Podcasts about Vindication

Are you facing mockery for choosing God over worldly gains? Will your radical faith leave you empty-handed? Discover how David and Daniel overcame impossible odds, and why your unwavering trust in the Lord guarantees you will never be put to shame!

Merriam-Webster's Word of the Day for August 8, 2026 is: nurture • NER-cher • verb Nurture is most often used to mean “to help someone or something grow, develop, or succeed.” It can also mean “to take care of someone or something that is growing or developing by providing food, protection, a place to live, etc.,” or “to hold something, such as an idea or strong feeling, in your mind for a long time.” // Art teachers nurture their students' creativity. // She nurtured a secret ambition to be a professional singer. See the entry > Examples: “Parks are good for people. They nurture community, even alongside busy roads and next to dense housing; especially there, in fact.” — Cheryl Fox, The Park Record (Park City, Utah), 3 July 2026 Did you know? When nurture was first adopted from Anglo-French into English in the 14th century, it referred, as it does in the phrase “nurture vs. nature,” to training or upbringing, i.e., to the care and attention given to someone or something that is growing or developing. It wasn't until a century later that the verb nurture settled into the language, first with meanings having to do with feeding and caring for young—meanings nourish had been, er, nurturing for a hundred years. The words come by their overlapping meanings etymologically: both come from the Latin verb nutrire, meaning “to suckle” or “to nourish.” The figurative use of nurture, meaning “to further the development of,” followed centuries later. Mary Wollstonecraft applied it in her 1792 book, A Vindication of the Rights of Woman, writing, “Public spirit must be nurtured by private virtue.”

Mary Wollstonecraft's "Vindication of the Rights of Men, in a Letter to Edmund Burke" is a powerful defense of individual rights and a critique of the prevailing political philosophies of her time. Addressing Edmund Burke's conservative views, Wollstonecraft argues for the inherent dignity and equality of all people, making a case for rational thought and justice over tradition and hierarchy. This work remains relevant today as it challenges us to reflect on the foundations of our rights and the importance of advocating for social justice. Its enduring themes of equality, reason, and the quest for human rights continue to inspire contemporary discussions about freedom and democracy.

PART 3 OF 3: EXCLUSIVE INTERVIEW WITH DON BOHANA — “The Road to Vindication” After 25 years of wrongful imprisonment… after refusing every plea deal… after fighting through a system that tried to break him…Part 3 is the explosive finale you've been waiting for. In this powerful conclusion, Don Bohana reveals: ·      The full details of the new Post-Conviction Petition for Declaratory Relief of Actual Innocence·      The irrefutable new evidence that demands his conviction be vacated·      What this moment means for his family, his freedom, and real justice in AmericaThis isn't just about one man. This is about exposing corruption, standing for truth, and proving that actual innocence matters. Don Bohana is a U.S. Army veteran, a devoted father, and a man who never stopped believing the truth would prevail. Now the world gets to see it happen. Part 3 of 3 — the most powerful episode yet — drops soon on The Take Your Power Back Show with Kim Yeater. This is the moment Don's long road to vindication truly begins. Don't miss the conclusion of this historic interview series. For updates, the full petition details, and ways to support Don's fight for justice, go to: Support Don's fight for justice here:https://www.givesendgo.com/don-bohanas-road-to-vindication-after-25 See all 3 Parts of the “Take Your Power Back show” Exclusive Interview with Don Bohana at:https://RoadToVindication.com Share this post far and wide. Tag everyone who needs to see real courage and real hope.The truth is winning. Don Bohana is coming home — in every way that matters. #JusticeForDonBohana #DonBohana #ActualInnocence #RoadToVindication #WrongfulConviction #TakeYourPowerBackShow #NeverGiveUp #USArmyVeteran Connect with Us:• Website: TakeYourPowerBackShow.com• Rumble: rumble.com/c/TakeYourPowerBackShow• Live Stream: rumble.com/TakeYourPowerBackShow/live• Social Media:o X:@realkimyeatero Facebook: kimberlyyeatero Instagram: Takeyourpowerback_kimyeatero TikTok: takeyourpowerbackshow• Email: TYPBProducer@gmail.comRelated Movement:TakeOurCaliforniaBack.com | TakeOurElectionsBack.com | TakeOurBordeBack.comSend us Fan MailSupport the show

He was put on child support at just 12 years old.By the time Lionel “TJ” Tillman learned what had happened, he says he was already $24,000 in debt—despite the court determining that he had not reached puberty when the child was conceived.After fighting the case for more than two decades, proving that the process involved extrinsic fraud, and eventually recovering his money, TJ is no longer focused solely on his personal battle.Now, he is taking the system to Congress.In this powerful episode of Inside the Vault with Ash Cash, TJ introduces the proposed Bringing Families Back Together and Child Support Equity Act of 2027—a bill created to promote equal parenting, due process, financial fairness, transparency, and healthier relationships between children and both fit and present parents.Ash and TJ examine some of the most controversial questions surrounding family court and child support:Can someone be held financially responsible without being the biological father?Why are some parents learning about child-support orders only after their wages are garnished?Are government incentives helping families—or rewarding separation?And why should a parent who is actively fighting to be present automatically be treated like an absent parent?TJ also makes one thing clear: this is not a “get out of responsibility” movement. He believes parents who refuse to support their children should be held accountable.His fight is for fairness, equal parenting, proper notice, due process, and a system that puts the well-being of children ahead of conflict between adults.This is not about mothers versus fathers.It is about choosing the children.Watch until the end to learn how you can support the petition, help bring the proposed legislation before Congress, and become part of a national movement to strengthen families.Sign the petition: change.org/childrenneedbothChapters00:00 – He was put on child support at 12 years old 01:08 – Message for entrepreneurs, coaches and business owners 02:05 – Protecting the family is a generational investment 03:04 – The Bringing Families Back Together and Child Support Equity Act 03:55 – “Take care of your children—this is not a free pass” 05:21 – Who is Lionel “TJ” Tillman? 05:38 – Put on child support at 12 and $24,000 in debt 06:32 – Could he have biologically fathered the child? 07:15 – Declared the legal father despite the court's findings 08:53 – Why outdated policies need to change 09:34 – Why TJ is taking the system to Congress 10:12 – Education, policy and prevention 11:18 – Does due process apply in family court? 11:51 – Equal parenting should begin at birth 12:53 – Georgia's legitimation process explained 14:11 – The $755 million child-support incentive claim 15:11 – Why TJ is pursuing federal reform 15:45 – Incentivizing families to stay together 17:25 – No parent should have to fight for their children 17:46 – Fathers ask for joint legal and physical custody 18:34 – How public assistance affected the family structure 19:35 – Who determines whether a parent is fit? 21:18 – Why must parents fight for more time with their children? 22:00 – “You want support, but you don't want support” 22:29 – TJ announces The Family Code podcast 22:39 – The hidden trauma families refuse to discuss 24:02 – Why TJ challenges the child-support system 24:17 – The Fourteenth Amendment and parental rights 26:47 – How claims can be presumed valid until challenged 27:43 – Proper notice before enforcement actions 28:17 – Discovering child support through wage garnishment 30:00 – Finding out on payday that half his check was gone 32:10 – Personal service versus substitute service 33:13 – Vindication after a 22-year fight 34:00 – The forged signature and vacant-house service 35:00 – Paying child support for 11 years 37:26 – Filing an insurance claim against Los Angeles County 38:30 – How TJ finally recovered his money 39:20 – Helping parents challenge their cases 40:15 – Why he refuses to assist irresponsible parents 41:41 – Raising his daughter as a single father 43:00 – Why children may need both parents 44:40 – The child-support-to-prison pipeline 46:47 – Are urban communities being disproportionately targeted? 48:14 – Is TJ trying to weaken child-support laws? 48:34 – Child support should apply to absent parents 49:22 – Equal parental rights and the “noncustodial” label 50:00 – Handling difficult co-parenting and parental alienation 51:10 – Support beyond money: emotional and physical presence 52:00 – Parenting education, therapy and conflict resolution 54:00 – What America could look like if the bill passes 56:47 – How viewers can support the proposed legislation 57:17 – TJ's direct appeal to Congress 58:25 – This is not men versus women 58:36 – Putting children ahead of pride and conflict 59:41 – “This is not an attack on women, men or child support” 1:00:04 – How to connect with TJ and the foundation 1:00:32 – Closing the VaultAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Vindication at last! On this weeks show we cover Fauci's senate hearing and delve into the entries in his personal diary that the mainstream media won't show you. MUST WATCH EPISODE LETS GOOOOOOOOOOO! We are a value for value podcast so please consider supporting the channel with your time (liking, sharing and commenting on our content) OR by sending some treasure to us using the methods below: Send Bitcoin to: bc1qsv6j2xjkg9vcmp5f4slgt95xk5mekjvndcty25 Send Solana to: oDhxvLuvNxg8Pi4d9YHGgfnUw524AE1PKjb6iNuYJqS Send Ethereum or US Tether to: 0x035cc00A983c3ecfC99029bE859DF9DC746Ac867 If you haven't set up a Crypto Exchange yet you can use our link here: https://www.coinspot.com.au/join/WRFH5C

#cuttheclutter Nearly two years after his death, the Supreme Court has given a clean chit to former PM Manmohan Singh in coal scam case, and has accepted CBI's closure report. ThePrint Editor-In-Chief Shekhar Gupta explains the intricacies of the case as well as SC order, and why this is a vindication of former PM. #CutTheClutter Ep 1872   --------------------------------------------------------------------------------------------- To read National Interest articles: https://theprint.in/national-interest/national-interest-coal-vs-coalition/544065/ https://theprint.in/sg-national-interest/jantar-chhu-mantar/544304/ To watch Walk the Talk: https://www.ndtv.com/video/walk-the-talk-with-kiran-karnik-192826 --------------------------------------------------------------------------------------------- #asusexpertbook  @ASUSIndia.official  Checkout the ASUS Expertbook series: https://www.flipkart.com/asus-expertbook-core-ultra-store

There's a particular kind of ache that comes from being doubted for something you know, deep down, to be true. Maybe it was a career change nobody understood, a relationship you had to walk away from, or a creative path that looked reckless from the outside.We sit with that ache instead of rushing past it—and we ask what it might actually be trying to teach us.In this episode, you'll hear about:Why moments of self-trust under pressure often become turning points.The difference between being wrong and simply being early or unfamiliar to others.A grounded, three-part practice for moving through doubt without abandoning what you know to be true.A guided meditation to help you reconnect with your own inner clarity.This episode also includes a conversation about why the discomfort of being questioned isn't necessarily a sign you've gone off course. Sometimes it's simply the friction of doing something the world hasn't caught up to yet. We look at what it really means to build self-trust in moments like these, and why vindication doesn't always arrive the way—or the speed—we expect it to.A moment to sit with: Being criticized doesn't mean you're wrong. If something still feels true after real thought, staying with it is its own kind of proof.If this episode resonates, we'd love for you to share it with someone who's questioning their own path right now—and if you're a Patreon subscriber, look out for this week's Magic Monday reflection, built around this very theme.Dr. Mensimah ShabazzJoin us as we build a global community of one million women who lead with wisdom, courage, compassion, and grace. Together, we are reclaiming our power, honoring our authentic selves, and inspiring one another to rise with confidence and purpose. Let us boldly affirm who we are—women of power and grace—and illuminate the way for generations to come. ♥️Resources & Links:Book a consultation: mensimah.com/harmony-consultJoin the community on Patreon: patreon.com/mensimahshabazzphdShop: shop.mensimah.comWebsite: https://www.mensimah.comInstagram: @mensimahshabazzphdYouTube: @mensimahsroundtableRegister as a Guest on PodMatch- https://www.joinpodmatch.com/mrtDonations:https://mensimahs-round-table.captiva...https://www.paypal.com/paypalme/MRTPo...

Shaun has more proof WE WERE RIGHT! PLUS, Michele Steeb, CEO and founder of Free Up Foundation and author of Answers Behind the RED DOOR: Battling the Homeless Epidemic, talks to Shaun about the massive failure in homelessness in Democrat-run cities and states, the obscene amount of unaccounted money in homelessness initiatives that do not work, and how Democrats have a high tolerance for failure. And Richard C. Lyons, author of But By The Chance of War and The DNA of Democracy Series, discusses the insurrection of our education system making America illiterate and too stupid to know they are stuck in unwillful slavery. See omnystudio.com/listener for privacy information.

Are you angry at all the evil that ravages this world? Where is God when sin seems to rule the day? Today, R.C. Sproul calls Christians to wait on the Lord, resting in the certainty that He will set all things right. Read the transcript: https://ligonier.org/podcasts/ultimately-with-rc-sproul/wait-for-gods-vindication/ Study Reformed theology with a free resource bundle from Ligonier Ministries: https://grow.ligonier.org/ A donor-supported outreach of Ligonier Ministries. Donate: https://donate.ligonier.org/ Explore all of our podcasts: https://www.ligonier.org/podcasts

God's righteous and persecuted servant cries out for vindication and rescue, by: 1. Pleading Christ's righteous heart. (vv. 1-5) 2. Pleading Christ's righteous rescue. (vv. 6-12) 3. Pleading Christ's righteous judgment. (vv. 13-15)

SNEAK PEAK-Witness a Powerful Candid Clip With Don Bohana! Witness a powerful candid interview with Don Bohana! For 25 years this U.S. Army veteran refused every plea deal and stood firm on his innocence in the 1994 drowning of Delores “Dee Dee” Jackson, MIchael Jacksons sister-in-law. Now the full truth is finally being told. Watch all three parts of this explosive, uncensored interview — “The Night Everything Changed” and the complete Road to Vindication — right now at: RoadToVindication.com See the new evidence. Hear Don's full account. Witness the petition that demands the court declare him factually innocent. Go to RoadToVindication.com and experience every part of the exclusive interview that is changing everything. The truth is coming out. Don Bohana is not backing down. Neither should we. https://RoadToVindication.com — watch the full series now. #takeyourpowerback #JusticeReformNow #donbohana #roadtovindication Send us Fan MailSupport the show

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

Send us Fan MailPosting from the archives again this week! We hope to be back recording new episodes in August. Until then, I know 3-years-ago Aaron and Marissa would love to have you join them for Proper 12.

Welcome to Day 2905 of Wisdom-Trek. Thank you for joining me. This is Guthrie Chamberlain, Your Guide to Wisdom. Day 2905 – Wisdom Nuggets – Psalm 143:1-12 Daily Wisdom Wisdom-Trek Podcast Script - Day 2905 Welcome to Wisdom-Trek with Gramps! I am Guthrie Chamberlain, and we are on Day 2905 of our Trek. The Purpose of Wisdom-Trek is to create a legacy of wisdom, to seek out discernment and insights, and to boldly grow where few have chosen to grow before. The Title for Today's Wisdom-Trek is: Out of the Dust, Into the Light – The Cry for Divine Direction and Vindication On our last expedition, on Day Two Thousand Nine Hundred Three, we crawled deep into the dark, damp, suffocating isolation of the cave with David in Psalm One Hundred Forty-Two. We listened as he poured out his raw legal complaint before the Lord, feeling entirely abandoned by humanity, with hidden snares waiting for him in the shadows. Today, as we advance our base camp along this ancient trail, we step directly into Psalm One Hundred Forty-Three, verses one through twelve. This is the final of the seven traditional penitential psalms, and it serves as a profound, sequential continuation of David's journey through intense distress. If Psalm One Hundred Forty-Two was the sharp, acute panic of being trapped in a cave, Psalm One Hundred Forty-Three represents the heavy, cumulative exhaustion of a long-drawn-out warfare. David is not just running from a temporary threat anymore; his soul is utterly spent, his energy is depleted, and the darkness is threatening to swallow him whole. Yet, as we will see, instead of throwing in the towel, David uses his remaining strength to lift his hands into the cosmic throne room, appealing to the character of the Sovereign King of heaven. The first segment is: Entering the Supreme Court: Appealing to Covenant Faithfulness Let us open our ears and hearts to the opening lines of this intense prayer, grouping together the logical thoughts found in verse one and verse two. Hear my prayer, O Lord; listen to my plea! Answer me because you are faithful and righteous. Don't put your servant on trial, for no one is innocent before you. David begins this trek with an urgent, deeply humble legal appeal, crying out, “Hear my prayer, O Lord; listen to my plea! Answer me because you are faithful and righteous. Don't put your servant on trial, for no one is innocent before you.” To fully appreciate what is happening here, we must view this through the ancient Israelite divine-council worldview. When David enters into prayer, he knows he is stepping directly into the celestial courtroom where Yahweh sits enthroned as the Supreme Judge, surrounded by His assembly of holy ones. Notice that David does not swagger into this heavenly court demanding rights based on his own moral perfection. In fact, he does the exact opposite. He submits a preemptive plea, begging, “Don't put your servant on trial.” Why? Because David possesses the profound cosmic wisdom to realize that in the brilliant, unblemished light of the Creator's holiness, no human being—not even a chosen, anointed king—stands completely innocent on their own merit. Instead of pleading his own righteousness, David brilliantly shifts the entire legal basis of his case to the character of God Himself. He demands an answer based entirely on two things: God's faithfulness and God's righteousness. In the ancient near eastern covenant framework, a king was legally and morally obligated to defend and protect his loyal subjects. David is essentially saying, “Lord, I am your covenant servant. I am imperfect, yes, but I am yours. Therefore, I appeal to your own cosmic reputation, and your sworn oaths of loyalty, to step down“from the judge's bench and act as my divine defense attorney." The second segment is: The Crushing Darkness: Paralyzed in the Shadow of Death The narrative takes a very heavy, somber turn as David describes the terrifying operations of his adversary, combining the logical thoughts found in verse three and verse four. My enemy has chased me. He has knocked me to the ground and forces me to live in darkness like those who died long ago. I am losing all hope; I am paralyzed with fear. David exposes his current reality, recounting, “My enemy has chased me. He has knocked me to the ground and forces me to live in darkness like those who died long ago. I am losing all hope; I am paralyzed with fear.” The language David utilizes here is intentionally cosmic, and deeply evocative of the underworld. When he says his enemy has knocked him to the ground, forcing him to live in deep darkness like those who died long ago, he is drawing a direct parallel between his current psychological state and Sheol—the dark, silent realm of the dead. In the ancient world, Sheol was seen as a place of absolute containment, ruled over by spiritual forces of chaos and decay. David feels as though his human persecutors, backed by the sinister principalities of darkness, have successfully driven him out of the vibrant land of the living and buried him alive in a spiritual tomb. Because of this prolonged, crushing environment, David experiences a profound internal collapse. He confesses, “I am losing all hope; I am paralyzed with fear.” In the original language, this means his spirit is completely overwhelmed, and his heart within him is numbed, desolated, and frozen. This is an incredibly important stop on our wisdom trek today. It reminds us that even the greatest spiritual giants can reach a point of complete emotional paralysis. The wilderness can get so dark, and the chase can last so long, that our internal resources completely run dry, leaving us numb to the world around us. The third segment is: The Pivot of Remembrance: Thirsting for Cosmic Intervention Right at the absolute nadir of his depression, when his heart is completely frozen, David makes a conscious, deliberate pivot in his mind, linking his thoughts together in verse five and verse six. I remember the days of old. I ponder all your great works and think about what you have done. I lift my hands to you in prayer. I thirst for you as parched land thirsts for rain. Faced with a paralyzing present, David forces his mind to look backward, declaring, “I remember the days of old. I ponder all your great works and think about what you have done. I lift my hands to you in prayer. I thirst for you as parched land thirsts for rain.” When you are paralyzed with fear, and can no longer see a way forward, the wisest thing you can do is look backward at history. David engages three distinct mental faculties here: he remembers, he ponders, and he meditates. He actively recalls the “days of old”—the cosmic history of Israel, the miraculous deliverance from Egypt, the splitting of the Red Sea, and the dramatic overthrow of the rebellious Canaanite kings. By contemplating these great historical works, David reminds his trembling heart that the God he serves is the undisputed Master of storm, sea, and human empires. This mental shift completely re-energizes his physical body. He moves from a posture of frozen paralysis to a posture of active, urgent worship, lifting his empty hands into the air. He describes his soul's condition using a striking agricultural metaphor: “I thirst for you as parched land thirsts for rain.” In the ancient Near East, a drought was not just an inconvenience; it was a terrifying threat of death and famine, often associated with the cosmic withdrawal of divine favor. David recognizes that he cannot fix his own drought. He stands before the Lord like cracked, dry, sun-baked desert soil, completely dependent on the sovereign windows of heaven opening up to pour out life-giving rain upon his parched soul. The fourth segment is: The Urgent S.O.S.: Pleading for the Morning Light The urgency of David's prayer reaches an absolute fever pitch as he strings together a rapid series of imperative petitions, grouping together the thoughts in verse Safen, verse eight, and verse nine. Come quickly, Lord, and answer me, for my depression deepens. Don't turn away from me, or I will die and go down to the grave. Let me hear of your unfailing love each morning, for I am trusting you. Show me where to walk, for I give myself to you. Rescue me from my enemies, Lord; I run to you to hide me. David sends out an urgent spiritual distress signal, crying, “Come quickly, Lord, and answer me, for my depression deepens. Don't turn away from me, or I will die and go down to the grave. Let me hear of your unfailing love each morning, for I am trusting you. Show me where to walk, for I give myself to you. Rescue me from my enemies, Lord; I run to you to hide...

Send us Fan MailPosting from the archives again this week! 3-years-ago Aaron and Marissa would live to have you join them for Proper 11. In this episode, we consider the readings for Proper 11, Year A:Genesis 28:10-19a & Psalm 139:1-11, 22-23; Isaiah 44:6-8 & Psalm 86:11-17; Romans 8:12-25; Matthew 13:24-30, 36-43. We discuss the Lord's promise to Jacob at Bethel, Isaiah's declaration of the Lord's unparalleled lordship alongside the absurdity of idolatry, Paul's beautiful exploration of the weight of glory, and Jesus' parable of the wheat and weeds.Notes:-Fr. Bruce Waltke's Sermon for July 9, 2023-Link to Death, Resurrection & the Life to Come playlist-"Hold Me Now/Whole Heart" song--The Bible Project--Bible Project on Jacob and Esau--Bible Project video on Isaiah--Bible Project video on Romans--Bible Project discussion, on Parable of the Weeds and Wheat--Kingdom, Grace, Judgment: Paradox, Outrage, and Vindication in the Parables of Jesus, by: Robert Farrar CaponOther Resources:--Dwell App--Metrical Collects7:07 Collect8:08  Genesis 28:10-19a & Psalm 139:1-11, 22-2327:08  Isaiah 44:6-8 & Psalm 86:11-1738:46 Romans 8:12-2556:09 Matthew 13:24-30, 36-43 Our outro music is an original song by our friend Dcn. Jeremiah Webster, a poet and professor whose giftedness is rivaled by his humbleness. You can find his published works, including After So Many Fires, with a quick Google.

Gods children will be vindicated and many will be launched in to their purpose at this hour. Those that have stayed the course and loyal to the most high will be used at this time. Judgement for the wicked is here and vindication for his children are here as well. The time is now! Stay close to God and lean on him no matter what.  Restoration, Vindication  and hope!Jeremiah 46John 15;19Send us Fan Mail

So much hype, so much doubt, so much noise, NSW Blues silence the critics after going to Brisbane behind enemy lines and stunning Queensland Maroons to win the Origin shield! But in a game mired by controversy, it's one that will be talked about for quite some time. Join Dan Ginnane, Blues icon Wade Graham, Maroons legend Shane Webcke and Ben Dobbin to review Origin 3 and they chat to some extremely vindicated Blues players who were more than willing to let us know how they truly felt! See omnystudio.com/listener for privacy information.

So much hype, so much doubt, so much noise, NSW Blues silence the critics after going to Brisbane behind enemy lines and stunning Queensland Maroons to win the Origin shield! But in a game mired by controversy, it's one that will be talked about for quite some time. Join Dan Ginnane, Blues icon Wade Graham, Maroons legend Shane Webcke and Ben Dobbin to review Origin 3 and they chat to some extremely vindicated Blues players who were more than willing to let us know how they truly felt! See omnystudio.com/listener for privacy information.

When Noni Madueke made his massive £48.5 million switch from Chelsea to Arsenal in the summer of 2025, a vocal section of the fanbase was openly unconvinced. Fast forward one season, and the electrifying 24-year-old winger has a Premier League title medal in his pocket and is currently lighting up the 2026 World Cup for Thomas Tuchel's England.In this episode, we break down how Madueke silenced his critics and established himself as a vital tactical weapon for both club and country:Unpacking the immense pressure of his cross-London transfer, the early fan skepticism, and how he quickly proved his worth to Mikel Arteta despite a frustrating autumn knee injury.A tactical look at his fearless, relentless ball-carrying abilities. We analyze how his direct, one-on-one menace provides Arsenal with the perfect rotational alternative to Bukayo Saka on the right wing.Reliving his crucial maiden strikes—including a clinical finish against Bayern Munich and a corner-kick goal against Leeds—culminating in a Premier League title and the agony of a shootout defeat to PSG in the Champions League final.Tracking his rapid rise on the international stage. We discuss his pivotal starts for England against Croatia and DR Congo, and how his pace is perfectly complementing Harry Kane as the Three Lions prepare to face Mexico in the Round of 16.Tune in as we discuss whether Madueke is finally ready to add ruthless end product to his game and cement his status as one of English football's premier attacking talents. Noni Madueke, Arsenal FC podcast, Premier League champions 2026, England national football team, Thomas Tuchel tactics, 2026 World Cup Round of 16, Chelsea transfer.

“This is surfing. The meanest people in the world get into making surfboards.” Surprising, then, that perhaps the most cartoonishly cheerful man in surfing right now also happens to be a board builder: Dan Mann. Dan's been something of a hot industry topic in recent months, following his contentious victory in Stab In The Dark X, when Kelly Slater elected to crown his own shaper the best of the decade, prompting several weeks of the surfing world accusing him of being a self-licking ice cream cone. But Dan would get a chance to respond soon enough. A month later, in the rapidly assembled Stab In The Dark starring Ethan Ewing, Dan's board, the same model, just scaled up to accommodate the mass and velocity of the Smooth Gorilla, made it all the way to the final, narrowly losing out to Hayden Shapes. Place some respect on the Mann's name. This episode also features Stab co-founder Sam McIntosh, filling the chair usually occupied by Dane Reynolds. Before you oil your pitchforks, here's the situation. It's proven difficult to lock Dane down week to week, given the number of businesses, projects, and assorted obligations currently competing for his attention. Frankly, we're lucky we've managed to get as much of him as we have. So, on the weeks Dane isn't around, we're experimenting with a slightly different flavour of StabMic: a more industry-focused edition, speaking with the people who keep the machinery of surfing turning, on both sides of the curtain. Think How Surfers Get Paid-lite. In this episode, Sam and Dooma shake Dan Mann by the shoulders and out fall the following opinions: surfboard shaping is too individualistic and needs more collaboration between the heads of the hydra; shit-talking is good for surfing, though he hates participating in it and especially hates being the recipient of it; PU boards are a bad habit we're collectively unwilling to quit; and the future of surfboard performance already exists, sitting in plain sight, waiting for us to develop the courage or financial incentive to embrace it. For those who still long for Dane, who miss him when he's not around, who've grown used to his weekly opinions arriving after decades of careful rationing, fret not. We've added a weekly Jordy and Dane segment. It will now exist in perpetuity, or until further notice. Alright then. Nature abhors a vacuum. This is episode #20 of StabMic.

We Want To Hear From YOU!He restoreth my soul. He leadeth me in the paths of righteousness for His name's sake." (Psalm 23:3)Notice what David didn't say.He didn't say:"Because I'm so faithful.""Because I deserve it.""Because I've earned it."David understood something.God restores because restoration reveals who He is.God guides because guidance reveals who He is.God forgives because forgiveness reveals who He is.God redeems because redemption reveals who He is.Every act of God is a testimony of His nature.The blessing isn't just about you.The blessing is revealing Him.Support the showwww.BibleDeliverance.org

Speaker: Brent Kercheville. Job 42 might be the most confusing part of the book. You will notice that Job 42:10 reveals that the Lord restored the fortunes of Job and the Lord gave him twice as much as he had before. What are we supposed to make of this ending? Is the message that, after your trials, everything […] The post Vindication After Suffering (Job 42) appeared first on Biblical Truths from West Palm Beach church of Christ.

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Gospel Unity Joe Gordon   Sermon Points: • The Picture of Unity • The Pathway of Unity • The Pattern of Christ • The Promise of Vindication

Evan Roberts and Tiki Barber revisit an old debate over which New York team would deliver the city's next championship, and Evan has to own a very loud miss after laughing off Tiki's belief in the Knicks. With the city still buzzing from a title celebration, the conversation turns to parades, predictions, and what comes next for a team now facing championship expectations. The hour also dives into a new manager decision stat that has Aaron Boone ranked at the top of baseball, much to Big Mac's delight, while Carlos Mendoza lands in the middle of the pack. Plus, the guys debate James Dolan's comments about the second apron, roast the Nets through Cinco de Lunch, and wonder whether sharp criticism from the Phillies broadcast could become the Mets' newest source of motivation.

A recent study examines if the hyper focus people with ASD experience is similar to creative flow, and the results have been a huge vindication for autistic people.I look at the 2 phenomena, the results of the study and the RTA method used. It's a happy wee episode, finally. Grma xStudy name: “In a state of flow: A qualitative examination of Autistic adults' phenomenological experiences of task immersion”Inline G Merch ⭐️www.Inlineg.myshopify.comInline G Patreon ⭐️www.patreon.com/TheInlineGFlutePodcastInline G will ALWAYS be free of charge, but signing up to the Patreon helps let this podcast reach new heights, if you can afford it. You'll also get to ask questions to upcoming guests as well as get early access to some episodes. Or if you'd rather not spend money, subscribing to my YouTube channel and following me on Facebook, Instagram and TikTok is a HUGE way to support the podcast. It'll cost you nothing, and it really makes a difference to the algorithm gods. So please interact however you can; like, comment, or subscribe, and help keep this podcast lit xIntro music: Rhythm=Power by Spodo Komodo. Used with permission. All rights reserved by the creator.Chapters:00:00 - The Cheshire Cat03:35 - Autism Spectrum Disorder09:30 - Money, Money, Money11:41 - Reflexive Thematic Analysis15:24 - Vindication for ASD

Billy and Jake hit News & Nonsense with GOATA vs WOATA takes and what Malik Nabers' value and injury profile really mean. They dig into the “scars” that shaped their playstyle, then fire off some vindication discussing why strong, backed‑up opinions matter. Next week tees up a full dive into what “value” actually is and how to weaponize it for dynasty edges.Join Our Discord: https://discord.gg/G77HWbDb7

A forest lookout sits alone in a glass tower at 2AM and spots flames crowning two distant pines — a fire only he can see. By dawn there's no smoke, no ash, no scorched earth... and no fire at all. From phantom flames that burn and vanish to the burned Bigfoot pulled from a Nevada blaze and the UFOs caught streaking through wildfire smoke, tonight we wander into the strange and unsettling things that appear when the forests burn.EPISODE BLOG PAGE (includes sources and full transcript): https://weirddarkness.com/ghostflamesREAD or DOWNLOAD the full transcript of this episode: https://weirddarkness.tiny.us/yjwtx7awFEATURED STORIES IN THIS EPISODE: The author of Frankenstein always saw love and death as connected. She visited the cemetery to commune with her dead mother. And with her lover. (Mary Shelley's Obsession With The Cemetery) *** A girl moves into a new apartment and discovers that a haunting doesn't necessarily have to be frightening. (Ghostly Happenings In My Old Apartment) *** The July 1886 murder at the Shawmut Avenue laundry was so shrouded in mystery that even the victim's name was uncertain. (The Wash-House Murder) *** Ghosts, high strangeness, and even Bigfoot – it appears they may all have something in common, and that would be forest fires. (Forest Fires and the Paranormal) *** How do you explain an experienced lookout reporting a blazing forest fire, only for it to disappear less than an hour later – leaving no trace? (Phantom Flames)CHAPTERS & TIME STAMPS (All Times Approximate)…00:00:00.000 = The Foreboding00:03:57.045 = Show Open00:05:40.844 = Phantom Flames00:21:25.265 = Forest Fires and the Paranormal00:35:10.279 = Mary Shelley's Obsession With The Cemetery ***0048:57.368 = Ghostly Happenings In My Old Apartment00:52:28.197 = The Wash-House Murder ***01:01:09.811 = Show Close*** = Begins immediately after inserted ad breakLISTEN ON PODCAST APPS: Look for this podcast on Apple Podcasts, Spotify, iHeart Radio, Amazon Music, Pandora, TuneIn Radio, and other podcast apps. Get a list of free listening apps here: https://weirddarkness.com/wdapps*No AI Voices Are Used In The Narration Of This Podcast*SOURCES and RESOURCES:“Phantom Flames” by F.A.Loomis from Idaho Magazine: http://ow.ly/beq730nL94u“Forest Fires and the Paranormal” by Brent Swancer for Mysterious Universe: http://ow.ly/ROYC30nL8n1“Mary Shelley's Obsession With The Cemetery” by Bess Lovejoy for the JSTOR Daily: https://tinyurl.com/y9cgd29w“Ghostly Happenings In My Old Apartment” by Cassie D, posted at MyHauntedLifeToo,com: https://tinyurl.com/ycexszvm
“The Wash-House Murder” by Robert Wilhelm, from the book “Wicked Victorian Boston”: https://amzn.to/2BGJOO0(Over time links may become invalid, disappear, or have different content. I always make sure to give authors credit for the material I use whenever possible. If I somehow overlooked doing so for a story, or if a credit is incorrect, please let me know and I will rectify it in these show notes immediately. Some links included above may benefit me financially through qualifying purchases.)WeirdDarkness® is a registered trademark. Copyright ©2026, Weird Darkness.Originally aired: March, 2021Weird Darkness opens a fire-themed descent that runs from a vanished forest blaze in 1976 Idaho through ghosts, Bigfoot, and UFOs born of wildfires, into Mary Shelley's graveyard education, a gentle apartment haunting, and an unsolved 1886 Boston murder.It opens with a U.S. Forest Service lookout stationed atop Pilot Peak in the Payette National Forest near Warren, high above the South Fork of the Salmon River, who woke sleepless at two a.m. in July 1976 and saw a bright orange triangle near a distant crest, then confirmed through binoculars two huge trees crowning out with flame. He calculated an azimuth with his fire-finder, radioed a two- to four-acre fire to the station fifteen air miles away, and watched it recede and vanish completely within forty minutes, leaving no smoke, no flame, and no charred ground at dawn six air miles out. Supervisors dubbed it the Pilot Peak phantom fire and sent smokejumper aircraft and hotshot crews to circle the ridge for nearly a week without finding a trace, until two months later a thousand-acre blaze on Zena Creek burned in roughly the same location he had reported.From there the episode widens into wildfires laced with the paranormal, beginning with the Curve Fire that struck South Mount Hawkins in the San Gabriel Mountains of California's Angeles National Forest on September 1, 2002, traced to a brittle 1935 wooden lookout tower and rumored to follow a cult ritual, after which hikers reported eyeless animals with hardened flesh and tall shadow figures akin to the Dark Watchers. It moves to the Battle Mountain Complex Fire near Battle Mountain, Nevada on August 6, 1999, where a letter forwarded to the Bigfoot Field Research Organization and a later call to investigator Thom Powell described firefighters capturing a burned, roughly seven-and-a-half-foot creature with a strong equine odor and near-human features. It closes with a July 2014 wildfire at West Kelowna near Vancouver, Canada, where a Castanet news video appeared to show an object shooting from a cloud, and a 2017 sighting by Arthur Frenette in New Hampshire's White Mountains, who watched a ball of fire plunge into Kinsman Ridge ahead of an out-of-control blaze.Next the episode turns to Mary Shelley, who in her 1831 introduction to Frankenstein traced her writing to her literary parents, though her mother, A Vindication of the Rights of Woman author Mary Wollstonecraft, died of puerperal fever days after her birth when Dr. Poignand removed the placenta with unwashed hands. Raised partly at her mother's grave in the St. Pancras churchyard, where she read her mother's work and escaped a strained home after father William Godwin remarried, the teenage Mary met Percy Shelley through the household and, at sixteen, declared love and reportedly first had sex among the tombstones. That fusion of reading, death, and forbidden knowledge surfaces in Victor Frankenstein's graveyard study of decay and in Godwin's 1809 Essay on Sepulchres, which framed visiting the illustrious dead as a form of communion the daughter carried into her novel of a creature assembled from corpses.From there the tone softens with a benign haunting recounted by a woman named Cassie, who moved into a larger, better-kept apartment over Christmas 2018 and lived there three months before moving in with her boyfriend. The internet blinked off repeatedly, cell reception failed in parts of the unit, electrical sockets quit working, bulbs burned out fast, and the shower switched itself on while she was away at classes. One night around one a.m. she and her boyfriend both heard the pitter-patter of bare feet in the kitchen, yet she never felt threatened, and when she left she said goodbye to whatever shared the space with her.The episode closes with the Wash-House Murder, the July 1886 killing of a Chinese laundryman found stabbed fourteen times in his Shawmut Avenue laundry in Boston's South End, his braided queue cut off and the five hundred dollars he had saved for a return to China gone. The victim's name was never certain, printed variously as Bin Chong, Ding Chong, and Wong Kong, and the case drew the Boston Police into a Chinatown governed by rival companies named Moy, Ching, Lee, and Sing. Detectives questioned the violent Moy company leader Ah Moy Chong and brought in New York interpreter Warry S. Charles, but the murder was never solved, and Charles himself was convicted of first-degree murder in 1908 after importing hatchet-armed assassins as a tong leader, leaving four dead in Chinatown.

Evan Roberts and Tiki Barber ride the wave of Knicks Finals excitement, starting with the messy and expensive scramble for tickets at Madison Square Garden and in San Antonio. With official sales unclear, resale questions growing, and fans weighing flights, hotels, and geo blocking, the road to seeing the Knicks chase a championship is already its own drama. The hour also digs into how reaching the Finals changes the Mikal Bridges trade debate, what a Knicks title would mean for Nets fans, and whether Jalen Brunson can fully claim the King of New York crown. Plus, Evan and Tiki hit on Drake Maye's future after New England's big AJ Brown move, the strangely quiet Jets, Giants kicking news, and a Knicks heavy edition of Posted and Toasted.

Brandon Sedloff and Brian Kosoy explore the transformation of retail real estate from distressed contrarian bet to institutional favorite. Kosoy, CEO of Sterling Organization, explains how his firm built a $4 billion vertically integrated shopping center platform by staying committed to retail through 15 years of headwinds—from the financial crisis through the retail apocalypse and COVID-19. He shares his unconventional path from failing out of Canadian schools to practicing real estate law in New York, then launching Sterling Organization in the summer of 2007, just as credit markets froze. They discuss: - Why vertical integration creates competitive advantages in tenant relationships and lease structuring that third-party management cannot replicate - The structural supply-demand imbalance driving a potential seven-year rent growth supercycle in grocery-anchored shopping centers - How being pigeonholed as "the shopping center guys" during a 15-year downturn created a durable moat as institutional capital returns to the sector - Why the average shopping center deal size makes it nearly impossible for large allocators to deploy $500 million quickly with quality managers - The difference between generating alpha in negative beta environments versus riding positive beta waves This episode examines how conviction through market cycles builds institutional platforms that can't be replicated by trend-followers or capital chasers. Links: Sterling Organization - https://www.sterlingorganization.com/about/ Juniper Square - https://www.junipersquare.com/ Brandon on LinkedIn - https://www.linkedin.com/in/brandonsedloff/ Topics: (00:00:00) - Intro (00:02:01) - Brian's background and career (00:16:08) - Building Sterling Organization (00:25:48) - Key stats for Sterling Organization (00:30:16) - Building conviction in the shopping center business (00:33:54) - Structural changes and themes for the industry in the future (00:40:23) - Vertical Integration (00:43:49) - Institutional Capital (00:46:20) - Common misconceptions about retail (00:50:19) - Things to keep an eye on

Is the Southern Baptist Convention about to split over women pastors? In this episode, William Wolfe sits down with Sam Webb and Jon Whitehead to unpack the fight over the pastorate, Dr. Al Mohler's Truth & Unity Amendment, and the vindication of Mike Law heading into Orlando. From Saddleback to the credentials committee, they explain why this is a first-order issue of biblical authority, anthropology, and SBC cooperation—and why 2026 may decide the fate of the SBC.   Timestamps: 0:00 – Intro: No More Women Pastors? 0:38 – Meet Sam Webb & Jon Whitehead 3:40 – How Mike Law Discovered the Women Pastor Problem 8:30 – The Rise of the Law Amendment in the SBC 13:40 – Why SBC Leaders Resisted for So Long 20:55 – Is This a Primary Theological Issue? Scripture & Sufficiency 27:40 – Anthropology, Gender, and the Battle for the SBC 32:45 – Mohler's Truth & Unity Amendment Explained 41:00 – Sam Webb's Warning to SBC, The Slippery Slope 45:40 – Call to Orlando: Final Challenge to Southern Baptists   ––––––   Follow Center for Baptist Leadership across Social Media: X / Twitter – https://twitter.com/BaptistLeaders Facebook – https://www.facebook.com/people/Center-For-Baptist-Leadership/61556762144277/ Rumble – https://rumble.com/c/c-6157089 YouTube – https://www.youtube.com/@CenterforBaptistLeadership Website – https://centerforbaptistleadership.org/   To book William for media appearances or speaking engagements, please contact him at media@centerfor­baptistleadership.org.   Follow Us on Twitter: William Wolfe - https://twitter.com/William_E_Wolfe Richard Henry - https://twitter.com/RThenry83   Renew the SBC from within and defend the SBC from those who seek its destruction, donate today: https://centerforbaptistleadership.org/donate/   The Center for Baptist Leadership Podcast is powered by American Reformer, recorded remotely in the United States by William Wolfe, and edited by Jared Cummings.   Subscribe to the Center for Baptist Leadership Podcast: Distribute our RSS Feed – https://centerforbaptistleadership.podbean.com/ Apple Podcasts – https://podcasts.apple.com/us/podcast/center-for-baptist-leadership/id1743074575 Spotify – https://open.spotify.com/show/0npXohTYKWYmWLsHkalF9t Amazon Music // Audible – https://music.amazon.com/podcasts/9ababbdd-6c6b-4ab9-b21a-eed951e1e67b BoomPlay – https://www.boomplaymusic.com/podcasts/96624 CastboxFM – https://castbox.fm/channel/id6132313 CastroFM – https://castro.fm/podcast/67110759-1bb9-4fd9-abcb-34113d42e945 CurioCaster – https://curiocaster.com/podcast/pi6894445 Fountain – https://fountain.fm/show/IURohE0rZPJr5h81wxbX Goodpods – https://goodpods.com/podcasts/center-for-baptist-leadership-565673 iHeartRadio – https://iheart.com/podcast/170321203 iVoox – https://www.ivoox.com/en/podcast-center-for-baptist-leadership_sq_f12419733_1.html Listen Notes – https://lnns.co/2Br0hw7p5R4 MoonFM – https://moon.fm/itunes/1743074575 PlayerFM – https://player.fm/series/3570081 PocketCasts – https://play.pocketcasts.com/podcasts/ddd92230-e3ff-013c-e7de-02cacb2c6223 PodcastAddict – https://podcastaddict.com/podcast/center-for-baptist-leadership/5090794 Podchaser – https://www.podchaser.com/podcasts/the-center-for-baptist-leaders-5696654 PodcastRepublic – https://www.podcastrepublic.net/podcast/1743074575 TrueFans – https://truefans.fm/center-for-baptist-leadership YouTube Podcasts – https://www.youtube.com/playlist?list=PLFMvfuzJKMICA7wi3CXvQxdNtA_lqDFV

One last riff about Paul Singer and Thomas Massie. Take it away, Ben. Michael Rabbitt tells you everything you need to know about the ordeal he and his five co-defendants faced in the Orwellian legal charade known as The Broadview 6 prosecution. They did nothing wrong other than exercise their right to protest against Operation Midway Blitz. Which is not wrong at all—exercising their rights, that is. Plenty wrong with the Blitz, of course. Michael is the Democratic committeeman of the 45th Ward in Chicago.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Actor and Hollywood stuntman Erik “All Day” Audé joins The Connect for one of the most intense stories ever told on the channel. In 2002, Erik was arrested in Pakistan after unknowingly being used as a drug mule and accused of attempting to smuggle narcotics through the airport. What followed was a nightmare: a death sentence, years inside one of Pakistan's most dangerous maximum-security prisons, brutal conditions, riots, violence, corruption, and the constant fear that he would be the next prisoner executed. Erik breaks down how he was deceived, what life on death row was really like, how he survived the prison system, and how he eventually fought his way back to freedom after proving his innocence. He also talks about his career as a stuntman, the importance of safety on film sets, and the lessons he learned from surviving the unimaginable. This is a story about betrayal, survival, faith, justice, and what it takes to keep fighting when the entire system is against you. Go Support Erik! Book: https://www.amazon.com/Years-Pakistan-Erik-Aud%C3%A9-Story/dp/B0D1YFHP5X Movie: https://www.amazon.com/Years-Pakistan-Erik-Aud%C3%A9-Story/dp/B07FSRBWGL Aude's Ice Cream Bar: https://www.instagram.com/audes_ice_cream_bar/ Tipsy Cow Bar and Grill: https://www.instagram.com/tipsycowshermanoaks/ Wine Bar: https://www.instagram.com/buvettela/ This Episode Is #Sponsored By The Following: Betterhelp! You don't have to be on this journey alone. Find support and have someone with you in therapy. Sign up and get 10% off at https://betterhelp.com/connect Lucy! Find LUCY near you at https://lucy.co/stores or save 20% on your first online order at https://lucy.co/CONNECT with promo code CONNECT. Join The Patreon For Bonus Content! https://www.patreon.com/theconnectshow 00:00 Erik Audé's Nightmare: Pakistan Death Row 01:39 Introducing Erik's Story & Book 03:11 Hollywood Stunt Work & Industry Dangers 07:53 On-Set Injuries & Stunt Safety Culture 13:32 Behind the Scenes: Drug Smuggling Logistics 16:08 Exploiting Drivers & Realities of US-Mexico Smuggling 18:29 Deception Schemes: Mules, Tragedy & Innocent Couriers 20:15 This Episode Is Sponsored By Betterhelp 21:24 How Eric Became a Dupe in the Drug Trade 29:34 Pakistan's Corrupt Justice System & Bribery in Courts 33:24 This Episode Is Sponsored By Lucy 34:56 Erik's Recruitment—A Glamorous Leather Industry Cover 46:14 Erik's First Suspicious Smuggling Trips 55:02 The Trip to Pakistan: Red Flags and Arrest 01:03:54 Jailed in Pakistan: Culture Shock and Danger 01:14:17 Abuse, Survival, and Corruption Inside Prison 01:25:27 Violence, Survival, and Learning the System 01:41:09 Erik's Survival Tactics and Prison Power Plays 01:54:28 Prison Riots, Boxing, and Navigating Pakistani Jail Hierarchy 02:15:33 Appeals, Pakistani Lawyers, and Winning Respect 02:35:07 Fighting for Freedom: Legal Maneuvering from the Inside 02:57:06 High Court, Vindication, and Leaving Pakistan 03:02:39 Return Home, Civil Suit, and Final Justice 03:11:00 Erik Today: Lessons, Life After Prison & Reflections 03:13:33 Final Thoughts & Where to Find Erik's Story Learn more about your ad choices. Visit podcastchoices.com/adchoices

Vindication by Bill Bennett

He will make your righteous reward shine like the dawn, your vindication like the noonday sun. [NIV]

WAKE UP AMERICA | THE SPLC WAS PAYING THE KKK, THE SUPREME COURT'S SECRET POWER GRAB, AND PETE DAVIDSON'S $700K MISTAKE Yesterday the federal government indicted the Southern Poverty Law Center on eleven counts of fraud and money laundering — accusing the organization of secretly funneling three million dollars in donor money to Klansmen, neo-Nazis, and Aryan Nations members through shell companies, in order to manufacture the very extremism they were raising hundreds of millions of dollars to fight. Today we go through every line of that indictment — the prepaid cards, the shell companies, the Unite the Right connection, the hate map that was used to blacklist conservative America, and the question nobody in the legacy media will ask: how many of the dots on that map were kept alive on donor money? Then Judge Andrew Napolitano joins us to break open a story buried for ten years — how Chief Justice John Roberts and four colleagues secretly rewired the Supreme Court in February 2016 while Justice Scalia was on vacation, erased two hundred years of constitutional precedent, and gave birth to the Shadow Docket. This one will make your blood boil. Plus O.W. Root of The Fitting Room joins us on Pete Davidson's $700K tattoo removal journey, the cleanskin vindication, and what his laser bill tells us about American men, identity, and a culture that confused trend-following for self-expression.

You Don’t Need It Mark Zoril Podcast Episode: #12 Podcast Date: 4/22/2026 Transcript  

Vindication is not a common word in our daily language, but the attitude behind it certainly is.  When we've been wronged or disbelieved, we usually feel compelled to make our case until the offending party admits their error.  We want the world to know that we were right.  But there are many biblical examples that demonstrate a very different response, so what should our attitude be in these situations? Join Julie Harwick for her insightful teaching on this subject.

Preached by Pastor Jared Kress on April 19, 2026.Main Idea: After the destructive release of the four horsemen, the natural question arises: What about the church? Here, as the fifth seal is broken, we see that there is a special protection and promise for those slain for Christ. Challenge:  What value do you place on living for Christ regardless of the cost? As the fifth seal is broken, we see:1. Those Slain for Word and Witness2. The Cry for Virtuous Vindication3. The Response of Pledge and Patience

What do you if someone suddenly thrusts a hand toward your face? You will instinctively block it or push it away. What do you do when someone hurls insults or accusations at you? You will probably have the urge to speak up in your defense or verbally attack that person. Human beings have a powerful innate desire to defend ourselves. But the people of God are commanded to respond to insult, injury, afront, accusation and persecution with patience and forebearence and trust the Lord completely for our ultimate vindication. To support this ministry financially, visit: https://www.oneplace.com/donate/791/29?v=20251111

We hope you enjoy today's Scripture reading and devotional aimed at equipping you for moral and spiritual transformation. Today's Bible reading is Job 19. To read along with the podcast, grab a print copy of the devotional. ESV Bible narration read by Christopher Ash. Follow us on social media to stay up to date: Instagram Facebook Twitter

Angela Stanton-King opens up about growing up in Atlanta, becoming a mother at a young age, and how the pressure to survive pushed her into hustling in the streets. Her path eventually led to two prison sentences, including the emotional experience of giving birth while incarcerated. In this episode of the Locked In Podcast with Ian Bick, Angela shares how she rebuilt her life after prison by writing books, starting a nonprofit, and becoming a voice for criminal justice reform. Her journey of survival, redemption, and reinvention ultimately led to receiving a presidential pardon, changing the course of her life forever. _____________________________________________ #angelastantonking #realhousewivesofatlanta #ianbick #lockedinpodcast #truecrime #federalprison #presidentialpardon #atlantageorgia _____________________________________________ Thanks to Married By The Mob for sponsoring this episode: Visit https://www.marriedbythemob.com/ to grab your tickets today! _____________________________________________ Connect with Angela Stanton-King https://www.instagram.com/theangiestanton/ _____________________________________________ Hosted, Executive Produced & Edited By Ian Bick: https://www.instagram.com/ian_bick/?hl=en https://ianbick.com/ _____________________________________________ Shop Locked In Merch: http://www.ianbick.com/shop _____________________________________________ Timestamps: 00:00 Growing Up, Family Background & Early Struggles 01:12 Childhood Rebellion & Teen Pregnancy 03:15 Dropping Out & Entering Street Life 06:10 Crime, Scams & First Arrests 09:00 First Prison Sentence & Life Behind Bars 13:00 Life After Prison & Reentry Struggles 15:15 Back to the Streets & Nonprofit Work 17:30 Second Prison Sentence & Motherhood in Prison 21:00 Loss, Grief & Family Separation 24:40 Federal Charges & Hitting Rock Bottom 27:00 Turning Point: Meeting Alveda King 28:35 Writing Her Story & Becoming an Advocate 33:35 Lawsuit, Vindication & Book Success 36:00 Rising Influence & Public Platform 38:40 Criminal Justice Reform & Trump Connection 42:54 Presidential Pardon & Redemption Story 46:01 How the Pardon Process Works 50:00 Advocacy, Recidivism & System Failures 53:15 Restitution & Barriers After Prison 56:00 Life After Prison & Public Judgment 01:00:00 Faith, Purpose & Advice for Others Learn more about your ad choices. Visit megaphone.fm/adchoices

Doree is justified in her weather worries and Elise discusses some potential weather-related travel interruptions. They also hear from listeners about shows to watch, exercises for during IVF, and some Trader Joe's beauty recs.To leave a voicemail or text for a future episode, reach Doree & Elise at 781-591-0390. You can also email the podcast at forever35podcast@gmail.com.Visit forever35podcast.com for links to everything they mention on the show or visit shopmyshelf.us/forever35.Follow the podcast on Instagram (@Forever35Podcast) and sign up for the newsletter at the free tier on Patreon! Hosted on Acast. See acast.com/privacy for more information.

For months, Donald Trump took slings and arrows for opposing further release of Epstein files. His haters claimed he was hiding something, but the latest release instead reveals the president was one of the first people to contact the police about Epstein's behavior. The show team reacts, then offers further insight on the viewership numbers for the All-American Halftime Show. Florida club owner Frankie Bianco joins after going viral for standing up to a deranged Bad Bunny fan. Watch every episode ad-free on members.charliekirk.com! Get new merch at charliekirkstore.com!Support the show: http://www.charliekirk.com/supportSee omnystudio.com/listener for privacy information.