated (in this new way) And what gets repeated—paired with the right amount of recovery—can become adaptation. That is how capacity grows. And that is where Kristen Holmes fits into Phase 3—not simply as an expert on recovery, but as a critical part of the science of change. And this idea—turning what we measure into meaningful change—is also shaping the larger vision for this podcast. At the start of this episode, we mentioned I'm developing a companion workbook called Journey of the Mind—a practical guide designed to help us move beyond simply listening to these ideas and begin applying them in our own lives. The workbook will guide us through five steps to apply what we have covered on this podcast. Understand. Reflect. Apply. Measure. Become More. With each episode we've covered. And this workbook began with an unexpected spark of inspiration. So, I want to offer one quiet acknowledgment: To the person who quietly helped illuminate this path for me—thank you. Sometimes, the people who influence the direction of our lives may never fully realize what their presence helped awaken. My vision from that the spark of inspiration I received will travel through this work and reach you listening—inspiring you to move forward on your own path and create a life that is more intentional, meaningful, and fulfilling than the one you knew before. Because inspiration doesn't end with the person who first awakens it. It moves through us. And when we act on it, one spark can grow into something that serves many others—far beyond what either person could have created individually. We'll explore the transformative power of human connection more deeply in Phase 4. Then, in Phase 5, we'll examine how connection, meaning, and contribution can become something that continues growing beyond us. But until then, we'll continue building this journey—one insight, one application, and one measurable change at a time. COMING NEXT Before we move on to Dr. John Medina and the next part of the Movement Loop, we'll cover our next Bonus Episode and show you what adaptation can actually look like in real life. In Bonus EP 3, I'm going to take you inside one of the clearest changes I've seen in my own data over the past several years: my resting heart rate. We'll look at where it started, how it changed, what may have contributed to that change, and why resting heart rate can become a useful window into how the body is adapting over time. Because this is really what Phase 3 is about. Not simply moving more. Not simply collecting data. But asking: Is what I'm doing actually changing my capacity? Then, for EP 406 we'll return to the research of Dr. John Medina and explore the next critical link in the Movement Loop: Attention. Because movement can activate the brain—but activation alone doesn't guarantee learning. The brain must first notice what matters. It must direct its attention. Hold that attention long enough to encode new information. And return to that information if it is going to become something we can remember and use. Dr. Ratey helped us understand how movement prepares the brain. Dr. Kristen Holmes helped us understand how recovery allows the brain and body to adapt. And Dr. Medina will help us understand what happens between those two points: How does an activated brain turn experience into learning? Because movement may open the door— but attention determines what enters and stays behind that door. And we'll reinforce one of the central ideas of this journey: What we repeatedly do shapes who we become. The question is: Are those repeated actions expanding our capacity and moving us toward our highest level of performance—or quietly holding us back? That's where we'll turn next as we continue building the Movement Loop. I'll see you next time. EP 405 — KEY TAKEAWAYS ✔ Why Kristen Holmes belongs in both Phase 1 and Phase 3 ✔ The difference between recovery for readiness and recovery for adaptation ✔ Why movement alone does not create improvement ✔ The relationship between stimulus → recovery → adaptation → greater capacity ✔ Why the goal is not maximum strain, but the right dose of strain ✔ How recovery completes the Movement Loop ✔ Why your weakest link can change as you improve ✔ How measurement provides feedback for better decisions ✔ Why downtime is part of the performance process ✔ The difference between accumulating strain and building capacity THE MOVEMENT LOOP Movement → Brain Activation → Attention → Learning → Recovery → Adaptation → Performance → Greater Capacity → Repeat Remember: Movement provides the stimulus. Recovery allows the adaptation. The goal isn't maximum strain. The goal is greater capacity. RESOURCES: Watch the full interview from 2021 here https://www.youtube.com/watch?v=TOgivjYhhW8 CLIP 1 Dr. Kristen Holmes https://www.youtube.com/shorts/Lg7FiWJKZc4 Phase 1: Regulation & Safety The Foundation Core Question: Is the nervous system safe enough to learn? Everything begins with regulation. Before we can focus, learn, lead, or perform, the brain first asks one fundamental question: Am I safe? Throughout Phase 1, our guests showed us that regulation isn't simply about reducing stress—it's about creating the biological conditions that allow the brain to learn, adapt, and thrive. Together we explored: Baland Jalal – how sleep, curiosity, imagination, and creativity prepare the brain for learning. https://andreasamadi.podbean.com/e/hypnagogic-genius-capture-your-best-ideas-at-the-edge-of-sleep/ Dr. Bruce Perry – why regulation, rhythm, and relationships form the foundation of every healthy nervous system. https://andreasamadi.podbean.com/e/safety-first-why-a-regulated-brain-is-the-key-to-learning/ Dr. Sui Wong – how lifestyle medicine and autonomic balance build lifelong brain resilience. https://andreasamadi.podbean.com/e/your-eyes-the-brain-s-early-warning-system/ Rohan Dixit – how heart rate variability gives us real-time feedback on our ability to regulate stress. https://andreasamadi.podbean.com/e/breathe-to-reset-how-hrv-tech-reveals-hidden-stress/ Dr. Kristen Holmes – how recovery metrics reveal our physiological readiness to perform. https://andreasamadi.podbean.com/e/kristen-holmes-from-whoopcom-on-unlocking-a-better-you-measuring-sleep-recovery-and-strain/ Dr. Antonio Zadra – how sleep and dreaming consolidate memories, regulate emotions, and generate insight. https://andreasamadi.podbean.com/e/when-brains-dream-how-sleep-integrates-emotion-insight-and-creativity/ Together these conversations taught us that sleep and stress regulation aren't optional—they're the operating system that allows every higher brain function to work. Phase 2: Motivation & Neurochemistry The Direction Core Question: What moves us into action? Once the brain feels safe, it becomes ready to pursue goals. In Phase 2, we explored the internal chemistry that transforms intention into action. Our experts helped us understand that sustainable motivation isn't about willpower—it's about aligning our beliefs, thoughts, attention, energy, and movement. Together we discovered: Bob Proctor — our beliefs determine the direction of our lives. https://andreasamadi.podbean.com/e/belief-first-the-neuroscience-of-motivation/ Dr. Carolyn Leaf — our thinking literally changes our brain chemistry. https://andreasamadi.podbean.com/e/thoughts-as-biology-how-your-mind-shapes-neurochemistry/ Dr. John Medina — attention determines what the brain encodes and remembers. https://andreasamadi.podbean.com/e/theory-of-mind-the-missing-link-between-attention-reward-and-motivation Dr. Anna Lembke- Dopamine, Motivation and Why the Brain Repeats Behavior https://andreasamadi.podbean.com/e/dopamine-nation-the-pleasure%e2%80%93pain-balance-that-drives-motivation/ Dr. Friederike Fabritius — managing our energy allows high performance to become sustainable. https://andreasamadi.podbean.com/e/fun-fear-focus-closing-the-motivation-loop/ Dr. Chuck Hillman & Paul Zientarski — movement activates the brain, preparing it to learn. https://andreasamadi.podbean.com/e/move-to-learn-how-movement-activates-the-brain-and-fuels-motivation/ By the end of Phase 2, we introduced what became The Motivation Loop, showing how beliefs influence thoughts, thoughts influence actions, actions create results, and results reinforce future beliefs. Phase 3: Movement, Learning & Human Performance The Transformation Core Question: How does movement change the brain—and how does recovery transform that change into performance? Now we're taking the next step. Phase 3 builds on everything we've learned so far. If Phase 1 created a regulated nervous system... If Phase 2 created motivation and direction... Phase 3 explains how the brain and body actually become stronger. Together we've explored this process through conversations with: Dr. Chuck Hillman & Paul Zientarski — why movement activates the brain before learning. https://andreasamadi.podbean.com/e/movement-first-how-a-20%e2%80%91minute-walk-lights-up-the-brain/ Dr. John Ratey — how exercise builds a healthier, younger brain. https://andreasamadi.podbean.com/e/movement-matters-how-every-move-rewires-the-brain/ Dr. Kristen Holmes — why recovery determines adaptation and readiness. Dr. John Medina — how attention transforms movement into lasting learning. Jason Whitrock — how metabolism and cellular energy fuel long-term performance. WHERE WE ARE GOING NEXT Phase 4 — Connection, Emotion & Social Intelligence The Human System Core Question: How do we thrive with other people? The brain didn't evolve in isolation—it evolved through relationships. In Phase 4, we'll explore emotional intelligence, empathy, communication, trust, leadership, and psychological safety to understand how our relationships shape learning, well-being, and performance. Phase 5 — Integration & Human Performance The Complete System Core Question: How do all the systems work together? In our final phase, we'll bring everything together—regulation, motivation, movement, emotion, relationships, learning, and recovery—into one integrated framework. We'll discover how these systems work together to create measurable improvements in our well-being, achievement, leadership, productivity, and results. REFERENCES: [i] A Neurologist Looks at Mind and Brain, published October 1, 2003, by Phirose Hansotia. [ii] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 134 with Dr. Kristen Holmes “Unlocking a Better You: Measuring Sleep, Recovery and Strain.” https://andreasamadi.podbean.com/e/kristen-holmes-from-whoopcom-on-unlocking-a-better-you-measuring-sleep-recovery-and-strain/ [iii] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 390 https://andreasamadi.podbean.com/e/what-gets-measured-gets-improved-sleep-recovery-peak-performance/ [iv]Neuroscience Meets Social and Emotional Learning Podcast BONUS EP 2 https://andreasamadi.podbean.com/e/find-the-gap-how-recovery-unlocks-predictable-performance
After Caroline Bicks was named the University of Maineʼs inaugural Stephen E. King Chair in Literature, she became the first scholar to be granted extended access by King to his private archives, a treasure trove of manuscripts that document the legendary writerʼs creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by one question millions of Kingʼs enthralled and terrified readers (including her) have asked themselves: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book?Bicks focuses on five of his most iconic early works—The Shining, Carrie, Pet Sematary, ʼSalemʼs Lot, and Night Shift—to reveal how he crafted his language, story lines, and characters to cast his enduring literary spells. While tracking King's margin notes and editorial changes, she discovered scenes and alternative endings that never made it to print but that King is allowing her to publish now. The book also includes interviews Bicks had with King along the way that reveal new insights into his writing process and personal history.Part literary master class, part biography, part memoir and investigation into our deepest anxieties, Monsters in the Archives: My Year of Fear with Stephen King (Hogarth, 2026)—authorized by Stephen King himself—is unlike anything ever published about the master of horror. It chronicles what Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about a grown-up English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine. Her academic books include Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; her popular writing has appeared in the Modern Love column of the New York Times and McSweeney's Internet Tendency, and includes a humorous Bard-themed cocktail book, Shakespeare Not Stirred. She is the co-host of the Everyday Shakespeare podcast. Recommended Books: Ben Lerner, Transcription Libby Edwardson, We Sent Them Down Singing Daniel Mason, Country People Chris Holmes is Chair of Literatures in English and Professor at Ithaca College. He writes criticism on contemporary global literatures. His book, Kazuo Ishiguro Against World Literature, is published with Bloomsbury Publishing. He is the co-director of The New Voices Festival, a celebration of work in poetry, prose, and playwriting by up-and-coming young writers. Learn more about your ad choices. Visit megaphone.fm/adchoices
After Caroline Bicks was named the University of Maineʼs inaugural Stephen E. King Chair in Literature, she became the first scholar to be granted extended access by King to his private archives, a treasure trove of manuscripts that document the legendary writerʼs creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by one question millions of Kingʼs enthralled and terrified readers (including her) have asked themselves: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book?Bicks focuses on five of his most iconic early works—The Shining, Carrie, Pet Sematary, ʼSalemʼs Lot, and Night Shift—to reveal how he crafted his language, story lines, and characters to cast his enduring literary spells. While tracking King's margin notes and editorial changes, she discovered scenes and alternative endings that never made it to print but that King is allowing her to publish now. The book also includes interviews Bicks had with King along the way that reveal new insights into his writing process and personal history.Part literary master class, part biography, part memoir and investigation into our deepest anxieties, Monsters in the Archives: My Year of Fear with Stephen King (Hogarth, 2026)—authorized by Stephen King himself—is unlike anything ever published about the master of horror. It chronicles what Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about a grown-up English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine. Her academic books include Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; her popular writing has appeared in the Modern Love column of the New York Times and McSweeney's Internet Tendency, and includes a humorous Bard-themed cocktail book, Shakespeare Not Stirred. She is the co-host of the Everyday Shakespeare podcast. Recommended Books: Ben Lerner, Transcription Libby Edwardson, We Sent Them Down Singing Daniel Mason, Country People Chris Holmes is Chair of Literatures in English and Professor at Ithaca College. He writes criticism on contemporary global literatures. His book, Kazuo Ishiguro Against World Literature, is published with Bloomsbury Publishing. He is the co-director of The New Voices Festival, a celebration of work in poetry, prose, and playwriting by up-and-coming young writers. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network
After Caroline Bicks was named the University of Maineʼs inaugural Stephen E. King Chair in Literature, she became the first scholar to be granted extended access by King to his private archives, a treasure trove of manuscripts that document the legendary writerʼs creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by one question millions of Kingʼs enthralled and terrified readers (including her) have asked themselves: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book?Bicks focuses on five of his most iconic early works—The Shining, Carrie, Pet Sematary, ʼSalemʼs Lot, and Night Shift—to reveal how he crafted his language, story lines, and characters to cast his enduring literary spells. While tracking King's margin notes and editorial changes, she discovered scenes and alternative endings that never made it to print but that King is allowing her to publish now. The book also includes interviews Bicks had with King along the way that reveal new insights into his writing process and personal history.Part literary master class, part biography, part memoir and investigation into our deepest anxieties, Monsters in the Archives: My Year of Fear with Stephen King (Hogarth, 2026)—authorized by Stephen King himself—is unlike anything ever published about the master of horror. It chronicles what Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about a grown-up English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine. Her academic books include Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; her popular writing has appeared in the Modern Love column of the New York Times and McSweeney's Internet Tendency, and includes a humorous Bard-themed cocktail book, Shakespeare Not Stirred. She is the co-host of the Everyday Shakespeare podcast. Recommended Books: Ben Lerner, Transcription Libby Edwardson, We Sent Them Down Singing Daniel Mason, Country People Chris Holmes is Chair of Literatures in English and Professor at Ithaca College. He writes criticism on contemporary global literatures. His book, Kazuo Ishiguro Against World Literature, is published with Bloomsbury Publishing. He is the co-director of The New Voices Festival, a celebration of work in poetry, prose, and playwriting by up-and-coming young writers. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/literary-studies
After Caroline Bicks was named the University of Maineʼs inaugural Stephen E. King Chair in Literature, she became the first scholar to be granted extended access by King to his private archives, a treasure trove of manuscripts that document the legendary writerʼs creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by one question millions of Kingʼs enthralled and terrified readers (including her) have asked themselves: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book?Bicks focuses on five of his most iconic early works—The Shining, Carrie, Pet Sematary, ʼSalemʼs Lot, and Night Shift—to reveal how he crafted his language, story lines, and characters to cast his enduring literary spells. While tracking King's margin notes and editorial changes, she discovered scenes and alternative endings that never made it to print but that King is allowing her to publish now. The book also includes interviews Bicks had with King along the way that reveal new insights into his writing process and personal history.Part literary master class, part biography, part memoir and investigation into our deepest anxieties, Monsters in the Archives: My Year of Fear with Stephen King (Hogarth, 2026)—authorized by Stephen King himself—is unlike anything ever published about the master of horror. It chronicles what Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about a grown-up English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine. Her academic books include Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; her popular writing has appeared in the Modern Love column of the New York Times and McSweeney's Internet Tendency, and includes a humorous Bard-themed cocktail book, Shakespeare Not Stirred. She is the co-host of the Everyday Shakespeare podcast. Recommended Books: Ben Lerner, Transcription Libby Edwardson, We Sent Them Down Singing Daniel Mason, Country People Chris Holmes is Chair of Literatures in English and Professor at Ithaca College. He writes criticism on contemporary global literatures. His book, Kazuo Ishiguro Against World Literature, is published with Bloomsbury Publishing. He is the co-director of The New Voices Festival, a celebration of work in poetry, prose, and playwriting by up-and-coming young writers. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/literature
After Caroline Bicks was named the University of Maineʼs inaugural Stephen E. King Chair in Literature, she became the first scholar to be granted extended access by King to his private archives, a treasure trove of manuscripts that document the legendary writerʼs creative process—most of them never before studied or published. The year she spent exploring King's early drafts and hand-written revisions was guided by one question millions of Kingʼs enthralled and terrified readers (including her) have asked themselves: What makes Stephen King's writing stick in our heads and haunt us long after we've closed the book?Bicks focuses on five of his most iconic early works—The Shining, Carrie, Pet Sematary, ʼSalemʼs Lot, and Night Shift—to reveal how he crafted his language, story lines, and characters to cast his enduring literary spells. While tracking King's margin notes and editorial changes, she discovered scenes and alternative endings that never made it to print but that King is allowing her to publish now. The book also includes interviews Bicks had with King along the way that reveal new insights into his writing process and personal history.Part literary master class, part biography, part memoir and investigation into our deepest anxieties, Monsters in the Archives: My Year of Fear with Stephen King (Hogarth, 2026)—authorized by Stephen King himself—is unlike anything ever published about the master of horror. It chronicles what Bicks found when she set out to unearth how King crafted some of his scariest, most iconic moments. But it's also a story about a grown-up English professor facing her childhood fears and getting to know the man whose monsters helped unleash them. Caroline Bicks is the Stephen E. King Chair in Literature at the University of Maine. Her academic books include Cognition and Girlhood in Shakespeare's World and Midwiving Subjects in Shakespeare's England; her popular writing has appeared in the Modern Love column of the New York Times and McSweeney's Internet Tendency, and includes a humorous Bard-themed cocktail book, Shakespeare Not Stirred. She is the co-host of the Everyday Shakespeare podcast. Recommended Books: Ben Lerner, Transcription Libby Edwardson, We Sent Them Down Singing Daniel Mason, Country People Chris Holmes is Chair of Literatures in English and Professor at Ithaca College. He writes criticism on contemporary global literatures. His book, Kazuo Ishiguro Against World Literature, is published with Bloomsbury Publishing. He is the co-director of The New Voices Festival, a celebration of work in poetry, prose, and playwriting by up-and-coming young writers. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/biography
Anthropics Investoren erwarten für Oktober einen Börsengang bei zwei Billionen Dollar, was der größte IPO aller Zeiten wäre. Pip erklärt, warum der Termin geschickt gewählt ist. Für OpenAI sieht er die Lage umgekehrt. Dort kommt der zweite Vertriebschef in einem Jahr. Zusammen mit Cerebras hat OpenAI dafür eine Variante gebaut, die vierzehnmal schneller antwortet. Danach vier Modellstarts in einer Woche, bei denen ausgerechnet DeepSeek die Preise um bis zu das Zwölffache erhöht, und Elon Musk sein neues Grok für objektiv das beste Modell hält. Bei den Finanzierungsrunden geht es um Databricks, Lovable, Legora und Cognition, dazu um die Frage, ob man das Geld gerade nehmen und liegen lassen sollte. Silver Lake holt Workday von der Börse. In der Schmuddelecke erlaubt die Trump-Regierung privaten Firmen offensive Cyberangriffe und beruft sich dabei auf Kaperbriefe aus der Verfassung. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf doppelgaenger.io/werbung. Vielen Dank! Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Aus der Community (00:02:05) OpenAI wechselt den Vertriebschef (00:12:20) Ultrafast mit Cerebras (00:17:16) Anthropic-IPO (00:29:10) Anthropic kauft Decart (00:30:34) Braucht man ein KI-Device? (00:32:36) Gemini 3.7 Flash (00:34:10) DeepSeek V4-Pro (00:34:47) Grok 4.6 (00:37:11) SpaceX (00:38:49) Databricks und Snowflake (00:42:40) Workday geht von der Börse (00:44:11) Lovable (00:49:26) Legora (00:52:28) Cognition (00:55:18) Mistral (00:57:22) Kaperbriefe (01:02:05) Truth API (01:03:28) Chronext (01:06:55) Apple zahlt Verlage Shownotes OpenAI holt den zweiten Vertriebschef in einem Jahr - bloomberg.com GPT-5.6 Sol läuft mit Cerebras bis zu 14-mal schneller - 9to5mac.com Anthropic peilt einen Börsengang bei 2 Billionen Dollar an - ft.com Anthropic verhandelt über Decart für 6 Mrd. - bloomberg.com Google stellt Gemini 3.7 Flash vor - blog.google DeepSeek bringt V4-Pro und erhöht die Preise um bis zu das Zwölffache - theinformation.com Grok 4.6 startet zuerst in Cursor - gizmodo.com SpaceX-Leerverkäufern gehen die Kugeln aus - cnbc.com Databricks sammelt 5 Mrd. bei 190 Mrd. Bewertung ein - cnbc.com Silver Lake verhandelt über eine Übernahme von Workday - reuters.com Lovable verdoppelt die Bewertung auf 13,3 Mrd. - trendingtopics.eu Legora verhandelt bei mindestens 10 Mrd. - ft.com Cognition verhandelt bei 40 Mrd. - bloomberg.com Mistral will bis 2030 ein Gigawatt in Europa bauen - aibusiness.com Trump lässt private Firmen offensive Cyberangriffe fahren - bloomberg.com Kaperbriefe stehen in der Verfassung - xcancel.com KI-Agenten greifen Taiwans Regierungssysteme an - ft.com Presseverbände klagen gegen Trumps Truth API - ft.com Chronext-Kunden warten auf Zahlungen und Lieferungen - wiwo.de Apple verhandelt mit Verlagen über Nachrichten für Siri - techcrunch.com
This week, it was a potpourri! We talked about how coffee can postively affect the microbiome. As long as you can tolerate it, it appears that coffee is a superfood! Then we found a shocker: chronic physiological stress leads to cognitive decline. So, stress reduction helps it! And did you know that Vitamin C can […]
Bloomberg reported that Cognition is in talks to raise new funding at a $40 billion valuation. The figure would place the AI startup among the highest valued private companies in the sector, below OpenAI's reported $80 billion to $90 billion secondary valuations and above xAI's $24 billion valuation after a $6 billion raise in May 2024. Amazon committed up to $4 billion to Anthropic and Google invested $2 billion via a convertible note, highlighting how capital and partnerships align in AI. Large model development requires significant compute built on Nvidia chips and long term cloud commitments, which drive funding needs. Cognition has focused on agent workflows for software development, including the 2024 debut of Devin, while enterprises pilot similar tools from larger platforms and startups. Late stage AI rounds often blend primary capital with secondary liquidity and can include strategic compute or cloud distribution agreements, raising the execution bar on unit economics and enterprise adoption.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.
In this episode of Trending in Education, host Mike Palmer is joined by Dr. Ozgur Bolat, associate professor of educational science and author of The Reward Trap: Why Rewards Backfire and the Real Science of Motivating Kids. Dr. Bolat shares his journey from seeking external validation to decoding the science of intrinsic motivation, offering practical strategies for parents and educators looking to move away from quick-fix rewards and build lasting resilience and internal drive in children. Key topics discussed include:
Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas
Large Language Models display an uncanny ability to construct human-sounding speech, and can synthesize concepts in novel ways. Is this because they are truly thinking like human beings in some way, or have they found a way to be human-like without reproducing the internal mechanisms of human thought? Chandra Sripada argues that LLM cognition is more human-like than we suppose, and offers evidence from the ways that cognitive scientists study actual humans. Blog post with transcript: https://preposterousuniverse.com/podcast/2026/08/10/363-chandra-sripada-on-how-llms-and-humans-are-cognitive-cousins/ Support Mindscape on Patreon. Chandra Sripada received an M.D. from the University of Texas and a Ph.D. in philosophy from Rutgers University. He is currently a professor of philosophy and psychiatry at the University of Michigan, where he holds the Theophile Raphael Research Professorship and directs the Weinberg Institute for Cognitive Science. He writes Cognition, Decoded, a Substack newsletter about AI, cognitive science, and philosophy. Web site Google Scholar publications PhilPeople profile Babbel is offering listeners up to 60% off. Go to https://babbel.com/MINDSCAPE. #ad Get 60% off an annual plan from Incogni using code MINDSCAPE at https://incogni.com/mindscape. #ad
If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects. In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge. So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below. Thank you and enjoy the episode!Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Substackhttps://substack.com/@theoccultrejects?r=7auau0&utm_campaign=profile&utm_medium=profile-pageCash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsDarkness, Sensory Deprivation, Caves, and Visionary ConditionsUstinova, Yulia. Caves and the Ancient Greek Mind: Descending Underground in the Search for Ultimate Truth. Oxford: Oxford University Press, 2009.Use for: caves, descent, darkness, ancient Greek revelation, incubation, inspired prophecy, and altered states in underground sacred settings.Lewis-Williams, David. The Mind in the Cave: Consciousness and the Origins of Art. London: Thames & Hudson, 2002.Use for: caves, prehistoric imagery, altered consciousness, visual phenomena, and symbolic environments. Use carefully because some interpretations of prehistoric religion remain debated.Eliade, Mircea. The Sacred and the Profane: The Nature of Religion. New York: Harcourt, 1959.Use for: sacred space, threshold, the difference between sacred and profane space, and the way religious environments reorganize orientation and meaning.Zuckerman, Marvin, and Nathan Cohen. “Sources of Reports of Visual and Auditory Sensations in Perceptual-Isolation Experiments.” Psychological Bulletin 61, no. 1, 1964: 1–20.Use for: sensory deprivation, perceptual isolation, visual/auditory sensations, and internally generated experiences under reduced stimulation.Wackermann, Jiří, Peter Pütz, and Carsten Allefeld. “Ganzfeld-Induced Hallucinatory Experience, Its Phenomenology and Cerebral Electrophysiology.” Cortex 44, no. 10, 2008: 1364–1378.Use for: Ganzfeld states, reduced/uniform sensory fields, pseudo-hallucinatory imagery, altered perception, and EEG findings.Metzger, Wolfgang. “Optische Untersuchungen am Ganzfeld.” Psychologische Forschung 13, 1930: 6–29.Use for: early Ganzfeld research and the effect of homogeneous visual fields on perception.Shenyan, O., et al. “Visual Hallucinations Induced by Ganzflicker and Ganzfeld Differ in Frequency, Complexity, and Content.” Scientific Reports 14, 2024.Use for: modern comparison between Ganzfeld and flicker-induced visual phenomena. Useful if connecting this episode back to the earlier flicker-light show.Candlelight, Firelight, Sacred Atmosphere, and the Agency of LightBille, Mikkel, and Tim Flohr Sørensen. “An Anthropology of Luminosity: The Agency of Light.” Journal of Material Culture 12, no. 3, 2007: 263–284.Use for: light as a social, material, emotional, and cultural agent; how light shapes atmosphere, social meaning, and movement.Bille, Mikkel, and Tim Flohr Sørensen, eds. Elements of Architecture: Assembling Archaeology, Atmosphere and the Performance of Building Spaces. London: Routledge, 2016.Use for: architecture, atmosphere, materiality, embodied space, and how built environments shape experience.Pallasmaa, Juhani. The Eyes of the Skin: Architecture and the Senses. Chichester: Wiley, 1996; later editions.Use for: architecture as multisensory experience, not just visual design; helpful for sacred architecture, embodied space, and sensory atmosphere.Barrie, Thomas. “Sacred Space and the Mediating Roles of Architecture.” European Review 20, no. 4, 2012: 505–514.Use for: sacred architecture as a mediating threshold between ordinary and sacred worlds.Kieckhefer, Richard. Theology in Stone: Church Architecture from Byzantium to Berkeley. Oxford: Oxford University Press, 2004.Use for: Christian sacred architecture, spatial theology, light, ritual orientation, and the way buildings teach belief through space.Do, Michael T. H. “Melanopsin and the Intrinsically Photosensitive Retinal Ganglion Cells: Biophysics to Behavior.” Neuron 104, no. 2, 2019: 205–226.Use for: melanopsin, intrinsically photosensitive retinal ganglion cells, non-image-forming vision, and the biological effects of light beyond ordinary sight.Hattar, Samer, H. W. Liao, Motoharu Takao, David M. Berson, and King-Wai Yau. “Melanopsin-Containing Retinal Ganglion Cells: Architecture, Projections, and Intrinsic Photosensitivity.” Science 295, no. 5557, 2002: 1065–1070.Use for: retinal ganglion cells that detect light for non-image-forming functions, including circadian-related pathways.Blume, Christine, Corrado Garbazza, and Manuel Spitschan. “Effects of Light on Human Circadian Rhythms, Sleep and Mood.” Somnologie 23, 2019: 147–156.Use for: light, circadian rhythms, sleep, mood, and why sunlight, darkness, dawn, and dusk matter biologically.Cajochen, Christian. “Alerting Effects of Light.” Sleep Medicine Reviews 11, no. 6, 2007: 453–464.Use for: light's alerting effects beyond image-forming vision.Stained Glass, Religious Light, and Visual TheologyBinski, Paul. Gothic Wonder: Art, Artifice, and the Decorated Style, 1290–1350. New Haven: Yale University Press, 2014.Use for: Gothic visual culture, sacred display, ornament, and the emotional force of medieval religious space.Camille, Michael. Gothic Art: Glorious Visions. New York: Harry N. Abrams, 1996.Use for: Gothic imagery, religious seeing, visual devotion, and sacred art as visionary environment.Kessler, Herbert L. Seeing Medieval Art. Peterborough, Ontario: Broadview Press, 2004.Use for: medieval Christian theories of seeing, devotional image use, sacred art, and vision as religious practice.Hamburger, Jeffrey F. The Visual and the Visionary: Art and Female Spirituality in Late Medieval Germany. New York: Zone Books, 1998.Use for: visionary art, devotional seeing, mystical imagery, and the relationship between images and religious experience.Freedberg, David. The Power of Images: Studies in the History and Theory of Response. Chicago: University of Chicago Press, 1989.Use for: why images provoke emotion, devotion, fear, desire, reverence, and acts of ritual engagement.Scrying, Black Mirrors, Crystal Gazing, and Reflective VisionCaputo, Giovanni B. “Strange-Face-in-the-Mirror Illusion.” Perception 39, no. 7, 2010: 1007–1008.Use for: mirror-gazing under low light, face distortion, visual instability, and apparitional perception.Caputo, Giovanni B. “Apparitional Experiences of New Faces and Dissociation of Self-Identity During Mirror Gazing.” Perceptual and Motor Skills 110, no. 3, 2010: 1125–1138.Use for: mirror-gazing, dissociation, altered self-recognition, and strange-face experiences.Caputo, Giovanni B. “Strange-Face Illusions During Inter-Subjective Gazing.” Consciousness and Cognition 22, no. 1, 2013: 324–329.Use for: prolonged eye-to-eye gazing, face alteration, dissociative effects, and interpersonal gaze phenomena.Caputo, Giovanni B. “Visual Perception During Mirror-Gazing at One's Own Face in Patients with Depression.” The Scientific World Journal, 2014.Use for: mirror-gazing research and altered self-perception. Useful for grounding black mirror and psychomanteum discussion, but do not overgeneralize.Moody, Raymond A., with Paul Perry. Reunions: Visionary Encounters with Departed Loved Ones. New York: Villard, 1993.Use for: modern psychomanteum practice, mirror-gazing, grief, and visionary encounters. Use cautiously; this is experiential/parapsychological material, not mainstream neuroscience.Roll, William G. “Psychomanteum Research: A Pilot Study.” Journal of Near-Death Studies 22, no. 4, 2004: 251–270.Use for: psychomanteum research, bereavement visions, mirror-gazing, and altered perception. Use cautiously.Northcote, Thomas. “Scrying.” In The Encyclopedia of Occultism and Parapsychology, edited by J. Gordon Melton. Detroit: Gale, various editions.Use for: general historical overview of scrying traditions, crystal gazing, mirror gazing, and divinatory seeing.Agrippa, Heinrich Cornelius. Three Books of Occult Philosophy. Translated by James Freake; edited and annotated by Donald Tyson. St. Paul, MN: Llewellyn, 1993.Use for: Renaissance magical imagination, correspondences, planetary symbols, spirits, and visual/ritual operations.Barrett, Francis. The Magus. London, 1801.Use for: later ceremonial magic, mirrors, spirits, correspondences, and visual ritual imagination. Use historically, not as scientific evidence.Magical Gaze, Concentration, Visualization, and Ceremonial PracticeCrowley, Aleister. Liber E vel Exercitiorum. In The Equinox.Use for: concentration, posture, breath, mental discipline, and the training of attention as a magical foundation.Crowley, Aleister. Magick: Liber ABA, Book 4. York Beach, ME: Samuel Weiser, various editions.Use for: ceremonial magic, concentration, visualization, ritual method, symbols, and disciplined magical practice.Regardie, Israel. The Golden Dawn. St. Paul, MN: Llewellyn, variousAlso want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball.
Episode Summary Marine mammal cognition researcher Dr. Kevin Woo joins Wild Connection to talk about what's actually going on inside the minds of seals and sea lions — how they perceive their world, make decisions, and adapt to one of the busiest, loudest urban waterways on the planet: New York Harbor.[Attachment] About the Guest Dr. Kevin Woo is co-director of the Center for the Study of Pinniped Ecology and Cognition (C-SPEC). His research spans sensory perception, cognition, and animal communication, with a focus on harbor seals and sea lions studied both in the field in New York Harbor and through cognitive testing at the Long Island Aquarium. And listen to an earlier episode with Dr. Kristy Biolsi back in 2021
Honeywell's Colin Hams, offering manager for the company's Experion Process Knowledge System, joined Control Amplified at the 50th Anniversary Honeywell Users Group meeting in Phoenix, Arizona, to talk more about Experion Cognition, which puts the Knowledge into PKS.
The new Medicare speech therapy CPT codes are coming, and adult SLPs need to prepare.In this episode, Jeanette Benigas, PhD/SLP, is joined by Katie Brown, SLP of Neuro Speech Solutions (@neurospeechsolutions), to discuss how the new code family replacing 92507 could impact adult speech-language pathology across Medicare Part B, skilled nursing, outpatient, home health, hospital outpatient, and private practice.They discuss how the new codes may affect reimbursement, scheduling, productivity, documentation, cognition treatment, Medicare Advantage, commercial insurance, and ethical billing. They also explore why accurate utilization data will be critical to future advocacy for higher reimbursement rates.Whether you own a private practice, work in a SNF, outpatient clinic, hospital, or even pediatrics, this conversation will help you understand what's changing, what questions remain unanswered, and how to start preparing now.Topics include:• The new adult SLP CPT codes• Replacing 92507• Medicare Part B billing• Cognition reimbursement• Documentation changes• Productivity concerns• Private practice and SNF implications• Ethical billing and future advocacyResources Mentioned
If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects. In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge. So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below. Thank you and enjoy the episode!Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Substackhttps://substack.com/@theoccultrejects?r=7auau0&utm_campaign=profile&utm_medium=profile-pageCash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsBibliographyCore Eye Anatomy, Retina, Optic Nerve, and Visual PathwaysBelliveau, A. P., & Somani, A. N. “Pupillary Light Reflex.” StatPearls. Treasure Island, FL: StatPearls Publishing, updated 2023.Cleveland Clinic. “Optic Nerve: What It Is, Function, Anatomy & Conditions.” Cleveland Clinic, updated 2024.Gupta, M., & Ireland, A. C. “Neuroanatomy, Visual Pathway.” StatPearls. Treasure Island, FL: StatPearls Publishing, updated 2022.Kolb, H. “Simple Anatomy of the Retina.” Webvision: The Organization of the Retina and Visual System. 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Fun fact, the art is all based on the eyeball.
In this episode Andrea Samadi explores Phase Three of the Brain's Operating System — how movement acts as the essential input that activates the brain, sharpens attention, boosts learning, and improves social and emotional readiness. Drawing on Dr. John Ratey's insights and related neuroscience research, she explains the chain reaction from exercise to brain activation, learning, and adaptation. The episode also emphasizes the vital role of recovery in consolidating gains so movement leads to lasting performance improvements, and invites listeners to adopt movement as a daily catalyst for clearer thinking, better learning, and greater resilience. Dr. John Ratey and The Movement Loop On this episode 404, we will: Explore where Dr. John Ratey's groundbreaking research fits within the Movement Loop. Understand how movement activates the brain and prepares us to learn. Discover why attention is the gateway to learning and performance. Connect Dr. Ratey's work with Dr. Chuck Hillman, Dr. John Medina, Kristen Holmes, and Jason Whitrock to see the bigger picture. Learn how movement becomes lasting human performance by building our capacity over time. How Movement Becomes Human Performance Welcome back to 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. If you've been following along through Season 16, you'll know we've been building what I call The Brain's Operating System for Human Performance—a neuroscience-based framework that explains how movement, recovery, and adaptation work together to improve our health, learning, leadership, and performance. Over the past seven years and more than 400 episodes, I've had the privilege of interviewing neuroscientists, physicians, psychologists, educators, and high performers from around the world. As I looked across those conversations, I realized they weren't isolated ideas—they were pieces of a much larger system. I've organized that system into five interconnected phases that mirror how we grow and develop throughout life. And as we work through each of these phases, we are sharpening our saw for productivity, learning and high performance, really, with the brain and body in mind. Phase 1 is Regulation and Safety, we launched on EP 384[i] with Dr. Baland Jalal on Sleep, Safety and Curiosity. This phase is where we build the foundation by understanding the nervous system, stress, emotions, and the importance of creating internal and external safety before learning or productivity can take place. Phase 2 is Neurochemistry and Motivation, where we explore how dopamine, reward, purpose, and emotional regulation influence our choices, habits, and sustained effort. Today, we're in Phase 3: Movement, Learning, and Cognition. This is where movement becomes the catalyst that prepares the brain to pay attention, learn, adapt, and ultimately perform at a higher level. In Phase 4, we'll discover how perception and social intelligence shape the way we understand ourselves, communicate, build trust, and strengthen relationships. And finally, in Phase 5, we'll bring everything together through integration and meaning—understanding how our experiences become wisdom, resilience, purpose, and lifelong growth. Each phase builds on the one before it, because meaningful human performance isn't created in a single breakthrough—it's built one system at a time. Together, they create what I believe is the brain's operating system for human performance. If we want to improve the entire big picture of our results, we can dive deeper into these 5 phases to see what areas we could sharpen a bit. I've put the individual episodes of each phase in the show notes for a deeper dive. Today's episode is one of those foundational pieces of Phase 3. Because before we can improve performance, we first need to understand how movement changes the brain. In Episode 402[ii], we introduced The Movement Loop, built around one simple idea: Movement is the input—it's what we do. Recovery enables adaptation—it's how the brain and body grow stronger. Performance is the outcome—the results we can see, feel, and measure. Every cycle expands our capacity to learn, lead, and perform. Every movement we make sends a signal to the brain and body. That signal activates the brain— increasing blood flow, neurotransmitters, and neuroplasticity—preparing us to pay attention and learn. When movement is followed by adequate recovery, the brain and body adapt. Those adaptations become the foundation for better performance—whether that's learning more effectively, thinking more clearly, leading with greater confidence, or improving our physical health. Then, in Episode 403[iii], Dr. Chuck Hillman helped us understand why movement changes the brain. We learned that every step increases blood flow, stimulates the release of Brain-Derived Neurotrophic Factor—BDNF, often called fertilizer for the brain—and prepares the neural networks responsible for attention, learning, memory, and executive function. But understanding why movement changes the brain is only part of the story. Today's episode answers a different question: What does that actually look like in real life? How does movement change our brain? To answer that question, we're returning to one of my favorite interviews from March of 2021 with Harvard psychiatrist and bestselling author Dr. John Ratey, whose groundbreaking book Spark helped change the way millions of people think about exercise and the brain. Now, you might be wondering... "Andrea, didn't we already review Dr. Ratey's work in Episodes 375[iv] and 376[v]?" We did. But today isn't simply another review. Today we're looking at Dr. Ratey's work through an entirely new lens. When we first interviewed Dr. Ratey in 2021, I knew movement was important. But after spending the past several years interviewing experts like Dr. Chuck Hillman, Dr. John Medina, Dr. Kristen Holmes, Jason Whitrock, and hundreds of others—and measuring my own health and performance data that we'll explore throughout this season's bonus episodes—I now understand exactly where Dr. Ratey's research fits inside the Movement Loop. As I looked across all of those conversations, I realized they weren't isolated ideas. They were pieces of one much larger system. Today we're in Phase 3: Movement, Learning, and Cognition—the phase where movement becomes the catalyst for attention, learning, adaptation, and ultimately, human performance. In the coming phases, we'll discover how perception shapes our relationships, and finally how everything integrates into meaning, purpose, and lifelong growth. Today's conversation with Dr. Ratey is one of the foundational building blocks of Phase 3. Why This Matters If you've listened to this podcast for a while, you've probably heard me talk about movement as one of the few true non-negotiables in my day. Before my workday begins, I've already made time to move—not because I'm training for a race or trying to burn calories, but because I know how profoundly movement changes the way my brain performs. The research is clear. Movement prepares the brain to pay attention, learn more effectively, regulate emotions, solve problems, and perform at a higher level. I've experienced these benefits firsthand. On the days I move first, I think more clearly, I'm more productive, and I'm better equipped to handle whatever challenges the day brings. And I imagine many of you listening have experienced the same thing. Once you begin noticing how movement changes your thinking, it becomes much easier to make it a consistent part of your life. Why This Season Exists That's why my hope for Season 16 isn't simply to convince you that exercise is good for you. My goal is much bigger than that. It's to demonstrate why movement is the first step in The movement loop—and to show how every movement you make begins a chain reaction that improves learning, health, leadership, and performance. With every cycle, we don't just improve today's performance. We expand our capacity for tomorrow. As we continue exploring the Movement Loop, I hope you'll begin to see movement differently—not as another item on your to-do list, but as the input that starts an entire system of growth. Because movement isn't the goal. Movement is the beginning of everything that follows. CLIP 1: Movement Primes the Brain for Learning CLIP 1: Social Connection Reflection: Where This Fits in the Movement Loop Let's review Clip 1 with Dr. Ratey. When we listen to his clip through that lens, every sentence he shares fits into a specific step of the process. Reviewing Dr. Ratey, through the lens of The Movement Loop. Step 1: Movement — The Input Everything begins with movement. Movement is the signal we send to the brain and body that says: "It's time to wake up. It's time to adapt." Without movement, nothing else in the loop begins. As Dr. Ratey says, movement is what "turns on the thinking parts of the brain." Step 2: Brain Activation — The Brain Wakes Up This is exactly what we learned from Dr. Chuck Hillman in Episode 403[vi]. Movement immediately increases blood flow to the brain. It activates neurotransmitters. It stimulates Brain-Derived Neurotrophic Factor, or BDNF, helping create the ideal environment for learning. Dr. Hillman explained what is happening biologically. Dr. Ratey in this clip explains what we experience. You suddenly feel more alert. More awake. More ready to think. These are two perspectives describing the same process. Step 3: Attention — The Gateway to Learning This is where Dr. Ratey's words become especially powerful. He says movement helps us: stay with an idea, focus longer, evaluate information, scan our memory, and even simply stay seated long enough to think through something difficult. That isn't just better concentration. That's improved attention, and attention is the gateway to learning. Without attention, very little learning takes place. Movement prepares the brain to pay attention. At this point in the interview, he was referencing the fact that I had told him that I couldn't write podcast episodes on this topic without moving my body first. In the early days of this podcast, I found the science very difficult to understand. A couple of times I did think that this topic was over my head, and almost considered giving up. Almost, but very grateful for where the learning took me that I stuck it through. Step 4: Learning — Building New Neural Connections Once attention improves, learning becomes possible. Now the brain can: process information, make connections, strengthen memories, and solve problems more effectively. This is why Dr. Ratey says movement helps us become better learners, better thinkers, better workers, and better students. Movement isn't improving muscles first. It's improving the brain's ability to learn. Step 5: Social and Emotional Readiness One part of this interview that has become even more meaningful over time is Dr. Ratey's explanation of social connection. Exercise doesn't only improve cognition. It also changes our emotional state. We're more motivated. We regulate our emotions more effectively. We become more socially connected. Dr. Ratey explains that movement increases oxytocin—the hormone associated with bonding and trust—making us more open to connecting with others. That's important because learning has always been social. Whether we're in a classroom, at work, or with our families, our ability to connect with other people influences how well we learn, communicate, and perform. Movement prepares us socially as well as cognitively. Step 6: Recovery and Adaptation — Where Lasting Change Happens Now here's the part I didn't fully appreciate back in 2021. Everything Dr. Ratey has described prepares the brain for learning. But those immediate benefits only become lasting improvements if they're followed by recovery. Recovery is when the brain consolidates learning. It's when new neural connections are strengthened. It's when adaptation occurs. This is where Kristen Holmes' work on recovery fits perfectly into the Movement Loop. We recently reviewed our 2021 interview with Kristen Holmes on EP 390[vii] “What Gets Measured Improves: Sleep, Recovery and Strain” and it was Dr. Holmes who was (not the first person to notice I could improve my recovery scores) but was stern enough with me about how important that it was, and how I was leaving a lot off the table by not getting enough sleep/recovery to match the strain I was pushing towards each day. Movement starts the process. Recovery completes it. And together (this won't happen if one is missing) they create adaptation—the biological changes that make us stronger, healthier, and more resilient over time. The Big Takeaway When I first interviewed Dr. John Ratey in 2021, I thought he was teaching us why exercise is good for the brain. Today, after years of interviewing neuroscientists, physicians, performance experts, and educators, I see something much bigger. Dr. Ratey wasn't simply describing the benefits of exercise. He was describing the beginning of a biological process. Movement is the input. Movement activates the brain. The activated brain becomes ready to focus, learn, regulate emotions, and connect with others. Learning, when supported by recovery, leads to adaptation. Adaptation creates better performance. And every time we move through that cycle, we expand our capacity to meet tomorrow's challenges. That's the Movement Loop. It's not just a framework for understanding exercise. It's a framework for understanding how the brain changes, how we grow, and how human performance is built—one movement at a time. Review and Conclusion: The Movement Loop and Dr. Ratey To review and conclude what we've learned today. Dr. Ratey's interview wasn't simply about why exercise is good for the brain. It was about how movement begins a chain reaction that changes the way we learn, think, and perform. We discovered that: Movement is the input. Every movement sends a signal to the brain and body that it's time to change. Movement activates the brain. Blood flow increases. Neurotransmitters increase. BDNF begins preparing the brain for learning. The activated brain becomes ready. Attention improves. Executive function sharpens. Emotions become easier to regulate. We're even more prepared to connect with other people. That readiness makes learning possible. New neural pathways begin to form. The brain starts building stronger and more efficient networks. But learning doesn't end when the workout ends. Recovery allows the brain to consolidate what movement started. Recovery creates adaptation. Adaptation creates better performance. And every time we complete that cycle, we expand our capacity for whatever challenge comes next. That's the Movement Loop. This is something I've learned through years of interviewing experts, but also through measuring my own health and performance. The days I move first are always the days I think more clearly. I'm more creative with what I write as well as making connections that are important to me in my day to day life. More productive- with the entire day (family and work). More patient (with my kids and family) More present and clear. Movement doesn't solve every problem. But it prepares us to solve problems better, and increases our capacity and productivity. And that's a lesson I'll keep coming back to throughout this season. Coming Next… But there's still one important question we haven't answered. If movement activates the brain... why do some people consistently improve --while others seem to work just as hard without seeing the same results? Is it genetics? Motivation? Discipline? Or is there something happening between today's effort and tomorrow's performance? Next week, in Bonus Episode 2, we'll answer that question. We'll explore what I call The Predictable Performance System and look at years of my own recovery data to understand why recovery isn't simply the opposite of training—it's the bridge between today's habits and tomorrow's performance. Because the difference between predictable and unpredictable results... isn't luck. It's recovery. It's how we are balancing how much we push with how much we are able to rest/recover. Until next time... Keep moving. Protect your recovery. Build your capacity. And continue designing your brain's operating system for lifelong learning, health, leadership, and human performance. Thanks for joining me. I'll see you next week. RESOURCES: Clip 1 Movement and the Brain Dr. Ratey https://www.youtube.com/shorts/bbB4NzsnyQ0 Clip 1 Social Connection Dr. Ratey https://www.youtube.com/shorts/bbB4NzsnyQ0 Phase 1: Regulation & Safety The Foundation Core Question: Is the nervous system safe enough to learn? Everything begins with regulation. Before we can focus, learn, lead, or perform, the brain first asks one fundamental question: Am I safe? Throughout Phase 1, our guests showed us that regulation isn't simply about reducing stress—it's about creating the biological conditions that allow the brain to learn, adapt, and thrive. Together we explored: Baland Jalal – how sleep, curiosity, imagination, and creativity prepare the brain for learning. https://andreasamadi.podbean.com/e/hypnagogic-genius-capture-your-best-ideas-at-the-edge-of-sleep/ Dr. Bruce Perry – why regulation, rhythm, and relationships form the foundation of every healthy nervous system. https://andreasamadi.podbean.com/e/safety-first-why-a-regulated-brain-is-the-key-to-learning/ Dr. Sui Wong – how lifestyle medicine and autonomic balance build lifelong brain resilience. https://andreasamadi.podbean.com/e/your-eyes-the-brain-s-early-warning-system/ Rohan Dixit – how heart rate variability gives us real-time feedback on our ability to regulate stress. https://andreasamadi.podbean.com/e/breathe-to-reset-how-hrv-tech-reveals-hidden-stress/ Dr. Kristen Holmes – how recovery metrics reveal our physiological readiness to perform. https://andreasamadi.podbean.com/e/kristen-holmes-from-whoopcom-on-unlocking-a-better-you-measuring-sleep-recovery-and-strain/ Dr. Antonio Zadra – how sleep and dreaming consolidate memories, regulate emotions, and generate insight. https://andreasamadi.podbean.com/e/when-brains-dream-how-sleep-integrates-emotion-insight-and-creativity/ Together these conversations taught us that sleep and stress regulation aren't optional—they're the operating system that allows every higher brain function to work. Phase 2: Motivation & Neurochemistry The Direction Core Question: What moves us into action? Once the brain feels safe, it becomes ready to pursue goals. In Phase 2, we explored the internal chemistry that transforms intention into action. Our experts helped us understand that sustainable motivation isn't about willpower—it's about aligning our beliefs, thoughts, attention, energy, and movement. Together we discovered: Bob Proctor — our beliefs determine the direction of our lives. https://andreasamadi.podbean.com/e/belief-first-the-neuroscience-of-motivation/ Dr. Carolyn Leaf — our thinking literally changes our brain chemistry. https://andreasamadi.podbean.com/e/thoughts-as-biology-how-your-mind-shapes-neurochemistry/ Dr. John Medina — attention determines what the brain encodes and remembers. https://andreasamadi.podbean.com/e/theory-of-mind-the-missing-link-between-attention-reward-and-motivation Dr. Anna Lembke- Dopamine, Motivation and Why the Brain Repeats Behavior https://andreasamadi.podbean.com/e/dopamine-nation-the-pleasure%e2%80%93pain-balance-that-drives-motivation/ Dr. Friederike Fabritius — managing our energy allows high performance to become sustainable. https://andreasamadi.podbean.com/e/fun-fear-focus-closing-the-motivation-loop/ Dr. Chuck Hillman & Paul Zientarski — movement activates the brain, preparing it to learn. https://andreasamadi.podbean.com/e/move-to-learn-how-movement-activates-the-brain-and-fuels-motivation/ By the end of Phase 2, we introduced what became The Motivation Loop, showing how beliefs influence thoughts, thoughts influence actions, actions create results, and results reinforce future beliefs. Phase 3: Movement, Learning & Human Performance The Transformation Core Question: How does movement change the brain—and how does recovery transform that change into performance? Now we're taking the next step. Phase 3 builds on everything we've learned so far. If Phase 1 created a regulated nervous system... If Phase 2 created motivation and direction... Phase 3 explains how the brain and body actually become stronger. Together we've explored this process through conversations with: Dr. Chuck Hillman & Paul Zientarski — why movement activates the brain before learning. https://andreasamadi.podbean.com/e/movement-first-how-a-20%e2%80%91minute-walk-lights-up-the-brain/ Dr. John Ratey — how exercise builds a healthier, younger brain. Dr. Kristen Holmes — why recovery determines adaptation and readiness. Dr. John Medina — how attention transforms movement into lasting learning. Jason Whitrock — how metabolism and cellular energy fuel long-term performance. WHERE WE ARE GOING NEXT Phase 4 — Connection, Emotion & Social Intelligence The Human System Core Question: How do we thrive with other people? The brain didn't evolve in isolation—it evolved through relationships. In Phase 4, we'll explore emotional intelligence, empathy, communication, trust, leadership, and psychological safety to understand how our relationships shape learning, well-being, and performance. Phase 5 — Integration & Human Performance The Complete System Core Question: How do all the systems work together? In our final phase, we'll bring everything together—regulation, motivation, movement, emotion, relationships, learning, and recovery—into one integrated framework. We'll discover how these systems work together to create measurable improvements in our well-being, achievement, leadership, productivity, and results. REFERENCES: [i] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 384 https://andreasamadi.podbean.com/e/hypnagogic-genius-capture-your-best-ideas-at-the-edge-of-sleep/ [ii] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 402 “Phase 3 Intro: How Movement Builds the Brain https://andreasamadi.podbean.com/e/movement-loop-how-everyday-action-rewires-your-brain-and-boosts-performance/ [iii] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 403 “How movement Activates the Brain and Learning” https://andreasamadi.podbean.com/e/movement-first-how-a-20%e2%80%91minute-walk-lights-up-the-brain/ [iv] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 375 https://andreasamadi.podbean.com/e/how-exercise-primes-the-brain-insights-from-dr-john-rady/ [v] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 376 https://andreasamadi.podbean.com/e/move-eat-connect-3-science-backed-keys-to-brain-health-ep-376/ [vi] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 403 “How movement Activates the Brain and Learning” https://andreasamadi.podbean.com/e/movement-first-how-a-20%e2%80%91minute-walk-lights-up-the-brain/ [vii] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 390 https://andreasamadi.podbean.com/e/what-gets-measured-gets-improved-sleep-recovery-peak-performance/
Patrick McKenzie (patio11) is joined by Garrison Lovely, journalist and author of Obsolete: The AI Industry's Trillion-Dollar Race to Replace Us and How to Stop It, to map the three-sided debate over AI risk and why the arguments keep talking past each other. They then turn to what cheap cognition does to surveillance that already exists: FinCEN receives roughly 4 million suspicious activity reports a year and reads almost none of them, ICE agents run about a million queries against that database annually, and every podcast ever recorded is now transcribable for approximately nothing. The conversation covers capabilities denialism, ablated open-weights models, the fraud supply chain, and why AI is also unusually good at writing the Regulation E letter that gets your bank to fix your problem.–Full transcript available here: https://www.complexsystemspodcast.com/cheap-cognition-and-the-end-of-practical-obscurity-with-garrison-lovely/ –Presenting Sponsors: Mercury, MongoDB & ChainguardComplex Systems is presented by Mercury—radically better banking for founders. Mercury's new feature Command brings an LLM directly into your banking interface, so checking balances, finding invoices, or sending a wire is as easy as asking. Apply online in minutes at https://mercury.com/. What's the point of building faster with AI if your database can't keep up? MongoDB's native data model mirrors the language LLMs already speak. Ship at the speed of AI while staying ACID compliant at Fortune 500 scale. Start building at https://mongodb.com/ai.If attackers are using AI to weaponize code faster than any team can review it, your scanners won't save you. Chainguard builds libraries and container images from source, verified all the way down, with near-zero CVEs and zero malware. Build safely at https://www.chainguard.dev/. –Links:Obsolete: The AI Industry's Trillion Dollar Race to Replace Us―and How to Stop It: https://www.amazon.com/Obsolete-Power-Profit-Machine-Superintelligence/dp/1682196305 –Timestamps:(00:00) Preview(00:43) Intro(01:51) The three-sided debate over AI risk(05:32) Power, politics, and the tech backlash(09:49) Capabilities denialism and AI tells(14:54) The obsoleting machine(17:09) The Turing test is dead(18:56) Languages for free, then software engineering(22:37) Sponsors: Mercury | MongoDB(25:09) How high up the stack do the models decide?(29:11) Surveillance and cheap cognition(32:38) Podcasts, FinCEN, and the end of practical obscurity(38:35) Section 702 and the data broker loophole(39:35) Sponsor: Chainguard(40:55) Section 702 and the data broker loophole (cont'd)(47:23) Institutional friction and a million ICE queries(53:29) Security through obscurity no longer works(54:54) Scams, fraud, and ablated models(1:00:20) Personal utility versus societal backlash(1:02:16) AI as a tool for redress(1:06:34) State capacity and regulating what you understand(1:12:37) Tobacco, nuclear, and AlphaFold: strangle it or steer it(1:18:37) Where to find Garrison and the book(1:20:32) Wrap
Making Sense of Fractions with Susan Empson, PhD ROUNDING UP: SEASON 4 | EPISODE 23 For quite a few adults, fractions were a stumbling block in their education, causing them to lose their footing and begin to doubt their ability to make sense of math. But this doesn't have to be the case for our students! In this rerelease of an episode from Season 2, we're talking with Susan Empson, PhD, about big ideas in fractions and how we can make them more meaningful for our students. BIOGRAPHY Susan B. Empson is an emerita professor in the department of Learning, Teaching, and Curriculum at the University of Missouri. She and Vicki Jacobs are currently collaborating on a National Science Foundation research grant to study elementary teachers' learning and development centered on teaching in ways that are responsive to children's mathematical thinking in the domain of rational numbers. Her research on children's thinking about fractions is the topic of her 2011 book, Extending Children's Mathematics: Fractions and Decimals (with co-author Linda Levi), and she has published widely in refereed journals, including Cognition and Instruction, Journal for Research in Mathematics Education, Educational Studies in Mathematics, Teaching Children Mathematics, and Journal of Mathematics Teacher Education. She has been a researcher of Cognitively Guided Instruction since 1989 and is a co-author of Children's Mathematics: Cognitively Guided Instruction (1st and 2nd editions). RESOURCES Extending Children's Mathematics: Fractions and Decimals book by Susan B. Empson and Linda Levi Mathematics Teacher: Learning and Teaching PK-12 journal articles authored and co-authored by Susan Empson TRANSCRIPT Click here for a full episode transcript.
When does experience become a liability, and who gets to decide? In this episode of The Valley Current®, Jack Russo examines one of the most uncomfortable constitutional questions of our time: what happens when the people entrusted with the nation's highest offices begin to lose the cognitive capacity those roles demand? From the 25th Amendment to the life tenure of federal judges, the discussion explores why America's legal safeguards often fall short when mental decline is suspected. Jack also unpacks the extraordinary case of Judge Pauline Newman, the growing gap between lifespan and "brain span," and why public opinion is a poor substitute for legal process. For corporate boards, family offices, and government leaders alike, the lesson is clear: succession planning is far easier than a constitutional crisis. Jack Russo Managing Partner Jrusso@computerlaw.com www.computerlaw.com https://www.linkedin.com/in/jackrusso "Every Entrepreneur Imagines a Better World"®️
Cognition is a strange word and not a very commonly used word by practicing educators. It feels academic and detached from the act of a group of young people learning in the world. But when you look at the dictionary definition of cognition – the "mental action or process of acquiring, storing, retrieving, and processing knowledge and understanding through thought, experience, and the senses" – this seems pretty fundamental to what we do as educators. But what if our understanding of what it means has been heavily biased by the Western modern scientific paradigm? For example, in the difference between learning the world and learning about the world via the linguistic labels and symbols that we create to represent aspects of it. My guest this week Jinan KB is offering a radically different description of what natural cognition or the biological roots of cognition actually are. I absolutely love these kinds of conversations as they challenge me right at the edge of my understanding given my own conditioning into particular ways of knowing about the world. It also feels like a really alive part of what we're being asked to do now more broadly, to explore more generously and expansively the space of what is possible in order to think differently about the educational and societal predicaments in which we find ourselves.Jinan is a researcher of natural cognition, cognitive autonomy, and the biological foundations of knowing. His work began with a simple but unsettling question: How do human beings naturally create knowledge? In 1991, Jinan stopped reading in order to investigate this question directly. For over four decades, he has explored this question through lived engagement with children, non-literate artisans, indigenous communities, design education, and everyday life. He is the author of Awakening Beauty and works with parents, educators, designers, researchers, and communities interested in recovering the conditions that allow human intelligence to flourish naturally.Website: https://www.jinankb.in/ LinkedIn: https://www.linkedin.com/in/jinan-kb-5386676/ Impact of literacy on consequence: https://youtu.be/WG9uJN-UoSc?si=0sMtaGuwLe4sn9Py How do Children Create knowledge? How teaching is detrimental to a child's learning: https://www.youtube.com/watch?v=s3A-8iI4Py4 What is Knowledge? Implicit and explicit knowledge: https://www.youtube.com/watch?v=3IhVFs4b8wA How schools and toys are damaging children? https://www.youtube.com/watch?v=z72rbcSpi7o Storytelling and Reading are damaging for children: https://www.youtube.com/watch?v=k4ZVLDpfzLI Word and the World form two different Cognitive systems: https://www.youtube.com/watch?v=4qHZNIEwio8&t=1s
AI has taken the grunt work out of many everyday tasks; from writing emails to planning dinner. But are we starting to rely on AI to think for us and could it be making us dumber? Today, Steven Shaw from the University of Pennsylvania on his research into cognitive surrender, AI and your brain. Featured: Steven Shaw, post-doctoral researcher at Wharton Marketing School, University of Pennsylvania and incoming associate professor at Kings College London.
If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects. In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge. So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below. Thank you and enjoy the episode!Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Substackhttps://substack.com/@theoccultrejects?r=7auau0&utm_campaign=profile&utm_medium=profile-pageCash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsCore Sleep Paralysis ScienceSharpless, Brian A., and Jacques P. Barber. “Lifetime Prevalence Rates of Sleep Paralysis: A Systematic Review.” Sleep Medicine Reviews 15, no. 5 (2011): 311–315.Sharpless, Brian A. “A Clinician's Guide to Recurrent Isolated Sleep Paralysis.” Neuropsychiatric Disease and Treatment 12 (2016): 1761–1767.Cheyne, J. Allan, Steve D. Rueffer, and Ian R. Newby-Clark. “Hypnagogic and Hypnopompic Hallucinations during Sleep Paralysis: Neurological and Cultural Construction of the Night-Mare.” Consciousness and Cognition 8, no. 3 (1999): 319–337.Cheyne, J. Allan. “Sleep Paralysis and the Structure of Waking-Nightmare Hallucinations.” Dreaming 13, no. 3 (2003): 163–179.Cheyne, J. Allan. “Situational Factors Affecting Sleep Paralysis and Associated Hallucinations: Position and Timing Effects.” Journal of Sleep Research 11, no. 2 (2002): 169–177.Solomonova, Elizaveta. “Sleep Paralysis: Phenomenology, Neurophysiology and Treatment.” In The Oxford Handbook of Spontaneous Thought: Mind-Wandering, Creativity, and Dreaming, edited by Kieran C. R. Fox and Kalina Christoff. Oxford University Press, 2018.Baland Jalal / Panic-Hallucination / TreatmentJalal, Baland. “How to Make the Ghosts in My Bedroom Disappear? Focused-Attention Meditation Combined with Muscle Relaxation (MR Therapy): A Direct Treatment Intervention for Sleep Paralysis.” Frontiers in Psychology 7 (2016): 28. doi:10.3389/fpsyg.2016.00028.Jalal, Baland, and V. S. Ramachandran. “Sleep Paralysis and ‘The Bedroom Intruder': The Role of the Right Superior Parietal, Phantom Pain and Body Image Projection.” Medical Hypotheses 83, no. 6 (2014): 755–757.Jalal, Baland. “The Neuropharmacology of Sleep Paralysis Hallucinations: Serotonin 2A Activation and a Novel Therapeutic Drug.” Psychopharmacology 235, no. 11 (2018): 3083–3091.Jalal, Baland, Lucia Moruzzi, Andrea Zangrandi, Matteo Filardi, Claudio Franceschini, Fabio Pizza, et al. “Meditation-Relaxation (MR Therapy) for Sleep Paralysis: A Pilot Study in Patients with Narcolepsy.” Frontiers in Neurology 11 (2020): 922. doi:10.3389/fneur.2020.00922.Folklore, Myth, and the Old HagHufford, David J. The Terror That Comes in the Night: An Experience-Centered Study of Supernatural Assault Traditions. Philadelphia: University of Pennsylvania Press, 1982.Hufford, David J. “Sleep Paralysis as Spiritual Experience.” Transcultural Psychiatry 42, no. 1 (2005): 11–45.Adler, Shelley R. Sleep Paralysis: Night-mares, Nocebos, and the Mind-Body Connection. New Brunswick: Rutgers University Press, 2011.Davies, Owen. “The Nightmare Experience, Sleep Paralysis, and Witchcraft Accusations.” Folklore 114, no. 2 (2003): 181–203.Bond, John. An Essay on the Incubus, or Nightmare. London: Printed for D. Wilson and T. Durham, 1753.Golzari, Samad E. J., et al. “Sleep Paralysis in Medieval Persia — The Hidayat of Akhawayni (?–983 AD).” Neuropsychiatric Disease and Treatment 8 (2012): 229–234.Cross-Cultural Sleep ParalysisHinton, Devon E., Vuth Pich, Dara Chhean, and Mark H. Pollack. “‘The Ghost Pushes You Down': Sleep Paralysis-Type Panic Attacks in a Khmer Refugee Population.” Transcultural Psychiatry 42, no. 1 (2005): 46–77.Hinton, Devon E., Vuth Pich, Dara Chhean, Mark H. Pollack, and Richard J. McNally. “Sleep Paralysis among Cambodian Refugees: Association with PTSD Diagnosis and Severity.” Depression and Anxiety 22, no. 2 (2005): 47–51.Jalal, Baland, and Devon E. Hinton. “Rates and Characteristics of Sleep Paralysis in the General Population of Denmark and Egypt.” Culture, Medicine, and Psychiatry 37, no. 3 (2013): 534–548.Jalal, Baland, Joseph Simons-Rudolph, Bamo Jalal, and Devon E. Hinton. “Explanations of Sleep Paralysis among Egyptian College Students and the General Population in Egypt and Denmark.” Transcultural Psychiatry 51, no. 2 (2014): 158–175.Jalal, Baland, Andrea Romanelli, and Devon E. Hinton. “Cultural Explanations of Sleep Paralysis in Italy: The Pandafeche Attack and Associated Supernatural Beliefs.” Culture, Medicine, and Psychiatry 39, no. 4 (2015): 651–664.Olunu, Esther, Ruth Kimo, Esther Olufunmbi Onigbinde, Mary-Amadeus Uduak Akpanobong, and Inyene Ezekiel Enang. “Sleep Paralysis, a Medical Condition with a Diverse Cultural Interpretation.” International Journal of Applied and Basic Medical Research 8, no. 3 (2018): 137–142.Sensed Presence / Body Map / Shadow Person NeuroscienceArzy, Shahar, Margitta Seeck, Stephanie Ortigue, Laurent Spinelli, and Olaf Blanke. “Induction of an Illusory Shadow Person.” Nature 443 (2006): 287.Blanke, Olaf, Stephanie Ortigue, Theodor Landis, and Margitta Seeck. “Stimulating Illusory Own-Body Perceptions.” Nature 419 (2002): 269–270.Blanke, Olaf, Theodor Landis, Laurent Spinelli, and Margitta Seeck. “Out-of-Body Experience and Autoscopy of Neurological Origin.” Brain 127, no. 2 (2004): 243–258.Ionta, Silvio, Lukas Heydrich, Bigna Lenggenhager, Michael Mouthon, Eleonora Fornari, Dominique Chapuis, Roger Gassert, and Olaf Blanke. “Multisensory Mechanisms in Temporo-Parietal Cortex Support Self-Location and First-Person Perspective.” Neuron 70, no. 2 (2011): 363–374.Blanke, Olaf, Polona Pozeg, Masayuki Hara, Lukas Heydrich, Andrea Serino, Akio Yamamoto, Toshiro Higuchi, et al. “Neurological and Robot-Controlled Induction of an Apparition.” Current Biology 24, no. 22 (2014): 2681–2686.Alien Abduction / Modern Mythic MaskMcNally, Richard J., and Susan A. Clancy. “Sleep Paralysis, Sexual Abuse, and Space Alien Abduction.” Transcultural Psychiatry 42, no. 1 (2005): 113–122.Clancy, Susan A. Abducted: How People Come to Believe They Were Kidnapped by Aliens. Cambridge, MA: Harvard University Press, 2005.Blackmore, Susan. “Abduction by Aliens or Sleep Paralysis?” Skeptical Inquirer 22, no. 3 (1998): 23–28.Clinical Sleep / Narcolepsy / REM BackgroundAmerican Academy of Sleep Medicine. International Classification of Sleep Disorders. 3rd ed., text revision. Darien, IL: American Academy of Sleep Medicine, 2023.Scammell, Thomas E. “Narcolepsy.” New England Journal of Medicine 373, no. 27 (2015): 2654–2662.Saper, Clifford B., Patrick M. Fuller, Nigel P. Pedersen, Jun Lu, and Thomas E. Scammell. “Sleep State Switching.” Neuron 68, no. 6 (2010): 1023–1042.Brooks, Patricia L., and John H. Peever. “Identification of the Transmitter and Receptor Mechanisms Responsible for REM Sleep Paralysis.” Journal of Neuroscience 32, no. 29 (2012): 9785–9795.Avidan, Alon Y., and Phyllis C. Zee, eds. Handbook of Sleep Medicine. Philadelphia: Lippincott Williams & Wilkins, 2011.Visual / Art HistoryFuseli, Henry. The Nightmare. 1781. Oil on canvas. Detroit Institute of Arts.Myrone, Martin. Gothic Nightmares: Fuseli, Blake and the Romantic Imagination. London: Tate Publishing, 2006.Powell, Nicolas. Fuseli: The Nightmare. London: Allen Lane, 1973.Also want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball.
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
A preview of my latest book: "Emergent Cognition: Rethinking the Role of Thinking and Memory in Sports & the Neuroscience of Skill" Links: https://www.amazon.com/dp/B0H7QLCJNL http://perceptionaction.com/ My Research Gate Page (pdfs of my articles) My ASU Web page Podcast Facebook page (videos, pics, etc) Subscribe in iOS/Apple Subscribe in Anroid/Google Support the podcast and receive bonus content Credits: The Flamin' Groovies – ShakeSome Action Mark Lanegan - Saint Louis Elegy via freemusicarchive.org and jamendo.com
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
Is civilization making us smarter while disconnecting us from what matters most? In this episode, John Vervaeke speaks with Kyle Koch, a deep nature connection practitioner and new father living in an intentional community in northern British Columbia, about applying Vervaeke's work to raise children with belonging and connectedness. Together they explore cognition as a dynamical developmental process, how culture increasingly "raises" children through screens, and how capitalism functions as a dominant religion shaping salience and meaning. Drawing on Iain McGilchrist's work and 4E cognitive science, they discuss left-hemisphere dominance, relevance realization, extended families and credible role models, narrative as "keeping track of tracking," and the need for a meta-narrative beyond a chaos of stories. The conversation also examines kinship and Indigenous worldviews, personhood as promise-making, the "spirit of place," pilgrimage as transformative theoria, and a civium model that combines Dunbar-scale communities with a more virtuous virtual world—including retreats like Reconnecting with the Real. Timestamps 00:00 – Welcome to the Lectern 02:00 – Fatherhood and meaning 05:00 – Questions on development 06:30 – Cognition as dynamic 07:30 – Culture raises kids 09:00 – Capitalism as religion 11:00 – Alloparenting and nature 13:00 – Becoming self and person 17:30 – Trust and role models 22:00 – Narratives and consumption 26:00 – Spirit of place 31:30 – Promises and personhood 33:30 – Narrative practice tracking 39:30 – Meta-narrative challenge 43:00 – Why we cannot return 43:30 – The synergy of civilization 44:30 – Civium at Dunbar scale 46:30 – Making the virtual sacred 49:30 – Can the virtual be virtuous? 51:00 – Keeping civilization's gifts 57:30 – Hospicing modernity and cultural transition 1:00:00 – Capitalism and demographic collapse 1:02:30 – Pilgrimage and theoria 1:09:00 – Apprenticeship to sages 1:11:30 – Eldership and the sage symphony About Kyle Koch Kyle Koch is a nature connection expert who bridges philosophical concepts and embodied reality through tracking, bird language, and nature-based core routines. Coming from a background in Evolve Move Play, he helps people reclaim an innate sense of belonging within the natural world. Learn more: https://www.ecologyofconnection.com Email: kyle@ecologyofconnection.com Resources The Matter With Things: https://channelmcgilchrist.com/the-matter-with-things Evolve Move Play: https://www.evolvemoveplay.com/ Follow John Vervaeke Website: https://johnvervaeke.com/ X: https://x.com/DrJohnVervaeke YouTube: https://www.youtube.com/@johnvervaeke Patreon: https://www.patreon.com/johnvervaeke Thank you for listening!
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
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________________________________________
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
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 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