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This week, we discuss Stripe's singularity letter, its $8B Open Router buy, and AI job anxiety. Plus, Matt plays “Bot or Not” on another podcast. Watch the YouTube Live Recording of Episode 587 Runner-up Titles Inertia wins again We don't talk about the Pope very much. Better than this year's storage arrays AI pimps its own ride Chonking machine A lot of chonk opportunity I'm tired of tech people being all fancy Thanksgiving with Ed Zitron No religion, no politics, no AI My recommendation: try harder Teletubbies for Adults. Rundown Singularity, Models and Routers Scoop: Stripe says "the singularity" has begun Stripe strikes mega-deal for OpenRouter Hugging Face reportedly in talks to be acquired for $13B Routing is coming for the frontier AI labs Terminator Judgement Day: August 29, 1997 2:14 a.m. Eastern Time The AI backlash goes mainstream 52% of Americans Now More Concerned Than Excited About AI, With Under-30s Crossing a Majority for the First Time Why Is Everyone In Tech So Sad? The AI Hater's Manifesto 40 Years of Infrastructure as Code: Ansible → Terraform → Kubernetes → Crossplane → AI Agents Relevant to your Interests Cursor Origin review: An engineer's perspective Claude can now pull data from your browser tabs and keep working on your desktop OTel Isn't Going Well (And I Made A Spreadsheet About It) Walmart is finally launching Apple Pay support next week Broadcom debt deal expected to reach upwards of $70 billion, sources say Anthropic-Backed Ode Acquires Casper Studios to Expand Corporate AI Deployments OpenAI 'will be a public company in 2027' or sooner, CFO Friar tells employees Google Aims to Boost AI With Purchase of Spirit Airlines Data The website that created an AI clone of its editor in chief OpenAI Jalapeño: Better Than Nvidia Blackwell An Inside Look at the Relay Market Powering Token Resellers and Fraud Meta settles social media addiction case with California, other states for $16.7 billion Hundreds of leaked AWS keys give full control over corporate accounts Free Hardened Container Images | Minimus Cyber startup Minimus shuts down, returns cash to investors Nonsense Apple Releases New Polishing Cloth Jason Kelce promotes mailing pee to data centers, Liquid Death Paradox Inc. Movie From Daniel Roher, Lord Miller, Universal In Works Conferences WeAreDevelopers NA, Sept 23-25, 2026, Discount Code: DEVPOD50 25 Free Tickets DevOpsDays Graz, Sept 4-5, 2026 Cloud Foundry Summit, Sept. 21st to 22nd, Heidelberg, Coté speaking. DevOpsDays Rockies, Sept. 22 – 23, 2026, Discount Code: 26DODSWEDEFTALK DevOpsDays Dallas, Sept 28-29, 2026 DevOpsDays Vilnius, Sep 30 - Oct 1, 2006, Lithuania. DevOpsDays Prague, Oct 5, 2026 - Coté speaking. DevOpsDays Istanbul, Oct 24th, 2026, Coté keynoting. VMware User Group, Orlando, Oct 20-22, 2026 Cloud Native Denmark, Nov 19th, 2026, Copenhagen, Coté keynoting. Build Stuff, Dec 2-4, 2026, Vilnius, Lithuania. cfgmgmtcamp, February 1st to 3rd, 2027, Ghent. SCALE 24x Pasadena, CA, April 1-4, 2027 SDT News & Community Join our Slack community Email the show: questions@softwaredefinedtalk.com Free stickers: Email your address to stickers@softwaredefinedtalk.com Follow us on social media: Twitter, Threads, Mastodon, LinkedIn, BlueSky Watch us on: Twitch, YouTube, Instagram, TikTok Book offer: Use code SDT for $20 off "Digital WTF" by Coté Sponsor the show Sponsor more podcasts with Failover Media Recommendations Brandon: Tuner ** Your AI Project Doesn't Need More Agents Matt: Tech, Texas & What It Takes to Run a Podcast with Matt Ray | Cyber Chat Ep. 5 Line of Duty Anti-pick: Roku's New Slop Channel Coté: Mini MLC, 30L.
India's truck transition could become a $1 trillion opportunity as heavy-duty trucking shifts from diesel to LNG and electric trucks.In this episode of The Core Report Weekend Edition, Govindraj Ethiraj speaks with Anirudh Bhuwalka, Founder and MD of Blue Energy Motors, about what this transition could mean for India's manufacturing, logistics and commercial vehicle industry.India has around 4 million medium and heavy-duty trucks on the road, with roughly 250,000 new trucks sold every year. The shift from fossil fuels to alternate fuels could create a $1 trillion manufacturing opportunity, even before accounting for logistics, charging infrastructure, energy services and the wider transport ecosystem.Blue Energy Motors has already put more than 1,200 LNG trucks on Indian roads and accumulated around 100 million kilometres of operating data. Bhuwalka explains why LNG can work as a transition fuel for long-haul trucking, how lower operating costs can help fleet operators recover the higher upfront cost, and why LNG infrastructure is beginning to improve across key industrial corridors.Electric trucks could change the economics even further. Falling battery and renewable energy costs are improving electric truck economics, but range, charging time, infrastructure and upfront capital remain major barriers. Blue Energy Motors is testing battery swapping on the Mumbai-Pune corridor and using an energy-as-a-service model to separate the battery cost from the truck.The conversation also looks at why India cannot simply copy China's electric truck model. It also examines how scale could bring down LNG truck costs over time, where heavy-duty trucks fit into the EV transition, and how LNG, electric, biogas and hydrogen may serve different use cases.Bhuwalka also explains how connected trucks are changing fleet economics. Blue Energy Motors tracks around 120 data points in real time and uses artificial intelligence and machine learning for driver scoring, predictive maintenance, fuel efficiency, safety and fleet productivity.Could India's $1 trillion truck transition become one of the country's biggest manufacturing and investment opportunities?Watch the full conversation for insights on electric trucks in India, LNG trucks, heavy-duty trucking, EV adoption, battery swapping, commercial vehicles, logistics, clean mobility, truck manufacturing, predictive maintenance and transport decarbonisation.Chapters:01:03 The Journey from AMW to Building Blue Energy Motors03:24 Blue Energy Motors Enters a Market Dominated by Giants04:46 The Challenge of Building a New Truck Brand07:04 What 1,200 Trucks Taught Blue Energy Motors10:13 Did the War Disrupt Blue Energy Motors?10:53 Are There Enough LNG Stations in India?11:58 How Blue Energy Motors Built Its Truck Platform16:14 Who Buys a Blue Energy Motors Truck?17:43 Have Blue Energy Motors' Early Buyers Broken Even Yet?18:54 Will LNG Trucks Get Cheaper With Scale?19:23 Why Blue Energy Motors Is Betting on Both LNG and Electric23:26 Where Heavy-Duty Trucks Fit Into the EV Transition24:11 Why China Is Pulling Ahead in Battery Technology26:25 Why LNG and Electric May Serve Different Trucking Needs27:21 Are Hybrids the Missing Link in Clean Trucking?28:01 Can India's Biogas Push Power Heavy Transport?30:41 How Connected Trucks Are Closing the Engineering Feedback Loop35:55 What It Takes to Build a B2B Truck Brand36:59 The Financial Roadmap for Blue Energy MotorsIndia's truck transition could become a $1 trillion opportunity as electric trucks in India, LNG trucks and battery swapping reshape heavy-duty trucking. The shift could transform India's manufacturing, logistics, commercial vehicles and clean mobility sectors. Watch the full conversation on EV adoption, truck economics and the future of transport in India.#ElectricTrucks #LNGTrucks #EVIndia #Manufacturing #Logistics #TheCoreReport #TheCore
Charley Boorman sits down with Jase to tell the real story behind Long Way Round, the trip with Ewan McGregor that changed the motorcycle adventure genre forever. From how the idea came together over dinner in London, to the Dakar Rally training that got him ready, to riding the Road of Bones across Siberia to Magadan, Charley walks through the trip that shaped his life. They get into what four months on the road with Ewan actually taught him, how the Sky One deal came together, the Long Way Down and Long Way Up follow-ups, riding Australia end to end, 53 miles of hard enduro at the Isle of Man with David Knight, and what makes Jett Lawrence and Marc Marquez operate on a different level than the rest of us. Whether you've watched every Long Way series or you're finding Charley for the first time, this is the interview that goes deeper than the shows ever could. Watch on YouTube: https://www.youtube.com/watch?v=OS_mpK0lzxg Listen on Spotify/Apple → https://pod.link/1335551721
Coming to you from Whidbey Island, Washington this is Stories From Women Who Walk with Story Prompt Friday and your host, Diane Wyzga. What Women Still Fight For. We women have to fight for so much: being seen, heard, understood, listened to, recognized, and acknowledged. Despite progress, we are still taking a stand to transform women's rights, to overcome barriers to the freedom of voice, opinions, and expression. Little Human to Big Human. Maybe like me you wonder if there was a thru-line of advocacy between who you were as a Little Human Girl and who you are today as a Big Human Woman. What if the courage to speak up, to speak out, to say what you mean and mean what you say was always there? What It Takes. Maybe it's a matter of time, confronting your story, upending your upbringing, and challenging yourself to be a testament to truth. Or a situation that demands a response. Women whose pioneering work laid a foundation for future feminist thought, greater autonomy and independence shared a commitment: I may be one among many but I can draw many to me like fillings to a magnet and together we will do the work that's calling to be done. Click HERE for 36 examples of women who took a stand Devil Within. I was a strong-willed handful as a child. Today we might say I was spirited; but back then I was told “A dimple in the chin means a devil within.” I still have a dimple in my chin. That never left me. What I had to unlearn was a voice that society, religion, patriarchy, and education quieted down. Who am I? The courage to be one's self begins with digging down into the soil of that self. Who am I? What treasure trove of skills, experiences, wisdom, and more can I uncover? And when I uncover the story of myself how do I bestow permission on me to be that self? For a long overdue lesson in courage I turned to Virginia Woolf who knows well the “supreme difficulty of being oneself.” "Let us simmer over our incalculable cauldron, our enthralling confusion, our hotch-potch of impulses, our perpetual miracle — for the soul throws up wonders every second. Movement and change are the essence of our being; rigidity is death; conformity is death: let us say what comes into our heads, repeat ourselves, contradict ourselves, fling out the wildest nonsense, and follow the most fantastic fancies without caring what the world does or thinks or says. For nothing matters except life.” ~ Virginia Woolf Story Prompt: Turn back the pages of time, find that chapter when you were bright and shining, alive and curious, energetic, perceptive, or outspoken. Who is the Little Human Girl that used to be you? Where is she now? Write that story and share it out loud! Thank you for listening. I'm glad to have you here. You're invited: “Come for the stories - stay for the magic!” Speaking of magic, Subscribe & Spread this episode with a generous 5-star review & comment—it helps us all—& join us next time! AND! Stop by my Quarter Moon Story Arts website during reconstruction, email me [info@quartermoonstoryarts.net] to arrange a no-obligation Discovery Call, and stay current with me as Quarter Moon Story Arts on Substack while I renovate that site, too. Stories From Women Who Walk Production Team Podcaster: Diane F Wyzga & Quarter Moon Story Arts Music: Entering Erdenheim by Steve Schuch & Night Heron Music ALL content and image © 2019 to Present Quarter Moon Story Arts. All rights reserved. Enjoy my work? Share & attribute it to Diane Wyzga of Stories From Women Who Walk podcast with a link back to the original source.
Operation Midnight Hammer through the eyes of the people who flew it. Capt. Williams and MSgt. Matos led F-16 maintenance for the 55th, keeping the jets flying for the Wild Weasels who flew Suppression of Enemy Air Defenses ahead of the B-2 strike on Iran. Watch the three-part documentary series on Afterburn Defense: Part I: https://youtu.be/MgQ_X3Td8a4 Part II: https://youtu.be/GzdL6NMYDso Part III: https://youtu.be/Xc22-2RaGSY This is the full interview. Nobody told them what was coming. The configuration changes did. In this conversation, they explain what it actually takes to put one jet in the air, how a young maintenance team learned under pressure, why the flight hours downrange ran far past anything they see at home, and the airman who did not get to work the night of the strike. Before Iran, the 55th Fighter Squadron fought over Yemen in Operation Rough Rider, a sustained air campaign against Houthi surface-to-air threats. What they learned there is the reason the F-16 Block 50 and the AGM-88 HARM were part of the strike package on June 21st, 2025. These interviews were recorded between December 2025 and January 2026 and preserve firsthand accounts from pilots, maintainers, intelligence officers, and logistics airmen who were there. 0:00 Getting the Jets Out the Door 7:03 Maintainers Working Across Airframes 9:49 Rough Rider and a Young Maintenance Team 15:54 The Configuration Changes That Tipped Us Off 18:07 What It Takes to Generate One Sortie 20:36 Maintenance Roles During Midnight Hammer 25:09 The Airman We Benched on the Super Bowl Have a story? Want to Connect? Advertise? https://theafterburnpodcast.com/contact/
"If you know something, you ought to be teaching it to others." Episode Chapters [00:01:36] When People Consider You a Role Model [00:03:23] Why Helping Others Changes You Too [00:07:20] Becoming the Person People Expect You to Be [00:09:31] What It Takes to Be a Good Role Model [00:10:00] Managing Emotions and Treating People with Respect You may be a role model without realizing it. In this episode, Lee Cockerell shares what happens when people look up to you and how that responsibility shapes the way you lead. Lee reflects on his evolution from an intimidating young manager to someone known for teaching and helping others. Being a good role model doesn't require a prestigious title. It comes from controlling your emotions and treating people with respect. Consistently showing up and making time to help others says more about a leader than a title ever will. Read my blog for more from this episode. Resources CockerellStore.com The Cockerell Academy About Lee Cockerell Mainstreet Leader Jody Maberry Travel Guidance Magical Vacation Planners are my preferred travel advisors. Reach out to have them help plan your next vacation. You can reach them at 407-442-2694.
How Do You Hit 4000 Episodes Without Losing Your Mind?Today I'm talking with Glenn Hebert and Jamie Jennings from Horses in the Morning, the flagship show of the Horse Radio Network. They just crossed 4000 episodes, and since they're doing this daily, I had a ton of questions for them. Mainly: how do you keep your sanity?Glenn has been on this show more times than any other guest, and I'll drop links to a few of those past conversations below. We also talk about how "Glenn the Geek" became a verb around here. I cut in a few times throughout the episode to riff on some of what we discussed, so stick around for that too.Key TakeawaysSegments (not just guests) are what let a daily or frequent show survive without constantly reinventing the wheel.A real community needs a place to talk to each other, not just a place to listen to you.Feedback from your most invested listeners (not just anyone) should shape what you keep and what you cut.You're making the show for the 80% who like it, not the 20% who don't.If you're trying to make money at this, budget 10-12 hours of work for every hour that airs.How Glenn and Jamie Keep Finding Something to Talk AboutJamie's husband asked her, before episode one, what she was even going to talk about after two weeks of a horse podcast. Sixteen years and 4000 episodes later, that question still makes her laugh. Glenn books guests only a week or two out, and most of the show's four or five segments per episode get planned the morning of. Being daily is actually their advantage: they can be timely in a way most podcasts can't.Glenn admitted he basically is the show-prep service. Google Alerts and AI help him find stories faster than they used to, but the instinct for what's worth talking about still comes down to two to three hours of prep every single morning.Segments Give You Somewhere to Put Your EnergyJamie is the storyteller. Glenn plays the guy reacting to it. He described their pre-show routine: Jamie says she's got a two-to-five-minute story, and he already knows it's going to stretch to twenty minutes and he'll happily let it. That contrast, he said, is what makes the show work.I've got segments on this show too (if you've got a favorite segment idea, feel free to send it in). One of mine is built entirely out of "this happened because I have a podcast." I used to do a "last five in five" segment where I'd share the last five things I listened to in under five minutes. Segments are useful because they don't all have to show up in every episode. When I don't have a big topic, I create a "podcast stew," a handful of small segments squeezed together. Takes the pressure off having to fill a big block of time. An episode just needs to be as long as it needs to be, and not a minute more.Building a Community That Actually Talks to Each OtherGlenn says their community basically built itself once Patreon entered the picture around nine or ten years ago. One of their own listeners asked if she could start a private Facebook group for supporters, and it's grown into 30+ subgroups on everything from dogs to mental health, plus in-person meetups they run themselves.Glenn's take, and I completely agree: the biggest mistake podcasters make when they say they want "community" is giving people no actual place to talk to each other. A community needs a room. You don't have a true community until they can talk to each other.They get supporters directly involved too, birthdays shouted out on air, segment material submitted by listeners themselves. If your audience made the content, you already know they're going to like it.Sponsors Who Become Part of the FamilyJamie and Glenn have given away more than $80,000 in prizes over 16 years, largely funded by sponsors who get a bit of airtime in exchange for product. Those relationships often graduate into full paid sponsorships once the brand sees it works.That reminded me of Mark from Beyond Bourbon Street, a past guest of mine (episode 829) who had Two Chicks Walking Tours as a sponsor, then had listeners telling him in his own community that they'd taken the tour and loved it. That's the kind of proof that makes a sponsorship renewal conversation easy. And speaking of sponsors sticking around, I was listening to The New Media Show recently and they mentioned Todd Cochran had GoDaddy as a sponsor for around 21 years.The Focus Group Most Podcasts Never Bother WithThis one floored me. On top of their broader Patreon community, Glenn and Jamie run a rotating focus group of 12 listeners every six months whose entire job is to comment on every single episode. Every episode gets six to twelve honest responses.That feedback has actually killed segments. They ran an "animal psychics" bit for a while that they personally enjoyed, but the negative response outweighed the positive, so it's gone. If you're going to ask your paying supporters for their opinion, you have to actually be willing to act on it.Handling the People Who Don't Like YouGlenn's rule, learned from ten years running an improv theater company: about 80% of any audience likes what you do, and 20% doesn't. That ratio doesn't change. You're not making the show for the 20%. You're making it for the 80%, and Glenn pointed out the harder truth: a chunk of that 20% keeps listening anyway, seemingly just to be annoyed. Let it go.Jamie added that after the true fans and the negative Nellies, there's a middle group whose feedback is the most useful because it's the most honest.The Most Popular Segment Isn't the GuestsRight before hitting 4000 episodes, they surveyed listeners on favorite segments. The winner, by a wide margin: pure host banter, just Glenn and Jamie talking about nothing. Their biggest listener complaint over the years has actually been "too many guests." As Glenn put it, people come for the content and stay for the hosts.What It Takes to Actually Make Money Doing ThisGlenn's advice for a new podcaster wondering when the money shows up: niches make it easier. Horse people, in his words, are some of the most dedicated hobbyists out there, and that's true of plenty of other niches too (cycling, golf, you name it). Small and mid-sized brands without their own ad agencies are the sweet spot, and Glenn's found 80% of his sponsors by showing up in person at trade shows and introducing himself at every booth. He's been to 38 of them.Their own listener survey backs this up: 80% of listeners said they'd bought a product because Glenn and Jamie talked about it on air in the last year. Only 25% said the same about a banner ad. Trust is the entire business model.Doing the Show Sick, and Other Real-Talk MomentsWhen you're sick, you address it up front. Ignoring it doesn't work, because that's all the listener will focus on anyway. Same goes for a loud remote recording location. Be real about it and people roll with it, sometimes they even prefer it.One detail that stuck with me: Jamie trains off-the-track thoroughbreds and has personally placed horses with listeners in 18 different states just by mentioning them on the show. That's community turned into real-world impact.My Takeaways After the InterviewA few things I wanted to add once the mic was off:Contact info matters more than you think. Always tell people how to reach you. For me that's schoolofpodcasting.com/contact. Someone recently told me they'd left a comment on my Substack and had no idea if I'd seen it, because I hadn't told them how I check for those.Where to find ideas, beyond hanging out with your audience the way Glenn does: Google Alerts will tell you anytime a topic (or your name) gets mentioned online. Answer the Public shows you what people are actually typing into search around a topic. Or just Google your topic and scroll down to "People Also Ask."On Podcast Movement: I wasn't selected to speak this year, which is the first time that's happened after speaking at every single one. Disappointing, sure, but I'll still be there to say hi, and I'll have more free time than usual. I am speaking at the Empowered Podcasting Conference in Charlotte, NC, and at PodIndy in November.Lesson...
Karan is the co-founder and CEO of Cartesia, an AI model lab building the next generation of real-time voice intelligence.In this episode, Karan explains what all goes into building an AI Lab company; from massive datasets to pre-training to post-training to inference engines, plus a heavy dose of relentless evaluation, and obsession with every layer of the stack. We also discuss why existing AI benchmarks are not very helpful, why latency is voice AI's biggest enemy, and why maintaining context across long conversations is one of the hardest unsolved problems.Apart from the cutting edge tech, this conversation explores Karan's journey from researcher to founder, building Cartesia with a five-person founding team, raising capital, and realizing why first-time founders waste years focusing on things that ultimately don't matter.If you want a glimpse into the future of AI, watch this episode.00:00 Trailer01:35 From DPS to Stanford to Cartesia02:35 From Counter-Strike to AI04:28 Another Research Paper Wasn't Enough06:48 The Story Behind “Cartesia”07:17 Building a Company With Five Technical Founders09:55 From Research Project to Startup13:54 Cartesia's Audacious Vision for 203515:07 What It Takes to Build an AI Model Company18:55 Why Faster AI Doesn't Have to Be Worse24:12 AI Still Has a Memory Problem27:14 Why Context Builds Trust27:50 Will AI Replace Entire Account Teams?29:16 Hold Music Is Now a Salesman30:30 Designing AI That Feels Personal31:31 Building With Cartesia34:48 Voice AI's Enemy #1: Latency38:50 Why AI Labs See the Future First41:32 Finding Product Market Fit45:34 Great Products Don't Need Much Marketing48:12 Very Few Things Matter52:26 How Big Can Voice AI Get55:53 Fundraising And Mission58:15 What Cartesia Is Ultimately Building-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the centre of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail
In this podcast episode, FM editor-in-chief Oliver Rowe summarises the content in the August digital edition of the magazine. Rowe details an article that discusses the history of business jargon, when it should be used, and when it should be avoided. He also shares details about several articles related to artificial intelligence, including one on how AI is changing early career paths for finance professionals. He also shares details on a technical article with the headline "Why Time Matters in Carbon Accounting". Rowe closes with a summary of leadership columns and an explanation of how members can access the current edition and the library of past editions. What you'll learn from this episode: Highlights of an article about business jargon, including a mention of where some terms originated. Details of the August edition's leadership columns, including the debut column of the new CIMA president, Alfred Ramosedi, FCMA, CGMA. A mention of a practical article for finance professionals with the headline "What It Takes for a CFO to Lead Operations and Tech". Rowe's reminders on how members can access the digital edition.
In this podcast episode, FM editor-in-chief Oliver Rowe summarises the content in the August digital edition of the magazine. Rowe details an article that discusses the history of business jargon, when it should be used, and when it should be avoided. He also shares details about several articles related to artificial intelligence, including one on how AI is changing early career paths for finance professionals. He also shares details on a technical article with the headline "Why Time Matters in Carbon Accounting". Rowe closes with a summary of leadership columns and an explanation of how members can access the current edition and the library of past editions. What you'll learn from this episode: Highlights of an article about business jargon, including a mention of where some terms originated. Details of the August edition's leadership columns, including the debut column of the new CIMA president, Alfred Ramosedi, FCMA, CGMA. A mention of a practical article for finance professionals with the headline "What It Takes for a CFO to Lead Operations and Tech". Rowe's reminders on how members can access the digital edition.
AI can help us work faster, produce stronger outputs, and appear more capable. But what happens when technology allows us to skip the slow and uncomfortable stage where real learning takes place?In this episode, Charles Good speaks with Whitney Johnson, one of the world's leading experts on personal disruption and the S-Curve of Learning.Whitney explains why every period of growth begins with uncertainty and discomfort, accelerates through a productive sweet spot, and eventually reaches mastery. The challenge is recognizing when mastery has become a plateau and finding the courage to become a beginner again.AI may weaken the signals that once pushed people toward a new challenge. Output can remain strong even when learning has slowed. Professionals may look productive while the capability beneath their work stops growing.Charles and Whitney discuss how to recognize restlessness and boredom as signals for change, why learning agility becomes more valuable as technology advances, and how trust, character, and human presence continue to compound.They also explore how professionals can use AI as a mirror for development by reviewing conversations, improving their listening, challenging their assumptions, and identifying one way to become 5 percent better.Key Takeaways• Growth follows an S Curve with launch, acceleration, and mastery phases.• AI can muffle the signal that it's time to disrupt yourself.• Naming the feelings associated with change can reduce fear and restore your ability to act.• Learning agility is a capability that remains valuable across roles and technological shifts.• Trust, character, and presence become more important as knowledge becomes easier to access.• Small improvements practiced consistently can produce extraordinary long term growth.Chapters00:00 Introduction to the series and the pattern of growth in the AI era02:11 Why leaders feel less confident despite working harder04:43 Unpacking the phases of the S Curve and their feelings06:07 The importance of naming feelings to tame fear07:02 AI's role in muffling the jump signal and delaying growth07:54 Recognizing restlessness and boredom as growth signals09:00 The importance of wrestling with discomfort for growth09:26 What compounds for leaders and what is at risk10:19 The importance of trust, character, and presence in leadership11:44 The meta skill of learning agility in the AI era12:53 The risk of skipping the slow part of growth with AI13:30 The value of doing things you're bad at for growth14:18 Signals that indicate you're on the right growth curve16:31 Whitney Johnson's personal disruptions and internalized lessons18:56 Signs you're at the top of your growth curve21:50 Managing multiple S Curves in your career and life22:16 Using AI as a mirror for self development23:28 The importance of human qualities in the AI age24:20 The power of small, consistent improvements over time25:01 How to connect with Whitney Johnson and further resourcesWhitney Johnson is the bestselling author of Disrupt Yourself and Smart Growth. Her S Curve framework helps individuals and organizations understand how growth feels, why people plateau, and what it takes to begin climbing again.This episode is part of The Good Leadership Podcast milestone series, What It Takes to Keep Rising in the AI Era.Subscribe to The Good Leadership Podcast: [Apple Podcasts] | [Spotify] | [YouTube]LinkedIn: linkedin.com/in/charlesagoodSubstack Channel (Outlearn to Outperform): charlesgood.substack.comLinkedIn Newsletter (The Outlearn Advantage): [Subscribe]The Institute for Management Studies
Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu
Artificial intelligence can accelerate output, but sustainable performance depends on whether human and organizational capability can keep pace.In this milestone episode of The Good Leadership Podcast, Charles Good speaks with renowned leadership and HR thinker Dr. Dave Ulrich about turning individual talent into organizational capability that creates value for customers, investors, employees, and communities.Dr. Ulrich explains the difference between competence—what an individual knows, does, and embodies—and capability—what an organization consistently does well. He explores how leaders can combine AI with the distinctly human strengths of vision, innovation, emotion, and wisdom while protecting judgment, honest feedback, human connection, and the future leadership pipeline.The conversation also examines the danger of mistaking increased activity for stronger capability, why leaders must become better observers of themselves and others, and how careers and leadership brands must evolve in an AI-driven world.This episode is part of our milestone series, What It Takes to Keep Rising in an AI Era.CHAPTERS00:00 Introduction and Milestone Series Announcement01:23 Dr. Ulrich's Perspective on Turning Talent Into Value02:09 The Difference Between Competence and Capability03:21 Leadership Challenges in a Volatile World04:16 The Multiplier Effect of Human Ingenuity and AI05:40 Four Pillars of Human Ingenuity: Vision, Innovation, Emotion, and Wisdom07:41 The Risks of Output Racing Ahead of Capability08:37 Creating Value for Stakeholders10:21 Signals of Building Activity Versus Capability12:56 The Power of Honest Feedback in Leadership14:20 Leadership in the AI Age: Human Connection and Feedback16:17 The Risks of Automation and the Future Leadership Pipeline19:05 Redefining Careers and Developing Future Leaders22:28 Self-Interrogation and an Evolving Leadership Mindset27:10 A Critical Leadership Skill: Observation and Reflection32:59 Building a Leadership Brand in the AI Era34:36 Final Reflections and Connecting With Dr. UlrichSubscribe to The Good Leadership Podcast: [Apple Podcasts] | [Spotify] | [YouTube]LinkedIn: linkedin.com/in/charlesagoodSubstack Channel (Outlearn to Outperform): charlesgood.substack.comLinkedIn Newsletter (The Outlearn Advantage): [Subscribe]The Institute for Management Studies
Ep 131 - What It Takes to Become a Top Equine Vet w/Randy Lewis Crockett Carothers Randy Lewis is one of the most well known equine veterinarians in the country especially West TX. We talk about the struggles of making it through veterinarian school and then building a profitable business once you get out. Review Wizard:https://www.reviewwizard.io/io-demo486587?am_id=crockett9437Sponsorship:https://form.jotform.com/251243256767057Diversified Payments:https://www.diversifiedpayments.com/wealthycowboyhttps://form.jotform.com/260584054076054The Wealthy Cowboy VIP:https://www.skool.com/the-wealthy-cowboy-vip-6536/about?ref=d30cd83cb8824bc7885158a8ec9366a5
AI can make your work faster, cleaner, and more impressive. But can it also create an illusion of competence—improving the output while the judgment, expertise, and original thinking underneath it stop developing?In this special episode of The Good Leadership Podcast, Charles Good speaks with bestselling author and globally recognized business thinker Dorie Clark about what it takes to win the long game in an AI-driven world.Dorie explains why the abundance of information is making human attention increasingly scarce and why leaders must differentiate through clarity, a distinctive perspective, and ideas grounded in real experience. She also discusses the lasting value of trusted relationships, reciprocity, personal branding, visibility, and becoming known for something meaningful.The conversation explores why many capable leaders are working harder while feeling less confident about their futures, how short-term wins can conceal long-term capability erosion, and why AI-generated polish should never be confused with genuine expertise.Dorie also shares practical guidance for:Using AI as a thinking partner rather than a thinking substituteDeveloping judgment through deliberate practice and reflectionInvesting AI-created time in skills, relationships, and strategic assetsBuilding a visible platform and recognizable point of viewKnowing when to persist and when to repositionTaking small actions that compound over five or ten yearsThe episode also examines the potential for AI to transform healthcare and human longevity—and why the biggest opportunities may emerge when technological innovation is paired with distinctly human judgment and relationships.Dorie Clark teaches executive education at Columbia Business School and is the bestselling author of The Long Game, Reinventing You, Stand Out, and Entrepreneurial You. She has been named four times to the Thinkers50 list of the world's leading business thinkers. This conversation is part of our milestone series, What It Takes to Keep Rising in the AI Era.02:25 Having a long-term strategy in a speed-driven world03:28 Scarce attention in an age of abundant information04:32 The value of in-person experiences and relationships05:04 Differentiating through clarity and perspective06:30 Why leaders are working harder but less confident07:04 Learning AI skills through deliberate practice08:59 The myth of career stability and reinvention09:59 Personal branding, visibility, and adaptability11:16 Building reciprocal human relationships12:17 Short-term wins versus long-term capability13:11 The artisan mindset and generalist skills14:22 The illusion of competence created by AI16:04 The capabilities most at risk in the AI era16:33 Andrew Ridgeley, likability, and relationships18:32 Trust and the power of a strong network19:19 Building judgment through difficult problems20:08 Using AI as a sparring partner20:52 Investing time in strategic assets and skills23:15 Dorie's focus on video, content, and AI mastery24:07 Career lessons and the importance of a platform 26:21 Community and accurate information in career growth27:08 Long-term success and the need for patience28:02 Using analytics and feedback to improve29:42 When to stay the course or reposition30:48 Exponential growth and patience32:17 Small actions that create long-term returns33:42 AI, healthcare innovation, and human longevity34:12 One practical step for building long-term capability36:34 Braces and the power of consistent action37:07 Final advice on relationships and deliberate practice
Hunting A Serial Killer in Canada: Retired Toronto Police Talks About the Case That Shocked a Nation. From tracking one of Canada's most notorious serial killers to leading the heartbreaking investigation into the murder of an eight-year-old child, retired Toronto Police homicide detective Hank Idsinga spent three decades confronting humanity at its darkest. The Law Enforcement Talk Radio Show and Podcast social media like their Facebook , Instagram , LinkedIn , Medium and other social media platforms. Every homicide detective knows that one case can define a career. The Podcast is available for free on the Law Enforcement Talk Radio Show and Podcast website, also on Apple Podcasts, Spotify, YouTube, iHeartradio and most major podcast platforms. #LawEnforcementTalk #Free #Podcast #Radio For Hank Idsinga, there were several. Supporting articles about this and much more from Law Enforcement Talk Radio Show and Podcast in platforms like Medium , Blogspot and Linkedin. As a Retired Homicide investigator with the Toronto Police Service, Idsinga became one of Canada's most respected detectives. During a remarkable 30-year career, he investigated countless murders, managed some of the country's highest-profile homicide investigations, and ultimately became the Major Case Manager responsible for one of the largest murder investigations in Canadian history. The conversation is available on the Law Enforcement Talk Radio Show and Podcast website, Facebook, Instagram, YouTube, Apple Podcasts, Spotify, iHeartRadio, and most other major podcast platforms, where audiences continue discovering firsthand accounts from those who have lived them. Today, Idsinga joins the Law Enforcement Talk Radio Show and Podcast to discuss Hunting A Serial Killer in Canada, the emotional realities of homicide investigations, and the experiences that inspired his #1 National Bestselling Book, The High Road: Confessions of a Homicide Cop. The compelling interview is available on the Law Enforcement Talk Radio Show and Podcast Website, Facebook, Instagram, YouTube, Apple Podcasts, Spotify, iHeartradio and other major Podcast platforms, where audiences continue discovering extraordinary stories from the people behind the badge. Hunting A Serial Killer in Canada: Retired Toronto Police Talks About the Case That Shocked a Nation. A Childhood Promise Became a Lifelong Mission Some careers begin with opportunity. Hank Idsinga's began with tragedy. When he was just ten years old, he learned that his grandfather had been murdered during the Holocaust by the Nazis in World War II. That revelation changed his life forever. "From the age of ten, I knew I wanted to become a homicide detective." Rather than simply dream about police work, Idsinga dedicated himself to achieving that goal. Throughout his youth, he prepared for a career in law enforcement, eventually joining the Toronto Police Service. The episode is available across major platforms including their website, Apple Podcasts, Spotify, YouTube, with highlights shared across their Facebook, Instagram, and LinkedIn profiles. What began with patrol assignments eventually led him into homicide investigations, where his remarkable memory, analytical skills, compassion, and determination quickly earned the respect of fellow detectives and supervisors alike. Finding Answers for Families During Their Worst Moments Hollywood often portrays homicide detectives as emotionless investigators driven only by evidence. Reality is far different. Idsinga explains that homicide detectives do much more than process crime scenes and interview suspects. They also become the people responsible for delivering devastating news to grieving families. One moment they may be chasing a dangerous suspect. The next, they're standing on a family's front porch explaining that someone they love will never come home. "You have to investigate the crime, but you also have to care for the people left behind." That balance between relentless investigation and genuine compassion became one of the defining characteristics of Idsinga's career. Available for free on the Law Enforcement Talk Radio Show and Podcast website, also on Apple Podcasts, Spotify, Youtube and most major Podcast networks. Hunting a Serial Killer Who Terrorized Toronto Perhaps no investigation better demonstrates the complexity of modern homicide investigations than the hunt for one of Toronto's most notorious serial killers. Hunting A Serial Killer in Canada: Retired Toronto Police Talks About the Case That Shocked a Nation. Between 2010 and 2017, eight men, most with connections to Toronto's Gay Village, vanished. At first, the disappearances appeared unrelated. As investigators dug deeper, disturbing patterns began to emerge. The investigation quickly expanded into the largest homicide investigation in Toronto Police Service history. Multiple agencies joined the effort, including: Toronto Police Service (TPS) Ontario Provincial Police (OPP) Royal Canadian Mounted Police (RCMP) Numerous regional law enforcement partners Multiple specialized task forces As the Major Case Manager, Hank Idsinga coordinated an extraordinarily complex investigation involving hundreds of investigators, forensic experts, analysts, intelligence officers, and partner agencies. The pressure was immense. The community demanded answers. Families desperately wanted their loved ones found. The media closely followed every development. The Investigation That Finally Solved the Murders Years of painstaking investigative work eventually led detectives to Bruce McArthur, a self-employed landscaper. The Podcast is available for free on the Law Enforcement Talk Radio Show and Podcast website, also on Apple Podcasts, Spotify, YouTube, iHeartradio and most major podcast platforms. Investigators uncovered horrifying evidence. The remains of several victims had been concealed inside large planter boxes located on properties where McArthur had worked. McArthur ultimately pleaded guilty to eight counts of first-degree murder. He received a sentence of life imprisonment with no eligibility for parole for 25 years. For investigators, however, the arrest represented more than solving a case. It provided long-awaited answers for grieving families and brought an end to years of fear within Toronto's community. "Every victim deserved answers. Every family deserved the truth." Standing Before the Cameras Throughout the serial killer investigation, Hank Idsinga became the public face of the case. That responsibility required more than investigative skill. It required calm leadership. Every news conference carried enormous significance. Families were listening. The public wanted reassurance. Reporters demanded updates. Criticism surrounding aspects of the investigation intensified as the case unfolded, but Idsinga consistently appeared composed, compassionate, and professional while communicating difficult developments to the public. Hunting A Serial Killer in Canada: Retired Toronto Police Talks About the Case That Shocked a Nation. The Law Enforcement Talk Radio Show and Podcast continues bringing listeners real conversations from the front lines of crime, policing, trauma, survival, and healing. His ability to balance transparency with investigative integrity earned widespread respect throughout Canada. The Murder of an Eight-Year-Old Child While serial murder investigations receive national headlines, some of the most emotionally devastating cases involve children. Idsinga also served as the lead investigator in the murder of an eight-year-old child, a case that tested every investigator involved. Few crimes affect detectives more profoundly than those involving children. The emotional weight follows investigators long after the evidence has been processed and the courtroom proceedings conclude. "Child homicide investigations stay with you forever." Those cases remind detectives why every decision matters and why justice requires both determination and extraordinary attention to detail. What It Takes to Solve Homicides Television often portrays homicide investigations as quick breakthroughs driven by dramatic confessions. Idsinga explains that reality is far more methodical. Successful investigations depend upon countless hours of: Crime scene analysis Witness interviews Forensic science Intelligence gathering Surveillance Digital evidence Interagency cooperation Persistence Sometimes one overlooked detail changes everything. Sometimes cases take years. Sometimes detectives refuse to quit because families deserve answers. Those lessons form the backbone of Idsinga's investigative philosophy. From Homicide Detective to Bestselling Author After retiring from policing, Hank Idsinga chose to preserve those lessons in writing. His memoir, The High Road: Confessions of a Homicide Cop, became a #1 National Bestseller. The Book offers readers an honest look inside homicide investigations while revealing the emotional realities rarely discussed outside police circles. Hunting A Serial Killer in Canada: Retired Toronto Police Talks About the Case That Shocked a Nation. Supporting articles about this and much more from Law Enforcement Talk Radio Show and Podcast in platforms like Medium , Blogspot and Linkedin. Rather than glamorizing violence, Idsinga focuses on the human side of policing. Readers discover the victories. The heartbreak. The difficult conversations. The impossible decisions. And the tremendous responsibility detectives carry every day. The book has been praised for bringing authenticity to the true crime genre while honoring both victims and investigators. The Human Side of Homicide One of the strongest themes throughout Idsinga's career is empathy. He never viewed victims as case numbers. Every investigation represented someone's parent. Someone's child. Someone's friend. Someone whose family deserved answers. That perspective helped shape the detective he became and continues to influence the stories he shares today. The episode is available across major platforms including their website, Apple Podcasts, Spotify, YouTube, with highlights shared across their Facebook, Instagram, and LinkedIn profiles. "The investigation doesn't end with an arrest. Families carry that loss forever." Why These Stories Matter Today Public fascination with true crime, Social Media, streaming documentaries, and crime podcasts has never been greater. Millions follow investigations on Facebook, debate cases on Instagram, and listen through the Law Enforcement Talk Radio Show and Podcast website, Apple Podcasts, Spotify, YouTube, iHeartradio and other Podcast platforms. Hunting A Serial Killer in Canada: Retired Toronto Police Talks About the Case That Shocked a Nation. Yet few hear directly from the investigators who actually solved these cases. That is what makes Hank Idsinga's conversation so compelling. Rather than sensationalizing violence, he provides an honest look at the investigative process, the emotional cost of homicide work, and the professionalism required to pursue justice under extraordinary pressure. His remarkable career demonstrates that solving murders requires far more than intelligence. It requires patience. Compassion. Leadership. And an unwavering commitment to victims who can no longer speak for themselves. For anyone interested in policing, criminal investigations, or the realities behind some of Canada's most significant homicide cases, Hank Idsinga's interview offers a rare opportunity to hear firsthand from the detective who helped bring one of the country's most notorious serial killers to justice while serving victims and their families with dignity throughout an extraordinary Retired Homicide career. Listen to the Law Enforcement Talk Radio Show and Podcast on their website, Facebook, Instagram, YouTube, Apple Podcasts, Spotify, iHeartRadio, and most major podcast platforms. Be sure to follow us on X , Instagram , Facebook, Pinterest, Linkedin and other social media platforms for the latest episodes and news. Learn and get access to money saving tips and how to increase your net worth at www.LetSavings.com Download the Free Ebook about ways and tips to improve your health. You can get the ebook for free at www.LetHealthy.com Get the Free Clubhouse App, it is Drop In Social Audio. Think of it as your own talk radio show on your phone, and best of all it is free. Be sure to look for me and follow me, that's John J Wiley or @letradioshow you can do all that here. The Law Enforcement Talk Radio Show and Podcast social media like their Facebook , Instagram , LinkedIn , Medium and other social media platforms. You can contact John J. “Jay” Wiley by email at Jay@letradio.com , or learn more about him on their website . Find a wide variety of great podcasts online at The Podcast Zone Facebook Page , look for the one with the bright green logo. Be sure to check out our website . Hunting A Serial Killer in Canada: Retired Toronto Police Talks About the Case That Shocked a Nation. Attributions Amazon Wikipedia Simon and Schuster Facebook Facebook Group Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
What It Takes to Lead Sports Host Broadcasting: Inside ISB with CEO Ursula Romero This week's podcast special guest is Ursula Romero, CEO and Managing Director of International Sports Broadcasting (ISB), a global leader in host broadcasting operations for the world's biggest sporting events. With over two decades of experience shaping modern broadcast standards, Ursula has worked on every Olympic and Paralympic Games since 1996, as well as major global events including the FIFA World Cup and the Invictus Games. An award-winning producer and director, she is also a passionate filmmaker with a Master's in Cinematography from the London Film Academy. Today, she leads ISB into a new era of technological innovation, diversifying global sports coverage and driving the future of live sports production. In this conversation, Ursula opens up about what it really takes to build a career in sports broadcasting from the ground up, how she navigated the transition from producer to CEO, and why storytelling still sits at the heart of great sports coverage—despite all the technological change. What You'll Discover: How Ursula built a 30-year career in global sports broadcasting From summer jobs as a liaison officer and driver at World Cups and World Championships to directing live sport and leading host broadcasting for Global Sporting Events What it actually takes to be a CEO in sports broadcasting Ursula unpacks the shift from head of production to running ISB, taking over during the pandemic, handling bids, clients, pressure, and the emotional reality of stepping into her late father's role. Why storytelling will always beat technology How Ursula thinks about live sport as storytelling, what's changed (and what hasn't) from the 1980s to the age of TikTok, and how to use new platforms without losing narrative quality. The future of live production, social media, and AI in sport Ursula shares how vertical video, second-screen experiences, fan engagement tools, and AI are reshaping coverage—and how to use these tools without diluting the human side of sport. Practical career advice for aspiring sports broadcasters Why work experience beats classroom theory, how to get your foot in the door, the importance of saying "yes" to unglamorous roles, and why exploring non-mainstream sports can be your edge—and much more. Connect with Ursula: LinkedIn: https://www.linkedin.com/in/ursula-romero-1b938810/ Instagram: https://www.instagram.com/ualecrim/ SBI Website: https://isbtv.es/ SBI Instagram: https://www.instagram.com/isbc_live
Bryce Daley, coach at Salisbury School, shares how to get recruited for basketball, what families should know about a post grad year for basketball, and how prep school can help players prepare for college.In this episode of the PREP Athletics Podcast, we talk with Bryce about his own path from Salisbury School to Division I basketball at UMass Lowell, what surprised him about the D1 level, and how he now helps players develop at Salisbury.We also cover campus visits, all-boys school advantages, player development, playing time expectations, college placement, NEPSAC exposure, reclassification, post-grad eligibility questions, and what it takes to become a scholarship-level guard.⏰ Timestamps:00:00 - Episode Intro01:57 - Bryce Daley Joins the Podcast02:09 - Bryce's Basketball Background and Prep School Path08:10 - From Salisbury School to UMass Lowell10:41 - What Playing D1 Basketball Really Takes13:18 - Why Salisbury School Works16:45 - All-Boys School and Player Development21:43 - Sports Requirements, Strength Training, and Post-Grad Academics27:38 - College Placement and Playing Time Expectations32:18 - NEPSAC Exposure, Reclassing, and Post-Grad Decisions41:02 - NEPSAC Class A and What It Takes to Be a D1 Guard45:33 - The Future of Prep School Basketball and Quick Hitters52:24 - How Prep School Changed Bryce's Life53:52 - Bryce's Contact Info and Final PREP Athletics CTA
AI is not just changing the tools leaders use. It is changing how leaders think, learn, make decisions, and build confidence in their own capabilities.This episode is part of our 300th episode milestone series, “What It Takes to Keep Rising in an AI Era.”In this conversation, Charles Good sits down with Alison McCauley to explore one of the most important leadership questions of this moment: how do we use AI without losing the judgment, curiosity, and human capability that make leadership matter?Alison argues that many organizations are doing AI backward: investing heavily in the technology while underinvesting in the people, workflows, and cultural conditions required to turn AI into real value. Alison shares why curiosity matters, why engagement may be more important than ROI in the early stages, how productivity gaps are forming inside organizations, and why the best AI users treat the technology as a thought partner rather than an answer machine. You'll also hear why leaders need to keep their own thinking in the lead, what it means to become a “compounded human,” and the simple rule that can help you use AI without outsourcing your judgment.For more than two decades, Alison McCauley has helped organizations turn emerging technology into real human-centered value. She is a bestselling author, social scientist, and AI strategist advancing human potential globally. As a digital transformation strategist focused on human-centered technology adoption, she works at the intersection of AI, behavior, and strategy.Chapters:00:00 Celebrating Milestones in Leadership Conversations03:29 The Role of Curiosity in Thriving with AI06:29 Engagement Over ROI: The New Focus for Organizations09:11 Navigating the Learning Curve of AI11:56 Understanding the Emotional Landscape of Leadership14:30 The Dunning-Kruger Effect and AI Usage17:09 Building Mental Muscles: The Discipline of Thought20:22 Opportunities for New Graduates in the AI Era23:13 Compounding Benefits of AI Engagement25:48 The Human Element in AI-Driven Work28:43 Personal Growth Through Change31:08 Key Behaviors for Effective AI UsageSubscribe to The Good Leadership Podcast: [Apple Podcasts] | [Spotify] | [YouTube]LinkedIn: linkedin.com/in/charlesagoodSubstack Channel (Outlearn to Outperform): charlesgood.substack.comLinkedIn Newsletter (The Outlearn Advantage): [Subscribe]The Institute for Management Studies
The More Sibyl Podcast Presents: 계속 선택하는 사랑 |Almost Forty Years, Three Countries, and What It Takes to Stay — The One with the Ibikunles: Episode 4 (2026)Love is patient, love is kind. Love hopes all things, and love endures all things (1 Corinthians 13:4-7).We all know this scripture. But the word I keep circling back to is endures. We love to dwell on the soft, beautiful things love brings, and we quietly skip past the harder truth tucked inside that verse. To endure something means there is resistance. It means something is pushing against you, and love is what holds on while it tries to pull you apart.In this episode, I sit down with a couple who have endured, with love, for almost forty years, across three countries, and they are still choosing each other.Dr. Tayo Ibikunle and Dr. (Mrs.) Tokunbo Ibikunle met more than once before they ever said yes (God, it seems, enjoys arranging these things), though she'll tell you plainly he wasn't her type at first. “Surely not you,” she told him. What followed was Nigeria, England, America, a letter written to her father, and a marriage that shaped how their own sons are being prepared for theirs.We get honest about the things that quietly pull marriages apart. The years it can take to learn to say sorry and mean it. What it means for home to feel like a castle instead of a battleground, and what happens to a man when respect is withheld until he shuts down. What it means to trust your husband's place instead of quietly outsourcing it, and we ask the harder question about female friendships: at what point does leaning on your community of women friends start filling a role that should be his? The endless, urgent pull of black tax, and the “tyranny of the urgent” that taught them to protect what was theirs first. What immigration actually reveals about a marriage. The fifteen-minute rule that rescued their worst arguments. And why, in her own words, “the older I get, the more I'm enjoying being married.”If you are newly married, in the thick of it, or quietly wondering how anyone stays connected across decades, this one is for you. Press play.
On this episode of The Jon Gordon Podcast, I sit down with legendary UConn basketball coach Dan Hurley for an honest and energizing conversation about building a championship culture, relentless leadership, and the transformative power of purpose-driven coaching. From his early days learning from a Hall of Fame father, to guiding players through the rigors of college basketball's toughest moments, Dan opens up about the realities of forming a great team in the era of the transfer portal, NIL, and social media. He shares the values at the core of the UConn program—relentless competitive effort, mindful communication, a dedication to the "pack," and the consistent pursuit of growth—explaining how accountability and care come together in an environment that pushes players past their limits and deepens their belief in themselves. Throughout this conversation, Dan reflects on the importance of love-tough coaching, the urgency of making an impact on young lives, and the crucial role that faith and self-development play in his own journey. Whether he's describing his mindful approach to team-building, the vulnerability he shares with his athletes, or the discipline and devotion it takes to chase excellence, Dan's story is both inspiring and down-to-earth. About Dan: Dan Hurley is one of the most accomplished coaches in modern college basketball, leading the University of Connecticut back to national prominence since becoming the program's 19th head coach in 2018. In eight seasons at UConn, Hurley has compiled a 199-75 record, guided the Huskies to six consecutive NCAA Tournament appearances, three Final Fours in four seasons, back-to-back NCAA National Championships in 2023 and 2024, and a national runner-up finish in 2026. His teams won 13 consecutive NCAA Tournament games from 2023-25, with all 12 victories during the championship runs coming by double digits—an NCAA record. Since 2022-23, UConn has posted a 126-28 record, the most successful four-year stretch in program history. Hurley was named the 2024 Naismith College Coach of the Year and BIG EAST Coach of the Year after leading UConn to a school-record 37 wins, BIG EAST regular season and tournament championships, and the program's sixth national title. He owns the highest winning percentage in BIG EAST history (minimum 100 conference games) and has developed 12 NBA players, including four lottery selections, during his tenure. Prior to Connecticut, Hurley transformed programs at Rhode Island and Wagner, leading Rhode Island to consecutive NCAA Tournament appearances and Atlantic 10 regular-season and tournament championships. Across 16 collegiate seasons, he has amassed a 350-180 career record. A Jersey City native, Hurley was raised in one of basketball's most accomplished families. His father, Hall of Famer Bob Hurley Sr., built the legendary St. Anthony High School program, while his brother Bobby won two NCAA championships at Duke. Before entering the college ranks as a head coach, Hurley compiled a 223-21 record in nine seasons at St. Benedict's Prep, developing multiple future NBA players. Known for his relentless competitiveness, player development, and elite two-way teams, Hurley has become one of the defining figures in college basketball. In 2025, he became a New York Times bestselling author with his memoir, Never Stop: Life, Leadership and What It Takes to Be Great. Additional Resources: Do you feel called to share your story with the world? Check Out Gordon Publishing Follow me on Instagram: @JonGordon11 Every week, I send out a free Positive Tip newsletter via email. It's advice for your life, work, and team. You can sign up here and catch up on past newsletters. Ready to lead with greater clarity, confidence, and purpose? The Certified Positive Leader Program is for anyone who wants to grow as a leader from the inside out. It's a self-paced experience built around my most impactful leadership principles with tools you can apply right away to improve your mindset, relationships, and results. You'll discover what it really means to lead with positivity—and how to do it every day. Learn More About the Certified Positive Leader Program Want to impact more people? Do you feel called to do more? Would you like to impact more people as a leader, writer, speaker, coach, and trainer? Get Jon Gordon Certified if you want to be mentored by me and my team to teach my proven frameworks, principles, and programs for businesses, sports, education, and healthcare.
Ever wonder how a debut novel can open doors far beyond book sales?This week's guest is Kailey Holbrook, award-winning filmmaker, author, and film student at USC. Kailey published her first 500+ page novel while still in high school. Since then, she's built an engaged online following, navigated the worlds of publishing and filmmaking, and learned firsthand what it takes to connect with readers in a crowded marketplace.In this conversation, Kailey shares how she found her audience, built a creative community, and leveraged storytelling skills across multiple platforms. Whether you're writing fiction or nonfiction, you'll discover practical lessons on visibility, audience growth, and creating opportunities that extend well beyond your book.Key TakeawaysWhy a Book Can Become Your Biggest DifferentiatorDiscover how publishing a book can create unexpected opportunities, boost credibility, and help you stand out in ways most people never consider.The Audience Mistake Many Authors MakeLearn why trying to reach everyone often leads to reaching no one, and how identifying the right readers can transform your marketing results.What Social Platforms Are Really Telling YouFind out how Kailey uses TikTok, Instagram, and Goodreads to learn what readers care about and build genuine engagement without feeling promotional.The Hidden Power of Creative CommunityExplore how surrounding yourself with fellow creators can spark new ideas, fuel motivation, and help your work reach a wider audience.What It Takes to Turn a Book into a ScreenplayGet an inside look at the biggest differences between writing a novel and writing for the screen, plus the skills every aspiring screenwriter should develop.Why Successful Authors Stay FlexibleLearn how adapting your writing, marketing, and creative process can help you stay relevant as your audience and goals evolve.Tune in now!Here's how to connect with Kailey:Tik Tok: @kaileyholbrookauthorYoutube: @KaileyHolbrookAuthorInstagram: @kaileyholbrookLinkedIn: Kailey Holbrook*************************************************************************When Book Marketing Feels Overwhelming, Clarity Changes EverythingIf you know your book deserves more visibility, but marketing feels confusing or inconsistent, the Author Influencer Circle helps nonfiction authors build authority, attract opportunities, and market with confidence.Learn more about the Author Influencer Circle and turn your book into money making opportunities!*************************************************************************
Your work has never looked better. So why do you feel less sure of yourself than you did five years ago?In this episode of The Good Leadership Podcast, behavioral economist Dan Ariely (author of Predictably Irrational and Misbelief) unpacks a quiet problem facing ambitious leaders right now: AI is polishing your output faster than you're actually growing your capability. The result is an "illusion of competence" — work that looks sharper while the person behind it quietly stalls.We get into:Why effort and outcome have decoupled — and why working harder is making leaders feel less in controlThe hidden forces shaping how you judge yourself: effort discounting, social comparison on speed, and attribution errorWhat still compounds in the AI era (hint: it's the meta-capacities machines can't touch)What "context collapse" does to leaders who lean too hard on their toolsThe simple weekly decision-review habit that will still be paying off in 10 yearsThis is the kind of conversation that changes how you think about your own thinking. If your performance reviews and your nervous system are being trained on different signals, this one's for you.CHAPTERS00:00 The Confidence Gap: Knowledge vs. Perception03:46 The Dangers of False Confidence in the Age of AI04:28 The Learning Process: Embracing the Messy Middle07:35 AI's Role in Knowledge Acquisition: A Double-Edged Sword10:32 The Temptation of AI: Short-Term Gains vs. Long-Term Growth 12:09 Measuring Success: The Risks of Misguided Metrics14:09 Innovation and AI: Who Really Benefits?15:05 The Impact of Measurement on Innovation16:56 Employee Engagement and AI Integration19:39 Balancing AI and Human Capital20:44 The Gap Between Knowing and Doing21:20 The Role of Planning in Change23:31 Defining Boundaries with AI23:51 AI as a Tool for Self-Reflection25:44 The Importance of Impact in Academia29:04 Skills to Protect in an AI World32:53 Fostering a Joy of LearningListen, then ask yourself: What am I doing not just to do more but to become someone whose judgment I trust more, year after year?
Tfue was the most-watched Fortnite streamer on the planet. Eleven million Twitch followers. Eleven million YouTube subscribers. Over 1.7 billion views. And in 2023, he walked away from it all. Not because he failed. Because he was done. Now he's running 120 miles offshore in the Gulf chasing swordfish, flats fishing in Indian Rocks Beach, and building one of the fastest-growing fishing channels on YouTube under the name C-Few. I really loved this conversation. Some of the moments that stood out to me: - The way Tfue talks about gaming and fishing as two sides of the same obsession — when he's in, he's ALL in. Whether that's streaming 15 hours a day or going 4-for-5 on swordfish before noon. - His take on what it actually takes to be the best in the world at something — genetics, age, reflexes, mindset — and how a lot of it applies far beyond gaming. - The moment he realized a flats boat was too dangerous for anyone he invited on it, and how that pushed him into bigger water and bigger fish. - How he learned sword fishing almost entirely on his own — YouTube, trial and error, and just going 120 miles out into the Gulf until he figured it out. - His honest answer when I asked if competitive gaming and competitive fishing are comparable. He doesn't sugarcoat it. The answer surprised me. 00:00 Introduction 01:00 From #1 Fortnite Streamer to Fisherman 03:35 What It Takes to Be the Best in the World at Gaming 07:00 How Tfue Fell in Love with Fishing 09:45 The Boat Progression — Simmons Flats Boat to 43-Footer 14:00 Sword Fishing the Gulf — Running 120 Miles Offshore 17:00 Sponsors, Growing C-Few, and Building a Fishing Channel 21:00 The Bahamas, Exumas, and Dream Fish 27:30 Retiring at 28 — What That Actually Looks Like 32:00 Gaming vs. Fishing: Competitive Mindset Breakdown 38:00 Fortnite by the Numbers — The Scale Is Hard to Believe 44:00 Enhanced Games, Adderall in Esports, and Performance 51:00 Skill Ceiling, Genetics, and What Makes a Great Competitor 55:00 AI, Chess, and Cheating at the Highest Levels 1:01:00 Dream Catches, Bluefin Tuna, and What's Next 1:07:00 Golf, Card Counting, Casinos, and Random Life Stuff 1:14:00 Where to Find Tfue — Tfue and C-Few
This week, we're breaking down one of the most talked-about launches of 2026: Jessi Jean's $1.2 million launch of her Yap On Camera challenge (completely organic!) in under two weeks. But this episode isn't just about Jessi Jean. It's about what her launch can teach YOU about strategy, positioning, and the mindset work that actually makes or breaks your results.In this episode, we talk about:Why the "power of one" (one client, one offer, one problem) is the fastest path to a million-dollar business and how Jessi Jean executed it perfectlyWhy a challenge format outperformed what a course would have done, and what that means for how you package your next offerThe two-month tease strategy she used to build demand before the doors ever opened and how to apply this to your own launchesWhy she closed the doors and turned down money in the moment, and the long game thinking behind that decisionHow she created extraordinary positioning in a saturated space without a new topic, just better framingThe difference between selling the feature vs. selling the fear, and which one actually convertsThe limiting belief every entrepreneur needs to kill: "Why would anyone work with me when someone else teaches more?"Why purpose over popularity is the mindset shift that removes the fear of visibility almost entirelyWhy your subconscious mind is running 95% of your business and what to do about it!Listen to similar episodes:246. The 4 Thought Loops That Are Costing You $5k+ Every Month238. The CEO Morning Routine That Made Me $600K Online222. Part 1: What It Takes to Quantum Leap - The Identity Shift Behind My $33K Week223. Part 2: What It Takes to Quantum Leap - The Identity Shift Behind My $33K WeekP.S. When you rate and review the podcast, you'll receive my Connect to your Higher Self Visualization as a thank you: Click here to claim your gift. Ways to Work with Nora:1:1 Coaching Waitlist – Add your name to the waitlist to be the first to learn when spots open.90-Minute Intensives Waitlist – Limited openings for deep-dive, high-impact sessions. Join the waitlist to be notified when spots become available.Courses – Explore Nora's signature programs:Full Throttle – The ultimate business strategy courseElite – Business energetics + identity work coursePodcasting for Business Growth – Turn your podcast into profitConnect with Nora – Follow her on Instagram @iamnoravirginia for updates, tips, and inspiration.
Lead Pastor Josh Carstensen continues our series on Mark.Two people come to Jesus in Mark 5. A synagogue ruler whose daughter is dying. And a woman who's been sick for twelve years, who's tried everything, spent everything, and is getting worse.They couldn't be more different. One is powerful and named. The other is anonymous and desperate. But they both end up at the same place: on their knees, out of options, reaching toward Jesus.What's striking is that he doesn't respond to them the same way. One gets immediate healing. But the other has to wait through the worst moment of his life first.If you've ever prayed hard for something and felt like heaven was quiet, this one is worth your time.Thank you for listening to this message from Northwest Hills Community Church in Corvallis, Oregon, on June 14, 2026, at 10:30am. You can find us online at nwhills.com.Key Moments(00:00) Welcome(1:00) Message: When You've Run Out of Options, Come to Jesus(5:46) Why Three Gospel Writers Tell This One Story(7:21) Jesus Challenges Our Brokenness and Authority(13:40) Jairus: A Father at the End of His Rope(16:08) The Woman Who Bled for Twelve Years(21:46) Why People Come to Jesus, and What It Takes(27:29) Jesus Heals Differently: Publicly, Privately, on His Timeline (31:17) Application: Coming to Jesus Now, Whatever Stage You're In
Therapist and author, Prentis Hemphill joins Dr. Thema for an honest conversation about loneliness and divine identity. They explore family, sexuality, embodied living, and the need to break draining cycles. Prentis Hemphill is the bestselling author of What It Takes to Heal, a groundbreaking exploration of healing, justice, and transformation. A therapist, somatics teacher, facilitator, political organizer, and writer, Prentis is also the founder of The Embodiment Institute and a leading voice in embodied leadership and collective healing. For over a decade, Prentis has worked with individuals and organizations through their most challenging moments of change—navigating leadership transitions, conflict, and the alignment of practice with values. Grounded in an embodied approach, their work ensures that our intentions aren't just ideas, but are fully lived, felt, and practiced. Before founding The Embodiment Institute, Prentis served as the Healing Justice Director at Black Lives Matter Global Network and was a lead somatics teacher with generative somatics and Black Organizing for Leadership and Dignity (BOLD). They hold an M.A. in Clinical Psychology and have provided therapeutic services in low-cost mental health clinics, centering marginalized communities. Don't forget to subscribe and share. Mixed & Edited by Next Day Podcast info@nextdaypodcast.com
Senator Angela Alsobrooks joins hosts Rebecca Rettig and Renato Mariotti to discuss the three outstanding issues she needs resolved before voting Clarity off the Senate floor. Plus, insights into Jamie Dimon's criticism over stablecoin yield. And, former Congressman George Santos named person of the week for the Kalshi insider trading investigation. - Timecodes: 00:00 Sen. Alsobrooks on Ethics 00:26 Welcome to The Policy Protocol 00:56 CFTC Greenlights Perps in 24 Hours 02:33 OFAC Sanctions Iran's Nobitex 05:56 Offshore Exchanges Coming Onshore 07:05 Senator Angela Alsobrooks Joins 07:45 Defending the Yield Compromise Against Jamie Dimon 10:21 What's Needed for an Ethics Compromise 12:17 How ClarityHelps Underbanked Constituents 15:03 Why More Democrats Aren't on Board 16:25 What It Takes to Get Clarity Across the Line 17:13 Sen. Alsobrooks's Approach 18:33 Why Clarity Is the World's Only DeFi Legislation 21:27 Senator Lummis Pushes Back on Jamie Dimon 22:31 George Santos Named Person of the Week
Industrial network protocols decide whether a machine talks or stays silent. Chuck from Horner Automation breaks down how they win, fade, and converge.Chuck has spent 36 years at Horner Automation and lived through what the industry once called the fieldbus wars. Before Horner became known for its all in one controllers, it spent a decade building specialty IO modules for GE Fanuc during the era of DeviceNet, SDS, InterBus S, PROFIBUS, and CANopen. His core argument is that most of those early protocols were technically fine. The ones that became standards won on the commercial weight of the companies backing them, not on superior specifications, with EtherCAT a rare exception that succeeded largely on technical merit.Trust is the recurring theme. Industry adopts slowly, and for years Ethernet was dismissed as too unreliable and not deterministic enough for control until Ethernet/IP, PROFINET, and Modbus TCP proved themselves. Today the market has settled around a big four set of protocols, and Chuck does not expect it to narrow further. For high speed motion he points to EtherCAT and PROFINET IRT as the implementations he most respects, since both step away from standard Ethernet at the device level to reach submillisecond timing.The episode is also a reality check on building your own hardware. Chuck and Dave describe how custom development routinely costs teams hundreds of thousands to millions of dollars, and how the real trap is obsolescence and maintenance rather than the first build. On the product side, the standout is FPD-Link, a serialization technology borrowed from automotive that carries video, touch, and power over one coaxial cable. Working with Safe Fleet, a maker of ambulances and fire trucks, Horner now mounts rugged displays up to seven meters from the PLC while still programming everything as one device.Looking ahead, Chuck argues that every PLC should now be treated as a data device first, because digitizing the process is the prerequisite for doing anything useful with AI. He also flags cybersecurity as the next burden for application engineers, with new mandates forcing both manufacturers and integrators to implement protections that were once optional. At Automate, Horner is showing HMI Connect and a 300 dollar CPU 151 that packs 18 IO points, wireless connectivity, and edge capability into a micro PLC.About Chuck and Horner AutomationChuck is a technical brand ambassador at Horner Automation, where he has spent 36 years across applications, product management, and education. An electrical engineer who started in the automotive industry, he now produces in depth tutorials on industrial protocols for the Horner APG YouTube channel. Horner Automation is a privately held controls manufacturer best known for its all in one PLC and HMI controllers, edge ready PLCs, and rugged hardware for industrial and mobile applications.Timestamps0:00 Introduction2:20 Chuck's Background and 36 Years at Horner Automation9:20 End User Engineer vs OEM Manufacturer Perspective13:20 New at Automate: HMI Connect and the CPU 151 Edge PLC21:30 The Fieldbus Wars and the History of Industrial Protocols24:20 What It Takes to Implement a Protocol Stack29:30 Why Protocols Win: Commercial Force vs Technical Merit32:40 Will Industrial Protocols Ever Converge?40:30 High Speed Motion: EtherCAT, PROFINET IRT, and Ethernet/IP44:40 FPD-Link: Rugged Remote HMI for Ambulances and Fire Trucks55:00 PLCs as Data Devices and the Push Toward AI1:02:40 Cybersecurity Mandates Coming for Application EngineersReferencesHorner Automation: https://www.hornerautomation.comAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Understanding Plant Networks: https://www.joltek.com/blog/understanding-plant-networks-how-industrial-connectivity-evolvedIndustrial Ethernet Reliability: https://www.joltek.com/blog/industrial-ethernet-reliabilityDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub
It is about what happens after the divorce papers are signed, when you are trying to build something new, when you are afraid to be vulnerable again, when you have been hurt, and you are wondering if you can trust someone with your whole heart.This is Part 3. In Episode 73, they covered institutional trust. In Episode 74, the parent-child relationship. Now they go to the most intimate place of all.In this raw, honest conversation, Dr. Marjorie & Dr. Michael tackle trust in intimate relationships, the kind that matter most and hurt the hardest. From the revelation that not meeting your partner's love language is a form of infidelity, to the impact of divorce on how we show up in future relationships, to the defense mechanisms we develop when trust has been shattered — this conversation covers the ground that matters.For anyone who has been hurt in love, who is scared to try again, who is wondering how to rebuild trust in a relationship after betrayal or divorce, this episode is for you. Because vulnerability in relationships is not a weakness. It is the only way through.Inside the Episode:Holes in the Boat. A hundred small violations of trust — the dishes, the chores, the unmet requests — will sink the ship just as surely as one cannonball. Micro-moments destroy trust more completely than any single dramatic betrayal. This is the framework that reframes everything about rebuilding relationships.Not Meeting Your Partner's Love Language Is Betrayal. "I have told you what my love language is. You continue not to honor it. And it is exhausting me." Infidelity does not always look like an affair. There are many ways to be disloyal.How to Rebuild Trust in a Relationship. Rebuilding trust after betrayal is brick by brick, through small, consistent actions that restore safety over time. Trust that has been tested and rebuilt through co-parenting after divorce is more durable than trust that has never been challenged.Who You Become When Trust Is Shattered. The defense mechanisms. The punishing behavior. The identity shift. This is the conversation about rebuilding trust after divorce and breakup that nobody wants to have, but everybody needs.Vulnerability in Relationships. You cannot microwave vulnerability. "I am more afraid of being somewhere I cannot trust than I am of being hurt." The case for staying open — and why the risk is worth it.Key Themes:• Micro-moments destroy trust more than major betrayals• Holes in the boat metaphor - small leaks accumulate• Love languages and not honoring partner's needs as infidelity• Foundation of marriage: friendship vs. romance• Can't prove counterfactuals - what-if scenarios are unknowable• Defense mechanisms in relationships after trauma• The BRAVING framework for building intimate trust• Vulnerability and the courage to be truly seen• Physical presence and being present (not on phone)
India's GPU footprint is on track to grow 40x by 2030, from ~50,000 today to a couple of million. That number is bigger than any public forecast. Sharad Sanghi has the unusual standing to make it: he built Netmagic into India's most significant datacenter business, and he's now running Neysa, the only neo cloud in India that Semi Analysis has rated, backed by Blackstone.In this episode of Intelligent Indians, Rajinder Balaraman and Sharad cover:1. Why neo clouds exist as a category, and what hyperscalers structurally can't do for one market 2. The ITQ case study: how to define ROI before infrastructure 3. The three infra mistakes that quietly cost AI teams 10x their compute spend 4. Why power, not GPUs, is the real bottleneck, and why 50% of India's data centre capacity sits in one city 5. What India's AI Mission could actually unlock in the next phaseIf you're building AI infrastructure in India, tracking the space as an investor, or working on policy in the area, this is the operator view. From someone whose entire balance sheet depends on getting the call right.Chapters 00:00 India's AI Moment The Big Picture02:00 Welcome Introducing Sharath of Neysa03:30 How He Built India's First Data Centre with NetMagic06:00 How ChatGPT Sparked the Idea for Neysa18:00 India is 2nd Largest AI Consumer 21:00 50,000 GPUs Today. 2 Million by 2028 24:30 Neysa vs AWS, GCP, Azure 28:00 Why Indian Banks Are Early AI Adopters31:30 Financial Services, Healthcare, Manufacturing 35:00 PhonePe, Perfios, Hungama - Real AI Use Cases in India38:30 Why Most AI Projects Stay in Pilot and Never Reach Production52:30 GPU Obsolescence Risk — How Neysa Manages It55:00 Healthcare, Education, Agriculture — Where Founders Should Build58:30 IIT Bombay and the Bharat Gyan Project1:01:00 Why India Needs to Keep Its AI Talent at Home1:04:00 Why He Refused to Flip the Company Outside India1:06:30 What It Takes to Make India the AI Research Capital of the World
In This Episode of Business Lunch: We explore the five critical shifts entrepreneurs must make to scale their business to seven figures per month. Hosted by industry experts, it covers strategies for leveraging sales, increasing profits, and building transferable value without working more hours or sacrificing ownership.Chapters:00:00 Introduction: The Power of Strategic Lunches00:18 Host and Guest Introduction00:53 Overview of the Episode's Focus01:18 What It Takes to Reach Seven Figures Monthly02:12 Evolution of the Business Growth Framework03:11 Target Audience and Business Criteria04:10 Achieving Seven Figures in Revenue05:07 Leveraged Efforts and Results06:03 Importance of Exit-Ready Business07:00 Myth Busting: Work Less, Earn More07:29 The Value of Real Business Impact08:23 Overcoming Overwhelm and Market Confusion09:20 Generating Consistent Profits and Wealth09:46 Premium Valuations and Exit Strategies12:36 The Five Shifts to Scale to 7 Figures13:02 External Shifts: Sales, Profits, and Value14:01 The SPV Framework for Impact16:00 The Leverage Sales Shift17:25 Case Study: Digital Marketer's Transformation20:19 Building Evergreen and Recurring Revenue22:16 Content Marketing and Lead Generation Strategies24:38 Systematizing Sales for Growth26:02 Calculating Sales Growth Potential27:54 Analyzing Sales Variance and Opportunities28:52 Global Expansion and Replication29:34 Wealth Building Through Acquisitions30:03 Introducing the Epic Deal Fast Track Program31:00 How to Get Started with Business AcquisitionsConnect with me on social:TikTok: Check out my TikTok HereInstagram: Check out my Instagram HereFacebook: Check out my Facebook HereLinkedIn: Check out my LinkedIn HereSubscribe to my YouTube
In this episode of HUNTR Podcast, we sit down with Taylor Thomas for one of the most raw and honest conversations we've had about the obsession behind chasing giant whitetails. Taylor opens up about surviving brain surgery, walking away from the day-to-day grind of business ownership, moving his family from Tennessee to Iowa, and what he believes it actually takes to consistently hunt mature bucks at a high level. We dive deep into Iowa land, off-market farm deals, and why most hunters never reach the next level because they simply aren't willing to do the work. Sit back, relax, and enjoy the show.00:00 – What It Takes to Hunt Giant Bucks11:51 – Meeting Taylor Thomas & ATA Stories15:01 – Sales, Spam Calls & Business Life23:48 – Growing Up Hunting in Tennessee32:26 – Taylor's Brain Tumor & Emergency Surgery48:39 – Life After Surgery Changed Everything54:06 – Moving to Iowa for Giant Bucks1:17:16 – Buying an Iowa Farm Off-Market1:33:03 – The Reality of Hiring & Building a Team1:38:16 – Why Most Hunters Never Find Great Land2:08:10 – Buying & Flipping Iowa “Unicorn” Farms2:19:34 – Hyper-Management, Genetics & Giant Deer2:42:16 – Passing Down Hunting Traditions & Work EthicFollow Taylor:https://www.instagram.com/fulldrawhuntco/SUBSCRIBE TO THE CHANNEL:https://www.youtube.com/c/HUNTRTUBEShop HUNTR Merch:https://wearehuntr.com/HUNTR Podcast is presented by:Hoyt Archery: https://hoyt.com (Code HUNTR for 20% off apparel)DeerGro: https://www.deergro.com (Code HUNTR for 15% off)Predator Camo: https://www.predatorcamo.com/ (Code HUNTR for 20% off)Beast Broadheads: https://beastbroadheads.com/ (Code HUNTR for 10% off)Lone Wolf Custom Gear: https://www.lonewolfcustomgear.com/ (Code HUNTR for 10% off your first purchase)RackHub: https://www.rack-hub.com/huntr (Code HUNTR for 10% off)Pure Wildlife Blends: https://www.purewildlifeblends.com (Code HUNTR for 10% off)Primos: https://www.primos.com/ (Code HUNTR for 15% off)Bushnell: https://www.bushnell.com/ (Code HUNTR for 15% off)HHA: https://www.hhasports.com/
What does it take to open your home to 99 children — and love every single one of them?Lorraine Howard did exactly that. A certified professional photographer, award-winning artist, mom of 8, and former foster parent to 99 children (102 total when you count her foreign exchange student and birth kids — yes, really), Lorraine joins Monica Samuels and co-host Julie Orcutt for one of the most powerful Moms Club conversations we've ever had.This isn't just a story about foster care. It's a story about what love actually looks like when it costs something.ABOUT LORRAINE HOWARD:Lorraine Howard is a certified professional photographer based in California whose love of storytelling through portraits grew directly from her years as a foster and adoptive mom. After noticing that not one of her 99 foster children arrived with a single photograph, she made it her mission to give every child a visual record of their story. She now donates senior portrait sessions to foster kids aging out of the system — giving them something priceless before they face the world alone.
This is a conversation about the invisible emotional weight people carry into work every single day and what happens when someone finally makes space for it. I sat down with Courtney O'Brien (HR leader and community builder) to explore the hidden emotional world inside modern workplaces. We unpack psychological safety, anxiety, spirituality at work, powerful questions, collective healing, and why humanity may be the most overlooked leadership skill of our time. From fear of flying and nervous system regulation to AI companionship and emotional intelligence in corporate culture, this episode challenges the idea that professionalism requires disconnection from ourselves. Courtney shares why she believes work can become a spiritual teacher, how shame silently shapes human behavior, and the simple practices that help people reconnect to themselves—and each other. Show Partners: Get your MENTAL FITNESS BLUEPRINT here! A special thanks to our mental fitness + sweat partner Sip Saunas Personal Socrates: Better Question, Better Life Connect with Marc: https://konect.to/marcchampagne Timestamps: 00:00 — “Who are you?” 01:18 — Growing up highly sensitive and learning to articulate emotions 02:43 — Loss, spirituality, and feeling connected to those who came before 03:22 — Why HR became Courtney's “spiritual practice” 05:02 — Bridging humanity and corporate systems 06:34 — Leading with vulnerability at work 07:23 — Why most people secretly want deeper conversations at work 08:55 — Small experiments that change team culture 10:12 — Using psychological safety exercises in meetings 11:32 — The surprising reaction people have when given permission to share 13:09 — Relief, humor, and connection in the workplace 14:33 — The power of better questions 15:52 — Why powerful questions changed Courtney's life 16:30 — “What do we owe each other?” 18:23 — Grace, challenge, and collective tension 19:53 — Why mentally fit teams operate differently 20:24 — Shame, judgment, and emotional healing 20:59 — Courtney's nervous system reset practices 22:21 — Learning to care for yourself without disconnecting from the world 23:18 — Why healing practices became simpler over time 24:28 — The grounding practice Courtney uses daily 26:36 — The wisdom of the body and collective consciousness 27:08 — Overcoming a fear of flying through physical regulation 28:51 — How posture changes emotional states 29:34 — AI, consciousness, and intuitive leadership 31:53 — Translating “woo-woo” for corporate culture 33:16 — Why spiritual thinkers and tech founders sound increasingly similar 34:33 — Using AI for self-reflection and deeper questioning 35:08 — The rise of AI companionship and emotional projection 36:53 — The danger of judging conversations too quickly 37:52 — What gives Courtney hope for the future 39:19 — “What It Takes to Heal” and collective belonging 40:10 — Why anxiety points toward what matters most 41:20 — Final reflections on humanity, healing, and meaningful work * Special props
Bernie Meehan spent over 40 years on the front lines as both a paramedic and firefighter — responding to emergencies most people never have to witness. In this episode of Locked In with Ian Bick, Bernie opens up about the realities of a career spent in the chaos of life and death situations, the calls that stuck with him long after the sirens stopped, and what four decades in emergency services really does to a person. He breaks down what first responders actually deal with behind the scenes — the adrenaline, the trauma, the toughest moments — and the perspective on life that only comes from spending 40 years running toward danger when everyone else runs away. _____________________________________________ #Paramedic #Firefighter #FirstResponder _____________________________________________ Connect with Bernie Meehan: https://www.facebook.com/bernie.meehan/ _____________________________________________ 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 40 Years as a Paramedic and Firefighter — Bernie's Story 00:20 Growing Up in the Country and How It Shaped Everything 01:08 The Family Influence and the Moment He Knew Fire Service Was His Calling 02:11 His First Steps Into EMS and the Fire Service 02:52 Why He Chose Fire Service Over Everything Else 04:08 The Real Relationship Between Fire Service and Law Enforcement 05:06 What a Career as a Paramedic Actually Looks Like 06:12 Early Paramedic Training and the Manhattan Stories Nobody Forgets 07:18 How EMS Has Changed and Why It's Harder Than Ever 08:21 Paramedic vs Nurse — The Differences Nobody Talks About 09:44 How the Drug Crisis Changed Paramedic Work Forever 11:02 EMS Then vs Now — How the Opioid Epidemic Changed Everything 12:54 The Career Defining Moments That Changed How He Sees the Job 13:57 Why Paramedics and Firefighters Don't Get the Recognition They Deserve 15:07 Urban vs Rural EMS — The Quality Gap Nobody Discusses 17:17 Fire Service in Rural Areas and the Ambulance Access Problem 18:52 What It Takes to Become a Firefighter Today vs Then 20:16 The Training Education and Ongoing Learning Nobody Warns You About 22:23 The Biggest Challenges Facing New Firefighters Today 23:28 The Non Emergency Calls That Take Up More Time Than You Think 25:06 Holiday Hazards and the Thanksgiving Stories He'll Never Forget 27:29 How They Prioritize Calls and the Unusual Emergencies Nobody Expects 29:23 The Moments of Real Danger and What Command Leadership Really Looks Like 31:03 How Experienced Responders Predict Emergency Outcomes 32:13 The Most Common Calls and the Hidden Problem of Hoarding 34:34 Hoarding Obesity and the Unique Rescue Challenges Nobody Talks About 38:33 The Real Causes of House Fires and the Safety Culture That Could Prevent Them 40:31 How Modern Technology Is Changing Firefighting and Response Times 43:33 On the Scene — Incident Priorities and the Animal Rescues Nobody Expects 46:09 Coping With Tragedy — How First Responders Handle the Emotional Toll 51:10 How Mental Health Support for First Responders Has Finally Started to Change 54:28 Why There Is Never a Quiet Day in EMS — The Workload Reality 57:24 How Public Perception of First Responders Has Shifted 59:07 The Teamwork Between First Responders That Saves Lives 01:00:50 The Most Difficult Calls — Crashes and How Auto Technology Is Changing Everything 01:06:37 The Practical Safety Tips That Could Save Your Life in a Car Emergency 01:10:58 Holiday Emergencies — Fireworks Nightclubs and the Calls Nobody Plans For 01:14:50 The Unique Calls From Nightlife and What They Taught Him 01:20:26 How First Responders Are Adjusting to New and Changing Risks 01:26:39 Supporting Mental Health After Tragedy — What Actually Works 01:33:31 What It's Really Like Responding Inside Prisons and Institutions 01:37:37 Fire Safety in Prisons and Large Events — What Nobody Talks About 01:41:11 Mental Health Advocacy and the Long Road to Trauma Recovery 01:49:19 His Advice for New First Responders and How the System Needs to Change 01:51:31 Career Reflections — What 40 Years on the Front Lines Taught Him 01:54:10 Why He Embraced the Chaos and What It Gave Him 01:56:00 Final Thoughts — The Power of Change and What Comes Next _____________________________________________ 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
Bernie Meehan spent over 40 years on the front lines as both a paramedic and firefighter — responding to emergencies most people never have to witness. In this episode of Locked In with Ian Bick, Bernie opens up about the realities of a career spent in the chaos of life and death situations, the calls that stuck with him long after the sirens stopped, and what four decades in emergency services really does to a person. He breaks down what first responders actually deal with behind the scenes — the adrenaline, the trauma, the toughest moments — and the perspective on life that only comes from spending 40 years running toward danger when everyone else runs away. _____________________________________________ #Paramedic #Firefighter #FirstResponder _____________________________________________ Connect with Bernie Meehan: https://www.facebook.com/bernie.meehan/ _____________________________________________ 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 40 Years as a Paramedic and Firefighter — Bernie's Story 00:20 Growing Up in the Country and How It Shaped Everything 01:08 The Family Influence and the Moment He Knew Fire Service Was His Calling 02:11 His First Steps Into EMS and the Fire Service 02:52 Why He Chose Fire Service Over Everything Else 04:08 The Real Relationship Between Fire Service and Law Enforcement 05:06 What a Career as a Paramedic Actually Looks Like 06:12 Early Paramedic Training and the Manhattan Stories Nobody Forgets 07:18 How EMS Has Changed and Why It's Harder Than Ever 08:21 Paramedic vs Nurse — The Differences Nobody Talks About 09:44 How the Drug Crisis Changed Paramedic Work Forever 11:02 EMS Then vs Now — How the Opioid Epidemic Changed Everything 12:54 The Career Defining Moments That Changed How He Sees the Job 13:57 Why Paramedics and Firefighters Don't Get the Recognition They Deserve 15:07 Urban vs Rural EMS — The Quality Gap Nobody Discusses 17:17 Fire Service in Rural Areas and the Ambulance Access Problem 18:52 What It Takes to Become a Firefighter Today vs Then 20:16 The Training Education and Ongoing Learning Nobody Warns You About 22:23 The Biggest Challenges Facing New Firefighters Today 23:28 The Non Emergency Calls That Take Up More Time Than You Think 25:06 Holiday Hazards and the Thanksgiving Stories He'll Never Forget 27:29 How They Prioritize Calls and the Unusual Emergencies Nobody Expects 29:23 The Moments of Real Danger and What Command Leadership Really Looks Like 31:03 How Experienced Responders Predict Emergency Outcomes 32:13 The Most Common Calls and the Hidden Problem of Hoarding 34:34 Hoarding Obesity and the Unique Rescue Challenges Nobody Talks About 38:33 The Real Causes of House Fires and the Safety Culture That Could Prevent Them 40:31 How Modern Technology Is Changing Firefighting and Response Times 43:33 On the Scene — Incident Priorities and the Animal Rescues Nobody Expects 46:09 Coping With Tragedy — How First Responders Handle the Emotional Toll 51:10 How Mental Health Support for First Responders Has Finally Started to Change 54:28 Why There Is Never a Quiet Day in EMS — The Workload Reality 57:24 How Public Perception of First Responders Has Shifted 59:07 The Teamwork Between First Responders That Saves Lives 01:00:50 The Most Difficult Calls — Crashes and How Auto Technology Is Changing Everything 01:06:37 The Practical Safety Tips That Could Save Your Life in a Car Emergency 01:10:58 Holiday Emergencies — Fireworks Nightclubs and the Calls Nobody Plans For 01:14:50 The Unique Calls From Nightlife and What They Taught Him 01:20:26 How First Responders Are Adjusting to New and Changing Risks 01:26:39 Supporting Mental Health After Tragedy — What Actually Works 01:33:31 What It's Really Like Responding Inside Prisons and Institutions 01:37:37 Fire Safety in Prisons and Large Events — What Nobody Talks About 01:41:11 Mental Health Advocacy and the Long Road to Trauma Recovery 01:49:19 His Advice for New First Responders and How the System Needs to Change 01:51:31 Career Reflections — What 40 Years on the Front Lines Taught Him 01:54:10 Why He Embraced the Chaos and What It Gave Him 01:56:00 Final Thoughts — The Power of Change and What Comes Next _____________________________________________ 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
Today, we're diving into the oddly specific habits that have genuinely made the biggest difference in business growth, confidence, creativity, and income… and spoiler: most of them have nothing to do with “traditional” business strategy. From visualization and embodiment rituals to fake deadlines, manifestation hacks, and NEVER showing my bare nails, we're unpacking the unconventional habits that ACTUALLY made a difference in my business! In this episode, we talk about:Why visualization and meditation completely changed the trajectory of my businessThe surprisingly powerful reason I rarely show my bare nailsHow I use calendar blocking as a manifestation tool for signing clientsThe “daydream conversation” technique that keeps turning into real-life opportunitiesShifting your self-concept by imagining yourself as peers with people you admireWhy taking consumption breaks can massively improve creativity and intuitionThe weird productivity hack of creating fake urgency and internal deadlinesHow paying for things motivates me to actually follow throughUsing other people's feedback to strengthen your intuition instead of override itWhy celebrating results before they happen can completely shift your energy and actionsThis episode is part mindset, part business strategy, part unhinged personal habits… but every single one of these practices has genuinely shaped the way I show up, make decisions, and grow my business.If you love behind-the-scenes conversations, manifestation meets strategy, and hearing the real habits I swear by, you're going to love this one.Listen to Similar Episodes: (TAKE OUT FOR EMAIL) 238. The CEO Morning Routine That Made Me $600K Online222. Part 1: What It Takes to Quantum Leap - The Identity Shift Behind My $33K Week223. Part 2: What It Takes to Quantum Leap - The Identity Shift Behind My $33K Week212. 5 Simple Ways to Build Confidence in Business187. The Most Underrated Tip for Being a Better CoachP.S. When you rate and review the podcast, you'll receive my Connect to your Higher Self Visualization as a thank you! Click here to claim your gift. Ways to Work with Nora:1:1 Coaching Waitlist – Add your name to the waitlist to be the first to learn when spots open.90-Minute Intensives Waitlist – Limited openings for deep-dive, high-impact sessions. Join the waitlist to be notified when spots become available.Courses – Explore Nora's signature programs:Full Throttle – The ultimate business strategy courseElite – Business energetics + identity work course Podcasting for Business Growth – Turn your podcast into profitConnect with Nora – Follow her on Instagram @iamnoravirginia for updates, tips, and inspiration.
What It Takes to Build Wealth - Brainz Podcast with Marc Esrig and Ron SchinikMarc Esrig and Ron Schinik, founders and co-managing members of New Blueprint Partners LLC, join the Brainz Magazine podcast for a powerful conversation on investment strategy, real estate, and building long-term value in today's market.In this episode, they share insights from decades of experience across acquisitions, finance, and asset management, breaking down what it really takes to build a successful investment portfolio. From due diligence to protecting downside risk, they reveal the key principles that guide every deal they make. The conversation explores how to identify strong opportunities in uncertain markets, the importance of experience and specialization, and why understanding your partners and tenants is critical to long-term success. Marc and Ron also discuss common mistakes investors make when scaling too quickly and how to balance risk with ambition.This is an insightful and practical episode for anyone interested in investing, business growth, and making smarter decisions in an unpredictable financial landscape.With podcast host Miceal O´KaneHope you'll enjoy the episode! Hosted on Acast. See acast.com/privacy for more information.
In this episode of the Sports Tech AllStars Podcast, we present Jeffrey Hintz, VP of Innovation at Indiana Sports Corp and Executive Director at SportsTech HQ.The conversation explores why Indianapolis - home to the NFL Combine, NCAA headquarters, IndyCar, the new Cadillac F1 team, and 50 plus sports tech companies - is one of the most overlooked sports tech ecosystems in the world, and how Jeff is building the infrastructure to put it firmly on the global map.TakeawaysIndiana is home to the NFL Combine, NCAA, IndyCar, USA Football, USA Track and Field, USA Gymnastics, the Cadillac F1 team and the National High School Sports Federation The Indiana Sports Corp was the first sports commission ever founded in the US, established in 1979, and has since inspired over 350 similar organisations globallySports Tech HQ is not an accelerator or co-working space Infrastructure that connects startups to pro teams, governing bodies, universities and investorsIndiana offers a genuine competitive advantage for sports tech companies: denser access to decision-makers and faster traction than larger, noisier marketsConsolidation in athlete performance tech is accelerating - the trend is toward one platform that manages and measures everythingYouth sports globally is still largely run on spreadsheets and payment apps - the disruption opportunity remains wide openThe over-monetisation of fans is an emerging tension in US sport, mirroring conversations already happening in European footballLocal fans create the atmosphere and the product - losing them to pricing pressure risks killing the golden gooseFan-owned models like the German 50+1 rule preserve exactly what makes sport worth watching in the first placeTo learn more, visit: https://sthq.org/Get in touch with Jeffrey Hintz at: linkedin.com/in/jeffrey-hintzHosted by Rohn Malhotra from SportsTechX - Leading source of Investment and Innovation insights in sports. As promised, here's your small surprise:Unlock your 30-day growth plan (worth €49) on the SportsTechX Intelligence Hub for free!Simply verify your company details and you get access to 1,500+ investors, programmes, initiatives and events in the sportstech ecosystem.Here's how to get set up and if you'd like a walkthrough of the platform, feel free to book a call here.More from SportsTechX:Explore the SportsTechX Intelligence Hub, an interactive database of over 8,000 sports tech companies, 8,000+ deals, 1,000+ investors, programs and events - HEREDownload the latest Global Sports Tech Ecosystem Report - HERESign Up for the Sports Tech Weekly Newsletter for more news, features & insights on Sports Tech - HERE Stay Connected and follow for more:LinkedInYouTubeSpotifyApple PodcastChapters00:00 Introduction 02:53 The 2016 Ryder Cup 05:14 What It Takes to Run a Four-Year Event in Six Days 06:50 From PGA Tour to Indiana Sports Corp 08:24 Why Indiana Is a Sports Tech Hub Most People Don't Know About 09:27 The NCAA, IndyCar, Cadillac F1 and the Density of Indiana's Sports Ecosystem 10:23 What SportsTech HQ Actually Does 11:35 Indiana as a Landing Pad for International Sports Tech Companies 12:26 Highlights: Six Techstars Cohorts, 66 Companies and Growing 14:26 Indiana Is the Middle Coast And That's a Competitive Advantage 17:33 Trends in Sports Tech: Consolidation, Youth Sports and AI-Driven Decisions 19:21 The Fan Economy, NIL and the Always-On Club Brand 22:12 Youth Sports Still Runs on Spreadsheets 23:14 Are Fans Being Over-Monetised? 25:47 Sports Betting, Distraction and the Warning Signs in the US 26:10 The 50+1 Rule, Fan Ownership and the Golden Goose Problem 29:20 FC Union Berlin, International Fans and Why the Model Matters 31:09 How Tech Can Help Clubs Balance Local and Global Fandom 32:33 What the Next 12–18 Months Look Like for SportsTech HQ 34:26 Peak Sports Tech Vegas, the Final Four Summit and Mark Cuban 37:41 Favourite Sporting Moment
The Modern Therapist's Survival Guide with Curt Widhalm and Katie Vernoy
Why Therapists Stop Working with Kids and What It Takes to Stay: Sustainability, Boundaries, and Pivots for the Long Haul Curt Widhalm, LMFT, and Katie Vernoy, LMFT push back on the field's quiet stereotype that working with kids is the "starter home" of private practice, the place clinicians put in time before graduating to a cardigan and a wing-back chair. Working with kids and teens is not entry-level work. It is some of the most clinically and physically demanding work in the profession, and it has a sustainability problem that rarely gets named honestly. Curt and Katie examine why so many therapists who work with kids and teens hit a wall around the five-year mark, and why that wall is rarely about clinical depth. They unpack the sensory toll, the parent communication load, the school and provider coordination, the cost of running a play therapy room, and the way a child caseload can quietly distort a clinician's sense of what is developmentally typical. They also talk about how to build a long-haul career working with kids, teens, and families without becoming, in Curt's words, "a cynical, glitter-covered shell of a human being." This is a conversation for therapists in private practice, supervisors of clinicians who work with minors, and anyone weighing whether to keep working with kids, scale back, or pivot. In this episode, we discuss: Why working with kids is not a lesser clinical specialty Why the work is hard to sustain, and why "burnout" alone does not fully explain it How shifting from kid sessions to family work and parent work extends the clinical impact The sensory, physical, and administrative load of working with kids Why parents contact child therapists more than adult clients contact their own therapists The financial and logistical reality of running a play therapy room How a clinical caseload can distort a therapist's sense of typical development When a pivot to adult, family, or parent work is healthy, and when it is avoidance Timestamps: 00:15 — The "starter home" stereotype, and the five-year wall 06:03 — The 167-hour problem and why kid work is family work 10:08 — The sensory and physical toll 12:58 — Caseload diversification and structuring the day 19:41 — The unpaid hours: parents, schools, and the village 23:43 — The play therapy industrial complex 27:59 — Keeping up with kids' culture without losing yourself 30:19 — How a clinical caseload distorts the sense of typical development 33:09 — Expectations, moral injury, and what "fix my kid" really costs 35:01 — When a pivot is survival, and when it is avoidance Full show notes and resources: mtsgpodcast.com Join the Modern Therapist Community Patreon: https://www.patreon.com/c/mtsgpodcast Facebook Group: https://www.facebook.com/groups/therapyreimagined Modern Therapist's Survival Guide Creative Credits Voice Over by DW McCann — https://www.facebook.com/McCannDW/ Music by Crystal Grooms Mangano — https://groomsymusic.com/
Mea Culpa welcomes David L. Marcus. Marcus has worked as a foreign correspondent, education reporter, and columnist at the Boston Globe, Miami Herald, Newsday, and U.S. News. David L. Marcus has worked as a foreign correspondent, education reporter, and columnist at the Boston Globe, Miami Herald, Newsday, and U.S. News. As South America bureau chief for the Dallas Morning News, he shared the Pulitzer Prize for International Reporting, for a series about violence against women. David wrote two books about education and parenting, What It Takes to Pull Me Through and Acceptance. His articles have appeared in the New York Times, Vanity Fair, Newsweek, and GQ magazine to name a few. Marcus is the cousin of the infamous lawyer, Roy Cohn. He wrote a quintessential piece about Cohn for Vanity Fair shortly after his death. Davis and Michael discuss Roy Cohn's relationship and influence on Trump and Rubert Murdoch and how it plays out today.
On this episode, we sit down with Princess Noura bint Faisal Al Saud, the visionary behind Saudi Arabia's first-ever fashion week and CEO of JAY3LLE. She shares the journey of building a fashion industry in the Kingdom from the ground up navigating risk, resistance, and ultimately proving that Saudi talent can compete on the global stage. Drawing from her years living and studying in Japan, she explains how that experience shaped her global mindset and approach to entrepreneurship. Princess Noura also reflects on her transition from the private sector to contributing to the strategy of the Saudi Fashion Commission, helping lay the foundation for a thriving creative ecosystem. The conversation explores the impact of Saudi Vision 2030, the rise of Saudi soft power, and the country's shift from being consumers to becoming innovators. She also discusses fashion as a form of cultural storytelling, from Founding Day to Saudi Cup and what it takes to build a sustainable global brand while preserving authenticity. On a more personal level, she opens up about risk-taking, self-doubt, and the values instilled in her by her parents. Princess Noura shares her perspective on success beyond titles and achievements, offering insight into the mindset required to lead meaningful change and position Saudi Arabia as a growing force in global fashion and culture. 0:00 Intro2:47 Building an Ecosystem Before the Fashion Authority8:11 Facing Resistance and Calculated Risk-Taking14:57 The Fashion Supply Chain & Job Creation16:14 Evolution of the Abaya & Saudi Design19:03 International Reactions to Saudi's Fashion Movement21:38 Fashion as Soft Power & Saudi Storytelling24:37 Founding Day & The Saudi Cup Dress Code29:53 Preserving Authenticity in a Changing Saudi Arabia32:04 Background: Studying in Japan36:37 Building Cultural Bridge44:22 Returning Home for Vision 203047:10 Introducing JAY3LLE: A Saudi Fashion Brand49:41 Golf as Lifestyle & The Brand's Vision53:27 What It Takes for Saudi Brands to Compete Globally56:04 The Business of Fashion & The Saudi Fashion Fund1:00:03 Competing in a Demanding Saudi Market1:01:22 Arab Brands Going Global 1:03:16 Defining Personal Success1:05:59 Self-Doubt, Imposter Syndrome & Seeking Mentors1:10:00 Lessons from Parents1:12:19 No Regrets, Growth Through Mistakes1:16:26 Closing Reflections
What does it really take to build an e-commerce business when you are starting from scratch?In this episode of High Voltage Business Builders, Neil sits down with Ganesh, an IT professional who joined Voltage Business Builders while looking for a way to build something beyond his career.Ganesh shares how he went from having little e-commerce experience to launching his first Amazon brand, Moga, a stainless steel kitchen product brand built around quality, affordability, and products he personally understands.They talk about why there are no shortcuts in business, how planning makes execution easier, and what Ganesh learned after crossing $100K in sales within his first six months on Amazon. He also breaks down the importance of product validation, using data to optimize campaigns, managing capital for growth, and building a business step by step while still working full-time.In this episode, we cover✅ Why Building Beyond Your Career Requires a Bigger VisionA stable career can provide security, but it does not always create the flexibility, ownership, or long-term growth many entrepreneurs want. This episode shows how e-commerce can become a path to building an asset outside of traditional employment.✅ The Reality of Launching on AmazonAmazon is not a set-it-and-forget-it business. Listings, images, descriptions, ads, seasons, and buyer behavior change constantly. Sellers have to pay attention to the data, test carefully, and keep adjusting based on what the market is showing them.✅ What It Takes to Cross $100K in SalesEarly growth comes from execution, not theory. Crossing $100K in sales requires product validation, aggressive learning, campaign testing, and a willingness to move through the messy launch phase before the business becomes more stable.✅ Why Business Becomes a Family Learning ExperienceBuilding a business affects more than the founder. When family is involved, the journey becomes a way to teach ownership, discipline, delayed gratification, and what it looks like to build something from the ground up.✅ Why There Are No Shortcuts to Building a Real BusinessA real business is built in phases. The foundation matters. Planning matters. Execution matters. The best path is not copying someone else's template, but learning the basics, adapting the process to your situation, and continuing to move forward.
When Jeff Wang stepped into the CEO role at Windsurf, it was not part of some long-term succession plan. It happened in the middle of a full-blown crisis. In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Jeff to unpack the wild chain of events that followed the collapsed OpenAI acquisition, the founders leaving for Google, and the intense 72-hour window Jeff had to help save the company and protect 250 jobs. He shares how Windsurf navigated that moment, how the Cognition deal came together, and what it has been like leading one of the most closely watched teams in AI coding ever since. Jeff also gets into what made Windsurf so strategically valuable in the first place, from shipping early breakthroughs in autocomplete, chat, context engineering, and agent workflows, to building one of the first generally available coding agents on the market. Beyond the origin story, the conversation goes deep on go-to-market strategy, why free products worked early on, how token economics changed the game, and why enterprise AI adoption takes far more than handing teams a tool. They also explore Windsurf 2.0, the shift toward managing multiple agents at once, how Jeff uses AI in his own CEO workflows, and why founders need to obsess over painful problems, customer conversations, and product-market fit instead of flashy demos. Key Highlights: 00:00 - The 72-Hour Crisis That Changed Everything Jeff shares the short version of the OpenAI, Google, and Cognition saga, and what it was like stepping into the CEO role during a company-defining emergency. 01:40 - Why Big Tech Wanted the Windsurf Team A look at the execution speed, product breakthroughs, and agent innovations that made Windsurf one of the most valuable teams in AI coding. 04:10 - The Future of Coding Is Multi-Agent Jeff explains why developers are moving from one-on-one AI assistance to managing many agents at once, and how Windsurf 2.0 is built for that shift. 08:54 - How Free Became Their Growth Wedge From free autocomplete to on-prem enterprise deals, Jeff walks through Windsurf's early PLG motion and how it created awareness and pipeline. 13:10 - The Hard Truth About AI Pricing A candid discussion on token costs, self-serve subsidies, pricing pressure, and why raising prices can reveal whether you truly have product-market fit. 16:13 - Why Enterprise AI Sales Are Top-Down Jeff shares how Windsurf sells into large companies by focusing on transformation, adoption, security, and measurable outcomes instead of seat counts. 20:51 - What It Takes to Drive Real AI Adoption Why playbooks, training, and solving a meaningful first use case matter more than just rolling out a shiny new tool to an engineering team. 24:40 - Jeff's AI Workflows as CEO Jeff reveals how he uses AI and custom playbooks for go-to-market research, outreach preparation, and spotting product trends before opening dashboards. 32:32 - Jeff's Advice for Every Product Founder Build around painful problems, talk to hundreds of prospects, and learn to enjoy rejection because that is often where the real insight comes from. Resources:
Strategies for getting out of your head and thriving in a chaotic world. Prentis Hemphill is a writer, political organizer, therapist and somatic facilitator. They are the author of the national bestseller What It Takes to Heal: How Transforming Ourselves Can Change the World, and host of the acclaimed podcasts Finding Our Way and Becoming the People. Prentis is also the Co-founder of The Embodiment Institute (TEI), a training and research organization that applies somatic practices to individual, organizational and collective care through a healing justice framework. In this episode we talk about: What embodiment really means How our bodies may be communicating more information to our brains than we realize Practices to feel more at home in your body and regulate your nervous system The "head, heart, gut" way of listening to different kinds of intelligence Why so many of us are pulled out of our center in modern life Micro-interdependence Simple, everyday ways to humanize one another in an era of mass vilification How cultural pressures contribute to anxiety and burnout How to set boundaries Identifying what you truly care about Join Dan and Emmy Award-winning journalist Allison Gilbert at 92NY on May 17th for a live conversation about how mindfulness can deepen connection and combat loneliness, available in person and via streaming. Register here. Join Dan, Sebene Selassie and Jeff Warren for Meditation Party, a 3-day immersive retreat at the Omega Institute in Rhinebeck, NY, October 16–18, 2026. Register here. Get the 10% with Dan Harris app here Sign up for Dan's free newsletter here Follow Dan on social: Instagram, TikTok Subscribe to our YouTube Channel This episode is sponsored by: Rosetta Stone — Language learning that's immersive and intuitive. Start your journey at https://www.rosettastone.com/happier Fatty 15 — The first essential fatty acid discovered in over 90 years, designed to support healthy aging at the cellular level. Get 15% off a 90-day starter kit at https://www.fatty15.com/happier with code HAPPIER To advertise on the show, contact sales@advertisecast.com or visit https://advertising.libsyn.com/10HappierwithDanHarris
Most entrepreneurs think community is just “networking.” They're missing the bigger opportunity.In this episode, Eric Berman and I break down what it really takes to build communities that actually work not shallow rooms full of business cards, but real relationship-based environments where trust, accountability, ideas, and opportunities compound over time.We talk about how Speakeasy Mastermind grew from a small San Diego gathering into a multi-city model, what makes the right room so valuable, and why more entrepreneurs than ever are quietly paying what Eric calls the “isolation tax” by trying to do everything alone.We also go deep on leadership, follow-through, culture, the kind of people who belong in high-value rooms, and the mistakes that can destroy a community before it ever has a chance to grow.In this episode, Eric and I break down:Why great communities are built on relationships, accountability, and contributionHow Speakeasy Mastermind evolved from an informal mastermind into a scalable multi-city modelWhat the “isolation tax” is and why entrepreneurs pay for staying disconnectedWhy the right room can accelerate growth faster than strategy aloneHow to identify the right leaders when building a communityWhy follow-through is one of the rarest and most valuable entrepreneurial traitsThe difference between people who contribute energy and people who drain itWhy not every successful entrepreneur is qualified to lead a roomHow to choose the right core members when launching a chapterWhat kinds of entrepreneurs thrive best in communities like SpeakeasyWhy ego, entitlement, and poor culture fit can quietly destroy a roomEric's backstory: building an early social network, losing it in the dot-com crash, and reinventing himselfHow offering value first created his opportunity with Brian TracyWhy active listening and service are still underrated business superpowersThe principle behind Eric's upcoming book, Proximity PaysWhy the best relationships are built by listening well, serving first, and doing what you say you'll doWhat excites me most about this conversation is that it's really about something deeper than masterminds or meetups.It's about proximity, it's about choosing better rooms and it's about understanding that the people around you shape the speed, quality, and direction of your future.TIMESTAMPS00:00 Eric Berman's Backstory and Why Community Matters 00:38 What It Takes to Build Relationship-Based Business Communities 01:06 The Origins of the Original Speakeasy Mastermind 03:36 Turning Community-Building Into a Scalable Business Model 05:56 What Made the Original Mastermind So Valuable 07:28 The “Isolation Tax” Entrepreneurs Pay 08:49 Why Every Business Owner Needs the Right Room 10:01 How Speakeasy Expanded Into Multiple Cities 10:31 What Makes a Chapter Work or Fail 12:50 The Business Model Behind Speakeasy 14:47 Who Belongs in the Room and Who Doesn't 18:38 Eric's Early Social Network, Dot-Com Crash, and Reinvention 21:28 How Brian Tracy Became a Career-Making Relationship 23:27 How to Get Involved as a Member or a Captain 24:46 Why Follow-Through Matters So Much in Leadership 26:17 How AI Now Supports Community Notes and Shared Wins 27:33 Why Small, Intimate Rooms Win 30:20 Eric's Book: Proximity Pays 31:20 Active Listening, Service, and Relationship Leverage 32:29 Why Doing What You Say Matters More Than Ever 34:00 Final Thoughts on Friendship, Trust, and Long-Term CommunityDiscover More
We don't heal alone – we heal in connection. Prentis Hemphill, alongside co-host Sue Marriott, traces their path from social organizing to somatic therapy, revealing how personal healing and collective transformation are deeply intertwined. Together, they explore how inherited myths, power dynamics, and collective trauma shape both our inner worlds and our social systems. This episode invites therapists and change makers alike to consider healing as more than an individual process—it's relational, embodied, and political in impact. Prentis offers grounded reflections and practical tools for working with the body, navigating power, and engaging in healing that extends beyond the self. “When we are courageous, we can do the unexpected and start to mold the world around a vision bigger than one produced by fear. Every inch of progress, every ounce of love, every truly meaningful action from here on out will happen through courage, not comfort.” – Prentis Hemphill Time Stamps for Where Personal Healing Meets Collective Change (295) 06:15 The interplay of interpersonal and systemic dynamics 09:31 The challenge of updating therapeutic practices 16:49 Impact of myths on human behavior 20:32 Reflections on current political climate and collective trauma 24:10 The myth of “American Exceptionalism” 36:50 Self-care and community engagement 40:07 Resources for healing and transformation About our Guest – Prentis Hemphill Prentis Hemphill is the bestselling author of What It Takes to Heal, a groundbreaking exploration of healing, justice, and transformation. A therapist, somatics teacher, facilitator, political organizer, and writer, Prentis is also the founder of The Embodiment Institute and a leading voice in embodied leadership and collective healing. For over a decade, Prentis has worked with individuals and organizations through their most challenging moments of change—navigating leadership transitions, conflict, and the alignment of practice with values. Grounded in an embodied approach, their work ensures that our intentions aren’t just ideas, but are fully lived, felt, and practiced. Before founding The Embodiment Institute, Prentis served as the Healing Justice Director at Black Lives Matter Global Network and was a lead somatics teacher with generative somatics and Black Organizing for Leadership and Dignity (BOLD). They hold an M.A. in Clinical Psychology and have provided therapeutic services in low-cost mental health clinics, centering marginalized communities. Prentis has contributed to Atlas of the Heart (Brené Brown), The Politics of Trauma (Staci K. Haines), You Are Your Best Thing (edited by Brené Brown & Tarana Burke), and Holding Change (adrienne maree brown). They are also the creator and host of the acclaimed podcasts Finding Our Way and Becoming the People, which have surpassed over a million downloads. At its core, Prentis' work challenges the complacency of mainstream therapeutic models, infusing healing with the rigor of justice, repair, and accountability. They believe that reclaiming feeling and relationship creates space for true transformation—in ourselves, our movements, and the world. Prentis lives on a small farm in Durham, NC, with their partner, Kasha, their child, and two dogs. !!NEW OPPORTUNITY!! READING POD STARTING MAY 1ST! Looking to deep dive into Prentis’s book? Co-host Sue Marriott is hosting a weekly Zoom reading pod – with a potential author Q&A at the conclusion. First session starts May 1st. $10/session and $5/session for our Supercast and Neuronerds. Learn more and reserve your spot – HERE! Resources for Where Personal Healing Meets Collective Change with Prentis Hemphill (295) The Embodiment Institute – Training institute, research entity, and culture change engine that strategically develops people and organizations to be agents of transformation in families, social movements and the environment. Prentis’s Website – Resources and information “Becoming the People” – Prentis’s podcast What It Takes to Heal; Published 2024 by Penguin Random House -Prentis’s book, get your copy today! Beyond Attachment Styles course is available NOW! Learn how your nervous system, your mind, and your relationships work together in a fascinating dance, shaping who you are and how you connect with others. Online, Self-Paced, Asynchronous Learning with Quarterly Live Q&A’s – next one April 13, 2026! Earn 6 Continuing Education Credits – Available at Checkout As a listener of this podcast, use code BAS15 for a limited-time discount. Get your copy of Secure Relating here!! You are invited! Join our exclusive community to get early access and discounts to things we produce, plus an ad-free, private feed. In addition, receive exclusive episodes recorded just for you. Sign up for our premium Neuronerd plan!! Click here!!
Ryan Pineda and Brian Davila sit down with podcast host Sean Kelly of the Digital Social Hour to break down how he built a 2,000-episode show from the ground up, covering everything from landing A-list guests like Andrew Tate, Charlie Kirk, and Donald Trump Jr., to the clipping strategy, monetization model, and networking secrets behind one of the fastest-growing podcasts in the game.Connect with Sean - https://www.instagram.com/seanmikekelly/https://www.youtube.com/@DigitalSocialHour__________If you want to start your real estate investing business, we'll give you 1:1 coaching, seller leads, software, & everything you need. https://www.wealthyinvestor.comIf you're a business owner who wants to get in peak physical shape, we can help! https://www.allproceo.comJoin our private mastermind for elite business leaders who golf. https://www.mastermind19.comJoin free Bible studies and workshops for Christian business leaders. https://www.tentmakers.us__________CHAPTERS:0:00 - Introduction & Sean's Guest Roster. 1:33 - The Keys to a Successful Podcast. 2:18 - Starting From Zero: Blurry iPhones & Rented Studios.4:29 - Overcoming Limiting Beliefs & Making Excuses. 7:55 - The Business Behind Viral Clips & Monetization. 13:30 - Getting the Right Views vs. Going Viral. 17:00 - How Sean Lands Big-Name Guests. 27:00 - What It Takes to Build a Sellable Podcast. 35:52 - Should You Start a Podcast in 2025? 40:00 - Enjoying Your Content & Finding Longevity. 47:00 - Networking Secrets & Building Communities.
This week on the Black Rifle Coffee Podcast, Logan Stark sits down with AG Gregoroff, founder of Toehold Flip Flops, for one of the most wild and jaw-dropping conversations we've had yet. AG opens up about growing up in a violent gang-infested neighborhood, selling guns as a teenager, and facing 16 years in prison after a corrupt drug raid changed his life forever. After two years behind bars, he walked free and built a life defined by obsession, discipline, and legacy. Now the founder of one of the most premium flip flop companies on the planet, AG shares how his father's final words fueled a mission he refuses to quit. If you're an entrepreneur, a veteran, or someone chasing redemption this episode is a must listen. TOPICS COVERED: ● Growing up surrounded by violence and addiction ● Wrongful charges, jail time, and fighting for justice ● Why obsessive craftsmanship drives Toehold's success ● Military contracting and training Marines in survivability ● Finding peace in the ocean and purpose through pain ● Legacy, fatherhood, and building something that lasts TIMESTAMPS: 00:00 – Psychotically Obsessed with Perfecting Everything in Life 04:41 – Growing Up in Vista, CA: Gangs, Cows, and Condos 19:23 – Teen Gun Sales, Mormon Disguise, and Street Smarts 23:51 – Getting Raided and Facing 16 Years in Prison 27:44 – Life in Jail: Surviving Violence and Corrupt Systems 31:15 – Prison Rules, Stabbings, and the Cost of Integrity 35:08 – Getting Out and Starting Over with Nothing 39:02 – Becoming a Military Contractor and Security Pro 42:21 – Training Marines for Survival in Helicopter Crashes 46:09 – Developing the Art of Situational Awareness 50:44 – Jiu-Jitsu as a Lifeline Inside and Outside of Jail 55:37 – The Apple Store Job That Shifted AG's Focus 59:55 – AG's Father Passes Away: A Message That Changed Everything 01:04:33 – Building Toehold from Grief, Purpose, and Obsession 01:09:10 – What It Takes to Make Something Truly Great 01:14:44 – AG on Legacy, Faith, and Doing Hard Things 01:21:03 – Why He'll Never Sell Out—And Doesn't Care if You Buy 01:28:17 – Final Thoughts: Work Ethic, Redemption, and Purpose
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