Podcasts about Deployment

Spencer sits down with Jen from Wives of the Armed Forces to talk about the real side of deployment no one prepares you for: how to build your support system, spend money without guilt, avoid resentment, and actually come out the other side stronger as a couple and a family. Topics Covered The Two Deployment Mindsets — Grind mode vs. survival mode, and how to figure out which one fits your season Giving Yourself 24 Hours — Why you need to sit in the hard feelings before jumping into action mode Buying Back Your Time — House cleaners, grocery delivery, nannies, and why paid help is sometimes the smartest financial decision you can make Combat Zone Tax Exclusion — How a deployment can increase take-home pay and where that extra money should go Per Diem Conversations — How to have the "how much of this is yours vs. ours" talk before resentment builds up The Slam Clicker Problem — Balancing crew culture and going out with budgeting as a team Leaning on Neighbors — Why asking for help actually builds stronger relationships, and how to get comfortable saying yes Reserve vs. Active Duty Deployment Differences — How the experience differs and how to tap into the broader civilian community for support On-Base Resources — MWR childcare hours for deployed families, on-base playgrounds, and the new children's museum at JBLM Give Parents a Break (GPAB) — $40/month per child in childcare support for families with a deployed service member SGLI and VRED — Making sure life insurance is maxed ($500K) and all documents are copied before departure TRICARE Changes on Activation — How Reserve TRICARE costs drop when a service member activates MLA Database & Credit Card Fee Waivers — Step-by-step: how to check eligibility and apply for annual fee waivers on Amex, Chase, Citi, US Bank, and Bank of America cards SCRA Benefits — How to get annual fees waived on cards opened before active duty, including the Capital One Venture X timing trick The Monthly Money Meeting — Ramit Sethi's approach to keeping finances connected across the distance Lifestyle Inflation — Why income level doesn't determine financial health, and the trap of spending expanding to match earnings Freezer Meal Strategy — How Jen prepped 32 meals for under $2.50 each to avoid convenience food spending during solo parenting Military Spouses as CFO — Reframing the home front role as a financial contribution, not just a sacrifice Resources & Tools Mentioned wivesofthearmedforces.com — Community, blog, and Instagram for military spouses; free deployment checklist available (search "Wives of the Armed Forces deployment checklist") Military OneSource — Benefits, counseling, and support resources for military families care.com — Vetted nanny and caregiver search; free or discounted for military families Walmart Plus — Grocery delivery; Amex Platinum travel credit can cover the membership Rocket Money — Budgeting and spending tracker (Jen's go-to for daily financial check-ins) MLA Database (DMDC) — Search "MLA database single record search" to check eligibility for credit card annual fee waivers militarymoneymanual.com/umc3 — Free Ultimate Military Credit Cards course covering MLA and SCRA fee waivers shop.militarymoneymanual.com — The Military Money Manual book (use code PODCAST for a discount) Books Mentioned Money for Couples by Ramit Sethi — How to have productive money conversations as a team; includes the monthly money meeting framework The Seven Principles for Making Marriage Work by John Gottman — Relationship exercises that work great done remotely during deployment Find Jen and the Wives of the Armed Forces community on Instagram and Facebook. Download their free deployment checklist at their website.    Spencer and Jamie offer one-on-one Military Money Mentor sessions. Get your personal military money and personal finance questions answered in a confidential coaching call. militarymoneymanual.com/mentor Over 22,000 military servicemembers and military spouses have graduated from the 100% free, Ultimate Military Credit Cards Course available at militarymoneymanual.com/umc3 If you want to maximize your military paycheck, check out Spencer's 5 star rated book The Military Money Manual: A Practical Guide to Financial Freedom on Amazon or at shop.militarymoneymanual.com. If you have a question you would like us to answer on the podcast, please reach out on instagram.com/militarymoneymanual. Spencer and Jamie offer one-on-one Military Money Mentor sessions. Get your personal military money and personal finance questions answered in a confidential coaching call. militarymoneymanual.com/mentor Over 22,000 military servicemembers and military spouses have graduated from the 100% free, Ultimate Military Credit Cards Course available at militarymoneymanual.com/umc3 If you want to maximize your military paycheck, check out Spencer's 5 star rated book The Military Money Manual: A Practical Guide to Financial Freedom on Amazon or at shop.militarymoneymanual.com. If you have a question you would like us to answer on the podcast, please reach out on instagram.com/militarymoneymanual.

Episode 252 of the Transition Drill Podcast, traumatic brain injury recovery, veteran transition, and rebuilding identity for veterans, first responders, and anyone navigating life after service, injury, or high-performance careers. You'll hear Chrisanne Gordon on the hidden cost of brain injury, the fight to be believed, and what it takes to turn personal adversity into a mission that changes lives.Dr. Chrisanne Gordon didn't arrive in the veteran community through military service. She arrived through medicine, personal hardship, and a realization she couldn't ignore. Chrisanne followed generations of family medicine into becoming a physician. She worked emergency medicine, then later moved into rehabilitation medicine, driven by a simple question she couldn't let go of: what happens to patients after they leave the ER?That question became personal after suffering a traumatic brain injury herself. As a physician suddenly becoming the patient, she experienced something she'd spent a career treating but had never fully understood. Cognitive changes, loss of speech, identity disruption, depression, and the realization that medicine didn't yet have all the answers changed how she viewed recovery and the people living through it.Then she read reports suggesting returning veterans with traumatic brain injuries were being dismissed, misunderstood, or categorized incorrectly. What started as concern turned into action. Chrisanne volunteered at the VA in Ohio expecting to help however she could. Instead, she found herself evaluating veterans returning from Iraq and Afghanistan and witnessing firsthand how difficult recovery, diagnosis, and transition could become when invisible injuries met institutional systems. Her work eventually challenged accepted practices and redirected her professional life entirely. From there came advocacy, research initiatives, congressional outreach, documentary filmmaking, and eventually the creation of a nonprofit focused on supporting veterans living with traumatic brain injury. Along the way she helped elevate awareness, pushed for recognition of TBI as a defining wound of modern conflict, and continued listening directly to veterans rather than speaking for them.Today, Chrisanne's mission extends through speaking, education, books including Guarding Our Guardians: Guaranteeing America's Veterans a Future from Deployment to Employment, and ongoing support for veterans and their families.CONNECT WITH THE PODCAST:Instagram: https://www.instagram.com/paulpantani/WEBSITE: https://www.transitiondrillpodcast.comLinkedIn: https://www.linkedin.com/in/paulpantani/SIGN-UP FOR THE NEWSLETTER:https://transitiondrillpodcast.com/home#aboutQUESTIONS OR COMMENTS:paul@transitiondrillpodcast.comSPONSORS:GRND Collective: Premium, veteran-owned sportswear built for those who show up, outwork the excuses, and give 100%. Score 15% off your order at thegrndcollective.com using promo code TRANSITION15 at checkoutBlue Line Roasting: Premium, law-enforcement-owned coffee roasted to fuel the shift. A portion of every order directly supports law enforcement families facing line-of-duty injury or loss. Save 10% at bluelineroasting.com with promo code Transition10Frontline Optics: Premium eyewear founded by a firefighter and built to withstand the job. Every single purchase helps support the First Responders Children's Foundation, serving families who've paid the ultimate price. Save 10% off your pair at frontlineoptics.com using promo code Transition10

In this episode of The Edge of Risk Podcast by IRMI, Joel Appelbaum speaks with Cherie Baker, vice president of enterprise risk management at Ilitch Companies, about how captive insurance can serve as a strategic tool for managing risk, deploying capital, and supporting business growth. Ms. Baker shares her career journey through brokerage, healthcare, automotive, and enterprise risk management, and explains how Tenda, the organization's captive insurer, has evolved from its original employee benefits focus into a broader risk financing vehicle supporting multiple lines of coverage. The conversation explores how Tenda utilizes both the Michigan and Cayman domiciles, works alongside commercial insurers and reinsurers, and evaluates opportunities across health, property, casualty, management liability, executive life, and other emerging risks. Ms. Baker discusses the importance of aligning captive strategy with organizational objectives, building relationships across diverse business units, and continuously reassessing captive utilization as market conditions change. She also shares insights into the role of entrepreneurship in captive innovation, the value of industry involvement through Captive Insurance Companies Association and Amplify Women, and why organizations should regularly revisit how their captives can support evolving and interconnected risks.

You can learn a lot about IPv6 from books, videos, and podcasts (such as this one), but it’s hard to beat hands-on experience. John Osmon joins our hosts to discuss how to set up your own IPv6 environment. They cover what John has done, including low-cost home lab options, how it’s impacted his thinking, and... Read more »

You can learn a lot about IPv6 from books, videos, and podcasts (such as this one), but it’s hard to beat hands-on experience. John Osmon joins our hosts to discuss how to set up your own IPv6 environment. They cover what John has done, including low-cost home lab options, how it’s impacted his thinking, and... Read more »

As AI becomes increasingly capable of generating code, many developers are asking the wrong question. Instead of asking whether AI will replace developers, a better question is: What skills become more valuable when code generation becomes easier? The answer may be AI Deployment Ownership. About Jason Sherman Jason Sherman is a serial entrepreneur, filmmaker, author, and technology founder best known for building practical solutions that bridge the gap between emerging technology and real-world business problems. He is the founder and CEO of Vengo AI and has launched multiple technology platforms throughout his entrepreneurial career. Jason is known for his direct, hands-on approach to innovation, focusing on execution, product development, AI implementation, and helping businesses leverage technology without losing sight of operational realities. His perspective combines startup experience, software development expertise, product strategy, and a strong belief that technology should solve actual business problems rather than chase trends. Links: Facebook, Twitter / X, YouTube, LinkedIn, Website AI Deployment Ownership Changes the Developer Role Historically, many developers focused on implementation. Their value came from translating requirements into working code. Today, AI can assist with much of that work. That shifts responsibility upward. Developers are increasingly expected to understand: Architecture Infrastructure Security Deployment Automation The ability to oversee an entire system becomes more important than writing every line manually. Insight: AI raises the importance of systems thinking. Why Building Is No Longer Enough Many AI-created applications work perfectly in development environments. Production introduces a different reality. Organizations need: Monitoring Logging Security controls CI/CD pipelines Recovery procedures These are areas where experience matters significantly. An application that functions correctly in a demo environment may fail quickly when exposed to real-world usage patterns. AI Deployment Ownership Requires Infrastructure Knowledge One of the strongest themes from the conversation was ownership. Developers who understand deployment gain an advantage by moving beyond simple application development. Key capabilities include: Server management API security Automated deployments Version control workflows Environment management These responsibilities cannot be delegated entirely to AI. Action: Learn how applications move from development into production. The Rise of the Technical Operator The next generation of developers may resemble technical operators rather than pure coders. Their responsibilities include: Reviewing AI output Managing architecture Protecting infrastructure Maintaining reliability This shift mirrors previous technology transitions. Tools become easier. Responsibility becomes greater. AI Deployment Ownership Creates Career Protection Developers concerned about long-term career relevance should focus on areas where judgment matters. AI can generate code. It cannot reliably assume accountability. Organizations still need professionals who can: Evaluate tradeoffs Assess risks Make deployment decisions Own outcomes That ownership creates value. Conclusion The future belongs to developers who understand entire systems rather than individual code files. AI Deployment Ownership represents a practical path forward for developers looking to remain relevant in an increasingly automated environment. Stay Connected: Join the Developreneur Community

In this episode, we kick things off by examining a massive competitive move that could fundamentally reshape the less-than-truckload landscape. Amazon announced the full expansion of its LTL service to all destinations, rolling out a traditional hub-and-spoke network capable of moving palletized freight anywhere nationwide at lower costs than legacy carriers. The service includes next-day live pickup, same-day drop-trailer options, real-time GPS tracking, and automated appointment scheduling, positioning the e-commerce giant as a serious threat to incumbent trucking companies like FedEx Freight, Old Dominion, and Estes. Next, we shift over to the autonomous trucking sector, where PepsiCo and Gatik have launched the largest commercial driverless freight deployment to date. This multi-year strategic partnership brings fully driver-out trucks into PepsiCo's consumer goods supply chain, with operations already live across Texas, Arizona, and Arkansas serving around two hundred fifty retail locations. These autonomous trucks maintain a ninety-nine percent on-time track record with no safety drivers in the cab, and a South Carolina production facility is set to begin mass-producing Level four autonomous trucks in the second half of twenty twenty-seven. Finally, we explore the trans-Pacific shipping market, where new tariffs are fueling an unusually early frontloading frenzy and peak season. Rate hikes and surcharges that took effect June first sent Asia-to-U.S. West Coast prices soaring fifty-one percent to four thousand eight hundred thirty-six dollars per forty-foot container, while East Coast prices jumped twenty-five percent. With the U.S. Trade Representative announcing new tariffs on sixty countries over forced labor concerns, the National Retail Federation has moved the expected peak season to June from July and predicts June import volumes will run five percent higher than May. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

The New York Knicks are in the NBA Finals, but what should have been a historic night for New York City turned into chaos after the official Bryant Park watch party following Game 3 against the San Antonio Spurs. Reports and videos from the scene showed crowd disorder, property damage, fights, and clashes that left many asking one question: What went wrong with the NYPD deployment plan? On this episode of The Finest Unfiltered, retired NYPD Lieutenant John Macari, Eric Dym, and Marlon Bethel break down the events that unfolded in Bryant Park, examine the challenges of policing massive spontaneous celebrations, and discuss whether the NYPD accurately anticipated the size and behavior of the crowd. Topics Include: What happened at the Bryant Park Knicks watch party Crowd management and public safety failures Was the NYPD deployment sufficient? The challenges of policing championship-level events Lessons learned before future Knicks Finals games How social media and viral gatherings change police planning What NYC can expect if the Knicks win the NBA Championship As New York experiences Knicks fever unlike anything seen in decades, the stakes for public safety, crowd control, and emergency preparedness have never been higher. Was this an unavoidable consequence of a city celebrating? Or were there warning signs that should have led to a different deployment strategy? Join the discussion and let us know your thoughts in the comments. Sponsored By Kalshi On Kalshi, you're trading against peers in a live market — meaning there's no house. And as the probability changes, you can buy in and out of your position. For a limited time, download Kalshi and use code FINEST to get $10 when you trade $10. Sign up here: http://kalshi.com/r/FINEST 18+ only. Restrictions and eligibility requirements apply. Event contract trading involves risk and may not be suitable for all investors. Prices, values, and available markets may differ from those mentioned. For more information see kalshi.com/regulatory. Hashtags #Knicks #NBAFinals #BryantPark #NYPD #NYCNews #KnicksFans #MadisonSquareGarden #PublicSafety #NewYorkCity #NBA #JohnMacari #TheFinestUnfiltered #SanAntonioSpurs #CrowdControl #BreakingNews ️ New to streaming or looking to level up? Check out StreamYard and get $10 discount! https://streamyard.com/pal/d/5689366474915840 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, we kick things off by examining a massive competitive move that could fundamentally reshape the less-than-truckload landscape. Amazon announced the full expansion of its LTL service to all destinations, rolling out a traditional hub-and-spoke network capable of moving palletized freight anywhere nationwide at lower costs than legacy carriers. The service includes next-day live pickup, same-day drop-trailer options, real-time GPS tracking, and automated appointment scheduling, positioning the e-commerce giant as a serious threat to incumbent trucking companies like FedEx Freight, Old Dominion, and Estes. Next, we shift over to the autonomous trucking sector, where PepsiCo and Gatik have launched the largest commercial driverless freight deployment to date. This multi-year strategic partnership brings fully driver-out trucks into PepsiCo's consumer goods supply chain, with operations already live across Texas, Arizona, and Arkansas serving around two hundred fifty retail locations. These autonomous trucks maintain a ninety-nine percent on-time track record with no safety drivers in the cab, and a South Carolina production facility is set to begin mass-producing Level four autonomous trucks in the second half of twenty twenty-seven. Finally, we explore the trans-Pacific shipping market, where new tariffs are fueling an unusually early frontloading frenzy and peak season. Rate hikes and surcharges that took effect June first sent Asia-to-U.S. West Coast prices soaring fifty-one percent to four thousand eight hundred thirty-six dollars per forty-foot container, while East Coast prices jumped twenty-five percent. With the U.S. Trade Representative announcing new tariffs on sixty countries over forced labor concerns, the National Retail Federation has moved the expected peak season to June from July and predicts June import volumes will run five percent higher than May. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

Gordon Chang and Rick Fisher analyze China's "grayzone" activities and maritime intimidation near Taiwan. They discuss the deployment of massive Coast Guard vessels and Taiwan's asymmetric defense strategy to prevent beach invasions. (12)1950 NAIROBI

Welcome to The Daily Wrap Up, an in-depth investigatory show dedicated to bringing you the most relevant independent news, as we see it, from the last 24 hours (6/9/26). As always, take the information discussed in the video below and research it for yourself, and come to your own conclusions. Anyone telling you what the truth is, or claiming they have the answer, is likely leading you astray, for one reason or another. Stay Vigilant. !function(r,u,m,b,l,e){r._Rumble=b,r[b]||(r[b]=function(){(r[b]._=r[b]._||[]).push(arguments);if(r[b]._.length==1){l=u.createElement(m),e=u.getElementsByTagName(m)[0],l.async=1,l.src="https://rumble.com/embedJS/u2q643"+(arguments[1].video?'.'+arguments[1].video:'')+"/?url="+encodeURIComponent(location.href)+"&args="+encodeURIComponent(JSON.stringify([].slice.apply(arguments))),e.parentNode.insertBefore(l,e)}})}(window, document, "script", "Rumble");   Rumble("play", {"video":"v78vcdm","div":"rumble_v78vcdm"}); Source Links (In Chronological Order): Federal Court Overturns Historic Fluoride Ruling as Trump Admin Fights to Keep Fluoride in the Water Digital Embassies: Host Countries Build Data Centers For Foreign Nations To Access New Tab (19) Former Congresswoman Marjorie Taylor Greene

Privileged Access Management has outgrown the vault. In this episode, Matthias sits down with lead analyst Alejandro Leal, author of KuppingerCole's newly released PAM Leadership Compass, to explore how the definition of privilege itself has changed, what NHIs and agentic AI mean for PAM, and why deployment sovereignty is now a boardroom conversation. Key Topics: ✅ How the definition of "privilege" has shifted from admin accounts to dynamic runtime identity capabilities✅ PAM convergence with IGA, CIEM, ITDR, SIEM, and SOAR — the end of the standalone PAM product✅ Non-Human Identities (NHIs) and agentic AI: the silent accumulation of machine privilege✅ Just-in-time access: the gap between concept and operational reality✅ Deployment sovereignty: who controls the keys to the kingdom — SaaS, on-prem, or hybrid?✅ AI and ML in PAM: separating genuine innovation from marketing inflation "Most enterprises can tell you the number of employees they have — very few can tell you the number of machine identities." If that sounds familiar, this episode is for you.

Privileged Access Management has outgrown the vault. In this episode, Matthias sits down with lead analyst Alejandro Leal, author of KuppingerCole's newly released PAM Leadership Compass, to explore how the definition of privilege itself has changed, what NHIs and agentic AI mean for PAM, and why deployment sovereignty is now a boardroom conversation. Key Topics: ✅ How the definition of "privilege" has shifted from admin accounts to dynamic runtime identity capabilities✅ PAM convergence with IGA, CIEM, ITDR, SIEM, and SOAR — the end of the standalone PAM product✅ Non-Human Identities (NHIs) and agentic AI: the silent accumulation of machine privilege✅ Just-in-time access: the gap between concept and operational reality✅ Deployment sovereignty: who controls the keys to the kingdom — SaaS, on-prem, or hybrid?✅ AI and ML in PAM: separating genuine innovation from marketing inflation "Most enterprises can tell you the number of employees they have — very few can tell you the number of machine identities." If that sounds familiar, this episode is for you.

Cody Boden grew up in Grand Junction Colorado, the son of an alcoholic father in a coal mining town. In this episode of Locked In with Ian Bick, Cody shares how he found his purpose in the U.S. Army — becoming a sniper with the 1st 40th Cavalry, earning two Purple Hearts from a bombing and a VBIED attack, and witnessing horrors overseas that would follow him home forever. When he returned from war the military forced him into medical retirement — leaving him without the only life he'd ever known. What followed was a bar altercation, drug dealing, a 15 year sentence he served 5 years of, and a battle with opiate addiction he finally won in June 2017. Now he faces his greatest fight yet — terminal liver failure connected to an illness contracted during deployment, waiting for a donor since May 2023.This is a story of war, trauma, addiction, prison, redemption, fatherhood and faith — and a man the system tried to throw away who refused to give up. _____________________________________________ #PurpleHeart #VeteranStory #TrueCrime _____________________________________________ Connect with Cody Boden: Tiktok: https://www.tiktok.com/@onemoremission Instagram: https://www.instagram.com/cody.boden/ Youtube: https://www.youtube.com/@UCtFo-QFNRfa-_c4uZh0WKyg Facebook: https://www.facebook.com/profile.php?id=61580807506052 Donation: livingdonorreg.upmc.com _____________________________________________ Hosted, Executive Produced & Edited By Ian Bick: https://www.instagram.com/ian_bick/?hl=en https://ianbick.com/ _____________________________________________ Shop Locked In Merch: http://www.ianbick.com/shop _____________________________________________ Timestamps: 00:00 Purple Heart Army Sniper to Federal Prison — Cody's Full Story 00:21 Growing Up in a Coal Mining Family and the Childhood That Shaped Everything 04:13 High School Struggles and the First Time Drugs Entered His Life 07:07 The Family Coal Mining Business and How Everything Started to Change 11:28 His Father His Grandfather and the Discipline That Defined His Childhood 16:01 Losing His Grandfather and the Moment He Turned to Drugs to Cope 18:59 Joining the Army to Escape — The Decision That Changed Everything 21:31 Army Training and How He Fought His Way Through Early Addiction 28:35 Making It to Army Sniper School and What Life in Alaska Really Looked Like 37:18 Preparing for Deployment and Adapting to the Most Extreme Environments Imaginable 41:01 Life in Alaska — Brutal Weather Brutal Training and What It Built in Him 45:00 Deploying to Iraq — His First Combat Experience and What He Wasn't Ready For 51:40 The Toughest Missions and the Friends He Lost in Battle 54:00 Survivor's Guilt — What It Does to You When the People Next to You Don't Make It 01:00:43 Heavy Combat Devastating Losses and What Leadership in War Really Looks Like 01:11:04 Coming Home — Injuries Forced Retirement and the Painkillers That Started Everything 01:18:50 A Bar Fight An Arrest and His First Real Taste of Jail 01:27:49 How Addiction Took Over and What It Did to His Family 01:30:47 Fighting for Custody of His Kids While Fighting His Own Demons 01:39:10 Prison — The Legal Troubles the Politics and What Survival Really Looks Like Inside 01:46:35 Prison Life Racism and the Mental Health Programs That Started to Help 01:58:03 Therapy Childhood Trauma and the First Real Steps Toward Recovery 02:05:00 Life After Prison Meeting Katherine and Then the Hepatitis C Diagnosis 02:12:42 Building a New Life Staying Clean and Finding Professional Purpose 02:18:11 The Terminal Liver Disease Diagnosis and the Transplant Journey Nobody Prepares You For 02:26:26 Medical Hardships Finding Hope and the Faith That Kept Him Going 02:32:23 The Delays the Donors and the Nightmare of Navigating the Medical System 02:36:09 What His Family and Legacy Give Him the Will to Survive 02:43:17 Reflection Gratitude and What Moving Forward Really Looks Like _____________________________________________ To advertise on the show, contact sales@advertisecast.com or visit https://advertising.libsyn.com/LockedInWithIanBicka Learn more about your ad choices. Visit podcastchoices.com/adchoices

Mary and Jerry discuss how military families can prepare for deployment and readjust when a spouse returns, drawing on Jerry's four Navy deployments and a demanding year of workups on the USS Enterprise. They emphasize getting paperwork and finances in order (will, power of attorney, SGLI, joint accounts, budgeting), having hard conversations about worst-case scenarios, and maintaining an emergency fund for inevitable breakdowns like appliances and cars. They share practical ways to stay connected, including videotaping a deployed parent reading children's books, numbering letters in the pre-internet era, and creating continuity through shared routines like reading the same book and praying together. They highlight the importance of community support, honest communication about roles and chores, getting counseling when needed, and recognizing the challenges of reintegration, especially when the returning spouse tries to take charge or feels disengaged. Connect with us and our podcast on Instagram @themaryandjerrypodcast Support the work of the show and get bonus content by supporting us on Patreon: https://www.patreon.com/user?u=17005526

Over 40 members of the defence forces recently took part in an intensive four-day training exercise at Fort Davis in County Cork as part of a programme to develop them into the next leaders of the defence forces. Our southern reporter, Jamie O'hara went to Fort Davis to meet with some of those taking part.

President Cyril Ramaphosa says South Africa will dispatch envoys across Africa and other continents following recent immigration tensions within the country targeting foreign nationals. Speaking alongside Kenyan President William Ruto in Pretoria yesterday during the stateve visit, Ramaphosa said the envoys will engage countries and stakeholders on migration challenges. He stressed that South Africans are not xenophobic and want to live peacefully with fellow Africans. Bongiwe Zwane spoke to Tshepo Matseba Managing Director at Reputation 1st Group

(00:00) — Welcome and origin spark: Kiki's path starts without an “aha” and a teacher's nudge changes everything.(02:24) — First shadowing, open-heart: A six-hour quadruple bypass leaves her captivated.(03:48) — Type B and present: Owning a goal without over-planning in high school.(04:29) — Balancing D2 hoops and premed: Small-school community and time management pay off.(07:19) — Burnout and a late college switch: Signing in July and embracing a non-linear path.(08:55) — Making premed work: Professors, small classes, and athlete study groups.(10:03) — The grind of student-athlete life: Exhaustion, rigid schedules, and living by the calendar.(11:38) — What gave way: Long-distance friendships and less family check-ins.(13:24) — First app cycle misses: 506 MCAT, six-week prep, content over practice, and low volunteering.(17:17) — Reapplicant moves: Earlier timing, pharmacy tech year, and next-day secondaries.(19:54) — Widening the net: Adding DO schools and securing acceptances.(20:53) — Discovering HPSP: Out-of-state sticker shock leads her to the Navy.(23:39) — Parents' buy-in and commissioning: From doubts to pride; acceptance to October commissioning.(26:16) — Military match realities: Deployment risk and the “assignment” mindset.(30:29) — Final takeaway: Keep trying—“what's meant for you won't miss you.Kiki didn't have a dramatic origin story—no early illness or single defining moment. A high school anatomy teacher's question and a mesmerizing first shadowing of a six-hour open-heart surgery nudged her toward medicine. She kept living fully as a type B student who played Division II basketball, learning time management the hard way: rigid schedules, constant travel, and studying through exhaustion. In this conversation, Kiki unpacks being a reapplicant after a 506 MCAT and limited volunteer hours, what she fixed the second time—earlier timing, practice questions over rereads, quick secondaries—and why she initially applied to only two schools. She explains how medical transport and later working as a pharmacy technician broadened her clinical lens. When out-of-state tuition topped $80,000, she took a hard look at Navy HPSP, did her homework beyond recruiter promises, and chose the scholarship—even after getting off a local waitlist later. Kiki shares how she reframed setbacks, how much community mattered, and what realistically concerns her about the military match: deployment and accepting “assignments.” Her closing message to premeds is clear and steady—keep doing the work, stay intentional, and trust that what's meant for you won't miss you.What You'll Learn:- How a D2 athlete built time management without sacrificing premed- What went wrong in her first cycle and how she changed it- Why she chose Navy HPSP and how she evaluated the trade-offs- Ways transport and pharmacy tech roles expand clinical exposure

Sacrilegious Sunday is back, and this one goes from Hollywood acting debates to full-blown current-events chaos.Chino, Homeboy, and the crew open with the eternal question: Denzel Washington vs. Will Smith, one-take greatness, Training Day, Malcolm X, Glory, After Earth, Mark Wahlberg, and why some actors have magic while others just memorize the damn script.Then the show swerves into the news: Charlie Kirk free-speech fallout, people getting fired or arrested over social media posts, First Amendment lawsuits, settlements, and why everyone suddenly remembers free speech when the consequences start costing real money.From there, the military-veteran segment kicks in hard: veteran mental health, DD-214 life, military trauma, civilian coworkers, social anxiety, sleep deprivation, CPAP survival, and the secret inner violence of trying to complete a grocery list without losing your soul.Then it gets political: retired military speech rights, Pete Hegseth, UCMJ threats, military pensions, Mark Kelly, illegal orders, and whether retired service members still get to speak like citizens or get treated like property of the government forever.And because the country apparently woke up and chose “what the hell is this?” the crew digs into the viral claim that Congress is trying to “merge” the U.S. military with the IDF. Is it literally one combined military? No. Is the 2027 NDAA pushing deeper U.S.-Israel defense technology, weapons industry, cyber, drone, quantum, and military-industrial integration? Yeah, and that still deserves every side-eye in the room.Also: deployment cheating, Jody energy, Ring cameras, girlfriends making terrible choices, and the most awkward “get out of my house” moment ever caught on camera.This episode hits:Denzel Washington vs Will SmithCharlie Kirk free speech settlementsFirst Amendment hypocrisyVeterans and civilian lifePete Hegseth and retired military speechUCMJ and military pensionsMark Kelly illegal orders controversyU.S. Israel military integration2027 NDAAIDF, AIPAC, defense tech, cyber warfareDeployment cheating and Jody storiesChino & Homeboy podcast chaosLike, comment, subscribe, and tell us which part made you laugh, rage, or question the structural integrity of this country.TIMECODES00:00 Intro, Sacrilegious Sunday begins00:46 Denzel Washington, improv, and one-take greatness14:44 Mark Wahlberg, Two Guns, Shooter, Fear, and The Departed18:37 The avocado/testicle detour, because of course23:56 Charlie Kirk posts, free speech, firings, arrests, and settlements28:02 Military segment begins28:40 Veteran life, DD-214 reality, trauma, and civilian coworkers31:27 Disabled vets, hearing loss, social anxiety, and threat assessment42:52 Retired military free speech, UCMJ, Pete Hegseth, pensions49:10 Viral claim: Is the U.S. military merging with the IDF?58:27 Deployment cheating, Jody story, girlfriend camera disaster01:03:02 “Third time she cheated”, trust, proof, and why people stay01:09:13 The other guy finds out whose house it really is

We are now closer than ever before to living in a world where AI agents are smart enough to run our power grids and manage water supplies. How do we keep them from going rogue? Sarah Guo sits down with Maxim Bar Kogan, founder and CEO of Onyx Securities, to explore the complexities of supervising and securing autonomous agents at the enterprise level. Maxim explains Onyx's product as an AI control plane, which oversees the permissions and flexible contexts of agents while balancing latency, cost, and reliability. He also discusses how current controls have insufficient context to monitor agent intent, tradeoffs for gradual model rollout, the need for vendor-independent oversight, and Israel's growing AI and security talent ecosystem. Plus, why Maxim is all-in on AGI. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @maximbarkogan  Chapters: 00:00 – Cold Open 00:45 – Maxim Bar Kogan Introduction 01:10 – AutoGPT and Betting on Agent Actions 05:17 – What Onyx Product Does 07:47 – State of Deployment in Large Enterprises 09:58 – Securing Agents 12:45 – Why Proxies Don't Work 14:11 – Why Onyx Trains Its Own Models 18:38 – Onyx's Talent Culture 21:24 – Mechanistic Interpretability 23:35 – How Onyx Builds Customer Trust 25:10 – Mitigating Risk at the Foundational Level 27:45 – Phased Rollout of Glasswing and Daybreak 29:11 – Large Enterprise Holdouts 30:46 – Onyx and the Larger AI Security Space 32:36 – Should Labs Address Model Trust and Governance?  36:56 – What Needs to Happen in Security 39:14 – Why Maxim is AGI-Pilled 41:15 – Conclusion

The Pure Report welcomes Dan Kent, Everpure's new Field CTO for Federal, to the studio to discuss the critical intersection of advanced technology and public services. Dan, who recently joined Everpure, brings decades of experience in the Federal space, including senior roles at companies like Cisco and as a CTO, where he developed a passion for leading teams and tackling challenging engineering problems. Our conversation kicks off by exploring the unique complexities and high stakes of working with government agencies, which range from managing the massive data sets of the Social Security Administration (supporting 300 million citizens) to deploying mission-critical IT components in the most extreme environments, such as on battleships, in military vehicles, and even in space. Dan asserts that the Federal AI tipping point has passed, driven by the competitive global landscape, executive orders, and the government's immense data holdings—which require AI to glean insights. With an estimated 4,000 AI use cases already in pilot across various agencies (from Air Force platform maintenance to IRS fraud detection), the biggest obstacles remain the outdated infrastructure and the pervasive challenge of data quality. Dan highlights that infrastructure is not yet generative AI-ready, with data locked in silos and complicated by time-sensitive, duplicated, or decades-old information, leading to self-induced mistakes and ethical concerns like misidentification. Our discussion shifts to how Everpure is positioned to solve these foundational issues. Dan explains the necessity of modern infrastructure that enables automated data pipelines for continuous cleaning, classification, and transformation into vector databases (RAG). This automation is key to ensuring AI applications have accurate, timely context, thereby eliminating security risks and self-inflicted errors. Finally, we address the critical human element, emphasizing that while a skills gap exists, the outlook is positive: AI should be treated as a co-worker to boost efficiency and help the federal workforce achieve its citizen-focused missions more effectively. To learn more, visit: https://www.everpuredata.com/solutions/industries/government/cost-efficiency.html Check out the new Everpure digital customer community to join the conversation with peers and Everpure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 01:15 Dan's Career Journey 04:41 Supporting Federal Agencies 09:35 AI Tipping Point for Fed 13:31 State of Government Infrastructure 19:47 AI Trust and Compliance 25:25 Workforce Impacts of AI 33:11 Everpure for AI in Fed 36:45 Hot Takes Segment

Understand how to close the gap between AI experimentation and enterprise production. Shub Agarwal, Founder of the AI Trust Lab at USC and author of Successful AI Product Creation: A Nine-Step Framework, shares his AI product management framework for taking enterprise AI strategy from demo to production, drawing on two decades of product leadership at Amazon and Fortune 50 firms. He breaks down why experimentation must tie directly to business OKRs, the four mindset shifts leaders need to scale AI responsibly, and how the AI Trust Lab is building a benchmark evaluation framework for AI model trust and governance. Key Moments: Why 80% of AI Projects Never Reach Production (02:13): Shub traces the root cause of stalled AI programs to a missing system for moving from demo to deployment. Most teams have no repeatable path to production. Shub's Nine-Step Framework for Building AI Products (06:00): Most AI projects start with a cool model instead of a painful problem. Shub walks through the three phases of his framework: discovery, execution, and excellence. The Case Against "Fix Your Data First" (12:41): Conventional wisdom says clean your data before building AI. Shub challenges that, arguing modern LLMs offer far more flexibility with imperfect data. Four Mindset Shifts for Scaling Enterprise AI (16:35): Shub outlines the four shifts separating organizations that scale AI from those that stall, from measuring AI performance differently to embedding trust from day one. Inside Shub's AI Trust Lab at USC (23:54): Major foundation models are already being benchmarked on trust and safety. Shub explains the lab's mission to build a standardized evaluation framework for AI model governance. Why Enterprise AI Governance Needs Multiple Disciplines (28:36): AI models can be sycophantic, manipulative, or lack candor. Shub argues that building trustworthy AI demands an interdisciplinary approach. Key Quotes: “I think the fundamental problem that organizations are facing today… is not that they have a lack of experimentation in the demo aspect. The challenge is they don't know how to take those demos to production, and that is where I saw the gap.” - Shub Agarwal “I do think data is the fuel for AI… But I think today organizations are crippled by this ‘fix your data, and then we'll build AI', and they never build AI. They never build use cases that are adding value.” - Shub Agarwal “There's no FICO scores for models, so I decided to create one. I built this lab… bringing the computer scientists, the researchers, the applied AI researchers, the policy, and the communication people together to think of what is trust, define it, and ultimately measure and evaluate it.” - Shub Agarwal Mentions USC AI Trust Hub Successful AI Product Creation: A Nine-Step Framework by Shub Agarwal Four Steps to Epiphany: Successful Strategies for Products That Win by Steve Blank Masters of Scale podcast with Reid Hoffman Guest Bios  Shub Agarwal is an associate professor of professional practice at the University of Southern California, an industry executive, and an advisor to start-ups and academic institutions. He holds an MBA from the University of California, Los Angeles (UCLA), and an MS from Carnegie Mellon University (CMU). He is the author of two books: Solve Catch-22 of Product Management and Successful AI Product Creation: A 9-Step Framework. He has made significant contributions to the fields of artificial intelligence and machine learning, holding several U.S. and global patents for his work, and is also a published author of several technical research papers. With around two decades of extensive experience in product management and leadership, his journey has been marked by a relentless pursuit of leveraging AI technologies to create impactful products that redefine industry standards. His industry experience includes leadership roles at Amazon, Silicon Valley start-ups, and other Fortune 50 firms. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical definition and requirements for navigating Enterprise AI. You’ll learn how to distinguish between consumer-grade tools and the strict standards required in regulated industries. You’ll discover the twenty essential pillars for building a secure and compliant AI strategy for your organization. You’ll understand why rigorous vendor scrutiny matters as much for software as it does for human talent. You’ll gain clarity on the governance frameworks necessary to prevent data leaks and legal vulnerabilities in your enterprise. 00:00 – Introduction 03:15 – Defining Enterprise AI vs. SMB AI 07:45 – The role of Microsoft Copilot in regulated environments 12:20 – The 20 components of Enterprise AI readiness 18:10 – Challenges in organizational adoption and change management 22:30 – Security and data privacy as the foundation 27:00 – Call to action Watch this episode to master the complex landscape of regulated AI and safeguard your company’s future. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-enterprise-ai-101.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights, we are talking about Enterprise AI 101. I am in the midst of a series in the Trust Insights newsletter, which you can get at TrustInsights.ai/newsletter. Part one was last week on seven different aspects of enterprise AI. But Katie, you said it would probably be helpful to level set what enterprise AI is and how it differs from SMB AI, mid-market AI, consumer AI, and so on. Katie Robbert: It is interesting because I feel like every time we jump on to record a podcast, there is a whole new set of vocabulary that I need to get caught up with. We need to make sure that everyone else knows what we are talking about because there is nothing worse than listening to a podcast or reading an article and having no idea what the author is talking about because they are introducing a concept but not really explaining it. I wanted to take this episode to talk about what enterprise AI is. Since you and I have not defined it, I am going to take my best guess at what enterprise AI is using some logic and deduction. I could be wrong, and that is why I think it is worth covering. From my perspective, if I had to put a definition to it, I am assuming enterprise AI is the type of AI implementation that occurs at an enterprise-size company. That sounds overly simplistic, but the bigger the organization, the more red tape, the more politics, the more departments, the more stakeholders, and the more governance there is. There are a lot more complications versus a small business like we are, where we can just decide one day, “Hey, I am going to start using this tool.” There are no real hurdles to go through. Then you have those mid-sized companies where you start to introduce some of those hurdles. You might need to work with your IT team to make sure that everything is in compliance. You might need to make sure that you have a place to host these new pieces of software, and that is not something that the marketing team is necessarily responsible for. Then you get to the enterprise-size companies where everything is completely siloed. Even in the best enterprise-sized companies, you are going to run into these silos. Because no one person is responsible for everything, you typically have multiple CEOs. Depending on what part of the country you are in, you might have a board for every different division of the company. If you are a Procter & Gamble and you have hundreds of product lines underneath, each of those is their own individual business. Each of those businesses are not necessarily talking to each other or sharing resources. That is my logical guess at what enterprise AI is. Christopher S. Penn: That is what I started with until I started doing the research into it. I realized that is not what it is. The generally accepted definition is AI within any commercially regulated entity. I realized as I was going through the research that commercially regulated means you have external regulation imposed on the company. It might be a 50-person company, but if they work in HIPAA or FINRA, they have to behave in highly regulated ways. Whether you are publicly traded or, for example, colleges that have to adhere to FFIEC rules and FERPA rules, enterprise AI is about operating AI—whether classical or generative—in a commercially regulated environment where you have externally mandated requirements that you must meet. Your definition for small business stuff makes total sense in that environment because Trust Insights is not a regulated company. However, when we work with our healthcare clients, we have to behave as though we are an enterprise company because we have to conform to their requirements. Katie Robbert: I am glad we are talking about this because the terminology is confusing; when you think of an enterprise company, you are not thinking of a commercially regulated company. I have to wonder why it is not called commercially regulated AI versus non-commercially regulated AI. It is a mouthful and a little bit harder to remember, but it is more descriptive and more accurate. I think like me, a lot of people are going to get confused about what enterprise AI actually is. Christopher S. Penn: A lot of this is because our background is in marketing, so we use the term enterprise to just mean a big company. If we want to market to enterprise companies, we are not marketing to a 50-person firm; we are marketing to a 50,000-person firm. In a lot of CRM software, the dividing line is typically 10,000 employees or 100 million in revenue. This is especially relevant because you see a lot of AI companies like Anthropic and OpenAI in a fight with Microsoft to try and gain a foothold into those enterprises. Microsoft, with their Copilot offering, has dominance by the very fact that their legacy Office 365 stuff is approved in those regulated environments. Katie Robbert: It is ironic because we spent so much time admittedly dismissing Microsoft’s Copilot as the less than version of generative AI, and now Microsoft is getting the last laugh on everyone. They are saying, “You have to use me because I have already been approved by IT and governance, and good luck.” You are stuck with whatever I decide to give you. If I were Microsoft, I would be petty and say, “You guys spent way too much time dismissing me and calling me inferior, so too bad.” Christopher S. Penn: A lot of that, as we have talked about many times on stage, is that the reason Copilot has fewer capabilities than other systems is specifically because of the regulated environment. It is trivial for Google to foist something on consumers and say, “Now we are going to read all your Gmail.” That does not fly in a regulated industry. Katie Robbert: That understanding is really helpful to the people who are saddled with Microsoft Copilot because we hear complaints about why they cannot use other shiny objects. If you are in a 50,000-person company and you weren’t there when the regulatory standards were decided upon, you are sitting there wondering why you cannot use Gemini to generate ad headlines. Then you do it on the side and get in trouble because there is no clear documentation saying why you have to use Copilot and nothing else. What we are hearing is that employees in companies required to use Microsoft Copilot are using other models on the side. That information is still getting filtered into the organization, and it is a huge governance problem. Christopher S. Penn: Completely. In enterprise AI, there are 20 different components to being ready. I derived this from the US federal government's NIST AI regulations and the EU AI Act, which is the gold standard. Katie Robbert: I want to see if you can get all 20. Christopher S. Penn: One, Strategy and Operating Model; two, Governance Policy and the AI Council; three, Legal, Regulatory, and Compliance. Katie Robbert: Are you reading this off a screen? Christopher S. Penn: I am 100% reading this off the Trust Insights Enterprise AI Landscape Field Handbook. Katie Robbert: Fine, continue. Christopher S. Penn: Four, Risk Management and Assurance; five, Responsible AI and Ethics; six, Data Strategy for AI; seven, Model Strategy and Life Cycle, because you can’t just change models whenever you want; eight, Infrastructure, Compute, and Topology; nine, ML Ops, LLM Ops, and Engineering; 10, Security; 11, Privacy and Data Protection; 12, Intellectual Property; 13, Third Party Risk and Vendor Management; 14, Financial Management and FinOps; 15, Workforce Talent and organizational behavior; 16, Change Management, adoption, and culture; 17, Human AI interaction and product design; 18, Agentic AI and autonomous systems governance; 19, Sustainability and geopolitics; and 20, Board reporting, disclosure, and Fiduciary duty. Katie Robbert: I just heard a whole lot of new job opportunities listed. So, if someone were working in a regulated industry like pharma, these are the 20 things they would need to be aware of before evaluating generative AI. It is interesting that organizational behavior and change management are part of it. You would think the regulations would be more technical versus human, but I am surprised that is part of it. Christopher S. Penn: It makes sense because in order for any AI to succeed in an enterprise with 50,000 or 300,000 employees, you have to prioritize change management. Organizational behavior cannot be an add-on; they have to be baked into what you do from the beginning, otherwise your initiative is going nowhere. Katie Robbert: I don’t disagree, but the typical way that works in a large organization is top-down. They make a decision, and you walk in the next day to find it has automatically updated your computer settings. Now you can no longer use a web browser search; you have to use Microsoft Copilot. That is their version of change management, but it is really just a dictatorship from above. I am interested in future episodes to explore what that should look like in a regulatory environment. Christopher S. Penn: We have known for two years that adoption is the hardest part. Deployment is easy compared to adoption. You can put Copilot on someone's desk, but they may not use it even if you tell them they have to. It comes back to how you get them to see the benefits. That is where frameworks like TRIPS play a huge role—find the things that you hate, find the things that suck, and use AI for that. Get that one thing off your plate. Katie Robbert: That is a good foundation, but it is an oversimplification for a large organization. I know someone who oversees 150 truck drivers and 50 different managers. The layers are so deep. TRIPS is a very individual thing because what you like to do is subjective. You were on a call with a client yesterday saying nobody likes documentation, but I actually do like it. My scoring would look different than yours. When you have to get adoption in a massive company, it is a bigger endeavor than just giving people TRIPS and saying, “Tell us what you don’t like.” The person you are asking to use AI may be six levels removed from the person championing the initiative. Christopher S. Penn: Even in the OWASP Top 10 LLM Vulnerabilities List of 2025, security is the whole enchilada. Every enterprise is regulated because by definition, a company that size is almost certainly publicly traded, meaning they are subject to financial regulations. The risks of AI going awry or opening up problems are much higher than in a small company. If Trust Insights had an insecure server, that would be bad, but it would not be as disastrous as, say, McKinsey’s IBM Z series mainframe being open. Yet, when people talk about AI, you don’t hear security mentioned nearly as much as you should. Katie Robbert: It is true. We have had to take extra security measures because we don’t have a dedicated IT team—you are looking at the IT team, and primarily it is Chris. We don’t have any wiggle room to set things up haphazardly. We have to do it right from the start. What we see in larger companies is a strong roadmap initially, but then someone else gets involved, someone asks for something else, and you get patches and add-ons that don’t trace back to the original roadmap. By the end, you are wondering what the original goal was. The bigger the organization gets, the harder it is to maintain control. It becomes a snowball effect. Christopher S. Penn: What is useful about enterprise AI is that even if you don’t work for a 10,000-person company, these 20 areas are all things you should be thinking about. Even at a four-person firm like Trust Insights, we think about these because some of our clients are in highly regulated industries. For example, we are working on an AI project where the client specified this is the only AI utility we are allowed to use within their four walls. Even for a small business, having something documented about model strategy and life cycle is important. As of the day we are recording this, Google Gemini 3.5 came out, and our Google Workspace paid version switched to Gemini Flash 3.5. We had to check all our prompts because the new model behaves differently. Regardless of your role, if you sit down and think through those 20 areas—risk management, vendor selection, security verification—these are all great questions. Katie Robbert: There is a good starting place for this. You can find our downloads at TrustInsights.ai/StrategicToolkit. There is also a free version at TrustInsights.ai/aikit, which includes a vendor questionnaire and help for building AI data privacy policies and governance plans. We have already templated these things out. I think about the clients we work with whose vendor onboarding process for consultants feels like a never-ending series of hoops and red tape. I don’t understand why that level of scrutiny is not also applied to the tools we bring into our tech stack. We are renting space in those tools and freely giving them our data. Those companies now have our data and will use it for their own benefit. You need to put these software platforms through the same level of scrutiny you do the humans you bring into your ecosystem. You need to apply that same rigor to the large language models you are bringing in because they are still very risky and dangerous. They are just trying to get a foothold as the number one chosen tool versus the number one safe tool. Christopher S. Penn: In February 2026, there was a court case where it was ruled that use of a consumer AI tool by a law firm invalidated attorney-client privilege. The judge ruled that this is no longer privileged information. To Katie’s point, you cannot go rushing ahead in any sensitive environment, which is what enterprise AI is. You have to be doing your homework. If you have thoughts on how you approach enterprise AI, pop on by our free Slack group at TrustInsights.ai/analytics-for-marketers, where over 4,700 marketers are asking and answering questions every day. Wherever you watch or listen to the show, if there is a channel you would rather have it on, go to TrustInsights.ai/tipodcast. Thanks for tuning in; we will talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Our services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, Martech selection and implementation, and high-level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members such as a CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What? livestream webinars, and keynote speaking. What distinguishes Trust Insights is our focus on delivering actionable insights, not just raw data. We are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet we excel at explaining complex concepts clearly through compelling narratives and data storytelling. This commitment to clarity and accessibility extends to our educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you are a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

Send us Fan MailThe infrastructure is being built, but is the workforce ready? During the Connected America Conference in Dallas, host Jessica Denson sat down to discuss the growing need for digital skills training. We break down the areas that require immediate focus and why digital literacy is the 'hidden gap' that could determine the success of broadband deployment across the U.S. Recommended links:Heather Gate LinkedInConnected Nation website

At four years old, Andrew Meari stood at the Tomb of the Unknown Soldier in Arlington National Cemetery and quietly told his mother: “Mommy, I'm going to be a soldier.” He never changed his mind. Years later, PFC Andrew Meari deployed to Afghanistan with the 101st Airborne Division, 1-502nd Infantry. On November 1, 2010, during combat operations in Kandahar, Andrew was killed in action while protecting his brothers during a motorcycle VBIED attack. He was 21 years old. In this powerful Memorial Week conversation, Gold Star Mother Denise Williams joins The MisFitNation to share the story behind the uniform… the little boy who loved history, the young man who dreamed of service, the soldier who stood forward, and the legacy that continues long after his final mission. But this episode goes deeper. It explores the reality of military sacrifice. The unimaginable moment a parent receives the call. The bond between soldiers. The strength of Gold Star families. And how purpose can still exist after devastating loss. Denise also shares:

Pastor Matt opens by honoring Memorial Day, reflecting on John 15:13, where Jesus says, "Greater love has no one than this, that someone lay down his life for his friends." He draws a connection between the sacrifice of soldiers and the sacrifice of Christ, reminding the congregation that sacrifice for the good of others is honorable and meaningful. From there, he transitions into the ongoing series through the Book of Acts, picking up in Acts 9:18–42 with the life of Saul following his dramatic conversion on the road to Damascus. The central big idea of the message is this: God develops who He calls. Calling may be immediate, but preparation is a process. Pastor Matt points out that what Acts describes as "many days" was actually closer to three years — a detail filled in by Paul himself in Galatians 1:17 — and that Saul largely disappeared from public view for another 8 to 10 years after that. These hidden seasons, Pastor Matt argues, were not wasted time. God was rewiring Saul's theology, building humility, developing ministry skills, and clarifying his calling. The key truth is that God often does His deepest work in seasons no one sees. Pastor Matt draws four practical lessons from the passage. First, immediate obedience does not mean immediate results — faithfulness precedes fruitfulness. Second, God uses hidden seasons to prepare people for future impact, and a calling may determine your direction, but preparation determines your capacity. Third, opposition should be reframed as confirmation rather than contradiction, because following Jesus will not make life easier, but it will bring new meaning, purpose, and power. Fourth, every believer needs a Barnabas — someone to believe in them, bridge the gap, and speak life into them — and many are also called to be that Barnabas for someone else.

Today, we address the challenges that military families go through. Heather Gray Blalock joins Jim Daly to share about how she and her kids got through her husband's passing, after he died in combat. Then, Danny and John will offer encouragement to military families with children. Find us online at focusonthefamily.com/parentingpodcast. Or call 1-800-A-FAMILY. Receive the book Faith, Hope, Love and Deployment for your donation of any amount! Take the 7 Traits of Effective Parenting Assessment Remembering Fallen Heroes and Their Families Counseling Consultation and Referrals Resources: Military Issues Support This Show! If you enjoyed listening to the Focus on Parenting Podcast, please give us your feedback.

For review:1. The U.S deployed the aircraft carrier USS Nimitz and its accompanying strike group into Caribbean waters this week.2. US President Donald Trump declared on Saturday that the US and Iran were finalizing a deal to end the war.Iran has agreed to give up its stockpile of highly enriched uranium as part of an agreement with the US to end the war, two US officials tell the New York Times.3. The Israel Defense Forces on Saturday said it hit Hezbollah sites in the terror group's strongholds in south and east Lebanon overnight following evacuation warnings.4. US Secretary of State Marco Rubio stated on May 22, that peace negotiations regarding Ukraine are currently suspended, though Washington remains ready to resume its mediating role if future discussions prove constructive.5. Poland will receive its first batch of F-35 stealth fighter jets under a deal signed with the United States.6. On Thursday, President Trump said in a Truth Social post he was "pleased to announce that the United States will be sending an additional 5,000 Troops to Poland."It wasn't clear whether the troops would be based in the country permanently or on a rotational basis.7. Secretary of State Marco Rubio is casting new doubt on NATO's relevance to the United States after key allies recoiled from backing Washington's “Operation Epic Fury.”8. A drone wingman built by General Atomics has resumed flying roughly one month after crashing in the California desert shortly after takeoff. In a press release Thursday, the company said its YFQ-44A aircraft “returned to flight testing following a round of safety reviews and software enhancements.” The drone is being developed for the Air Force's Collaborative Combat Aircraft (CCA) Program. 

Join us as we explore the unique experiences of City Councillor and RCAF reservist, Captain Amit GAUR, who shares insights from his deployment to Canadian Forces Station Alert in Nunavut, his service philosophy, and his vision for the future of Parksville. Discover how military discipline, community service, and environmental advocacy intersect in his work.Key TopicsDeployment to CFS Alert in NunavutImpact of military discipline on municipal leadershipCommunity service and environmental advocacy in ParksvilleArctic sovereignty and Canada's defense strategyFuture vision for Parksville and local developmentChapters00:00Introduction to Amit Gore's Unique Journey02:29The Influence of Military Training on Public Service05:01Cultural Roots and the Desire to Serve07:30Insights from Working with Seniors09:47Deployment to CFS Alert: Mission Overview12:20On-the-Ground Relationships and Political Climate14:51Daily Life and Responsibilities at CFS Alert17:22Reflections on the Deployment Experience21:07Logistics and Morale in Military Operations25:32Leadership Ethos in the Military26:38Political Journey and Community Service28:47Accomplishments in Local Governance34:41Environmental Advocacy and Community Development35:47Key Issues Facing Parksville40:32Vision for Parksville's FutureResourcesCity of Parksville Official WebsiteCanadian Forces Station Alert Follow us on Facebook, Instagram and LinkedIn!

May 21, 2026: Today might be the most consequential single day for the future of work in all of 2026. In a two-hour window yesterday afternoon, OpenAI's AI autonomously solved an 80-year-old math problem, Anthropic announced its first-ever profitable quarter at $10.9 billion in revenue, SpaceX filed a $1.75 trillion IPO, and every major tech CEO was summoned to Washington for an AI executive order signing. We break down what all of it means for leaders and workers. Then: Microsoft and EY just committed $1 billion to deploy AI inside every major enterprise on the planet — finance, tax, HR, supply chain, healthcare. This is the ERP moment for AI, and it's happening now. And finally: California's Governor signed the first executive order by any U.S. governor aimed at protecting workers from AI displacement. We look hard at what it actually does and what it doesn't.

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

Send us Fan MailPeaches is back with the May 20 Daily Drop, and this one's got everything: shrinking bonuses, carrier problems, Iranian escalation, NATO chaos, and the Pentagon throwing half a billion dollars at counter-drone tech.The United States Army keeps pushing force transformation while Europe braces for more U.S. troop withdrawals. Poland is openly stressing about losing American presence—and the billions that come with it. Meanwhile the United States Navy says the plumbing drama aboard the USS Gerald R. Ford was exaggerated, while the new Boeing MQ-25 Stingray finally moves toward deployment. Then the United States Air Force cuts reenlistment bonuses, grounds the entire Northrop T-38 Talon fleet after another crash, and keeps testing rapidly deployable special operations aircraft built for the next fight.Overseas? Iran is setting up control over the Strait of Hormuz, U.S. intelligence says mines are already in place, NATO accidentally shot down a Ukrainian drone over Estonia, and everybody keeps inching closer to a larger regional problem.Bottom line: the future battlefield is moving faster than the bureaucracy trying to manage it.⏱️ Timestamps:00:00 Tasty Gains & OTS Updates 02:00 Las Vegas OTS? 03:00 More U.S. Troops Leaving Europe 05:00 Why Poland Wants Americans to Stay 07:00 Army Transformation Hits Resistance 09:00 Legacy Equipment vs Modern Warfare 11:00 USS Ford Plumbing Drama 13:00 Boeing MQ-25 Stingray Cleared for Deployment 15:00 Super Hornets Landing on Iwo Jima 17:00 Air Force Slashes Reenlistment Bonuses 21:00 Why Bonuses Actually Disappear 24:00 Northrop T-38 Talon Fleet Grounded 26:00 AFSOC's Deployable Skyraider Concept 29:00 Pentagon Drops $500M on Counter-Drone Systems 31:00 Pete Hegseth Reviews Military Legal System 33:00 Donald Trump Eyes Iran Again 35:00 Taiwan Becomes a Negotiating Chip 37:00 Iran Tightens Grip on Hormuz 39:00 NATO Shoots Down Ukrainian Drone 41:00 U.S. Finds Mines in the Strait 43:00 Final Thoughts

In this episode, we kick things off by examining autonomous trucking's major geographic expansion as Einride deploys cabless electric trucks in Ohio in partnership with EASE Logistics. This proof-of-concept deployment represents a significant shift beyond the Sun Belt, bringing SAE Level 4 autonomous technology to the industrial Midwest for the first time. Operating between warehouses in Marysville, the project is part of Ohio and Indiana's Truck Automation Corridor initiative to evaluate real-world impacts on safety and freight efficiency. Next, we explore the equipment sector where Volvo Trucks North America has unveiled a completely redesigned D13 engine engineered to meet 2027 EPA standards taking effect January 1st. The next-generation powerplant slashes nitrogen oxide emissions by a staggering eighty-three percent and particulate matter by fifty percent, making it Volvo's cleanest engine ever. With compacted graphite iron block construction, a higher compression ratio, and innovative fourteen-wave piston design, the engine delivers up to 540 horsepower while fundamentally redefining heavy-duty performance and environmental compliance. Finally, we cover a sudden leadership shakeup at the western Class I railroad as BNSF's chief operations officer departed after just five months in the top operations role. Matt Garland, a twenty-five-year BNSF veteran who took the COO position on January 1st, has been replaced by Craig Morehouse, who will now oversee the entire operations organization. The abrupt transition comes as Berkshire Hathaway's new leadership pushes BNSF to further improve its operating ratio and operational performance. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

ALSO: People and businesses in Speedway gear up for the Indy 500, Rookie drivers milk a cow, medical debt relief, Kentucky primary loss, and Caitlin Clark plays a big role in the Indy 500.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we kick things off by examining autonomous trucking's major geographic expansion as Einride deploys cabless electric trucks in Ohio in partnership with EASE Logistics. This proof-of-concept deployment represents a significant shift beyond the Sun Belt, bringing SAE Level 4 autonomous technology to the industrial Midwest for the first time. Operating between warehouses in Marysville, the project is part of Ohio and Indiana's Truck Automation Corridor initiative to evaluate real-world impacts on safety and freight efficiency. Next, we explore the equipment sector where Volvo Trucks North America has unveiled a completely redesigned D13 engine engineered to meet 2027 EPA standards taking effect January 1st. The next-generation powerplant slashes nitrogen oxide emissions by a staggering eighty-three percent and particulate matter by fifty percent, making it Volvo's cleanest engine ever. With compacted graphite iron block construction, a higher compression ratio, and innovative fourteen-wave piston design, the engine delivers up to 540 horsepower while fundamentally redefining heavy-duty performance and environmental compliance. Finally, we cover a sudden leadership shakeup at the western Class I railroad as BNSF's chief operations officer departed after just five months in the top operations role. Matt Garland, a twenty-five-year BNSF veteran who took the COO position on January 1st, has been replaced by Craig Morehouse, who will now oversee the entire operations organization. The abrupt transition comes as Berkshire Hathaway's new leadership pushes BNSF to further improve its operating ratio and operational performance. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

US Vice President JD Vance says the planned deployment of more than 4,000 US-based troops to Poland has been delayed rather than canceled.

DOD Abruptly Halts Troop Deployment to Poland To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A sermon by John Zeigler entitled "His Departure Our Deployment" from Acts 1:8-11 and other Scriptures

From Addition to Multiplication: Reimagining the Future of the Church In this insightful episode, Pastor Jack King interviews Bill Couchenour, the Director of Deployment for Exponential. They dive deep into the mathematical and spiritual necessity of moving beyond "Level 3" addition churches into "Level 5" multiplying movements. Bill shares the staggering statistics of church decline and offers a hopeful, strategic path forward that looks less like a corporate franchise and more like the way Jesus actually spent His time. -- The distinction between addition (adding members) and multiplication (planting churches that plant churches). -- Why 73% of Jesus' time was spent with the twelve disciples rather than just the crowds. -- The concept of "ecclesiology" following "missiology" (letting the mission dictate the form of the church). -- Real-world examples of "microchurches" and unique expressions of faith, like the "Jungle Gym" ninja ministry. -- The "Tipping Point" theory: Why reaching 16% of churches as reproducing entities could change the landscape of the U.S. -- Addressing the "feudal kingdom" mentality and moving toward a city-wide "kingdom lens." Scriptures for Further Study -- Matthew 28:18-20 -- Ephesians 2:10 -- Matthew 7:24-27 This is episode 1276. ******* This is the radio program with the music removed. By the way, I have written a new book, and you can find it here: https://www.amazon.com/Dreams-Visions-Stories-Faith-Pastor/dp/161493536X

In this episode, Frank La Vigne sits down with his Red Hat colleague Christopher Newland for a deep dive into the evolving challenges and opportunities at the intersection of AI, open source, and enterprise technology.Fresh off attending both IBM Think and Red Hat Summit, Christopher Newland shares insights from two very different industry perspectives—executive strategy and hands-on engineering. Together, they explore the elusive “last mile” problem in AI adoption, the rise of agentic systems, the critical role of harnesses and runtimes, and why memory management is becoming the next frontier.Plus, they discuss the practical realities and future potential of tools like OpenShift AI, IBM Bob, and open source alternatives. Whether you're a developer grappling with implementation details or a leader focused on ROI, this episode has something for everyone navigating today's fast-changing AI landscape.LinksChristopher on LinkedIn -https://www.linkedin.com/in/cjnuland/Watch this episode on YouTube -https://www.youtube.com/watch?v=xlkPPt5YeY0Time Stamps00:00 Comparing IBM Exec and Red Hat Conferences05:24 Challenges in AI implementation06:56 Challenges in scaling microservices11:38 Integrating AI with project management14:23 Debate on AI model vs. harness16:54 Discussing model evolution and limitations22:54 Affordable Power BI Courses Bundle25:19 Separating and managing runtimes27:02 Using semantic routing for requests30:15 Agent memory and compression basics36:02 New AI approach and vision38:49 Developing a multi-agent system40:40 Importance of data chunking

Join us for a Global Wind Report 2026 special looking at the critical role of the workforce in enabling wind deployment at scale. Neil Mellin and Katy Hall from NES Fircroft reveal how expanding talent access and fostering diversity can accelerate offshore wind projects and unlock untapped markets worldwide. The panel also discuss the critical importance of predictable project pipelines for attracting the right talent, especially in emerging markets like APAC and Central Asia. Discover how targeted pathways, apprenticeship programs, and community engagement are shaping the future of offshore wind's workforce, bringing fresh perspectives from regions historically underrepresented in energy.We break down:Why the offshore wind industry faces a skills bottleneck in HV electrical technicians and how to overcome itThe role of diversity and inclusion in expanding the talent pool — from gender to regional representation— and why it's a strategic advantageInnovative strategies NES Fircroft employs, such as returnship programs and Indigenous community collaborations, to build long-term local capacityHow stable policies, long-term project certainty, and partner-led community initiatives are vital for industry growthThe fascinating link between renewable energy expansion and regional economic development, and how inclusive recruitment can catalyze this processWith Katy Hall, Global Head of Diversity, NES Fircroft and Neil Mellin, Regional Business Development Director - Renewables, NES FircroftGWEC's Offshore Wind Podcast is hosted by Stewart Mullin, GWEC's Chief Industry Officer, and Rebecca Williams, GWEC's Deputy CEO, who leads on all GWEC's Offshore Wind work.The podcast, or 'show' as Stewart still likes to call it, features leading voices from across the sector, whether that is large OEMs, key supply chain manufacturers or political leaders driving policy, to talk about how we can all work together to deliver on offshore wind's enormous potential.Follow Stewart on LinkedIn hereFollow Rebecca on LinkedIn here and Instagram hereFollow GWEC on LinkedIn here and Instagram here

In this episode of the Shift AI Podcast, Pete Johnson, Field CTO of AI at MongoDB, joins host Boaz Ashkenazy for a wide-ranging conversation on where AI agents are actually delivering ROI — and what still needs to happen before enterprises can trust them with customers.Pete shares his origin story as a self-taught programmer who got his start on a TRS-80 in 1981, traces how MongoDB was born into a world of cloud, mobile, and internet that relational databases were never designed for, and explains why vector search sits at the intersection of MongoDB's document model and modern AI use cases.The conversation digs into Pete's "Customer Agent AI World Tour," where he has met with enterprises in over a dozen cities and heard a consistent message: production-grade agents are real, ROI is measurable, but the deployments are employee-facing and human in the loop. Pete explains the three things blocking the jump to customer-facing agents at scale, governance, observability, and evaluations, and why that challenge mirrors the early days of HTTPS standards for e-commerce.Boaz and Pete also explore the growing conversation around sovereign AI and on-prem inference, why Apple's edge device ecosystem may be the quiet wildcard in the infrastructure debate, and how MongoDB's Atlas platform lets organizations deploy data across 125-plus hyperscaler data centers worldwide.The episode closes with a forward-looking discussion on the future of software engineering, Werner Vogels' five skills for tomorrow's engineer, and why Pete's two-word forecast for the future of work is "not doomsday" — backed by a compelling contrast between the bank teller and the tollbooth worker as a framework for thinking about automation and job transformation.This episode is essential listening for enterprise leaders, developers, and anyone thinking seriously about where agentic AI is today versus where it is headed.Chapters[00:00] From Intellivision to TRS-80: Pete's Tech Origin Story[03:13] What MongoDB Is and Why the Document Model Matters[06:23] Joining MongoDB and the Vector Search Opportunity[07:50] What Pete Is Hearing on His AI World Tour[09:06] Why Fortune 500s Start with Employee-Facing Agents[11:11] Security, Governance, and the Three Big Blockers to Customer-Facing AI[14:29] How Software Engineering Is the Canary in the Coal Mine[16:56] Sovereign AI, On-Prem Inference, and the Cost of Tokens[20:14] The Apple Edge Device Wildcard[21:19] How MongoDB's Atlas Platform Fits a Hybrid Cloud World[23:21] Using AI Agents Is Programming in English[25:50] Werner Vogels on the Five Skills Every Engineer Will Need[27:17] The Future in Two Words: Not Doomsday[28:08] Bank Tellers vs. Tollbooth Workers: Why Most Jobs Will Level UpConnect with Pete JohnsonLinkedIn: https://www.linkedin.com/in/petecj2/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

Mock-интервью с Николаем Лебедевым - DevOps/SRE-инженер, 17 лет в Linux, 4 года AWS EKS. Stack: Terraform, Flux, Cassandra, Kafka, Vault, SOPS. Два часа - много практики, много каверзных вопросов. ЧТО СПРАШИВАЛИ ☁️ AWS: EKS и IRSA, VPC с нуля (CIDR, multi-AZ, multi-region), managed K8s vs self-hosted, Elasticache, Golden Signals и метрики SRE.

You are listening to a presentation given at the 2025Michigan Conference Cedar Lake Campmeeting. We pray you will be blessed!

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Tyler Cloutier, founder of Clockwork Labs and creator of SpaceTimeDB. They explore how SpaceTimeDB functions as more than just a database—it's essentially a distributed operating system that merges server logic with data storage, enabling real-time applications and time-travel capabilities. The conversation ranges from the technical architecture of databases and operating systems to the philosophy of distributed systems, touching on everything from Unix and Linux to how SpaceTimeDB could revolutionize AI-generated software deployment. Tyler explains how their system reduces the complexity of building real-time applications, makes deployment simpler for both humans and AI agents, and why games like their MMORPG BitCraft Online drove them to create this new infrastructure. They also discuss the future of the internet, the role of bots in gaming, and how SpaceTimeDB fits into the broader landscape of cloud computing alongside tools like Cloudflare, Vercel, and Docker. For more information, visit spacetimedb.com or check out Clockwork Labs on GitHub and Twitter.Timestamps00:00 Stewart introduces Tyler Cloutier, founder of Clockwork Labs, discussing the origin of SpaceTimeDB's name inspired by Einstein's theory and its time travel capabilities that store all operations indefinitely05:00 Tyler explains SpaceTimeDB as more of an operating system than a database, using tables instead of file systems while running code in a sandboxed environment with full atomic properties10:00 Discussion of how SpaceTimeDB replaces both Node.js and Postgres by merging web server and database functionality, eliminating separate deployment concerns15:00 Tyler explains JavaScript execution through Chrome's V8 engine and JIT compiling, leading to Node.js creation for server-side JavaScript development20:00 Explanation of stateless web servers versus stateful game servers, and why games require in-memory state management for real-time performance25:00 Tyler introduces reducers and real-time subscriptions, questioning why more applications aren't real-time when state changes should update immediately30:00 Discussion of Facebook as essentially a text-based MMO, comparing social media architecture to game server requirements and the need for unified systems35:00 Tyler explains ACID properties in databases: atomic, consistent, isolated, and durable, using game item trading examples40:00 Comparing SpaceTimeDB to smart contract systems without cryptocurrency or global consensus, positioning it as a smart database with centralized trust45:00 Tyler reveals SpaceTimeDB uses 43% fewer tokens than Postgres for AI-generated applications, making it valuable for vibe coding platforms50:00 Conversation shifts to bots in games and proof-of-human concepts, with Tyler proposing biometric systems and discussing potential in-person gaming applications55:00 Closing discussion about tracking AI-driven traffic through UTM parameters and finding SpaceTimeDB at spacetimedb.comKey Insights1. SpaceTimeDB is fundamentally a database that runs application code directly inside it, combining what traditionally required separate systems like Postgres and Node.js. Users compile their application logic into WebAssembly or JavaScript and upload it to run within the database itself. This architecture provides high performance because the entire server backend operates inside the database environment. The system also features time travel capabilities, storing every operation and change to data persistently and indefinitely, allowing users to set application state back to any earlier point in time. This makes SpaceTimeDB more accurately described as an operating system rather than just a database, where the abstraction is that everything is a table rather than a file.2. The inspiration for SpaceTimeDB came from building BitCraft Online, an MMORPG where all players exist in a single persistent world and rebuild civilization together. Traditional MMO backends required complex custom solutions to handle real-time state, with game servers storing state in memory and periodically writing to databases. This complexity existed because games cannot afford the latency of constantly delegating to distant databases like traditional web applications can. SpaceTimeDB solved this by making the database fast enough to handle real-time requirements directly, eliminating the need for separate game servers. This same performance advantage that benefits games also applies to web applications, which is why SpaceTimeDB evolved from a game-specific tool to a general-purpose platform.3. SpaceTimeDB functions as a distributed operating system where each database acts like a process in an actor model system, similar to Erlang or Scala Akka. Databases can send messages to other databases and be spawned across a cluster for horizontal scaling. This represents an overlay operating system running on top of Linux rather than competing with it, providing a distributed abstraction across many machines while Linux handles device drivers and hardware support. The vision is for the cloud to function as a single enormous computer running one operating system, where developers simply publish their programs without managing separate services, deployment, routing, networking, or persistence infrastructure.4. The real-time capabilities of SpaceTimeDB address a fundamental limitation in how most web applications work today. Traditional web servers are stateless, delegating all state to databases and accepting network round-trip latency for each request, which is why users often must refresh pages to see updates. SpaceTimeDB allows queries to be subscribed to, maintaining open connections that stream changes whenever query results update. This makes applications like Discord, Facebook, or banking systems naturally real-time without requiring page refreshes. The historical accident that more things are not real-time represents a problem SpaceTimeDB solves by unifying the web world with the game world's real-time requirements.5. SpaceTimeDB implements ACID properties—Atomic, Consistent, Isolated, and Durable—ensuring database operations are reliable and safe. Atomic means operations either fully happen or not at all, preventing issues like item duplication in games when trading between players. Consistent means declared invariants like unique usernames are always enforced. Isolated means concurrent operations do not interfere with each other. Durable means changes persist even if computers restart, with varying levels from in-memory on one machine to disk storage across multiple geographic locations. These properties are managed through reducers, functions inspired by React Redux that fold changes into application state incrementally.6. For AI and large language models, SpaceTimeDB offers significant advantages in building and deploying applications. Testing showed that creating applications with SpaceTimeDB uses 43% fewer tokens compared to Postgres implementations, costs less, has fewer bugs, and is easier to extend. This matters because the primary cost for vibe coding platforms is tokens. As more software gets written in the next twelve months than ever before, there is insufficient focus on infrastructure required to run all this AI-generated software. SpaceTimeDB positions itself as ideal for LLMs to target because of its simplified deployment model where developers just publish code and the system handles everything behind the scenes.7. SpaceTimeDB can be understood as a smart contract system without cryptocurrency or global decentralized consensus. Like blockchain smart contracts, it executes code with atomic, consistent, isolated, and durable properties, but avoids the expense and slowness of requiring all computers worldwide to agree on everything. Instead, it offers centralized trust where users trust Clockwork Labs not to modify deployed contracts, rather than the trustless but extremely costly blockchain approach. This makes it functionally similar to Cloudflare's durable objects but with full relational database capabilities. The system exists before the networking layer where Cloudflare operates, handling deployment, server, and database functions while Cloudflare could provide DDoS protection in front of it.

Send us Fan MailMatt Fitzpatrick is the CEO of Invisible Technologies, an AI platform used to improve models for more than 80% of the world's leading AI companies, including Microsoft, AWS, and Cohere. The company has raised $100 million and scaled to $134 million in revenue, making it one of the fastest-growing AI companies globally.Before joining Invisible, Matt was the Global Head of QuantumBlack Labs at McKinsey, where he led large-scale AI and data engineering efforts and helped enterprises move from experimentation to production.In this episode, Matt draws on years spent inside enterprise AI deployments to challenge the gap between model progress and real-world adoption, and to explain why most organizations still struggle to turn AI into measurable business outcomes.In this conversation, we discuss:Why enterprise AI adoption lags far behind model performance improvements, and why most organizations still struggle to turn technical progress into real business impactThe hidden role of messy, fragmented legacy data, and why decades of accumulated systems make it nearly impossible to deploy reliable AI at scaleWhy defining “good” output in generative AI is far harder than expected, and how unclear standards stall deployment across high-stakes enterprise workflowsThe case for redesigning workflows from scratch, and why layering AI on top of existing processes fails to create meaningful efficiency gainsWhy most AI initiatives fail due to lack of business ownership, and how separating technology teams from operators prevents projects from reaching productionHow fear-driven narratives about job loss are slowing adoption, and why AI is more likely to shift work toward higher-value tasks than eliminate roles entirely Explore this conversation: 00:00 Intro and Fun Fact 03:57 Matt Fitzpatrick's Path From McKinsey to Invisible Technologies 09:56 Scaling Enterprise AI with Modular Platforms and Clean Data 12:44 The Crucial Role of Expert Human Feedback in Model Training 17:56 Why 95% of Enterprise AI Projects Never Reach Production21:38 The Missing Link: Why True AI Transformation Requires Business Ownership 26:54 Overcoming AI Fear and the Reality of Jevons Paradox 32:24 Responsible AI: Governing Outcomes Over Technology 39:05 The Future of Work: Moving From Administration to Innovation 44:12 Where to Connect with Matt Fitzpatrick and Invisible TechnologiesResources:Subscribe to the AI & The Future of Work NewsletterConnect with Matthew on LinkedInAI fun fact articleOn How Allison Baum Gates Reveals the Secrets to a Successful VC Career

Let us know what you think!    Security Halt's Med Group - https://zcform.com/QA5QsClick the link for a FREE consultation with My Med Team to see how we can help. What happens after the uniform comes off? In this powerful episode of the Security Halt! Podcast, Chad Spivack—military veteran, author, and mental health advocate—opens up about the realities of combat, the weight of transition, and the internal battles that follow service.This conversation dives deep into veteran mental health, identity loss, and the path toward healing. Chad shares how storytelling, vulnerability, and intentional self-work became the foundation for rebuilding his life—and how other veterans can do the same.If you're navigating transition, struggling with purpose, or supporting someone who is, this episode delivers real, actionable insight grounded in lived experience.Timestamps00:00 – Introduction: Why veteran stories matter and the power of shared experience 01:17 – Chad's background and why he wrote Now What 02:18 – Early life, military service, and defining moments 05:08 – From enlistment to pursuing psychology 06:58 – Airborne school challenges and training experiences 09:18 – Deployment to Afghanistan during COVID and transition insights 10:39 – Depression, suicidal thoughts, and mental health awareness 12:33 – Post-military life and the struggle with identity loss 14:02 – Inspiration behind the book and storytelling approach 15:24 – Themes of purpose, struggle, and resilience 17:41 – Vulnerability as a tool for healing 20:18 – Why sharing stories matters in the veteran community 22:34 – Fear, honesty, and writing authentically 24:21 – Opening up to family and loved ones 26:49 – Journaling as therapy and reflection 30:29 – Identity, purpose, and resilience after service 32:17 – Transitioning during COVID and life changes 35:47 – Reintegration struggles and redefining goals 39:24 – Community, fitness, and mental health strategies 43:32 – Goal setting and avoiding external distractions 45:41 – Practical transition and mental health advice 50:36 – The importance of self-care and support systems 56:00 – Where to find Chad and his book 57:31 – Final thoughts on storytelling, healing, and growthSponsored by: Transcend Use my referral link to book a consultation for Peptide Therapy http://transcendcompany.com/DenyCaballero Pure Liberty Labs Use Code: SECURITY_HALT_10 Instagram: https://www.instagram.com/purelibertylabs/ Website: https://purelibertylabs.com/ PRECISION WELLNESS GROUP  Use code: Security Halt Podcast 25 Website: https://www.precisionwellnessgroup.com/ SPECIAL FORCES FOUNDATION Instagram: https://www.instagram.com/specialforcesfoundation_/ Website: https://specialforcesfoundation.org/ Request Help: https://specialforcesfoundation.org/get-support/  Security Halt Mediahttps://www.securityhaltmedia.com/Instagram: @securityhaltSupport the showProduced by Security Halt Media

Iran rejects a U.S. proposal to end the war and offers a different peace plan. Thousands more U.S. troops are deployed to the Middle East as President Trump considers seizing Iranian oil infrastructure. An unprecedented verdict against Meta and Google finds the tech giants responsible for mental health issues like anxiety and depression. Want more analysis of the most important news of the day, plus a little fun? Subscribe to the Up First newsletter.Today's episode of Up First was edited by Gerry Holmes, Tara Neill, Brett Neely, Alice Woelfle, and HJ Mai. It was produced by Ziad Buchh, Nia Dumas, and Chris Thomas.We get engineering support from Neisha Heinis. Our technical director is Carleigh Strange, and our deputy Executive Producer is Kelley Dickens.(0:00) Introduction(01:58) Iran Rejects US Peace Proposal(05:47) Troop Deployment(09:30) Social Media TrialTo manage podcast ad preferences, review the links below:See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

NPR has confirmed the U.S. is sending thousands of paratroopers from the 82nd Airborne to the Middle East, raising questions about whether this is an escalation in the war or a pressure tactic to force Iran to the negotiating table.Lebanon expelled Iran's ambassador as Israel threatens to move the country's border northward and use the "Gaza model" in the south of Lebanon, with more than a million people already displaced. And Congress is inching toward a deal to fund the Department of Homeland Security, but President Trump says he's probably not going to be happy with it, leaving TSA workers still without pay.Want more analysis of the most important news of the day, plus a little fun? Subscribe to the Up First newsletter.Today's episode of Up First was edited by Gerry Holmes, Andrew Sussman, Kelsey Snell, Mohamad ElBardicy, and Alice Woelfle.It was produced by Ziad Buchh and Nia Dumas.Our director is Christopher Thomas.We get engineering support from Neisha Heinis. Our technical director is Carleigh Strange.And our Supervising Producer is Michael Lipkin.(0:00) Introduction(01:53) 82nd Airborne Deployment(05:55) Israel Threatens Lebanon Invasion(09:39) DHS Funding NegotiationsTo manage podcast ad preferences, review the links below:See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy