Podcasts about depend

In this episode of Pray the Word on Judges 6:15–16, David Platt reminds us that God's presence is our strength in weakness.Explore more content from Radical.

Today, we are goingto look at the second mark of a genuine believer: “We rejoice in ChristJesus.” NoticePaul did not say we rejoice in our accomplishments. He did not say we rejoicein our denomination. He did not say we rejoice in our church attendance. Rememberwhen Jesus sent out the seventy-two, two by two, and they came back reportingwhat they had experienced. They said they had seen this and that, and that eventhe demons were subject to them. Jesus said, "Do not rejoice in that,but rejoice in the fact that your name is written in heaven" (Luke10:20). Jesus teaches us that our joy should be found in our relationship toHim. That is what Paul reminds us of here. Theword rejoice literally carries the idea of boasting or glorying in. So what arewe proud of? Jesus! What do we celebrate? Jesus! Who do we talk about? Jesus! Religionboasts about what people do. Christianity boasts about what Christ has done. Rememberthe Pharisee in Luke 18. He prayed, "God, I thank You that I am notlike other men." Then he proceeded to list all of his accomplishments.The publican, however, bowed his face before God, beat his breast, and criedout, "God, be merciful to me a sinner." Jesus said that manwent home justified rather than the Pharisee who had done so much. You see,true believers boast in Christ alone. Ilove the words of Galatians 6:14: "But God forbid that I should boastexcept in the cross of our Lord Jesus Christ." So when someone praisesus, we reflect that praise back to God. We thank Him. When something goodhappens in our lives, we thank God. When we see spiritual fruit, we give gloryto God. Why? Because we know where it all came from. Remember, Jesus said inJohn 15:5, "Without Me, you can do nothing." Now,the third mark that distinguishes a genuine believer is probably the mostimportant of all. Paul says here that: “We have no confidence in the flesh.”This statement strikes at the heart of human pride. The flesh refers to ourfallen nature. Paul says that true believers place no confidence in themselves.None. Zero. Our culture today teaches just the opposite. Believe in yourself.Trust yourself. Follow your heart. Depend on your abilities.  Scriptureteaches something very different. Jeremiah 17:9 says: "The heart isdeceitful above all things, and desperately wicked; who can know it?" InRomans 7:18, Paul proclaimed: "For I know that in me (that is, in myflesh) dwells no good thing." The flesh cannot save us. The fleshcannot please God. The flesh cannot produce righteousness. The flesh cannotearn heaven. Only Jesus Christ can do that. John19:30 records the triumphant words from the cross of Calvary, where Jesusproclaimed: "It is finished." Not partly finished. Not almostfinished. But finished. Completely. Totally finished. His death on the crossand His shed blood accomplished propitiation and satisfied God completely forour sins. Everything necessary for salvation was accomplished in Jesus Christ. Thatis why true believers have no confidence in the flesh. Our confidence is not inourselves. Our confidence is in Jesus Christ and in Who He is. The Holy Spiritnow dwells in us, and whatever He leads us to do, we can accomplish because itis God who works in us both to will and to do of His good pleasure. Solet me ask you a personal question today. Which of these three marks bestdescribes your life? Do you worship God from the heart? Do you rejoice in JesusChrist? Do you have any confidence in the flesh, or is your confidence inChrist alone? Or are you still trusting your own goodness, your churchmembership, your baptism, or your religious activities? My friend, the truebeliever worships God in the Spirit, rejoices in Christ Jesus, and placesabsolutely no confidence in the flesh. That is the difference between religionand a relationship with Christ. 

When I was at my most anxious in my relationships - I realised I had been depending on things outside of me being a certain way for me to feel safe, secure or calm. Can you relate?If so - I'm going to be talking today about the difference between internal and external safety.And I'm going to offer you concrete exercises to do to start building your internal safety muscle.So you can feel emotionally safe and secure - regardless of what's going on outside of you.Mentioned in the episode:1:1 coaching with Rebecca - email rebecca@rebeccaorecoaching.com

Por que algumas pessoas se aproximam quando sentem medo, enquanto outras se afastam?

O poema trata da dependência emocional extrema e da perda da identidade em um relacionamento abusivo ou tóxico.

Spontaneous short episodes recorded Off The Cuff from the heart and life of Matt Knoll

In This Episode AI is changing how businesses operate—but according to Ryan Redding, leadership remains the ultimate competitive advantage. In this episode, Adi Klevit interviews Ryan Redding, founder of Eightfold Advantage and former owner of Leverage, about his entrepreneurial journey from building a side hustle to growing, scaling, and successfully selling a digital marketing agency. Ryan shares how he transformed Leverage from a one-person operation into a global agency serving clients across North America, Australia, and the United Kingdom. Adi and Ryan dive deeply into the impact of AI on modern businesses. Ryan explains how his team embraced AI early, empowering employees to experiment with new tools and identify opportunities to improve efficiency. Rather than viewing AI as a threat, the company leveraged it to eliminate repetitive tasks, improve productivity, and allow team members to focus on higher-value work that required creativity, strategy, and human connection. The conversation also explores why AI cannot replace leadership. While technology can provide information, automate workflows, and accelerate analysis, Ryan argues that the biggest challenges in business are still human challenges. Accountability, communication, culture, trust, and leadership development remain areas where people—not technology—drive outcomes. Perhaps the biggest takeaway is that sustainable growth starts with the owner. Ryan explains that businesses often become trapped when leaders try to control every decision and solve every problem themselves. By investing in people, building strong systems, and creating accountability, business owners can build organizations that grow without requiring constant personal sacrifice.  

Spontaneous short episodes recorded Off The Cuff from the heart and life of Matt Knoll

Os pets deixaram de viver no quintal e passaram a ocupar um espaço muito maior dentro das nossas vidas: o coração, o sofá, a cama e, muitas vezes, o centro da dinâmica familiar.Mas será que todo esse amor está sempre fazendo bem?Neste episódio do PetDoc Cast, vamos falar sobre a transformação da relação entre humanos e animais nas últimas décadas e entender como os pets passaram a ser vistos como verdadeiros membros da família.Vamos discutir os benefícios dessa mudança, como a evolução da medicina veterinária, o fortalecimento do vínculo afetivo e o aumento da expectativa de vida dos animais. Mas também vamos abordar um tema que gera reflexão: quando o excesso de cuidado pode se transformar em um problema?

If your income disappeared tomorrow, how prepared would you be to replace it?In this solo episode, Amy Sylvis explores one of the biggest questions facing today's workforce: what happens if artificial intelligence significantly changes the employment landscape? Using recent layoffs from major companies as a starting point, Amy examines the risks facing mid-career and late-career professionals and why the consequences of job loss often become more severe with age.She shares statistics on reemployment challenges, discusses the growing importance of multiple income streams, and offers practical examples of how professionals are beginning to diversify beyond a single paycheck. From consulting businesses and intellectual property to dividend investing, private lending, and real estate, Amy encourages listeners to think proactively about creating options before they need them. This episode isn't about fear. It's about understanding risk, recognizing what you can control, and preparing for a future that may look very different from the past.Connect with Amy Sylvis:https://www.linkedin.com/in/amysylvis/Contact Us:https://www.sylviscapital.comhttps://www.sylviscapital.com/webinarinfo@sylviscapital.com00:00 Are AI-Related Layoffs Just Beginning?02:43 What Can We Actually Control?03:34 Why Job Loss Hits Harder Later in Life06:15 How Professionals Are Hedging Against Career Risk07:46 Building Income That Doesn't Depend on Your Time09:21 Is the Workforce Ready for What's Coming?10:34 Diversifying Income Like You Diversify Investments

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Links and Offers Mentioned in the show:Buy quality at Quince.com/BusinessGame - get free shipping and 365-day returns! Now available in Canada too!Receive up to 45% Off Native Path Collagen: Head to explorenativepath.com/Sarah,To try it risk-free with a 365-day money-back guarantee.Try QUO for free PLUS get 20% off your first 6 months when you go to Quo.com/BUSINESSGAMESupport this show by supporting the shows sponsors at SarahWestall.com/ShopLinks to Buy and Information for the Peptides Mentioned in the Show:MUST Sign up as a VIP to see certain peptides like Retatrutide at limitlesslifenootropics.com/vip-club-registration/?uid=116&oid=1&affid=10134
Purchase the most effective weight peptide available, Next Generation GLP-1 Retatrutide - use code Sarah to save 15%: www.limitlesslifenootropics.com/product/retatrutide-ha/?ref=vbWRE3JSee the Peptide stack for weight loss stack in the Ultimate Peptide Guide for Weight Loss and Muscle Preservation. This guide provides common dosages and guidance on the peptide stack used by Sarah:sarahwestall.substack.com/p/the-ultimate-peptide-guide-for-weightMasterpeace: Protect your body, dream better and be healthier. Remove Heavy Metals micro-plastics, toxins. Learn more and buy at masterpeacebyhcs.com/shop/?ref=11308MUSIC CREDITS: Down to the Wire – Nonstop Producer Series: Broad Media Internet LicenseCopyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.Disclaimer: "As a journalist, I report what significant newsmakers are claiming. I do not have the resources or time to fully investigate all claims. Stories and people interviewed are selected based on relevance, listener requests, and by suggestions of those I highly respect. It is the responsibility of each viewer to evaluate the facts presented and then research each story furtherSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

ChristoffelSneijdersis an MCC coach, behavioral expert, and creator of the 3 Brains Intelligence Theory—an approach that integrates neuroscience, coaching, and emotional intelligence by aligning the Head, Heart, and Gut. He has trained over 12,000 people in 33 countries and speaks on leadership, trauma, burnout, and personal transformation from a deeply embodied, science-based perspective.

Palavra ministrada no dia 24 de maio de 2026 pelo diácono Sodre.

The Bible says, “The LORD God of Jacob blesses everyone who trusts him and depends on him” (Psalm 146:5 CEV). In this message series, Pastor Rick talks about the ways you need to depend on God so that you can receive his blessing in your life.If you put your security in your bank account, your job, or your investments, it won't make you more secure—because you can still lose all those things. Pastor Rick explains through this message how you get God's blessing when you depend on his wealth and not your own. To support this ministry financially, visit: https://www.oneplace.com/donate/1103/29?v=20251111

The Bible says, “The LORD God of Jacob blesses everyone who trusts him and depends on him” (Psalm 146:5 CEV). In this message series, Pastor Rick talks about the ways you need to depend on God so that you can receive his blessing in your life.When you are criticized or attacked, you'll be tempted to defend yourself. In this broadcast, Pastor Rick explains why you need to let God be your defender and, instead of retaliating, respond like Jesus did when he was insulted. To support this ministry financially, visit: https://www.oneplace.com/donate/1103/29?v=20251111

God wants to bless your life. The problem is that people don't always choose to live in ways that God can bless. Listen to this series by Pastor Rick as he walks through Jesus' most famous sermon, the Sermon on the Mount, where he shared the Beatitudes—the conditions for receiving God's blessing on your life. They still apply to your life today!The Bible says, “The LORD God of Jacob blesses everyone who trusts him and depends on him” (Psalm 146:5 CEV). In this message series, Pastor Rick talks about the ways you need to depend on God so that you can receive his blessing in your life.If you really want God's blessing on your life, you can't depend on what your feelings or your gut tells you. Join Pastor Rick as he shares in this message how to get God's wisdom so that you will have fewer dead ends and failures in your life. To support this ministry financially, visit: https://www.oneplace.com/donate/1103/29?v=20251111

Quick Summary of This Post This post explores why thought leadership is no longer optional for small recruitment businesses, why most attempts at consistency fail, and how to build a practical five-component system that keeps your voice in your market without requiring you to start from scratch every week. If you run a recruitment business with a small team and want to stay visible without it consuming your diary, this is the framework you need. Think about the last time you sat down to write a piece of content for your business. A LinkedIn post. Something you had been meaning to send to your database. A short article you had planned weeks ago and kept pushing back. Now think about what stopped you. Not the first time. Not the second time. But eventually, what made you put it down and never pick it back up? For most recruitment business owners we speak to, the answer is the same. It was not a lack of ideas. It was not a lack of things to say. It was that there was no system underneath it. No repeatable process that kept it moving when life got in the way. And because there was no system, the moment a client called, the moment a candidate needed managing, the moment anything urgent appeared, the content went to the bottom of the pile. Again. Every week, it sits there; one week, your competitors show up and you don’t. This post is going to change that. What You Will Learn Why thought leadership matters more than ever in the current recruitment market The three reasons consistency breaks down for small recruitment businesses The five components of a thought leadership system that holds What the system looks like in practice for a typical recruitment business owner Why Thought Leadership is not Optional Right now Sometimes, thought leadership gets talked about like a nice-to-have. Something you do when things are good, when you have time, when business is ticking along. It is not a nice-to-have. And the market data from 2025 into 2026 is making that clearer than ever. 181 UK recruitment businesses entered liquidation in the six months to August 2025. That is an 18% year-on-year increase. The majority of those were micro and small agencies. Founder-led businesses, boutique specialists, people who were very good at what they did. But they were not visible enough in their market to weather the harder periods. The businesses that came through that same period in good shape were not always the most skilled. They were the most visible. They were the ones whose names came up when clients and candidates needed support. They had stayed present. Their competitors had gone quiet. Visibility in a difficult market is not about vanity. It is about being the name that comes to mind first when someone is ready to move. And thought leadership is the most sustainable way to build that kind of presence. When you share your perspective on what is happening in your sector, when you talk about what good hiring looks like, when you help your market think through a challenge they are facing, you are not just posting content. You are positioning yourself as the expert they should call. And that positioning compounds over time. The problem is that most recruitment business owners know this. They have heard it before. And yet, consistent thought leadership remains the first thing dropped when the diary fills up, which brings us to why. Why Consistency Fails Without a System There are three reasons thought leadership breaks down for small recruitment businesses, and each one needs a different solution. Time Running a small recruitment business means wearing multiple hats. You are billing, managing clients, sourcing candidates, running the team, and handling the finances at the same time. Content creation competes with all of that and rarely wins, because it rarely feels as urgent as whatever else is on your list. Perfectionism This one is less talked about but equally common. Many recruitment business owners hold back from publishing because they are waiting for something to be good enough. They want the post to be perfectly worded, the topic timely, the thinking original. And while they are waiting, their competitors are posting imperfect things that are still building their authority. The Absence of a Process This is the root cause underneath the other two. When content creation depends on motivation, on finding a window in a busy week, on inspiration striking at the right moment, it will always lose to the urgent. The only way to solve for time and perfectionism is to remove the decisions. To build a repeatable process that runs regardless of how the week is going. That is what we mean by a thought leadership system. Not a complicated content calendar. Not a full-time marketing operation. A simple, repeatable structure that keeps your voice in your market consistently, without requiring you to reinvent the wheel every week. The Five Components of a Thought Leadership System That Holds The word system can make this sound more complex than it needs to be. Each of the five components below is about reducing the number of decisions you have to make. Component One: One Primary Source of Thinking This is where your content starts. For most recruitment business owners, the richest source of thought leadership material is the work they are already doing. The conversations they are having with clients. The patterns they are seeing in their sector. The questions candidates are asking reveal something about the market. You already have opinions on all of this. The system starts with capturing them, even in rough form. A voice note on your phone after a client call. A short note in a document when something catches your attention. You are not creating ideas from scratch. You are capturing what is already there. Component Two: Your Primary Channels For recruitment businesses, two channels do the heavy lifting and work together: LinkedIn and email. LinkedIn puts you in front of your market in real time. It builds your profile with the people who could hire you and keeps your name visible in a marketplace where most of your competitors are silent. Email keeps you present with the people already in your world: the ones who have spoken to you before, engaged with your content, or been a contact for years. Those people are already warm. Together, LinkedIn and email give you reach and depth. LinkedIn builds the audience. Email deepens the relationship. These two channels, used consistently, will outperform five channels used sporadically every time. Component Three: a Repurposing Engine This is the part that makes a system genuinely sustainable. One piece of thinking should not produce one piece of content. A perspective you have on the candidate market this quarter can become a LinkedIn post, a section of an email to your database, a talking point in a client conversation, a short article, and a podcast topic. The thinking is the same. The formats are different. You are not working harder. You are working the same, thinking harder. This is where many small businesses leave significant value on the table. Component Four: a Scheduling Rhythm That Does not Depend on Motivation This means deciding in advance when content goes out, blocking time in the diary to batch-create it, and using scheduling tools to publish at the right time. You are not sitting down on a Monday morning, wondering what to post. You have already decided. When the time is blocked, you show up and create. When the content is scheduled, it goes out whether you are in a client meeting or on the other side of the country. Component Five: Team Involvement Many founder-led businesses overlook this because they assume thought leadership is the founder’s job alone. It does not have to be. If you have a consultant who is brilliant at what they do and knows the sector, their perspective is thought leadership. If you have someone close to the candidate market, their observations are thought leadership. The founder’s voice is still central, but a system that draws on the wider team is far less vulnerable to the week getting away from you. What This Looks Like in Practice Here is a simple picture of what this looks like for a typical recruitment business owner. On a Monday morning, you spend fifteen minutes reviewing the notes you have captured over the previous week. Client conversations, market observations, or something you read that sparked a thought. From those notes, you identify two or three topics that feel relevant right now. In a focused hour midweek, you draft content on those topics. Not perfect content. First-draft content. A LinkedIn post, a short note that might become an email. You are not writing for an audience yet. You are getting your thinking down. That draft goes through a quick review, either by you or a team member, and is scheduled to go out within the next week or two. The scheduling tool handles the timing. You do not think about it again until the next Monday morning. Over a month, that process produces eight to twelve pieces of content from your own thinking, without requiring a significant time investment, without depending on inspiration, and without competing with the urgent work in your diary. That is a thought leadership system. Not complicated. Not expensive. But consistent. And consistency is what builds the visibility that turns into clients. The businesses that do this well are not doing more than you. They have removed the friction. They are not waiting to feel inspired. They are following a process that keeps moving regardless of what else is happening. Thanks, Denise and Sharon How We Can Help If this framework sounds like what your business needs, consistent marketing activity, a system that keeps running even when you are fully focused on delivery, and the support of people who understand how recruitment businesses actually work, that is exactly what Superfast Circle is built for. We have just completed a significant update to the Superfast Circle programme. Members get access to recruitment-specific content resources, the frameworks to build your own thought leadership system, one to one coaching and marketing strategy session with us alongside group coaching calls. This is not a course you buy and never look at again. It is an ongoing membership with a rock solid guarantee in which your marketing builds momentum week by week, and you are never left to figure it out alone. If you would like the details on the updated programme, email us at support@superfastrecruitment.co.uk, and we will send everything across. The post How to Build a Thought Leadership System That Runs Without You appeared first on Superfast Recruitment.

Episode 151 of 2 Minute Disciple Season 5 explores Jesus' instructions to the twelve apostles in Matthew 10:5–10. Before sending them out, Jesus gives them authority to heal, cast out demons, and proclaim the Kingdom of Heaven. Then He tells them something surprising: travel light. No extra money. No extra supplies. No backup plan. Why? Because Jesus wanted His disciples to learn a lesson that remains essential for every follower of Christ: the mission of God depends on His authority, not our resources. In this contemplative Christian podcast episode, Nick guides listeners through a peaceful rhythm of slowing down, reading Scripture, noticing, meditating, responding in prayer, and practicing a daily spiritual habit. Together, we reflect on the tension between preparation and dependence, and the invitation to trust God when we feel under-resourced or unqualified. This episode is for anyone wrestling with fear, hesitation, insecurity, or the belief that they need “just a little more” before obeying God's call. It is a reminder that Jesus often sends us before we feel ready so that we learn to depend on Him completely.

(3) Professor Richard Epstein analyzes the legal history of birthright citizenship and Donald Trump's executive order, arguing that the 14th Amendment has been misinterpreted and that the child's status should depend on the parent's.1923 SCOTUS

OPEN HEAVENSMATALA LE LAGI MO LE ASO SA 31 ME 2026(tusia e Pastor EA Adeboye) Manatu Autu: Faalagolago tau o le Leoleo Mamoe Lelei  (Depend Only On The Good Shepherd) Tauloto Tusi Paia: Ioane 10:10 “O le gaoi, e leai se tasi mea e sau ai na ‘ona ia gaoi, ma fasi, ma fa‘aumatia; ‘ua ‘ou sau ‘ina ‘ia latou maua le ola, ‘ia maua atili ai lava.”Faitauga - Tusi Paia: Salamo 37:3-7Na folo e se tagata se tapui (mea faataulaitu) sa faapea e avea ai ia ma mauoa, peita'i na tumau pea le mativa, e le'i mauoa. E le gata i lea, na logoina ia o le a maliu pe a mavae le fitu tausaga ona o le tapui ua ia folo. Ina ua toe lua vaiaso na totoe e ola ai ma ua sioa atu le oti i ona fofoga, na tamoe mai i le RCCG. Ina ua ia faamatala mai ia i matou lona puapuaga na o matou ta'u i ai, o lo matou Atua o le lavea'i sili. Na ia ofo lona ola mo Keriso, ma ina ua o matou faia se tatalo puupuu mo ia, na ia pua'i mai i fafo le tapui sa ia foloina (tulou). Na toaga mai i le lotu mo le lua vaiaso, olioli ona o le fa'asa'olotoga na faia e le Atua mo ia. Ona faafuase'i lea ona le toe sau i le lotu. Ina ua o matou asiasi atu ia te ia na faapea mai, “Na ta'u mai e le tagata taulasea fai vai na faia le tapui, ia te a'u, na sesē lo'u foloina o le tapui.” Na ia toe folo se isi tapui, ma o le taimi la lea, na matua'i mauoa lava ia. Na ta'u e le taulasea ia te ia o le a soifua pea mo ni tausaga se fitu e fa'aaogā ma fiafia i ana oa ma ina ua lata ina uma le taimi (fitu tausaga) na faafuasei ona maua sana togafiti poto. Na ia filifili e alu e nofomau i Lonetona ma lona manatu, o temoni na o Aferika e mafai ai ona galulue. Peita'i o le tala faanoanoa na maliu I luga o le vaalele. O lo'u tamā i le Alii, Pa Josiah Akindayomi e masani ona fai mai, “O le a tausia oe e lou tamā i soosemea o ia te ia. Afai o ia te ia le leaga, o le leaga lena na te fa'aaogā e tausi ai oe, afai o ia te ia lelei, o le lelei fo'i e te maua mai ia te ia.” O le tiapolo e leai ma se mea lelei o i ai; afai e te alu i ai i soosemea e te mana'o ai, ia e mautinoa poo le a lava na te avatu ia te oe, e leaga. Peita'i o Iesu, o ia o le leoleo mamoe lelei; soosemea na te aumai e mautinoa lava e lelei. E tusa ai ma le Tusi Paia faitau o le asō, o le faamoemoe ma le faalagolago i le Atua lou auala i lona agalelei. O i latou e faamoemoe iā Iesu o le a maua le mana e avea ai ma atalii o le Atua (Ioane 1:12). O le a ia i latou le pule i le malosi uma o le fili ma e leai se mea e afaina ai i latou (Luka 10:19). E leai se auupega a le fili e fa'asagatau ia te i latou e manumalō (Isaia 54:17) ma latou te aai i mea lelei o le laueleele (Isaia 1:19).Le au pele e, e te faamoemoe ea tau lava o le leoleo mamoe lelei? O i latou e faamoemoe ia te ia e faapei o le mauga o Siona. Poo le a lava le tulaga e o'o i ai, e lē fa'agae'etia i latou. Ia e fiafia lava oe i le Alii ona foaiina lea e ia ia te oe o mea e mana'o i ai lou loto (Salamo 37:4). I le suafa o Iesu, Amene.

Data serve para conscientizar sobre os riscos do uso do tabaco e as táticas utilizadas pela indústria, além de promover medidas eficazes para acabar com a dependência. Brasil na lista dos ganhadores de prêmio por controlar tabaco. 

What if the right tools could help you scale your business without adding more chaos to your life? In this episode, I'm pulling back the curtain on the exact digital and non-digital tools I use to run my coaching and education business—from course platforms and AI tools to productivity systems, banking, podcasting, and content creation. After nearly 10 years of creating content online, I've refined the systems that actually save me time, simplify my workflow, and help me serve clients better. I'm sharing the platforms I personally use every day, the mistakes I made early on (yes, including trying to avoid paying for Zoom

Somos estimulados a buscar a nossa independência, nossa cultura nos diz que ela é um bem indispensável. É triste constatar que, muitas vezes, deixamos estes valores nos afetarem a ponto de pensarmos que sequer precisamos depender de Deus. Não são poucas pessoas que dizem crer em Deus, mas que não levam a vontade de Deus em conta na sua vida diária, nas decisões que toma e nas prioridades que estabelece. A Bíblia nos ensina um caminho oposto, o caminho da dependência total de nosso Deus, o caminho de uma vida que sabe que somente nosso Criador e Pai pode nos dirigir e sustentar. Ninguém pode desfrutar de uma vida plena sem confiar e depender do Senhor. Viver nesta bendita dependência é um privilégio, experimente viver assim, você jamais se arrependerá.

No Braincast 634, Carlos Merigo, Cris Dias, Hiago Vinícius e Luiz Yassuda discutem o vibe coding, a nova febre da IA que promete permitir que qualquer pessoa crie aplicativos, dashboards, automações e protótipos apenas descrevendo o que quer. A conversa passa por Claude, Codex, Lovable, Replit, Bolt, Cursor, Manus, low-code, SaaSpocalipse, token maxing e a fantasia do “unicórnio de uma pessoa só”. Afinal, estamos diante de uma revolução criativa, em que mais gente pode transformar ideias em produtos, ou de uma fábrica de gambiarras em escala industrial? Também entram no papo os riscos de segurança, vazamento de dados, dependência das big techs, código ruim, Shadow IT, empresas tentando substituir times inteiros por IA e a importância de repertório, critério e bom gosto num mundo onde executar ficou mais fácil, mas saber o que pedir continua sendo o grande desafio. No Qual é a Boa, ainda tem Cinemático sobre Obsessão, jogos como Crimson Desert e The Last Caretaker, o Anti-Authoritarian Toolkit, IA em Curso, The Traitors e Momento Faustão. -- CONHEÇA OS CURSOS DA ESCOLA DE IA DA PUCPR https://posdigital.pucpr.br/areas/escola-de-ia?utm_source=podcast&utm_medium=braincast&utm_campaign=pucpr_externo_leads_ativacao-1_escola-ia&utm_content=audio_atributo_26-05-17 -- 04:17 PAUTA 05:37 O que é vibe coding 08:31 Origem e ferramentas 09:52 É programação mesmo 14:50 SaaSpocalipse e limites 19:59 Dilema do monstro 25:30 Token maxing e tralha 27:50 Low code e democratização 30:37 Agentes e checagem 34:10 Programadores e IA 34:52 Autocomplete e Vibe Code 38:52 Hype e corrida da IA 39:56 Segurança e dados 41:45 Automação pessoal útil 43:55 SaaS pequeno vs grande 46:07 Sites leves sem WordPress 49:57 Canva e custos ocultos 57:09 Dependência e mediação 59:45 Legado corporativo e suporte 01:02:57 Habilidades e formação 01:11:40 Bom gosto e repertório 01:12:46 Curiosidade como profissão 01:15:03 Educação e base teórica 01:18:00 A febre dos prompts 01:18:50 QUAL É A BOA 01:28:56 Toolkit anti autoritário 01:34:38 Cupom IA em Curso 01:35:24 Reality The Traitors 01:40:06 Momento Faustão -- ✳️ TORNE-SE MEMBRO DO B9 E GANHE BENEFÍCIOS: Braincast secreto; grupo de assinantes no Telegram; e episódios sem anúncios!

Have you been fighting the weeds yet this spring? Whether it is in gardens, yards, or fields, the weeds seem to be a perpetual problem. But we often give little thought to the native weedy species.

One week ago, the state of Louisiana’s Legislative Auditor’s office released its annual fiscal review of Grambling State University’s athletics program for the year ending June 30, 2025. The school was cited for a few audit irregularities and quickly announced that changes were being made.  That wasn’t the biggest news, though. Grambling’s athletics department lost $5.1 million for the fiscal year ending June 30, 2025.  Revenues were reported at $9.2 million versus annual expenses of $14.3 million. In percentage terms, Grambling’s revenue was only 64% of the amount needed to sustain the athletics programs at the current level. Grambling wasn’t the only north Louisiana public university whose athletics spending exceeded revenue last year The same Louisiana Legislative Auditor also filed reports earlier in 2026 for Louisiana Tech University, Northwestern State University and the University of Louisiana at Monroe. Each of these four football-playing state universities located north of Alexandria reported losses in their athletics programs for the year ending June 30, 2025.  Grambling’s massive deficit grabbed the recent news headlines, but there is a troubling commonality among Louisiana public schools not named LSU. Louisiana Tech recently cut an expensive deal (rumored to be in the vicinity of $8 million) in order to exit Conference USA and join the more geographically-suitable Sun Belt Conference. That move may turn out to be prudent for the Bulldogs over the long-term.  Louisiana Tech’s annual travel expenses as part of Conference USA totaled nearly $3.5 million.  Nearby Sun Belt rival UL-Monroe’s travel costs for the same year were $2.3 million.  Louisiana Tech is expected save $1 million or more annually on its travel expenses beginning this fall by moving to the Sun Belt Conference. Let’s look under the hood at each of these four universities’ athletics spending.  We’ll finish with a few common sense (cheap) ideas on how to achieve break-even in the future. Grambling State University – 5,200 students (2024/2025 school year) Grambling is nationally known for its athletics and its exceptional marching band.  It was bit surprising to learn that Grambling’s football program had lost $2.5 million in the most recent year.  That amounted to about 50% of the athletic department’s annual deficit of $5.1 million. The football team’s travel costs of $1.1 million last fall were higher than all three of the other north Louisiana pubic schools. The expense summary also showed nearly $160,000 was spent to cover the costs of the school’s spirit groups (for one or more road trip performances). Grambling’s men’s and women’s basketball teams each posted losses in excess of $900,000 for the most recent year. Grambling (like Northwestern State) participates NCAA’s FCS small college football division.  The G-men play in the Southwestern Athletic Conference (SWAC). Louisiana Tech University – 12,145 students (Fall, 2025) The Bulldogs are based in Ruston.  Louisiana Tech’s campus is less than six miles east of Grambling via Interstate 20.  The Bulldogs have been competing in Conference USA and a part of the NCAA FBS major college football division.  As mentioned earlier, Louisiana Tech moves into the Sun Belt Conference this fall. Audit results for Louisiana Tech’s athletics department last year showed a loss of $11.875 million. Football lost “only” about $1.6 million for the year.  Louisiana Tech’s men’s and women’s basketball teams each ran a deficit of about $700,000 apiece.  Other competitive sports at Louisiana Tech lost another $2.6 million.  The school’s income statement showed “non-program specific” athletic costs with a $6 million shortfall. As noted earlier in this report, Louisiana Tech’s overall travel costs playing in far-flung Conference USA were easily the highest in the group.  The Dogs’ annual total of $3.5 million for travel exceeded Grambling ($2.6 mm), UL-Monroe ($2.3 mm) and Northwestern State (less than $1 million). Northwestern State University – 8,402 students (Fall, 2025) The Demons from Natchitoches, Louisiana came the closest to break-even within its athletics programs among these four state schools.  Northwestern State participates in the NCAA’s FCS small college football division in the regionally-aligned Southland Conference. Northwestern State posted a relatively benign loss of $167,245 for the fiscal year ending June 30, 2025.  The Demons’ men’s basketball program ran the largest deficit at more than $300,000.  The football team came up short by $280,000. Noteworthy, Northwestern State collected nearly $1.5 million in annual student fees to help support athletics.  That was about 10% of the school’s athletics spending.  It was the highest total among these four north Louisiana state schools. University of Louisiana at Monroe (ULM) – 8,678 students (Fall, 2025) Sun Belt Conference member ULM (like Louisiana Tech) competes in the NCAA’s FBS major college football division.  ULM is expecting to benefit from Louisiana Tech’s arrival in the Sun Belt Conference this fall with increased attendance and revenues expected at home games in all major sports. The Warhawks’ athletics budget is the smallest among the NCAA’s 136 FBS major colleges.  ULM’s overall school budget has been prone to massive shortfalls in recent years, too.  That means that ULM’s $1.5 million athletics department loss in fiscal year 2025 is much harder to cover. Audit results showed the UL-Monroe football program lost a whopping $3.8 million in the most recent report.  The Warhawks’ men’s and women’s basketball teams lost a combined $2.5 million.  Ouch! The school’s institutional support has kept the ULM athletics department afloat for years.  Significantly higher fan support for the Warhawks football and basketball programs is needed immediately.  Otherwise, the school may have no other choice but to consider returning to the NCAA FCS small college athletics division. A few suggestions from SwampSwami to achieve fiscal break-even These four north Louisiana state universities are located within 100 miles of each other.  Each school is a very large and important employer in its home city. These state schools must immediately address their athletics spending and move quickly toward achieving fiscal balance.  At the same time, they must also work harder and more creatively to raise sports revenues over the long-term to grow the athletics programs. First things first – Take immediate cost cutting measures – The simplest and fairest way is to voluntarily reduce athletics spending by cutting a certain percentage across the board.  That could come in the form of job reductions or, perhaps, an across-the-board pay cut for staff making more than $30,000 per year.  For example, a 5% mandatory spending reduction in Year 1 may spur some voluntary budget trimming beyond that level.  Yes, this likely means one less assistant coach, one fewer support staffer, one less charter flight, etc.  The athletics departments must take a hard look at streamlining operations.  Learn to do more with less. Refuse to play long-distance road games unless the school earns a significant profit by participating – UL-Monroe’s football team hits the road for at least two “Clobbering Time” payday games every season. They are often paid more than $1 million to play at large universities such as LSU, Texas A&M, and Alabama with huge stadiums.  ULM receives more money from some of these massive “visitor” paychecks than playing a home game in front of a sold-out stadium.  There are also downsides from being on the receiving end of a couple of massive road losses every season, too.  The football team and local fans can become a bit demoralized about the team’s chances for the remainder of the season. Now, let’s try to grow the revenue side with a few cheap ideas Stimulate increased student, alumni, and hometown support – Student and local support for the athletics programs within each of these four communities (Grambling, Ruston, Natchitoches, and Monroe) must improve.  Local fans want to see their sports teams having a chance to win more than they lose.  Identify sports which are cost-effective and give the school the best chance to hang a new (and long overdue) championship banner. Success in any of the major sports at these four schools can go a long way in rejuvenating and expanding the school’s athletic support base. Improve local marketing and promotion – It may sound corny but handing out free tickets to youth at local elementary, junior high school and high schools gets parents and guardians to purchase tickets, too.  A positive game day experience for that youth can plant a valuable seed about attending that college some day.  Each of these four north Louisiana public universities have thousands of empty seats available at football and basketball games.  A purposeful campaign to encourage and engage more youth at nearby college sporting events will pay future dividends. Inject more game day excitement – Utilize the pregame, quarter breaks, halftime, and post-game times to get fans more engaged.  Experiment with creative new (and inexpensive) ideas to keep the game experience fresh for all ages of fans. They will be more likely to return if they are having more fun at the games. Relentlessly promote ahead – There are only a few home football games played each fall.  Make each game special with its own promotion.  There are, perhaps, twenty home basketball, baseball or softball home games, too.  Give thought as to how to make each home game unique for fans. Target every recent (last few years) ticket purchaser by sending a weekly email.  Remind them of the school’s upcoming weekly sports schedule, special promotions, and discounts.  Utilize all types of social media to reach a wider audience to spread the word about upcoming college athletic events and team opponents. Depend on your own athletics staff to get the word out – Sadly, we live in a world with fewer and fewer exceptional local newspapers.  It is incumbent on each school’s athletics department to take an aggressive role in publicizing and promoting all ticket-based sporting events.  Fans want to know about the school’s upcoming games and events, so take the initiative! The post North Louisiana’s College Sports Programs are Underwater appeared first on SwampSwamiSports.com.

Take Me To The Club EP26 Show: Take Me To The Club Artist: Stefan Makepeace Air Date: 22 May 2026 Genre: House / Funky House / Tech House / Jackin' House / Deep House The Biggest House Tracks here with your host Stefan Makepeace on Data Transmission Radio. We have a ton of amazing house tracks, perfect for kicking starting your weekend. ________________________________________________________ ## STEFAN MAKEPEACE MUSIC Break Me - www.beatport.com/release/break-me/5381431 Up High - www.beatport.com/track/up-high/20671847 Body Talk - www.traxsource.com/track/13582961/…lk-original-mix Missing You - open.spotify.com/track/1PZ8bAiKJv…8350148ecc794371 Out Of Control (Stefan Makepeace Remix) - open.spotify.com/track/6FU5qtcTab…ae6232895e1c4a40 I Don't Depend - open.spotify.com/track/3ECedsTBAo…3b0019e91c4346e5 ________________________________________________________ ## SOCIALS Instagram | www.instagram.com/stefanmakepeace/ Tiktok | www.tiktok.com/@stefanmakepeace Soundcloud | on.soundcloud.com/r5JeC5FLNQQ4uNxm6 Spotify | open.spotify.com/artist/6hfJIuSp2…42Txu0C2h09XVbWw Beatport | www.beatport.com/artist/stefan-makepeace/1210080 Traxsource | www.traxsource.com/artist/836379/stefan-makepeace ________________________________________________________ #vocalhouse #housemix #techhouse #poolparty #houseparty #gymworkout #gymmix #summermix #underground #djmix Tracklist: 1) Keep My Mind - Roel 2) Music In Me - Wh0, Anelisa Lamola, Revival House Project 3) Moving Along (Extended Mix) - Eachother 4) Housefire (Original Mix) - Mike Speaks 5) Give a Little Love - Block & Crown 6) My Life Is A Disco (In The Mix) - MIXMASTERS, Mellizos 7) Keep on Walkin' feat. Nada Leigh - Nada Leigh, St. Croix 8) Zero Sleep (Smudged Soul Bootleg) - Zero B vs ATFC 9) Be Mine - Eva, Ellie Scougall 10) Squeeze My Heart - Reiss Ruben 11) Good Vibrations - Tensnake, Bobby Harvey, Sarah Bird 12) Starlight - Fran Ares, Sergiodnine 13) Runnin' (Extended Mix) - Alex Lynch, Finn Penny 14) Milton Shadow - Black Night (Extended Mix) - Milton Shadow 15) Dance With U - DJ Mes, Turntables Night Fever 16) Again Do It Again (Extended Mix) - Milk Bar & Antonio Contino Originally broadcast on Data Transmission Radio. Listen live and explore the archive: https://radio.datatransmission.co

What if the reason you feel exhausted isn't that you're doing too much, but because your business is designed in a way that requires you to do everything? You've tried taking time off, hiring more people, and getting more organized, but nothing has changed. You're still the one solving problems, making decisions, and holding everything together. You're not doing anything wrong, but you're stuck. Your business is structured around you, and when everything flows through you, you don't get relief, space, or time to lead. The key isn't working less, but it's about redesigning how your team operates. When that shifts, your team will step up, problems will stop stacking up, and you will get time back. In this episode, you'll discover why burnout is a symptom (NOT the root problem), the hidden ways businesses become owner-dependent, why high-performing entrepreneurs become the bottleneck, what needs to shift to create real time freedom, and how sustainably profitable businesses are intentionally designed. Trust us–no amount of rest will fix a business that still requires you to carry it all. If your business looks successful on paper but feels exhausting in real life, this conversation will help you understand why—and what to do next. Ready to build a business that supports your life? Join Kaitlyn Beaver and Dr. Sabrina Starling now.Profit by Design is a Tap the Potential production. Show Highlights:Stepping out of the weeds to redesign your businessFrom Dr. Sabrina: An example of a business owner stuck in fear-based leadership.Treating surface-level problems doesn't help you get unstuck.Our action plan supports you in real time.One step at a time gets you closer to change. There is no magic pill.The difference between entrepreneurs who burn out vs. profitable businesses with a thriving, healthy business owner (It all comes down to systems and a willingness to invest in coaching.)Info about our upcoming free workshop, How to Reclaim 10 Hours per Week (and pay yourself more!) Click here for more info!Understanding the dangers of unprofitable revenueResources:If you're realizing this isn't burnout—it's structure, join us in our workshop, How to Reclaim 10 Hours per Week (and pay yourself more!) We'll show you exactly how to redesign your business so it no longer depends on you. Register here.Mentioned in this episode:Jumpstart Your Business!You've built a successful business—but it's still running you. Join us to reclaim 10 hours a week and finally step into your role as the owner—register now: https://www.tapthepotential.com/jumpstart.

Federal Medicaid cuts could mean a loss of at-home services for Arizonans with disabilities. We'll meet three Arizonans whose families rely on those services. Plus, a University of Arizona astrophysicist and 2026 Guggenheim Fellow who posts about all things space on Instagram.

Glen chats with Lee Wetherington about Jack Henry's newly minted survey of nearly 200 credit union and bank CEOs, exploring how strategic priorities and top concerns differ by group and how they've shifted over time. Also, a treasure trove of new industry data- on consumer payment choice, household economics, crypto/stablecoin usage, and a breezy 269-page NCUA stroke of GENIUS for your holiday reading pleasure.      Links related to this episode: Jack Henry's 2026 Strategy Benchmark Study: https://discover.jackhenry.com/strategy-benchmark-study-2026 FRB Services' Diary of Consumer Payment Choice: https://www.frbservices.org/news/research/2026-findings-diary-consumer-payment-choice The Kansas City Fed's paper on stablecoin use: https://www.kansascityfed.org/documents/15703/PaymentsSystemResearchBriefing26Noll0410.pdf The Federal Reserve Board's 2025 Survey of Household Economics and Decisionmaking (SHED): https://www.federalreserve.gov/publications/files/2025-report-economic-well-being-us-households-202605.pdf   CU Today on the NCUA's proposed approach to implementing the GENIUS Act: https://www.cutoday.info/THE-feature/NCUA-s-New-Stablecoin-Framework-Sparks-Debate-Over-CUs-Best-Digital-Dollar-Strategy  Join us for our next CU Town Hall- Wednesday May 20 at 3pm ET/Noon PT- a live and lively interactive conversation tackling the major issues facing credit unions today. In this session, John will dissect OpenAI's new personal finance offering. The Town Hall is free to attend, but advance registration is required:  https://www.cutownhall.com/   Follow us on LinkedIn:  https://www.linkedin.com/company/best-innovation-group/   https://www.linkedin.com/in/jbfintech/  https://www.linkedin.com/n/glensarvady/

Sir Thomas has a full-on crashout when Fanny reveals that she wishes to refuse Henry Crawford. He cross examines her until she cries, and she narrowly escapes him finding out about her crush on Edmund. But after absolutely tearing her to shreds, he still gets a fire going in the East Room. The next day, Fanny finds herself alone with Henry Crawford.Topics discussed Fanny's empty fireplace, the "little privations" the Bertrams have bestowed on Fanny, Becca's 1-4 scale for categorizing peoples' morals vs. how good a hang they are, the conditionality of Fanny's position at Mansfield Park and the transactional nature of her relationship with the Bertrams. A COURT OF MIST AND FURY SPOILERS AT 42:40Patron Study Questions come from Ghenet and Avi.l Topics discussed include whether Sir Thomas would have believed Fanny if she told him about Henry's debauchery and Fanny's refusal to break her convictions about Henry.Becca's Study Questions: Topics discussed include how Sir Thomas and Fanny's relationship shifted during the fight and the result of the mismatch between them, the symbolism of the fire, and why this chapter reads more like Proposalgeddon than the last chapter.Funniest Quote: She was preparing to obey, when Mrs. Norris called out, “Stay, stay, Fanny! what are you about? where are you going? don't be in such a hurry. Depend upon it, it is not you who are wanted; depend upon it, it is me” (looking at the butler); “but you are so very eager to put yourself forward. What should Sir Thomas want you for? It is me, Baddeley, you mean; I am coming this moment. You mean me, Baddeley, I am sure; Sir Thomas wants me, not Miss Price.” But Baddeley was stout. “No, ma'am, it is Miss Price; I am certain of its being Miss Price.”Questions moving forward: Will this be a love story? What's going to happen in this conversation between Henry and Fanny? Why are they unchaperoned?Who wins the chapters? FannyGlossary of Terms and Phrases:imputing (v): giving the blame or credit for to some person or cause.misapprehension (n): an understanding or belief about something that is not correctGlossary of People, Places, and Things: Fire Island, A Court of Thorns and RosesNext Episode: Mansfield Park Volume III Chapters 2-3Our show art was created by Torrence Browne, and our audio is produced by Graham Cook. For bios and transcripts, check out our website at podandprejudice.com. Pod and Prejudice is transcribed by speechdocs.com. To support the show, check out our Patreon! Check out our merch at https://podandprejudice.dashery.com.Instagram: @podandprejudiceTwitter: @podandprejudiceFacebook: Pod and PrejudiceYoutube: Pod and PrejudiceMerch store: https://podandprejudice.dashery.com/

Proverbs 3:5-6 - Dependance Determines Direction |   Series: Homegrown: Parenting in Proverbs  | Sam Holm, Lead Pastor |  Preached 5-17-26 10:45am  Tag: Proverbs, Parent, Parenting, Plants, Wisdom, Christian, Solomon, Leadership, Bible, Family, Faith, Children, Kids, Mom, Dad, Graduation, Grad, Life, Crutches, Lean, Depend

Proverbs 3:5-6 - Dependance Determines Direction |   Series: Homegrown: Parenting in Proverbs  | Sam Holm, Lead Pastor |  Preached 5-17-26 10:45am  Tag: Proverbs, Parent, Parenting, Plants, Wisdom, Christian, Solomon, Leadership, Bible, Family, Faith, Children, Kids, Mom, Dad, Graduation, Grad, Life, Crutches, Lean, Depend

In this message from Matthew 5, Pastor Caleb teaches through the first two Beatitudes: “Blessed are the poor in spirit” and “Blessed are those who mourn.” This sermon explores humility, dependence on God, surrender to Jesus, grief, repentance, and the comfort God brings through His presence, forgiveness, and restoration.Click here to view the episode transcript. (00:00) - Welcome and Introduction to the Beatitudes (00:44) - Understanding the Sermon on the Mount (02:10) - Why Crowds Traveled to Hear Jesus (04:35) - A Different Kind of Revolution (06:13) - The Beatitudes Must Lead Us to Jesus (07:24) - Matthew 5 and the First Two Beatitudes (07:50) - What Does Blessed Really Mean? (11:45) - Present Assurance and Future Promise (13:10) - What It Means to Be Poor in Spirit (15:52) - Solomon and the Emptiness of Life Without God (20:24) - The Kingdom of Heaven and Full Dependence on God (27:07) - Depend on God Daily, Not Just in Crisis (29:23) - Blessed Are Those Who Mourn (31:29) - Learning to Grieve in a Healthy Way (35:06) - Mourning Sin, Suffering, and What Breaks God's Heart (37:16) - God's Comfort, Forgiveness, and Restoration

Pastor Luke spoke about being fully dependant on the Lord, like David was when he faced Goliath.

When the alarm sounds and lives are on the line… leadership gets real. On this episode of The MisFitNation Show, host Rich LaMonica welcomes Fire Captain Mark Andrew, author of Leading Through the Heat—a powerful leadership book built from real lessons learned in one of the most demanding professions on earth. In the firehouse, there is no room for: ❌ Micromanagement ❌ Ego ❌ Empty commands ❌ Fake leadership There is only trust, preparation, teamwork, and decisive action. After decades in the fire service, Mark Andrew has seen what separates leaders who earn loyalty from those who lose it. In this conversation, Mark shares the hard truths about leadership that apply far beyond emergency response—whether you lead in business, the military, entrepreneurship, education, or everyday life. We dive into:

Teacher: Rob Zimmermann Download Sermon Notes  Watch Episode Give Online: http://westgatechapel.org/give Chapters (00:02:18) - Praise for the Sovereign God(00:06:28) - Mother's Day at Westgate(00:07:37) - Wonders of Westgate Chapel Welcome!(00:09:44) - How to Invite Everyone to VBS(00:14:02) - Donating to our Kids Ministry(00:15:44) - A Moment of Remembrance(00:20:07) - The Lord's Supper(00:28:54) - Depend on You(00:37:59) - First Things First(00:39:48) - Psalm 51: Making Jesus Lord of Our Lives(00:44:32) - Paul's Making Jesus the Lord of My Life(00:53:36) - Case Study(00:57:44) - The Perfect Image of Sin(01:05:44) - David's Sin in Second Samuel(01:07:16) - David's Personal Experience in Repentance(01:15:13) - Psalm 51(01:16:37) - Putting Your Faith in Jesus First(01:27:30) - Put Jesus First

Who Do I Depend On?

What if the very thing that tried to define you doesn't get the final say? In Week 2 of our Hidden Figures series, Pastor RJ Ciaramitaro explores the life of Jabez—a man whose name literally meant pain, yet whose prayer changed his trajectory. This episode challenges us to stop accepting limiting narratives and start praying bold prayers that align with God's purpose. Discover how to: • Break free from labels tied to your past • Ask God for more without guilt • Depend on God's presence in every step • Trust Him to turn pain into purpose Your past may explain you, but it doesn't have to define you.

NOAA affects your daily life more than you think, from the weather forecasts you check to the seafood you eat, yet most people have no idea how important it really is. In this episode, we break down what NOAA actually does, why it matters for your safety, food, and environment, and what could happen if funding cuts weaken its ability to operate. Ocean science plays a critical role in predicting hurricanes, managing fisheries, protecting marine wildlife, and understanding climate patterns like El Niño and La Niña. But when programs are cut or overlooked, the consequences ripple through communities, economies, and ecosystems in ways most people never see coming. This conversation with Jeff Watters from Ocean Conservancy reveals the hidden systems behind NOAA, why public science infrastructure matters, and what's at stake if we stop paying attention. Support Independent Podcasts: https://www.speakupforblue.com/patreon Help fund a new seagrass podcast: https://www.speakupforblue.com/seagrass Join the Undertow: https://www.speakupforblue.com/jointheundertow Connect with Speak Up For Blue Website: https://bit.ly/3fOF3Wf Instagram: https://bit.ly/3rIaJSG TikTok: https://www.tiktok.com/@speakupforblue Twitter: https://bit.ly/3rHZxpc YouTube: www.speakupforblue.com/youtube  

Neste episódio, o podcast da Nova Acrópole do Brasil propõe uma reflexão filosófica sobre identidade, vida social e dependência, destacando a necessidade de compreender o ser humano para além de suas definições superficiais. A partir da perspectiva das tradições clássicas, é apresentada a visão de que o indivíduo possui uma natureza tripla — instintiva, humana e superior — e que a verdadeira identidade é uma conquista construída por meio do autoconhecimento, da razão e do desenvolvimento de virtudes. Nesse sentido, a filosofia surge como um caminho para reconhecer e harmonizar essas dimensões, orientando o ser humano em direção a uma vida mais consciente e significativa. O diálogo evidencia que a vida social não é apenas uma condição de convivência, mas um campo essencial de aprendizado, onde o indivíduo desenvolve suas capacidades mais nobres. Ao mesmo tempo, alerta-se para os riscos da perda da individualidade diante de relações baseadas na dominação, submissão ou na mentalidade gregária. A proposta filosófica apresentada — alinhada à visão da Nova Acrópole — reforça a importância de uma formação integral, que une ética, sociopolítica e filosofia da história, permitindo ao ser humano atuar de maneira livre, responsável e consciente dentro da sociedade, sem abrir mão de sua identidade. Por fim, o episódio destaca que a filosofia, enquanto prática viva, é fundamental para uma escola que busca formar indivíduos mais humanos e comprometidos com o bem comum. Ao estimular o autoconhecimento, o desenvolvimento de valores e a capacidade de amar, a reflexão sobre esses temas torna-se indispensável para a construção de uma sociedade mais justa e fraterna. Assim, o podcast convida o ouvinte a transformar conhecimento em ação, fortalecendo sua identidade e contribuindo ativamente para a harmonia coletiva. Participantes: José Roberto e Danilo Gomes Trilha Sonora: Piano Sonata nº 16 em Dó Maior, K. 545 (1º movimento) – Mozart

Your Influence Doesn't Depend on Your Title! Influence Without Authority? Supersize You Annual Challenge Day 116! Join us every day in 2026 for a quick challenge that is all about you Improving and creating the life you want! https://www.facebook.com/ThrivingSharon Ask your questions and share your wisdom! #supersize #supersizeyouannualchallenges #doonethingeverydaytosupersizeyou #communication #no #saynokindly #SUPER #uncoverwhatis #currentsituation #reflectonresults #obstacles #strengths #influence #noauthority

Are you avoiding reporting a workplace injury because of your immigration status? You're not alone and that fear is more common than you think.  If fear is holding you back, start with a conversation. It's free, confidential, and focused on protecting your rights.  At Pacific Workers, we see it every day, injured workers across California choosing not to act. Not because they don't have rights, but because of fear and misinformation.  But here's the truth: your right to medical care and Workers' Compensation benefits does NOT depend on your citizenship status.   This is a special episode created for you.  Carmen Ramirez, alongside attorney David Mojica from Pacific Workers, opens up a conversation around a topic that affects so many people on a deeply personal level.  But the goal is clear: to give you real information, provide clarity, and support those who need answers today.  Because we understand something important: this doesn't just impact your job… it impacts your life.  The fear is real, but it shouldn't stop you  In many communities, the presence of ICE has created uncertainty. This has led many injured workers to avoid reporting accidents, skip medical appointments, or walk away from their cases altogether.  But here's what you need to know: a Workers' Compensation case is completely separate from immigration enforcement.    If you're injured and afraid, here's what you should do TODAY:  Report your injury as soon as possible   Seek medical treatment immediately   Do NOT discuss your immigration status with your employer   Speak with a Workers' Compensation attorney   Understand that workers' comp hearings are Not immigration proceedings     At Pacific Workers, we believe something very simple: your work deserves protection, and so do you. And sometimes, more than laws… what matters most is understanding that behind every case, there's a real story.    Not sure what to do next? Talk to someone who understands your rights, your consultation is free and completely confidential.   Drop your questions and comments on our social media channels or message us directly on Instagram: @workcomptalk |  @pacificworkerscomp For more help, check out Stand Together Contra Costa in the link below. They offer free immigration legal services and 24/7 support.  https://standtogethercontracosta.org/   

Early bird discounts for the San Francisco World's Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP!From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability.We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify's customer simulation defensible, and what he learned from the Sydney era at Bing.We discuss:* Mikhail's path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify* Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company* Shopify's internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools* Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output* Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation* Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans* Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point* How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era* Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed* What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start* Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams* What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more* Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers* Why AutoML finally feels real in the LLM era, and where auto-research still falls short today* Why Tangle, Tangent, and SimGym become much more powerful when combined into one system* What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify's data gives it a moat* How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions* Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs* How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications* Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice* Shopify's new UCP and catalog work, including runtime product search, bulk lookups, and identity linking* Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice* Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads* Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice* Who Shopify is hiring right now across ML, data science, and distributed databases* The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early onMikhail Parakhin* LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/* X: https://x.com/MParakhinTimestamps00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify00:01:16 Why Shopify Is Talking More About AI00:02:29 Internal AI Adoption at Shopify and the December Inflection00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead00:10:55 Why Shopify Built Its Own AI PR Review System00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents00:18:24 Tangle: Shopify's Reproducible ML and Data Workflow Engine00:21:19 Why Tangle Is Different from Airflow00:26:14 Tangent: Auto Research for Optimization and Experimentation00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers00:33:06 The Limits of Auto Research00:36:36 Why Tangle, Tangent, and SimGym Compound Together00:37:20 SimGym: Simulating Customers with Shopify's Historical Data00:42:47 The Infra Behind SimGym00:46:00 Why SimGym Gets Better with Real Customer History00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories00:51:55 CRPs, Clustering, and Category-Level Customer Behavior00:53:30 UCP, Shopify Catalog, and Identity Linking00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models00:59:13 Real Shopify Use Cases for Liquid01:03:00 Can Liquid Scale into a Frontier Model?01:09:49 Hiring at Shopify: ML, Data Science, and Databases01:10:43 Sydney at Bing: Personality Shaping and AI Character01:13:32 Closing ThoughtsTranscript[00:00:00] swyx: Okay. We're here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome.[00:00:08] Mikhail Parakhin: Thank you. Welcome.[00:00:10] swyx: I don't even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don't know, I don't know, uh, you know, it's, uh, people va-variously refer you as like CEO or, or, uh, I don't know what that, that, that said previous role at Microsoft was.[00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft's business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything.[00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time.You've obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi's QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering.I think more-- it's just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true?[00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we've-- Shopify, you know, at this stage of its development, we're developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory.So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don't have to research or, or lose context every- Yestime. And a little bit tongue in cheek, I tweeted that, “Hey, we've, we've done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I'm more of a SQL, SQLite fan. But, uh, yeah, very similar things that we've already done here. The point is, yeah, we're very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously.[00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart.What are we looking at here? What ?[00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of-[00:03:05] swyx: Yeah ...[00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total.Uh, green is just total. So you could see that it approaches really % by now. It's hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing.Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe.[00:03:52] swyx: Yeah.[00:03:52] Mikhail Parakhin: The other thing I would claim you could see is that, uh, CLI-based tools and tools that don't require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they're not exactly shrinking, but they're not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they're, they're not experiencing as, as fast of a growth.[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you're just kind of doing a, a daily sur-survey or something.[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don't use anything less than Opus four point six.”[00:05:09] swyx: Oh .[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.But, uh, we try to discourage people from using anything less than that.[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that's, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it's also really interesting that no one was kind of abusing it in twenty twenty-five.Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it's still like, you know, probably, probably gave fifty percent.[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It's still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don't know what it tells me. It's like it feels not ideal, to be honest.Or maybe it's okay. We'll see.[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what's the concern?[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it's just, it's kinda strange.[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let's, let's call it that.I'll just, I'll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they're all considering some kind of token budget, right? Like I think it's something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they're, they're underutilizing coding agents.Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don't know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.[00:08:02] Mikhail Parakhin: Well, I mean, you're, you're baiting me. I, I like... This is my favorite topic. Uh, if you let me, I'll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you're, you know, this, uh, the- ...the cake seller says you don't need enough cakes.”You know? Like, of course. Uh, but, uh, I actually, uh, think that's undeserved. I think he, he's actually right. Uh, I do think- He,[00:08:33] swyx: he's directionally correct.[00:08:35] Mikhail Parakhin: Yeah. Yeah. He's directionally correct for sure. Uh-[00:08:37] swyx: Who knows what the right number is? Yeah.[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.One is that it's not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don't communicate with each other. That's almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.So people don't like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn't get tired.And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It's, uh, it's this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.But since they write so much more of it, like more of it will make it into production. So you have to- You still[00:10:26] swyx: have[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn't have any review tools. Do you just use like, like let's say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don't know if you've had those specialist review tools.[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven't found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it's so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.At peer review tool, uh, time, you want to run the largest models. That means, I don't know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don't want, like, a big swarm of, uh, of, uh, agents.So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that's, that's why I feel like I haven't found good tools, so we are using our own for peer review for now.[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?[00:12:38] Mikhail Parakhin: Mm-hmm.[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we're now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.You know, this is productivity, right? ‘Cause y- presumably there's more stuff going into the code base and more, more features getting worked on. I'm curious about the backlog, right? Like the, the, the-- I actually don't mind a pro-level model taking an hour or two hours to review my PR, because I've dealt with humans who take a week to review my PR, right?And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there's some trade-off here where, like, it still doesn't make sense.[00:13:18] Mikhail Parakhin: Exactly. That, that's exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.It's real problem is since there's so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so deployment cycle becomes much longer. Uh, so it actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like thinking for an hour, because then, then you, you don't have to spend all that time during testing and rolling, you know, rolling back the deployment.[00:14:03] swyx: Yeah, totally. That's still worth it. You know, you don't look at the individual, look at the aggregate, and look at the, the, the change in the aggregate system.[00:14:11] Mikhail Parakhin: Exactly.[00:14:11] swyx: I'm kind of curious if, like, there's this PR mentality and, like, c-- the, the, the CICD paradigm will be changed eventually. Some people are like, obviously a lot of people want new GitHub, but I even wonder if, like, Git is the problem, right?Like, is that the bottleneck? Is the concept of a PR a bottleneck? Do you guys use stack diffs? I don't know if, uh, that's a, like, a merge queue stack diff type of thing.[00:14:34] Mikhail Parakhin: We, we use, we use Stacks, we u- we use Graphite. We worked with, uh, Graphite a lot. Uh, so we use Stack, uh, PRs. I think, uh, like that's clearly the overall CICD in general, and the interaction with the code repository right now is the, clearly the sort of the, the main issue and the bottleneck for us, uh, and highest top of mind.I would say we probably need a different metaphor or different whole design of how to process it in new agentic world. I haven't seen anything dramatically better yet. I, I think everybody right now is just trying to keep their head above the water ‘cause, ‘cause there, there's so many PRs and then everybody's CICD pipelines start creaking, the, the times are increasing, the number of bugs slipping by increasing, and you have to, have to clap on down.And so we are a little bit in this situation when we need to first stabilize that story and then start thinking, hey, what, what it could be a completely different and new world, which I haven't... I know some people working on it. I haven't seen something, like anything super compelling yet, but clearly the old thing were designed for humans will need to be morphed into something new.[00:15:53] swyx: One of the thing that I, I think about is kind of like the merge conflict is basically a global mutex on the whole system, right? And in, in hu- in human organizations, we do have something like that. It's the company standup. But like, other than that, it's like it's actually fitting for us to be somewhat decentralized, somewhat plugged into one stream of information source, but somewhat lossy.Like it's okay, you know, that, that not every delivery is like atomic consistency. Like we're not dealing with a database sometimes.[00:16:27] Mikhail Parakhin: This is a very good point, uh, because since humans don't write code too fast, you know that global mutex is not too bad. Once you-[00:16:36] swyx: Yes ...[00:16:37] Mikhail Parakhin: start writing code at the speed of machine, it becomes the, you know, the bottleneck.Then what do you do? Maybe, and I can't believe I'm saying this because I, I'm long-- lifelong opponent of, uh, microservices, and I always thought that was, like, a really bad idea. And now that you're saying it, like, maybe in new guys like microservices will make a comeback, you know, because then you, you can ship things independently in tiny things and, and the managing all that complexity automatically will be much easier.I don't know. Like, we'll s-- we'll have to see.[00:17:10] swyx: Yeah. I mean, I don't know what the Microsoft or, or Shopify thing is, but I, I read this paper from Google where they have a monorepo that deploys into microservices, right? And then, uh, the other concept that I think about a lot is the Chaos Monkey concept from, from Netflix.Being able to create, like, this robust system where, um, uh, you know, you, you have the service discovery, you have the, uh, the independent, independent microservices discovery and, and, uh, you know, probably going to be a fair amount of duplication. That's how an organic system sort of scales, uh, that, that you have that...I don't know how you call it. Slack? Robustness? Depend-- uh, d-duplication. I, I, I forget the-- I, I'm-- And this-- those-- these are not exactly the terms- Hmm ... I'm looking for, but I c-can't really think of the words. Okay. I was gonna go into Tangent and Tangle. Uh, so, uh, we, we sort of discussed the overall stats that, uh, Shopify has.Uh, but, you know, I, I think some, some pretty cool stuff that you guys are working on is your ML experimentation, uh, and your, your sort of auto tr-research training pipeline. Presumably you're much closer to this one because it's, it's a sort of personal hobby of yours. How, how would you explain them in, together?I thought we have a slide that, like, uh, has the s- the system diagram.[00:18:24] Mikhail Parakhin: Yeah. Tangle first and then Tangent as a-[00:18:27] swyx: Yeah ...[00:18:28] Mikhail Parakhin: as a thing on top of Tangle. And, uh, Tangle is the third generation, I claim, of, uh, systems of, uh, running any data processing, but a bit with a skew for ML experiments, but not necessarily. Any sort of data processing tasks where you need to iterate, share, and you have scale so that you want maximum efficiency.You know how, like, normally you would work, you would-- Imagine you're a data scientist or an ML practitioner, you would get Jupiter notebooks or, or maybe you would get, uh, you know, Pyth- your Python scripts, and you would manage the data, and you produce those TSV files, and you put them in some JFS or something.Then you would notice that, oh, it has this, uh, weird missing values. You go and write another script that, uh, goes and replaces them with, uh-[00:19:20] swyx: Ah ...[00:19:21] Mikhail Parakhin: dash S. And then, then you, then you run some, some, uh, “Oh, I need to filter bots.” And so you run some light GBM model that, uh, removes the bots. And then, then you like-- And then you, you kind of like get into shape, and then you start experimenting, and you run multiple experiments, and then you're like, “Oh my God,” like, “this experiment is worse.”You undo, and you cannot get to previous result. And like, “Ah, what did I do?” Like that. Again, then, then you finally like get everything working. Then you like start throwing it over the fence to production. You, you replicate it, those things don't work, and then sometimes you like don't notice that you forgot some feature naming and the, the features don't match.But then, like imagine you, you did everything, and then six months later you're like, have to repeat it because now there's more data, or you wanted to do another pass, and you're like, “What, what did I do?” Or like, or like, “This script crashes now,” or the, “the path has changed.” And then, then you're trying to, like you spend another month just doing ar- digital archeology on your own, you know, history, right?Now multiply that by many, many teams. Now imagine you got an intern that you wanna ramp up. Now you have to show that intern, “Oh, you know, look, here's the folder, there's the scripts, you know, ask your cloud agent to do, and then, uh, to, to figure it out.” And then cloud agent does something, and then you're, “Ah, yeah, right, right, it was the wrong folder.I forgot to tell you, I actually have this other thing I forgot myself.” And, and that's, that's the, like, the daily life we all, uh, all know it, uh, if, if you're a data scientist, machine practitioner, ma- machine learning practitioner or, uh, or even like any data managing, uh, person.[00:21:00] swyx: Yeah. So I, I used to do this, uh, f- uh, on the quant finance side, uh, in, in my hedge fund.So we did this before Airflow, and then, uh, obviously Airflow came along and, uh, then more recently Dagster, uh, I would say is like, in my mind, what I would use for that shape of problem, uh, where you had to materialize assets and create a pipeline.[00:21:19] Mikhail Parakhin: And that's, that's very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule.It's less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don't wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.”All that is very hard to do with Airflow. It's very easy to do with Tango. Tango is m- more about, it's everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don't need to understand fully. You, you grab-- you clone somebody else's experiment or somebody else's pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click.So then the... You don't have to port it into any other system to, to run in production. You can just run the same experiment. It's, it's fully production ready. And, and it's, uh, it has lots of... Again, as I said, it's third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach.And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes.So even if the version changed, but if the output didn't change, nothing is being rerun. It's very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it's not repeated multiple times. It's automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don't have to coordinate for that.Like, you don't have to know that other people are starting it. You now, it's very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it's very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone.And everybody knows also it's fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there.[00:24:06] swyx: Uh, so, so people can, uh... It's open source. Go to the GitHub repo and, and, uh, check it out.Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven't really solved that, uh, strictly, right?Like, we develop really, really well in, in Python notebooks, but then, you know, that's obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense.Uh, it surprised me that the savings could be this much, but maybe I just haven't worked at your scale where there's so much duplication, uh, that people just rerun because they change a single ID upstream.[00:25:10] Mikhail Parakhin: It does, yeah. But it's not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on.Then- Yeah ... somebody else in some department you don't know existed runs the same task, but on a newer version.[00:25:27] swyx: Yeah.[00:25:27] Mikhail Parakhin: Like right now, you can't, in, in most of the organizations, you can't even find out about it so that you can't even measure that you're spending that time twice, right? Here- Yeah ... if everybody's on Tango, that's detected automatically and detected that the output is the same.And then for that person, all it looks like is like experiment just suddenly moved, jumped forward, right? Uh, uh- Yeah ... so that's because, because the, there's network effect of multiple people helping each other.[00:25:51] swyx: Yeah. This is one of those things where it's designed to be a platform from the beginning rather than an individual developer's tool from the beginning, right?And, and everything's gonna streams down from there. That is the sort of Tango, uh, orchestrator, and it's, it manages jobs. We've seen a few versions of this, and this is obviously, uh, uh, the sort of, uh, unique approaches that you guys have, have, uh, figured out. And then there's Tangent.[00:26:14] Mikhail Parakhin: Yeah. And Tangent is basically an automatic auto research loop that can help and kind of do your work for you.Uh- ... you know, uh, effectively, effectively, Andrej Karpathy recently popularized it with auto research. Yes. Remember he said like he was, uh, speed running this, uh... Yeah, uh, you know the story. The, here we're basically bringing the same capability into Tango so that, uh, the, uh, Tangent can analyze it. It's just an agent that can run multiple experiments, figure out what can be changed, and keep on rerunning it, keep on modifying until, uh, maximizing some goal, some loss function, whatever you need to, to achieve.And in general, I would say if you're not using auto research-like approach in whatever you do, like literally whatever you do, then you're missing out. We saw at Shopify that taking like a wildfire, anything where you can put measurements can be done dramatically better. Our-[00:27:19] swyx: Mm-hmm ...[00:27:20] Mikhail Parakhin: uh, speed of, uh, templatization HTML, uh, completely new UX tem- uh, templatization of, uh, reducing latency for liquid themes.Uh, we-- Our, uh, search, uh, recently we moved from It's hard even, uh, quote from eight hundred QPS to forty-two hundred QPS with the same quality just by pure optimizations and not a research loop that kept running and changing code in our index serve on the same number of machines, just increasing the throughput.We, we managed to improve the quality of gisting and machine learning process. Uh, you know, gisting is the prompt compression technique that[00:27:59] swyx: allows for[00:28:00] Mikhail Parakhin: lower latency and, and lower and, uh, actually higher quality slightly. So like literally whatever different walks of life, and it doesn't have to be AI related.Uh, we, we had a reduction in, uh, storage because the agents would go and find data sets that clearly are derivative, uh, and then you don't need to store things twice. You know, we, we, we found somewhat embarrassingly that it was one of the largest tables was hashing random IDs into another random ID, and we literally- Oofput only one. So it was translating, yeah, two random IDs hashed[00:28:36] swyx: into[00:28:37] Mikhail Parakhin: each. So, so[00:28:37] swyx: it has access to the code as well, so it can, it can check the, like what, what the hell is it doing?[00:28:42] Mikhail Parakhin: So there, there cou- it could be run in two levels. You, uh, you know, at the superficial level, it could just use ex-existing components and, uh, reshuffle them.Uh, you know, like you can grab- Yeah ... uh, XGBoost, and you can grab some, some Py- PyTorch module, and then can grab some, you know, grab another tools and, and combine them. At a deeper level, since Tangle is all sort of CLI based underneath you, every, every component is a wrapped really CLI, uh, call and a YAML file, it can analyze code and create new components and, and, uh, keep on iterating as well.So, so you can, you can both have quick modifications of existing t- uh, pipelines with the, with components that are already there pre-baked, or you can create new components, uh, and-[00:29:29] swyx: Yeah ...[00:29:29] Mikhail Parakhin: keep iterating on those. So auto research is, again, this is probably the, the thing I was excited the most in the last two months happening, and we see it taking like, like totally like a wildfire.Just, uh, everybody, every day, every... well, every day, every minute, I would, uh, have somebody Slack message saying, “Oh, look how much better I made it.” And, uh, it's all throughout the research.[00:29:53] swyx: Is this democratized in some way in, in the sense that like is it your ML, uh, engineers and researchers doing this, or is it your regular PMs and software engineers also have the ability to auto-- to use Tangent?[00:30:07] Mikhail Parakhin: This is an awesome question. Like, Tango in general and Tangent in particular are extremely democratizing. Like they- Yeah ... they are the main tools for- ‘Cause I don't[00:30:15] swyx: need the details.[00:30:16] Mikhail Parakhin: Yeah. Exactly. Initially used by ML and AI engineers, but then literally, as you said, PMs are like the highest user right now is one of PMs on our org, uh, Sartak and he was, he was number one by, by usage of, of this ‘cause they're just, uh, energetic and knowledgeable, and now it, it unlocks a lot of capability where you don't have to co-change code manually.[00:30:39] swyx: I mean, I mean, because it kind of cuts out the ML, ML engineer from the process because the, the, the PMs have the domain knowledge and the ability to think about, uh, from first principles about, okay, what, what results do I want? And they can-- they even have the access to the data that, that needs to go in.So it's like in some ways, like this is the magic black box that we've always wanted for, for training and, and for, uh, I guess, uh, uh, hill climbing, whatever.[00:31:04] Mikhail Parakhin: It's basically cloud code for your AI development- ... uh, situation, right? Like now, now you don't have to know exactly how algorithms work. You can just, uh, bring your domain knowledge and expertise and product knowledge and iterate within Tangent until you've gotten the results that you need.[00:31:21] swyx: In my previous roles, every time that someone has pitched AutoML, you know, I've always been like, “Uh, this is not, this is not gonna work. It's, you know, it's, it's always gonna be a flop.” Somehow it's working now. I mean, presumably the answer is now we have LLMs and it's good enough, right? It's, it's an emergent property that we can do auto research, but like, it doesn't feel that satisfying that how come we didn't do this before, right?Like we just did like parameter search and like, I don't know. That's maybe that's it.[00:31:48] Mikhail Parakhin: Yeah. Bayesian optimization and hyperparameter optimization was, was the one that, or facet of AutoML that was used very actively, which incidentally also built into, uh, Tango. But, you know, I know Patrice Simard very well, and, uh, he was such a, uh, such a proponent of AutoML, and he put, like literally spent careers trying to democratize it.Without LLMs, it just turned out to be very hard. Like it, you, you would have flexibility within certain narrow domain, but it was hard to wider scale, and now with LLMs suddenly it's like magic wand, and so suddenly everybody- ... is an AutoML expert.[00:32:28] swyx: Yeah, I, I think it's multiple things, right? Like I'm, I'm just gonna bring up the, the, the chart again, right?Like LLMs can do the monitoring very well. That is the very potentially unbounded, super unstructured. It can do the analysis very well, it can do the... Uh, and basically it is much more intelligence poured into every single step. Uh, there's maybe nothing structurally changed about AutoML, but this is just m-more intelligent and more unstructured.[00:32:53] Mikhail Parakhin: Exactly.[00:32:54] swyx: Any flaws that you've run into? Like everyone is like drinking the Kool-Aid, oh my God, time savings, uh, you know, performance improvements. Like what, what, uh, issues have you have, uh, come up?[00:33:06] Mikhail Parakhin: This is really cool. It's not a solution to all the world's problems for sure. The limitations are usually the ones I-- And this is where we get into a bit of a subjective territory.Uh, I can only share what I've, I've seen so far, and I'm sure the situation, uh, is changing, and, you know, maybe after I say it, like many people will reach out and say, “Hey, what about this?” And you don't know that, and then, then we'll be probably right. But what I've seen is auto research is very good at doing kind of obvious things that you don't have bandwidth to do or you didn't notice or maybe you're not aware of like the-- some standard practices.It is not good at doing something completely out of distribution, something that, you know, you have to think for, for multiple days, uh, and, and do something like none of this. So, so it's, uh, I, uh, set an experiment once, uh, on, on my sort of, uh, hobby thing, and I let it run for, uh, ended up, uh, several weeks run, uh, you know, it's like full production kind of scale, so it, you know, slow runs and, and it ex-- it performed in the end, uh, over four hundred experiments, and only one was successful.I'm like, “Okay, that's, that's good.” But-[00:34:18] swyx: But it saved time.[00:34:19] Mikhail Parakhin: Yeah, I saved time. Like it, it was the, that thing. Yeah, if I, if I were doing four hundred experiments myself, my betting average, as I said, would have been much higher, I'm sure. But also, first of all, it would take me like three years to do four hundred experiments.And, uh, I didn't have to do them. Like the machines were just, uh, the price of electricity did that. So, and I got one improvement, uh, that in, uh, my, my-- Honestly, when I was starting that experiment, my thinking was to go and show that, “Hey, Andre, maybe you just don't know how to optimize.” And I was super smart because in, in my pro-problem, it was optimized for many years, and it was like fully improved.Uh, and I didn't expect it, you know, auto research to find anything at all. Yet it did. So instead of making fun of Andre, I ended up, uh, a big, big supporter. Yeah, that's exactly the tweet. Yes.[00:35:10] swyx: You and Toby really, really go back and forth on-online a lot, which is really funny. Uh, think of it as, as an eval for the optimalness of the code it's running on.Uh, it's almost like it reminds me of like a Kolmogorov complexity thing, but, uh, I guess it's-- there's some optimal thing that you're trying to sort of reduce down to, I guess. Um, and so, so you, you, you know, you should congratulate yourself that you had, uh, you know, uh, ninety-nine percent, uh, optimality.[00:35:36] Mikhail Parakhin: Exactly, yeah. I think Andre really deserves a lot of credit for popularizing this approach. This is, uh, this is incredibly, I think, powerful and cool and You know, the, uh, even him, him just mentioning it led to a lot of gains in a lot of places in the industry, so we should be thankful.[00:35:56] swyx: Yeah. I think he also has a just...I don't know what it is. Like, um, you know, it, it is a simple self-contained project that people can take and apply to other things, which is, is, is one thing, but also just the name. Just like somehow no one, no one managed to call their thing auto research. It's just naming things is very important. I think that that is mostly, uh, our coverage of Tango and, and, uh, Tangents.I think obviously, you know, there's a lot of, uh, ML infra at, at Shopify that people can, uh, dive into. We're about to go into SimGym, but before I do that, any, any other sort of broader comments around this whole effort? Like where is it, where is it leading to?[00:36:36] Mikhail Parakhin: As a segue to SimGym, like all those things start composing strongly.And, uh, you could see a huge unlock when you can look at each one of the tools and, and you see, oh, they're extremely useful. Uh, Tango is useful by itself. Auto Research is useful by itself. SimGym is useful by itself. If you combine all three, you create like synergetic effect. I think that's why we wanted to even, uh, cover them today is because this is something that if you go back even, you know, five years ago, would've been unthinkable.Uh, replicating that, uh, would, would be either incredibly costly or impossible, right? With probably thousands of people are required.[00:37:20] swyx: Well, we have serverless human, uh, serverless intelligence, right? Like, uh, so yes, you do have thousands of hu-- of, of intelligences, not just, not humans. And that's, that's close enough, right?Even if they're not AGI, they're, they're close enough to do the, the task that you need them to do. And, and, you know, that's, there's plenty for, for a lot of routine work, knowledge work. Okay, let's get into SimGym. Um, this is one of those things I, I was surprised to see actually it's apparently your, uh, one of your most popular launches, and I think something that, uh, I think Sim AI, I think Yunjun Park, who did the Smallville thing, there's a very small cottage industry of people trying to do like the simulate customer thing.I think a lot of people maybe don't super trust this yet because they're like, well, obviously they would just do what you prompt them to do, right? But maybe just think, uh, tell us about the sort of inspiration or origin story.[00:38:10] Mikhail Parakhin: That's exactly actually the thing I wanted to cover, because if you don't have the historical data, all you can do is prompt a-agents in a vacuum, and they will do exactly what you prompt them to do.In fact, when I first proposed it, and this is a bit of, um, my brainchild initially, if I, I can boast, even Toby said like, “But wouldn't they, they just repeat what, what you tell them?” And, uh, but I'm like, “Yes, except Shopify has decades of history of how people made changes and what there is, uh, there, what it resulted in terms of sales.”So now what we can do is we can-- we have this... It's not, it's a noisy data. There's a small, usually websites, uh, you know, like things, things are never in isolation. It's almost never AB experiment. It's always AA experiment when there's has two meanings, but basically, you know, in different time you run two different things.But if you aggregate in general, uh, like everything together, and you apply, uh, denoising and collaborative filtering like approach, you can extract a very clear signal. And then you can optimize your agents. And that's why it took so long. It took almost a year of that optimization of just us sitting and fiddling, and, and we had this internal goals of correlation of hitting-- internal goal was to hit zero point seven correlation with, uh, add to cart events, for example.Like that, that if we run real AB test experiment, that it should, it should go and, and rep-uh, replicate, uh, same sort of success that, that humans had or lack thereof. And it, it took forever, and I don't think that's easily replicatable because, uh, like who else would have that data? You have to have this historic, you know, decades, uh, worth of data.And now, now the, like the other thing you need is in-infrastructure and the scale, right? Because, uh, w- again, what we found, uh, stat sig results, you need to run a lot of simulations, a lot of agents, and, and it's-- Those are expensive things. Like you're, you're making actions in the browser because you want a real friction.You want to, to be able to get the image like of what humans will see because you wanna, uh, detect effects like, “Hey, if I make my images larger, will I have more sales or l- uh, fewer sales?” And like usually people's intuition here, by the way, is that I increase my images, I will have more because they look nicer.You know, designers all look sparse and big images. Like usually your sales tank, right? But, but, uh, you know, from HTML, all the characters look the same only the, the size tag looks different, right? So it's very hard. So you have to take visual information, you have to run this in simulated browser environment on the big farm and, and of course, you have to have, uh, like very, very expensive model, good model with multi-model model.So all this it's-- is what's taken so long and, uh, to share my personal fail a little bit there, Sean, is like, you know, we always had this bias to-- for like large company bias. You know, we always, uh, whenever you-- we do, we're like, “Hey, we'll run an experiment,” right? We make, make a change, and we will run an experiment and then, uh, see, uh, see which one's better or like, “No, this is worse,” and most of them are worse, so you discard it and keep iterating, hill climbing.And we're like, “Oh, like smaller merchants, they cannot get stat sig results. They cannot really run experiments simply because, you know, in a week there would be not enough data for them.” So we thought from this perspective. What we didn't realize is that most people don't have A and B, they just have one thing, and they need suggestions of What A and B should be.So, uh, we first build this, hey, we run simulation on two separate teams and, and, uh, say, “Hey, which one is better?” We then morphed it into, and very recently just released it, when you have just your site, your theme, we run over it and we say, “Hey, here's what predicted values of, of, uh, uh, conversions are, and here's how we think you should modify it to increase your conversions.”And then circling back to what you started with, the proof is in the pudding. Like, if we are not correlating with reality, like, people will not be using it. And, uh, thankfully, we see literally every day more users than the previous day. So, so right now, uh, right now- It's working. Yeah. I'm-- Right now my problem is how to pay for it all because the so our major thing is how to optimize the LLMs, do distillation, how to run the headless browsers, uh, and handful browsers, uh, uh, cheaper so that we can accommodate the increase in traffic.[00:42:47] swyx: Yeah. I, I understand that you, uh, you published a lot of technical detail at GTC, so I was just gonna bring it up a little bit. I think s- was this in, in con-conjunction with some kind of GTC presentation? Or something like that, right?[00:42:59] Mikhail Parakhin: Well, we, yeah, we, we did it in several place, but yeah, we had the engineering- Yeahblog, uh, as well. Yeah.[00:43:05] swyx: Yeah. So you're running, uh, GPT OSS. Uh,[00:43:08] Mikhail Parakhin: the, this is an older version. You know, now we run multimodal model. But yeah- Yeah ... GPT OSS, we still run GPT OSS as well for[00:43:15] swyx: And then you have the VMs, and you also have browser-based. I really like this one where it you said, “It violates almost every assumption that standard LLM serving is designed for.”And then you had like, basically orders of magnitude differences between everything.[00:43:29] Mikhail Parakhin: Exactly. Which is, which, uh, which was, you know, a bit of a challenge to implement, like when, like even simple things. Uh, be- since it violates all the assumptions, for example, multi-instance GPUs, like MIGs don't work as well.But we needed, uh, to get MIG to work because, ‘cause otherwise it's way too expensive. And so we had to deal with the, yeah, with, uh, lots of infrastructure and, and, uh, work with, uh, uh, Fireworks and CentML, uh, you know, to help with optimizations and browser-based, as you mentioned. Yeah, like, takes a village.[00:44:04] swyx: Okay. So there's a lot of like, I guess, experimentation in the infrastructure so far, and you've published more or less what you have here. I guess I'm, I'm less familiar with CentML. I, I don't do, uh, that much work in this, this part of the stack. But why was it the sort of preferred instance platform?[00:44:22] Mikhail Parakhin: There are really three probably top companies. There used to be, uh, uh- Three top companies, uh, at least I was aware of that did, uh, LM optimization. You know, together Fireworks and Santa ML, not necessarily in that order. Santa ML recently got acquired by NVIDIA. Uh, what they did is if you have a model and you want to optimize it to a specific prof-- uh, profile of usage, uh, they would go and do it.And, uh, we work with, with those companies, uh, this was work particularly in with Santa ML and NVIDIA to get them the best possible results out of it. And, and sometimes you, you have to retune depending on, like sometimes you want the maximum throughput, sometimes you want minimal latency, sometimes you want like the cheapest, right?And, yeah, or some combination. And so yeah, these are people who would come and help you.[00:45:14] swyx: I see. I see. Yeah, yeah. I'm familiar with these people for the LLM, you know, autoregressive stack. But the other interesting category of these optimizers is also the diffusion people, whereas like Fel and, you know, uh, Pruna recently has come up a lot as well, which I think is like really underappreciated, uh, at least by myself, because I, I thought, oh, all the workload would be LLMs, but actually there's a lot of diffusion as well.[00:45:38] Mikhail Parakhin: Exactly.[00:45:38] swyx: There's a lot here, so I, I, I... it's, it's, uh, it's, it's, it's hard to cover. But I, I do think like people underappreciate the importance of customer simulation, basically. I think this is something that I'm candidly still getting to terms with. Uh, you know, uh, you also-- your team also like prepared this, like, really nice diagram.Uh, I, I assume this is AI generated.[00:46:00] Mikhail Parakhin: Yeah, it looks-[00:46:01] swyx: Maybe it's not.[00:46:01] Mikhail Parakhin: Yeah, it looks, uh, Gemini-ish. Yeah, but, uh, uh, honestly, I, I don't know where, where the hell they generated. It looks, look, uh, looks like it's, uh, Google. But the interesting part, John, that, that, uh, we haven't covered, but I, I wanted to mention is if your store had previous customers, rather than it's a new store, you're like new merchant just launching things, it helps tremendously in just correlation and forecast.Yeah, we take your previous, uh, customer's behavior, and we create agents that replicate those specific distribution of, of customers that you get, and then we a- we apply those to your changes, and then that, that raised raw, you know, the re-- uh, just correlation with the add to cart events or to-- with conversion or whatever it, it, it may be, uh, quite dramatically.So, uh, replicating humans in general seems like an interesting, cool challenge.[00:46:58] swyx: As a shareholder, I think this is the-- like if people are Shopify shareholders, they should really deeply understand this because this is basically the moat. The, the more you use Shopify, the more it will just automatically improve, right?Like you're, you're doing the job for them.[00:47:13] Mikhail Parakhin: Yeah, that's what we started with. Like, uh- ... uh, otherwise, if you're just a startup, I wouldn't do it if, uh, you know, if it was my startup because Without the data, it, yeah, as, as you said, it's, it's exactly the case that, uh, whatever you say in prompt, that's, that's what the agents will be doing.[00:47:30] swyx: The statistician in me wants to like really satisfy the sort of, um, statistical intuition, I guess. Um, to me it's kind of, uh, the, the word that comes to mind is, um, ergodicity. Uh, so let's say a, a customer takes this path, customer takes this path, customer takes this path, right? Um, the... In my mind, the way I explain it is like, okay, here, here's the ninety-five percentile, here's the five percentile, and here's the median, right?Um, but to me, what SimGym is potentially doing is that it can, uh, modify... It can sort of model the sort of in-between sort of journeys as well, that, that maybe are dependent on the previous states. This may be like a very RL-type conclusion where like basically the summary statistics, if you only did naive AB testing, you only have the, the statistics at, at, at a certain point, and you only judge based on the sort of overall summary statistics.But here you can actually model trajectories. Does that make sense? Or-[00:48:31] Mikhail Parakhin: That makes total sense because like, well, that, that makes even more sense that maybe even you realize bec- because-[00:48:38] swyx: Okay. Please,[00:48:38] Mikhail Parakhin: please. Yes ... we do-- Yeah. The, so internally, uh, we have this system, we talked about it briefly once at NeurIPS.We have a huge HSTU-based system that models the whole companies, uh, and their possible paths. And like- Yeah ... what you are, what you are showing, like actually at any point of time, you can either model the user's behavior or you mo- can also think about, uh, the whole merchant as a company, as the entity that acts in the world.You can model that as well. And then you can do, can do counterfactuals. In your graph, like in your blue graph, uh, if you're... Imagine in the center there, uh, somewhere in the middle, you would have an intervention. I give that person a coupon, or I don't know, I send a personal thank you card, or give a discount in some- somewhere.And then you can, uh, then you can do forward rollouts from that counterfactual. So what would have happened with that intervention or without the intervention? And you can even ch- change where that intervention, uh, in time can happen, right? Like some- where, where in this journey. So we, we do this at the Shopify scale for our merchants, and then if we notice that something that they can be fixing, like there's a strong counterfactual, like we have Shopify policy, they basically get a notification like, “Hey, we think your...something is wrong with your-” I don't know, Canadian sales. Like, uh, it looks like it's misconfigured. Here's what you need to do. Or do you think like, uh, you have to set up this campaign with these parameters? And we do that at the buyer level to literally offer discounts or cashback or, or things to buyers.So this is-- I'm getting very excited. Like this is my sort of area of, uh, interest, I guess, and, and hobby. But being able to m-model something complex as human beings or companies and model counterfactuals on it, where you can have interventions in the future and optimize when to make intervention, what kind inter-- uh, what kind of intervention to make.It's such an unlock that previously was completely impossible. Like the-- it was, it was always dreamed of, but never... Like how would you even simulate it without LLMs or HTUs? I think very, very exciting times.[00:50:59] swyx: I just wanted to, uh, to maybe illustrate this. I, I'm not the best illustrator, but I, I am a conceptual statistics guy.And y-you know, you cannot just do this. Like this is a dimensionality AB test doesn't do, right? Like, uh, because it doesn't have the, the, the change over time, uh, stochastic nature, uh, and it doesn't have the sort of contextual like... Here's all the context to this point. Um, okay, cool. Um, that's SimGym.You're, you're gonna burn a lot of tokens on this thing. But you're, you're one of the, the only scale platforms in the world that can, uh, that can do this across a huge variety of workloads, right? I'm even curious on a sort of human, uh, research level of like, well, do, does retail behave d-differently from like clothing sales?D-does that behave differently from electronic sales? I, I don't know. I don't know what else you guys... The Kardashian shoppers, do they differ from like people who buy, uh, I don't know, cars and, uh, whatever.[00:51:55] Mikhail Parakhin: Well, very different, and different sensitivities and different modes of, uh, shopping and, and different levels of what's important.Now, to-totally, you can do aggregations at, uh, at a store level. You can do aggregations at a different, uh, category level. I don't know if, uh, you know, for our statisticians among us, I couldn't believe, but we-- recently we're looking at it, and we had to bring back, uh, CRPs, you know, Chinese restaurant process.It's a, like, way of aggregating and, like, naturally grow clustering. So across... Specifically to answer questions that, uh, like you were just posing on how, how if, if buyers behave different categories. And I'm like, “I haven't seen CRP since two thousand and one.” It's[00:52:37] swyx: so What? It's so- What is... No, I haven't, I haven't seen this.No. This is not in my training. Uh,[00:52:44] Mikhail Parakhin: but, but yeah, it, uh, uh, it actually, like the, the-- there was a very popular kind of theory, popular neurips HTML circles in early two thousands, uh, kind of nice. And now, now it has practical applications, uh- Yeah ... that we were resurrecting.[00:53:03] swyx: Yeah, amazing. Uh, I, I can see, I can see how this is like a, uh, a fun job for you where you get to apply all these things.Um, yeah, yeah, so super cool. Super cool. So, okay, so, so anyone who, who knows what CRPs are and has always wanted to use them at work, uh, they should, they should definitely join Shopify. Okay, so w-we have a lot and but I, I'm, I'm being mindful of the time. I, I do wanted to, to sort of cover some other things.Um, I-I'll give you a choice, UCP or Liquid?[00:53:30] Mikhail Parakhin: Liquid. I think, I think on UCP, you know, like UCP is very important for us and, and it just we are-- UCP, we have a structured, uh, discussions, and you can read about them, and we have, uh, blog posts, and we have a big release this week, in fact, like with our catalog.Oh,[00:53:46] swyx: okay.[00:53:46] Mikhail Parakhin: Uh, yeah,[00:53:46] swyx: but- Le-I mean, we, we can, we can discuss the, the, the release briefly because we'll release this after the-- after it's already announced so whatever. There's a catalog that you guys are doing?[00:53:55] Mikhail Parakhin: Yeah. So we are, we are- Okay ... we are bringing in capabilities of a whole, uh, Shopify catalog.Basically, you now you can search for products, you can do lookups by specific ID, you can do bulk lookups when you need to bring m-multiple products. You don't need to know in ad-in advance what you're trying to show or to sell or check out. Like, you can now, you can now have this decided at, at runtime, and this big area for investment for us for both non-personalized and personalized searches, trying to provide basically a win-window into whole universe of products that are being sold everywhere in the world.And Shopify is really not exactly, but almost like a super set of any-anything being sold. Now we are bringing it into UCP and, uh, and, uh, identity linking is another big thing for us, uh, so that you, you can use, uh, like Google or whatever, whatever identity you have, uh, they're minimizing friction.[00:54:56] swyx: Yeah. So[00:54:57] Mikhail Parakhin: yeah, big release for us.But Liquid AI of course we never talk about, and the problem might be more, more aligned with what we d-discussed previously on this chat.[00:55:07] swyx: Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don't know why. I'm curious on your explanation. I think you, you, uh, you can make things very approachable.And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid?[00:55:23] Mikhail Parakhin: You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it's called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities.They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they're used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it.And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It's non-transformer architecture that's more complicated than sta-state space and really difficult to code if you-- if I'm being honest. But it's, um, very efficient. It's, uh, subline-- sub, uh, quadratic in, in length of your context.Uh, it's very compact way to represent things, and that's a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it's basically on par with transformers, and if you do hybrids with transformers, it's, it's even better.That's why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels l

Living the Gospel, neighbor to neighbor   Matthew 28:18-20 Matthew 4:19 Romans 10:14-15   None of this matters if you don't have a burden for the lost. We talk about the things we love. Maybe we need to reconnect with our love for Jesus. We are becoming people who BOLDY GO: Be present Outside the box Love intentionally (Mark 12:30-31) Depend on the Spirit (John 15:4-6) Listen & learn You   Matthew 9:35-38   Crossroads – about heaven, earth, and the journey in between. Connect with us Crossroads Linktree: https://linktr.ee/CrossroadsFairOaks?utm_source=linktree_profile_share<sid=29f93fab-45f2-4463-9a37-f4ad802326f8

Would you like access to our advanced agency training for FREE? https://www.agencymastery360.com/training Are you struggling to scale your agency or are you unknowingly the thing holding it back? At what point does your growth stop being a systems problem and start becoming a leadership one? Today's guest shares what it to break through those ceilings. After scaling quickly off the back of a strong network, he made the critical decision to systemize everything before growth turned him into the bottleneck. By leveraging documentation in a smart, intentional way, he built a foundation that allowed the agency to grow without everything running through him. In this conversation, he unpacks the realities of working with enterprise clients, the often uncomfortable shift from operator to CEO, and why—despite all the noise, AI is actually increasing the need for human judgment, taste, and leadership, not replacing it. Ted Harrison is the CEO and founder of Neuemotion, a fast-growing B2B creative agency working with enterprise brands. Before launching his agency, he spent seven years at Twitter (later X), where he led advertiser production, helping global brands create better-performing content at scale. After navigating the chaos of a major corporate transition, Ted left to build an agency where he could control decisions, scale creative impact, and architect a business on his own terms. In this episode, we'll discuss: Avoiding the trap of confusing early traction with a scalable model Leveraging documentation early Enterprise clients as a double-edged sword Subscribe Apple | Spotify | iHeart Radio Sponsors and Resources E2M Solutions: Today's episode of the Smart Agency Masterclass is sponsored by E2M Solutions, a web design and development agency that has provided white-label services for the past 10 years to agencies all over the world. Check out e2msolutions.com/smartagency and get 10% off for the first three months of service. Toggl: Most agencies are losing 15–30% of their profit every year: lack of time tracking, messy manual timesheets, scope creep, untracked revisions, and all those "quick" client requests that never get billed. Toggl has created a fast, interactive way to uncover exactly where your margins are leaking. Start your investigation now at toggl.com/smartagency and use the code SMARTAGENCY10 at checkout for a 10% off annual plans. The Hidden Trap of Scaling Expertise Leaving Twitter a year after the acquisition ultimately created opportunities for Ted's newly founded agency. Many had left long before him, had already found new jobs, and proved to be valuable contacts for potential clients. Ted tapped into this powerful network, and the access to enterprise clients helped him build momentum and fast growth. However, that same advantage creates a structural risk: those clients don't initially trust the agency, they trust you. This is where most founders get stuck. They confuse early traction with a scalable model. In reality, they've just extended their personal brand into a slightly larger container. The real challenge is transferring trust. If you don't systemize your thinking, your decision-making, and your taste, every new client reinforces dependency. The agency grows, but so does the founder's involvement. And eventually, growth slows, not because of demand, but because of capacity. Documentation as a Scaling Weapon (Not a Nice-to-Have) Luckily for Ted, by the time he started the agency, he already understood the importance of documenting processes, which has helped him greatly as he initiates his transition out of operations. Instead of relying on shadowing, tribal knowledge, or ad hoc training, Ted documented his thinking through a book, internal frameworks, and structured onboarding. Every new team member consumes that context upfront. This does two things most agencies miss: First, it compresses onboarding time. Instead of months of "figuring it out," team members immediately understand how decisions get made. Second, it creates consistency without rigidity. The team isn't copying Ted, but they're operating from the same mental model. This is the difference between delegation and true scale. Without documentation, you're forced to stay involved because no one else "thinks like you." With it, you create a system where people can make aligned decisions independently, while still bringing their own perspective. The Operator → CEO Shift Is Uncomfortable (But Necessary) Ted is currently in the most dangerous phase for any founder: the transition from doing to leading. At ~20–30 employees, the cracks start to show. You can't be in every decision. You can't touch every client. And you can't be the quality control layer anymore. This is where many founders regress. They step back in when things break. They reinsert themselves into delivery. They become the "fixer" again. But that behavior reinforces the very bottleneck they're trying to escape. The real shift is identity, not activity. As an operator, your value comes from execution. As a CEO, your value comes from clarity, structure, and direction. If you don't make that shift intentionally, the agency will stall right at the point where it should scale. AI Is Not Replacing Agencies, It's Exposing Them At his agency, Ted's team is using AI in two ways. At the client level, they're mostly building agents, using it to clean up audio and video, and using its output as a starting point. Internally, they have their own "TedGPT", which has proven to be a great tool to scale knowledge. When it comes to how his enterprise clients are using it, Ted has seen that rather than replacing agencies with AI, they're hiring agencies to fix what AI broke. Why is this? Because AI lacks taste, context, and lived experience. It can generate and optimize. But it can't decide what matters. That's where agencies still win, if they position correctly. The real risk for agencies is doing work that AI can replace. Low-level execution, undifferentiated production, and generic output are already commoditized. Enterprise Clients Are a Double-Edged Sword Something Ted wishes he'd known before working with enterprise clients is that it introduces a level of complexity most founders underestimate. Long payment terms. Free pitch work. Endless stakeholder input. Constant shifting priorities. It's both harder and structurally different. Like most founders who have worked with enterprise clients, Ted eventually realized that the bigger the client, the more operational friction you inherit. That doesn't mean you shouldn't work with them, but it does meanyou need to build systems that protect your agency from them. Without strong positioning, pricing discipline, and process control, enterprise clients will consume your team, and your margins. Do You Want to Transform Your Agency from a Liability to an Asset? Looking to dig deeper into your agency's potential? Check out our Agency Blueprint. Designed for agency owners like you, our Agency Blueprint helps you uncover growth opportunities, tackle obstacles, and craft a customized blueprint for your agency's success.

Chuck and Kevin Klinkenberg explore why progress comes from people who stop waiting for permission and start doing things locally. They look at incremental developers, neighborhood groups, and the limits of top-down systems in cities like Kansas City. Along the way, they wrestle with incentives, housing, and how much order a city actually needs. Additional Show Notes Kevin Klinkenberg (LinkedIn) The Messy City Podcast (Spotify) The Messy City (Substack) The Messy City (Site) Chuck Marohn (Substack)   This podcast is made possible by Strong Towns members. Thank you!

Greg talks about a card he received from a group saying we have to follow the Old Testament laws to be saved, then he answers questions about what happens to everyone who doesn't trust in Jesus, making a case God sustains the universe, and a couple in a celibate same-sex marriage serving in a church.   Topics: Commentary: Your salvation doesn't depend on following the Old Testament laws. (00:00) If Jesus really is the only way, does that mean everybody else goes to Hell? (31:00) How can I make the case that God sustains our contingent universe without referencing the Bible? (45:00) What do you think about a couple in a same-sex marriage who live together celibately functioning as worship leaders in a church? (51:00) Mentioned on the Show:  Better Together Conference – April 17–18 in Phoenix, AZ Upcoming events with Stand to Reason speakers Reality Student Apologetics Conference – April 24–25 in Los Angeles, CA Related Links: One Way or Any Way? Part 1 and Part 2