Podcasts about gifted

Day 1,641.Today we assess a hectic weekend of diplomacy, with Andy Burnham and other world leaders in Kyiv to mark Ukraine's Independence Day – his first foreign trip as Britain's new Prime Minister. He came ready to hand over blueprints for Storm Shadow cruise missiles, not long after President Zelensky discussed fresh supplies of missile interceptors with his French counterpart Emmanuel Macron. Meanwhile, Ukraine has targeted another major Russian online retailer – Ozon – for the first time, and Russia's ambassador to London Andrei Kelin was withdrawn to Moscow with no explanation.Contributors: Dominic Nicholls (Host on Ukraine: The Latest). @DomNicholls on X.Antonia Langford (Freelance Journalist in Kyiv). @Antonialford on XWith thanks to John Foreman CBE, Chatham House associate fellow and former UK defence attaché to Russia.Producer: Rachel PorterSenior Producer: Lilian FawcettVideo Producer: Sophie O'Sullivan and Tom SteedSocial Producer: Aaron WheelerStudio Director: Meghan SearleExecutive Editor: Francis DearnleyCreated by David KnowlesNOW IN FULL VIDEO WITH MAPS & BATTLEFIELD FOOTAGE:Every episode is now available on our YouTube channel shortly after the release of the audio version. You will find it here: https://www.youtube.com/@UkraineTheLatest CONTENT REFERENCED:If you're interested in sponsoring Dom's upcoming defence event, email our colleagues at events@telegraph.co.ukRussian Offensive Campaign Assessment, August 23, 2026 (Institute for the Study of War)https://understandingwar.org/research/russia-ukraine/russian-offensive-campaign-assessment-august-23-2026/Burnham hits back after Kremlin accuses him of prolonging war in Ukraine (Antonia Langford for The Telegraph)https://www.telegraph.co.uk/world-news/2026/08/24/kremlin-attacks-burnham-over-storm-shadows-for-ukraine/EMAIL US:Contact the team on ukrainepod@telegraph.co.uk. We continue to read every message, and seek to respond to as many as possible.HIGHLIGHTS:British Storm Shadow missile blueprints exchanged for 'goldmine of Ukrainian battlefield data'Coalition of the Willing meet in Kyiv in Ukraine's Independence Day, including Andy Burnham Hosted on Acast. See acast.com/privacy for more information.

Today on Karl and Crew, we kicked off our weekly theme, “Spiritual Gifts.” Dr. Sam Storms and Alex McFarland joined us. Dr. Sam Storms, the founder and president of Enjoying God Ministries and author of “Understanding Spiritual Gifts,” explained how spiritual gifts are expressions of the Holy Spirit’s work through believers and why every Christian has a role in building up the church. Then Alex McFarland, a Christian apologist, author, evangelist, religion and culture analyst, and a national talk show host, explained why every believer is spiritually gifted and how recognizing and using those gifts helps the church function as God intended. We then turn to our listeners and ask, "If spiritual gifts are given to build up fellow followers, how are you using your gifts right now?" You can hear the highlights of today’s program on the Karl and Crew Showcast. If you're looking to hear a particular segment from the show, look at the following timestamps:Call Segment [ 11:04 ]Dr. Sam Storms [ 19:55 ]Alex McFarland [ 39:20 ]Donate to Moody Radio: http://moodyradio.org/donateto/morningshowSee omnystudio.com/listener for privacy information.

Today on Karl and Crew, we kicked off our weekly theme, “Spiritual Gifts.” Dr. Sam Storms and Alex McFarland joined us. Dr. Sam Storms, the founder and president of Enjoying God Ministries and author of “Understanding Spiritual Gifts,” explained how spiritual gifts are expressions of the Holy Spirit’s work through believers and why every Christian has a role in building up the church. Then Alex McFarland, a Christian apologist, author, evangelist, religion and culture analyst, and a national talk show host, explained why every believer is spiritually gifted and how recognizing and using those gifts helps the church function as God intended. We then turn to our listeners and ask, "If spiritual gifts are given to build up fellow followers, how are you using your gifts right now?" You can hear the highlights of today’s program on the Karl and Crew Showcast. If you're looking to hear a particular segment from the show, look at the following timestamps:Call Segment [ 11:04 ]Dr. Sam Storms [ 19:55 ]Alex McFarland [ 39:20 ]Donate to Moody Radio: http://moodyradio.org/donateto/morningshowSee omnystudio.com/listener for privacy information.

Today on Karl and Crew, we kicked off our weekly theme, “Spiritual Gifts.” Dr. Sam Storms and Alex McFarland joined us. Dr. Sam Storms, the founder and president of Enjoying God Ministries and author of “Understanding Spiritual Gifts,” explained how spiritual gifts are expressions of the Holy Spirit’s work through believers and why every Christian has a role in building up the church. Then Alex McFarland, a Christian apologist, author, evangelist, religion and culture analyst, and a national talk show host, explained why every believer is spiritually gifted and how recognizing and using those gifts helps the church function as God intended. We then turn to our listeners and ask, "If spiritual gifts are given to build up fellow followers, how are you using your gifts right now?" You can hear the highlights of today’s program on the Karl and Crew Showcast. If you're looking to hear a particular segment from the show, look at the following timestamps:Call Segment [ 11:04 ]Dr. Sam Storms [ 19:55 ]Alex McFarland [ 39:20 ]Donate to Moody Radio: http://moodyradio.org/donateto/morningshowSee omnystudio.com/listener for privacy information.

Eddie Smith made waves across the boating world with his decision to place Grady-White Boats into a Purpose Trust rather than sell it for hundreds of millions of dollars. Senior Editor Chris Dixon sits down with longtime steward of the brand to learn more about his journey from the son of an orphan to the top of the boatbuilding mountain. Learn more at pmymag.com Subscribe to Power & Motoryacht magazine at pmymag.com/subscribe Subscribe to our FREE newsletter Learn more about your ad choices. Visit megaphone.fm/adchoices

What connections may exist between childhood GATE programs, intelligence experimentation, MK-Ultra and claims of ritualized trauma?Today, Robert Kalil welcomes Chris Mathieu, founder and host of Forbidden Knowledge News and the Forbidden Knowledge Network, for a deep and uncensored conversation about controversial programs and the hidden systems that may operate behind them.Chris has built an independent media network dedicated to investigating suppressed history, unexplained phenomena, covert operations, consciousness, technology and subjects frequently ignored by mainstream media. In this episode, Rob and Chris examine reports surrounding gifted-and-talented education programs, trauma-based conditioning, MK-Ultra, SRA testimony and the possibility that vulnerable individuals have been monitored or selected from an early age.Topics may include:• The origins and purpose of GATE programs• Gifted children and unusual screening procedures• MK-Ultra and trauma-based conditioning• Claims involving SRA and organized abuse• Intelligence agencies and behavioral experimentation• Memory fragmentation and recovered memories• Manipulation of consciousness• Suppressed testimony and institutional protection• Independent media and censorship• Chris Mathieu's work with Forbidden Knowledge NewsThese subjects include disputed allegations and personal testimony. This program is presented for discussion, investigation and educational purposes. Viewers should research the evidence and reach their own conclusions.We go deep. You decide.Typical Skeptic Podcast #2777Live at 4:00 PM EasternChris Mathieu / Forbidden Knowledge News:https://www.youtube.com/@ForbiddenKnowledgeNewshttps://forbiddenknowledge.news/

In this episode, Dr. K unpacks the unique challenges of high intelligence, explaining why gifted individuals are exceptionally prone to chronic anxiety, overwhelm, and severe burnout. Moving beyond the popular myth of intelligence as a pure superpower, he breaks down the neurobiology of an undisciplined mind, why smart people develop fragile coping habits, and how to stop using calculations as a shield against taking action. What to expect in this episode: The Burden of a High IQ: Why having superior cognitive ability directly multiplies your daily anxiety, as your brain's capacity to run complex calculations forces you to build highly robust predictive models of everything that could go wrong. The 100-Problem Trap: Why gifted individuals easily freeze up; while an average brain might predict a single solvable problem, a high-IQ mind predicts 100 future problems but remains physically limited by time and space to only solving 10 of them. The Compression Delusion: How smart people waste their mental energy trying to "whittle down," reshape, and calculate their way out of problems to make them perfectly solvable, rather than simply taking a single step forward. The Curse of Early Success: Why gifted kids fail to build discipline because their fast minds allowed them to bypass studying entirely, leaving them saddled with fragile habits that collapse under real-world challenges. The 800-Pound Tiger: Why a highly intelligent, undisciplined mind is like trying to control a wild tiger on a leash, and why IQ tests completely fail to measure your actual ability to regulate your thoughts and set rational goals. Why Geniuses Earn Less: A look at the surprising economic data showing that while income generally increases with IQ, it actually craters at the very highest levels of intelligence. The EQ Buffer: How individuals can leverage high emotional intelligence (EQ) to buffer cognitive gaps, utilizing deep empathy and emotional regulation to build strong social relationships and excel at work. Dr. K's NEW Guide to Love, Sex, & Relationships is here! Order now: https://bit.ly/4dO3x0VHG Coaching : https://bit.ly/46bIkdo Dr. K's Guide to Mental Health: https://bit.ly/44z3SztHG Memberships : https://bit.ly/3TNoMVf Products & Services : https://bit.ly/44kz7x0 HealthyGamer.GG: https://bit.ly/3ZOopgQ Learn more about your ad choices. Visit megaphone.fm/adchoices

Stay Connected With UsWebsite: anchorfaith.comAnchor Faith Church Facebook: www.facebook.com/anchorfaithAnchor Faith Church Instagram: www.instagram.com/anchorfaithPastor Earl Glisson Facebook: www.facebook.com/earlwglissonPastor Earl Glisson Instagram: www.instagram.com/earlglisson

Our thoughts on the season 2 finale of X-Men '97!

In today's episode, lead singer Nicolle Brown from the gospel music band "Nikki D & The Sisters of Thunder" joins Dr. Sheila White for a real conversation about the groovy passion of gospel blues music. Together, they discuss how the band's groovy music has allowed them to perform internationally on stage and inspire hope to those that need joy and positivity in their lives.

LET'S GET INTO SADIE SINK'S ROLE IN SPIDER-MAN BRAND NEW DAY! A lot to say and discuss! 

Planet X-Men and Power of X-Men have feels! So let's get into this Exodus focused episode!

In today's episode, Gospel Artist and Singer Malinda Baker joins Dr. Sheila White for a real conversation about the soulful pursuit of Gospel music from her new single Pursue (Malinda's Anthem). Together, they discuss how believing and pursuing a spiritually divine purpose in a person's life can help lead them to advance the kingdom of God.

Gifted to Serve: Gifts of Faith - Matt 14:22-33 - Deacon Casey Solgos by

Ryder Lee Joins Jay Weidner As They Discuss The New Documentary Psychic Agent: The Gate Program Available Now On Amazon Prime Video: https://www.primevideo.com/detail/0MCKET0R327G2WFPL3L19X3HR4?ref_=atv_dp_share_cu_r

A Qatar-donated Boeing jet gifted to and used by President Donald Trump as a temporary Air Force One may no longer be headed to his presidential library, at least for now, according to multiple reports, with Eric Trump telling NBC News there are different plans for the aircraft. Read the full story on Forbes: https://www.forbes.com/sites/antoniopequenoiv/2026/08/05/qatar-gifted-air-force-ones-future-grows-unclear-as-trump-library-plans-fall-into-doubt/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Stay informed on current events, visit www.NaturalNews.com  - mRNA Technology and Its Implications (0:11) - Mechanism of mRNA Vaccines (3:29) - Impact of mRNA Vaccines on Elderly Population (6:58) - Biological Weapons and Depopulation Agenda (10:08) - Research and Findings on mRNA Persistence (10:25) - Challenges and Strategies for Protection (47:31) - Second Amendment and Gun Rights (56:50) - Introduction of Mickey Willis and Azai (69:23) - Impact of Digital Media on Youth (69:39) - Future of Film and Digital Media (73:14) - Philosophy of Non-Violence and Compassion (75:58) - Impact of Internet and Media on Youth (81:14) - Technology as a Tool or Weapon (82:42) - Purpose and Impact of the Film "Shine" (84:18) - Standing Up for Principles (86:32) - The Role of Principles in Success (94:03) - The Power of Harmony and Frequency (102:16) - The Connection Between Music and Truth (106:15) - The Future of Film and Technology (117:34) - Upcoming Projects and Final Thoughts (124:55) Watch more independent videos at http://www.brighteon.com/channel/hrreport  ▶️ Support our mission by shopping at the Health Ranger Store - https://www.healthrangerstore.com ▶️ Check out exclusive deals and special offers at https://rangerdeals.com ▶️ Sign up for our newsletter to stay informed: https://www.naturalnews.com/Readerregistration.html Watch more exclusive videos here:

Contact Marko Gelo, he's a Mortgage Broker!604-800-9593 cell/text Vancouver403-606-3751 cell/text CalgaryCall Marko via WhatsApphomefinancingsolutions.caIf family is helping with your down payment, timing matters more than you'd think. Most people don't know that gifted funds sitting untouched in your account for 90 consecutive days are no longer treated as a gift at all — they become your own money in the eyes of your lender. That distinction can open up mortgage programs a fresh gift wouldn't qualify for, and it's just one of several rules worth knowing before you rely on family funds. In this episode, I broke down the full picture — who can gift you money, how much paperwork is really required, and what happens when funds come from abroad.CLICK HERE to be redirected to the blog version of this episode.CLICK HERE to be redirected to Mortgagenomics Canada Podcast YouTube ChannelCLICK HERE to download Marko's award-winning Mobile Mortgage App! Hosted on Acast. See acast.com/privacy for more information.

COUNT YOUR BLESSINGS

Join Walter Sterling as he discusses compacting history with Tartaria, technology gifted earlier than conventionally known and more on WABC.

At 10 years old, Jesus appeared at the foot of Eileen Mestas's bed and gave her an assignment for her life. What followed wasn't a single miracle—it was decades of watching everything He said come true.Eileen Mestas says Jesus appeared at the foot of her bed and told her He was taking her on a journey. Over the years, He showed her the man she would one day marry before they ever met, guided her family through multiple adoptions, answered an impossible prayer to breastfeed her adopted premature twins after eight years without nursing, and orchestrated events she believes only God could have arranged. Through every season, Eileen says she watched Jesus faithfully and miraculously fulfill what He had spoken from the very beginning.

River of Life is an inter-denominational, interracial, Spirit-filled church located in the heart of Wakulla County, Florida. We share the sermons from our services in the hopes they'll reach others determined to worship God in spirit and truth.

River of Life is an inter-denominational, interracial, Spirit-filled church located in the heart of Wakulla County, Florida. We share the sermons from our services in the hopes they'll reach others determined to worship God in spirit and truth.

Gifted to Serve: Gifts of Generosity - Matt 14 :3-21 - Chris Easley by

We are Compelled to Serve by and for God's Glory . . . I. You are Gifted by God to Serve! (10) II. Your Service is a Stewardship of God's Grace! (10) III. Our Service has Categories and Cautions! (11) Practical Implications o What are you internal and external barriers to sacrificial service? o What is your next step to discovering & using your gifts? o How can you evaluate your service, in community, for danger signs?

Leadership failures rarely happen overnight. More often, they begin with unseen pressures, unresolved pain, and unhealthy patterns that slowly erode a leader from the inside out. In this special compilation episode, we bring together four candid conversations with experienced church leaders who openly share the personal struggles that nearly derailed their ministries. Rather than focusing on leadership strategies or organizational systems, this episode explores the inner life of church leaders. From burnout and unprocessed grief to stress management, character formation, and radical honesty before God, don’t miss the lessons that can help you build healthier, more sustainable ministries. Use these conversations as discussion starters with your teams, asking not simply how the ministry is doing, but how the leaders themselves are doing. Unprocessed Loss Eventually Takes Its Toll Bob Riedy reflects on the burnout that brought his ministry to a halt and the painful realization that decades of accumulated loss had never been properly grieved. The death of a family member, staff transitions, declining attendance after COVID, fractured relationships, and personal shame all compounded over time because he kept pushing forward instead of processing what he had experienced. His story is a powerful reminder that even successful leaders are vulnerable when they ignore the emotional weight of ministry. Key Takeaway // Pastors experience constant loss, but simply “moving on” isn’t resilience. Healthy leaders intentionally process grief with God before it silently grows into burnout, anxiety, or shame. Listen to the Full Episode // Burnout, Breakthrough, and the Road to a Healthier Ministry with Bob Riedy (May 29, 2025) Simple Practices Build Long-Term Resilience Charles Stone shares practical neuroscience-backed habits that help leaders reduce stress and remain emotionally healthy. Rather than offering complicated solutions, he begins with something every leader can practice immediately: intentional breathing. By slowing the body’s stress response through simple breathing exercises, pastors can create space to think clearly, regulate emotions, and respond wisely during difficult seasons. Key Takeaway // Resilience isn’t built during a crisis. It’s developed through consistent daily practices. Simple rhythms like intentional breathing can help pastors lower stress before it becomes overwhelming. Listen to the Full Episode // Leading Through Stress Without Burning Out with Charles Stone (July 3, 2025) Crisis Reveals Character Allen Holmes explains that gifted leaders often reach a defining crossroads when crisis exposes weaknesses beneath their talent and experience. Every leader eventually faces a moment when they must decide whether to hide behind their abilities or allow God to shape their character through hardship. Churches that embrace this perspective create cultures where leaders remain, grow, and mature instead of running from difficult seasons. Key Takeaway // Leadership development isn’t primarily about improving skills. Rather, it’s about allowing God to form character. Crisis often becomes the catalyst that moves leaders toward deeper maturity and lasting influence. Listen to the Full Episode // Why Gifted Leaders Still Fail: Lessons from 25 Years of Ministry with Allen Holmes (March 5, 2026) Honesty Before God Leads to Healing Scott Landry vulnerably recounts one of the darkest moments of his life after personal failure left him isolated, angry, and questioning God. Rather than meeting condemnation, he encountered God’s presence in the middle of his raw honesty. His story illustrates that true healing begins when leaders stop pretending, acknowledge what’s really happening inside, and allow God to meet them in their deepest pain. Key Takeaway // God doesn’t meet the version of us we project to others—He meets the person we truly are. Healing begins when leaders stop hiding and bring their deepest struggles honestly before Him. Listen to the Full Episode // Leading After You Lose Everything: Redemption, Honesty & The Fight with Scott Landry (December 18, 2025) This compilation reminds us that leadership health begins long before visible success or failure. Healthy churches are built by healthy leaders, and healthy leaders cultivate habits that address the condition of their hearts, not just the effectiveness of their ministries. Whether you’re leading through growth, challenge, or personal struggle, these conversations invite you to slow down, examine your inner life, and take intentional steps toward long-term faithfulness in ministry. Thank You for Tuning In! There are a lot of podcasts you could be tuning into today, but you chose unSeminary, and I'm grateful for that. If you enjoyed today's show, please share it by using the social media buttons you see at the left hand side of this page. Also, kindly consider taking the 60-seconds it takes to leave an honest review and rating for the podcast on iTunes, they're extremely helpful when it comes to the ranking of the show and you can bet that I read every single one of them personally! Episode Transcript Rich Birch — Hey friends, welcome to the unSeminary Podcast. So glad that you’ve decided to tune in. You know, the subtitle of our podcast is Stuff You Wish They Taught in Seminary. Rich Birch — And today I wish there was like a big underline under that word wish. I wish this conversation was a robust conversation when I was in school. Today we’re listening in on four senior leaders talking about really stuff that was going on on the inside when nobody was watching. Rich Birch — This is a good time of year for you and I as executive leaders to pull back and to think deeply about the kind of structures we’re building as an organization that really create a flourishing environment for our people. You know, we asked earlier in the year what your greatest fears were for this year and 24.8% of you indicated that staff health, structure, morale, succession were your top fear this year. And so today’s conversation, what we’re doing is listening in on four senior pastors who get really honest on the show about what was happening on the inside of them. Rich Birch — One of the churches that I was an executive pastor at, about a decade after I left, the senior leader made tragic decisions in their own personal life that ended up burning the church down, literally to the place where that organization doesn’t exist anymore. And from my understanding, actually the building is going to be demolished. Literally there’s nothing left. Rich Birch — A ministry that impacted tens of thousands of people doesn’t exist today because of the decisions of a senior leader. And so at this time of year, in the summertime, I want to challenge us to listen carefully to some leaders that have been through some horrific moments and have come out the other side more healthy. And I want us to learn from these moments and say, hey, what can we do to structure our ministry so that we can be the healthiest possible environments we can possibly be? Rich Birch — So up first, we’ve got Bob Riedy at Church of the Open Door, who hit a wall on a Sunday morning a few years ago, and he learned about ungrieved losses that took 40 years of ministry to see. So let’s listen in to Bob and see what you and I can learn as we think about the future. [Clip 1 Begins] Bob Riedy — I really do think that loss, and especially not properly processing and grieving loss, was a big part of what was behind all of this. In 2018, I lost my brother, my younger brother, who was really my wingman in so many ways. That was a loss that I think in some ways I’m still wrestling with. Bob Riedy — During the pandemic, we lost, pick a number, a lot of loss as far as attendance and stuff. At first, we were off for about four months, but then when we came back, people began to trickle in, and it was clear to me that we lost hundreds of people as well from our church during that time. That was probably a profound thing for me. Bob Riedy — We lost some key staff members as well during that time. We lost some friends because of the politics and some of the difficulty of that. And so I think loss was a big part of that. Bob Riedy — My counselor has really helped me a lot with that, not properly grieving loss. Terry Wardle is one who said that ministry is a series of ungrieved losses. And I found that I really wasn’t doing that good. I was shaking it off and just moving forward, shaking it off and moving forward, and not really grieving it and bringing God into it and surrendering it to Him. Bob Riedy — And I think part of what happened with that as well was I was concerned about what people thought. We were this powerhouse of a church, growing like crazy, and all of a sudden, like every other church, we were struggling. Bob Riedy — And I remember the day that I burned out. That afternoon my children all came to our apartment. My wife and I were living downtown, and my daughter reminded me of this the other day. And she said, Dad, do you know what you said to me that day? And I said, no. She said, Dad, you said to me, what are people going to think of me that this happened to me? Bob Riedy — And that was a loss that I wasn’t able to really properly wrap my head around. I was more concerned about how this was going to look with me as a Christian leader in our community. And then I think one of the places that the anxiety came from as well was shame. Bob Riedy — One of the things the enemy, I think, came after me with was like, you’ve been telling people here in York how to live for the last 15 years, and you let this happen to you. And wow, I wrestled with that as well. So I think that loss and worrying what people were concerned about or what they were thinking of me, and then the shame of wrestling through that as a senior pastor whose church had been top 100 churches. Bob Riedy — I taught church leadership and biblical preaching at Lancaster Bible College, and this happened to me. And so I really wrestled with just the fallout of all that. [Clip 1 Ends] Rich Birch — What are people going to think? Bob’s daughter quoting him back to him is a part of this thing I just will not shake. Ungrieved loss, it compounds. Rich Birch — Loss is a part of your role. It’s a part of my role. But what Bob learned the hard way is that shaking it off and just kind of moving on will cost you more in the long run than sitting with it and processing it. Rich Birch — Charles Stone is up next. He’s a friend, a pastor, a counselor, and did his PhD on stress while he got diagnosed with three serious conditions in the same year. And we’re going to focus on an easy kind of habit that he has installed or practiced that he has installed that I think you could use in the midst of your processing, of dealing with even those kinds of ungrieved losses. Let’s listen in on what Charles has to say today. [Clip 2 Begins] Charles Stone — Well, I kind of get thematically. One involves how we breathe. These are practices that can help us develop resilience. Charles Stone — One has to do with how we breathe. Two have to deal with our emotions. One has to do with our thinking. One has to do with contemplative practices, spiritual practices. Charles Stone — One has to do with something called certainty. One has to do with gratitude. One has to do with relationships. And one has to do with sleep. So any of those, I’m game for any of those that you feel like we ought to touch on one or two of them. Rich Birch — Well, what about maybe breathing? Why don’t we start right at the top where you started? And then, you know, we’ll scratch the surface. There’s a ton we could, obviously, we want people to pick up a copy of the book, but let me start with breathing. That might be kind of interesting. Charles Stone — Yeah. Well, actually, this ended up being the first one because it’s very easy to do. Now, I think around 75 times plus, you’ll find the word breath or breathe in the scripture. Charles Stone — And Genesis, God breathed into Adam. He became a living being. I think his early part of Acts, Jesus breathed on the early church, the Holy Spirit came. Charles Stone — And then David, the psalmist wrote, let everything that has breath praise the Lord. Now, here is the interesting neuroscience insight. Deep breathing activates a key set of nerves that come directly from my brain, just come from my spinal cord. But these are these are key nerves that they’re in pairs. Charles Stone — One of them is called the vagus nerve, comes from the word vagrant. What does a vagrant do? He wanders around. So this nerve wanders around our hollow organs, our heart, our lungs, our stomach. So there’s a lot of communication back and forth. Charles Stone — Scientists have found that deep breathing, especially on the exhale, when you exhale a little longer, it actually engages this nerve, which lowers the stress response. And here’s here’s why you practice it. I call it the sniff breath. Charles Stone — Now, the actual term is “a psychological sigh”, but here’s how it would work when you’re facing some stress. Just breathe in, you know, a little more and then breathe out through your mouth. Now on the out breath, you want to be a little longer than the in breath, doing that three or four or five times. Charles Stone — Labs all around the country found it lowers the stress response. So that is a simple, a portable tool you can take. Anybody can use anywhere. Charles Stone — So yeah. [Clip 2 Ends] Rich Birch — I love Charles. He’s such a practical leader. Listen, I’d pick one of those practices this week and really try to install it. Rich Birch — I love that he started with breath work because it’s really the easiest one to start with. You can install it today. Literally, he gave you everything you need, but maybe look at the other ones. Pick up his book, “Stress Less”. Got a bunch in there. Rich Birch — Well, coming up next, we’ve got Allen Holmes. He’s been the senior pastor at Definition Church in Greensboro for 25 years. He has watched gifted leaders crash long enough that he’s built a framework around this to understand it. Let’s listen in to what Allen has to bring to this conversation. [Clip 3 Begins] Allen Holmes — It’s interesting when I, one of the other real key moments for me is I went back to do my doctorate of ministry degree at Gordon Conwell in redemptive leadership. And so much of what we were studying is how God works in the crisis, in these pressure moments to expose the unfinished places in our character so that we can grow and become more like Jesus and therefore maximize our kingdom impact in the world. Allen Holmes — And one of my professors, Dr. Powers, he actually wrote a book called Redemptive Leadership. It’s a simple little book, but profound, where he describes leadership development in five stages. And stage one is a skilled leader where you get a leadership role just based on your skill. So maybe the ability to preach. And so they call you to be the pastor. Allen Holmes — That’s how I became the pastor of my first church. I could preach. I hadn’t done anything else, but they let me be a pastor because I could preach. Allen Holmes — And then the second stage is a principal leader where you begin to understand why you do what you do. But the third stage, which is so important, is the character stage. And in order for a leader to go through the character stage, God always uses a crisis to bring him into that stage. But when he comes into that stage, he has a choice. In that stage, he can open his heart and allow God to do that deeper work, or he can go back and hide behind his skills and principle. Allen Holmes — And that’s what pastors do. A lot of times the reason you see this turnover every, you know, depending on what statistic you read, every two to four years, pastors are leaving churches is because they come into a church and they have this honeymoon season. And then all of a sudden there’s a crisis that exposes some things and they start floating their resume and hiding behind their skill rather than allowing God to deal with their character so that they can advance and become a transformative, redemptive leader. Allen Holmes — So I think one of the things that’s been so true for us is we’ve just tried to say to people when there’s a crisis, don’t panic, don’t run away. See it as an opportunity. Allen Holmes — In fact, I ended up doing my dissertation on the idea that if we could teach this model to leaders, that it would cause them to respond differently in the crisis instead of running from it, they would run to it and open their heart. And God could use that to really propel them into their redemptive future. And the research said that was true. Allen Holmes — And so we’ve tried to really work that in our culture to understand when something goes wrong, don’t run away and don’t hide. Let’s run into it and trust God to meet us there so that this thing God works redemptively to use it for your benefit and to launch you into your future. Allen Holmes — And because that’s been our culture, people have stuck around. I mean, my lead team, Rick has been here 25 years. He was he’s actually here two Sundays longer than I’ve been here. Eric’s been here 24 years. Jonathan’s been here 19 years. Chelsea’s been here almost this year will be 14 years. Steve’s been here 10 years. Allen Holmes — I mean, so they’ve just been here a long, long, long time. And that but that’s why they’ve seen these moments and we’ve helped them to find God in it so that it actually works for us instead of against us. [Clip 3 Ends] Rich Birch — Allen walked us through the five stages. The third one he talked about is character. And that’s really where I think you and I, our greatest test is at. And this is where we meet our crisis. You should go back and listen to Allen’s episode. Super helpful. Rich Birch — Coming up next for our last part of the conversation today is a friend of mine, Scott Landry. He’s the senior pastor of The Bridge near Ottawa. He’s been on the podcast before. Rich Birch — Next is a clip from a book that was coming out or that has come out called “The Fight”. And it’s the moment that he lost everything and that he was alone in a house in the Northwest Territory. The reason why I wanted this on is because I think Scott models for you and models for me an open transparency with ourselves, with our people. Rich Birch — That is something that I think you and I should live with our live with and should think through. How can we be this transparent with the people around us? Heads up. This is a heavy clip and it’s worth every second of your attention to lean in and hear what Scott shares. [Clip 4 Begins] Scott Landry — I hope that’s a powerful moment in the book because it was it’s genuinely the most powerful moment in my life. And this was this was kind of at the crescendo of my my breaking point. So after my marriage and my life specifically falling apart and I kind of lived in a place of isolation. Scott Landry — I was living in the North, Canadian North, and I was yeah, I was lost. I was I was angry like I had so much anger. And it was so, yeah, I talk about in the book and I was angry and ultimately I was angry at myself, but I was also angry at God. Scott Landry — And because even after, again, making a mess of my own life, he didn’t make a mess of my life. Nobody made the mess in my life. I made the mess in my life. Scott Landry — And but then after that, I was trying to do everything right. And I was trying to do the right thing, do the right thing. And I was like, God, when are you going to start intervening on my behalf? Scott Landry — And so, you know, being the preacher that I am, I was like, I got all the Bible verses that tell me that you’re going to like now is you’re going to do the redemptive thing. You’re going to show up. You’re going to move. Scott Landry — You’re going to fix. You’re going to redeem. You’re going to restore. Scott Landry — You’re going to repair. You can do all the R words. And and nothing was happening like it was like and it was almost as if I and I literally heard nothing. And I’d like to say I didn’t feel anything, but I did. Scott Landry — It was just this this anger that was welling up inside of me like a like a pot boiling and eventually it just I just became unhinged. Like I was alone and I was completely isolated. I was in this empty house and I just started crying out like and yelling out and I threw through things. Scott Landry — I I used words I’ve I’m ashamed to admit I used. Like I mean, I was as unhinged as could possibly. I was like, I God, if I saw you face to face, I would give you the…like I told him all this stuff. Scott Landry — And and what I found in that moment was like and I again, I talk about in the book, but like I yelled, God, I don’t even believe in you anymore. I’m done. Like I like I don’t I don’t believe. You’ve promised me that you would never leave me. You would never forsake me. And that’s exactly what you’ve done. I’ve told people that you would never leave them and forsake them. And yet you’ve done that to me. You are you are dead to me. I don’t believe in you anymore. Scott Landry — And I even now I still feel this and I’m just talking about it. But like this is and this is I know some people are going to roll their eyes at this. But like genuinely, when I heard myself say that, I felt this…like over me, over my house. It was like this eerie like pause. Scott Landry — And I heard if I’ve ever heard the voice of God, I heard a voice say, well, then who are you yelling at? And it was like this, like… Rich Birch — Beautiful. Scott Landry — And in that moment, it was like my anger was it wasn’t my degree. It wasn’t my Bible. It was it was my anger was my evidence that God was present right then and right there. And because my anger was directed at him. Scott Landry — And he knew that I was angry with him and he met me at the place of my anger and he was waiting. And this is the part that I still, I can’t do this, what’s in my head and in my heart justice. But it was God was saying, I’ve been waiting for you at this place your whole life. Rich Birch — Wow. Scott Landry — You you have been hiding from this anger from your childhood, from your young adulthood. And I’ve been waiting for you to meet me here at your anger. And I’ve I’ve wanted you to know that I would be here waiting for you. Scott Landry — And if you met me on the top of the tallest mountain and if you look me face to face and if you were to give me the finger, you would find me there waiting because I am waiting at who you really are, not who you’re pretending to be. Scott Landry — And everyone around you, you’ve got them fooled. And you’re a used car salesman and you can spin the Bible verses and you can do all that other stuff. But I know who you really are. And I’m waiting for you to finally be honest with yourself about who you really are. Scott Landry — And now that you finally are, now we can do something about that together. And that was the moment that God truly revealed himself to me. And that’s when I, for the first time in my life, truly discovered who I was. Scott Landry — And yeah, that’s the moment that I hope anybody who ever meets me or talks to me or listens to me or reads it, like that’s the part that I long for people to have before it costs them, like it cost me. [Clip 4 Ends] Rich Birch — I love that moment when Scott said he heard, who are you yelling at? Man, that sticks with you. Rich Birch — Listen, four different stories. What I’m trying to do is raise for you the question about the inner life of what’s going on at your church with the senior leaders around you. Rich Birch — What if you were to go back and look at these four conversations and maybe say, OK, this one might be the best for us to lead a conversation with, and send them one of those, send your team one of those episodes. And say, hey, I’d love for us to take some time this summer. Rich Birch — Maybe you go and relax on a back deck somewhere or jump in somebody’s boat or spend a little extra time at the office. Because there is a little bit of slack at this time of year. And ask the question, hey, what’s going on on the inside of us as leaders at the church? How are we developing and how could this conversation help us ask some different questions than we normally do in our team meetings? Rich Birch — All the links to those four conversations are down below. Thanks for sticking through this this entire conversation. I know there was a lot today, but we’ll see you next week with our regularly scheduled programs, kind of back to what we normally do here at unSeminary. Rich Birch — Thanks for being a part of the podcast. Thanks for doing what you’re doing, including the hard work of the inner life to lead your people in a way that makes a huge difference. Thanks for being engaged in the local church. Take care, friends.

Planet X-Men and Power of X-Men breakdown every plot point and reference in this episode!

She dated two alcoholics named Brian, back to back, and it led her to build a healing community serving thousands.   Andrea Ashley is the host of The Adult Child Podcast, a show that dives into the lasting effects of growing up in dysfunctional families, codependency, toxic shame, complex trauma, and addiction patterns, discussed with raw, unapologetic honesty. Since launching in March 2021, the show has grown to roughly 60,000 monthly downloads. Andrea is also the founder of The Shitshow, a paid healing community built to support listeners working through their own childhood dysfunction. She shares: ◼️ How dating two alcoholics named Brian, back to back, led her to realize her childhood was more than “less than ideal”, it was trauma ◼️ Why she launched the podcast the moment the idea hit her, driving over the Golden Gate Bridge ◼️ How a cold DM to a comedian led to her first big break: an invitation onto Dr. Drew's podcast ◼️ Why she stepped away from the show for ten months, and how she kept her community running through it ◼️ How The Shitshow, her paid healing community, works, and why ad revenue and membership keep the show sustainable ◼️ What she actually looks for in a guest pitch (and why authenticity beats a templated compliment every time) ◼️ Why she's finally starting 1:1 coaching, imposter syndrome and all   Follow Andrea Ashley: ◼️ Apple - https://podcasts.apple.com/us/podcast/adult-child/id1552579027 ◼️ Spotify -https://open.spotify.com/show/4T65uJfo4skTLkpcHYyw9k ◼️ The Shitshow Healing Community - https://www.adultchildpodcast.com/shitshow ◼️ IG - https://www.instagram.com/adultchildpod/    Want to grow your visibility through podcast guesting? Explore how PodWritten can help: https://podwritten.com/services/   Bonus tips and resources: ◼️ Blog: https://podwritten.com/blog/ ◼️ Instagram: https://www.instagram.com/podwritten/ ◼️ LinkedIn: https://www.linkedin.com/company/podwritten   Questions or want to say hey? Email us at sam@podwritten.com Please leave us a review on Apple Podcasts or Spotify.

Episode Transcript (provided by Riverside - forgive any errors): https://link.sheldongayisbugn.com/s3e20transcriptFollow I Must Be BUGN on IG @sheldongayisbugnSummary:In this episode, I sit down with Dr. Bowen Tyler Marshall, a licensed psychotherapist, PhD, and author specializing in ADHD, autism, relationships, and trauma - for a conversation I've been excited to have for MONTHS. If you're eager to discuss the challenges of neurodivergent relationships, THIS IS YOUR EPISODE. We dig into the science of late diagnosis, why so many people get completely missed by traditional frameworks, and how neurodivergent traits that look like rejection or emotional unavailability are actually nervous system responses being misread as character flaws. We even discuss something that might need me to hold a few hands...when healing and growing into who you actually are, not everyone in your life can make that journey with you. This one is for the late diagnosed, the ones who always felt like something was off, and everyone doing the work to live authentically and have healthy, fulfilling relationships.Key Topics:Different brains create different relationship needs.Late diagnosis changes more than identity.Compassion isn't the same as sacrifice.Relationships need understanding, not assumptions.Misunderstanding isn't the same as rejection.Healing sometimes costs you your closest relationships.Not every conflict is personal.Stay in Contact with Dr. Bowen Tyler Marshall:Follow Bowen on IG: https://www.instagram.com/drbotyler/Subscribe to the Substack (@bowentylermarshall)Connect with him on LinkedIn: https://www.linkedin.com/in/bowen-marshall-phd-lpcc-s-b62b2621/Resources and Links:SPARK Autism research program: sparkforautism.orgCHADD - ADHD research: https://chadd.org/research/Autism Science Foundation: autismsciencefoundation.orgThe Trevor Project (LGBTQ+ youth mental health): thetrevorproject.orgHelpful Links:Want to go deeper? Schedule a short call to explore working with me as a speaker or personal self-discovery coach: sheldongayisbugn.comFree list of Therapists for Melanated and Marginalized Groups: https://sheldongayisbugn.com/#resourcesFree GroupMe Community for Talented and Gifted adults: https://groupme.com/join_group/108040800/igLaxqNGND Connect - Online community for neurodivergent people: ndconnect.appUmbrella ND - Non-profit focused on neurodivergent advocacy: https://umbrellaopensdoors.org/resourcesKeywordsneurodivergence, ADHD, autism, relationships, couples therapy, mental health, therapy, communication, late diagnosis, self-awareness, compassion neurodivergent relationships, self-compassion, boundaries, marginalized communities, late diagnosis, neurodivergent healing, emotional understanding, trauma, identity, compassionIntro and Outro music provided by byrdversion1 - "Understand" from the album Nevermore Hosted on Acast. See acast.com/privacy for more information.

My Baby Passed In The Car Because I Blacked Out w/ Carolina Ayala | The Hopeaholics Podcast #348 Chad and Natalie sit down with Carolina Ayala, founder of The Happier Life Project, a recovery community organization built to give people the peer support she never had. She was drinking, using, and married by 15, and had watched two of her closest friends get killed before she was old enough to graduate. One night she drank too much, blacked out, drove home, and didn't take her daughter Crystal out of the car seat. Crystal spent three hours in the car and didn't make it. Carolina did nearly four years in prison for child endangerment and used meth for the next two decades because it was the only way she knew to get through what happened.#TheHopeaholics #redemption #recovery #AlcoholAddiction #AddictionRecovery #wedorecover #SobrietyJourney #MyStory #Hope #wedorecover #treatmentcenter #natalieevamarieJoin our patreon to get access to an EXTRA EPISODE every week of ‘Off the Record', exclusive content, a thriving recovery community, and opportunities to be featured on the podcast. https://patreon.com/TheHopeaholics Go to www.Wolfpak.com today and support our sponsors. Don't forget to use code: HOPEAHOLICSPODCAST for 10% off!Follow the Hopeaholics on our Socials:https://www.instagram.com/thehopeaholics https://linktr.ee/thehopeaholicsBuy Merch: https://thehopeaholics.myshopify.comVisit our Treatment Centers: https://www.hopebythesea.comIf you or a loved one needs help, please call or text 949-615-8588. We have the resources to treat mental health and addiction. Sponsored by the Infiniti Group LLC:https://www.infinitigroupllc.com Timestamps:0:00:00 - Cold open: the night Crystal died0:00:40 - Welcome / What is the Happier Life Project0:03:01 - The gap between treatment centers and what happens after0:05:10 - Treatment, psych wards, jails, prison0:05:51 - Getting pregnant, giving son to CPS0:06:38 - Getting custody, relapsing, returning to smoke meth0:07:02 - Rock bottom in 2021: not wanting to be alive anymore0:07:34 - Losing faith in God, 12-step, everything she held onto0:08:12 - Outside the crisis hospital, too scared to go in0:09:16 - Finding RA International's peer support center in Temecula0:10:55 - The idea for the Happier Life Project0:13:19 - Divorced parents, alcoholic stepdad, being the oldest0:14:41 - Gifted programs and nothing in the adult world making sense0:15:08 - First joint at 12 as a freshman in El Paso0:16:51 - Following friend into gang life0:17:43 - The four runaways: Jasmine shot in the head, Cynthia murdered and burned in the desert0:19:48 - Why she stayed on the street instead of going home0:20:43 - Meeting her husband at 12, marrying at 150:21:13 - Moving to Arizona with a Mormon family to clean up0:23:38 - Husband falls out of car going 55mph0:25:26 - The miscarriage that wasn't: Danielle is alive and 310:26:36 - Leaving her husband and daughter on her first birthday0:28:34 - The guilt cycle: couldn't face going back, so she never did0:30:32 - Daughter goes to a Mormon colony in Mexico0:32:27 - Federal charges at 19, domestic violence, psychiatric holds0:33:40 - Trying to kidnap her daughter and failing0:35:42 - Arrested at eight months pregnant for cocaine trafficking0:35:58 - Crystal born, alcoholic mother-in-law tried to sell her0:38:09 - Crystal's Death0:39:46 - Prison for child endangerment0:40:25 - Blood alcohol, no drugs found, what the DA had threatened0:41:08 - Planning to jump off the 15 freeway / bail raised to $250K0:42:59 - Not qualifying for the substance abuse program because the charge wasn't drug-related0:43:47 - No contact with anyone under 18, including siblings and daughter0:45:06 - Two beers, blowing a .080:45:40 - Back in on violation / introduced to meth in prison0:46:04 - What meth did that nothing else could0:47:36 - First time in treatment0:49:08 - Guilt, shame, and unaddressed grief 0:49:37 - $20 worth of meth, sentenced to five years in state prison0:51:11 - Cooper Fellowship: three years sober, the longest stretch yet0:52:01 - CBT: the first thing that actually made sense to her0:53:23 - The pre-cycle nobody ever addressed0:55:37 - This time in recovery is different0:57:34 - Almost not coming back to the rooms after the last relapse0:58:00 - What actually matters today: the daily work, not the time1:05:32 - Crystal's birthday, deciding to honor her for the first time1:06:01 - Psychic medium speaks with Crystal1:09:26 - Chad and Natalie / walking through pain vs. sitting in it1:15:41 - The Happier Life Project: where to donate, how to find them

Gifted to Serve: Gifts of Hospitality - Matt 13:31-33, 44-50 - Rachel Hoskins by

ANDREW GARFIELD BRINGS A SWAGGER TO SPIDER-MAN THAT WE WEREN'T READY FOR!

Mike is flying solo for another Coffee Time Kicks episode, and he brought a good one. He sits down with photographer and content creator Adham Abousalem, better known as Chef Doomy, for an honest conversation about building a craft instead of chasing a following.They get into why this wave of oversaturated releases is quietly handing power back to the collector, what actually makes someone a sneakerhead (it has nothing to do with brand loyalty), and how New Balance helped make comfortable runners cool again in a post-Yeezy world.Adham shares his origin, from collecting dubstep band tees and borrowing a friend's camera to becoming one of the most respected shooters in the space. He talks openly about how COVID drained his motivation, why ComplexCon changed his trajectory, and why shaking a hand still beats sending a DM.Then there's the Ronnie Fieg moment. Adham explains how a genuine, no-agenda conversation at an All-Star weekend Kith panel led to Ronnie gifting him a 1-of-36 Kith x Nike Air Max 95 in "Beetroot," with a handwritten note he won't forget.It is a real conversation about doing the work for the love of it, staying positive, and letting the opportunities come when they come.Follow Chef Doomy: @chefdoomy on Instagram and Twitter/X.New here? Subscribe to the Sneaker History Podcast, leave a review, and tell us your favorite Kith release or your favorite Chef Doomy photo in the comments.SUPPORT THE SHOW:Donate Through Venmo: https://venmo.com/u/sneakerhistoryBuy Me A Coffee: https://buymeacoffee.com/nickengvallEarly Access, Exclusive Videos, and Content On Patreon: https://patreon.com/sneakerhistorySneaker Business Insights: https://www.thesneakernewsletter.comTrack Your Collection: https://www.sneakerhistory.com/archive/If you are interested in advertising to our audience, contact us: podcast@sneakerhistory.com[Links contain affiliate links; we may receive a small commission if you purchase after clicking a link. A great way to support the pod!]—––––—––––—––––—––––—––––—––––—––––—––––Our podcast is proudly...Recorded on Riverside: http://www.riverside.fm/?via=sneakerhistoryHosted & Distributed By Captivate: https://bit.ly/3j2muPbDisclaimer: The views and opinions expressed in this program are those of the speakers and do not necessarily reflect the views or positions of any entities they represent.This podcast uses the following third-party services for analysis: Spotify Ad Analytics - https://www.spotify.com/us/legal/ad-analytics-privacy-policy/

Sebastian Shaw is DEAD! Who did it? Let's review X-Men Hellfire Mystery! 

Your next murder-mystery obsession…Gifted psychic medium, acclaimed jazz musician, and author of the new murder mystery Let the Murderer Say Amen- Carolyn Marie Wilkins is back!This time, she's pulling back the curtain on her entire process: how the story came together, what it really takes to get published, and how her gifts as a medium show up on the page.In this episode you'll learn about:-The real cult that inspired her fictional "fake bishop"-Why writing a first draft feels like channeling spirit-The truth about rejection, editors, and endless rewrites-Her advice for knowing if you're truly meant to writeIf you love a good book, or you've ever thought about writing one yourself, this is the insider's tour you don't want to miss.Let the Murderer Say Amen releases July 28th — pre-order link below.Show Notes:Let the Murderer Say Amen (A Psychics and Soul Food Mystery) https://a.co/d/0euGD1yYWebsite: https://www.CarolynWilkins.com Facebook:  https://www.facebook.com/carolyn.wilkins.3114/Instagram:  @Jemaya7Get Carolyn's first episode on Spirit Speakeasy: Mediumship Meets Jazz with Carolyn Wilkins' Akashic Trance Piano HealingListen now: https://www.buzzsprout.com/2084888/episodes/11835507Watch the video: https://www.joyfulmedium.com/blog/mediumship-meets-jazzLink to Carolyn's second Spirit Speakeasy episode: "Mediumship, Mystery & Murder" (Wham Bam Club)https://www.buzzsprout.com/2084888/episodes/17699515 Carolyn Marie Wilkins is the author of Let the Murderer Say Amen - the second in the Psychics and Soul Food Mystery Series. Her other books include Murder At The Wham Bam Club, Death at a Séance, Melody for Murder and Mojo for Murder. Carolyn's stories have appeared in Festive Mayhem and Wolfsbane: Best New England Short Stories of 2023.She is a Professor at Berklee College of Music and has represented her country as a Jazz Ambassador for the U.S. State Department. An initiated priestess of Jemaya, the African goddess of motherhood, Carolyn is also a psychic medium and Reiki Master. For more about Carolyn, visit her web page: https://www.CarolynWilkins.comGet Joy's Free "Sign Magnet" 3 Day Mini Course HERE https://www.joyfulmedium.com/sign-magnetJoy's Website: www.joyfulmedium.comInstagram: @JoyfulMediumTikTok: @JoyfulMediumFacebook: @JoyfulMediumFacebook Group: Joy's Soul SpaYouTube: Psychic Medium Joy Giovanni 

Flinkman and Dayspring discuss SDCC 2026 Marvel Legends reveals!

Are you struggling to find an educational path that fits your bright or twice-exceptional child? Join Jae on the Homeschool Your Kids podcast as he sits down with Dr. Mary Grace Stewart, founder of Ideal for Gifted, to discuss how to tailor learning to the individual needs of unique students. Discover how to move beyond the one-size-fits-all approach and embrace an education that truly respects the human element of learning.In this episode, Dr. Mary Grace shares her extensive journey in education, beginning in 1978 and evolving into a global nonprofit mission. She explains how the 2020 pandemic acted as a catalyst for change, revealing the deep gaps in traditional schooling for gifted and twice-exceptional children. You will learn about the concept of cognitive nourishment and why many students who struggle in traditional classrooms actually thrive when given the right tools and autonomy.We also dive into the challenges of the modern school system, the importance of soft skills in the age of AI, and how to handle asynchronous development where a child might be advanced in math but struggle with basic writing. Dr. Mary Grace uses her famous shoe store analogy to explain why choice is the most important factor in a child's success. Whether you are a veteran homeschooler or just starting out, this conversation provides the encouragement and practical advice you need to follow your child's bliss and support their unique growth.https://ideal4gifted.orgDr. MaryGrace Stewart is the Founder and Executive Director of IDEAL4Gifted, an online homeschool and enrichment program for gifted and multi-exceptional learners ages 5–14.MaryGrace began her career in 1978 as an art and drama teacher and taught in three states and nine school districts. In 1997, she took a turn toward gifted education and has never looked back. Since then, she has worked with gifted and multi-exceptional learners, presented to parents and educators nationally and internationally, and become a recognized advocate for children whose educational needs are often misunderstood.A state- and national-award-winning educator, speaker, and lifelong champion of gifted learners, MaryGrace believes that education should flex to fit the child—not the other way around.Chapters0:00 Welcome to Homeschool Your Kids Podcast2:50 The 2020 Pivot and Starting Ideal for Gifted7:15 Building a Program During a Crisis12:30 Going Global and Becoming a Nonprofit17:45 The Problem with Standardized Testing22:10 Education in the Age of AI27:00 Asynchronous Development in Gifted Kids32:15 Redefining the Human Element of Learning37:40 The Shoe Store Analogy for Education42:20 Challenges of the Public School System47:05 Understanding Twice Exceptional Behaviors51:50 Lessons from Personal Experience and Self-Care54:30 Final Advice for Homeschooling FamiliesIf you found this video helpful, please give it a thumbs up and subscribe to our channel for more expert interviews and homeschooling tips. Visit our website at homeschoolyokidsexpo.com to find upcoming events and resources in your area.#homeschooling #giftedchildren #2e #education #parenting

In this week's episode of the podcast, the conversation focused on the critical role of home environment and systems in supporting executive function—especially for neurodivergent families. It's not about Pinterest-perfect organization, but about designing spaces and routines that work with your brain, not against it. Key Takeaways Design Over Discipline: Many executive function challenges are solved through smart environment design, not just motivation or checklists. Moving decisions from the brain into the environment can reduce overwhelm and increase independence. Zones Make Life Easier: Creating dedicated zones—like reading nooks, creative spaces, launch zones near exits, and recharge corners—helps reduce cognitive load and makes daily transitions smoother. Visible, Forgiving Systems Win: The discussion explored the power of visual cues (checklists, labels, color coding) over constant verbal reminders. Systems should be simple, obvious, and evolve as your family's needs change. Links and Resources from Today's Episode Thank you to our sponsors: CTC Math – Flexible, affordable math for the whole family! The Learner's Lab – Online community for families homeschooling outside-the-box learners! The Lab: An Online Community for Families Homeschooling Neurodivergent Kiddos The Homeschool Advantage: A Child-Focused Approach to Raising Lifelong Learners Raising Resilient Sons: A Boy Mom's Guide to Building a Strong, Confident, and Emotionally Intelligent Family The Anxiety Toolkit Sensory Strategy Toolkit | Quick Regulation Activities for Home Affirmation Cards for Anxious Kids Why Typical Organization Systems Fail Neurodivergent Homeschoolers and What Works Instead Homeschool Planning For Kids: Helping Your Child Get Organized Organizing Your Homeschool in a Tiny House Executive Function Struggles in Homeschooling: Why Smart Kids Can't Find Their Shoes (and What to Do About It) How Adventuring Together Grows Confidence, Curiosity, and Executive Function Understanding Executive Function Skills in Gifted and Twice-Exceptional Children Strengthening Executive Function Skills: A Conversation with Sarah Collins Strengthen Executive Function Skills The Best Books for Teaching About Executive Functions Skills 7 Executive Functioning Activities for Small Children RLL #84: Exploring Education and Executive Function with Seth PerlerThe Unmeasured Executive Functioning Issue RLL 20: Helping Your Kiddo with Executive Function Skills Struggles | A Listener Question RLL LIVE | Improving Executive Functions Helping Kids Who Resist: Low-Demand Homeschooling for Autonomy and Skill-Building Why Is Finishing So Hard? Helping Neurodivergent Kids Cross the Finish Line Why Typical Organization Systems Fail Neurodivergent Homeschoolers and What Works Instead  

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

LOG July 23 After subpoena for reporting on Qatar-gifted jet as AF1 is withdrawn after argument exposing what Judge Subramanian called "hiccups to say the least," this stand-up. Judge Subramanian has Inner City Press FOIA case against DOJ https://courtlistener.com/docket/73400542/1/lee-v-united-states-department-of-justice-antitrust-division/Today'shearing live coverage:X https://x.com/innercitypress/status/2080353396679193063Threads https://www.threads.com/@innercitypressinsta/post/DbJRERdFrn0Blueskyhttps://bsky.app/profile/innercitypress.bsky.social/post/3mrdgfkacmc2u

We're in Ephesians 4:7-12 to explore God's design for spiritual gifts and the grace that fuels them. Paul reminds us that our spiritual gifts aren't given just for our own benefit. We are graced so that we can reflect that grace to others and build up the body of Christ. Walking through verses 8-10, we examine Christ's triumph on the cross over darkness, His incarnation, and His ascension, which paved the way for Him to equip His church. When we step into our God-given gifts, the church is strengthened, and God is glorified. But when we hold back, the entire body suffers. Join us as we challenge ourselves to identify, pray over, and actively operate in the gifts God has given us.WHAT IS NEUE THING?Neue Thing is a non-profit ministry, founded by Cherie Wagner, that exists to equip women with the Word of God. Cherie's life-long passion is two-fold: knowing Jesus Christ and making Him known. Author of Found On My Knees, Awake O Sleeper, Rest, Hope, Psalms for Life, and Knowing Your Name, Cherie writes Bible studies for women that will encourage them to know and believe God's Word, equip them to live it, and empower them to take it and transform this generation for Jesus Christ.CONNECT Website: https://neuething.org/Email Subscription link:https://neuething.us2.list-manage.com/subscribe?Instagram: https://www.instagram.com/neuethinginc/Facebook: https://www.facebook.com/neuethingGive to Neue Thing: https://neuething.org/give/RESOURCESProverbs: The Wise, The Fool, The WickedKnowing Your NameFound On My Knees: The Journey from Brokenness to BlessingAwake O Sleeper: EphesiansRest: 30 Days of Exploring God's Invitation to RestHope: Tethered to an Unwavering GodPsalms for Life

In this powerful and hope-filled conversation, Barb speaks with counselor, spiritual director, and author Sheila Wise Rowe about her book Seeds of Racial Healing: Fifty-Two Devotions for Navigating Through Trauma. With over thirty years of experience counseling trauma survivors and ministering to marginalized communities, Sheila offers a deeply compassionate, trauma-informed approach to addressing both personal and systemic racism through the lens of Scripture. They talk about how racial trauma impacts the body and soul, why rest and resilience are essential for Christians of color, and how being rooted in God's Word can nurture healing and justice.  If you are seeking Christian encouragement, spiritual formation, and practical tools for navigating racial trauma, this conversation offers hope, wisdom, and a path toward renewal.   RESOURCES FROM THIS EPISODE Connect with Sheila on Facebook Connect with Sheila on Instagram Seeds of Racial Healing: Fifty-Two Devotions for Navigating Through Trauma Visit Sheila's Website   ABOUT OUR SPECIAL GUEST Sheila Wise Rowe (MEd, Cambridge College) has over thirty years of experience offering counseling and spiritual direction to individuals, couples, leaders, and trauma survivors. Sheila also spent a decade ministering to unhoused and abused women, children, and youth in Johannesburg, South Africa, where she was a lay pastor and taught Christian counseling and trauma-related courses. Sheila is a speaker, trainer, and writer, authoring the award-winning Healing Racial Trauma and Young, Gifted, and Black. She and her husband, Nicholas Rowe, live in Boston, Massachusetts, and coauthored Healing Leadership Trauma.

This week on Sustainability Now!, we bring you highlights from The Exchange: Data Centers 101, a summer series of weekly community conversations organized by UofL's Envirome Institute to examine data center impacts in areas such as health and the environment. The June 10th event was focused on Data Centers & Health, examining the threats they pose to human health. The guest was LaTricea Adams, founder of Young, Gifted and Green, exploring the health impacts of rapid data center development and expansion. Watch the full presentation at https://www.youtube.com/watch?v=MqNhuz0yxok&list=PL3EGWMSHI12br0ncQSSC9GP4aOB3pHOwf&index=2 The Exchange: Data Centers 101 is a new initiative from the Green Heart Project and the Superfund Research Center at UofL's Envirome Institute. Community interest in understanding more about the potential impacts of data centers led to the focus on this topic. You can watch all of the webinars in the series at https://www.youtube.com/playlist?list=PL3EGWMSHI12br0ncQSSC9GP4aOB3pHOwf The series concludes with a Film Screening and Panel coming up this week on Thursday, July 23rd at the YMCA, 1720 West Broadway, with a Film Screening at 6:00pm, followed by a panel discussion starting at 7pm. UofL's Envirome Institute, Green Heart Louisville and the UofL Superfund Research Center invite you to the culmination of their summer series called The Exchange: Data Centers 101 with an in-person film screening and panel. The evening will begin with a 6pm screening of The Beginnings of the Internet's Energy Crisis, followed by a panel discussion at 7 pm. The panel will cover community perspectives, health impacts, environmental impacts, and policy implications related to data centers. Panelists include: Byron Gary - a Senior Attorney at Kentucky Resources Council. He holds degrees in political science and philosophy from the University of Louisville and a J.D. from the University of Louisville Brandeis School of Law. Elisa Owen - a Senior Energy Organizer in Kentucky. She works with the Sierra Club's Beyond Coal Campaign, collaborating closely with the Kentucky Chapter as their cheap energy specialist. Before joining the Sierra Club in February 2025, Elisa served as Kentucky Interfaith Power and Light's Executive Director. Donovan Taylor - who has been leading tours in Louisville's historic West End since 2012 and now operates a guided bus tour throughout the West End's 10 neighborhoods. Taylor's goal is to teach people more about West End neighborhoods, including the triumphs of residents there. He also serves as a Chickasaw community leader. Moderated by Dr. Ted Smith - the Director of the Center for Healthy, Air, Water and Soil, Co-founder of the Envirome Institute, and Interim Associate Dean for Research in the School of Public Health and Information Sciences. Register to attend in person: https://www.eventbrite.com/e/the-exchange-data-centers-101-film-screening-and-panel-tickets-1992856537550?aff=oddtdtcreator Register to attend virtually: https://us02web.zoom.us/meeting/register/0jZWH5BHQ1uXzHHzn_q7jg#/registration  As always, our feature is followed by your community action calendar for the week, so get your calendars out and get ready to take action for sustainability NOW! Sustainability Now! is hosted by Dr. Justin Mog and airs on Forward Radio, 106.5fm, WFMP-LP Louisville, every Monday at 6pm and repeats Tuesdays at 12am and 10am. Find us at https://forwardradio.org

Gifted to Serve: Gifts of Evangelism - Matt 13:1-23 - Deacon Casey Solgos by

BREAKING NEWS! Marvel Comics is leaving NYC after 90 years and heading to Burbank, CA. Plus, a new editor-in-chief!

This episode is sponsored by TeachHoops.com, where you get direct access to veteran coaches who have faced every tough locker room situation you can imagine — real mentorship, one question away. Today we examine a painful case study every coach recognizes: the loaded roster that never comes together. Talent doesn't guarantee wins, and the missing ingredient is almost always trust — between players, between players and coaches, and between the team and the plan. We break trust down into coachable components. You'll learn how to build vulnerability-based trust through structured team activities, how your own consistency as a coach either deposits or withdraws from the trust account, and why role clarity is the fastest trust-builder there is. We also cover the moment most coaches miss: how you respond to a player's mistake in a big game tells the whole roster whether your words about trust are real. Trust isn't a poster on a wall; it's a system of daily behaviors you can install starting tomorrow. This episode gives you the blueprint. When you're ready to talk through your own locker room challenges with coaches who have solved them, find your mentors at https://teachhoops.com/. Learn more about your ad choices. Visit podcastchoices.com/adchoices

P.M. Edition for July 1. Today President Trump flew to North Dakota in new digs: the Air Force One plane that was gifted by Qatar. Journal national security reporter Marcus Weisgerber discusses the refurbishments to the plane as well as the controversies surrounding it. Plus, the U.S. has declined to renew the signature trade agreement with Canada and Mexico, putting the pact's future in doubt—we'll get into the economic consequences. And the U.S will now offer federal education funding for short training programs for jobs in fields like healthcare and cybersecurity. WSJ reporter Lauren Weber discusses how it works. Alex Ossola hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.