Podcasts about Seed

What do you do when people criticize you, misunderstand you, or challenge what you believe? Tim Tebow joins me for a conversation about what it really means to stand your ground without losing respect for the person across from you. We talk about handling criticism, knowing when to speak up and when to walk away, getting out of the comparison trap, and surrounding yourself with people who will tell you the truth. Check out Tim's newest book, 40 Days to Look Again: https://timtebow.com/books/  Learn more about Tim's foundation and how you can get involved: https://timtebowfoundation.org/  Additional resources Tim mentioned in this episode: https://www.missingkids.org/home  Bark Phone Get help. Text "THORN" to 741741 to confidentially speak with a trained counselor. Join me on Supercast for ad-free episodes, bonus content, and AMAs: https://jefferson.supercast.com/ Leave me a voicemail to be featured on the show! https://www.jeffersonfisher.com/ask-jefferson  Order The Next Conversation Workbook: https://www.jeffersonfisher.com/workbook Thank you to our sponsors: Seed. Go to https://www.seed.com/jefferson and use code 25JEFFERSON to get 25% off your first order. Cozy Earth. Upgrade Your Every Day. Get 20% off at cozyearth.com/jefferson or use code JEFFERSON at check out. BetterHelp. Click https://betterhelp.com/jeffersonfisher for a discount on your first month of therapy. Order my book, The Next Conversation, or listen to the full audiobook today. Like what you hear? Don't forget to subscribe and leave a 5-star review! Suggest a topic or ask a question for me to answer on the show!  Want a FREE communication tip each week? Click here to join my newsletter.  Join My School of Communication Watch my podcast on YouTube  Follow me on Instagram  Follow me on TikTok Follow me on LinkedIn Learn more about your ad choices. Visit megaphone.fm/adchoices

Your blood sugar can look perfectly normal for up to 14 years while insulin resistance quietly builds underneath. Dave Asprey and I get into why most doctors never test for it, why the restaurant "tallow fries" you ordered may not be what you think, and what happened to my cholesterol, gut bacteria, and inflammation markers after 90 straight days of carnivore. 93% of Americans are metabolically unhealthy. In 2018, that number was 88%. It's getting worse, not better. And most people won't find out until years after the damage starts, because the test that could catch it early usually isn't ordered. There's no medication for high insulin, so why check? In this conversation, Dave Asprey and I go after the things that keep people stuck as sugar burners: high insulin, seed oils hiding in "healthy" menu items, oxalates in so-called superfoods, and the idea that your metabolism slows down just because you got older. Spoiler: a 6,800-person study says otherwise. What if your labs look fine and you're still years into a metabolic problem? In this episode, I sit down with biohacker Dave Asprey to talk about why so many people are stuck burning sugar, and what it takes to get your metabolic flexibility back. We start with insulin, the marker most doctors skip, and why a fasting insulin over 6 may be a warning sign even when your glucose and A1C look normal. Dave and I also break down why high insulin, not salt, may be what's pushing your blood pressure up. From there, we get into seed oils and why "high smoke point" doesn't mean what most people think. A study on cooking oils found that the oils with the highest smoke points produced the most harmful polar compounds, while one oil most people are told not to cook with produced the least. Dave and I don't fully agree on this one, and that's part of the fun. I also share the results of my 90-day carnivore experiment, including full blood work, stool tests, and a review from a heart surgeon. My total cholesterol went from 300 to 450. What the rest of my markers showed may surprise you. Dave shares his own carnivore experiment, which went very differently, and we talk about where the upper limit should be. Plus: how Dave gave himself gout in three days, why almond flour gave me recurring styes, the right way to think about gut diversity, and why your metabolism isn't fast or slow, it's efficient or inefficient. If you care about insulin resistance, seed oils, keto, carnivore, fasting, gut health, or longevity, this one's for you. About Dave Asprey Dave Asprey is a biohacker, entrepreneur, and host of The Human Upgrade podcast. He founded Bulletproof and wrote The Bulletproof Diet, one of the first major books to bring intermittent fasting and keto to a mainstream audience. He's also the author of Heavily Meditated and the founder of Danger Coffee, and he's involved with Upgrade Labs. After losing 50 pounds in three months on keto, Dave spent the next decade experimenting to lose the last 50. That journey shaped his data-driven approach to metabolic flexibility, longevity, and biohacking. Health Disclaimer This podcast is for educational purposes only and is not medical advice. Always consult a qualified healthcare professional before making changes to your diet, exercise, supplements, or health routine. FOLLOW DAVE ASPREY Website: Dave Asprey | Official Website About Dave Asprey: Biohacking, Longevity & Human Performance Instagram: Dave Asprey (@dave.asprey) YouTube: Dave Asprey FOLLOW BEN AZADIInstagram: Ben Azadi (@thebenazadi) • Instagram profileFacebook: Ben Azadi, FDN-PTikTok: Ben Azadi - Metabolic Freedom on TikTokLinkedIn: https://www.linkedin.com/in/benazadiYouTube: https://www.youtube.com/@BenAzadiWebsite: Ben Azadi

Most of us think of inflammation when an ankle swells or a cut turns red. It's our body's immune system, and without it, we couldn't fight infections or repair injuries.  But when it stays switched on, this chronic inflammation can drain your energy, can affect your health over the years, and even lead to heart disease. Eventually, 1 in 3 of us will develop an inflammatory disease. In today's episode, Professor Luke O'Neill - one of the world's top 1% immunologists - explains why that matters and what you can do about it. He explores how everyday foods can push inflammation up, or help calm it. He also explains why balance matters more than banning foods like sugar, red meat, gluten, and dairy. As for your gut, you'll learn how anti-inflammatory foods like plants, fibre, and a healthy microbiome can help keep inflammation down.  By the end of the episode, you'll know how to eat, rest, and enjoy life in ways that can keep inflammation in check.  Could the way you eat help your body's healer do its job without going into overdrive? 

Bitkey ditched seed phrases for a recovery-focused 2-of-3 design: an app key, hardware key, and Block server key that use phones, hardware, and cloud access to help people retain control of their funds.Clay Garrett, Bitkey lead at Block, joins Stephan Livera to explain the design trade-offs: safety as security plus recoverability, cloud backup that needs your hardware to decrypt, and why Bitkey does not treat vendor lock-in as its ethos.They dig into recovery paths, multi-vendor trade-offs, transfer-without-hardware limits, Recovery Contacts and inheritance, a working FROST prototype, what covenants could move on-chain, chain code delegation, and privacy-conscious Electrum options.Timestamps:00:00 — Intro00:19 — Clay Garrett Joins — Bitkey at Block00:56 — Bitkey V2 and the 2-of-3 Model03:03 — Safety Means Access and Recoverability04:11 — App+Hardware Spends Without Block07:09 — Cloud Backup and Instant Phone Swap09:27 — Lost Hardware and the 7-Day Delay12:14 — Vendor Lock Is Temporary, Not Ethos15:44 — Multi-Vendor After Coldcard21:20 — Which Wallet Fits Your Threat Model?26:49 — $5 Wrench and Transfer Without Hardware30:04 — Recovery Contacts and Inheritance36:06 — FROST Prototype Already in the Repo38:04 — On-Chain Looks Like Single-Sig39:34 — Covenants Could Move Policy On-Chain42:09 — Chain Code Delegation — BIP 8946:10 — Electrum, Mempool, and Custom Servers52:08 — Industry After Coldcard Changed55:21 — Wallet Plus Free-for-Life Co-SigningLinks: https://x.com/clay_garretthttps://bitkey.world/Stephan Livera links:Follow me on X: @stephanliveraSubscribe to the podcastSubscribe to Substack

Welcome back to Voices of Your Village and today is a rebroadcast of an episode with Larissa Geleris and we got to dive into when your nervous system is overwhelmed by your child's sensory needs. I, for one, have this at my household. I have a sensory mismatch with one of my children and my husband has a sensory mismatch with our other child and so we're constantly navigating this in real life. And at the end of this episode, Rach and I came together for a breakdown of what this looks like in real life in our households. I hope this one is hitting you at a good time, y 'all. It's a reminder I think we all need. All right, let's dive in.  Connect with Larissa: Instagram: @steadyparents Website: https://www.steadyparents.com/ Connect with us: Instagram and TikTok: @seed.and.sew  Seed and Sew's NEW Regulation Questionnaire: Take the Quiz Order Big Kids, Bigger Feelings now!  Website: seedandsew.org Credits: Host: Alyssa Blask Campbell Co-host: Rachel Lounder Production/Editing: Kristin Mork-McVeigh Graphics: Kayla Kurland-Davis/ Beki Rohrig Music by: Ruby Adams and  Bensound Learn more about your ad choices. Visit podcastchoices.com/adchoices

This week, the Hemp Show takes place in New York City, at the intersection of Industrial Hemp and Fashion Avenue. Last month, the National Industrial Hemp Council held its annual Global Industrial Hemp Fiber Summit at the Fashion Institute of Technology in Manhattan. With two full days of presentations, panel discussions and networking opportunities, the "Seed to Style" summit attracted people from all parts of the hemp supply chain, as well as people from the worlds of fashion, design and global textiles. This episode offers a snapshot of what the intersection of hemp and fashion looks like in 2026. We talk to Shannon Mayer, dean of the Jay and Patty Baker School of Business and Technology at FIT; Patrick Atagi, president and CEO of the National Industrial Hemp Council; and Curie Chang, founder and CEO of Ofibera, a luxury hemp apparel company. Learn more National Industrial Hemp Council of America https://nihcoa.com/ NIHC Global Summit https://nihcglobalsummit.com/ FIT's Jay and Patty Baker School of Business and Technology https://www.fitnyc.edu/academics/academic-divisions/business-and-technology/index.php Ofibera https://www.ofibera.com/ Bonus: NIHC Industrial Hemp Annual Market Guide https://nihcoa.com/new-nihc-industrial-hemp-market-guide-now-available-online/ The NIHC's first annual guide to U.S. hemp production and markets, which debuted in print on hemp paper at the Seed to Style summit at FIT, is now available online. ONE PLANT in Lancaster https://www.oneplant.film/lancaster Tickets: ONE PLANT double screening, Oct. 24, Zoetropolis Cinema Stillhouse https://www.eventbrite.com/e/lancaster-farming-sunflower-present-one-plant-tickets-1998324956739 Thanks to Our Sponsors! Jungmaven jungmaven.com IND HEMP indhemp.com Americhanvre Cast-Hemp americhanvre.com King's AgriSeeds kingsagriseeds.com Forever Green, distributors of the KP4 Hemp Cutter hempcutter.com

Nutrition advice often reduces food to calories, nutrients, and a list of rules to follow. But the more we learn about how the body responds to different foods, the clearer it becomes that this way of thinking tells only part of the story. Today, I'm joined by cardiologist and public health expert Dr. Dariush Mozaffarian, director of the Food is Medicine Institute at Tufts University and author of the new book Food Is Medicine. We explore what we still get wrong about food and what changes when we begin treating it more like medicine. Here's what we discuss: Why eating less and exercising more may not lead to lasting weight loss, and what to focus on instead How to choose foods that work with your appetite, metabolism, and microbiome rather than relying on calorie counting What GLP-1 medications can teach all of us about hunger, cravings, and why weight isn't simply about willpower How much protein you actually need and why the source matters What the science says about seed oils and which processed foods are worth avoiding Over the years, I've learned to focus less on counting calories or chasing a single nutrient and more on the quality of the food itself. When you eat real, whole foods, the signals that regulate hunger and energy can begin to work with you instead of against you. Eating well should be less about following rules and more about understanding what helps your body function at its best. View Show Notes From This Episode Sign up for Dr. Hyman's Brainshaping Academy to learn how to nourish the biological systems that support your mental, emotional, and cognitive health https://drhyman.com/products/brainshaping?utm_source=dr_hyman_show&utm_medium=newsletter&utm_campaign=may_27&utm_content=link Get Free Weekly Health Tips from Dr. Hymanhttps://drhyman.com/pages/picks?utm_campaign=shownotes&utm_medium=banner&utm_source=podcast Sign Up for Dr. Hyman's Weekly Longevity Journalhttps://drhyman.com/pages/longevity?utm_campaign=shownotes&utm_medium=banner&utm_source=podcast Join the 10-Day Detox to Reset Your Healthhttps://drhyman.com/pages/10-day-detox Join the Hyman Hive for Expert Support and Real Resultshttps://drhyman.com/pages/hyman-hive This episode is brought to you by Cozy Earth, Made In, Fatty15, Pique, Paleovalley, and BIOptimizers. Upgrade your sleep setup with cozyearth.com and enjoy 20% off with code HYMAN. Explore kitchen essentials at madeincookware.com and save 10% off your first order with code HYMAN-HIVE. Head to fatty15.com/HYMAN today and use code HYMAN for 15% off your 90-day subscription Starter Kit. Elevate your daily wellness ritual at piquelife.com/hyman and enjoy 20% off plus free gifts. Shop nutrient-rich foods and supplements at paleovalley.com/hyman and save 15% off your first order. Shop Magnesium Breakthrough at bioptimizers.com/hyman and save 15% with code HYMAN. 0:00 The paradox of rising obesity, calorie intake, and the importance of food quality over willpower 2:00 Introduction to Food is Medicine and Dr. Hyman's mission 4:25 How medical training ignored nutrition and early challenges to low-fat dogma 7:48 Cardiac diets, processed carbs, and failures in nutrition science translation 10:54 The food system's role in chronic disease and its national impact 12:15 Food as a health, economic, environmental, and equity issue 15:05 How science, policy, and business shaped the modern food system 18:14 Fixing the food system using the same forces that created it 19:04 The birth of modern nutrition science and discovery of vitamins 20:15 Deficiency diseases, vitamin fortification, and enriched grains 23:16 The green revolution, famine fears, and calorie-focused agriculture 27:04 Low-fat advice, tobacco-era marketing, and the rise of ultra-processed food 31:29 Misapplied nutrient thinking: fat vilification and chronic disease 33:30 Why calories in, calories out fails for obesity and weight control 36:53 Thermogenesis, brown fat, and food quality's impact on metabolism 39:25 The microbiome's role in calorie harvesting and weight regulation 41:03 Real food vs. calorie counting for long-term weight control 43:09 GLP-1, the microbiome, and how the food system hijacks appetite 48:27 Feeding your inner garden: microbiome foods, prebiotics, and protective foods 50:47 Food as medicine: adding good foods vs. deprivation 52:36 Food as a fast, personalized therapy for chronic disease 54:11 What ultra-processed food is and why it's harmful 59:59 Why junk food isn't food, and the protein craze explained 1:00:44 How much protein you need and why the source matters 1:03:55 Best protein sources and the dangers of processed meats 1:05:58 Seed oils, trans fats, omega-6s, and omega-3 deficiency 1:13:57 Why food system change is needed, not just individual willpower 1:15:20 Policy levers for fixing agriculture, labeling, regulation, and nutrition science 1:17:29 How health care can drive food as medicine 1:21:58 How investing in healthy food can reshape markets and outcomes 1:22:44 Food industry lobbying, misinformation, and policy challenges 1:25:07 Business innovation to shift toward healthier food products 1:26:03 Ultra-processed foods as “coal” and replacing them with clean food 1:26:25 What listeners can do in their communities and with officials

In episode 2133, Miles and guest co-host Matt Lieb are joined by Pod Yourself A Gun & Mad Yourself A Man, Vince Mancini, to discuss… This Year’s “MAHA Summit” Is Full Of Celebrity Dirtbags, Zoomer on TikTok says that MILLENNIALS are preventing their generation from working, Gov Abbott DECLARES (makes empty gesture) DIESEL DISASTER! A Long-Dead Sci-Fi Writer Probably Shouldn’t Be In Charge Of AI Safety and more! Vance, RFK Jr. and 'Iron' Mike to headline MAHA Summit Dr. Oz to Appear On a Panel Moderated by Russell Brand — Who is Currently Facing Rape Charges in the UK The Benefits of Snake Oil - Dr.Berg Isaac Asimov Showed Us How to Avoid the AI Apocalypse One of science fiction’s greatest writers warned us about AI. Could his ideas help us avoid a dystopian future? Could revisiting Asimov’s laws help us avoid AI’s ‘Chernobyl moment’? Noted Brain Genius Tyler Winklevoss Thinks Isaac Asimov Already Solved the AI Apocalypse Google Taps Asimov's Three Laws of Robotics for Real Robot Safety Asimov's Laws Won't Stop Robots from Harming Humans, So We've Developed a Better Solution What Isaac Asimov Reveals About Living with A.I. “Runaround” In: I, Robot by Isaac Asimov Runaround By Isaac Asimov Elon Musk, Sam Altman, and the Misreading of Science Fiction LISTEN: Blend by BrainstorySee omnystudio.com/listener for privacy information.

Do you ever feel like you're chasing certain things to finally feel joy? Or like your joy needs to be earned? It turns out many of us do, and it is hugely impacting how we feel in our daily lives.  In this episode, you are going to understand exactly how to bring more joy into your life and how to reap the incredible mental and physical health benefits (even if you're struggling right now). My expert guest is Dr. Kelly McGonigal. She is a renowned health psychologist, a Stanford University lecturer, and one of the world's leading experts on the science of stress and joy. 

We've learned a lot over the years of parenting and we're reflecting on some of the mistakes made and lessons learned.  We want to share five things we wish we knew earlier when the kids were young.Today's Show Sponsor is SEED.  Use code 25Tripp for a discount at seed.com/trippWe're thrilled to be a Toothpillow partner and share with you a free assessment and $500 off treatment! https://tripp.toothpillow.com/Send us Fan Mail

The Sower and the Seed | Matthew 13 by Christ Covenant

WE GOT MOUTHWASH STUDIO IN THE HOUSE! We are thrilled to welcome Strategy Director & Partner Alex Tan and Creative Director & Partner Abraham Campillo to the program! Mouthwash is a design studio centered on new ideas and defining experiences, partnering with this generation's leading brands and innovators to influence culture. Their approach challenges industry standards, adopts new technologies, and strives for a lasting positive impact. Over time, their work has naturally led them towards industry leaders across art, architecture, fashion, sustainability, technology, and beyond. They've worked with brands like Apple, Bonnie Clyde, Fender, Google, Lululemon, Nike, The North Face, OpenAI, Rimowa, Seed, and the Utah Jazz to name a few. They recently opened an all-day café in Los Angeles: Café Tondo. They also write Findings, a newsletter on the influences and trends that are quietly shaping our culture. And they make a successful magazine: Mouthwash Magazine, an exhibition in print for contemporary culture, art, and photography. Mouthwash is a creative powerhouse, a studio started by friends who met on Instagram and just liked working together, and they haven't looked back since, and they never will. Enjoy!

Wednesday In this episode of Seeing Without Seeing, we come together in prayer and faith to seek the Lord concerning the pressing issues affecting our world today. This Christian podcast is a place for daily prayer, spiritual encouragement, and faith-based support as we lift up our children, our parents, and our loved ones who are facing sickness and life's challenges. We also pray for our leaders, asking God to release His wisdom, protection, and divine direction across the earth. If you are seeking peace, strength, and a deeper connection with God, this moment of prayer and encouragement is for you. Thank you for being a faithful supporter of this podcast. Your prayers, your shares, and your willingness to join your faith with ours help spread the message of hope, healing, and Kingdom living. There is power in agreement, and we are grateful to stand with you as we believe God to move mightily. If you have a prayer request, you may text 843-790-4229 or submit it by email to seeingwithoutseeing2020@gmail.com. Please place “Prayer Request” in the subject line. If you feel led to sow a seed into this ministry: • You may send via Zelle using the email seeingwithoutseeing2020@gmail.com • For PayPal instructions, email us with “Seed” in the subject line • Cash App: $seeingwithoutseeing Thank you again for your continued support. Please share this episode with others and join us as we grow in faith, walk in purpose, and seek the presence of God together.

Tuesday In this episode of Seeing Without Seeing, we come together in prayer and faith to seek the Lord concerning the pressing issues affecting our world today. This Christian podcast is a place for daily prayer, spiritual encouragement, and faith-based support as we lift up our children, our parents, and our loved ones who are facing sickness and life's challenges. We also pray for our leaders, asking God to release His wisdom, protection, and divine direction across the earth. If you are seeking peace, strength, and a deeper connection with God, this moment of prayer and encouragement is for you. Thank you for being a faithful supporter of this podcast. Your prayers, your shares, and your willingness to join your faith with ours help spread the message of hope, healing, and Kingdom living. There is power in agreement, and we are grateful to stand with you as we believe God to move mightily. If you have a prayer request, you may text 843-790-4229 or submit it by email to seeingwithoutseeing2020@gmail.com. Please place “Prayer Request” in the subject line. If you feel led to sow a seed into this ministry: • You may send via Zelle using the email seeingwithoutseeing2020@gmail.com • For PayPal instructions, email us with “Seed” in the subject line • Cash App: $seeingwithoutseeing Thank you again for your continued support. Please share this episode with others and join us as we grow in faith, walk in purpose, and seek the presence of God together.

Cherie Calbom, MS, nutritionist and author of The Truth About Seed Oils, joins the Strong Ambition Podcast for a friendly debate about one of the most controversial topics in nutrition: Are seed oils actually harmful?Cherie explains why she believes industrial seed oils should be avoided, while Rhyland challenges whether the human evidence supports labeling seed oils as toxic. They discuss inflammation, randomized controlled trials, anecdotes versus population evidence, beef tallow, processed foods, calories, food sensitivities, and why the overall dietary pattern may matter more than any single ingredient. Despite disagreeing on seed oils, the conversation finds plenty of common ground around whole foods, sustainable nutrition, individual differences, and the importance of actually enjoying your life and the people you share it with.In This Episode, We Discuss:Cherie's personal health transformation and journey into nutrition.Why juicing and whole foods became central to her nutrition philosophy.The argument that seed oils oxidize and create harmful byproducts.Are seed oils actually “toxic” in the human body?What randomized controlled trials can tell us about seed oils.Biological mechanisms versus real-world human outcomes.Anecdotes, food sensitivities, and population-level recommendations.Why Rhyland believes individual ingredients are often unnecessarily villainized.Seed oils versus overall calorie intake and dietary patterns.Is beef tallow actually healthier than seed oils?Why Japan creates an interesting challenge to the seed oil argument.Correlation versus causation in nutrition research.GMOs, food processing, chemicals, and the appeal to “natural.”Why different dietary restrictions can all improve adherence for different people.Finding the right balance between whole foods and flexibility.Listening to your body and recognizing individual food sensitivities.Why relationships, community, and enjoying food also matter for health.Where Rhyland and Cherie ultimately agree despite their differences.Episode Roadmap00:47 – Cherie's Health Transformation & Journey Into Nutrition04:24 – Juicing, Whole Foods & Cherie's Current Diet09:47 – The Truth About Seed Oils11:01 – Why Cherie Believes Seed Oils Are Toxic13:33 – Rhyland Challenges the Evidence Against Seed Oils17:37 – Where Rhyland & Cherie Agree — and Disagree19:52 – Personal Stories, Food Sensitivities & Anecdotal Evidence22:41 – Can Seed Oils Be Part of a Healthy Diet?25:07 – Is the Ingredient the Problem or the Overall Diet?26:55 – Beef Tallow vs. Seed Oils29:27 – Calories, Processed Foods & the Bigger Health Problem30:38 – Japan: A Challenge to the Seed Oil Argument34:30 – GMOs, Cancer & Asking What the Evidence Actually Shows35:00 – Correlation vs. Causation in Nutrition Research37:40 – Why Rhyland Doesn't Think Seed Oils Are the Main Problem39:38 – Dietary Restrictions, Adherence & Finding What Works for You41:26 – What Does “Chemical” Actually Mean in Nutrition?42:42 – Does Natural Automatically Mean Healthier?44:03 – How Much Processed Food Can Fit Into a Healthy Diet?46:24 – Food Sensitivities & Individualizing Your Nutrition48:00 – Why Relationships and Community Matter for Your Health50:33 – Where Rhyland & Cherie Find Common Ground52:08 – Cherie's Current Ambition & Making Healthy Eating Enjoyable1:XX:XX – Rhyland's Reflectionhttps://amzn.to/3MU4EBg@juiceladycherie

We're ONE WEEK away from the launch ofThis Wasn't the Plan is out! In todays episode Mac and Kenz give us the inside scoop on the book, the parts that hit close to home. Then it's straight into real life: Mac is 38 weeks pregnant and navigating contractions after emergency surgery, while Kenz is juggling house projects, horse-girl dreams, and the general chaos of September. They also get honest about learning to accept help, slowing down, and making more room for joy—plus plenty of side quests along the way!! ⁠Preorder This Wasn't The Plan!⁠ Fill out the form HERE to get your preorder goodies! SEEDS: Download the Seed app and take one small step today: plant your first seed and see what God can grow! Learn more about your ad choices. Visit megaphone.fm/adchoices

Former Hell's Angels enforcer Joby Walters shares his incredible journey from outlaw life to Jesus, redemption, and freedom. Listen to his powerful story of identity, forgiveness, and grace.What happens when the life you thought would give you freedom, brotherhood, and belonging becomes the very thing holding you captive?Joby Walters has lived a story most of us could only imagine. A Vietnam veteran, cowboy, former Hell's Angels enforcer, and man who once faced decades in federal prison, Joby spent years searching for identity, family, respect, and belonging.But his greatest transformation didn't come from changing his circumstances. It came when Jesus met him in the middle of his darkness.Joby's story is a powerful reminder that the things we chase for belonging and freedom can never satisfy the deepest places in our hearts.Chapters:[00:00] Podcast Preview[01:18] Topic and Joby Walters' Introduction[04:25] Growing Up in Troubled Times[05:40] The Seed of Gang Life[07:58] Chasing Identity[08:38] Searching for Family[12:15] From Biker to Cowboy[13:30] Running From Himself[14:37] Breaking Free From Addiction[15:26] How He Met the Hell's Angels[18:46] Meeting Sonny Barger[21:50] Becoming a Hell's Angel[23:15] The False Promise of Brotherhood[26:07] Prison and the Outlaw Life [27:03] When Jesus Started Breaking Through[28:12] The Box That Changed His Life[29:50] The Moment Everything Changed[33:00] Knowing Jesus & Sharing Him [35:20] The Mission Behind Cut Free [38:06] What People Are Really Searching For[40:19] “I'm Too Far Gone”[41:10] Joby's Prayer for the AudienceResources mentioned:

Late Season Control Options for Sericea Being Prepared for Wheat Pests Will Kansas be a Top 10 Milk Producing State?   00:01:05 – Late Season Control Options for Sericea: K-State Extension rangeland management specialist Molly Reichenborn starts today's show as she explains late season control for sericea lespedeza and what options are available. Options for Late Season Sericea Control   00:12:05 – Being Prepared for Wheat Pests: Jeff Whitworth, K-State crop entomologist, keeps the show going as he discusses insecticide seed treatments for wheat and what pests growers should keep an eye out for as wheat gets established.   00:23:05 – Will Kansas be a Top 10 Milk Producing State?: Ending the show is K-State dairy specialist Mike Brouk saying Kansas is well positioned to break into the Top 10 as one of the top milk producing states.      Send comments, questions or requests for copies of past programs to ksrenews@ksu.edu.   Agriculture Today is a daily program featuring Kansas State University agricultural specialists and other experts examining ag issues facing Kansas and the nation. It is hosted by Shelby Varner and distributed to radio stations throughout Kansas and as a daily podcast.   K‑State Extension is a short name for the Kansas State University Cooperative Extension Service, a program designed to generate and distribute useful knowledge for the well‑being of Kansans. Supported by county, state, federal and private funds, the program has county Extension offices statewide. Its headquarters is on the K‑State campus in Manhattan. For more information, visit Extension.ksu.edu. K-State Extension is an equal opportunity provider and employer.

We're rounding out September in Ontario and plenty of winter wheat is already in the ground. For those just getting around to it now, conditions have turned decidedly dry in some areas. Which begs the question — if there's moisture at 2.5 to 3 inches down, do you seed to moisture or stick with the... Read More

If you wait for perfect conditions, you may never sow. This week on One More Thing, we talk about being faithful with what God has put in your hand, taking the step of obedience, and trusting Him with the harvest.

The girls flag football season is underway, with Romeoville, Lincoln-Way East and Minooka earning high sectional seeds as games begin today.Become a supporter of this podcast: https://www.spreaker.com/podcast/friday-night-drive--3534096/support.This episode includes AI-generated content.

Monday In this episode of Seeing Without Seeing, we come together in prayer and faith to seek the Lord concerning the pressing issues affecting our world today. This Christian podcast is a place for daily prayer, spiritual encouragement, and faith-based support as we lift up our children, our parents, and our loved ones who are facing sickness and life's challenges. We also pray for our leaders, asking God to release His wisdom, protection, and divine direction across the earth. If you are seeking peace, strength, and a deeper connection with God, this moment of prayer and encouragement is for you. Thank you for being a faithful supporter of this podcast. Your prayers, your shares, and your willingness to join your faith with ours help spread the message of hope, healing, and Kingdom living. There is power in agreement, and we are grateful to stand with you as we believe God to move mightily. If you have a prayer request, you may text 843-790-4229 or submit it by email to seeingwithoutseeing2020@gmail.com. Please place “Prayer Request” in the subject line. If you feel led to sow a seed into this ministry: • You may send via Zelle using the email seeingwithoutseeing2020@gmail.com • For PayPal instructions, email us with “Seed” in the subject line • Cash App: $seeingwithoutseeing Thank you again for your continued support. Please share this episode with others and join us as we grow in faith, walk in purpose, and seek the presence of God together.

Caleb Schafer, Lead Pastor of Redeemer's Church, concludes our series on Evangelism, Prophecy, and how they both play a part in seeing people come to faith in Jesus. September 27th, 2026 | 9.27.26 Category: Stewardship, Work, Word

In this episode, former Johnny's Seeds seed rep-turned-garden manager Wesley Palmer doubles down on why seed reps are a small farm owner's untapped resource.   Subscribe for more content on sustainable farming, market farming tips, and business insights!   Get market farming tools, seeds, and supplies at Modern Grower. Follow Modern Grower:  Instagram  Instagram Listen to other podcasts on the Modern Grower Podcast Network:  Carrot Cashflow  Farm Small Farm Smart  Farm Small Farm Smart Daily  The Growing Microgreens Podcast  The Urban Farmer Podcast  The Rookie Farmer Podcast  In Search of Soil Podcast Check out Diego's books:  Sell Everything You Grow on Amazon   Ready Farmer One on Amazon **** Modern Grower and Diego Footer participate in the Amazon Services LLC. Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.

The Seed of Commitment | He Pursues, We Respond | Abundant Life Church | Pastor Erin Thomas

Generosity is not supposed to be something we occasionally do; it is supposed to become part of who we are as we abide in Jesus, trust Him, and bear fruit. In this message from the Domino Effect series, Pastor Josh reminds us that God did not give us our time, gifts, resources, and opportunities to build our own kingdoms, but to build His. If we consume everything God gives us, we may never discover what He could have grown through us.If you're new to Harbor or want to get connected in any way click this link to get your New Here gift, find upcoming events or get involved!https://harborchurch.com/connect

As harvest gets underway across the country, the American Seed Trade Association is reminding farmers to take precautions to keep treated seed out of the grain supply. NAFB News ServiceSee omnystudio.com/listener for privacy information.

On this episode of We Talk Weekly After The Talk on WPPM-LP Philadelphia 106.5 FM, Charles Gregory, Classy Lady Sparkle, Lawrence, and Big Exclusive bring a full show filled with wellness, politics, culture, entertainment, sports, and community-centered conversation.The show opens with special guest Sherry Calbom, widely known as The Juice Lady, for a powerful conversation about juicing, seed oils, whole-food nutrition, celery juice, acid reflux, menopause, hair loss, organic produce, and her latest book, The Truth About Seed Oils. She also reflects on her time working with George Foreman and shares why health is connected to purpose.The conversation continues with local political discussion around Philadelphia leadership, youth engagement, social media campaigning, Mayor Cherelle Parker, and the importance of bringing both young voices and experienced voices to the table.The crew also covers trending stories in sports, politics, music, fashion, and entertainment, including Ryan Garcia vs. Conor Benn, federal hiring questions, Beyoncé's B'Day anniversary, American fashion, Kendrick Lamar's upcoming film project, Lil Durk's legal situation, the return of the NFL, and a tribute moment for Philly artist PnB Rock.Lauren closes things out with The Sizzle, covering Beyoncé and Selena fan reactions, Chris Rock and the Oscars conversation, Madagascar sequel talk, and the ongoing Remy Ma, Papoose, and Claressa Shields drama.As always, We Talk Weekly brings real people, real conversations, and solutions-based commentary for the community.Support independent journalism and community-centered media by subscribing, sharing, and donating at wetalkweekly.com.Become a supporter of this podcast: https://www.spreaker.com/podcast/we-talk-weekly-news--2576999/support.Subscribe to We Talk Weekly News' YouTube channel for full podcast video show episodes:https://www.youtube.com/@WeTalkWeeklyTVFollow We Talk Weekly News across all social media platforms for exclusive content, breaking updates, and behind-the-scenes access:Instagram: https://www.instagram.com/wetalkweeklyTwitter (X): https://twitter.com/WeTalkWeeklyFacebook: https://www.facebook.com/wetalkweekly

Guests Jon and Steph Hietbrink continue our series on Psalms. This week they share from teaches from the Psalms 27 on the theme of Presence.

Adam continues in Into the Fields with a message on the power of a planted seed.#iamembraceEmbrace is one church, made up of multiple campuses and network churches. We exist to relentlessly reach the next person for Jesus.Give Herehttps://www.iamembrace.com/giveStay ConnectedWebsite: iamembrace.comEmbrace Church Facebook: facebook.com/iamembraceEmbrace Church Instagram: instagram.com/iamembraceAdam Weber YouTube: https://www.youtube.com/@adamaweberAdam Weber Facebook: https://www.facebook.com/adamaweberAdam Weber Instagram: instagram.com/adamaweber

This sermon examines the divine oath sworn by the pre-incarnate Christ on Mount Moriah, emphasizing that God's promises are secured by His own unchangeable nature rather than human effort. The message connects the first biblical mention of love and the doctrine of providence in Genesis 22 to the typological foreshadowing of Christ's death and resurrection in Isaac's experience. It distinguishes between the earthly seed represented by sand and the spiritual seed represented by stars, identifying the latter as the church united to Christ. The preacher challenges believers to reject comfortable passivity, arguing that genuine faith is evidenced by costly obedience and that God's sovereign providence continues to work behind the scenes of human trials. Ultimately, the sermon calls for a restful trust in God's immutable character and a life marked by visible fruit of regeneration.

The Juice Lady Talks Seed Oils, Juicing & Natural HealingOn this episode of We Talk Weekly After The Talk, we welcome Sherry Calbom, widely known as The Juice Lady, for a powerful conversation about juicing, whole-food nutrition, seed oils, and natural wellness.Sherry shares her personal healing journey, how juicing changed the direction of her life, and why she believes food can play an important role in supporting the body. She also discusses her latest book, The Truth About Seed Oils, and breaks down the growing conversation around which oils people may want to avoid and which oils she believes are better choices.The conversation also touches on celery juice, green juice, acid reflux, menopause, skin care, hair loss, organic produce, and her experience working with legendary heavyweight champion George Foreman.This episode is about practical wellness, better food choices, and learning how to support your body from the inside out.Disclaimer: This conversation is for informational and educational purposes only and should not replace medical advice. Please consult a qualified healthcare provider before making major changes to your diet, supplements, or wellness routine. #WeTalkWeekly #TheJuiceLady #SherryCalbom #SeedOils #Juicing #NaturalHealth #Wellness #Nutrition #AfterTheTalkBecome a supporter of this podcast: https://www.spreaker.com/podcast/we-talk-weekly-news--2576999/support.Subscribe to We Talk Weekly News' YouTube channel for full podcast video show episodes:https://www.youtube.com/@WeTalkWeeklyTVFollow We Talk Weekly News across all social media platforms for exclusive content, breaking updates, and behind-the-scenes access:Instagram: https://www.instagram.com/wetalkweeklyTwitter (X): https://twitter.com/WeTalkWeeklyFacebook: https://www.facebook.com/wetalkweekly

Woe to the Serpent's Seed Matthew 23:29-36 Huntingdon Missionary Baptist Church Sunday, September 27, 2026 Evening Service

Power Up: Mark 8:14-20 KJV In this episode of Seeing Without Seeing, we come together in prayer and faith to seek the Lord concerning the pressing issues affecting our world today. This Christian podcast is a place for daily prayer, spiritual encouragement, and faith-based support as we lift up our children, our parents, and our loved ones who are facing sickness and life's challenges. We also pray for our leaders, asking God to release His wisdom, protection, and divine direction across the earth. If you are seeking peace, strength, and a deeper connection with God, this moment of prayer and encouragement is for you. Thank you for being a faithful supporter of this podcast. Your prayers, your shares, and your willingness to join your faith with ours help spread the message of hope, healing, and Kingdom living. There is power in agreement, and we are grateful to stand with you as we believe God to move mightily. If you have a prayer request, you may text 843-790-4229 or submit it by email to seeingwithoutseeing2020@gmail.com. Please place “Prayer Request” in the subject line. If you feel led to sow a seed into this ministry: • You may send via Zelle using the email seeingwithoutseeing2020@gmail.com • For PayPal instructions, email us with “Seed” in the subject line • Cash App: $seeingwithoutseeing Thank you again for your continued support. Please share this episode with others and join us as we grow in faith, walk in purpose, and seek the presence of God together.

MAPS Abs and Butt: https://mapsabsandbutt.com Code: LAUNCH for 40% off. Includes Butt Building Diet Guide, Women's Supplement Guide, at-home dumbbell version and a free second program at checkout (Muscle Mommy or Muscle Mommy 15).   In this episode the guys break down how to cut fat and keep muscle — covering why you need to be in a good metabolic position before you start cutting, why letting food create the deficit beats trying to burn calories through exercise, why cutting volume during a cut is counterintuitive but essential, and why increasing low-stress activity like walking is the secret sauce for getting lean without losing muscle. They also cover Adam's friend who dropped total cholesterol from 293 to 201 and LDL from 200 to 124 using Dose alongside dietary changes, a processed food study linking ultra-processed foods to hidden intramuscular fat in the thighs, the first reversal of adult obesity rates in decades and why GLP-1 is almost certainly responsible, new data showing GLP-1 improving psoriasis before weight loss even begins suggesting an independent autoimmune mechanism, Sal two and a half weeks into MAPS Anabolic for the first time in years and the strength gains already surprising him, Adam's friend Ryan DeVita finally diagnosing the pec major tear that has been baffling Adam for months, and Sal noticing a dramatic difference in digestion after running out of Seed for two weeks. Then they coach live callers submitted through mplivecaller.com.   Mind Pump Fitness Coaching: https://mindpumpfitnesscoaching.com 1.9 NASM CEUs   SPONSORS Dose for Your Liver: https://dosedaily.co/MINDPUMP35 Code: MINDPUMP35 for 35% off first month subscription. Adam's friend dropped total cholesterol from 293 to 201 and LDL from 200 to 124 — all natural liquid supplement with clinical studies behind it.   Seed Daily Synbiotic: https://seed.com/mindpump Code: 25MINDPUMP for 25% off your first month. Sal ran out for two weeks and noticed a huge difference in digestion — confirms it works best as a daily consistent habit.   Butcher Box: https://butcherbox.com/mindpump No code needed. Free ribeyes, NY strip steaks or filet mignons in every order for a year plus $20 off first box.   LINKS Submit a live caller question: https://mplivecaller.com Mind Pump Store: https://mindpumpstore.com Maps Fitness Products: https://mapsfitnessproducts.com Instagram: @mindpumpmedia   0:00 - Intro 2:18 - How to cut fat and keep muscle — why your body wants to lose muscle when you diet 8:35 - Step 1: Are you in a good metabolic position to cut from? 9:59 - Step 2: Let food create the deficit — stop leaning on exercise to burn calories 13:03 - Step 3: Lower your training volume — this is the opposite of what most people do 16:00 - Step 4: Increase low stress activity — walking is the secret sauce for getting shredded 18:18 - Step 5: Eat in ways that produce satiety — whole foods and high protein 23:31 - Adam's friend drops cholesterol from 293 to 201 using Dose and dietary changes 28:33 - Ultra-processed food study — hidden intramuscular thigh fat linked to processed food intake 33:41 - Adult obesity rates reversed for the first time in decades — GLP-1 is almost certainly responsible 42:43 - GLP-1 improving psoriasis before weight loss begins — independent autoimmune mechanism 52:51 - Sal two and a half weeks into MAPS Anabolic Phase 1 — why he is only adding 2.5 pounds at a time 57:35 - Ryan DeVita diagnoses Adam's pec major tear that has been baffling him for months 1:00:28 - Sal runs out of Seed for two weeks and notices a huge difference in digestion 1:04:15 - Caller: Kyle (British Columbia) — returning caller, 6 pounds of lean mass in 90 days, gets MAPS Upper Lower and Powerlift package 1:19:01 - Caller: Justin (California) — law enforcement, 3-4 night shifts a week, 4-5 hours sleep, gets MAPS 15 and sleep strategy 1:31:07 - Caller: Casey (North Carolina) — returning caller, Hashimoto's, gluten-free 90 days, no scale change but all markers improved

Jessica and Dave welcome Dr. Pamela Ray to discuss how hygienists can improve periodontal communication, help patients take ownership of their disease, and guide them toward informed decisions about their health. This episode was brought to you by our friends at Net32.   What We Talked About: Overcoming barriers to periodontal treatment acceptance.   Using the oral-systemic connection to motivate patients.   Effective language and communication strategies.   Strengthening communication with periodontists, physicians, and referral sources.   Handling patients who delay or decline treatment.   Practical tips for creating meaningful conversations that lead to action.   Call to Action: Find your passion while practicing patient care.   Guest: Dr. Pamela Ray — drpamelaray@gmail.com   Hosts: Jessica and Dave — jatkinson@endeavorb2b.com | Dtorres@endeavorb2b.com

Friday In this episode of Seeing Without Seeing, we come together in prayer and faith to seek the Lord concerning the pressing issues affecting our world today. This Christian podcast is a place for daily prayer, spiritual encouragement, and faith-based support as we lift up our children, our parents, and our loved ones who are facing sickness and life's challenges. We also pray for our leaders, asking God to release His wisdom, protection, and divine direction across the earth. If you are seeking peace, strength, and a deeper connection with God, this moment of prayer and encouragement is for you. Thank you for being a faithful supporter of this podcast. Your prayers, your shares, and your willingness to join your faith with ours help spread the message of hope, healing, and Kingdom living. There is power in agreement, and we are grateful to stand with you as we believe God to move mightily. If you have a prayer request, you may text 843-790-4229 or submit it by email to seeingwithoutseeing2020@gmail.com. Please place “Prayer Request” in the subject line. If you feel led to sow a seed into this ministry: • You may send via Zelle using the email seeingwithoutseeing2020@gmail.com • For PayPal instructions, email us with “Seed” in the subject line • Cash App: $seeingwithoutseeing Thank you again for your continued support. Please share this episode with others and join us as we grow in faith, walk in purpose, and seek the presence of God together.

Thursday In this episode of Seeing Without Seeing, we come together in prayer and faith to seek the Lord concerning the pressing issues affecting our world today. This Christian podcast is a place for daily prayer, spiritual encouragement, and faith-based support as we lift up our children, our parents, and our loved ones who are facing sickness and life's challenges. We also pray for our leaders, asking God to release His wisdom, protection, and divine direction across the earth. If you are seeking peace, strength, and a deeper connection with God, this moment of prayer and encouragement is for you. Thank you for being a faithful supporter of this podcast. Your prayers, your shares, and your willingness to join your faith with ours help spread the message of hope, healing, and Kingdom living. There is power in agreement, and we are grateful to stand with you as we believe God to move mightily. If you have a prayer request, you may text 843-790-4229 or submit it by email to seeingwithoutseeing2020@gmail.com. Please place “Prayer Request” in the subject line. If you feel led to sow a seed into this ministry: • You may send via Zelle using the email seeingwithoutseeing2020@gmail.com • For PayPal instructions, email us with “Seed” in the subject line • Cash App: $seeingwithoutseeing Thank you again for your continued support. Please share this episode with others and join us as we grow in faith, walk in purpose, and seek the presence of God together.

In this Special Edition of the Flavors of Northwest Arkansas podcast, we're talking about Evening at the Farm at Apple Seeds Teaching Farm in Fayetteville. We talk with Apple Seeds Event Manager Carol Weaver, who walks us through what Apple Seeds is and what they do, and how Evening at the Farm has grown over the years. You'll also hear from Chefs Case Dighero and Tyler Rogers about the night, the menu, and more. This is a night that always sells out, and this year will be no different because there aren't many tickets left. The dinner is October 10th and you can get your tickets at appleseedsAR.org.

From the earliest days of open-weight models to becoming the neutral routing layer for more than 10 million developers, OpenRouter is one of the clearest bets that the future of AI will be multi-model. In this episode, OpenRouter co-founder & CEO Alex Atallah, with AMP's Anjney Midha returning with swyx to unpack how OpenRouter emerged from the first wave of Llama, Alpaca, Mistral, and Midjourney, why model diversity mattered before it was consensus, and how a company dismissed as “just a wrapper” became critical infrastructure for the AI ecosystem.We go deep on the product and distribution lessons behind OpenRouter: why model labs can spend billions training a checkpoint and still struggle to get it into developers' hands, how Mistral helped prove the value of a competitive inference marketplace, why OpenRouter chose focus over expanding into fine-tuning, memory, and other adjacent products, and how its rankings became a real-time map of how AI usage was changing. Alex also explains OpenRouter's early experiments with model fusion, why they deleted the first version and brought it back years later, and how the platform grew to more than 10 trillion tokens per day.Finally, Anjney explains why Stripe and OpenRouter fit together, why token fraud may become one of the defining security problems of the AI economy, and why the next wave of fraud won't just come from humans but from autonomous agents attacking increasingly valuable token flows.We discuss:* Why OpenRouter bet early that no single AI model would win everything* Alpaca, Llama, and open models becoming impossible to ignore* Why Discord's early AI deployments exposed the limitations of closed models* Why model labs can spend billions on training and still fail at distribution* How OpenRouter became a neutral distribution layer for model developers* Why VCs dismissed OpenRouter as “just a marketplace” or “just a wrapper”* The Mistral price war and the first real proof of an inference marketplace* How Midjourney scaled through Discord and what it taught the AI ecosystem* Why crypto infrastructure became a dress rehearsal for generative AI* OpenRouter vs. LM Arena and why their missions are fundamentally different* Why focus became one of OpenRouter's biggest strategic advantages* Anthropic's early focus on AI pair programming and coding* The OpenRouter products that were prototyped but never launched* MOM, OpenRouter's early Mixture of Models experiment* Why model fusion failed in 2024 — and why it works much better now* How OpenRouter's leaderboard became a live map of the AI industry* OpenClaw, auto-routing, and agents reshaping AI usage* How OpenRouter reached 10+ trillion tokens per day* Why inference gateways are increasingly becoming targets for fraud* Why Stripe's fraud infrastructure is strategically important to OpenRouter* The coming rise of agentic fraud and attacks on the token economy* What changes and what stays the same as OpenRouter joins StripeAlex Atallah* LinkedIn: https://www.linkedin.com/in/alexatallah/* X: https://x.com/alexatallah* Website: https://alexatallah.comAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney/* X: https://x.com/AnjneyMidha* AMP: https://www.amppublic.com/Timestamps00:00:00 Introduction00:02:12 Alpaca, Llama, and the Multi-Model Bet00:06:04 Discord, Open Models, and OpenRouter's Origins00:14:28 Why “One Model Wins” Was the Wrong Bet00:17:27 Why Model Labs Struggle With Distribution00:23:04 “Just a Wrapper”: Why VCs Misunderstood OpenRouter00:27:58 Bootstrapping OpenRouter Through Community00:36:16 Crypto, Midjourney, and the Early Generative AI Ecosystem00:43:38 Mistral and the Birth of the Inference Marketplace00:47:10 OpenRouter vs. LM Arena00:52:08 Focus, Anthropic, and Roads Not Taken00:59:34 Mixture of Models and Model Fusion01:02:44 Sonnet, OpenClaw, and OpenRouter's Explosive Growth01:09:03 Why Stripe Acquired OpenRouter01:12:45 Fraud and the Emerging Token Economy01:17:47 The Coming Wave of Agentic Fraud01:19:07 What's Next for OpenRouter at StripeTranscriptIntroduction: OpenRouter, Marketplaces, and Pub-Sub as a Product PrincipleSwyx [00:00:00]: Okay, we are here in Anja's house, which is where all big startups in San Francisco start.Anjney Midha [00:00:08]: Howdy.Swyx [00:00:08]: And, congrats on Cursor, Mistral. I don'- God knows what else. You got so much stuff going on.Anjney Midha [00:00:17]: There's, there's a lot going on. Well, OpenRouter is probably the - has been the most, I would say, like, one I'm excited about recently.Swyx [00:00:24]: Yeah. And we have Alex, first time on the pod, but,Anjney Midha [00:00:27]: Thanks for having me.Swyx [00:00:27]: You've been in the IE a few times. I appreciate every time you've shown up, for the community. Congrats. I just, like, what a journey. When I was looking back at your past posts, one of the earliest principles that I saw you write as a product person is sub as a product principle. And I wanted - you to maybe explain how you think about what should exist in the world.Anjney Midha [00:00:49]: Yeah. The sub piece, which was early 2023, I didn't think about it until we talked like 10 minutes ago, is about how there is like a way of thinking about products as an intersection between subscribing to data and publishing data. And marketplaces are an easy example of this. You have suppliers that are publishing some product to a SKU. And the SKU is like a sub topic that a consumer is subscribing to and just going to, like, consume whenever they want. And humans consume in a very, like, discreet, ad hoc way. It's not very scalable. all their attention is on the topic when they're buying the thing, and their attention is nowhere else when that happens. agents and consumers of inference don't act like that. They're consuming continuously, and they're changing the SKUs that they consume from all the time. So OpenRouter is like a blend between a normal API experience and a marketplace where we create model slug. We have the auto router. We have all kinds of, like, product SKUs that you can subscribe to. And then you can, like, continuously add, like, derive value and make decisions based on those consumers.Alpaca, Llama, and the Multi-Model BetSwyx [00:02:11]: Yeah. This is something that was more consensus now, but not consensus when you guys started, which was that there is such a demand for swapping models and changing things out and, that people would not use the native SDKs. I guess, for each of you, what was your realization moment that this would be it? I, - You've, you've given a talk at EIE about Alpaca as,Anjney Midha [00:02:33]: Yeah.Swyx [00:02:33]: One of your inspiring moments.Anjney Midha [00:02:35]: Alpaca, I can, like, rehash the Alpaca moment for a sec. Like, the very beginning, at the end of 2022, OpenAI was the only game in town. There was, like, OpenAI, Cohere,Swyx [00:02:47]: Yes.Anjney Midha [00:02:48]: And then a smattering of, like, early attempts at open weight models.Swyx [00:02:54]: Yeah.Anjney Midha [00:02:54]: When Llama came out in January of 2023, it was like, “Wow, really exciting. This is really big.” It outperforms 3 on, one or two benchmarks. but you can't chat with it. It wasn't like - It wasn't an engaging model, but it seemed like someone just needed to fix a couple things and do some RLHF on it to get it all the way there. And Alpaca was the first model that I saw that did that. It only took $600 to do. A team at Stanford generated a bunch of synthetic data, tuned Llama, and made Alpaca, billion parameter model. Or was - Maybe it was thirteen billion parameters. And it was so good. Like, I was just, like, on an airplane using it. I, - in many cases, I, like, you could not discern a ChatGPT versus an Alpaca result. And I figured if it was this easy to make a model, one, we have a whole new way of monetizing data for the first time. you can just, like, take really valuable data and turn it into a service in $600. and that cost will probably go down over time.Swyx [00:04:03]: When you - So sorry. when you say monetizing your data as, what eventually will become an MCP endpoint or as a training data for a model?Anjney Midha [00:04:12]: Yeah, training data for a model.Swyx [00:04:13]: Awesome.Anjney Midha [00:04:13]: Like, an abstract way of saying like, “Hey, I have this data.”Swyx [00:04:15]: Compress it into a model.Anjney Midha [00:04:16]: Like, it makes sense for me in my product, but, like, I could repackage it in the form of a model and sell it. And so it's just a whole new business model for the economy. It also, of course, provides, like, a way of following what Frontier Labs are doing, but in a way that, like, a single developer or a small team of developers can roll on their own. And so - Whenever you have an example of that, like a breakout app that's doing really well, and then some framework for imitating it with - in your own flavor, you have an immediate ecosystem of, like an immediate ecosystem, like, should arise because there's just a huge gap between the, like, decisions that the single company is making and all of the variations in those decisions that, like, a wider ecosystem can create themselves. And so then, you need a marketplace to, like, discover all of those, services and all of those products. There wasn't any place on the internet that, like, was like a home base for LLMs in terms of seeing how much they were being used and seeing who was using them and why.Swyx [00:05:29]: The closest would be Hugging Face.Anjney Midha [00:05:30]: Hugging Face was the closest at the time, yeah.Swyx [00:05:31]: They just started Hugging, like, a few years ago before that.Anjney Midha [00:05:34]: Yeah, and Hugging Face also didn't have the closed-source models.Swyx [00:05:37]: Yeah.Anjney Midha [00:05:38]: And they didn'- you couldn't use the models at the time. and there wasn't data about who was using them. There were, like, a bunch of differences between OpenRouter and Hugging Face, and those differences felt really critical to me, especially when I was just trying to learn about LLMs and, like, why people are choosing, like, Different little ones that are emerging over time.Discord, Open Models, and the Origins of OpenRouterSwyx [00:06:03]: Got it. And then, Ansh, no stranger to wanting more model diversity, at the time, you're a couple of years into your Anthropic journey, which we covered in the previous podcast as well. What was your introduction to Alex?Alex Atallah [00:06:16]: Well, the introduction was, I think, thirteen years before that.Swyx [00:06:20]: Oh.Alex Atallah [00:06:20]: But the OpenRouter handshake happened right over there, if you remember.Anjney Midha [00:06:23]: Yeah.Alex Atallah [00:06:24]: Which - So Alex and I, met, I believe as sophomores now, if I remember at the Stanford Review,Anjney Midha [00:06:32]: That's rightAlex Atallah [00:06:32]: Meeting for the first time.Anjney Midha [00:06:33]: I think so, yeah.Alex Atallah [00:06:35]: Yeah.Anjney Midha [00:06:35]: Yeah.Alex Atallah [00:06:35]: So Stanford Review was the libertarian newspaper on campus at Stanford that Peter Thiel started back in the day. And, whatever-- for whatever reason, I, Alex and I both showed up to one of the meetings, and I remember, the editor-chief was a mutual friend of ours. Lisa was really a really great editor-chief, where, part of an editor-chief's job is to assign responsibilities to people and make sure the work gets done. and I, I may be misremembering the details, but I remember wanting to. It was surprising to me that at the time there was no dedicated technology section in the newspaper.Alex Atallah [00:07:11]: YouSwyx [00:07:13]: Because it's political, right?Alex Atallah [00:07:14]: It is primarilySwyx [00:07:14]: Like, it's talkingAlex Atallah [00:07:15]: It originally started as like aAnjney Midha [00:07:16]: Yes.Swyx [00:07:17]: Yeah, states and all those things.Alex Atallah [00:07:17]: Correct.Swyx [00:07:18]: Yeah.Alex Atallah [00:07:18]: But it, - To take us back in time, you may remember this, but, there was this technology, legislation that was being debated called, the Net Neutrality Act. And net neutrality is, like, inherently this political concept, right? It's, it's about the regulation of - internet broadband access. And so there was a community of us who were technologists, but also debating the politics of the technology. And I thought the Review would be a great place - to, like, write about that. And I was working on, I think, a net neutrality article, and I remember proposing, “Well, maybe we should start a technology section.” And Alex was one of the only people who said, “Yes, that would be cool.” And said. I forget whether we ended up writing stuff together, but - that's when we first met,Alex Atallah [00:08:03]: Was 2011 or twelve. I forget which year it was. It was one of those.Anjney Midha [00:08:09]: Yeah.Alex Atallah [00:08:09]: It was at Old Union, if I remember correctly.Alex Atallah [00:08:11]: That's where we used to meet. But, along the way, Alex and I have had a chance to, To hang out often. And probably the time when we had the most professional overlap was when I was running the platform at Discord, and it had become this explosive platform for cryptoSwyx [00:08:32]: YeahAlex Atallah [00:08:32]: And NFTs in the middle of the pandemic.Swyx [00:08:35]: Which also, by the way, you were in charge of safety and security as well, right?Alex Atallah [00:08:38]: I was the head of platform, which meant all of the crypto - the DAO and NFT launch security debugging fell onSwyx [00:08:45]: And their phishing and.Alex Atallah [00:08:47]: The phishing, the social engineering attacks, the katana DDoS that we were getting hit by. but it's around the time I first started teaching security at scale at Stanford, CS 153. And Alex was on the, - at OpenSea at the time, and I was trying to figure out how we could defend against all these attacks that we were. Like, and at peak, I forget, if you remember how much NFT volume was running throughSwyx [00:09:10]: DiscordAlex Atallah [00:09:10]: Discord, but it was, like, a meaningful amount of, like, it was, like, several billion dollars in NFT volume of GMV, so to speak, were running through the platform, and it was all coming from OpenSea. It was these, like, buy, sell,Swyx [00:09:20]: TheAlex Atallah [00:09:21]: ServersSwyx [00:09:21]: The D in DAO is Discord.Alex Atallah [00:09:25]: Yes. And so that's when I think we had hung out professionally. But a year after that, OpenAI gave Discord early access to GPT. Sorry, three. No, it was five. Yeah, five, which is the RL version of three. And that's around the time we made a Discord bot with, OpenAI for internal deployment, and that's when I realized we would need. Like, since I was part of the deployment team.Anjney Midha [00:09:50]: What was the use case?Alex Atallah [00:09:51]: There were two that were. And there's, there's a post now called “Discord is Your Place for AI with Friends” that somebody sent me recently that I wrote, and published in twenty-three. But There were two use cases. One was Clyde, which was the - like, a party friend inside of Discord that could help you set up your Discord server and talk to you about onboarding and get your friends to hang out more. and then there was content moderation. And one of the realizations we had with content moderation was - it would refuse to moderate. Like, it would just refuse our prompts because the The training was. We were very early in the training era, and it would just. Our prompts would trigger it, its, like, guardrails. And we told OpenAI, “Hey, guys, we need access to the weights because if we're gonna be doing content moderation at scale, we had 250 million monthly active users, we need more reliability that the model will do what we need it to.” And they said, “Well, sorry, guys, that's not how this works. We're a closed-source company.” And so that was my first realization that we needed open models, and the enterprises would need more control over capabilities, and then ultimately would need some control plane or management system to orchestrate these open models. But there weren't no good - there were no good open alternatives until maybeAlex Atallah [00:11:10]: Six months later when Llama came out. And six months after that, I led the series A into Mistral, which was started by Guillaume and the Llama team. And - That, - Around that time is when I remember hearing about Alex launching OpenRouter and going, “These worlds are gonna collide, and I don't know when it'll make sense to team up.” But Alex was so early and could see. I think he was totally right about this ecosystem starting with Llama that then needed, like, a, an easy layer to manage for, especially for. I was approaching it from the enterprise perspective because I had been that, like, the. As the VP of platform at Discord, it was my job to ensure that when we deployed models to, like, 250 million users, they did what we wanted them to. And that was very hard, because if you outsourced it to the labs and they controlled the guardrails and their guardrails are their safety policies. Forbid the model from responding to your prompts. That was quite catastrophic.Swyx [00:12:05]: Yeah. But what, a moderation is the thing that they want to support. And obviously, beyond that, they would - OpenAI would work with you, presumably to give you a moderation endpoint, which they offer for free.Alex Atallah [00:12:16]: It was an interesting use case, that - So they did give us a moderation endpoint. However, as you guys know, every Discord server is like a mini deployment of itself. And so the use case was instead of having human moderators that have to interpret the norms of the community, you just give the, - Often, like every, subreddit, Discord servers, public ones have their own rules that the user, the users create.Swyx [00:12:41]: Oh, yeah. We run the LinkedIn Discord in. Yeah.Alex Atallah [00:12:43]: And then humans used to read those norms and then enforce it every day manually, like observing each message in these communities. And these communities have like millions of users. So we had a 5,000+ person team globally in the, on the Discord content moderation team. These are outsourced contractors who had a really tough job. And so the idea was instead, if you could give the norms of that server To the LLM, then the LLM would do custom moderation for that server. It's almost like a, like context moderation for that server. And many of those servers' norms just violated OpenAI's rules. And so - It was like we had our own custom eval. So each server had its own custom eval. But Discord-- at the time, OpenAI's evals, we were all soAlex Atallah [00:13:28]: Primitive in our thinking about how to deploy these LLMs that often the training prompts were super handed. It said, “Oh, anything about Harry Potter, anything that has trademarked content, don'- refuse.” And if it was a fan - Harry Potter fan community, this is a real use case, that had content moderation, the LLM would just refuse.Swyx [00:13:48]: Yeah.Alex Atallah [00:13:49]: And that was just not precise enough.Anjney Midha [00:13:52]: Another one that we heard was like if someone was trying to write like a detective story, and there's one chapter with a lot of violence, like maybe someoneAlex Atallah [00:14:01]: RightAnjney Midha [00:14:01]: Like kills someone, the LLMs would just refuse to, like, help with that part of the story.Alex Atallah [00:14:07]: Yeah.Anjney Midha [00:14:07]: And then - like, we used to be like, okay, this is not like structurally inherent to LLMs. There must be, like, some choice out there so that I can, like, switch to another model, when I'm getting, like, a refusal or a bad result from the main one that I have. And that, like, tension also drove me for a marketplace.Why “One Model Wins” Was the Wrong BetSwyx [00:14:28]: Yeah. I think that is well accepted now. What was it like back then when you were raising or, starting this? did people get it? what was the, some of the struggles? I like getting stories out of him about how other VCs don't get it. So like anything you wanna, talk about, now - Let's, let's call it, that the early journey of OpenRouter is done, right? You can obviously talk about some of the early days stuff.Anjney Midha [00:14:54]: Well, I was gonna say that, like, the biggest objection we got is big model win, which is - all of theSwyx [00:15:03]: Scaling laws.Anjney Midha [00:15:04]: Huh?Swyx [00:15:04]: Scaling laws.Anjney Midha [00:15:05]: Yeah, scaling laws, and natural network effects are just gonna accrue to one company, which will be - It'll be a Google-style monopoly, just like how Google won the search market, by a large margin, and you'll just be fighting for scraps at the end. That was probably the biggest objection we got. it is interesting that Google won the search engine race with such a huge margin. I think, like, had there been more interesting benchmarks or had, like, search engines been, - had people, like, seen them a little bit more like LLMs where they're services that you can build companies on top of, that might not have been the case. but LLMs don't merely have a user interface. They're also, like, ways of building entirely new businesses. And, a Google-level monopoly would be like the Dutch East India Company times, quadrillion in magnitude because the whole economy ends up, like, depending on the one monopoly as well. So it didn't seem like would be a really crazy outcome if that happened. And it's also less likely because the economics of, like, creating good competitors are much, like, much more decentralizable.Alex Atallah [00:16:25]: Everything Alex said is true, And I came at it from a completely different perspective, whichSwyx [00:16:31]: Yes, this is why we're here.Alex Atallah [00:16:32]: The scaling laws were never - In my mind, were always a feature, not a bug for why OpenRouter would be very valuable. Because, I was one of the first investors in Anthropic, and it was obvious to me that other researchers in our friends - I went to grad school for machine learning, and I just had a lot of friends in the ML community who it was very obvious to us that the bitter lesson holds. And so I was like, “Oh, fantastic. Now we have at least two proof points that compute scaling works.” It was OpenAI and Anthropic. and by the time I think we decided to team up on OpenRouter, I had already invested in Mistral and Black Forest Labs and Luma. So there was multiple model companies and teams that I was, working with.Why Model Labs Struggle With DistributionSwyx [00:17:14]: But you did other modalities, whereas this is literallyAlex Atallah [00:17:16]: Across different modalities, yesSwyx [00:17:17]: Text.Alex Atallah [00:17:18]: Exactly. And it was so obvious to me that an ecosystem of different kinds of models were being created, and that this whole narrative of, like, Only one company will dominate like Google was, well, like maybe true, but one, I don't believe that. But two, there was so much extraordinary innovation happening across several different research teams. But the shared problem I was noticing across all of them was often, the research teams were fantastic at figuring out how to reason about new capabilities. They think in terms of capabilities, but never - like, are not developer mindset-oriented. Like, what happens after the training is done and the checkpoint comes out? Like, you'd be shocked how, like, similar the early training teams at OpenAI, sorry, Anthropic, BFL, Mistral, were in their, like, default approach to. Taking their research out of the, lab and scaling their impact, which is often, oh, the checkpoint is done, put it out as an API, done, and then there'd be crickets. in the case of Claude, the first Claude checkpoint was done a year before they released it internally. And then ChatGPT came out, and we decided, okay, yes, it's a good idea to release a Claude version externally.Alex Atallah [00:18:34]: And they had no plan, like no plan for how to get developers to try it out. And so if you go to the Claude one blog post, you'll notice there are, like, three developer examples for users of the API, and one is a Discord bot, and the second is Vivian, my wife's startup called Juny Learning, ‘- And then there was, like, Notion, because these were all friends of, like, the Anthropic Because that's how - like, last minute the planning was around, hey, once the model's done training, how do you get it out to the world? There was no distribution platform that understood what developers needed, all the key management, provisioning, like, simple, like, endpoint management, versioning control. Like, all these things that the scientists and researchers go, “ that's plumbing. I don't really think about it.”Swyx [00:19:15]: Implementation detail.Alex Atallah [00:19:16]: Right. And instead, Alex came at it from that perspective. And so, it was so obvious to me that, like, every single lab I was funding would spend - like, literally sometimes billions of dollars into training, and then a checkpoint would be done, and there'd be crickets, like, during early access because they're like, “Oh, that's right.”Alex Atallah [00:19:35]: It's hard to use a checkpoint to make anything. You need a whole bunch of plumbing around it to make it usable by a developer. And so by the - I think - it was so obvious to me that a distribution platform like OpenRouter was critical to have in the ecosystem if we wanted there to be competition to Google. Like, unless-- ‘cause with Google, DeepMind is done training a new checkpoint, and then they push a button, and it gets blasted out across all their surfaces from Google Docs to,Swyx [00:20:01]: Everywhere, even if I don't want it.Alex Atallah [00:20:02]: Everywhere. You wanna know about, like, on Android, like, overnight, they can deploy a new checkpoint to, like, a billion devices, right? And that invisible infra advantage, distribution advantage, most people don't realize, but until OpenRouter showed up, - you had to think about all of that yourself as a model lab. And it was very daunting. at Anthropic, I think it took, well, more than twelve months to get to our first 10 million in revenue. And in contrast with Black Forest Labs, I remember the early days, you guys had a conversation with the BFL team, and, it was so simple for OpenRouter to say, “Oh, no problem. Like, the day you launch, we can send 1 million developers to you.” that was crazy. That was like a step function change in, like, an hour.Swyx [00:20:46]: Is that a real number, a million?Alex Atallah [00:20:47]: I,Swyx [00:20:48]: Okay. All right.Alex Atallah [00:20:48]: I think today it's, like, 4 million. How many developers are on OpenRouter today?Anjney Midha [00:20:52]: Over ten,Alex Atallah [00:20:54]: Yeah.Anjney Midha [00:20:54]: Over 10 million, but, like, it's, it's hard to, youAlex Atallah [00:20:59]: I, yeah, I don't know how to. Yeah.Anjney Midha [00:21:00]: We do a lot of, like, account duping work, but, noAlex Atallah [00:21:04]: If you could get 1,000 developers, just to put in context If you get 1,000 developers who try the model on day one after you release it and just, like, do inference and give you feedback, that's a thousandAnjney Midha [00:21:15]: That's hugeAlex Atallah [00:21:16]: More developers than they knew how to get to on their own.Swyx [00:21:19]: Well, BFL had a reputation, but yes.Alex Atallah [00:21:21]: They had one in Stable Diffusion.Swyx [00:21:22]: Yeah.Alex Atallah [00:21:23]: And with Mistral, I don't know if you guys remember, but the first checkpoint they released was, like, torrents. It was, like, torrent weights.Swyx [00:21:31]: Yeah, they just put up a magnet link.Alex Atallah [00:21:33]: Yeah, there was no API.Anjney Midha [00:21:34]: Yeah.Alex Atallah [00:21:34]: Because they didn'- they weren't infra people.Alex Atallah [00:21:37]: ? Like, it's like, okay, download these weights, and you guys go figure out how to host it.Swyx [00:21:39]: Well, he has a story on his side, yeah.Anjney Midha [00:21:41]: Yeah, in addition to the, like, building a really good developer experience around it, the marketing that we do on, like, for different models is totally different and perceived totally differentlyAlex Atallah [00:21:54]: RightAnjney Midha [00:21:54]: From the marketing that a model lab does for itself.Alex Atallah [00:21:56]: Yes, 1,000%.Anjney Midha [00:21:57]: Right? We are like a, neutral layer looking at this market like it's a big dark room with all the corners completely obscure to users, and users are walking into the room and, like, feeling aroundAlex Atallah [00:22:09]: YeahAnjney Midha [00:22:09]: And trying to figure out what objects to grab off the tables and, like, build into, their companies. And it's just an insane way of working. Like, models are not products where you can just enumerate all their features onto a web page. They're all black boxes, including the open weight ones. So you need to, like, shine lights on all corners of this room, so that people can see what makes this model good, and you need the company shining that light to be a neutral third party, which is what we specialize in. So the, like. It'- In addition to developer experience, there's also, like, a very important, like, marketing and product packaging componentAlex Atallah [00:22:50]: YeahAnjney Midha [00:22:50]: And a way of, like, routing and discovering models becomes, like, critical to your market as a provider or a model lab or a server tool and more in the future.“Just a Wrapper”: Why VCs Misunderstood OpenRouterAlex Atallah [00:23:03]: And this value, to your earlier point about how many VCs, like, just don't. One of my biggest frustrations is that venture capitalists, many of them, like, just don't have any operating experience in the field. so unlike a traditional investor who's just maybe come up through the ranks as, like, a associate working on financial modeling or maybe hasn't been a real operator in the field for, like, more than ten years, which is a big part of the industry now, I had just arrived at a16z, like, a year after running the platform. And so I knew what the challenges were of, like, building a real - great developer experience and like, being able to create a working piece of software with a model. And there were a few, I won't name names, but there were investors who were looking at OpenRouter, and, felt at the time, like, when I would compare notes with people, that it was just, I quote unquote, “just a marketplace.”Swyx [00:23:59]: Yeah, just a thin layer, just aAlex Atallah [00:24:00]: CorrectSwyx [00:24:00]: JustAlex Atallah [00:24:01]: A wrapper or whatever on other people's APIs. And I was like, “You have no idea how strategic the value that OpenRouter has created by being able to orchestrate even three.” APIs in production. The amount of both engineering work and community design that goes into getting that live and running in production at the scale the OpenRouter team had started just doesn't happen by default. And that was one of the things that stood out to me about Alex from the earliest days. Like, he just understood, like, - from a systems perspective, like, how do you get these flywheels going? Like, that stood out to me with OpenSea when we were working together on the NFT integration at Discord. Like, Alex had a level of community-- like, systems thinking on how you get these flywheels going that most scientists and machine learning people just don'tAlex Atallah [00:24:48]: Think of. Like, we often think in terms of training.Swyx [00:24:52]: It's a linear stage.Alex Atallah [00:24:53]: It's this linear pipeline.Swyx [00:24:53]: There's no loop yet.Alex Atallah [00:24:54]: Yeah. It wasn't until much later that the modern context feedback loop cycle really got standardized in the industry. But at the time, if you remember, machine learning was like. Like, mostly we did a lot of ML, like, when I was in grad school on a laptop. So you just, like, download a dataset, ran some ablations, and you looked at the loss curves, and you're like, “Great, I made AI.” And the idea that you have to, like, deploy those capabilities, collect feedback trajectories, then, like, put those into a continuous loop, like, came much later. And it was very counterintuitive to the - like, the traditional AI mindset. I do remember doing the investment phase for, OpenRouter, I just didn't try and educate a bunch of other VCs on why it was not just a marketplace. I was like, “ what? I'm just gonna invest.”Anjney Midha [00:25:41]: Yeah.Alex Atallah [00:25:41]: And I'm going to, like, take the opportunity to partner with Alex, and if - no other VCs get it, that's totally fine. ‘Cause at the time, - it was not obvious, I think, to several of the investors that, like, OpenRouter was not more than just a wrapper around APIs. And - that infuriated me. And I was like, “ what? I don't have time to debate you. I'm - we're gonna, we're gonna invest.” And then I think, like, a month later, Matt Murphy marked it up by 10x. Like, - I think. I forget what the exact money was and so on, but, to his credit, Menlo Ventures realized, “Okay, there's much more strategic value here as well.” Maybe you didn't hear all these conversations behind the scenes But that frustrated me a lot. there's a lot of this, like, opining about wrappers. and if you're like, “Oh, an app is just a wrapper on a model,” then, like. And, OpenRouter is, like, this wrapper on top of other APIs, and this is the most stupid, reductive framework.Alex Atallah [00:26:31]: And so it's clearly somebody who has no experience deploying product at scale.Swyx [00:26:34]: It's the thing you dismiss other things with. Like, you're a - everyone's a wrapper on everything, right? Like, and there's, there's some Some wrappers have value.Alex Atallah [00:26:40]: Investors are wrappers and LPs, right?Alex Atallah [00:26:42]: Like venture capitalists. So, yeah, it's all wrappers down, all down to bare metal, I guess, and like energy.Swyx [00:26:46]: Yeah, there - When I started the whole AI engineer, I guess, the coining, in 2023, like, that was, like, the number one pushback is that this is no value. You should just train models.Anjney Midha [00:26:56]: Right.Swyx [00:26:57]: And, yeah, obviously this is, like. you guys are one of the testaments to the fact that you can build very valuable wrappers, but also very valuable model companies.Alex Atallah [00:27:06]: It's so, hard to be. Like, the day a model launches, the fact that you have an OpenRouter, endpoint for that model frequently at the top of Hacker News on day one, people don't realize the amount of work that goes into accomplishing that. And OpenRouter used. Like, that would happen over and over again, and I remember going, “People have no idea how hard that is.”Alex Atallah [00:27:30]: That's not.Swyx [00:27:31]: Yeah, we've covered some of the inference engineering that goes behind,Alex Atallah [00:27:34]: YesSwyx [00:27:34]: Some of - with Base Ten and all those. Well, today you have, all those, like, cool code name things that people guess what Oxy Alpha is and all those things. But, like, I guess one of the things that you're teasing is, how do you get that initial flywheel going, right? Because today you have your scale and your reputation, all these things, so obviously you - you're driving immense distribution. But when you were early on, when it's mostlyBootstrapping OpenRouter Through CommunityAlex Atallah [00:27:55]: The bootstrap, yeah.Swyx [00:27:56]: Yeah.Alex Atallah [00:27:56]: What was the bootstrap like?Anjney Midha [00:27:58]: To bring it back to early Discord days, I think we, like, initially connected with. This is an OpenSea story, technically. But, and we initially connected when you were at Discord, and we talked about, like, - the Axie Infinity server.Alex Atallah [00:28:13]: Oh, yes. Yes.Anjney Midha [00:28:14]: This server was, like, the biggest server at theAlex Atallah [00:28:17]: YeahAnjney Midha [00:28:17]: At Discord.Alex Atallah [00:28:18]: That's right.Anjney Midha [00:28:19]: And you were like, constantly bumping up theAlex Atallah [00:28:22]: The limits on the server. Oh, my GodAnjney Midha [00:28:24]: Of how many people could be in the server.Swyx [00:28:24]: For those who don't know, like, 10% of Philippines was Axie.Alex Atallah [00:28:29]: Was on that server. That's a big hit.Swyx [00:28:31]: It was, like, a meaningful contributor to the GDP of the country.Alex Atallah [00:28:33]: It was an NFT, like, crypto game, but itSwyx [00:28:35]: It was like a Pokémon breeding thing.Anjney Midha [00:28:36]: Yeah.Alex Atallah [00:28:36]: Yeah. Similar. Yeah. There was battling, there was breeding, and then there was, like, a marketplace for trading.Swyx [00:28:43]: Earn as well.Alex Atallah [00:28:45]: Yeah, earn. And, like, the graphics were really cute and fun, and you like, you get emotional about your Axie that you make. So to, like, start a community like that, which we had to do many times at OpenSea with every early project, for us to create a marketplace for it, we need to make sure that the, like, the community wants it.Anjney Midha [00:29:09]: Right.Alex Atallah [00:29:09]: And it's like building something that people want and going and telling them about it. Like, you can do that on a one basis, but there's way higher leverage to do that in a community where everyone can talk to you at the same time. So we spent a lot of time, like, building things that the community really wanted. We did the same thing for OpenRouter. And, like, the Axie community was one of, like, a zillion communities we did that with. And Anj, like, saw us doing it and. ‘Cause you could just see people sharing OpenSea links constantly in that Discord. Like, users sharing links is a really clear indicator that, like, something important is going on. So we spent, a lot of time, like, first figuring out what the gap is in the technology that people care about. Like, what was the actual problem that needs to be solved? in early LLM days, it was, OpenAI refusing to finish the prompt or,Anjney Midha [00:30:09]: YeahAlex Atallah [00:30:10]: To, like, complete the task. It was also.Anjney Midha [00:30:13]: Inability to customize models. and so there are communities that, like are just completely blocked on that issue, and those are the communities that are most useful to learn about and dive into and explore.Alex Atallah [00:30:28]: Something that really struck me at that time, - as I was just hearing your talk, I remember noting - you may not remember this, but we - we had these, like working, Zoom calls that we were doing a sprint around for, like this OpenSea integration with Discord. and, we'd, we'd - it was myself, my engineering team. I think you were there. And I remember, Alex, in the middle of one of those calls, just like there was like silence. we were all like, “Oh, yeah, this totally makes sense. Let's do this.” And then there's - every, like everybody aligned. And Alex was like, “No, this makes no sense to me.” And everyone's - I remember going, “What? Like, it works. Like, you click on a link and this, then it bounces you out to, like, OpenSea.” And he was like, “It's not a good user experience. Yeah, we should not do this.” And I remember going, he was the only one person out of all of us to raise his hand and go, yes, it made sense from a technical implementation perspective. Like, we were bouncing the user out into the, into OpenSea. And so it kinda checked the box of the product manager's requirements on both sides. But Alex went one step further and was like, “ what would be better, guys? If we just embedded the experience right here inside of Discord so the link opened up as an embedded iframe, and you can just check out right there.”Alex Atallah [00:31:47]: And not one person on the call, and there's like seven of us who had met, like, week after week.Swyx [00:31:52]: And it's the guy who doesn't work for Discord.Alex Atallah [00:31:53]: And it's the guy who doesn't work for Discord.Swyx [00:31:55]: Like, technically, you benefit if they bounce.Alex Atallah [00:31:57]: Exactly. And that was, like, adversarial. To keep the user inside of Discord would be adversarial to OpenSea. And yet Alex put that user experience first. And I was like, “That's special.”Swyx [00:32:08]: Wow.Alex Atallah [00:32:08]: Because it's very hard to have somebody who's technical like Alex and understands the developer flow, but also understands the best user experience and wants to prioritize that. And that's two sides of the flywheel that if you can get spinning, like is often hard to stop. And you just reminded me, like that one was one of those moments where I go, I - I realized I gotta be better at user experience because I should have been the one who came up with that, and I didn't. And I learned from you. And, I think that went into one of our case studies for the PM training program at Discord.Swyx [00:32:34]: Whoa.Alex Atallah [00:32:36]: I don't know if it there is Because ofSwyx [00:32:38]: You need an Alex is the conclusion.Alex Atallah [00:32:40]: Yeah. You need an Alex. And this is why I'm not, nobody should be surprised why Stripe decided like they had to buy OpenRouter because it's a really rare combination of people who understand the machine learning community, the developer experience, and the user experience. And putting all that together has resulted in this extraordinary scale that very few other marketplaces have been able to achieveWindow AI, BYOM, and Finding the Right Form FactorSwyx [00:33:02]: Yeah.Alex Atallah [00:33:02]: Over the last, five years.Swyx [00:33:04]: Yeah. Well, we should talk about the other reasons for acquisitions, whichAlex Atallah [00:33:07]: Yes, we should.Swyx [00:33:07]: You've written about. I wanna proceed somewhat chronologically as well. So - there is a point that, one of the questions that, Dave from H of Zero sent in was, when did it - really started to work? And you brought up Mixtral. I don't know if you wanna bring up that story.Alex Atallah [00:33:22]: Oh, yeah.Swyx [00:33:23]: Which obviously you overlap with, so.Anjney Midha [00:33:26]: Yeah, the MoE was. I don't know when. there's no like one moment where I was like, “Oh, this is, officially starting to work.” It wasSwyx [00:33:36]: The moment where you had a Chrome extension, like, really super early on.Anjney Midha [00:33:39]: Oh, yeah. But, well, - yeah. So before OpenRouter, I wanted to, like, explore a bring-your-own-model experiment. And,Swyx [00:33:47]: Which anyone familiar with crypto is like, yeah, Phantom and all these things.Anjney Midha [00:33:50]: Yeah. So it felt like doing a MetaMask analogy for AI would be a fun way of exploring that. And at the time, there were no AI apps. There were probably as many AI apps that were, like, hitting AI - like, hitting an LLM via an API call as there were, like, games just doing it in JavaScript. like there was a, there was a moment in time where it could have been the case that web apps call LLMs through the browser, like through some desktopAlex Atallah [00:34:27]: Yes.Anjney Midha [00:34:27]: Managed app that is controlled by the user. and of course, there are like, I think, many reasons that did not happen. But back when the days were that primordial, I built a Chrome extension called Window AISwyx [00:34:43]: With Plasmo.Anjney Midha [00:34:44]: With Plasmo.Swyx [00:34:45]: I had come across early on, and I was like, “Who's gonna use this?” You did.Anjney Midha [00:34:49]: Plasmo had a couple, like, I think Phantom was using it. there were some other, like real companies using it.Alex Atallah [00:34:56]: It was like a shim.Swyx [00:34:57]: React for Chrome extension. It compiles to allAnjney Midha [00:35:00]: Yeah.Alex Atallah [00:35:00]: I see.Anjney Midha [00:35:00]: Like Next.js for Chrome extensions.Swyx [00:35:01]: Next.js, Next.js.Alex Atallah [00:35:02]: Okay.Anjney Midha [00:35:03]: And yeah, built Window AI on top of it. The creator of Plasmo, like started contributing code to Window AI, in GitHub, and that turned out to be Louis VicchiAlex Atallah [00:35:15]: Oh, you'Anjney Midha [00:35:15]: Who is the founder of OpenRouter.Alex Atallah [00:35:17]: That's right. You have told me this is how you met Louis. Yes.Anjney Midha [00:35:19]: Yeah.Alex Atallah [00:35:19]: Okay.Anjney Midha [00:35:20]: So, that allowed users to like configure which model they wanted to use for a web page in their browser, and then, like the app would just call out to that model when it needed to do things. not the right form factor for LLMs, but, it's like fun experiment. You learn a lot, and like I open sourced it. And the main learning is like, okay, this has to be an API, and it has to look a little bit - like, there has to be more of a developer experience here and more of a discovery experience as well. Like, I don't know where to use these models, and a little Chrome extension is not gonna help me discover. It's not enough real estate. I need more space. I need visuals. I need graphs. I need, examples. I need images. I need to, like, I need to be able to, like explore both as a human and as an agent.Crypto, Midjourney, and the Early Generative AI EcosystemAlex Atallah [00:36:10]: Yeah.Anjney Midha [00:36:10]: So that's how OpenRouter came to be.Alex Atallah [00:36:13]: A meta point that.Alex Atallah [00:36:16]: I think is underappreciated, but Alex is reminding me, is that we were quite lucky that we were so. we were, like, adjacent to the crypto community in those days. Because in hindsight, crypto ended up being like a dress rehearsal for generative models, right? If you think about the Axie experience, Alex is totally right, there were not that many AI apps at the time. And while I was dealing-- my job was to be the head of platform at Discord, which meant to be a general purpose place for communities and friends to create-- for developers to create apps and bots and, other services that could be deployed across Discord. And while 80% of the attention at the time was being spent on crypto, because that's where all the NFT volume was, there was, like, twenty percent of my time I was spending with a friend, who would get hotbot with me and ask me for. We would play Magic: The Gathering on weekends, and he was working on a little Discord bot that could take a text input and turn it into an image, and it was called Midjourney. YouSwyx [00:37:15]: Is that David?Alex Atallah [00:37:15]: It was David Holz.Alex Atallah [00:37:16]: He was a good friend. And David and I have both been failed ARVR founders, in the before that. And, I remember this. Midjourney was one of the fastest-growing communities we had after Axie Infinity started to peter off. And many of the, like, the abstractions and the infrastructure decisions we made to scale Axie happened just in time because they. Axie did this and then fell off a cliff. And then as Midjourney was taking off, we, like, explicitly decided to help David make the server, the Midjourney server, as the primary place for interaction with the model, because it was very hard for people to understand how to use the model if they couldn't see other people using it and copy them. And so the single-player Midjourney web app on its own, like midjourney.com, had, like, terrible retention because people would show up, they'd see this empty field. It's like E 2, and they would type in, like, cat or dog. And it was, like, paralyzing for them to have this blank canvas that they had to fill because they'd never used an AI model before. But instead, in a Discord server, you could see other people using it and riff off of their prompt, and the engagement was off the charts. And so scaling, Midjourney from zero to, like, 10 million monthly actives was a much smoother approach Axie Infinity. And so,Swyx [00:38:29]: Don't forget the best of four pictures, and you choose one.Alex Atallah [00:38:31]: The best, yeah, and then the other, weSwyx [00:38:32]: Which is the feedback loop.Alex Atallah [00:38:33]: The RLHF feedback loop, which, by the way, separately, like, Tom Brown, David and I used to play Magic: The Gathering on weekends. And so, like, it was one group of friends would hang out, and we'd. Like, these concepts were all being discussed all the time. But, there was.Alex Atallah [00:38:47]: I think there were few of us who bridged both the crypto worlds and the AI worlds. And compared to crypto, where it was - the question was always, what's the use case, for this technology? There was never any need to ask that for AI because it's, like, the use case was so visceral. It was like, I can create now anything at - I can imagine. I can write novels, I can code. And the infrastructure that those of us who believed in the distributed systems, like, value of crypto, like the censorship resistance part, found this use case that was explosive. And I think between Midjourney, the, Claude was a Discord bot launch, that we were using internally as an LLM. ElevenLabs had a TTS model that we had on Discord as well. Like, Discord became this petri dish for, like, early apps to innovate. And I don't think it's a coincidence that they found a home there before OpenRouter gave the world, like, a public home store or, like, a, storefront. Discord was this, like, almost petri dish storefront that - had, like, piggybacked on the infra we'd built for crypto communities. And then I think Alex was one of the first people to realize, wait a minute, like, these apps need their own home, on the internet. And then OpenRouter, to me, was a continuation of that community's needs. And of course, there was the crazy distribution that you enabled for a lot of these developers.Why OpenRouter Couldn't Just Live Inside DiscordSwyx [00:40:07]: So then my question is, how come you were. My perception is OpenRouter is not that Discord-centric, right? You have a Discord.Anjney Midha [00:40:14]: Yeah.Swyx [00:40:14]: And you use it to engage your community, but it's not like Midjourney where, like, no, that is like the primary way people experience OpenRouter.Anjney Midha [00:40:21]: Yeah, Midjourney, like, it really helps to see visually really quickly how people are using the model and how to prompt it.Swyx [00:40:29]: Yeah.Anjney Midha [00:40:29]: And I think that is partly why the server was so critical. It's like it is the user experience. It adds a ton.Swyx [00:40:36]: Yes.Anjney Midha [00:40:37]: And you can go the whole mile with just, like, prompting via Midjourney, like, the, via the Midjourney Discord server, getting your images and then sharing them and having fun. For OpenRouter, for LLMs, like, you need a lot of user experience around LLMs to make them, like, really usable.Swyx [00:40:54]: Charge point.Anjney Midha [00:40:55]: And yeah.Anjney Midha [00:40:57]: The, like, seeing the examples of other people is also not as useful because it's a lot of stuff to read. It takes a long time.Swyx [00:41:03]: Yeah.Anjney Midha [00:41:04]: You need, like, based integration. Not possible to do in a Discord server. You need, Or technic- it's possible. I shouldn't say that. It's just not a great developer experience. you need, like, - you need governance for. At the point where you got based integration, now you need governance for managing the LLMs that have access to it, the data policies, which teams. All that stuff needs a lot more than a Discord server can provide. So it's justSwyx [00:41:30]: YeahAnjney Midha [00:41:30]: It's not the right.Alex Atallah [00:41:32]: Well, in addition, you're not wrong, but also there's the very important distinction that, Midjourney was an end user application.Swyx [00:41:40]: Right.Alex Atallah [00:41:40]: And, that's why Discord, which has 250 million monthly end consumers, made, it made sense for Discord to be a host for that application experience. What I knew was gonna happen soon after Midjourney found explosive product-market fit, because we. I think when Midjourney launched, from launch to $100 million revenue run rate, it was less than eight months. And shortly thereafter, Stable Diffusion launched. And, all of us used to hang out in the Discord server. There, I think it was the,Swyx [00:42:13]: The Stability Discord?Alex Atallah [00:42:14]: It was theSwyx [00:42:16]: Yeah, LAION.Alex Atallah [00:42:16]: Yeah, the LAION Discord server.Swyx [00:42:17]: The image community that spawned Stable Diffusion.Alex Atallah [00:42:19]: The image community. Yeah. And so when Stable Diffusion came out, I realized- Oh, now other people can build their own Midjourney.Alex Atallah [00:42:27]: Because until then, Midjourney did not have an API, so they were a stack company, right? They were training their own models, and they were deploying them as an application. But if you wanted to build your own Midjourney, there was no API of that quality. and I think E two was still quite primitive. Like, Midjourney had great quality. And then when Stable Diffusion came out, suddenly there was this new person who - there was - this new capability in the world, which is a developer could create their own Midjourney. And that, I think, created the need for something like OpenRouter, because then you need an API to. If you - if you had the creativity of David Holz and you had Stable Diffusion as the model and you wanted to put these things together, how could you do that without having to figure out how to host the weights? And what OpenRouter, - the shape of OpenRouter enabled is that. Right? When you have open model alternatives to closed applications, OpenRouter's value in the world becomes extraordinary because now any developer can just show up and use theStable Diffusion and the Need for a Model API LayerSwyx [00:43:20]: You just love model diversity.Anjney Midha [00:43:21]: Did you just say the shape of OpenRouter?Alex Atallah [00:43:23]: Oh, no.Anjney Midha [00:43:25]: Were you in cloud? What is this the real Han?Alex Atallah [00:43:26]: I've been, I've been - I'm, I'm misaligned now. I've been overtrained. I've been using Cloud way too much, haven't I?Swyx [00:43:34]: Claude-ish is what people would say.Alex Atallah [00:43:35]: Claude-ish. Oh, God, I gotta untrain myself.Swyx [00:43:38]: Okay. - And I just wanna cap off the Mistral side. my TLDR is there was a Mistral price war, is what they called it, right? Like, round about NeurIPS is twenty-three or twenty-four.Mistral and the Birth of the Inference MarketplaceAnjney Midha [00:43:47]: Yes. DecemberSwyx [00:43:48]: They launched, the Mistral 8x7B, and like the price went down like 80%.Anjney Midha [00:43:54]: Yeah.Swyx [00:43:54]: To me, that's very positive because it's like the first, like, real competition to host Mistral. Is there more?Anjney Midha [00:44:01]: Yeah, that was. I'm, like, trying to remember it, all the things that happened. It. Like, we saw that model come out and immediately saw people say that it was the best model in the world.Alex Atallah [00:44:15]: Yes.Anjney Midha [00:44:15]: Like, this was, to my knowledge, the first time an open weights model was called that in real seriousness.Swyx [00:44:22]: It's hype, right? Is it?Anjney Midha [00:44:25]: It was hype. It was hype. It was also, like, hype from AI influencers at the time. And there were many examples where it was, like, outperforming four. So people really wanted to try it out and see, is this gonna be true for me too? And if so, at what price? And, the, like, inference landscape was really messy.Alex Atallah [00:44:49]: Yes.Anjney Midha [00:44:50]: We cleaned it up. - it allowed, like, providers to compete on price, so we could give you just the best price in one spot. And so it was, I think, the first clear example of, like, a provider marketplace working in a way that adds value to end developers.Alex Atallah [00:45:08]: Sean, you may not remember this, but I think we met for the first time a few days after Mistral came out at NeurIPSAnjney Midha [00:45:15]: Yeah.Alex Atallah [00:45:15]: At a luncheon.Swyx [00:45:16]: Yeah. That's where I also met BFL as well. Yeah.Alex Atallah [00:45:18]: And Guillaume was there.Swyx [00:45:19]: Yeah.Anjney Midha [00:45:19]: I was at NeurIPS at that time.Alex Atallah [00:45:20]: You were there too. And, we had just announced the Mistral investment, and I remember Guillaume was over there, and I remember turning to Guillaume and asking him, Like, “Is it is all the. Like, how are you feeling after the launch of Mistral and seven B?” And, him in his typical French fashion was like, “ it's a, it's an okay model. It's not that good.” And I was like. It was so, in contrast. But I remember him also saying that part of the reason he felt a lot of people Thought that it was better than four was because of the speed. - it was an MoE model that they had, like, absolutely figured out how to make super efficient. It was on the Pareto frontier. And this is an important thing about LLMs, right? Sometimes when they're faster, you think they're smarter, even though, like, if you did, N of, these common, like, evals that are - you do seven tries, and I don't remember. I think we should go back and figure out what the data says, but I wouldn't be surprised if it turns out, oh, on an N of seven attempts, four was smarter on evals, but the perception of on, like, or correctness would be smarter or more accurate. But, people, like, from a human preference perspective felt that it was faster because it - or smarter because it's so fast.Swyx [00:46:36]: Yeah. And most queries do not take that levelAlex Atallah [00:46:39]: Don't take that. That's true.Swyx [00:46:40]: Right? So this is the start of humans as routerAlex Atallah [00:46:42]: Yes.Swyx [00:46:42]: Which then eventually becomes OpenRouter as router of like theAlex Atallah [00:46:45]: Oh, that's interesting way to think about it. Yeah.Swyx [00:46:47]: Like, because humans are the routing mechanism. Like, I will ask the fast model first, and then if, like, oh, not good enough, I'm gonna upgrade manually.Alex Atallah [00:46:52]: Yes.Swyx [00:46:53]: But then he's gonna auto it.Alex Atallah [00:46:54]: I didn't, I hadn't thought of it that way, but that makes sense.Swyx [00:46:57]: Which then there's, there's a lot more techniques, like fusion. Fusion is the thing that we should talk about. Before I move on to those things, I just want to close off the early years. one thing that I observe, which you are also an investor in Arena.OpenRouter vs. LM ArenaAlex Atallah [00:47:10]: Right.Swyx [00:47:10]: And we talked about Midjourney having that feedback loop of, A, B, C, D, and choosing that very. being very important. And you understand the flywheel. So how come you didn't build Arena, and how come Arena didn't build OpenRouter?Anjney Midha [00:47:23]: Well, Arena started before OpenRouter, right?Swyx [00:47:27]: They had the school projectAnjney Midha [00:47:29]: Yeah, LMSwyx [00:47:29]: And then it became a company.Anjney Midha [00:47:31]: LM Arena, yeah.Swyx [00:47:32]: So, but, and I know you had some Arena experiences, like the up comparison type things.Anjney Midha [00:47:37]: Yeah.Swyx [00:47:37]: But you never really went as hard as Arena did.Swyx [00:47:40]: And,Anjney Midha [00:47:40]: In doing up experiences?Swyx [00:47:42]: Yes. And LM Arena did have a router project based on LM Arena ELOs, which they never commercialized.Anjney Midha [00:47:48]: It's hard to do a company that does both because one company is taking data and selling it, and the other company really can't by default. So, I think there is, like, a branding reason that there are two companies here. like, when you set up OpenRouter, there's no training, there are no prompts, right, aside from what your provider policy set. Like, OpenRou- like, OpenRouter can't see your prompts or completions. If you want to see that as an org, you have to opt into it and enable it. And so we're, like, pretty conservative and careful about data policy and security. And privacy. And LM Arena is like, their business model is like oriented around the labs and,Swyx [00:48:34]: Because they give it for free, right? You don't give it for free to give it for free.Anjney Midha [00:48:37]: Yeah.Anjney Midha [00:48:38]: But we do give some. We like have free endpoints too, but like those free endpoints, we, I think we're not collecting any prompts. We're not like monetizing the data unless you, opt into it for some reason.Alex Atallah [00:48:48]: This comparison. you're not the first person to ask me this, and Alex knows this, but I was the interim, like the founder, like first CEO of Arena for the first five months when, and we were helping Anastasios and Waylin spin out of Berkeley. And, I did invest in that before, OpenRouter, but it was very strange to me the comparisons that outside, folks would make between the two projects because the missions were completely different. The founding entity for Arena, we called it the AI Reliability Institute because it was there as an eval service. Like the data, so to speak, that they were originally, offering the labs was how do you make the evaluation of models more reliable than like the state of the art at the time, which was like really just finger in the wind.Alex Atallah [00:49:38]: That's what Anastasios and Waylin's PhD work was as scientists at Berkeley, was on statistical methodologies for correcting, eval estimates, based on like intrinsic biases and how you collected the data.Swyx [00:49:54]: Yes.Alex Atallah [00:49:54]: AndSwyx [00:49:54]: Style control.Alex Atallah [00:49:55]: Style control and stuff like that. And which is very much like a, hey, how. If you're a scientist and you're trying to. the highest expectation customer for Arena was always like a training and, like a researcher at a lab. Whereas the highest expectation customer from my perspective that Alex like really understood and was the mission was to serve was like a developer, right? Who then takes the result of the research and then produces an application that's deployed to the world. It was a completely different problem and person that these two teams were focused on. And so from the outside in. I don't know if you remember this, but I have a distinct memory of a few weeks before we did the term sheet, together for OpenRouter, I'd given you a call because we were trying to get a pooled data set together from OpenRouter and from Arena to, create like an open source repository of prompts. these projects were so different in their goals that it was totally normal to me to be like, “Oh, yeah, let's call Alex and see if he'd want to team up on pooling data,” because they're so different. We need. We don't have that data at all. We. Like, we didn't have API prompts. We didn't, we didn't have like what developers want to do with the models, which is very different from what researchers inside a model lab want to do before releasing the model.Swyx [00:51:15]: Yeah.Alex Atallah [00:51:15]: Does that make sense? And so to this day, I think you see that this difference, even though at a 30,000-foot level you could. I guess you could conclude that Arena and OpenRouter are adjacent, but, the roadmaps, the missions and so on at the time at least were like in very different directions.Swyx [00:51:36]: That ideal customer, I get. I totally get that.Alex Atallah [00:51:39]: Yes.Swyx [00:51:39]: As a founder, I want to own everything, right?Alex Atallah [00:51:41]: That's possible.Swyx [00:51:42]: Like this is clearly an adjacency that I'm like gonna explore that.Anjney Midha [00:51:45]: Own everything meaning like you don't know what to do yet, so you wanna like make sure you catch PMFocus, Anthropic, and Roads Not TakenAlex Atallah [00:51:51]: No, I think what heAnjney Midha [00:51:52]: As quickly as possible.Alex Atallah [00:51:53]: You want to own the entire infrastructure space, and so you expand to whatever demand you can capture.Swyx [00:51:58]: You want to have a play in each end.Alex Atallah [00:51:59]: Yeah, I think that's, that's hard, in reality, because serving multiple customers is difficult.Swyx [00:52:05]: Clearly, this is the one focus, right?Alex Atallah [00:52:08]: Yeah.Anjney Midha [00:52:08]: Yeah. I still think even in the age of AI, like focus is,Alex Atallah [00:52:12]: Is criticalAnjney Midha [00:52:13]: Underrated and critical, not just because you end up with a better product by focusing your humans on it, but also because the world knows what your focus is.Alex Atallah [00:52:22]: One thousand percent.Anjney Midha [00:52:23]: The world can map like, “Oh, I have this issue. Which brand out there is going to help me with that issue? This is the brand that's known for that focus.”Alex Atallah [00:52:31]: Yes.Anjney Midha [00:52:32]: So like if I want real attention on this issue, like this really matters to me, I should go with the brand that cares the most about it.Alex Atallah [00:52:39]: To underscore Alex's point about how important focus is, in the early days of Anthropic, it was not easy to. Like people think that the early days of Anthropic were like super easy because they were on their 3 guys who left, but it was very

Earlier this month, world model company Runway introduced GWM Worlds 2, a research preview that “turns high-fidelity video and audio generation into real-time interactive simulation.” Runway calls this an “autoregressive diffusion” model; with autoregressive describing how it generates over time.One new feature in particular caught our eye: WorldPrompt, a proposed input format for specifying a generated world and the actions within it. It allows you to fix some aspects of a simulated environment — including the first frame — and then create a series of timestamped events. The events, or actions, can even be prompted in real-time.To understand the implications of WorldPrompt, we spoke to Kamil Sindi, Runway's CTO, and Robin Kahlow, its Principal Research Scientist for generative video and multimodal AI. We also have exclusive comments from Anastasis Germanidis, co-founder & co-CEO of Runway, courtesy of a podcast swyx and Vibhu did with him.Who's building real-time interactive world models?First, some context about world models that can generate interactive video and audio in real-time.Runway is reportedly valued at $5.3 billion, based on its most recent fund raise of $315 million in February. Its first release, GWM Worlds, was launched last December.Alongside Runway, there are several other notable projects in this domain: Google DeepMind's Genie 3 (which also generates at 720p and 24 fps), Odyssey-2 Pro, and World Labs' RTFM (Real-Time Frame Model). We've summarized their differences in the following table:Given the complexity and massive latency demands of real-time video and audio generation (which we'll get into below), all of the projects listed above have limitations. For instance, Google notes that Genie 3 “can currently support a few minutes of continuous interaction, rather than extended hours.”But as our interviews with Runway show, real progress is being made.The central idea of WorldPromptWorldPrompt, a new feature in GWM Worlds 2, helps differentiate Runway from its competition. You can think of it as a control layer for characters, cameras and the environment. As Kahlow put it, it's a way to “control all the different subjects in the world” — similar to a computer game.“Like, if there's an NPC [Non-Player Character] somewhere, the NPC might walk up to you and say something. So you could achieve the same thing with this kind of model, where you can have very detailed control over everything in the scene.”As the name suggests, WorldPrompt is a prompting mechanism — not a programming language. So, unlike virtual world games like Minecraft or Roblox, GWM Worlds 2 doesn't offer scripting capabilities or the ability to control state. But there's a power to that, as Sindi pointed out.“You can create promptable worlds on-demand with video and audio in sync, across all these different domains and environments. That's not a distant-future hypothetical thing,” he said.But there are also limitations to prompting a world model. We asked how reliably the model would follow an instruction to create, for example, a law of gravity or a certain ability in a character?“Yeah, so it's a research preview,” Kahlow replied. “So it's not perfect, of course, and there are still flaws. It really depends on how difficult the action is. I would say movement works quite reliably.”Sindi added that more training plus scaling the data and models is resulting in “better following.”How a video model becomes a real-time runtimeDespite the current limitations of GWM Worlds 2 — especially if you compare it to pre-designed and scriptable worlds like Minecraft or Roblox — the true promise of world models like Runway is that they'll eventually lead to fully self-generated, real-time games and experiences. Which is an extremely hard engineering problem, as Kahlow reminded us.“There are two challenges. One is making the model not generate a whole clip at once. So instead, you want it to generate frame by frame while you're looking at it. And the other challenge is actually making the generation fast, so you can play it in real time.”GWM Worlds 2 offers real-time interactive worlds streamed in continuous 720p video at 24 frames per second (fps) and audio at 48,000 Hz.Runway achieved this firstly by taking its foundational audio-video generation model and fine-tuning it to the new WorldPrompt format, so the model can follow that. It then post-trains the model to generate autoregressively.“And after that, we work on making it real-time through distillation methods,” Kahlow added.Co-CEO Anastasis Germanidis offered more technical details in our podcast with him. He told us that the process starts from “bidirectional diffusion that basically generates an entire video at once and [makes] it autoregressive.” This allows the model to “generate one frame or a few frames at a time.”Germanidis described two possible forms of distillation in order to make it real-time: distilling a larger model into a smaller one or reducing its diffusion steps. As a general example, he said a model might go from around 50 denoising steps to four, with some quality loss but potentially comparable results.The challenges of real-time generationGermanidis admitted that there were issues with how it generates real-time interactive video.“The biggest challenge with autoregressive models is error accumulation,” he said. “You're feeding generated frames back into the model to generate the next frames, and if there are any small errors, they accumulate over time.”Sindi told us there are also challenges dealing with “infinite generations” of content.“There's all these challenges around what context to keep, what to discard that's not important. And so there's all these optimizations we have to think about, so we're not blowing up our GPU memory.”Another current limitation is long-term memory. “The model does not have perfect memory,” Kahlow said. “That's still an open research problem.”Causality and correctnessWhile performance is the primary challenge for Runway at this time, its world model also has to produce plausible consequences when a user takes different actions.Germanidis used the example of simulating football; he pointed out that online video training data contains more successful goals than failed goal attempts, so a video model might render the first more convincingly.“If I take this action versus this action, you want it to generate equally realistic outcomes,” he told us. “That's, I think, the big gap between video models and world models: that idea of counterfactual generation.”Sindi told us that evaluation gets harder the more complex interactions get.“If you have this multi-prompt, multi-character, multi-scene [environment], how do you really understand what was causal and what was not?”To try and solve that, Runway has some automated verifiable tests. But since GWM Worlds 2 is a research preview, Kahlow noted that doing tests yourself is also advisable — “trying out your model to see what doesn't work is really important.”More than gaming — there are agent use cases tooGaming is the obvious use case for what Runway is building, but there are others. Kahlow mentioned robotics — for example using a simulated environment to test how a robot works.Another, more intriguing, use case is to use it to test agents at scale.“Having thousands of simulated environments is much less challenging if you have a suitable model like GWM Worlds,” Kahlow said.But how does an agent know what's changed in the world — is there a structured state that it can read, or is it just the generated video and audio that it's consuming and understanding?“So there's no structured state here,” Kahlow replied. “It's just observing the same thing you might observe in real life, just [in this case] from cameras.”Sindi noted that GWM Worlds can also be used for “synthetic data generation for agents.”Finally, Germanidis suggested there's potential to use these world models alongside reasoning models.“You're maybe using some reasoning [for] planning of the scene, and then you're passing it into the diffusion head that's actually generating the pixels.”Anastasis Germanidis* LinkedIn: https://www.linkedin.com/in/agermanidis/* X: https://x.com/agermanidisTimestamps00:00:00 Introduction00:05:17 Runway's Origins and the Bet on Generative Video00:12:23 The Stable Diffusion Story00:18:44 Gen-2, Controllability, and the Weekend Hack00:23:02 From Video Generation to World Models00:28:03 Learning From the World, Not Just Language00:35:04 Sora, Runway's Existential Crisis, and Gen-300:39:39 Why Real-Time Video Is Inevitable00:43:06 Interface World Models: Software Without Code00:50:25 The Fully Neural Operating System00:55:11 World Models for Robotics01:02:32 Robot Policies and World Action Models01:07:47 The Lucid Dream Test01:11:41 Video Agents and Omni Models01:23:12 Artists, AI, and Creative Workflows01:27:14 Physical AI and the Future of World ModelsTranscriptIntroduction: Runway, Creative AI, and the Early ThesisSwyx [00:00:00]: Okay, we're here with, Anastassios from Runway, with, me and Vibhu in the studio. Welcome.Anastasis [00:00:08]: Good to be here.Swyx [00:00:09]: Congrats on all your success and progress with Runway. You're opening offices all over the world. Did you envision this when you first started out?Anastasis [00:00:16]: Not quite. I think even when we started, we had this idea that, It was more a matter of when, not if, we were seeing the early generative models of 2016, 2017, and just extrapolating, assuming, we resolution, quality increases predictably over time. There's gonna be a point where most of content will be generated, and that was maybe the initial thesis of Runway was we will need, as a result of those generative models, rethink how creative tools are made. and as we built out the research behind, our generative models, it then became clear that they were useful far beyond that as well.Anastasis' Background: Art, Simulation, and Machine LearningSwyx [00:00:57]: And it is more obvious now with, like, the real-world stuff and the world models that we'll talk about later. I'm just kinda curious how you go from a background in, like, Zocdoc and, computer vision into Runway. Like, take us back to that early conversations with Chris and, whoever else is on your founding team.Anastasis [00:01:14]: I was always splitting through those two worlds. One was the I had my own art practice. I was making a lot of interactive art, I think for a long time. and then on the other side, I was working in startups, and I was working as a ML engineer, as a backend engineer at different companies. I've always been interested in, coding and computation, and especially interested in simulation and brought it back into my early artwork as well. And at the same time, I was interested inSwyx [00:01:43]: The personal site has a few, right?Anastasis [00:01:44]: Yeah.Swyx [00:01:45]: Is there one that we should pull up? Just in case there's something that's like. I just like to go down memory lane.Anastasis [00:01:50]: Yeah.Swyx [00:01:50]: Okay, what is this?Anastasis [00:01:51]: So this was, a project that I made, I think back in 2015, where I built this software that would give, voice instructions to people in a gallery space. So it would coordinate interactions between people. And so it will first give you an identity, like you're an, architect, you're 30 years old, and, you like sports. and then it would match you with another person, and you have this completely generated interaction. language models were not quite there at the time, and so it was it was a mix of some templates and some, like, some Markov chain-generated text, and it would just completely simulate these small talk conversations between, everyone in the gallery space. so was always very fascinated on the one hand with, generative models and, like, the early machine learning work that was being at that time. But at the same time, there was this separate thread of simulation and what it means. Like, what can we learn about humans by creating those very simple models of their interactions and their behavior?Early Generative Art: pix2pix, GANs, and Uncanny ValleyVibhu [00:02:56]: Did you generate the prompts or, the 30-year-old, whatever? Was it you generating them? How'd you, how'd youAnastasis [00:03:03]: Exactly. So the program would just generate- those, from. Yeah, a lot of it would be Mad Libs style of justVibhu [00:03:10]: YesAnastasis [00:03:10]: You have lists of different professions, lists of different,Vibhu [00:03:14]: HobbiesAnastasis [00:03:15]: Personality types, lists of different, ages, things like that. And then it would just combine those things together. And then maybe the next project we go is, Uncanny Valley, Uncanny Road, which wasSwyx [00:03:27]: GansAnastasis [00:03:27]: One of the first projects that, we built with, one of my two co-founders, Chris. This was taking, pix2pixHD, which was one of the early image-to-image models that NVIDIA released back in 2016 or 2017. and it was a model that would take a semantic map of a scene and then generate a photorealistic, let's call it, output. very early days, so it was not very high-fidelity outputs, but it w I think was the first image-generation model that could generate at 1K resolution. And it was all trained on self-driving datasets. So the semantic categories it would support were only, things you would encounter on the road. So it would be pedestrians, traffic signs,Vibhu [00:04:16]: StoplightsAnastasis [00:04:17]: Bikes, stoplights. And so that was one of our first indications that we built this and people were making all this, like, very surreal imagery of, yeah, a million plus a million pedestrians or a million traffic signs or, like, gigantic humans. And it was a indication that you could take a model that was trained on this very boring dataset, essentially, of, like, not that many interesting things happen when you're on the road, and then you can repurpose it and go very out of distribution and make something that was artistically compelling. And that was It's a summary of the thesis of Runway in some ways, that you can take the same generative models, and if you look at them from another direction, if you build interesting tools around them and you give them to artists, they're gonna do things that you don't expect.Vibhu [00:05:02]: Very cool. I like the, UX of it. You're just given an empty canvas, try whatever, do whatever. And then the other one, like, you see everyone with wired headphones? Like, that's, that's a sign that it's, it's veryAnastasis [00:05:16]: The AppleVibhu [00:05:17]: YeahAnastasis [00:05:17]: Apple, your version.Vibhu [00:05:17]: Original ads. Yeah. Take us to today. You've been doing this for seven years at Runway. How have we got to this? Like, how do we go from driving simulator data to all this? And you cover the whole stack of generative media?From Creative Tools to a Research LabAnastasis [00:05:33]: Interestingly, we're almost back in, we're, we're full circle. We're, we're now applying our models and beyond creative tools into real-world scenarios. But it was a, it was a long journey. It was very early on we realized the first version of Runway was a way to easily use the, all the open source model of the day, things like pix2pix to. and give them to artists. That was the initial idea, is those models are too difficult to use if you're not a machine learning engineer. Like, what happens when you give them to artists? Very quickly, we realized we needed to build a research org, inside of Runway, and that happened maybe on year one. And, a lot of the mandate there was. The image-generation models of the time, the video generation models of the time, or there were barely any video generations all the time, but they were not quite there where they could be productionized and brought into tools that would be part of creative workflows. so we need to push the frontier of the research. And so maybe the first four years of Runway, research was almost happening on the background until there was a moment in 2022, with latent diffusion, with, DALL-E 2, where, there was that step function change, and you guys maybe remember around the time.Swyx [00:06:49]: I started in this space because of latent diffusion and Stable Diffusion.Anastasis [00:06:54]: Yeah.Swyx [00:06:54]: Because I was like, “Wow, this is not only, like, feasible, it is doable on consumer hardware.”Anastasis [00:07:01]: Exactly, yeah.Vibhu [00:07:01]: I think the delta is also huge. Like, I learned pix2pix. Like, this was intro to ML, the TensorFlow, like, Jupyter, Google Colab notebooks were like this, and then you have a sudden step function change, with diffusion and whatnot. Any other ones since that. Like, there were clear examples of what early diffusion were to get to here. Any other changes in key technology research?Green Screen, Rotoscoping, and Early RunwayAnastasis [00:07:26]: Between, 2018 when we started and 2022?Vibhu [00:07:29]: Yeah.Anastasis [00:07:29]: So one of the early work that we did in Runway was solving segmentation, image and video segmentation. It was a very important problem because most VFX involves essentially separatingSwyx [00:07:42]: RotoscopeAnastasis [00:07:42]: Subjects. Yeah, rotoscoping. Extremely manual process. Nobody enjoys doing that. and so a lot of the early days of Runway was building this tool. It was called Green Screen, and it was for a long time the main thing that people were using Runway for. It ended up being used in, Everything Everywhere All at Once and a bunch of other high-visibility films and series. But that was essentially, Runway for a long time was a post-production tool until latent diffusion and generat- Gen-1, Gen-2, happened.Swyx [00:08:12]: Cool. let's, let's go past that moment. You've come a long way. Then you started releasing your own models. Maybe describe that journey as well.Scaling Video Models and the Bet on 1,000 A100sAnastasis [00:08:20]: Yeah, so we go to the other point, yeah, in mid-2022 when it became clear that we're doing research at a fairly small scale of compute, and it became clear that, like, scaling laws would apply to, image and video gen in the same way that we're applying to language generation. So we made a big bet, and I think at so at the time, we signed this deal to build a cluster of a thousand A100s, which at the time we were a Series B startup. That was a almost, slightly irrational decision maybe, but we really believed that if we trained a video model at a large scale, we would get, like, a great model at the end. And at the time, the goal or we set the goal around fall of 2022 of what is, what does the latent diffusion, Stable Diffusion moment look like for video? And at the time, the best model of the time was called CogVideo. it was one of the early video models. It was very 256 by 256 resolution, very not very high quality. and so we decided we're gonna build out this cluster, and we're gonna just invest in, like, in building out our own video model. it became clear as we're training Gen-1 that it was difficult to get to fully. we wanted to build text-to-video, but it became clear to us that an easier starting point would be to start from video to video. Because when you have a stronger conditioning, it's, it's an easier problem to restylize an existing video versus generate the video from scratch. And so we released Gen-1 first back in, it was January of, 2023. Yeah.Vibhu [00:10:04]: It's just a fun visual podcast, honestly. Like, if we can see February 2023, what was the state of stuff?Gen-1: Video-to-Video and Depth ConditioningAnastasis [00:10:10]: It's so interesting ‘cause at the time when you see those results, you think this is so incredible, and this is like, it's almost like image generation or video generation is solved. And then you look back a few years after, and it's like, it's It's just like you get used to the results very quickly, with those models. But at the time when we started seeing those results, it was, it felt quite incredible, and the level of, like, quality that you could get. And, so the Gen-1 was a depth-conditioned video model, so it would turn. it would take a input video, it would predict. it would it would first convert it into the depth map, and then we would generate, pixels with a latent diffusion model.Swyx [00:11:01]: Yeah, very effective.Vibhu [00:11:02]: Yeah. I didn't realize how distracting the blog post would be. Sorry.Anastasis [00:11:05]: Yeah, but, one of my favorite examples of on those, on Gen-1 was both, if you go up to mode three or mode two, there was this storyboard use case where people would makeVibhu [00:11:18]: OohAnastasis [00:11:18]: WouldVibhu [00:11:20]: You can mess around with theAnastasis [00:11:20]: Make a city out of books or out of boxes, and then they would shoot a video with their phone and then translate it into a photo-photorealistic output. There was all these ways in which those models were starting to be used for storyboarding and also for really. and then if you go to mode four, like, of taking untextured 3D scenes and then turning them into photorealistic output. So we saw a lot of use cases early on where people that were familiar, were power VFX editors would just take a blender, render, and then they would get translated in with Gen-1 or create a scene in Unity and then take a capture a video of it and then translate into, restylize it. So I still think video to video is powerful. I think we had a recent video-to-video model as well, and it's one of my favorite ways of using those models is essentially using them to use ground truth video as, like, the initial inspiration and then translate into different styles or different outputs.Stable Diffusion, Stability AI, and Open SourceSwyx [00:12:23]: But I think we're gonna go into, like, the rest of Runway and catch people up to speed today. I did wanna cover the, let's call it the Stable Diffusion controversy, or, what happened with Stability AI, whatever. I think there was a two sides of the story. I think there's part of that is a normal thing of, like, people, join and leave companies, but what is the, retrospective now that, there's been some years behind it?Anastasis [00:12:49]: Yeah, it's a very, it's a very long story to go into. I think it wouldSwyx [00:12:53]: Which I remember you wrote a really long post about.Anastasis [00:12:56]: We would probably cover the whole hour to go into it in more detail. But, essentially, there was the latent diffusion paper that came in, I think that was at the end of, 2021. And then Patrick Esser, who was one of the researchers behind, latent diffusion, and he worked at Runway at the time, he built latent diffusion in collaboration with Robin Rumbach and a few other folks back, in the in, CompVis, which was, a labSwyx [00:13:26]: Like a research group, yeah.Anastasis [00:13:27]: And, after releasing the early latent diffusion model, they, essentially they were. the goal was to keep working on versions of the model, scale it up, incorporate new data, incorporate new tasks. And Stable Diffusion was the same model, but trained on more compute, and then with a few more tricks, like a classifier-free guidance paper came at some point, I think in the early 2022. And thatSwyx [00:13:52]: Which, like, was a big prompting improvement.Anastasis [00:13:55]: Yeah.Swyx [00:13:55]:?Anastasis [00:13:56]: That improved results. it was trained on better data, so like, the esthetic subset of LAION, but it was effectively, the same underlying architecture. And there was that big training run, that, happened on Stability's cluster. Stability financed that run. And looking back at that story, I think it was the work to build and train that model was done. It was a, it was a research project. It was done as part of, like, continuation of the latent diffusion work. It then, I think it the model became very successful, and it, I think there were the. And I think as a result of its success, other companies tried to, figure out the commercialization path for it. But for us, it was very important that we try to, we make sure that we. It was meant to be an open source research project, and so the we decided that we should continue releasing versions of it, since that was the original goal of Stable Diffusion, and that led to releasing Stable Diffusion 1.5. There was maybe a day of, a bit of, miscommunication there, but ultimately that was resolved very quickly within hours. so yeah, there wasSwyx [00:15:12]: OkayAnastasis [00:15:12]: Not a niceSwyx [00:15:13]: I just wanted to. you have toAnastasis [00:15:15]: Yeah.Swyx [00:15:15]: You're one of the main players in that journey, and so it's nice to hear from the source of, like, what happened. Yeah.Anastasis [00:15:22]: Yeah. I think it's all, it's all in the past nowSwyx [00:15:26]: YeahAnastasis [00:15:26]: I would say. and, like, both companies, Stability took its own path, Runway took its own path.Swyx [00:15:32]: Yeah. There's still. James Cameron is backing the new Stability, whatever they're doing with the Hollywood studios.Anastasis [00:15:38]: Right.Swyx [00:15:38]: I don't know what they are doing. I think one thing that impresses me, and I'm happy to move on, is that back in the that time, let's say, like 2021, 2022, there was this community of people that you were involved in that was researching all this stuff, right? And, like, from everyone I talked to who was active then, it seemed like it was fairly obvious that somebody would do the hero training run that would produce Stable Diffusion. So, like, I guess the question is, like, you had the you were you had made investments. You were you had the foresight. Is it accurate to say, like, that is reflective of, like, what people were thinking at the time? Or was it still very much like, “Well, we'll use it as, like, a post-production tool or something. I don't know.”? Like, where in the sentiment were we that maybe you can think back to, like, what the community was like back then?The Early Creative AI CommunityAnastasis [00:16:28]: I reminisce and I think very fondly those early years, from like 2018 to 2022, because it was a very small community that, as you said, were very convinced that this was gonna be a big thing. And at the time, anyone who. Because it was such a small circle and, everyone who would, like, be part of that circle and, like, make projects with it would, immediately get, go viral. so likeSwyx [00:16:55]: And you didn't know who they are, right? They're just some name on a, GitHub or Hugging Face somewhere.Anastasis [00:16:59]: Exactly, yeah. So I remember one of the first big viral moments of creative AI was, there was the neural style transfer paperSwyx [00:17:09]: HuhAnastasis [00:17:09]: ThatSwyx [00:17:10]: Something dreaming?Anastasis [00:17:11]: I think it was called neural style transfer.Swyx [00:17:14]: Okay.Anastasis [00:17:14]: There was also Deep Dream, the puppy sliceSwyx [00:17:16]: YesAnastasis [00:17:16]: Which was, also really cool. but, yeah, there was this project that, Jim Kogan, who was an early advisor of Runway and one of those,Swyx [00:17:25]: Marketing guysAnastasis [00:17:26]: Big, creative AI, folks, he literally just, like, showed a video of himself taking the New York Subway and going over the Williamsburg Bridge and then stylized it with, I think in the style of Van Gogh or, like, one, painter. And that was. Like, at the time, that was, like, so cool and it went viral and it was completely revelation to people that you could do this with generative models. And that was only, it was less than. It was maybe 10 years ago. So just, like, as an indication of, like, how quickly things have gone.Vibhu [00:18:02]: It's pretty crazy. Like, even since then, you've got people at every level of the stack. You've got devs, creatives, artists, hobbyists. You've got everyone using it. And for people that tried stuff early, they'll remember how hard it was to use regular diffusion, right? Like, nowadays, you can use your favorite ChatGPT image gen or whatever, give a sentence, get a beautiful output. But diffusion was like, the whole ultra HD, 4K, high resolution. Like, prompting these things was very different. anything you learned on the tooling side, like from the offerings you guys have now, so like creatives, devs, you really took the. Research and brought it to everyone to use. anything interesting there to share?From Gen-2 to Controllable Video GenerationAnastasis [00:18:44]: We had to build the entire model serving infrastructure for video diffusion models. There was nothing else, already, like, because we had Gen-2 was the first text-to-video model, I think, out in the market. So many things that we learn over time. I think the I think the biggest one was, like, we. it was very clear early on that text-to-video was not gonna be the answer. Like, you. Like, people wanted a lot more control than that, and so we invested in, like, control building on top of those models very quickly. how do you use the camera trajectory as control? How do you use an initial input frame as control? So that was a very early learning for us. With text-to-video was, like Gen-2 was an amazing, step function improvement in the quality of video models, but it was used much more in an exploratory way because there was nothing to ground it to. There was no reference that you could bring into it. There was no. You couldn't really control the camera motion. You couldn't control the object motion. And so the first year, in 2023, was really all about what are all the interesting ways in which we can condition those models? And it was a lot of just post-training rounds on top of the base model to figure out, like, what, -- how do people wanna control them? And so there was, like, this quick succession of the we it was called Motion Brush, which was you could, like, you could draw arrows and dictate where things should move in the scene.Vibhu [00:20:09]: That's so cool.Anastasis [00:20:09]: There was camera control that was you could just describe, like, how you want the camera to move in the scene. And because we work with filmmakers from the most of the history of Runway, we immediately got this feedback and got this, decided that this was worth investing in. And so control ability became a big theme, I think, very early on as we were building, as we were building those models. Something fun that I haven't really talked about too much was just how Gen-2 came to be out of Gen-1. So it was a bit strange because we announced Gen-2 two months after Gen-1 andHow Gen-2 Came From a Weekend HackVibhu [00:20:43]: We're accelerating.Anastasis [00:20:44]: It was before Gen-1 was even generally available. But Gen-1 was a depth-to-video model, so it would take a depth map and it would convert it into RGB. and we couldn't get, text or image-to-video to work directly, and that's why we started from depth to video. but, and we had discussions of like, okay, we need to spend the next six months investing in text-to-video, maybe increasing the compute scale or the model scale, like train a larger model. And I had this weekend project idea, which was, what if I take a model that, starts from text input and converts to depth maps and then use Gen-1 to convert the depth maps Into RGB?Vibhu [00:21:29]: It would probably work.Anastasis [00:21:30]: And so Gen-2 was that.Vibhu [00:21:32]: Oh. The hackathon pipeline.Swyx [00:21:35]: The weekend hackathon pipeline.Anastasis [00:21:36]: Yeah.Vibhu [00:21:37]: But it looks good.Anastasis [00:21:38]: And it worked pretty well. there were if you, with the knowledge that it has this, like, two-stage pipeline, you can tell in some cases that the structure of the video looks a bit off because you had to generate the depth first before you go into the output video. But it worked and it allowed us to bring this to our, to users very quickly. But it's now it's interesting because, like, people are coming back to this almost two-stage approach. Like, if you look at the Reve text-to-image model that came a few months ago, it had this planner model that would generate bounding boxes before it fed that into the diffusion transformer.Swyx [00:22:19]: Yeah, Ideogram also the same day.Anastasis [00:22:22]: Yeah.Swyx [00:22:22]: I remember that was very strange that both of them came out the same day with the same exact innovation.Anastasis [00:22:26]: It's a small community, I think.Swyx [00:22:28]: I'm like, this is like, this is completely coincidental, right?Anastasis [00:22:32]: People talk. So yeah, there's, there's definitely something into this approach. And, now, like every single like, video generation model in production uses a complex prompt completion pipeline under the hood. I think that's no secret that there is. ThatSwyx [00:22:48]: Humans are terrible at prompting.Prompt Rewriting, Camera Control, and the Seed of World ModelsVibhu [00:22:51]: I think across the board.Anastasis [00:22:51]: Yes.Vibhu [00:22:52]: But yeah, I think like the original Sora one blog post even told you that what happens after your input is rewriting your prompt. It's much more descriptive about what you would want.Anastasis [00:23:02]: Exactly. I, And there was the DALL-E 3 paper beforehand that, was the first public, description of the fact that synthetic captions and really detailed captions work really well. And then Sora built on that. Yeah, so it was 2023. We were releasing all these updates to Gen-2, like the camera control, Motion Brush. And there was something very interesting about camera control because it was the first time that you felt that instead of, like, you were creating video, you were creating a short video, you were navigating inside the world. And I think camera control was maybe the seed of some of the ideas that we had around world models and really opening up that research direction. We realized, it was this era and this series of, Gen-1 and Gen-2 models really proved to ourselves, yeah, this is theSwyx [00:23:56]: Cool.Anastasis [00:23:57]: So this is not the original camera control. This was the updated camera control on top of Gen-3. But yeah, I think it made those models usable to filmmakers, I would say. The so camera control was very popular. And so we realized, there is one way of seeing those models, which is, you're just as content creation machines, and there is the other way, which is you're. As you're predicting video in order to predict video well, you need to simulate the world in an increasing and increasing capacity. And if scaling laws apply on video, just like they apply on language models, then as we scale the compute that we put into those models, then they're gonna be able to simulate physics, they're gonna be able to simulate human actions and dynamics increasingly well and predictably well. That was the thesis about around our efforts on world models, and we spin up this research group to just focus on the world models and how do we turn the video generation models that we're building into something broader and something that would be useful beyond, also content creation as well.Swyx [00:25:04]: And that was roughly when?Anastasis [00:25:06]: Yeah, so that was inSwyx [00:25:06]: OhAnastasis [00:25:07]: In late 2023.Vibhu [00:25:08]: Interesting. like, I think, a lot of people have been saying a lot of video gen model companies have all pivoted to world models these days, but like, 2023, you're posting it. oneWorld Models: From Video Generation to SimulationSwyx [00:25:21]: It's, it's debatable whether it's a pivot.Vibhu [00:25:23]: Yeah.Swyx [00:25:23]: Like, arguablyVibhu [00:25:24]: YeahSwyx [00:25:24]: That's what you always had to do anyway, right?Anastasis [00:25:26]: It's in a way an expansionVibhu [00:25:28]: YeahAnastasis [00:25:28]: Of the applicationsVibhu [00:25:29]: YeahAnastasis [00:25:29]: Of the models as they become more capable.Vibhu [00:25:31]: The early signs, it seems like the original models you guy had, guys had, people would say it's very not bitter lesson pilled, right? You're adding, rewriting prompts, you're having all these one-off things, but that's just the state of the tech as it was versus the future of as you said, you can scale it up as, we can scale up to world models.Anastasis [00:25:50]: Yeah. So it just became. And if you looked at the outputs of Gen-2Vibhu [00:25:56]: YeahAnastasis [00:25:56]: It was not. I think it was not obvious to people that this would scale to become a general simulator of the world. Like, you had very limited movement, you had, very low fidelity or low resolution, like obvious mistakes in human anatomy, like all kinds of limitations. But it was just, the idea was that's just GPT-two, and GPT-two, it can barely generate, like, coherent sentences. Similar, Gen-2 can barely create coherent video, but if you scale it up, you're gonna. There is no reason why it shouldn't work in a way. It's, And I think that was. That's, that's always the mindset of Runway is like this extrapolation of, like, if, like, even when we started in 2018 and you looked at the results of the day, you need to look more at the trend of, like, where we were in 2018 versus when we were at the, when the first GAN came out in twenty, four 2014 or twenty, fifteen. And, you started from, like, thirty-two by thirty-two images of faces, and then by the time in 2018, you could generate, street images at the 1K resolution. And it was the same with world models, very early signs of something much bigger.Swyx [00:27:08]: Yeah. I was gonna say, like, it's diffusing into focus. Like, if you look at our visible output from year to year, it looks like a diffusion process itself.Anastasis [00:27:17]: Yeah.Vibhu [00:27:17]: Especially watching the early, like, old blog posts, you can really see the choppiness, the details.Anastasis [00:27:24]: Yeah. Like human civilization starting from random noise and thenVibhu [00:27:27]: YeahAnastasis [00:27:27]: Denoising intoSwyx [00:27:28]: Yeah. Just run it a hundred years.Anastasis [00:27:30]: Civilization.Swyx [00:27:30]: Yeah.Vibhu [00:27:31]: That's how you're on track, you're still noising, right?Swyx [00:27:34]: Yeah. I like the way that you guys phrased it when you, announced it in June, which is, oh, that you had a video essay. “The human mind is no longer the center of AI. Our world is.” Right? Which is, let's, let's call it the past five years of LLM-based AI is very much like trying to emulate human preferences and human speech. But now that's, like, mostly solved. I think that's, like, some of the context of your essay, which you also wrote around the time. And now it's like the focus is on modeling the world accurately.Scaling Laws for Video and Why Predicting Pixels MattersAnastasis [00:28:03]: Exactly, yeah. So the way we see it is, there is that, initial mission statement of DeepMind, which is, solve intelligence and then use it to solve everything else. But I think it's starting from everything else, could be valuable of, like, starting from. there is just so much complexity, and detail in the world that in order to. That it's, it's hard to learn directly from just human descriptions of the world. Like, we're assuming that, like, language models learn from everything that humans have written about the world, like our own understanding as of, the twenty twenties. And there is just so much that we don't know and so much that's not captured by existing text, about both the low level dynamics of the world, like we're not describing in detail. if I tell you to describe, like, how do you tie your shoes, that's a very difficult thing to describe in words, but it's very obvious thing to demonstrate. And so I think there's been. And there's, more of X paradox, like we're constantly underestimating all the complexity that goes into very, like, things that we do subconsciously as humans, and we don't even necessarily always have the words to describe them. And so in my mind, the simulating the world and simulating, physics, simulating the dynamics of the world has always been underestimated, compared to, we place too much emphasis on the things that are easy to talk about. but there is just all this complexity and richness of the world that if we just try and train directly on that observational data instead of training on how people describe the world, we would learn something new that we wouldn't otherwise know.Swyx [00:29:54]: You think that the present architectural paradigm is fine? You don't need, like, another layer, like JEPA, like another famous, New York AI leader would say?Anastasis [00:30:05]: We're a very pragmatic research lab. If, we have evidence that an approach works better than the approach that we're taking, then we have no qualms to taking it. We just have seen no indication that video prediction itself doesn't scale. And even if you look now, not just our work, but the work of others, you're seeing in robotics some of the most promising work, starts from video prediction models, and then you adapt them to also the action models, for example. so there is very little evidence that you need something else and that your time is better spent on a novel architectural change compared to improving data and improving the, and scaling the current approach. And so, We don't have any indication that. the, there is that counterargument that I think there was a tweet by Yann LeCun a few days ago that, understanding the dynamics of the world is very different than, generating, cute videos.Swyx [00:31:05]: And your answer is no, they're the same thing.Anastasis [00:31:07]: Yeah, they're the same thing.Swyx [00:31:08]: My cat videos are the same as understanding physics.Anastasis [00:31:11]: Right, because if you wanna generate. video models can cheat and, like, they could you could give, like, successive dif shots of the scene in a way that doesn't require you to simulate difficult physics. There is like, all these different ways in which you can hide the deficiencies of the model, and it's important not to be too tricked by the performance of the current video models. It's easy to, cherry-pick examples and think that video models are further advanced than they are. So there is a lot more work that we need to do to improve those models. But in my mind, very similar to language, and, like, we've. you go from barely coherent sentences to something that, could hold a conversation with a human to something that could can operate autonomously for a day and, like, create entire code bases. And the main difference, there is some architecture improvements along the way, but the main thing is scale. And so it's the same bet for video, and we have no indications that this is saturating. Like, we have benchmarks that we use for measuring the physics of those models, and we see those predictably improve as we scale those models. So there is. If you want to Google up, Physics-IQ, is one of those benchmarks that measures how well does the model perform at solid mechanics or fluid dynamics or optics.Vibhu [00:32:32]: I'm curious if you've seen any emergence, any scaling law around this.Swyx [00:32:37]: Yeah, he's saying there is a scaling law, right?Anastasis [00:32:39]: Exactly.Vibhu [00:32:40]: Yeah,Anastasis [00:32:40]: So the way those models, those benchmarks work is you. the researchers have gone and, like, captured, a few videos that are representative of different physical phenomena, and then you can take the first frame and then pass it through an image-to-video model and then generate a rollout that shows what should happen next. So you have, a ball hanging from the ceiling, and then you use that as input, and then you the model predicts how the ball should fall on the ground. and this measures. we have an intuitive understanding of physics. I know, you can imagine what will happen next if I drop this bottle. So it's measuring that same intuitive physics understanding of those models, and we've measured that at different model scales, and we see, and compute scales, and we see that the score on physics IQ predictably improves. There's other, tricks and techniques that you can make to improve the score even further, but even scale alone helps, in the model learning better physics.Swyx [00:33:40]: My main sympathy with Yann LeCun is the, Plato's cave allegory, right? Like, you're, you're, like, learning on the output of a thing, not the internal process of a thing, and it's very noisy. And, if only you could observe the internals of a thing. It's hard to observe the internals of a human mind, but you can very much observe, or at least we have a whole branch of science and physics that we're ignoring on how to model Physics and movement and, gravity and, other interactions. and we're just, like, throwing away all of that and just saying just scale data, which is very much the lesson of unsupervised learning, but it feels wrong. that's the main idea.Anastasis [00:34:21]: I think the history of machine learning is, at large, it feels wrong.Swyx [00:34:25]: Yeah. It's a bitter lesson, right? Yeah. It's, it's, it's the simple answer to that.Vibhu [00:34:29]: I guess, how much can you scale? So, like, even on, let's say, the video generation side, like, there's one side of video understanding. Video generation, are we still gonna have tools where it's like, I wanna generate two hours, twenty hours? there's a infra way to do it in batches and stitch it together, but, like, do we just keep scaling? Do we just continue long generation consistency, all that at scale? And, like, tying it into where we're at now from we looked at Runway two to four point fiveGen-3, Sora, and Runway's Scaling InflectionAnastasis [00:34:58]: Yeah.Vibhu [00:34:58]: Like, technically, what advancements have we made to today, and then where do you see things still going?Anastasis [00:35:04]: So part of the answer is definitely scale. and that was. We learned that lesson in a big way for with Gen-3. So Gen-3 was the model we released the year after, like in 2024. That was a few months after Sora was released. so yeah, there's an interesting story of that came to be as well. Gen-3 for us was, the first time that we really needed to build. we had to learn all the lessons that the language model world learned in two in three years in the span of a few months. one of the biggest changes of Sora was using diffusion transformers instead of convnets. So a lot of the early, latent diffusion models were all, convnets for the diffusion model part. And the diffusion transformer paper came at some point in 2023, and it showed scaling laws for image, diffusion transformers. And we realized at that point that we needed to invest in infrastructure for model parallelism, for really scaling training to larger than, a few billion parameter models. And we spent maybe the, most of the fall of 2023 building out our infrastructure for distributed training. And we had a lot of false starts and a lot of failure in trying to scale, image and video diffusion transformers. And at that point, February 2024, Sora comes out, and the results areAnastasis [00:36:35]: Very much superior to what Gen-2 could produce. There were a lot of, a lot of chatter on Twitter about Runway. Runway's done. like, there is no way Runway will catch up. And if you remember, also OpenAI in the early twenty-It felt very, like it's aSwyx [00:36:56]: To the moonAnastasis [00:36:57]: It's a formidable opponent now, but at that point, it, they were on the top of their game. nobody could even get close to them. There was maybe Gemini was just the first version of Gemini had just released. So when OpenAI came with Sora and it was such a big jump of like quality, it gave me, there was like an existential crisis for a few hours. But that, I think the amazing thing about Runway and like I think the, we've been around eight years now, which is almost we're dinosaur in AI, and we had to like, we had there was a lot of those moments we had to learn, adapt very quickly and build out skill set in the team that we didn't have. And so, if you ask anyone what is their favorite time at Runway that was there during that time, it was that push in like three months to get to a model better than Sora. and it, we scaled 10x the model scale, the model size and the, compute that we were training on. we figured out model parallelism. We had zero expertise in that. And then we came out with Gen-3 during that summer. So that was a big turning point, I think, for the company where the research org grew very quickly, and we really started pursuing this vision of the general world model, in earnest, I think after Gen-3 was out.Swyx [00:38:12]: Yeah. that's the amazing thing about building when you're building. There's no stack to. You have to invent everything yourself. You have to be completely full stack. Now I think like there are inference specialists like Fal or whatever that can help with like, model serving, and I think you guys work with them as well. but yeah, like it's, it. But at the time, it was just. It's very interesting to think about what you do when Sora comes out and people are questioning whether your company should still exist.Distillation, Turbo Models, and Real-Time VideoAnastasis [00:38:41]: Yeah. And yeah, there was no, there was no VLM of diffusion models. Like, we had to build the whole model serving infrastructure and make things efficient. And a few months after we released Gen-3, we released the Turbo version, which I think was the first step-distilled model in production.Swyx [00:38:56]: That was a whole trend that we covered as well. Yeah.Anastasis [00:38:59]: So that allowed us, to serve those models at the larger scale, ‘cause I think the first version of Gen-3 was quite, expensive to serve.Swyx [00:39:09]: I think the whole like trend in like consistency models, Lightning and, Turbo and all these things somehow didn't really stick around. I don't know if you have any reflections on this. Because at the time, I was like, “Well, everything should start with a distilled model first, and then you can upscale,” right? It. your bigger models just turn into fancy upscalers, but like you should always draft with a smaller model and faster model, right? Because you can get it so quickly, like near real-time.Anastasis [00:39:39]: Yeah. I would not be so sure to say that didn't stick around. I think that, it's, it's likely to. that there is a lot of step-distilled models that are actively used in production. there is still a gap in quality compared to the, non-distilled model. but in my mind, we're still. there is a two to three year offset from language models. So the things that, So it's just a matter of time before there is better distillation techniques. we use. Right now we have a real-time model core character that I think is the largest deployment of real-time video models, that's a step-distilled model, and it's actively being used. It's a very specific use case compared to a general video model. So this is aSwyx [00:40:27]: Very cool, by the way.Anastasis [00:40:27]: This is avatars stuff, right?Swyx [00:40:28]: Consistency, character.Anastasis [00:40:30]: Yeah. So this is a talking avatar, model. we were able to. we optimized the hell out of it, and it generates at 24 FPS, and it's a, it's a step-distilled autoregressive video model. So if we look at our world model direction, a big component of it is starting from the bidirectional diffusion that generates entire video at once and making autoregressive shows. So you generate one frame or a few frames at a time. so there's a lot that goes into that pipeline of getting to a real-time model. It's first you need to make it into a causal autoregressive model, and then you just turn it into. You need to do some additional step distillation to get it to be real-time. and I think that part is just starting. I'll be very surprised if we're, two years from now, we don't primarily use real-time models. To me, real-time video generation is just inevitable that, it has much better user experience, it's much cheaper to serve, and, the quality gap between the base model and the real-time model is only gonna close as we figure out better, distillation techniques. And we made a lot of progress there internally on maintaining the quality of the base model when we distill them.Swyx [00:41:49]: How much of this is transferable? So is it the same base model? Like if you're doing diffusion across the whole sequence and you're converting it to step autoregressive distillation, is this like distillation where you still need to train both, you can use the same base and converter? What's that process like to go from regular model to something that's real-time on a technical level?Anastasis [00:42:11]: So the nice thing about diffusion models is you have, two axes of distillation. So there is the. You can distill to a smaller model, which resembles what you do in LLMs, or you can distill in terms of taking less steps, less diffusion steps. So you could take a model that generates in fifty steps and generate in four steps and get to, You have some performance, degradation, but very often you get comparable outputs. So you can even take the large frontier model and distill it with step distillation and get to a real-time performance, and that's what we've seen. So, depending on the use case, in some cases we might also serve with a smaller model, but in a lot of use cases, we just use theSwyx [00:42:56]: Step distillationAnastasis [00:42:56]: The frontier model, and we're able to make it work in real-time.Swyx [00:42:59]: I think this might be a good time to cut over to his laptop to show off some of the real-time stuff that you're doing.Interface World Models and Neural SoftwareAnastasis [00:43:06]: This is one of the research updates that we did recently. so we've been working and f in getting our general world models to, different applications. one of them that we think is very compelling is using general world models as essentially, an interface, a universal interface to software. This is a version of our world model that's called an interface world model. and the idea is that it essentially, replaces, the, front end of a software application. It renders the pixels directly of an interface and is trained to predict what happens next as a result of, a click or another interaction you have with the interface. So this is all pixels. it's there is no HTML, CSS, React that's powering this interface. This is directly at the output of our real-time, video generation model, and it takes clicks directly as input.Swyx [00:44:09]: And drags, click and drag.Anastasis [00:44:12]: Right. So it supportsSwyx [00:44:13]: Ooh.Anastasis [00:44:14]: Yeah, clicks. It supports drags. it also supports scrolling. and the amazing thing about this is that you can effectively describe in the prompt how you want different elements, like what do you want the behavior of different elements to be. So it's almost you're you can turn, an interface from, markup language description of, like, an HTML interface, and instead you can just describe the interface. if I press this button, I expect this to happen. If I press this button, this should happen. And it's useful, we believe, both for prototyping, for, like, just testing, like, what different interactions would feel like. you can also add audio to it. So it's a video audio generation model. So you get you essentially can describe both what the visual outcome should be of your click and also what the if there is a sound effect that comes out of it. So we believe that's gonna be a much more flexible way of building software. Just render. It just, in why generate the code that generates the pixels? Just generate the pixels directly.Anastasis [00:45:18]: It's the end-to-end philosophy applying applied to front ends.Anastasis [00:45:25]: So we think there is a few interesting use case. So you can build creative tools on top of it.Anastasis [00:45:32]: We think that, for any use case that involves a lot of exploration or, like, educational use case where you wanna learn about a new concept and you want some visualization and like, and open-ended exploration, we think those this is a very powerful, approach. you can imagine new forms of, design, industrial design software that could emerge as a result of those models. And this is all, generated in real-time as well. So, you can build a lot of interesting camera transitions and forms of interaction that are very difficult to build otherwise. And one way in which we evaluate this is what if you try to generate the same interface with Claude by just, prompting Claude, “Here's an image reference of my interface that I made in Figma or that I created somewhere else. create this particular interaction,” which in this case it's, drag that object, upwards. and beyond it being slower, it's also very difficult to capture some interactions by just fully, with just LLMs. So we think that this is likely to be the way that a lot of the future, like, software in the future will be created. and one of the additional benefits is personalization might be a lot easier done with those models. Like, you can essentially try out different prompts based on who is visiting the interface. You can, more easily, prompt engineer the interface to have larger size, text for more accessibility reasons, or you can make this or, like, if you have a particular aesthetic preferences. So we're very excited about this approach. It's early days, and I think we'll need to, make it more cost-effective as well to serve those models ‘cause, running a real-time video model versus just purely rendering HTML, there's -- the computational needs are much higher. but we do see a lot of potential in this approach to building front-end interfaces.Swyx [00:47:47]: So we covered this similar thing with Flipbook before with our, Ethan Hara episode with Groq, video. And yeah, I think it's very engaging visually. I think it's maybe very good for education, but it's it does sound expensive. I think there's an upper bound to how expensive it will be, though, right? Like, the inference cost will go down over time. You'll figure out ways to optimize it. Effectively, when it pauses, you don't you're not receiving human input. You don't have to generate anything, right? So.Anastasis [00:48:14]: Yeah, you could also. Like, in this case, you have ambient motion, so there is parts of the screen that might. if you're let's say you wanna, visit Paris and then you get this interface that allows you to explore.Swyx [00:48:29]: People walking. Yeah.Anastasis [00:48:29]: You have people walking or, like, things happening. But, it's, it's a no Yeah, it makes it more expensive because you need to run the model all the time. Maybe you have some looping mechanism so you don't need to do that. But all those things, I think, is stuff we'll need to figure out.Toward a Fully Neural Operating SystemSwyx [00:48:44]: Yeah.Anastasis [00:48:44]: I think our first consideration is let's make this clearly find some use cases where it's clearly a much more compelling interaction compared to traditional interfaces. And then it's a matter of time before it becomes more cost-effective to serve.Swyx [00:48:58]: Yeah. When it comes to the people walking, I think the approach that makes the most sense to me is Nick.Anastasis [00:49:04]: Nick.Swyx [00:49:04]: Oh, God. I keep messing up their name. With Chris Manning and Fanny Yan. I don't know if you've come across them, where they. Mapped to some game engine. I think it's Unity or something, or Godot. And they you can script some NPC behavior behind that and train on that. Whereas here, you can really imagine whatever you want. Like, that is a UI, right? Like, and it feels, like, more tractable, I guess, to, create a world model of software that is interactable because we have many of examples of that, and you can, do your fancy RL environment stuff on that than it is scaling up to embodied and real-world physical use cases. But this is a nice first step.Vibhu [00:49:43]: Or, there's the opposite of you have, like, one B models, three 50 million parameter language models. It just gets so small that they're just predicting, like, fishes moving.Swyx [00:49:53]: Small models are now 120 B, so.Vibhu [00:49:57]: Ultra mini on device.Vibhu [00:49:58]: But, no, I think it, like, it puts it into perspective, at least the car one for me, like, the applications, right? The amount of work to do that, sure, you only make one model year car per year, but applying this, it's also a cost-saving to have to manually make all this, right? So it opens up a lot of possibilities, too. I'm curious if you extend this out two, three years, so where do you see things going even further?Anastasis [00:50:25]: Effectively, the end game of something like interface world models is you have, a fully neural operating system. So I think, Andrej Karpathy has written about that quite a while back. But it's, You, I think to me it's, it's a bit, it's a bit odd that, we have, for example, with an interaction with an LLM of today, you have this LLM that can talk to you about anything. It can You can take the conversation in any direction. You can It's very general, so it can solve all those different tasks, but you interact with it through a very rigid interface. And so to me, it's just a matter of time before the interface itself becomes learnable and becomes, part of the whole loop of, like, you're not just delivering. You're delivering an application end-to-end, and that means you're delivering the language model, but you're also delivering the render and the pixels and that's also a learnable component. And the concept of applications might not necessarily. I think we'll need to figure out new abstractions for software. the concept of application comes from this idea that you need, separate code bases to describe, to, for, to power each individual, tool and each individual application. But you might think of something a lot more unified if you're. if you have, a video model that's generating the interface as you go. so it can take context from an LLM and allow you to combine different functionalities that traditionally would live in different applications. So it's a, it's a way to solve, software end-to-end, effectively. We also see this as a powerful way to train computer use agents as well. so this is, one way to see this as. And in general, with world models, there is those two directions. One is world models for humans and world models forSwyx [00:52:24]: AgentsAnastasis [00:52:24]: To train agents.Swyx [00:52:25]: Yeah.Anastasis [00:52:25]: And so for every new work of, world models that we do, we have this both uses become possible. So this is a powerful synthetic data generator for training computer use models. It could become, a live, RL environment that you could use to do online RL with a computer use agent, and you can get wide diversity of different interactions, kinds of interfaces, just generated on the fly that, to improve the how robust the, your agent, becomes. So that's the same also with the world models that we're working on for a robotics use case as well.Long Context, Error Accumulation, and Autoregressive VideoSwyx [00:53:02]: Is there a research breakthrough that you're Waiting for that would unlock the next set of use cases that you really wanna pursue?Anastasis [00:53:10]: Long context is a very important one, so being able to maintain consistency for long periods of time, and that depends on the use case. So for our characters model, for example, or for the interface world model, it's easier to maintain long sessions of interaction. If you go into more open-ended worlds that you navigate and you take arbitrary actions in, we, like, there is more the context at which you can and duration which you can generate becomes limited much more quickly.Swyx [00:53:40]: Yeah.Anastasis [00:53:40]: So we see more degradation and error accumulation happening. so the biggest challenge with autoregressive models is error accumulation, is you're feeding generative frames back into the model to generate the next The next frames. And if there is any small errors, they accumulate over time. That's not a new problem. It's a problem that LLMs also have, and we've seen the ability to generate now really long outputs. So it's a solved problem, but it's definitely still a challenge.Swyx [00:54:08]: Yeah. And what is the state of the art? so for Grok, it would be like 10 to 20 seconds of context going in there for video.Anastasis [00:54:16]: With our characters models, we're able to generate up to 30 minutes of video autoregressively.Swyx [00:54:21]: Yeah. But that's just for the avatars.Anastasis [00:54:24]: Yeah. So if we look at, GWM Worlds, which is more our open-ended world exploration model, it's, it's on the order of a few minutes, which is Yeah, soSwyx [00:54:35]: Probably enough for people because you have to cut to the next scene anyway, right?Anastasis [00:54:40]: Yeah, it's not, it's not the ideal game experience if you have to restart every few minutes. So I think. But, I think it's. Yeah, for certain kinds of game experiences, you can work around it.

You're listening to Voices of Your Village, and today I'm joined by Libby Ward—creator, author, and the voice behind Honest Motherhood—and this conversation is the kind that makes you feel a little less alone in your own brain.We're talking about the mental load of motherhood, the pressure to be productive, and how hard it can be to rest even when your body is asking for it. Libby shares openly about self-doubt, overwhelm, and what it's looked like to move from doing what she should do to actually listening to what she needs.We get into nervous system support, repair over perfection, and what it means to raise kids differently than we were raised—while still being human in the process.Alright folks, let's dive in. Connect with Libby:Instagram: @libbywardWebsite: www.libbyward.comOrder the book: Honest Motherhood: On Losing My Mind and Finding Myself Connect with us:Instagram and TikTok: @seed.and.sew Seed and Sew's NEW Regulation Questionnaire: Take the QuizOrder Big Kids, Bigger Feelings now! Website: seedandsew.org Credits:Host: Alyssa Blask CampbellCo-host: Rachel LounderProduction/Editing: Kristin Mork-McVeighGraphics: Kayla Kurland-Davis/ Beki RohrigMusic by: Ruby Adams and  Bensound Learn more about your ad choices. Visit podcastchoices.com/adchoices

Looking for a cool-season cover crop to help suppress weeds and improve the soil in an empty bed over the winter? Wondering if crimson clover is right for you and, if so, when and how to sow and extinguish it? Today, Lisa and Layne discuss the ins and outs of cover cropping with crimson clover. They cover what cover crop is and why it is used, the benefits of cover cropping with crimson clover, planting times and sowing tips, when and how to extinguish the plants, and more. Listen to the podcast and discover how to incorporate beautiful and beneficial crimson clover as a cover crop in your field or garden!The video version of Lisa and Layne's conversation will be posted to The Gardener's Workshop's YouTube channel, where all “Seed Talk” episodes are organized into a ⁠⁠⁠⁠playlist⁠⁠⁠⁠. In addition, auto-generated transcripts are available for viewing on YouTube. If there is a question or topic you would like to hear discussed on a future episode of “Seed Talk”, please fill out the form linked below. We would love to hear your suggestions!Mentions:⁠⁠⁠⁠"Seed Talk" YouTube Playlist⁠⁠⁠⁠Online Course: Cool Flowers from Seed to HarvestOnline Course: Warm Flowers from Seed to HarvestShop: Cover Crop Seeds, Silage TarpEpisode 87 - Cover Crop Basics⁠⁠⁠⁠"Seed Talk" Topic Suggestion Form⁠⁠⁠⁠⁠⁠⁠⁠TGW YouTube Channel⁠⁠⁠⁠⁠⁠⁠⁠TGW iPhone App⁠⁠⁠⁠ (iOS App Store)⁠⁠⁠⁠TGW Android App⁠⁠⁠⁠ (Google Play)⁠⁠⁠⁠Sign up to receive our weekly Farm News!⁠⁠⁠⁠The ⁠⁠⁠⁠"Seed Talk with Lisa & Layne"⁠⁠⁠⁠ podcast is produced by ⁠⁠⁠⁠The Gardener's Workshop⁠⁠⁠⁠ and co-hosted by Lisa Mason Ziegler and Layne Angelo. Lisa is the founder and owner of The Gardener's Workshop, where Layne works as Seed Manager. Lisa is the award-winning author of ⁠⁠⁠⁠Vegetables Love Flowers and Cool Flowers⁠⁠⁠⁠ and the publisher of ⁠⁠⁠⁠Flower Farming School Online, Farmer-Florist School Online, and Florist School Online⁠⁠⁠⁠. Watch ⁠⁠⁠⁠Lisa's Story⁠⁠⁠⁠ and connect with her on social media. Layne is an avid gardener, seed starter, and engineer who loves learning and applying her technical knowledge to all areas of life, including gardening and growing flowers. Thanks for joining us!

Raphie flies solo but is joined by over 100 patrons live on Discord after the Padres beat the Dodgers 5-1 to move a game clear of the Phillies and Cubs and sit just one win away from the postseason.Want to get this show and tons of others early and ad-free? Plus access to post game shows, our vaunted PHT Discord server and more? Become a patron at patreon.com/padreshottub

You can know exactly how you want to eat and still find yourself reaching for something else when you're stressed or exhausted. I've been there. Our relationship with food is influenced by far more than nutrition knowledge, and blaming ourselves for struggling doesn't help us understand what's actually happening. Today, I'm joined by clinical psychologist Dr. Rachel Goldman, author of When Life Happens, to talk about the biology and psychology behind cravings, emotional eating, and food noise. We also explore how to care about nutrition without letting food rules take over your life. Here's what we get into: Why you may reach for food when what you really need is rest, comfort, or a break How to pause before an automatic food choice and figure out what's driving it How rigid food rules can turn one choice into a cycle of guilt and overeating How GLP-1s can quiet food noise, and what they can't change about your relationship with food How to build other ways to cope when food has become your go-to response to stress I know what it's like to want the cookie even when I know it isn't what I really need. Knowing better doesn't make us immune to being human. My hope is that this conversation helps you meet those moments with more curiosity and less blame, so your relationship with food can become a source of nourishment rather than stress. View Show Notes From This Episode Sign up for Dr. Hyman's Brainshaping Academy to learn how to nourish the biological systems that support your mental, emotional, and cognitive health https://drhyman.com/products/brainshaping?utm_source=dr_hyman_show&utm_medium=newsletter&utm_campaign=may_27&utm_content=link Get Free Weekly Health Tips from Dr. Hymanhttps://drhyman.com/pages/picks?utm_campaign=shownotes&utm_medium=banner&utm_source=podcast Sign Up for Dr. Hyman's Weekly Longevity Journalhttps://drhyman.com/pages/longevity?utm_campaign=shownotes&utm_medium=banner&utm_source=podcast Join the 10-Day Detox to Reset Your Healthhttps://drhyman.com/pages/10-day-detox Join the Hyman Hive for Expert Support and Real Resultshttps://drhyman.com/pages/hyman-hive This episode is brought to you by Seed, Paleovalley, Timeline, Rho, Sunlighten, and Seatopia. Make your microbiome part of your daily routine with seed.com/hyman and save 25% with code 25HYMAN. Shop nutrient-rich foods and supplements at paleovalley.com/hyman and save 15% off your first order. Support healthy aging and get up to 20% off when you subscribe on top of the new starting price of $79 at timeline.com/drhyman. Explore science-backed products at rhonutrition.com and use code HYMAN for 20% off the entire site. Discover why so many people are using sunlighten.com. Use code HYMAN to save up to $2,100 today and free shipping. Find a cleaner source of seafood. Check out seatopia.fish and use code HYMAN for free shipping on your first order. 0:00 Introduction: Food industry, brain/microbiome hijack, and Dr. Rachel Goldman 1:17 Challenges in our relationship with food and emotional eating 4:25 Societal messages, body image, and the problem with body comments 6:17 Cognitive behavioral therapy for eating behaviors 9:54 The power of pausing and mindful eating 16:20 Emotional vs. physical hunger and mindful eating 22:17 Stress, sleep, and their impact on hunger and food choices 25:21 The myth of willpower, cravings, and brain studies 31:39 Food addiction, biology, and focusing on controllable actions 35:15 Coping tools and stress management 38:32 Diaphragmatic breathing and managing stress 40:36 Orthorexia, restrictive eating, and seeking professional help 45:19 The restriction-binge cycle and building a healthy relationship with food 49:29 Creating and sustaining a healthy relationship with food 56:08 Critiques of psychiatric practice and the GLP-1 phenomenon 1:00:30 Psychological aspects of weight loss and GLP-1 medications 1:02:58 Fixing biology vs. addressing psychology in eating 1:03:44 Long-term strategies and resources for eating disorders 1:07:00 The psychology of eating, self-talk, and episode conclusion

Ever gotten in a fight with your partner over chores? Me too. You might be carrying more of the mental load.  It's a term many of us know, but awareness of it isn't enough. A lot of couples disagree on what things should be done, what a “good job” looks like, and some people just refuse to do more, period. Well, today, we get into the mental load conversation no one else is having.  I'm joined by Zach Watson, who is one of the few men who's actually leading the mental load discourse. Zach Watson is a certified Fair Play facilitator, has been featured in The New York Times, CNN, Forbes, and built an audience of more than 1.4 million followers. 

Dive into the invisible world of seeds, Sefra Alexandra, the self-titled “Seed Huntress.” She tells us about her global mission to preserve our planet's diverse seeds and protect our future food security by uncovering the stories of culinary and cultural significance rooted in these tiny powerhouses. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This Week In Startups is made possible by: Northwest Registered Agent: https://www.northwestregisteredagent.com/twistdomain Odoo: http://Odoo.com/twist Rippling: http://Rippling.ai/twist Plaud: http://Plaud.ai/twist Harmonic: http://harmonic.ai/ Today's show: Anthropic and OpenAI released cheaper models on the same day with Claude Opus 5.5 and GPT-6, and these VCs would still bet on open source. Jason brings on Dave McClure of Practical Venture Capital, Jeff Clavier of Uncork Capital, and Jenny Fielding of Everywhere Ventures to discuss the argument for open-weight models over the frontier labs. Learn why median seed valuations have more than tripled since 2017. Why does Dave say it's a buyer's market for secondaries right now? PLUS, does Meta's Muse leave any room for Instinct, and why are fintech and healthcare suddenly out of favor? Guests: Dave McClure on X: https://x.com/davemcclure?lang=en Practical Venture Capital: https://practicalvc.com/ Jeff Clavier on X: https://x.com/jeff Uncorked Capital: https://uncorkcapital.com/ Jenny Fiedling on X: https://x.com/jefielding Everywhere Ventures: https://www.everywhere.vc/ Relevant Links: Instinct reportedly seeking $1B at a $10B valuation (report via The Information) → https://cryptobriefing.com/ai-assistant-startup-instinct-targets-10b-valuation-in-new-funding-talks-report/ Meta's Muse, the agent the panel sees as Instinct's biggest threat, now on Mac → https://aiweekly.co/alerts/meta-ships-muse-for-mac-agent-now-acts-in-native-macos-apps Trump calls Jensen Huang onstage at the All-In Summit → https://thenextweb.com/news/trump-phoned-jensen-huang-onstage-at-the-all-in-summit-to-call-ai-fear-a-hoax AI-assisted intel error nearly triggered a US intercept of a Chinese ship (CNN via gCaptain) → https://gcaptain.com/ai-error-nearly-triggered-u-s-intercept-of-chinese-ship-cnn-reports/ Bernie Sanders' 50% AI sovereign wealth fund bill + Altman meeting (Fortune) → https://fortune.com/2026/06/18/bernie-sanders-ai-fund-bill-sam-altman-1000-payments/ Stanley Druckenmiller's AI-written WSJ op-ed (Axios) → https://axios.com/2026/08/26/stanley-druckenmiller-ai-writing-wsj-op-ed Claude Opus 5.5 and GPT-6 Sol/Luna launch the same day with price cuts (SiliconANGLE) → https://siliconangle.com/2026/09/22/anthropic-releases-claude-opus-5-5-and-openai-counters-with-two-cheaper-gpt-6-models/ Zipline: LAUNCH's "miss," later backed through the syndicate → https://www.flyzipline.com/ Micro1 → https://www.micro1.ai/ Founder University → **https://www.founder.university/** [VERIFY] The Syndicate: angel invest alongside LAUNCH (apply, waitlist) → **https://thesyndicate.com/** Timestamps: 0:00 What seed rounds looked like then: Fitbit's $2M seed 3:15 Jenny's Techstars NYC years and the two-unicorn mug 7:51 All-In Summit: Trump calls Jensen Huang onstage. Staged or not? 8:56 P(doom) roundtable + Dave's AI sovereign wealth fund prediction 10:08 Northwest Registered Agent - Got a new business idea? Northwest Registered Agent helps you bring it to life. Get a free domain, email, phone number, and more - with no purchase required! Learn more at https://www.northwestregisteredagent.com/twistdomain 13:35 The AI intel error that nearly triggered a US-China incident 17:44 "McClure's Law": a human with AI is the real risk 19:59 Odoo - The all-in-one business platform. Your first app is free! Get started today at https://Odoo.com/twist 21:37 Stripe + OpenRouter: an anti-fraud play, not a routing play? 23:07 Seed valuations: median $8M (2017) to $28M today 23:58 Instinct: $2.5B to a reported $10B in weeks 29:49 Rippling - Thanks to our partners at Rippling! Head to https://Rippling.ai/twist and get the only AI built to give you full visibility across your startup and take complex actions across your entire business. 44:01 Opus 5.5 vs. GPT-6 Sol: the same-day price war 46:51 Gun to your head: open-weight models or frontier labs? 51:00 Secondaries: the top 30 names vs. everyone else 52:30 Why fintech and healthcare are unloved (and Jason's Robinhood bet) Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp Follow Lon: X: https://x.com/lons Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Thank you to our partners: (0:00) PARTNER - AD BLURB (0:00) PARTNER - AD BLURB (0:00) PARTNER - AD BLURB Check out all our partner offers: https://partners.launch.co/ Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland Subscribe to our Substack Newsletters! TWiST: https://twistartups.substack.com/ TWiAI: https://www.thisweekinai.ai/ TWiVC: https://thisweekinvc.substack.com/ Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com