Podcasts about constraints

In this mini-episode, we explain constraints-led approach (CLA) in simple terms, and how it impacts your jiu-jitsu training.Get our Intro to Mechanics audio course, normally $79, FREE:https://bjjmentalmodels.com/freeintro⬆️ LEVEL UP with BJJ Mental Models Premium!The world's LARGEST library of jiu-jitsu audio lessons, our complete podcast network, online coaching, and much more! Your first week is free:https://bjjmentalmodels.comNeed more BJJ Mental Models?Get the legendary BJJMM newsletter:https://bjjmentalmodels.com/newsletterLearn more mental models in our online database:https://bjjmentalmodels.com/databaseFollow us on social:https://instagram.com/bjjmentalmodelshttps://threads.com/@bjjmentalmodelshttps://bjjmentalmodels.bsky.socialhttps://youtube.com/@bjjmentalmodels

What if constraints could actually make us more creative? This week, we use behavioral science to explore how limits on our time, resources, and choices can focus our attention, spark creativity, and help us make better decisions. With author David Epstein, we look at how creatives like Bach and Dr. Seuss used constraints to generate new ideas, and how identifying bottlenecks at work can help us focus on the problems that matter most. We'll also share practical ways to embrace constraints, think differently, and get more done. Topics [0:00] Intro and speed round with David Epstein [15:00] Choice overload and endless options [22:31] How constraints can unlock creativity  [29:53] Innovation and limitations [36:14] Great thinkers and the obstacles that met them [43:55] The Green Eggs and Ham Effect [51:10] AI and creativity [59:40] Finding the Bottleneck [1:04:02] Knowing your own constraints [1:11:13] Bach: the maestro of constraint [1:18:43] Grooving Session: Exploring the power of constraints ©2026 Behavioral Grooves Links About David Epstein Inside the Box: How Constraints Make Us Better Join us on Substack! Join the Behavioral Grooves community Subscribe to Behavioral Grooves on YouTube Support Behavioral Grooves Music Links Bach - Air on the G String (Orchestral Suite No. 3) London Grammar - Hey Now

What do you do when every option is on the table and you still end up with the same idea you always have? Jake and Damian tackle one of the quiet traps of creative work, and land somewhere counterintuitive: freedom doesn't make ideas bolder. Constraints do.Damian unpacks David Epstein's Inside the Box and the psychology behind why unlimited time, unlimited method and unlimited scope send the brain straight to its safest, most familiar answer and the three tools for building a better box: the Fake Deadline, the One Rule, and the Small Box.Along the way you'll hear Shane Parrish on the Henry Kissinger question that outed a staffer's "best work" as anything but; René Meulensteen on the Federer clips he used to teach Man Utd players to stay one point wide; Sarina Wiegman on the training session she deliberately ran with a terrible referee, just to see what her players would do with it; and Lewis Morgan on what he'd actually do with a spare £1,000 and a Facebook Marketplace login.The idea doesn't need better conditions. It needs a smaller box.Listen to the full episodes:Sarina Wiegman: https://shows.acast.com/the-high-performance-podcast/episodes/sarina-wiegman-the-leadership-secrets-powering-england-lioneLewis Morgan: https://shows.acast.com/3d30295e-f7fb-5af1-b618-30a8763cc75a/625c41bcfa71dd0012e7f614René Meulensteen: https://shows.acast.com/3d30295e-f7fb-5af1-b618-30a8763cc75a/67e02799511f1304b0d74225 Hosted on Acast. See acast.com/privacy for more information.

How can professionals and creatives transform burnout into sustainable productivity while creating a work life that actually supports the life they want?In this episode of Productivity Smarts, host Gerald J. Leonard sits down with Lindsay Barnett, workplace experience strategist and author of Working Hell to Working Well, to rethink what productivity can look like when doing more is no longer the goal.Lindsey shares how moving to a part-time schedule after the pandemic changed the way she approached work, time, and work-life harmony. Gerald and Lindsay explore why constraints can sharpen focus, how mindfulness creates space between stimulus and response, and why negotiation works better as collaboration than compromise.They also unpack the habits that keep people stuck in hustle culture, from constant email and social media access to the pressure to always be productive. Lindsay shares her "3 I's" of intellectual challenge, impact, and interaction as a way to identify what gives you energy and what may be missing from your work.The conversation also examines burnout in leadership, unrealistic expectations, and the fear of failure that can create unnecessary work. If you're feeling burned out, stuck, or ready to rethink how you work, this episode offers practical ways to build sustainable productivity and greater work-life harmony. Listen in for practical ideas you can start applying to your work and life today.What We Discuss[00:00] Introduction[02:01] Guest introduction: Lindsay Barnett[03:39] Lindsay's journey to workplace wellness[05:48] The power of personal experience[06:48] The power of constraints[08:17] Constraints create clarity[09:19] The water hose analogy[11:19] Practical steps to overcome burnout[13:50] Mindfulness and fluidity[15:59] Negotiation and networking skills[18:57] Shifting from hustle culture[20:09] Breaking the hustle pattern[23:36] Dealing with feeling stuck[26:53] Unsticking yourself through learning[27:47] Taking agency in your career[30:34] The research and depth behind Working Hell to Working Well[31:02] Why burnout is contagious in leadership[31:22] Fear of failure as a hidden driver of overwork[33:50] Constraints as a competitive advantage, from supply chains to teams[35:08] Where to find Lindsay, her book, and the new Recharge Lab[36:05] Final thoughtsNotable Quotes[07:07] "The words we use create the world that we see. And it creates the world that our body lives in." – Gerald J. Leonard[08:24] "There is something about constraint that creates such clarity." – Lindsay Barnett[10:05] "I'm using less water, I'm covering more ground, and I'm getting the job done. I'm being more efficient, and I'm conserving my energy. And that's the principle of constraints." – Gerald J. Leonard[11:47] "Mindfulness is that little gap between the piece of information that's coming your way and your response." – Lindsay Barnett[13:16] "The unexamined life is like driftwood—throw it out in the ocean and wherever the wave takes it, that's where it goes." – Gerald J. Leonard[16:29] "Negotiation looks more like, here's what I have on my plate. What do you have on your plate? How do we start the conversation? Not with, I want it this way, you want it that way, but really saying, where can we go together?" – Lindsay Barnett[19:37] "It's unsustainable to drive your car at 100 miles an hour and never stop for gas. The engine is just gonna burn up. But that's exactly what happens to us." – Gerald J. Leonard[20:11] "I'm a big believer in small actions over time yield big results." – Lindsay Barnett [25:09] "We oftentimes overly identify with work. And so we can feel super stuck, but there's a whole other aspect of life that we can use to charge our battery." – Lindsay Barnett[31:58] "Sometimes what happens is this fear of failure is driving our behavior to create certain standards of excellence that honestly we may not have the resources to actually deliver on." – Lindsay BarnettResource and LinksLindsey BarnettWebsite: barnettcoaching.comLinkedIn: Lindsay K. BarnettBook: Working Hell to Working Well New offering: The Recharge Lab (team experience launching soon)Productivity Smarts PodcastWebsite - productivitysmartspodcast.comGerald J. LeonardWebsite - geraldjleonard.comTurnberry Premiere website - turnberrypremiere.comScheduler - vcita.com/v/geraldjleonardKiva is a loan, not a donation, allowing you to cycle your money and create a personal impact worldwide. https://www.kiva.org/lender/topmindshelpingtopmindsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, I examine the career journey of Doc Iggy - an orthopedic spine surgeon and popular digital creator - through the lens of Career Identity Expansion. Known online via his handle @doc.iggy, he gained widespread attention for sharing his medical journey, lifestyle, and mentorship before stepping away from clinical practice to focus on content creation. SummaryThis episode explores how professionals can expand their career identities beyond traditional roles, using the story of Dr. Eggie to illustrate the concept of career identity expansion and multidimensional careers.Takeaways:Your professional identity is not limited to your job title.Expanding your career identity can include multiple roles and interests.Success in one area does not mean you must abandon other passions.Redefining your relationship with your career can lead to greater fulfillment.You can honor your past achievements while exploring new dimensions of yourself.Take the Career Identity Expansion Assessment  https://lifebeyondclinicalpractice.thrivecart.com/cie-assessment/Visit the website: www.lifebeyondclinicalpractice.com

More leads won't solve a growth problem if you make the wrong people responsible for converting, serving, or managing them. Before marketing becomes the bottleneck, most firms hit another ceiling entirely: talent. In this solo episode, Chris Dreyer, CEO of Rankings.io, explains why traditional hiring advice often fails in today's market, how firm owners should decide their next hire, and why recruiting, retention, and culture all begin with understanding the real constraint inside the business. He also shares lessons from scaling Rankings.io, including the costly mistake of hiring exceptional talent without giving them the resources needed to succeed. You'll learn: When a law firm should hire before increasing its marketing budget. How to recruit and retain top talent at a personal injury law firm. How to decide which position your law firm should hire next. The leadership mistakes that prevent great employees from succeeding. If you want a marketing partner that takes your growth as seriously as you take your team, head over to Rankings.io. Like what you hear? Hit Subscribe! We do this every week. If you want to keep learning from the best voices in PI, join us at PIMCON 2026. Buy your tickets now! Subscribe to our newsletter and get the freshest news every Monday: newsletter.rankings.io Get Social! Personal Injury Mastermind w/ Chris Dreyer powered by Rankings.io is on Instagram | YouTube | TikTok

Kelly has a fascinating conversation with New York Times bestselling author David Epstein about his new book “Inside the Box: How Constraints Make Us Better.” “Multitasking is the act of distracting yourself.”  “It is usually bad to be a maximizer.” “Too often, we reach for a dream when what we need is a textbook.”

Summary The episode explores how constraints can become a powerful catalyst for innovation rather than an obstacle to it. Drawing on my experience leading an innovation initiative in educational publishing, I describe "in-the-box innovation," a systematic approach that modifies existing products and resources instead of relying on unfettered brainstorming. The episode argues that solutions are often contained within the problem itself—a principle illustrated by a high-school program designed to address the shortage of teachers of color and by the World War II Corsair, whose dangerous landing problem was solved by altering lift on the opposite wing rather than redesigning the aircraft. Constraints, Jeff argues, clarify priorities, reduce the number of competing possibilities, and force creative thinking. He concludes by applying the principle personally, noting that limiting the information he consumes through social media has improved his attention and comprehension. The central idea Constraints aren't necessarily barriers to innovation—they can be the very conditions that make innovation possible. By limiting our options, constraints force us to focus, rethink what we already have, and discover solutions that might otherwise remain hidden. Connect / Referenced Inside the Box: How Constraints Make Us Better by David Epstein Systematic Inventive Thinking Podcast interviews with Systematic Inventive Thinking

The Theory of Constraints is the framework Josh uses to answer the question every ecommerce founder asks, which is what do I actually work on next.In this episode, Josh and Dylan walk through Josh's five-step version of the framework built specifically for ecommerce, including the four constraint areas he uses on coaching calls to find the real bottleneck instead of the obvious one.Inside this episode:The step that has to happen before you identify your constraint (skip it and you'll spend a month solving the wrong problem)The four constraint areas in every ecommerce business, acquisition, fulfillment, product dev, and ops, plus the exact symptoms that tell you which one you're sitting inWhy all roads lead back to acquisition, and what breaks underneath you every time you crank ad spendThe client who was certain her constraint was Meta ads performance, until Josh walked the process and found the real one hiding behind a $50,000 inventory orderWhy conversion rate optimization is almost never the answer (Josh can't name three coaching calls in the last six months where it was)The brand spending $2,300 a day on ads while launching five creatives a week, and the number Josh told them to hit insteadThe difference between optimizing existing resources and allocating new ones, and the one rule you can't break when you free up time to work on a constraintThe activity Josh gives most founders permission to stop doing this week, plus why "I don't have time for this" is already a diagnosisJosh draws the entire framework out on his iPad while he teaches it, so the YouTube version is worth watching if you want the visual. And if you've ever ended a quarter busy, drained, and no further ahead than you started, you probably weren't lazy. You were working on the wrong constraint.Loved this episode? Drop us a rating because we're going for #1 ecommerce podcast in the world and every single rating moves the needle.-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-► Visit Our Website For Training and Resources► Leave Us An Honest Rating, Email An Image Of Your Rating To team@theecommercealley.com, We'll Send You A $10 Amazon Gift Card As An Appreciation Gift!► Learn About Our Mentorship Program For Ecom Brands Making Over $10k/month► Checkout Our Software, Breezeway - Never Second-Guess Your Meta Ads Again►  Follow Josh on social media: YouTube | Instagram | Facebook | TikTok | 

How ethanol blends higher than E15 may offer benefits related to emissions reduction, octane enhancement, agricultural economic activity and energy security. Hosted by: Jeff Lenard About our Guest: John Eichberger, Executive Director, Transportation Energy Institute John has more than 25 years of experience in the fuels industry, including more than a decade leading the Transportation Energy Institute (formerly known as the Fuels Institute). Prior to that, he served for 15 years as vice president of government relations at NACS, with a focus on fuels- and energy-related issues.

The fear of losing the faith to another culture is producing the very thing it fears. Dr. James Spencer and Dr. Ashish Varma open a series on three forces pressing on the church, beginning with assimilation, and Varma's argument is that syncretism functions mostly as a fear discourse. The word gets aimed outward, at newer expressions of Christian faith in other places, by people whose own expression is so culturally saturated that they have stopped noticing it. He points out that "essence," the thing we say we are protecting, is a Greek philosophical category rather than a biblical one. His illustration is an apple. American students arriving in Germany recognize the fruit and still find it strange, because the apple in their heads is large, waxed, and unblemished, and a German student had the same reaction in reverse. The dandelion has a flower and we call it a weed. Every category came from somewhere. Spencer supplies the Old Testament counterweight: what scholars call syncretism there is specific and it is always about Yahweh. Israel does not swap gods so much as limit the one they have, keeping him for agriculture and hiring another for war, and the distortion becomes a denial. That distinction lets the conversation move without collapsing. Acts 2 puts the gospel into every dialect at once, and Acts 15 declines to make Gentiles into Jews. Varma, whose family is Indian, describes a mission history in which merchant, soldier, and missionary arrived together and in which becoming Christian meant becoming less Indian. He asks what an Indian Christian should do with Diwali, a festival about light overcoming darkness in which a god enters a demon's kingdom to rescue his bride, and offers the reception of Christmas in the Roman world as his analogy. His word for the move is not conform but transfigure. Spencer's is relocation under a different authority. Both insist on a constraint: Jesus the Jewish Messiah, which they argue is what makes the openness possible rather than what limits it. Chapters 0:00 Three forces on the church 1:33 Syncretism as a fear discourse 4:26 A German breakfast 6:09 What an apple looks like 7:51 Where the image came from 8:48 Why is that a weed? 9:43 Essence is not a biblical word 10:40 Syncretism in the Old Testament 13:04 Naming it in someone else 16:10 Acts 2 and every dialect 18:11 What was actually at stake 20:00 Mission history in India 21:42 Diwali and light over darkness 22:41 How Christmas landed on the 25th 24:14 Distortion becomes denial 26:11 Relocated under Christ 29:58 Transfigured, not conformed 30:12 Distinction through proximity 34:28 Were Israel's laws more just? 38:13 What actually made Israel different 42:18 Assimilation as the result of fear 45:03 The Jerusalem council 46:31 Glitter and what we miss 51:04 The litmus test 55:22 Constraints that enable Resources Download the Study Guide at https://www.thinkingchristian.org/about/framework Varma's essays on Diwali and Indian Christian identity: Substack The Society for Post-Supersessionist Theology Passages referenced: Deuteronomy 4 and 6, Daniel 1, Acts 2 and 7 and 15, Philippians 1 Follow and subscribe Subscribe to Thinking Christian: https://episodes.thinkingchristian.org Website: www.thinkingchristian.org Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.

This week's Tech Corner turns to a recent entry into the public trading space: Cerebras (CBRS). Rick Ducat turns to the various headwinds and tailwinds facing the company as it seeks to become a worthy competitor to Nvidia (NVDA). Partnerships with other chipmakers like AMD Inc. (AMD) give it plenty of muscle but reliance on chip development from TSMC (TSM) raise investor concerns. Rick also turns to technical analysis of the Cerebras' post-IPO stock and options activity. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Ben and Andrew begin with Nvidia's announcements of a new funding model for AI infrastructure, including the differences and similarities with railroad expansion 150 years ago, why LLMs were a gift and curse to Nvidia's business, the pressure on Nvidia coming from Google and Amazon, and the expanded blast radius as Nvidia works to mobilize third party funding. From there: Why the turnover at Google may actually be a good sign for Google's frontier efforts, and extended thoughts the future of AI-generated output, ideas and substantiation, and Anthropic's plan to watermark outputs. At the end: A question about the Vision Pro, the obstacles for Starlink Mobile, and a cranky emailer yields a clarifying answer on why Ben is excited about his vibe coded app.

Ben Emons believes Applied Materials (AMAT) remains strong in the AI trade despite Friday's post-earnings sell-off. However, China revenue shows some weakness, and Ben is keeping an eye on how that will affect demand for the company's products. Supply constraints also pose a challenge to future earnings strength. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Why does less freedom make you more creative? How can you use constraints effectively to help you get the most out of your creativity? Is AI helping you solve or avoid the real problem? In this podcast episode, Joe Sanok discusses creativity, constraints, and problem-solving with NYT bestselling author David Epstein, author of Inside the Box: How Constraints Make Us Better. They explore why having unlimited freedom can actually make people less creative, how constraints can unlock better ideas, and why subtracting commitments may be more effective than continually adding new ones. David also explains how AI can hinder creative problem-solving when it is used before the real problem has been identified, and offers advice for private practitioners navigating an increasingly AI-driven world while preserving the value of human connection.

Community acceptance may be emerging as one of the most consequential constraints on data center growth. On this episode of the Data Center Frontier Show, DCF Editor in Chief Matt Vincent speaks with Buddy Rizer, Executive Director of Loudoun County Economic Development, and Adam Waitkunas, founder of Milldam Public Relations, about the rising political and community resistance confronting data center development — and what the industry needs to do differently. After two decades working with the world's largest data center market, Rizer said the current level of opposition is unlike anything he has seen before. “The pushback is universal,” he said. “The talking points are fairly standard from community to community, and it has really become next to impossible to do business.” Rizer believes the industry's next major constraint may no longer be purely physical. “We thought it was going to be power, but it may be community acceptance and political durability.” The discussion examines how distrust can overwhelm even strong factual arguments. Rizer noted that Loudoun County's more than 250 data centers collectively use less than 10% of its water system and have generated enormous tax benefits, yet those figures increasingly struggle to break through. “You can't change how people think until you change how they feel,” Rizer said. Waitkunas argues that developers need to begin community engagement much earlier and give residents a meaningful role in shaping Community Benefit Agreements. Those agreements should go beyond financial contributions to schools, parks or community programs and include operating commitments involving issues such as noise, traffic and other local impacts. Rizer agreed that simply complying with regulations no longer demonstrates partnership. “When the noise ordinance says 55 and they come in at 54.5, that to me, that's not partnership.” The guests also discuss industry “unforced errors,” perceptions of secrecy surrounding anonymous LLCs and project filings, the growing influence of organized opposition groups, and the risk of companies waiting until a moratorium is already underway before attempting serious community outreach. For Rizer, developers increasingly need to manage two distinct measures of success. “Economic success and community trust are two different balance sheets,” he said. “Industry has to invest in both.” The conversation also explores the role of local governments, particularly smaller communities encountering hyperscale development for the first time; the value of bringing officials and residents through operating facilities that resemble what is actually being proposed; and whether the data center industry ultimately needs a formal standard for community engagement. Both guests expect conditions to become more difficult before they improve. Waitkunas anticipates more moratoriums without significant changes in industry behavior, while Rizer warned that data centers could increasingly become central issues in local political campaigns. “AI hasn't really created this conversation,” Rizer said, “but it definitely has put it on fast forward.” The central question for the industry is increasingly straightforward: not whether data centers have impacts, but whether developers can understand those impacts, mitigate them, communicate them honestly and build enough community trust to sustain the infrastructure expansion now underway.

What do you do when the most evidence-based intervention you have is also the one your patient is "not allowed" to receive? Constraint-induced movement therapy has decades of research behind it, yet its exclusion criteria — especially around cognitive impairment — rule out a huge share of the stroke survivors clinicians actually see. In this episode, hosts Erin Gallardo, PT, DPT, NCS, and Claire McLean, PT, DPT, NCS, talk with neuro OT Briana Elson, MS, OTR/L, BCPR, CBIS, about her decision to try modified CIMT with an 86-year-old stroke patient who had significant cognitive impairment and underlying dementia — a textbook exclusion — and why his profile, a left-handed man whose affected side was his dominant hand, made forced use feel natural and salient rather than confusing. Briana walks through how she built the protocol around safety and supervision in inpatient rehab: getting a physician order for the constraint mitt, educating and enlisting his wife, keeping a consistent daily schedule, breaking the three-to-six hours into non-consecutive blocks, and removing the mitt before late-day sundowning. She shares the outcomes that surprised even her skeptics — meeting the minimal detectable change on the Box and Block Test and a standardized motor scale within roughly thirteen days, plus real gains in grooming and feeding independence — alongside the honest caveats, including that his cognition never improved and that real-world delivery never looks as clean as the published article. It's a practical, encouraging conversation about reading the research, respecting its limits, and giving more patients a shot at an intervention they're usually told they can't have.

They're shamed, banned, or driven underground but “repugnant markets” have a way of surviving. Organ sales, MAID, even blood sales fall into the category. Nobel Prize-winning economist Alvin Roth comes to Canadaland to discuss his latest book, Moral Economics: From Prostitution to Organ Sales, What Controversial Transactions Reveal about How Markets Work.Further Reading:The Nobel Prize - Alvin E. RothCan economics save lives? - Alvin E. RothRepugnance as a Constraint on Markets - Alvin E. RothA Conversation with Alvin Roth - The ConversationKidney Paired Donation Program - Canadian Blood ServicesHost: Gemma BoothroydCredits: Gemma Boothroyd (Reporter), Bruce Thorson (Senior Producer), Chad Galloway (Audio Editing & Production), Tristan Capacchione (Senior Production Supervisor), Jesse Brown (Editor and Publisher)Fact Checking by: Julian AbrahamAdditional Music by: Audio NetworkPhoto: Linda A. Cicero, Stanford UniversitySponsors: Shopify: Sign up for your one-dollar-per-month trial today at https://shopify.caoxio: Head over to https://canadaland.oxio.ca and use code CANADALAND for your first month free! Article: Article is offering our listeners $50 off your first purchase of $100 or more. To claim, visit https://article.com/canadaland and the discount will be automatically applied at checkout.Can't get enough Canadaland? Follow @Canadaland_Podcasts on Instagram for clips, announcements, explainers and more.If you value this podcast, support us! You'll get premium access to all our shows ad free, including early releases and bonus content. You'll also get our exclusive newsletter, discounts on merch at our store, tickets to our live and virtual events, and more than anything, you'll be a part of the solution to Canada's journalism crisis, you'll be keeping our work free and accessible to everybody. Hosted on Acast. See acast.com/privacy for more information.

In this episode we respond to emails from Thirsty Horse, Joanne, Matt and Alan.  We share our gratitude for our listeners and reflect on how a listener community can become one of the most meaningful outcomes of a long-term investing project. We also provide an update on the Top of the T-Shirt fundraising campaign for the Father McKenna Center.  Next we answer two portfolio design questions about retirement drawdown constraints and how to fit them into the framework for portfolios with higher safe withdrawal rates.And THEN we our go through our weekly portfolio reviews of the eight sample portfolios you can find at Portfolios | Risk Parity Radio.Links:Father McKenna Center Donation Page (please mention Risk Parity Radio in the comment section with your donation):  Donate - Father McKenna CenterCharity Navigator Rating for The Father McKenna Center:  Charity Navigator - Rating for Father McKenna Center Inc.Bengen "Richer Retirement" Sample Portfolio at Portfolio Charts:  Richer Retirement Portfolio – Portfolio ChartsBill Bengen's "Richer Retirement" Content:  Bill Bengen's New Book | Charts & Tools for YouGolden Ratio Compared with Version w/o Alternative Investments:  Portfolio Backtester for ETFs and Asset Allocation | testfolioAfford Anything Risk Parity Portfolio Blueprint:  Afford Anything frank-vasquez-risk-parity-portfolio-BluePrint.pdf - Google DriveBreathless Unedited AI-Bot Summary:A week where stocks jump 3% to 5% and gold pops more than 7% can feel like the market is daring you to change your plan. We don't take the bait. We walk through what actually happened across major asset classes, why we still refuse to time markets, and how a diversified risk parity approach is designed to keep you steady when headlines and price moves get loud.We also start with something more important than portfolio math: the notes we received after my mom passed away, and what it means to build an audience that shows up for each other. From there, we share a progress update on our Father McKenna Center “top of the t-shirt” campaign, including matching funds, a Charity Navigator 100% rating, and a practical tip for tax-smart giving: donating appreciated shares can reduce capital gains while supporting a mission you care about.Then we get into two listener questions that hit the real world. First: if you're in the retirement drawdown phase and you can only use stock and bond ETFs or index funds, what would we actually hold and why? We talk safe withdrawal rate research, the role alternatives play, and what you might use as imperfect substitutes (value tilt, REITs, utilities, even gold miners) when gold and managed futures aren't on the table. Second: what if you're investing from New Zealand with limited fund access and a tax drag on US ETFs? We lay out a decision process for finding value-tilted funds locally, evaluating managed futures costs, and avoiding expensive “solutions” that quietly erase the benefit you're chasing.Support the show

Today, we dive into the 'Simulation Theory' and the unsettling reality of the odds of us living in a simulation. We explore the origin of the Simulation Theory, the odds of living in a simulation, and who controls reality. Welcome to Camp!

Send us Fan MailWe take rapid-fire questions from the field and get blunt about what's changing in low voltage work, from labor shortages to PoE terminations to fiber habits that build real trust. We also dig into estimating mistakes, dark fiber strategies, cybersecurity boundaries, and what to learn before stepping into design and the RCDD path.• livestream schedule change coming soon and how to stay connected • fundraiser progress update and pushing to hit the goal • LinkedIn profile lockout story and where to find our daily LinkedIn content • labor as the biggest estimating risk and why averages hide crew variability • adding risk management and assumptions to bids to protect schedules • cross-training as the fastest way to reduce labor constraints • pass-through versus traditional RJ45 terminations under PoE loads • importance of crimper maintenance and matching tool to connector • choosing a specialization based on passion and then stacking skills • certifications and credentialing that improve marketability and job access • AI data center growth and the bottleneck debate across GPUs, power, cooling, cabling, skilled labor • fiber testing growth beyond class fundamentals and why humility matters • contamination control, labeling, and documentation as the marks of a great tech • building customer confidence so clients request specific technicians • estimator mistakes under tight margins including site logistics and lessons learned • dark fiber and spare capacity rules of thumb with cost versus flexibility thinking • where cybersecurity responsibility starts for structured cabling teams • what to keep learning before moving from field work into RCDD and design • firestopping foam selection, inspector expectations, and safety concerns Also, keep in mind that I'm still doing the uh Save Our Sons walk-a-thon um uh ministry event, trying to raise money for um Living Hands Ministry in Dade City, Florida. So if you haven't donated yet, please go look at my LinkedIn profile, go look at my Facebook profile and donate to that cause. So if you ever consider joining the Let's Talk Cablin community, please make sure that you do so.Support the showKnowledge is power!  Make sure to stop by the webpage to buy me a cup of coffee or support the show at https://linktr.ee/letstalkcabling .  Also if you would like to be a guest on the show or have a topic for discussion send me an email at chuck@letstalkcabling.com Chuck Bowser RCDD TECH#CBRCDD #RCDD

This is a special bonus episode, an interview with Rivian's Chief Design Officer, Jeff Hammoud. We've got all the audio here, but it's worth heading over to our Youtube channel at dbtr.co/youtube to watch the video. We recorded this one live in Miami, in the Rivian Pavilion during Art Basel, with a R1T truck in a new eggplant-purple called Boreal parked just over Jeff's shoulder. Jeff runs a team of about 140 people across Irvine and Palo Alto — interior and exterior designers, color and materials, digital surfacing, perceived quality, even an in-house painter and a clay milling shop. And he has a strong opinion about what all that machinery is for: constraints are what separate designers from stylists. Anyone can make a beautiful concept car, but making the production version look like the concept is the challenge. We talked about why Rivian keeps making colors most people won't buy, and how limited-run “Studio Originals” work like sneaker drops. Jeff makes the case that AI is going to level the playing field in design — shifting the advantage from whoever illustrates best to whoever has the better idea. We got into which controls stay physical and why, the haptic halo wheels coming in R2, and how scent ties a car to memory. *** Premium Episodes on Design Better This ad-supported episode is available to everyone. If you'd like to hear it ad-free, upgrade to our premium subscription, where you'll get an additional 2 ad-free episodes per month (4 total). Premium subscribers also get access to the documentary Design Disruptors and our growing library of books. New premium subscriber benefit: we've launched a private Slack workspace…join now to connect with designers, product leaders & creative practitioners in our community. And get a behind-the-scenes pass to every episode with The Roundup, where each week we bring you insights and actionable tactics from recent episodes. You'll also get access to our monthly AMAs with former guests, ad-free episodes, discounts and early access to workshops, and our monthly newsletter The Brief that compiles salient insights, quotes, readings, and creative processes uncovered in the show. And subscribers at the annual level now get access to the Design Better Toolkit, which gets you major discounts and free access to tools and courses that will help you unlock new skills, make your workflow more efficient, and take your creativity further. Upgrade to paid Learn more about your ad choices. Visit megaphone.fm/adchoices

When vulnerability disclosures shrink the wild exploit window down to less than 24 hours (or even minutes), traditional manual patching and WAF rule creation simply cannot keep up.In this episode, Ashish sits down with Ammar Alim (Product Security Engineering Lead at Adobe) to break down how to build an automated, agentic WAF pipeline. Ammar shares how his team manages the scale and complexity of seven commercial and open-source WAFs (including AWS WAF, Cloudflare, Akamai, Azure WAF, Wallarm, and ModSecurity) by leveraging AI agents.Discover how to construct an agentic harness, orchestrate deep research agents to gather exploit POCs, and utilize multi-model architectures (e.g., Anthropic for rule generation and OpenAI as an LLM judge) to eliminate false positives and safely deploy virtual patchesGuest Socials -⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠Ammar's LinkedinPodcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Security, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions asked:(00:00) Introduction & The Currency of Speed in Security(02:00) Ammar Alim's Background: From Data Centers to Product Security at Adobe(05:00) The Multi-Vendor WAF Nightmare: Managing Scale Across 7 Products(08:30) Understanding False Positives vs. False Negatives in WAF Management(12:30) Why

AI made campaign creation easy, so the new bottleneck is knowing what to ship. Brendan Farnand, co-founder and chief evangelist at Knak, spent the past year studying how enterprise teams like OpenAI, Google, and YouTube build AI-native campaigns. He breaks down OpenAI's agent workflow that starts with an unstructured Slack message, builds a structured brief in Linear, then passes assets to a production platform for a human to add taste. He also explains applying Goldratt's Theory of Constraints to find the single bottleneck in your go-to-market process, and why speed to market can mean millions more customers reached.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI made campaign creation easy, so the new bottleneck is knowing what to ship. Brendan Farnand, co-founder and chief evangelist at Knak, spent the past year studying how enterprise teams like OpenAI, Google, and YouTube build AI-native campaigns. He breaks down OpenAI's agent workflow that starts with an unstructured Slack message, builds a structured brief in Linear, then passes assets to a production platform for a human to add taste. He also explains applying Goldratt's Theory of Constraints to find the single bottleneck in your go-to-market process, and why speed to market can mean millions more customers reached.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

"I think structure is more liberating than it is stifling, and it often takes people losing it to sort of realize that,” says David Epstein. Epstein is the author of the number one New York Times bestseller Range and the New York Times bestseller The Sports Gene. He has master's degrees in environmental science and journalism and has worked as a senior writer for Sports Illustrated and as an investigative reporter for ProPublica. He lives in Washington, DC. His newest book, Inside The Box, is out now.  00:00 - Why constraints drive creativity 06:35 - How mnemonics help you memorize 10:31 - The power of low-stakes practice 12:25 - The problem with youth sports  18:56 - The constraints-led approach in elite sports 22:25 - Good vs bad constraints 25:39 - Constraints at NASA & Pixar 31:07 - Why we always add instead of subtract 35:02 - The dizziness of freedom 41:29 - Experimentation as a practice 45:19 - The importance of self-reflection 49:02 - How to fix your fractured attention Referenced in the episode:  Read The Athletic's article about the constraints-led approach: https://www.nytimes.com/athletic/6665943/2025/09/29/sports-training-cla-coaching-wembanyana-ohtani For more about David Epstein, visit his website: https://davidepstein.com/  Buy Inside The Box here: https://www.amazon.com/dp/B0FNDSKWMY/?/ref=as_li_qf_sp_asin_il_tl?tag=mind0a3-20  Buy Range here: https://www.amazon.com/Range-Generalists-Triumph-Specialized-World/dp/0735214506/ref=as_li_qf_sp_asin_il_tl?tag=mind0a3-20  We hope you enjoy this episode, and feel free to watch the full video on YouTube! Whether it's an article or podcast, we want to know what we can do to help here at mindbodygreen. Let us know at: podcast@mindbodygreen.com. Learn more about your ad choices. Visit megaphone.fm/adchoices

- Apple's Q3FY26 Earnings and Call Set for Today - Last Guesses at Apple Earnings - CAICT: "Foreign" Smartphones Grow in China as Market Shrinks - MacRumors Tracks Lots of July Expansion for AppleCare+ - Qualcomm Says Revenue from Apple to Fall Sooner Than Expected - Apple Tosses Studio Display XDR Into Refurbished Store - Apple Sued Over Bogus Bitcoin Wallet in App Store - New Snoopy Special Hits Apple TV on Friday - Sponsored by Coveron: Save up to 76% off with code macos at coveron.com/macos - Catch Ken on Mastodon - @macosken@mastodon.social - Send Ken an email: info@macosken.com - Chat with us on Patreon for as little as $1 a month. Support the show at Patreon.com/macosken

Diesel prices have soared, driven by supply constraints through the Strait of Hormuz and by the ongoing war between Russia and Ukraine. Ukraine has been able to knock out a sizable percentage of Russia's refining capacity, shrinking global supplies. Where does that leave Brazil, which has been dependent on diesel imports from Russia? How do Brazil's price subsidies impact diesel supplies? What role could India play in supplying diesel fuel? What policy options, if any, does the Trump administration have to solve the supply problem? Join Jeff Mower, S&P Global Energy's director of Americas oil news, as he dissects these topics with Aaron Tucker, managing editor of Americas middle distillates, and Renato Rostas, Brazil oil price manager.

We're re-airing one of our favorite conversations: Season 4 guest Devin Finigan, chef and owner of Aragosta at Goose Cove in Maine. Since we first spoke with Devin, Food & Wine named Aragosta the #1 hotel for food and drink in the country, and her cookbook, A Kitchen on Goose Cove: Recipes from the Heart of Maine, has officially been released. This episode also holds special meaning for me, as I open up the episode with a tribute to my late mother, Sadie B — a lifelong food lover whose recipes, generosity, and "never let anyone leave hungry" spirit set the stage for a conversation with a guest who embodies that same philosophy through her cooking. Devin never went to culinary school. She dropped out of art school, followed her instincts, signed a restaurant lease she couldn't really afford, and built Aragosta into an award-winning destination — all while staying true to her own vision. If you've ever wondered what it looks like to take the unconventional path and make it work, this is the episode for you. Manifesto Connections “The riskiest thing you can do is play it safe” — leaving art school and signing her first restaurant lease on a handshake. “There is more than one right way in life” — no culinary school, no traditional path, and it worked anyway. “Constraints are opportunities” — building Goose Cove on a tight budget by leaning on local artisans, potters, and woodworkers.   Calls to Action Learn more about Aragosta at Goose Cove or make a reservation: aragostamain.com Follow Aragosta on Instagram: @aragosta.main Support our sponsor: Interabang Books — interabangbooks.com Watch Tricia's TEDx talk, “How I Redeemed 35 Years of Regret” — TriciaRoseBurt.com or https://youtu.be/KpMSGApoLKE?si=3-u3CaywtoWcKXIm Credits No Time to Be Timid is written and produced by Tricia Rose Burt. Episodes are produced and scored by Adam Arnone of Echo Finch. Executive producers: Amy Grant, Nancy Perot, and Sage Wheeler. No Time to Be Timid is a presentation of No Time to Be Timid Productions.

Points of Interest 00:00 – 02:50 – Introducing Owner's Clarity: Kristen Kelly welcomes Mike Grinberg back to the podcast to discuss why founders should develop personal clarity before focusing on positioning or marketing strategy. 02:50 – 05:30 – Why Successful Founders Still Feel Miserable: Mike explains how many founders build profitable businesses that ultimately fail to support their personal goals, values, or fulfillment. 05:30 – 07:10 – A Real-World Example of Misalignment: Mike shares the story of a founder whose business model eliminated the mentoring opportunities that gave his work meaning, despite excellent financial performance. 07:10 – 10:15 – Learning from Personal Experience: Reflecting on his own agency journey, Mike explains how removing himself entirely from client work created a leadership role that didn't align with his strengths or interests. 10:15 – 16:05 – The Four Constraints of Owner's Clarity: Mike introduces the four pillars of Owner's Clarity—purpose, identity, intent, and capacity—and explains how each influences business decisions and long-term satisfaction. 16:05 – 20:00 – Discovering Purpose Beyond the Business: Mike discusses how uncovering a founder's true purpose often requires deep personal conversations that go far beyond surface-level business objectives. 20:00 – 24:35 – Translating Clarity into Operational Decisions: The conversation explores how understanding intent and capacity influences hiring, delegation, automation, forecasting, and leadership responsibilities. 24:35 – 29:55 – Burnout Is an Alignment Problem: Mike explains that burnout is often caused less by workload and more by operating outside your strengths or building a business that conflicts with your identity. 29:55 – 33:45 – Building a Business Around Founder Constraints: Mike shares how Owner's Clarity influences technology investments, financial planning, reinvestment decisions, and sustainable business growth. 33:45 – 35:40 – Using the Constraint Alignment Test: Mike outlines when founders should revisit their Owner's Clarity, including after major business milestones, partnerships, or significant life changes. 35:40 – 38:25 – Think About Your Exit Early: Mike encourages founders to define how they want to feel at the end of their ownership journey, using that vision to guide strategic decisions from the beginning. 38:25 – 41:50 – Advice Mike Would Give His Younger Self: Mike reflects on the importance of designing a business that supports the founder, embracing healthy selfishness, and intentionally building a company around personal fulfillment rather than external expectations. Show Notes Connect with Mike Grinberg on LinkedIn Owner's Clarity Toolkit™ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On episode 9 of Third Loop, the Progressive Delivery team explores the complicated relationship between AI, automation, and human creativity. Kim Harrison, Adam Zimman, and Heidi Waterhouse discuss AI's ability to lower technical barriers and reduce toil, along with its tendency to strip away context, reinforce sameness, and confidently produce answers it does not understand. Along the way, they encounter whale dictionaries, ancient scrolls, AI-written memos, autonomous tractors, and the dangerously comfortable “Yes Bubble.”

Today we sit down with David Epstein, environmental scientist, investigative journalist, and author of the new book, Inside the Box: How Constraints Make Us Better. He joins us for a special interview to discuss a modern paradox that is plaguing today's society: how seemingly infinite choices and the illusion of absolute freedom bring overwhelm, paralysis, and uncertainty.   David Epstein uses science to explain how we can benefit from narrowing our options, and how, as contrary as it might feel, using self-imposed boundaries and constraints can tap into unexpected wells of focus and innovation.   Find David on Instagram or at his website or order his book here. ------------------------------------- COME TO ROME WITH US: Our 4th annual Bittersweet Life Roman Adventure is taking place this year from 1 to 7 November 2026! If you'd like to be part of an intimate group of listeners on a magical and unforgettable journey to Rome, discovering the city with us as your guides, find out more here. AD-FREE LISTENING: After well over 10 years on the air with little-to-no advertising, in 2026 we have finally made the difficult decision that this completely independent and self-funded show is no longer sustainable without it. HOWEVER! If you join us on Patreon, for as little as $3 per month, you will have access to all new episodes completely ad-free! ADVERTISE WITH US: Reach expats, future expats, and travelers all over the world. Send us an email to get the conversation started. GET TWO BONUS EPISODES PER MONTH: Pledge your monthly support of The Bittersweet Life at the $5 per month level or above, and you will have access to two all-new (and sometimes wacky) bonus episodes every single month. As well as ad-free listening, occasional live meet-ups, and access to our chat community. Visit our Patreon site to find out more. TIP YOUR PODCASTER: Say thanks with a one-time donation to the podcast hosts you know and love. Click here to send financial support via PayPal. (You can also find a Donate button on the desktop version of our website.) The show needs your support to continue. START PODCASTING: If you are planning to start your own podcast, consider Libsyn for your hosting service! Use this affliliate link to get two months free, or use our promo code SWEET when you sign up. SUBSCRIBE: Subscribe to the podcast to make sure you never miss an episode. Click here to find us on a variety of podcast apps. WRITE A REVIEW: Leave us a rating and a written review on iTunes so more listeners can find us. JOIN THE CONVERSATION: If you have a question or a topic you want us to address, send us an email here. You can also connect to us on Facebook or Instagram. Tag #thebittersweetlife with your expat story for a chance to be featured! NEW TO THE SHOW? Don't be afraid to start with Episode 1: OUTSET BOOK: Want to read Tiffany's book, Midnight in the Piazza? Learn more here or order on Amazon. TOUR ROME WITH TIFFANY: If you're traveling to Rome, don't miss the chance to tour the city with Tiffany as your guide!

Limits are the key to stimulating creativity, innovation, and collaboration. In a world that gives us seemingly infinite choices and prizes freedom above all else, we have an unprecedented number of options regarding what to do, who to be, and how to spend our time. All that choice is wonderful, but it is also overwhelming. The irony is that total freedom can be paralyzing, and unlimited resources don't necessarily lead to the biggest breakthroughs. In fact, overvaluing complete freedom can be disastrous for everything from starting a company to harnessing creativity to finding personal satisfaction. In his new book, Inside the Box: How Constraints Make Us Better, David Epstein argues that all of us — individuals, businesses, institutions, even societies — can benefit from narrowing our options. Epstein dives into the science and practice of constraints, exploring exactly when and how guardrails can be beneficial, whether we're working with limited resources or using self-imposed boundaries to tap unexpected wells of focus and innovation. Inside the Box reveals how boundaries create breakthroughs, and how setting the right constraints can help you become the most creative, productive, and satisfied version of yourself. David Epstein is the author of the number one New York Times bestseller Range and the New York Times bestseller The Sports Gene. He has master's degrees in environmental science and journalism and has worked as a senior writer for Sports Illustrated and as an investigative reporter for ProPublica. Katy Sewall is the host and creator of The Bittersweet Life podcast. She's a writer, podcast consultant, and a Public Radio professional frequently heard on 94.9 KUOW. She's also the former Program Director at Town Hall. Buy the Book Inside the Box: How Constraints Make Us Better Elliott Bay Book Company

In this episode of The Vibe With Ky Podcast, executive function coach Sean McCormick explains why ADHD procrastination is a signal that a task needs a different person, not more willpower.This one is for you if you have been called lazy your whole life and always suspected that word never quite fit what was actually happening.Sean McCormick, M.Ed., is a credentialed education specialist who taught public school special education before he understood his own ADHD. He founded Executive Function Specialists for students, built the Executive Function Coaching Academy to train educators, and co-founded an adult coaching practice called UpSkill Specialists. He joined me for this conversation after we spent three days together at NeuroDiversion 2026.The shift in this episode is small and it changes a lot. Executive function is not a measure of effort, it is a set of brain-based skills involving working memory, self-regulation, and task initiation. Once that lands, the question stops being why can I not make myself do this and starts being what does this task actually need.Here is what comes out of the conversation:- Procrastination often points to a task that needs a different person, which Sean calls who not how.- Constraints create real freedom, and Sean explains the fixed non-negotiables he keeps in every week.- One full unscheduled day, phone off, does more for an ADHD brain than another productivity system.- Sean shares what five years of sobriety did for his focus, decision making, and follow-through.CONNECT WITH SEAN MCCORMICKUpSkill Specialists, his adult executive function coaching practice: https://www.upskillspecialists.com/For educators thinking about coaching, the Executive Function Coaching Academy: https://www.executivefunctioncoachingacademy.com/CONNECT WITH KYWebsite: https://thevibewithky.com/Patreon, for the ADHD Deep Dive Library, exclusive neurodivergent content, and the Reverse The Mic segment that is not available anywhere else online: https://www.patreon.com/thevibewithkyFacebook subscription, for the ADHD Deep Dive Library, exclusive neurodivergent content, and the Reverse The Mic segment that is not available anywhere else online: https://www.facebook.com/thevibewithky/subscribe/Instagram: https://instagram.com/thevibewithkyTikTok: https://tiktok.com/@thevibewithkyThe ADHD Vibe Store: https://thevibewithky.podia.com/This podcast is for informational and entertainment purposes only and is not a substitute for professional medical or mental health advice. If you are struggling, please reach out to a qualified professional.

  Every once in a while I finish a trip and, after I get home and everything dries out, I realize there were a handful of lessons that weren't obvious while I was riding. So this week, rather than another recap of the Oregon Coast, here are a few takeaways from the trip."       Takeaway #1: Constraints can make for a better tour   Weather forced changes. My tent forced bigger changes. Transit schedules forced even more changes. We stopped trying to preserve the original plan and started asking what the best trip looked like now. The revised trip ended up being more memorable than the one we planned.   Bottom line: Sometimes constraints don't ruin a tour—they become the reason it's interesting.       Takeaway #2: I think my touring philosophy is changing   I've spent years optimizing for mileage. That usually means riding through interesting places instead of experiencing them. Tillamook was the perfect example. I've passed through multiple times and barely stopped. This time I spent the day exploring instead of chasing miles.   Bottom line: I'm increasingly interested in using the bike to get me to experiences, not simply accumulate distance.       Takeaway #3: "Ride your own ride" can happen on the same trip   Brock and I started from exactly the same place. We made completely different decisions. He chose the big riding day. I chose the slower exploration day. Both approaches ended with great stories.   Bottom line: There isn't one correct version of a successful bike tour.       Takeaway #4: Transit isn't cheating   We used buses multiple times. They made the route possible. They allowed us to adapt instead of abandoning the trip. They cost almost nothing.   Bottom line: Bikes and transit complement each other remarkably well.       Takeaway #5: Gear failures change everything   "My tent is not viable." That one sentence completely changed the weekend. Once your shelter becomes unreliable, every decision becomes different. Confidence is part of your gear list.   Bottom line: Reliability beats optimization.       Takeaway #6: Where did all the bike tourists go?   Peak season. Iconic route. Excellent infrastructure. Very few loaded touring bikes.   Possible explanations:   More gravel riders? More bikepacking? Different travel habits? Something else?   Bottom line: I'm seeing enough of a pattern that I don't think it's a coincidence anymore.       Takeaway #7: I'm excited about touring again   Fitness is coming back. Losing weight has made a noticeable difference. I'm thinking differently about how I want trips to look. I was planning the next ride before this one ended.   Bottom line: That's usually a sign you're headed in the right direction.

C.P. Gurnani is the Founder of Aionos, an AI-enabled company he launched in April 2024 after a 44-year career in the global technology industry. He is best known for leading Tech Mahindra, and his earlier career took him through HCL under Shiv Nadar and Perot Systems under Ross Perot Sr. He continues to serve as Chair of Mahindra Holidays and founder of Mahindra University. In this second appearance on DGTL Voices, C.P. sits down with Ed for a conversation that starts with a Bollywood song about perseverance and ends with why the world's real innovation is happening in places most people aren't watching. He walks through India's advantage at the intersection of talent, data, and the willingness to work within constraints, telling the story of a friend making cancer diagnostics affordable for under one dollar. He shares the three biggest misconceptions about AI, why he thinks the world is measuring it wrong, and what it takes to build a company where the mission is to have fun and create real impact. Plus his leadership formula: discipline, determination, dedication, and the two-way street of giving and taking that made him the leader he is. https://marxadvisory.com

In this episode of the Ecomm Breakthrough Podcast, host Josh Hadley shares his proven strategy for doubling business growth by identifying and eliminating bottlenecks. He recommends conducting a time study to pinpoint where time is being consumed, then hiring specifically to relieve that constraint. Whether it's PPC management or product sourcing, freeing up the founder's time allows focus on growth opportunities like new product launches or expanding sales channels. Josh cautions against hiring roles that don't address the real bottleneck, drawing from his own costly mistakes, and notes that while AI can help, human oversight remains essential.Bullet Points:Strategy for doubling and accelerating business growthImportance of identifying business constraints or bottlenecksConducting time studies to pinpoint where time is being spentHiring specifically to relieve identified bottlenecksRole of AI in business operations and the need for human oversightCommon bottlenecks in ecommerce, such as PPC management and product sourcingThe cycle of identifying constraints, hiring for them, and focusing on growthCaution against hiring roles that do not address the main constraintImportance of ongoing attention and iteration in the hiring processBuilding a scalable and sustainable business through disciplined approachesTimestamps:00:00:00 Introduction to the Ecomm Breakthrough PodcastHost Josh Hadley introduces himself, his ecommerce background, and the podcast's purpose of sharing strategies for business growth.00:00:56 The Key to Doubling Your BusinessJosh reveals his number one strategy for rapid business growth: identifying and hiring for the main constraint or bottleneck.00:01:52 Identifying the Business BottleneckAn example of how a growing ecommerce business hits a bottleneck, such as PPC management consuming the founder's time.00:02:52 How to Find Your ConstraintJosh recommends conducting a two-week time study for yourself and your team to pinpoint where time is being spent.00:03:52 Solving the Bottleneck: Hiring vs. AIDiscusses two solutions for a constraint: hiring a person or using AI, emphasizing the need for human oversight with AI.00:04:50 Reinvesting Time into GrowthOnce a constraint is solved, the founder's freed-up time must be redirected toward new growth opportunities like product launches.00:05:46 The Evolution of a Business OwnerDescribes the ongoing cycle of identifying a new bottleneck, hiring to solve it, and reinvesting time into further growth.00:06:40 The Danger of Hiring for the Wrong RoleJosh shares personal examples of hiring for roles that were not the actual business constraint, which proved to be mistakes.00:07:38 A Real-Life Hiring DilemmaJosh recounts his decision between hiring a Director of HR versus a Director of Ecommerce by identifying the true constraint.00:09:36 Final Warning and ConclusionA final caution against hiring roles based on what other companies are doing instead of focusing on your own bottleneck.Links and Mentions:E-commerce Platforms:  "Shopify": "00:00:00"  "TikTok Shop": "00:00:00"  "Amazon": "00:00:00"  Business Concepts and Tools:  "Hiring for the Constraint": "00:00:56"  "Time Study": "00:02:52"  "AI Agents": "00:03:52"  Recommendations:  "Hiring Strategy": "00:09:36"  Call to Action:  "Share and Subscribe": "00:10:21"Transcript:Josh Hadley 00:00:00  Today, I'm going to share with you the best way I know how to double a business and help it grow even faster. Welcome to the Ecomm Breakthrough Podcast, I'm Josh Hadley. I've scaled my own ecommerce brand from 0 to 8 figures, and I'm actively building towards nine figures in sales. This podcast is where I document that journey and share the systems, the strategies, and the lessons learned in real time so that you can learn what actually matters and scale your own business. My name is Josh Hadley. First and foremost, I'm a man of faith. I'm a husband to a beautiful wife and the father of four children. I've been selling in the e-commerce space for over a decade now, doing over $20 million in annual revenue and multi-millionaire on Shopify, TikTok shop and Amazon. And I am also the host of the number one business strategy podcast for ecommerce entrepreneurs, which is Ecomm Breakthrough. So let's dive into how I would double or grow a business. What is this magic trick that I'm saying that, hey, this is the exact best way that I know how to grow a business the fastest.Josh Hadley 00:00:56  And that simply comes down to hiring for the constraint of the business. Nothing more and nothing less. And I'm going to talk about why this is so important, because ultimately, every business at every part of its pathway is going to hit a constraint. This is also referred to as the bottleneck for a business where, you know, things are flowing, flowing, flowing until you get to the bottleneck. And then it just seems like things really slow down. Now let's talk about like what is the true constraint? Like, let's give you some ideas. When I say constraint to the business, I want to give you a few examples of what that actually looks like in an e-commerce perspective. And then we're going to talk about why hiring is the solution to that. Or it could also be AI by the way. So let's talk about that and how I'm going to basically relieve the bottleneck of the business and point it in a different direction. So let's talk about this. A new e-commerce entrepreneur that gets started on Amazon and finds some initial success.Josh Hadley 00:01:52  First and foremost, you've got to go find some products. You got to go source those products. You got to then get them launched up on Amazon. And then you need to manage those products and make sure that they're successful. Well, if you can continue to do that, rinse and repeat Time and time again. Eventually, it's going to get to a point in time where the founder is no longer able to do everything themselves, and ultimately, the bottleneck will reveal itself in whatever processes in the business takes the most time and is the most labor intensive. So let's for a hypothetical situation. Let's say PPC is the constraint. In this case, the business owner has launched product after product after product. They found really good opportunities, but then PPC just continues to suck up more and more and more of their time. So much so that now they spend less time coming out with new products, because what used to be only a few hours a week has now turned into 20 or 30 hours of their week.Josh Hadley 00:02:52  Just trying to manage and optimize PPC campaigns and day partying, and staying up to date with the latest and best practices on PPC. So if that's the case, and how do you reveal where your time is going? Go back and take a look at my two week time study. This is how you identify the bottleneck of the business by actually going through the processes, understanding where your time is going, and this applies to you and your team if you have an existing team. They should all be doing this time study. That's how you're going to reveal where the bottleneck in the business truly sits. So let's say we're going to go solve for this bottleneck. I've got two possible solutions. I can eit...

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

The right limits can lead to better ideas and better performance.David Epstein, bestselling author of The Sports Gene, Range, and Inside the Box: How Constraints Make Us Better, joins Wharton Moneyball to explore how constraints can strengthen creativity, focus, and decision-making.Epstein discusses the value of tight briefs and rapid prototypes, why first ideas are not always the best ideas, and why young athletes may benefit from playing multiple sports before specializing. Hosted on Acast. See acast.com/privacy for more information.

Great leadership doesn't begin with better answers—it begins with asking better questions. In this episode, Dean Newlund and Ashley Jablow explore how design thinking can help leaders create intentional cultures, embrace uncertainty, and make better decisions through empathy, experimentation, and purposeful action.   In this episode, Dean Newlund and Ashley Jablow discuss: Applying design thinking beyond product development Why organizational culture should be intentionally designed Innovation, leadership, and navigating uncertainty Creativity within organizational and personal constraints Designing meaningful careers and life transitions   Key Takeaways: Start every challenge by understanding the people involved before jumping to solutions. Ashley explains that designing with empathy leads to more effective decisions for employees, customers, and leaders alike. Favor small, fast experiments over waiting for perfect plans. Ashley encourages leaders to learn from each attempt, iterate, and build confidence through continuous experimentation. Building innovative teams requires creating enough trust and psychological safety for people to contribute ideas, take healthy risks, and learn from failures without fear. Constraints such as limited time, resources, or existing commitments can become productive design tools by narrowing focus and encouraging more creative problem-solving. When facing uncertainty, Ashley recommends asking, “What is the smallest, fastest, cheapest thing that I could do or try today to learn the most?” to identify low-risk actions that generate valuable learning and momentum.   "You kind of have to build the plane while you're flying it.” — Ashley Jablow   About Ashley Jablow: Ashley Jablow (Jab-lo, pronouns: she/her) is the founder of Wayfinders Collective and creator of Life Design School, a creative studio for people in career and life transition. A seasoned facilitator, speaker, coach, and design strategist, Ashley blends design thinking and innovation, emotional intelligence, and creative tools to spark clarity and action for teams and individuals navigating change. She's also the artist and author of 100 Days of Designing My Life, a guided journal series for reflection and reinvention.   Connect with Ashley Jablow:   Website: https://www.wayfinderscollective.com/ & https://www.lifedesignschool.co/ LinkedIn: https://www.linkedin.com/in/ashleyjablow/ Instagram: https://www.instagram.com/ashleyjablow/     See Dean's TedTalk “Why Business Needs Intuition” here: https://www.youtube.com/watch?v=EEq9IYvgV7I Connect with Dean:YouTube: https://www.youtube.com/channel/UCgqRK8GC8jBIFYPmECUCMkwWebsite: https://www.mfileadership.com/The Mission Statement E-Newsletter: https://www.mfileadership.com/blog/LinkedIn: https://www.linkedin.com/in/deannewlund/X (Twitter): https://twitter.com/deannewlundFacebook: https://www.facebook.com/MissionFacilitators/Email: dean.newlund@mfileadership.comPhone: 1-800-926-7370 Audio production by Turnkey Podcast Productions. You're the expert. Your podcast will prove it.

In this episode of Service Drive Revolution #368, Chris, Hogi, and Adam dig into one of the biggest and most expensive misconceptions plaguing dealership fixed operations today: the constant cry for "more traffic" and "advisor training." The team uncovers the real patterns behind hundreds of service departments and reveals a harsh disconnect—dealerships aren't running out of work; they are failing their technicians and losing their best producers. Chris and the crew break down why shoving more traffic into a shop running at 60% to 70% efficiency is an absolute recipe for disaster that tanks your customer satisfaction score. We unpack why service advisors secretly refuse to sell work when the shop is backed up, why the industry completely abandoned basic maintenance sales, and how to use the "Theory of Constraints" to treat your shop like a high-output factory. Plus: Adam talks about his $7,500 journey to becoming a certified master distiller, Chris debates the risks of blind-inducing backyard moonshine, and the guys try to figure out which "I" state they are traveling to next.

Host Richard Cunningham welcomes co-host John Coleman for July's Marks on the Market, joined by two veteran venture investors: Phil Jung, who helps lead the venture investing business at Sovereign's Capital, and Mark Phillips, co-founder and managing partner at Eleven Tribes Ventures. The panel digs into a historic first half of 2026 for venture capital, the "barbell" strategy reshaping early-stage investing, and why faith-driven investors need to understand both the opportunity and the constraints of the AI boom. Phil and Mark bring an early-stage, seed-focused lens shaped by operator-led, vertically focused founders — from wire harnessing to waste management — while John Coleman zooms out to the macro picture: record public market concentration, a $725 billion capex commitment from the hyperscalers, and the possibility that this technological shift could be ten times bigger and ten times faster than the Industrial Revolution. The conversation closes, as always, with reflections on Scripture and what it means to build — and rest — under God's sovereignty in the middle of historic disruption. Key Topics: A record-breaking first half of 2026: $412 billion invested in venture capital, more than all of 2025 combined The "barbell" approach to early-stage investing: mega AI valuations on one end, capital-efficient vertical AI solutions on the other Why physical products, deep tech, and hard tech are back in vogue after years out of favor Constraints on the AI boom investors should watch: capital expenditure limits, energy demands, and regulatory fragmentation Token costs, open-source models, and who ultimately controls the data Closing reflections from Psalm 127 and John 4 on surrender, weariness, and calling Notable Quotes: "It really is an unprecedented venture market in the moment that we have never seen before." - Phil Jung "Increasingly your software platform, your product is no longer the moat... it's distribution... and then it's integration." - Mark Phillips "We serve a God who's in control, who's unchanging, who created a universe far more complex than anything we're talking about here." - John Coleman

https://teachhoops.com/ Welcome to 5th Quarter Studios in Madison, Wisconsin. In this episode of Coach Unplugged, we dive straight into an uncomfortable question that might just ruin your week: Why do your drills look great, but your games look bad? Be honest with yourself. You've lived this exact nightmare. Tuesday's practice was a masterpiece. The three-man weave was humming, the shell drill was textbook, and the baseline energy was electric. But when Friday night comes around, your team looks like a group of complete strangers who have never shared a floor. Here is the blunt truth: It's probably not your players. It's your drills. Most drills teach kids to execute a rigid pattern, while games demand that kids independently solve fluid problems. Those are two completely different skills, and the first one does not automatically translate into the second. We break down the absolute science of motor learning, expose the hidden trap of "clean" block practice, and reveal how corrupting your favorite drills with simple live variables will completely transform your program's game-night execution. Many coaches fall into the trap of prioritizing aesthetic compliance over functional retention. The motor learning research has been definitive on this dynamic for decades: When you run a traditional three-man weave, there is zero defense, zero decision, and the pass is predetermined before the ball is even inbounded. Your athletes can run it flawlessly for four years and never once make an actual basketball read. They didn't forget how to play when the weekend arrives; they never learned it. They just learned how to run the drill. Clean practice feels like great coaching, but it is often just comfortable compliance. Random practice—introducing messy repetitions packed with real friction and visual triggers—looks significantly worse on Tuesday afternoon. Turnovers spike, spacing breaks down, and your assistant coaches might look at you funny. But this exact friction is what builds authentic Resilience Equity. Messy training forces your roster to scan the floor, read defensive hips, and think on the fly, directly optimizing your team's collective Decision IQ. To evaluate whether your daily practice architecture is actually preparing your athletes to win under postseason pressure, you must put every single drill track through this unyielding filter: "If this drill features zero defenders, zero live choices, and zero competitive consequences, are my players actively learning how to play basketball, or are they simply practicing choreography?" While foundational block work has a place for raw mechanics and youth form shooting, an elite varsity practice cannot survive on choreography. If your athletes spend less than ten minutes of a 120-minute practice script making a live read against a live body, your team's Effective Field Goal Percentage ($eFG%$) and Next Play Speed will plummet the second a defender scrambles their rhythm. You don't need to throw away your playbook or hunt for hundreds of new drills on the internet. You just need to intentionally corrupt the drills you already use by injecting one of three elements: Add a Defender: Introduce an active body—even a lazy or restricted one—to force a visual closeout read. Turn your standard two-ball passing drill into a dynamic 2-on-1 puzzle. Add a Decision: Give the offensive player two fluid options instead of one scripted path. Force them to read whether the defender has High Hands or a dropped hip. Add a Consequence: Keep score on everything. Make the drills highly competitive, put a clock on the execution, and ensure that someone wins and someone loses. Coach's Note: "Let practice be ugly. An ugly Tuesday is exactly how you buy a clean, efficient Friday night victory. Stop trying to protect your ego by keeping your gym tidy and structured. Throw your kids into the tactical deep end, force them to make live reads through the exhaust of a competitive drill, and watch your program's ceiling skyrocket. Pick just one drill this week and corrupt it. Step back, put the whistle away, and force your players to own the room." Title Ideas: Are Your Drills Destined to Fail? Why Great Practices Lead to Bad Games Stop Doing Basketball Choreography! How to Fix Your Practice Drills Why Messy Basketball Practices Actually Win More Games on Friday Night How to Add Decisions and Constraints to Your Favorite Basketball Drills Primary Keywords: Why basketball drills look great and games look bad, Coach Unplugged, TeachHoops, Coach Collins, basketball practice organization, small-sided games basketball, basketball decision IQ drills. Secondary Keywords: Block practice vs random practice motor learning, effective field goal percentage analytics, next play speed resilience, standard of tolerance, high-hands closeout reads, corrupting basketball drills, Types of Coaches (3).pdf, The Championship Coaching Fellowship. Description Snippet: "Why does your team look like a textbook powerhouse on Tuesday afternoon but completely freeze up when Friday night arrives? In this episode of Coach Unplugged, broadcast from 5th Quarter Studios in Madison, Wisconsin, Coach Collins exposes the ultimate coaching trap: running clean, robotic drills that feature zero live decisions. Discover how to 'corrupt' your favorite practice tracks by adding defenders, choices, and competitive consequences to build a player-led powerhouse that thrives under game-night pressure." Suggested Tags: #BasketballCoaching #TeachHoops #CoachCollins #CoachUnplugged #PracticeDesign #MotorLearning #BasketballIQ #HighSchoolBasketball #TeamCulture Are you looking to corrupt a specific half-court shooting drill this week to help your team transition out of a robotic perimeter rhythm, or are you preparing your application for The Championship Coaching Fellowship to get direct, season-long masterclass mentorship on your actual team workflows? Show NotesThe Motor Learning Paradox: Clean Tuesdays vs. Clean Fridays [BLOCKED PRACTICE] ───► Repetitive/No Defense ───► Looks Clean Tuesday ───► Fails Friday Night [RANDOM PRACTICE] ───► Messy/Live Decisions ───► Looks Ugly Tuesday ───► Wins Friday Night The Illusion of "Clean" Block PracticeThe Reality of "Messy" Contextual PracticeThe Ultimate Drills Filter: Where is the Decision?Corrupting Your Playbook: The 3 Simple VariablesYouTube SEO Strategy Learn more about your ad choices. Visit podcastchoices.com/adchoices

Back with another off the cuff vlog today, and this time, I share a smoothie of thoughts, including:The power of constraints boosting imaginationHow I produce more coherent work with constraintsMy experience flying Business Class from Copenhagen to Lisbon and the glaring spotlight of class systems in travelWhat Business Class and the coaching space have in commonWhat "classes" are we creating in our own ecosystems and are we happy about it? Is there anything we can change?Why I'd never run a 4000+ person program and why I never desire toHow I make my business work focusing on intimacy and build that into my business modelWHEW, a lot can go down in 10 mins! Let me know your thoughts in the Spotify comment section, leave a written review, and slide in my DMs (on Threads)!Connect with Chelsea:Get on the Ultimate Pitch Kit waitlist

Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Kathryn Nave is a Leverhulme Trust Early Career Fellow at the University of Edinburgh, and the author of the book A Drive to Survive: The Free Energy Principle and the Meaning of Life. In the book, Kate dives deep into the free energy principle and active inference, which are popular approaches to studying brains, minds, and organisms in general, and which are being used in artificial intelligence. Ultimately, Kate finds these approaches come up short as explanatory frameworks for life, and autonomy, and intelligence. Instead, Kate and many others advocate a framework that Kate calls constraint closure or closure of constraints, but also goes by the name organizational closure. This is a concept from philosophy and theoretical biology that people like Alvaro Moreno and Matteo Mossio have put forth in their 2015 book Biological Autonomy. The core ideas are also found in various forms from people like Robert Rosen, Stuart Kauffman, Alicia Juarrero, Terrence Deacon, and others. We discuss what constraint closure is, why Kate thinks it's a solid foundation to build on, and what if anything it means for cognitive science and brain sciences to embrace this constraint closure view. I highly recommend the book even if you're looking for a primer on the free energy principle and active inference. As we discuss, Kate's journalism experience has helped her become a wonderful communicator of these notoriously difficult concepts. Kathryn Nave @kathrynnave; @kathrynnave.eurosky.social. A Drive to Survive: The Free Energy Principle and the Meaning of Life Related episode: BI 241 Johannes Jaeger: Agency and the Cyborg Myth Mentioned in the episode: We Need To Rewild The Internet Beyond Control: Finding the Purpose of Enactive Cognitive Science 0:00 - Intro 5:39 - Journalism back to philosophy 15:56 - How Kate got into predictive processing etc. 21:30 - Predictive processing and phenomenology 30:45 - Organizational closure 37:37 - Constraint closure beyond the single cell 45:04 - Brain as metabolic 50:12 - Basal cognition 52:13 - Degeneracy 55:08 - Neutral networks 1:00:33 - AI and autonomy 1:08:12 - Meaning and mind 1:10:02 - Why do we need brains? 1:17:33 - Reframe neuroscience? 1:23:51 - Reifying models 1:27:43 - Free energy principle and active inference 1:37:16 - Tolerating as much variability as possible

This is Heather Hansman's third appearance on the podcast, and if you've been listening for a while, you know how much her work has shaped my own understanding of the West. Heather is an award-winning journalist and the author of "Downriver" and "Powder Days"—two books I reference constantly—and she's back today to talk about her brand-new book, Fierce Country: The Untold Story of Three Women Who Ignited America's Love for the Wild, which hits shelves the same day this episode is released. "Fierce Country" tells the stories of three women who were pivotal in shaping how we think about recreation and conservation today, yet whose stories have largely gone untold: Georgie White, one of the first Grand Canyon river guides; Anne LaBastille, an off-the-grid Adirondack guide and pioneering climate scientist; and Dolores LaChapelle, a legendary powder skier and environmental philosopher whose ideas helped launch the deep ecology movement. These are three complicated, larger-than-life characters, and Heather weaves their wild adventures together with her own personal story in a way that makes the book resonate no matter who you are. As always with Heather's work, it introduced me to ideas I didn't even know I needed to be thinking about. Heather and I talked about how she chose these three women and why their stories were left out of the history most of us learned, and we dug into some of the big ideas woven throughout the book—the heroine's journey versus the hero's journey, the superwoman myth, and the surprising research on men and women in outdoor sports. We discussed the double standards that outdoorswomen face, especially as parents, and the culture of harassment that unfortunately still exists in the outdoor world. We also chatted about Heather's recent investigative reporting on bots gaming the recreation.gov permit system, the importance of journalism and speaking truth to power, and how five years of living with these women's stories has changed the way Heather moves through the world. I loved this conversation, and I can't recommend "Fierce Country" highly enough—the book is out today, so be sure to click through to the episode notes and grab a copy. --- Heather Hansman Fierce Country: The Untold Story of Three Women Who Ignited America's Love for the Wild Heather's First and Second M&P episodes Full episode notes and links: mountainandprairie.com/heather-hansman-3 --- THANK YOU TO OUR SPONSORS: Mountain & Prairie is listener supported via Patreon, and brought to you with support from the Colorado Cattlemen's Agricultural Land Trust, MIRASOL: Looking at the Sun, Patagonia Books, the Rye Resurgence Project for their generous sponsorship. --- TOPICS DISCUSSED: 0:00 - Introducing Heather Hansman (again) and highlighting Patagonia books 7:00 - Book number three 8:08 - Overview of Fierce Country 12:05 - Why these women 16:22 - Georgie and Anne on the wild 19:31 - Anne vs. Thoreau 23:08 - Constraint drives creativity 26:12 - Driven by hardships 28:58 - The heroine's journey 30:45 - Female and male differences 35:40- To scold or not to scold 41:41 - Pitching the book 44:08 - Outward Bound 46:56 - Recreation.gov 50:07 - Slowing down 56:48 - Bad feminism 1:04:50 - What's next for Heather --- ABOUT MOUNTAIN & PRAIRIE: Mountain & Prairie - All Episodes Mountain & Prairie Shop Mountain & Prairie on Instagram Upcoming Events About Ed Roberson Leave a Review on Apple Podcasts

— Many parents, teachers, and caregivers spend years trying to manage a child's challenging behaviors, learning difficulties, emotional struggles, or lack of motivation—often addressing the symptoms without identifying the true cause. But what if there is one underlying factor acting as a bottleneck, quietly limiting a child's ability to thrive? In this episode, we explore a fascinating and innovative approach to child development through the lens of the Theory of Constraints—a framework originally developed to improve performance in complex systems. Applied to child psychology, this model asks a powerful question: What is the primary constraint preventing this child from reaching their full potential? That constraint may be emotional, such as anxiety, shame, grief, or unresolved trauma. It may be cognitive, such as executive functioning challenges, attention difficulties, or learning differences. Or it may be environmental, including family stress, social pressures, lack of support, or unmet developmental needs. Rather than trying to fix everything at once, the Theory of Constraints encourages us to identify and address the single most significant factor limiting growth. This conversation offers a compassionate and practical perspective on helping children flourish—not by asking, "What's wrong with this child?" but by asking, "What is standing in the way of this child's growth, and how can we help remove it?" Join us for an insightful discussion about unlocking potential, fostering well-being, and creating the conditions for every child to thrive. Valeria interviews Paula Noble — She is an Educational and Developmental Psychologist, and Director of Noble Psychology, with over 30 years of experience helping children and families navigate the complexities of growing up. Paula's work is shaped by more than her professional training—it's grounded in lived experience. As a teacher, psychologist, parent, and grandparent, she understands firsthand the challenges families face when a child is struggling with anxiety, learning, friendships, or emotional regulation—and how powerful the right support can be. Paula began her career in classrooms across Australia and Singapore before moving into school psychology in some of Sydney's leading independent schools. Today, she provides evidence-based assessment and therapy, helping families move from overwhelm to clarity, confidence, and connection. In recent years, Paula has also faced her own life-changing challenge—undergoing open heart surgery for a congenital condition. This experience has deepened her perspective, strengthening her belief in resilience, gratitude, and the importance of truly living. Alongside her clinical work, Paula is a creative writer. She has written two plays—Deli Chick and Brad Checks In—both staged at Sydney's iconic Old Fitzroy Theatre and is the author of the satirical novel Deli Chick. Whether working with families or speaking to audiences, Paula brings warmth, honesty, and insight—helping people feel understood, supported, and empowered to move forward. To learn more about Paula Noble and her work, please visit: https://noblepsychology.com.au/

David Epstein is a science journalist and author. Is discipline really freedom? When we feel overwhelmed, it can seem like the problem is that we have no freedom. But maybe the opposite is true: maybe we have too much freedom, too many options, and too little structure. So why can setting constraints become a superpower? And when you're overwhelmed, why might choosing limits actually make you feel more free? Expect to learn what the Green Eggs and Ham effect is, why having constraints is so unsexy, how setting limits powers learning, the real meaning of “The Road Not Taken”, why it is important to decide what not to do, how to tell if you have too much freedom or too many constraints, and much more… Sponsors: See discounts for all the products I use and recommend: ⁠⁠https://chriswillx.com/deals⁠⁠ Get 35% off your first subscription on the best supplements from Momentous at https://livemomentous.com/modernwisdom Get 10% discount on all Gymshark products at https://gym.sh/modernwisdom (use code MODERNWISDOM10) Get a free bottle of D3K2, an AG1 Welcome Kit, and more when you first subscribe at https://ag1.info/modernwisdom Get 15% off your first order of my favourite Non-Alcoholic Brew at https://athleticbrewing.com/modernwisdom Get ChatGPT to explore ideas, solve problems, and learn faster at ⁠https://chatgpt.com Timestamps: (00:00) How Dr. Seuss Changed Children's Books Forever (03:30) Why We Avoid Constraints (and Why That's a Mistake) (11:37) Why Is Choice So Overwhelming? (18:06) The Tension Between Freedom and Constraint (22:07) The Genius Behind General Magic (29:33) How Limits Power Learning (37:01) Why Fewer Options Feel Harder to Choose From (45:58) Is Anything Truly Original? (53:56) How to Break Free From Habit and Convention (56:45) Are Constraints the Secret to Great Design? (01:00:26) Is Multitasking Secretly Hurting You? (01:03:48) The Best Examples of Locking In (01:08:44) When Optimisation Turns Into Obsession (01:13:39) How to Use Constraints Without Being Trapped (01:15:41) What the “Road Less Travelled” Really Means (01:18:08) Where to Find David Extra Stuff: Get my free reading list of 100 books to read before you die: ⁠⁠https://chriswillx.com/books⁠⁠ Try my productivity energy drink Neutonic: ⁠⁠https://neutonic.com/modernwisdom⁠⁠ Episodes You Might Enjoy: #577 - David Goggins - This Is How To Master Your Life: ⁠⁠lnkfi.re/SN-Goggins⁠⁠ #712 - Dr Jordan Peterson - How To Destroy Your Negative Beliefs: ⁠⁠lnkfi.re/SN-Peterson⁠⁠ #700 - Dr Andrew Huberman - The Secret Tools To Hack Your Brain: ⁠⁠lnkfi.re/SN-Huberman⁠⁠ - Get In Touch: Instagram: ⁠⁠https://www.instagram.com/chriswillx⁠⁠ Twitter: ⁠⁠https://www.twitter.com/chriswillx⁠⁠ YouTube: ⁠⁠https://www.youtube.com/modernwisdompodcast⁠⁠ Email: ⁠⁠https://chriswillx.com/contact⁠⁠ - Learn more about your ad choices. Visit megaphone.fm/adchoices

King Charles III and the Revitalization of the Royal Navy. Guest: Gregory Copley. Copley highlights King Charles III's personal connection to the Royal Navy and his efforts to revitalize the service. Despite budget constraints, the UK is building new capital ships and submarines to maintain maritime power. The King's involvement is seen as crucial for maintaining military morale and national defense during periods of governmental incompetence. 121871