Podcasts about Omni

We hear a master recording of The Firm playing the Omni in Atlanta, on April 18, 1985. It's a very good show with great energy, and playing from Mr. Page. I play a very cool Satisfaction Guaranteed, a rollicking Full Circle, and we wrap it up with a bluesy Boogie Mama. Enjoy.

This time on the Regional Rasslin', Jammie Ward joins Ray Russell to talk Georgia Wrestling for July 1982! It's the Fourth of the July at the Omni featuring NWA World Champion Ric Flair vs. WWF Champion Bob Backlund! Plus, Roddy Piper banned from TBS studios but finds a loophole to return to the airwaves, Don Muraco searches for the "big white shark" Dusty Rhodes (who has the 'Eye of the Tiger', if you will), Orndorff vs, Snuka, the Freebirds vs. Wild Samoans, Wrestling #2 vs. Masked Superstar, Ole Anderson, Stan Hansen, Ivan Putski, Tommy Rich, Buzz Sawyer, Sonny King's lollipop, & much more!If you're enjoying WrestleCopia and interested in helping us continue to grow, please consider Subscribing to our Patreon to help us cover some of our costs! https://www.patreon.com/wrestlecopiaYOU CAN ALSO GIFT SOMEONE A PATREON MEMBERSHIP (OR ASK THEM TO GIFT YOU ONE) AT https://www.patreon.com/WrestleCopia/giftIncludes the $5 “All Access” Tier $9 "VIP Superfan" Tier, and "The ULLLTIMATE Tier", featuring our various VIDEO-CAST Series, Early Show Releases, our insanely detailed show notes (for the Grenade, Monday Warfare, Regional Rasslin, Puro Academy, & Retro Re-View), monthly DIGITAL DOWNLOADS for your viewing and reading pleasure, & more!HELP SUPPORT THE SELF-FUNDED WRESTLECOPIA BRAND, CONSIDER DONATING TO OUR PAYPALWRESTLECOPIA MERCHANDISE - https://www.teepublic.com/user/wrestlecopiaVisit the WrestleCopia Podcast Network https://wrestlecopia.comFollow WrestleCopia on “X” (Formerly Twitter) @RasslinGrenadeFollow & LIKE our FACEBOOK PAGE – https://www.facebook.com/RasslinGrenadeSubscribe to the WrestleCopia Youtube Channel at https://www.youtube.com/RasslinGrenade ★ Support this podcast on Patreon ★

In this episode, Ray Cochrane breaks down NVIDIA’s case for world action models, the shift that swaps a robot’s picture-describing backbone for one trained to predict what happens next. He also covers Perseverance closing in on the off-world driving record, a derelict SpaceX rocket stage hitting the Moon, and Anthropic’s rework of Claude Fable 5’s biology safeguards. Finally, he digs into Gemini Omni, Google’s undisclosed trip-planning rankings, the Danube’s record low, and iFixit’s call for Apple to unlock the iPad bootloader. – Want to start a podcast? It’s easy to get started! Sign up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a personal update. Wildfires in Eastern Oregon made for a rough week of heavy smoke, and a local building burned down, which he calls a real tragedy. Meanwhile, his work at Blubrry has centered on PowerPress fixes, where reproducing customer-reported bugs remains the biggest headache. Support tickets rarely carry enough detail, and the errors themselves are often too vague to diagnose. Consequently, he is leaning toward a stronger logging and error layer, and he asks experienced developers to share what actually works for them. Beyond VLAs: NVIDIA’s Case for World Action Models The featured story comes from NVIDIA’s developer blog, and it answers a question sitting underneath this year’s robot news. Why do robot arms fall apart the moment anything changes? Move a cup six inches, swap its shape, or change the lighting, and a policy that worked perfectly in training fails. The answer, according to NVIDIA, is not the robot but the model underneath it. For the last few years, the dominant approach has been the vision-language-action model, or VLA, built on an AI that originally learned to describe pictures. Consequently, it recognizes a banana it has never seen, in a kitchen it has never seen, yet it has no idea what that banana will do next. As the article puts it, such a model “does not learn what happens to a mug when the gripper closes, how a towel folds, where an object lands when released.” Because the physics never arrives with the model, every scrap of it has to come out of hand-recorded demonstrations. The proposed fix swaps the foundation entirely. Instead of building on a model that learned to caption images, a world action model builds on one trained to predict how video continues, so the physics is already paid for. Notably, these models output an action and a prediction of what the robot’s cameras will see, in the same pass. Cochrane likens it to forethought, imagining your own motion as you make it. NVIDIA’s implementation is Cosmos 3, pretrained on roughly 767 million images and 348 million videos of real-world dynamics. It ships in 4, 16, and 64 billion parameter sizes named Edge, Nano, and Super, and it runs in real time on a Jetson Thor board bolted to the robot itself. Cochrane recalls his dad owning one of those Jetson boards, and he asks anyone working in robotics to explain how the throughput figures fit together. However, he closes on an open question: where did 348 million videos actually come from? For deeper detail, he points listeners to the source article and to NVIDIA researcher Jim Fan. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Perseverance Closes In on the Off-World Driving Record Ars Technica reports that NASA’s Perseverance rover is about to take the record for most distance driven on another world. The mark sits at roughly 28 miles, set by NASA’s own Opportunity rover across more than fourteen years before it went quiet in 2018. As Cochrane works out on air, that averages about two miles a year. Perseverance will pass it in roughly five years instead. The difference is a navigation system called AutoNav. Since a radio signal takes several minutes to reach Mars, earlier rovers crept along pre-plotted routes and stopped every half meter to think. Perseverance carries a second computer dedicated to processing what its cameras see, so it plans while the wheels keep turning. Consequently, about ninety percent of its driving is autonomous, against roughly ten percent for Curiosity, and it averages around 110 meters an hour rather than 15 to 18. Cochrane notes researchers finding the rover at planned sites days ahead of schedule, and he wonders aloud whether world action models might drive the next one. A SpaceX Rocket Stage Slammed Into the Moon Next, Smithsonian Magazine covered the Falcon 9 upper stage that struck the Moon on August 5. That stage flew back in January 2025, carrying Firefly’s Blue Ghost and ispace’s Resilience landers, and it was never meant to end up there. SpaceX’s Julianna Scheiman says a mixture of solar activity and gravity nudged the derelict onto a lunar path after nineteen months adrift. Four tonnes of dead hardware arrived at about 5,400 miles per hour. Nobody watched it happen, and the reason is a nice bit of physics. It struck sunlit ground near a crater called Einstein, and no impact flash has ever been detected on the lit part of the Moon. However, the instruments caught the aftermath. South Korea’s Danuri orbiter imaged a dark new mark, while the European Southern Observatory’s Very Large Telescope picked up sodium and lithium in the plume, the lithium possibly shed by the rocket itself. Astrophysicist Jonathan McDowell quipped that he has “Sir Isaac Newton’s personal assurance that it did indeed hit the moon,” while planetary scientist Hannah Sargeant warns against making a habit of it. Cochrane points out the Apollo landing sites are still sitting up there. Anthropic Reworks Claude Fable 5’s Biology Safeguards Anthropic published a post on how Claude Fable 5 handles biology questions, and the bind is genuine. Biology is the textbook dual-use problem, since the knowledge behind reading your own lab results also helps someone build a weapon. Rather than refusing outright, a classifier watches for risky requests and quietly reroutes them to Claude Opus 5, a capable model without Fable 5’s biological depth. Anthropic calls that mechanism a fallback. The trouble was how often it fired on people doing nothing wrong. This update cut biology-related fallbacks by roughly 85 percent in Anthropic’s own testing, with expected overall drops of 67 percent on Claude.ai and 55 percent on Cowork. Genuinely dual-use territory still trips it, and Anthropic names virology, toxicology, and molecular design. Cochrane hit the old behavior himself and found it irritating, so he welcomes the refinement. Even so, he would rather see a false positive than a model helping someone produce a virus. Five Builders Put Gemini Omni Through Its Paces Google highlighted five builders working with Gemini Omni. Omni is a model rather than an app, and it generates video from text, images, other video, or audio, while also editing footage you already have. Google claims it “combines an intuitive understanding of physics with Gemini’s real-world knowledge,” citing gravity, kinetic energy, and fluid dynamics. As Cochrane observes, that is the same bet NVIDIA is making with robots, only pointed at video generation instead. He also flags a naming collision worth knowing about. NVIDIA calls its architecture an omni-model while Google’s product is simply Omni, two different things landing in the same week. Additionally, he encourages listeners to watch the demos, though he still senses a disconnect in AI-generated video and concedes that knowing its origin may color the impression. Gemini Wants to Plan Your Vacation Another Gemini piece, a how-to on trip planning, drew Cochrane’s sharpest take of the night. Gemini plugs straight into Google Maps, Flights, and Hotels, pulling live locations, reviews, and prices to build an itinerary. Switch on a feature called Personal Intelligence, and it reads across your Google apps, turning a messy trip-planning email chain into a clean master plan. Clever, but he calls it extremely concerning. Once these become services, he expects partnerships to quietly push particular hotels, restaurants, resorts, and destinations onto users. Notably, Google’s post never explains how any of it gets ranked, and the words sponsored, ad, affiliate, commission, and paid never appear once. There is no disclosure of a commercial arrangement, and no denial of one either. Meanwhile the post hands readers off to Viator to book tours without describing that relationship at all. Cochrane suspects the real effect shows up slowly, in the shape of small businesses continuing to disappear. The Senate Blocks a Rule on Who Controls Research Money Science reports that the Senate passed a temporary spending bill in the early hours of Saturday the 8th. The Senate’s version carries a one-paragraph rider the House version lacks, and that rider stops the White House Office of Management and Budget from finalizing a set of proposed rules. OMB builds the president’s budget, clears agency regulations, and controls how approved money actually reaches agencies. The bill itself is a stopgap, which prevents a shutdown without settling anything. The rules reach every organization that takes federal money, a pot of roughly $1.1 trillion across 41 agencies, about $150 billion of it research grants. They would let political appointees second-guess which grants get funded, allow awarded grants to be pulled when the work does not match presidential priorities, and put several countries off limits for research partnerships, China first among them. Senator Susan Collins pushed the block through after telling OMB director Russell Vought the proposal was deeply flawed, noting nearly 500,000 public comments, the vast majority opposed. However, the 90-6 vote is not law. Both chambers are on recess. Vought reportedly said the rule would not have been finalized before December anyway, and the block only lasts as long as the stopgap, which expires December 11. The Danube Falls to a Record Low ESA published a pair of Copernicus Sentinel-2 satellite images showing the same bend of the Danube, 45 kilometers upstream of Budapest, photographed a year apart. Cochrane calls the before-and-after shocking, going from green to brown completely. Wire reports put the Budapest gauge near 10 centimeters at the start of the month, about four inches of water, against a previous record of 33 centimeters set in 2018. The knock-on effects arrived fast. Budapest ran short on both power and drinking water, while the shrinking flow concentrated pollution in what remained. Romania hit record lows on its own stretch as well. Cochrane hopes the recovery is already underway. What a Heatwave Actually Does to the Power Grid That river story runs directly into a Carbon Brief factcheck. Nuclear plants cool themselves with river water, so when the Danube dropped, plants in Hungary and Romania throttled back and pulled roughly 2.5 gigawatts off the grid. Romania declared a state of alert in its energy sector, and its navy reportedly used explosives to steer more water toward a plant intake. Carbon Brief then walked through what heatwaves do to each way of making electricity. Nuclear loses efficiency when the cooling water is already warm, though its shutdowns are mostly regulatory rather than mechanical. Gas turbines pull in less air because hot air is thinner, costing capacity. Wind falls off hardest, since a heatwave is a big stalled dome of high pressure and nearly still air. Solar is the surprise: cells genuinely do get less efficient as they heat up, yet total output climbs anyway, with UK solar up 46 percent during a four-day June heatwave against the week before. Butterflies Are on the Move Everywhere A new study in Nature Ecology and Evolution covered 1,758 butterfly species, roughly one in ten of every species we have named. The team pulled 6,182 records from 105 countries, reading non-English research alongside 68 expert write-ups. Four out of five species pushed into new territory, and about 79 percent of the logged shifts traced back to climate change and extreme weather. Separately, 27 percent saw their range shrink somewhere, and 22 percent moved up or down a mountain slope chasing cooler air. That sounds like good news, and it really is not. Expansion means a boundary moved, not that butterflies are thriving, since a species can push its northern edge forward while its southern edge quietly collapses. Lead author Shawan Chowdhury says the shifts turn up on every continent where butterflies occur. Additionally, monitoring gaps leave Central Africa, Southeast Asia, New Guinea, and the Amazon Basin barely counted at all. Cochrane recalls hearing years ago that butterflies were disappearing in Hawaii, and he invites listeners spotting unfamiliar species locally to contribute what they see. Primates Make Friends Across Species Cochrane called this one a fun find. A study in the journal Primates, led by Cyril Grueter at Oxford, gathered 427 documented cases going back to the 1970s across 88 primate species and 127 partner species. Play and grooming dominated at 139 and 136 cases, alongside carrying, huddling, food sharing, and even adoption. Primates usually started the interactions themselves, with juveniles playing most, adult females handling grooming and caregiving, and adult males least likely to join in. The examples are remarkable. Japanese macaques on the island of Yakushima groom sika deer and climb on their backs, a silverback gorilla cradled a tiny wild bushbaby, and wild capuchins in Brazil adopted an infant marmoset in a bond that held for weeks. However, Grueter rejects the pet-keeping headline and prefers the hedged term proto-pet keeping. The actual claim is smaller and more interesting: curiosity, tolerance, caregiving, and play have roots running far deeper than humans do. iFixit Tells Apple to Unlock the iPad Finally, an opinion piece from Charlie Sorrel at iFixit struck a chord. This fall, iPadOS 27 drops support for a batch of older iPads, including the 8th-generation iPad, the third-generation Air, the fifth-generation mini, and the first-generation iPad Pros. Cochrane owns one of those Pros and reports it still works fine. Those devices will not break, but they stop getting OS and security updates until apps abandon them and the battery gives out. The obvious second life is Linux, except the bootloader stays locked. Apple’s iBoot will not load anything else, unlike a Mac, a PC, or most Android phones. Sorrel argues it “should be a user choice, not a vendor choice,” and Cochrane agrees flatly. You own the device, so why does Apple decide what runs on it? He compares the situation to jailbreaking, and he suspects most consumers have never pushed back simply because it never occurs to them. Nevertheless, he hopes an unlock eventually breathes new life into hardware that still works perfectly well. Cochrane wraps with housekeeping: become a GNC Insider at geeknewscentral.com/insider, email geeknews@gmail.com, subscribe to the newsletter, and grab a modern podcast app at podcastapps.com. He thanks GoDaddy for over twenty years of keeping the show on the air, and he signs off wishing listeners a wonderful evening. The post The Robot That Imagines First #1872 appeared first on Geek News Central.

AI is moving beyond text, images, and code. Now it's learning to read and write -- shall we say program -- DNA.In this episode of NEXT, John Koetsier talks with Eric Nguyen, co-founder and CEO of Radical Numerics, about the rapidly emerging world of biological AI.Nguyen and his team helped create Evo and Evo 2, generative foundation models for DNA, and are now working toward what they call “general biological intelligence”: AI systems capable of understanding biology across DNA, gene expression, methylation, proteins, and other biological signals.The potential upside is enormous. These systems could help scientists detect cancer earlier, develop treatments for antibiotic-resistant superbugs, understand disease more deeply, and eventually design countermeasures to emerging biological threats on demand.But the same capabilities introduce serious risks.Nguyen explains how AI could potentially generate biological sequences that retain dangerous functions while evading traditional sequence-matching detection systems ... essentially creating biological “deepfakes.” He also discusses why AI labs need to develop biodefense capabilities alongside increasingly powerful biological design tools.The conversation covers DNA foundation models, AI-generated viruses, biosecurity, pathogen detection, wastewater surveillance, attribution of biological threats, antimicrobial resistance, cancer detection, open-source versus closed-source biological AI, and why biology may be the next major frontier for artificial intelligence.00:00 AI-designed DNA and “deepfake” viruses00:21 AI is moving into biology00:55 Meet Eric Nguyen of Radical Numerics01:20 Why DNA is a language01:39 Teaching AI to read and write DNA02:54 What happens when AI can create biological systems?03:15 The promise and risks of programmable DNA04:32 The medical upside of AI-driven biology04:58 Moving beyond single-molecule drug discovery06:30 Can AI model the complexity of the human body?06:59 Biology has more data than we know how to use08:38 Where all the DNA data comes from09:12 The dangerous side of AI-generated biology10:01 What is a “deepfake virus”?12:41 Could AI make pathogens more dangerous?14:36 Putting AI biodefense on the front lines16:56 The three pillars of biodefense18:22 On-demand treatments for new diseases19:11 How close is this future?21:53 Why biodefense capabilities are falling behind24:36 Should powerful DNA models be open source?25:40 How Evo and Evo 2 were made safer26:49 Why Radical Numerics is keeping Omni closed27:55 AI-generated bacteriophages and superbugs29:23 Balancing breakthrough biology with biosecurity30:53 Using AI to detect cancer earlier32:08 Why cancer detection needs multiple biological signals33:49 “Sensor fusion” for biology34:24 Why a holistic view of medicine matters

Are you posting every single day and still feeling invisible? That is not a client attraction problem. It is a visibility problem, and it has a fix. In this episode Crissy Conner breaks down how to get coaching clients online without chaining yourself to your phone or feeding the algorithm 50 times a day.Find out more about joining OMNI https://thevisibleceo.com/omni The OMNI Method is a diversified visibility strategy built across social media, search and AI - designed for female entrepreneurs who want to be known, found and unforgettable without living online around the clock. If you are ready to build visibility that works harder than you do, learn more at https://thevisibleceo.com/omni Website: thevisibilityimpactshow.comBrand: thevisibleceo.com Instagram: instagram.com/itscrissyconner TikTok: tiktok.com/@crissyconner Facebook: facebook.com/crissyconner YouTube: youtube.com/@CrissyConner LinkedIn: linkedin.com/in/crissyconner 

Are you posting every single day and still feeling invisible? That is not a client attraction problem. It is a visibility problem, and it has a fix. In this episode Crissy Conner breaks down how to get coaching clients online without chaining yourself to your phone or feeding the algorithm 50 times a day.Find out more about joining OMNI https://thevisibleceo.com/omni The OMNI Method is a diversified visibility strategy built across social media, search and AI - designed for female entrepreneurs who want to be known, found and unforgettable without living online around the clock. If you are ready to build visibility that works harder than you do, learn more at https://thevisibleceo.com/omni Website: thevisibilityimpactshow.comBrand: thevisibleceo.com Instagram: instagram.com/itscrissyconner TikTok: tiktok.com/@crissyconner Facebook: facebook.com/crissyconner YouTube: youtube.com/@CrissyConner LinkedIn: linkedin.com/in/crissyconner 

At 6 years old, Kevin Keller was already "stealing" his father's cologne — a habit generously tolerated by his dad — and discovering that scent could change the way you felt before you ever said a word.That early fascination with transformation eventually became Fulton & Roark, the American fine fragrance house Keller co-founded in 2013 with $11,000 and a belief that perfumery could be both deeply personal and distinctly American.Raised in Atlanta and educated at Georgia State University and Wake Forest University, Keller began his career in journalism before turning to music and culture, where he profiled brilliant emerging musicians and artists. What stayed with him was a fascination with culture, place, and tradition — and how those forces subtly shape the way we carry ourselves in the world.Built without outside capital, Fulton & Roark has grown into a nationally distributed independent fragrance brand, available in nearly 500 retail locations including premier independent boutiques, select Ritz-Carlton and Omni properties, and Neiman Marcus stores and online. The brand has been featured in GQ, Vogue, and ELLE, among others, and its fragrance Roark's Cove was a finalist for the Universal Prestige category of the Fragrance of the Year Award from The Fragrance Foundation.He lives in Winston-Salem, North Carolina, with his wife and two children, and mentors young founders at Wake Forest University.In This Conversation We Discuss:[00:00] Introduction[01:08] Solid fragrances vs extrait de parfum[02:08] How the founder's love of fragrance began[03:16] Founding a niche brand with $11,000[05:26] Going to market without a DTC plan[07:39] Sponsor: IntelliGems[09:19] The GQ feature that forced them online[11:37] Building first websites on Squarespace[12:37] Sponsor: Klaviyo[14:47] Building the marketing flywheel[18:04] Sponsor: eFulfillment Service[19:40] Getting customers to pay for samples[22:37] Callouts[25:26] Why founders overrate daily metrics[27:30] Learning to delegate as a founder[27:54] Where to find Fulton & Roark[29:02] Final thoughtsResources:Subscribe to Honest Ecommerce on YoutubeAmerican Fine Fragrance fultonandroark.com/ Follow Kevin Keller linkedin.com/in/kevinwilliamkeller Book a demo today at intelligems.io/ Migrate and grow more klaviyo.com/honest Lower scale costs today FulfillmentService.com/honest If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

Colin spent eight years building Looker into a $2.7B Google acquisition. Then he left to compete with his own product. He thought traction would take a month—it took nine. A hundred demos got him five verbally-committed customers, and he lost all five. So he spent two months killing bugs, went on one podcast, and won every single trial that came out of it. Omni just raised over $250M.In this episode, Colin breaks down how to tell the difference between a product that's genuinely better and one the market just doesn't want, why founding with $30M didn't stop them from staying stingy, and the LinkedIn playbook that turned 6,000 connections into a 90% response rate.Why You Should ListenWhy losing every deal doesn't mean the idea is wrong—and how to know the difference.Why real differentiation shows up as "wow" moments in demos, not signed contracts.The LinkedIn social-selling playbook that built Omni's first pipeline.Why hiring sellers from your old industry hands you their Rolodex on day one.Keywords startup podcast, startup podcast for founders, product market fit, finding pmf, Omni, Colin Zima, Looker, business intelligence, BI tools, enterprise SaaS, AI analytics, social selling, LinkedIn outbound, founder-led sales, innovator's dilemmaChapters00:00:00 Intro00:01:58 The Moment of True Product Market Fit00:06:16 Losing All Five Deals and Doubling Down00:10:43 Leaving Looker to Compete With Looker00:17:39 Founding With $30M and Staying Stingy00:22:09 A Hundred Demos Before the Flywheel00:32:15 No Silver Bullet—Just Do More of Everything00:38:32 The LinkedIn Social-Selling Playbook00:46:07 Hiring the Best People You've Worked WithSend me a message to let me know what you think!

What is God like? How does who he is impact what we do with our lives? In "Now Showing," we walk through some of the attritbutes of God and why they matter for our daily living. In Part 2, Pastor Matt teaches about God's power, presence, and knowledge.Omniscience - God Knows Everything.God possesses perfect, complete knowledge of all things—past, present, future, actual, and possible. Because of this, he knows exactly what is happening in our minds and hearts at any moment. Nothing is invisible to him. While this can be a terrifying realization if we are not right with God, it can also be a comfort. If we reject God, his perfect knowledge means he knows all our wrongdoing. However if we have accepted Christ as our Lord then his perfect knowledge means he is able to comfort us perfectly. He knows our struggles and can help us through them.Omnipresence - God Is Present Everywhere.God is fully present everywhere; there is no place in creation where God is absent. As with the last attribute, this can be a terror or a comfort depending on your perspective. If we reject God, his inescapable presence is cause for trembling and fear. For Christians, his ever-present guidance is a delight. God is always near to us and able to help us in times of trouble. Because of this, we can trust him with our fears.Omnipotence - God Is All-Powerful.God possesses unlimited power and is able to accomplish everything consistent with His nature and will. As has been a consistent theme, this too is either terrible or magnificent news depending on our relationship to God. Those who rebel against God rightly fear punishment, knowing that God has the power to administer it. Those who trust in God rightly rely on his strength to defend and uphold us in our weakness.Because God is Omni, submit your life to Him.These three attributes reveal an incredible binary between those who follow God and those who reject him. On one hand, the one who rejects God knows God's reach is inescapable, that he sees all sin, and that he can punish that sin. The Christian, on the other hand, rests safely in the knowledge that those infinite attributes are on his side. God's power, presence, and knowledge are a comfort, not a curse to the one who trust in God. Will you place your trust in God today?

Amardeep Parmar from Bae HQ welcomes Shiv Sivakumar, Founder at OmniAmardeep Parmar:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Shiv: ⁠Website:

Are you already good at what you do but quietly wondering how to get even better? In this solo episode Crissy Conner pulls back the curtain on everything she changed in 2026 to level up her craft, starting with one belief. To be the best at what you do, you have to first be the best student of your work.Find out more in our Free Skool Community https://www.skool.com/thevisibleceo/aboutFind out more about OMNI https://thevisibleceo.com/omniThe OMNI Method is a diversified visibility strategy built across social media, search and AI - designed for female entrepreneurs who want to be known, found and unforgettable without living online around the clock. If you are ready to build visibility that works harder than you do, learn more at https://thevisibleceo.com/omni Website: thevisibilityimpactshow.comBrand: thevisibleceo.com Instagram: instagram.com/itscrissyconner TikTok: tiktok.com/@crissyconner Facebook: facebook.com/crissyconner YouTube: youtube.com/@CrissyConner LinkedIn: linkedin.com/in/crissyconner 

Watch This Episode On YouTubeI couldn't stop laughing during the funniest film of the year, directed by my guest, Elliot Connors. It's called FULL FRONTAL, or if you want to use the full title (and you should), it's I'M GOING FULL FRONTAL FOR MY NYU STUDENT SHORT FILM (2026), streaming today, August 4th, 2026 on NoBudge.I'd argue getting laughs is the hardest thing to do in independent film so that's why comedies are so relatively rare. And to do it in a short film -- that's a tough sell.But Elliot has the, um, goods: He is a 2026 Just for Laughs “New Face” for his adult animated series OMNI, about a dystopian virtual reality and the group of lovable curmudgeons trying to change it. BA from Yale, MFA from NYU, just one watch of FULL FRONTAL, you'll see what I mean. It's so, so fucking funny.But seriously: not safe for work. I'm in early on the Elliot Connors rocket ship and can't wait to get to the Moon.Hat tip to the absolute best friend of the pod, Hope Lawson, for making this conversation happen.In this episode, Elliot and I talk about:the insane reaction at NoBudge 50 for this film;how he describes the film and the reaction for people after watching it;how he got involved in filmmaking;the difficulty of making comedy work on screen;the importance of trusted people on what's funny;handling intimate filming and how he made the case and crew feel comfortable;why NoBudge?what NYU's writing program feels about streaming vs. festival rollouts;watching a lot of stuff versus a new perspective;what's next for him;the challenges of getting comedy shows produced nowadays.Elliot's Indie Film Highlights: PAJARITO (2026) dir. by Pilar Garcia-Fernandezsesma; JEFFREY, BAD DAY (2025) dir. by Theo Matza; COUPLES THEFTAPY (2026) dir. by Kiran ArainMemorable Quotes:" Kentucker was saying, "We probably got a good 20 seconds of penis there." And I was joking that I'd used a prosthetic, so it wasn't my real penis, but it was my real penis. And there's plenty of it in the film."" I grew up watching, like binging comedy sitcoms."" If the jokes are working for you on the page, chances are it'll translate."" So I'm always getting notes on my features of "this is not quite working." But when I write a half-hour comedy, I get a lot more positive feedback because I feel like I know the rhythm more."" The director of my program, when I said, "should I really be studying screenwriting at such a tough time for the industry?" He said, "It's always tough."Links:Follow Elliot On InstagramElliot Connors Website

Are you already good at what you do but quietly wondering how to get even better? In this solo episode Crissy Conner pulls back the curtain on everything she changed in 2026 to level up her craft, starting with one belief. To be the best at what you do, you have to first be the best student of your work.Find out more in our Free Skool Community https://www.skool.com/thevisibleceo/aboutFind out more about OMNI https://thevisibleceo.com/omniThe OMNI Method is a diversified visibility strategy built across social media, search and AI - designed for female entrepreneurs who want to be known, found and unforgettable without living online around the clock. If you are ready to build visibility that works harder than you do, learn more at https://thevisibleceo.com/omni Website: thevisibilityimpactshow.comBrand: thevisibleceo.com Instagram: instagram.com/itscrissyconner TikTok: tiktok.com/@crissyconner Facebook: facebook.com/crissyconner YouTube: youtube.com/@CrissyConner LinkedIn: linkedin.com/in/crissyconner 

The answer to everything.

Want more fishing content and fewer ads? Check out asfr.supercast.com for an ad-free, extended cut of your local fishing report. New subscribers get the first 30 days free. This week's Alabama Saltwater Fishing Report covers red-hot Gulf Coast fishing from bluewater current breaks to Dauphin Island beach trout.  Butch Thierry is joined by Angelo DePaola, who opens the show with an update on the Abaco Orange Beach development and how boating, fishing, waterfront access, and lifestyle are driving strong interest in the project. Tom Hilton of Realtime Navigator follows with a detailed offshore breakdown after the recent tropical weather, explaining how cleaner water, color changes, sea surface temperatures, altimetry, current, and structure should guide anglers looking for blue marlin, tuna, wahoo, mahi, and other pelagics. Capt. Myles Colley of It Just Takes Time joins next for a deep offshore conversation on big-game fishing, Omni sonar, tuna tubes, live baiting, trolling teasers, pitch baits, current direction, boat handling, and how aggressive drag pressure can help anglers land more billfish and tuna. He also shares a wild story about releasing a giant blue marlin years ago near the Nipple and Elbow area. To close it out, Capt. Cody Broughton checks in from Dauphin Island with a strong inshore report, breaking down the live-croaker beach bite for speckled trout, night fishing, major bite windows, rigging details, leader choices, croaker placement, flounder, tarpon, and how long the summer beach trout pattern may last. SPONSORS The Coastal Connection / Angelo DiPaola Sea Tow Foster Contracting / Fortified Roofing Pros Black Buffalo Eastern Metal Supply Fiber Plastics Inc Hilton's Offshore Charts / Realtime-Navigator Coastal Brew Bait Company Dixie Supply / Baker Metal Works Make Wake Marine Midway Lumber Admiral Shellfish Company Pure Flats Pike Consulting Group Community Fly Supply Deep South Crane Rentals SlipSki Solutions CCA Alabama Camper City Mobile Moffett Road Dentistry / Ricciardone Family Dentistry McCoy Outdoor Company The Orthopaedic Group / Dr. Michael Blackmer Alfa Insurance / Bryan Klein Ultra Wash 

Join us tonight for a special livestream reflecting on G-Fest 2026 in Rosemont, Illinois. Was it as great as the epic that was last year's con? Did Nathan survive the nine hours of programming he (foolishly) took on? Did Damon choke on his first panel? Will Omni Viewer explain why Snazzy was missing? All this and more in this fun discussion! Watch the original livestream here: https://youtube.com/live/Egv1e-dv1Ac.    Check out Nathan's spinoff podcasts, The Henshin Men and The Power Trip, and Henshin Power V3! We'd like to give a shout-out to our free MIFV MAX patrons on Patreon: Cordell Stevens, John Pannozzi, Jacob Heron, Cool Cat Videos, Bransbow, Sean Sullivan, Frankie Wolf, Russel Hale, FRIEN Jadge, Bob Hard, ArtsieSteph, Robert O'Brien, DD Chief, Kaye, Nobody, The Indiscrite One, Clayton Warden, Enigma, Dave Blanken, Patrick Greenlaw, Mikki, Josh Baughan, Shane Cochran, and Francis Chopin. You, too, can join MIFV MAX on Patreon to get this and other perks starting at only $3 a month! (https://www.patreon.com/monsterislandfilmvault) Buy official MIFV merch on TeePublic! (https://www.teepublic.com/user/the-monster-island-gift-shop). NEW MERCH NOW AVAILABLE! This episode is approved by the Monster Island Board of Directors. Podcast Social Media: MIFV Linktree: https://linktr.ee/monsterislandfilmvault Nate's Linktree: https://linktr.ee/nathan_marchand MIFV is a member of PodNation (https://podnation.tv/) MIFV is one of Feedspot's top 10 tokusatsu podcasts! (https://blog.feedspot.com/tokusatsu_podcasts/) MIFV is one of Feedspot's top 20 monster podcasts! (https://podcasts.feedspot.com/monster_podcasts/) www.MonsterIslandFilmVault.com #JimmyFromNASALives, #MonsterIslandFilmVault, #Podcast, #kaiju, #MIFV, #Godzilla, #gfestxxxi, #gfest © 2026 Moonlighting Ninjas Media

Join us tonight for a special livestream reflecting on G-Fest 2026 in Rosemont, Illinois. Was it as great as the epic that was last year's con? Did Nathan survive the nine hours of programming he (foolishly) took on? Did Damon choke on his first panel? Will Omni Viewer explain why Snazzy was missing? All this and more in this fun discussion! Watch the original livestream here: https://youtube.com/live/FjO9n1Fvox8. New discussion episodes are coming! Podcast Socials:                 Twitter/X: @HenshinPowerV3 The Markalite Lounge (official Facebook group) The Markalite Lounger (official Discord server) Power Rangers Legacy (Power Trip Facebook group) YouTube Channel: @HenshinPowerV3 Nathan's Linktree: https://linktr.ee/nathan_marchand

Steve Dennis and Michael LeBlanc open episode 308 with a check on Steve's number one prediction for 2026 — uncertainty reigns supreme — and it's holding up. The flat 10% global tariff is gone, replaced by a 10–12.5% range on nearly all U.S. imports, with steeper rates for Canada and Brazil. The new forced-labor justification behind the increases strikes Steve and Michael as ridiculous and unlikely to survive a court challenge. The retail numbers look better than the tariff logic. June retail sales rose 5.7% year over year, led by sporting goods (a World Cup bump) and electronics. Grocery managed just 1%. Steve walks through why: dollars flat, units falling, prices 33% above pre-COVID levels, food bank use climbing on both sides of the border. Albertsons is the clearest case — sales nearly flat, guidance cut, stock down 20% in a day. Then, recorded live in the pop-up studio at the CommerceNext Growth Show in New York, Ulta Beauty Chief Retail Officer Amiee Bayer-Thomas argues stores are queen. Thirty years into a career she calls a lattice rather than a ladder — seven roles at Ulta alone, including chief supply chain officer through 2020 — she now owns everything from site selection and store design to services, asset protection, eventing, and the omni guest journey. Stores, she says, are experiential hubs delivering "beautytainment" through 60,000 associates across 1,500 locations. The numbers: 47 million loyalty members, 95% of whom shopped in-store last year. A NielsenIQ study of Gen Alpha found the most AI-engaged shoppers still pick stores over apps, 57% versus 36%. Amiee's take on AI: it's an "and," not an "or." Technology can flag a replenishment; only an associate reads the confusion on a guest's face in the skincare aisle. Omni guests visit and spend three times more. She previews a Times Square flagship alongside outlet and small-format stores, plus an eventing calendar headed toward 140,000 events this year. In the news, Wayfair is expanding its store footprint. Shein is heading toward a Hong Kong listing at roughly half its 2022 valuation. Also on the radar: fuel costs rising again as back-to-school and holiday approach, and U.S. population growth of 0.25% turning retail into a fight over share. About UsSteve Dennis is a strategic advisor and keynote speaker focused on growth and innovation, who has also been named one of the world's top retail influencers. He is the bestselling author of two books: Leaders Leap: Transforming Your Company at the Speed of Disruption and Remarkable Retail: How To Win & Keep Customers in the Age of Disruption. Steve regularly shares his insights in his role as a Forbes senior retail contributor and on social media.Michael LeBlanc is a senior retail advisor, keynote speaker and media entrepreneur. Michael has delivered keynotes, hosted fire-side discussions hosted senior retail executive on-stage in 1:1 interviews worldwide. Michael produces and hosts a network of leading retail trade podcasts, including The Remarkable Retail Podcast, The Voice of Retail, The Food Professor, The FEED powered by Loblaw and the Global eCommerce Leaders podcast. He has been recognized by the NRF as a global Top Retail Voice for 2025 and 2026 and continues to be a ReThink Retail Top Retail Expert for the fifth year in a row.

In this episode, Kamille demonstrates the power and limitations of using Claude and the Model Context Protocol (MCP) to build an Airtable base from scratch. She walks through a live experiment where she prompts Claude to create a complex meal planner, including tables for recipes, ingredients, and a shopping list, as well as the necessary automations and interfaces. The team also dives into recent Airtable updates, including the new Slack integration and the ability to manage automations via MCP. They discuss the current limitations of AI-driven base building—such as the inability to edit existing interfaces or handle script steps—and compare the experiences of using Claude versus Airtable's Omni.

What if the future of analytics wasn't about building another middleware layer, but about getting closer to the actual business user? In this episode, Benjamin sits down with Chris Merrick, CTO and cofounder of Omni, to explore why semantic layers matter more than ever in an agentic world, how AI is reshaping embedded analytics and customer-facing data experiences, and the key strategies for keeping complex data models aligned across federated sources. Whether you're building analytics platforms, managing data infrastructure, or trying to make AI work at scale, this conversation is packed with practical insights on balancing governed analytics with exploratory AI, unifying disparate data sources, and capturing business intelligence beyond just the metrics in your warehouse. Tune in to discover how the next generation of analytics platforms will need to think about the entire business, not just the data.

Mike runs a solo Coffee Time Kicks episode and sits down with a friend of the show, Jason Faustino, Senior Director of Energy at Saucony. Fresh off the brand's Paris preview, Jason breaks down how Saucony builds its creative world: why knowing exactly who you are beats chasing anyone else, how collaborations really get vetted (it comes down to chemistry and gut), and how a small team stays two years out on product while making it look effortless.They get into the Westside Gun partnership and the passing of the torch from Jae Tips, why Jason sees himself as working for the artists as much as the brand, the Ride 1 revival, the slow-burn rise of the Guide 7, and a Crystal Caves colorway coming back on the Triumph 4. If you want to understand what makes Saucony tick right now, this is the one.In this episode:Why Saucony leans all the way into its own identityHow collabs actually get vetted, and why it comes down to a feelingThe Westside Gun and Jae Tips storyProtecting artistic freedom inside a corporate structureThe two-years-out product development timelineRide 1, Guide 7, Omni 9, and the Crystal Caves Triumph 4Why in-person community still beats the transactional stuffFollow Jason on Instagram @um.jf13. Saucony at saucony.com.Subscribe to the Sneaker History Podcast and drop your favorite Saucony in the comments so we know you made it to the end.SUPPORT THE SHOW:Donate Through Venmo: https://venmo.com/u/sneakerhistoryBuy Me A Coffee: https://buymeacoffee.com/nickengvallEarly Access, Exclusive Videos, and Content On Patreon: https://patreon.com/sneakerhistorySubscribe to The Sneaker Newsletter for industry insights: https://www.thesneakernewsletter.com/If you are interested in advertising to our audience, contact us: podcast@sneakerhistory.comCHECK OUT OUR OTHER SHOWS:For the Formula 1 Fans - Exhaust Notes: https://exhaustnotes.fmFor the Fitted Hat Fans - Crown and Stitch: https://crownandstitch.comFor the Cars & Sneakers Fans - Cars & Kicks: https://carsxkicks.comFor the Creators & Creatives - Outside The Box: https://podcasts.apple.com/id/podcast/outside-the-box-convos-with-creators/id1050172106[Links contain affiliate links; we may receive a small commission if you purchase after clicking a link. A great way to support the pod!]—––––—––––—––––—––––—––––—––––—––––—––––Our podcast is proudly...Recorded on Riverside: http://www.riverside.fm/?via=sneakerhistoryHosted & Distributed By Captivate: https://bit.ly/3j2muPbDisclaimer: The views and opinions expressed in this program are those of the speakers and do not necessarily reflect the views or positions of any entities they represent.This podcast uses the following third-party services for analysis: Spotify Ad Analytics - https://www.spotify.com/us/legal/ad-analytics-privacy-policy/

Just imagine a scientist going to the supermarket where he picks up a banana, an antenna from a blue crab and a whisker from a catfish. He takes them back to his lab, hooks them together and connects the whole thing to his computer. He calls it a “bananatrode” and uses it to detect chemicals from the human body called dopamine.While scientists don't actually do that, the scene is not so far-fetched. The crab antenna can detect tiny traces of amino acids in salt water. You see, crabs find their lunch by sensing and tracing down amino acids in the water. The catfish whisker can detect equally small amounts of amino acids in fresh water and they both can do so extremely quickly. Scientists know that these natural sensors work much faster and are more sensitive than man's rather cumbersome methods of laboratory analysis. For that reason they are being seriously studied for human medical use. One such application is a glucose sensor for an artificial pancreas. Oh, and the banana? That was for the scientist's lunch.It is amazing to consider that while the crab and the catfish are pretty low on the evolutionary ladder yet they have these wonderful abilities not even matched by twenty-first century technology. Surely, this is more reasonably evidence of superb design in the beginning by our Creator.Psalm 147:5"Great is our Lord, and of great power; His understanding is infinite."Prayer: Dear Father in heaven, You have filled Your creation with wonderful miracles of design so that men would seek You Who made them all. Preserve me from the pagan sin of putting the creation over You, the Creator. In Jesus' Name. Amen.Ref: Kay Cahill, “Animal Sensors,” OMNI. To support this ministry financially, visit: https://www.oneplace.com/donate/1232/29?v=20251111

In this episode, I discuss a part-time wrestler, full-time city constable Paul Kisselbach killed in the line of duty during September 1949. I also review the wrestling card from the Omni in Atlanta on April 22, 1984.

In this week's episode, we sit down with the with the founders of the OMNI International AI Film Festival, to discuss all the pressing issues central to current debates concerning AI filmmaking, from intellectual property protection to the democratisation of art-making.We also briefly discuss:A Face Only a Mother Could Love (2026) d. Robert GaudetteGuardians of the Burrow (2026) d. Jodie HeenanContact UsEmail: contact@jimmybernasconi.com

One of the greatest questions in biology asks how a single fertilized cell divides into many different cells - some become liver cells, skin cells, brain cells and bone cells. This is the ultimate way of asking the question, “Where do babies come from?”Human growth factor, a chemical produced by our body, is being studied to see if it is responsible for the development of all those different cells from a single cell. However, this is only one of thirty known chemicals that influence the development of new cells. Another powerful chemical, called GM-CSF for short, has now been synthesized as a drug and stimulates the development of white blood cells in the bone marrow. This drug has saved otherwise doomed patients who have lost virtually all of their bone marrow by exposure to radiation. Nerve growth factor is another being studied as a possible treatment for Alzheimer's disease and for use in re-attaching limbs.Do you notice where all of these incredible chemicals come from? They don't come from laboratories staffed with the best brains in science. They are produced by our bodies. Science should stand in awe at the evident wisdom behind those chemical structures and the way they work. And that's one of the best arguments that we were created by a wise and powerful Creator, not by mindless chemical accidents!Psalm 139:14"I will praise Thee, for I am fearfully and wonderfully made; marvelous are Thy works, and that my soul knoweth right well."Prayer: Dear Lord, I am truly fearfully and wonderfully made. I ask that you would guide medical researchers so that they may learn how to use the substances You have created for the earthly betterment of our condition and that You would be glorified through this. Amen.Ref: Carol Kahn, “Tapping The Healers Within,” OMNI. Image: 1TGK Human Transforming Growth Factor Beta3 Crystallized From, Nevit Dilmen, CC BY-SA 3.0, Wikimedia Commons. To support this ministry financially, visit: https://www.oneplace.com/donate/1232/29?v=20251111

In deze aflevering van De Schaal van Hebben testen Stijn Goossens en Nina van den Dungen de Ecovacs Winbot W3 Omni, een robot die zelfstandig je ramen wast. De Winbot W3 Omni is het topmodel in de brede lijn robotramenwassers van Ecovacs. Het apparaat zuigt zich met onderdruk vacuüm aan het glas vast en rijdt op rupsbandjes met ronde hoeken systematisch over het raam, terwijl het een reinigingsoplossing sproeit en met een afneembare dweilpad wist. De robot zit met een kabel vast aan een basisstation dat functioneert als een kleine wasmachine voor de pad: na drie tot vier ramen maakt het station de dweilpad in ongeveer een tot anderhalve minuut schoon en weer nat, met een aparte bak voor schoon en vuil water. Dankzij een ingebouwde accu en externe batterij werkt de robot mobiel, met een oprolbare kabel. Hij is bedoeld voor gladde oppervlakken zonder kieren: binnen- en buitenramen, glazen deuren en douchewanden. De robot reinigt tot ongeveer een millimeter van de rand en doet er zo'n drie minuten over per raam. De officiële prijs ligt rond de 650 euro, maar het apparaat is nu al zo'n honderd euro goedkoper te vinden. Ecovacs verkoopt ook compactere modellen voor ongeveer de helft van de prijs. In de test valt op dat de accu lang meegaat, ongeveer twee uur, en dat het een intuïtief, goed doordacht product is dat zich nooit losliet. Vooral voor lastig bereikbare buitenramen is de robot handig. Kritisch: het reinigingsmiddel is snel op, bij het loshalen blijft een natte afdruk achter waarvoor je een doek nodig hebt, en sneller dan met de hand is het niet per se. De bijgeleverde app werkt prettig voor handmatig sturen en pauzeren, maar is niet noodzakelijk.See omnystudio.com/listener for privacy information.

One of our Creation Moments listeners has written to ask how evolutionists explain the development of male and female. The problem is, if a mutation produced the first male, it isn't likely that another mutation would have produced the first female at the same time and in the same neighborhood. The writer further pointed out that studies now show that with the current genetic errors we all carry within us, one male and one female would not be enough to establish a new population of male and female creatures.The question is important. Evolutionists have admitted that their theory does not have a satisfactory explanation for how male and female could have developed. One evolutionist even noted that because evolutionists have no credible explanation, most textbooks simply ignore the question as obvious as it is.But evolutionists also point out that the problems in explaining the origins of male and female are even greater than those I've already mentioned. They now admit that their studies show that if creatures progress by evolution, creatures that reproduce sexually would be at a disadvantage. Even worse, sexual reproduction is, as one evolutionist put it, designed to weed out the very genetic variations that supposedly cause evolution!It is difficult to understand, then, how anyone could say that evolution offers a better explanation of life than the Bible's creation account.Genesis 6:19"And of every living thing of all flesh, two of every sort shalt thou bring into the ark to keep them alive with thee; they shall be male and female."Prayer: Dear Father, I thank You that in Your wisdom You have made us male and female, and that You have done so in a way which confounds man's rebellious wisdom. Help this fact be a witness to Your glory. In Jesus' Name. Amen.Ref: Kathleen McAuliffe, “Why we have sex,” OMNI. To support this ministry financially, visit: https://www.oneplace.com/donate/1232/29?v=20251111

The CPG Guys are joined in this episode by Christine Gambino, CEO of Omni, Omnicom's next-generation marketing and sales intelligence platform. It connects strategy, creativity, media, CRM, commerce, data, and AI to help brands grow with greater clarity, speed, and measurable business impact.Follow Christine on LinkedIn at: https://www.linkedin.com/in/christine-gambino-59683ab8Follow Omni online at: https://www.omc.com/omni/Christine answers these questions:Christine, you came up through Flywheel — one of the most commerce-native organizations in the industry — and now you're operating at the platform level as CEO of Omni. What did that journey from commerce practitioner to enterprise platform operator teach you about what CPG brands actually need from a marketing intelligence system?Your move from Flywheel to Omni was a deliberate cross-capability appointment. What was the mandate when you stepped into the CEO role, and what did you see as the biggest operational gaps you needed to close?At CES you demonstrated Omni live — walking through a campaign brief in real time, from media budget breakdowns to synthetic audiences to creator suggestions. For a CPG brand team watching that demo, what's the "aha moment" you're trying to create? In other words, What should click for them?You've been clear that Omni is additive, not a replacement — your words were: "It's meant to give real-time insights to teams and brands through our agentic environment." For CPG marketers who are worried about what AI means for their jobs, how do you make that case authentically, not just rhetorically?Omni now integrates Acxiom's 2.6 billion consumer profiles, Flywheel Commerce Cloud, and IPG's data and technology assets. That's a massive data convergence. How are you ensuring that consolidation creates clarity and speed for CPG clients rather than complexity?Omni is reported as having 41+ frontier LLM models and 24,000 monthly active users across 77 countries. What does it actually mean operationally to manage a platform at that scale — and how do you govern consistency of outputs when so many models and markets are in play?Flywheel's Commerce Cloud is described as the largest digital transaction data set in the world. Now fused into Omni, that commerce signal is live inside a broader marketing intelligence platform. For a CPG brand running retail media across Amazon, Walmart, and a dozen other RMNs, how does that change what's possible from a targeting and measurement standpoint?Omni includes what you're calling "Return on Consumer" — tracking consumer progression through Opportunity, Awareness, Interest, Purchase, and Loyalty — rather than just campaign metrics. That's a fundamental reframe. How ready are CPG brand teams to actually buy into that measurement model, and what's holding back adoption?There's a real tension in the industry right now between AI as efficiency play and AI as growth driver. Omnicom's own data points to 25-55% faster production times. But CPG CMOs are being asked to prove incrementality, not just efficiency. How does Omni help brands make that leap from "we're saving money" to "we're growing share"?GEO — Generative Engine Optimization — is becoming a major conversation as AI-driven search displaces traditional keyword rankings. Omni appears to have a point of view here through AI Optix on the PR side. How is the broader Omni platform thinking about brand visibility in an era where the algorithm deciding what consumers see isn't Google anymore?You are sitting at the intersection of the largest data asset in advertising, the largest commerce platform, and a next-gen agentic interface. If you had to name the one capability that CPG brands are most dramatically under-utilizing today — what would it be, and why?Christine, you came from Flywheel, which has deep roots in CPG. What's your message to the brand leaders, the commerce managers, and the retail media practitioners in our audience about what Omni means for them specifically — not for the holding company, but for them, in their day-to-day work?CPG Guys Website: http://CPGguys.comFMCG Guys Website: http://FMCGguys.comSheCOMMERCE Website: https://shecommercepodcast.com/Rhea Raj's Website: http://rhearaj.comLara Raj in Katseye: https://www.katseye.world/DISCLAIMER: The content in this podcast episode is provided for general informational purposes only. By listening to our episode, you understand that no information contained in this episode should be construed as advice from CPGGUYS, LLC or the individual author, hosts, or guests, nor is it intended to be a substitute for research on any subject matter. Reference to any specific product or entity does not constitute an endorsement or recommendation by CPGGUYS, LLC. The views expressed by guests are their own and their appearance on the program does not imply an endorsement of them or any entity they represent.CPGGUYS LLC expressly disclaims any and all liability or responsibility for any direct, indirect, incidental, special, consequential or other damages arising out of any individual's use of, reference to, or inability to use this podcast or the information we presented in this podcast.

Jim McDonald sits down with Dan Moore, Senior Director of CIAM Strategy and Identity Standards at FusionAuth, for an in-depth conversation on customer identity and access management. Dan explains how FusionAuth views authentication as the front door to any application and why control, deployment flexibility, and developer ownership are central to their approach. The discussion covers progressive registration, friction vs. usability, customization options, identity standards, the build vs. buy debate, risk-based MFA, and how AI agents will shape the future of customer identity. This episode and others is made possible with support from FusionAuth. Learn more at fusionauth.io/idac.Connect with Dan: https://www.linkedin.com/in/mooreds/Learn more about FusionAuth: https://fusionauth.io/idacBlog article mentioned: https://bobdahacker.com/blog/fifa-hackConnect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at http://idacpodcast.com00:00:00 Introduction00:01:18 What is FusionAuth?00:03:15 Dan's identity origin story00:04:19 Developer focus and ethos00:06:54 Authentication as the front door00:10:00 Balancing friction and usability00:15:24 Customization in CIAM00:18:10 What sets FusionAuth apart00:20:33 FusionAuth's customer sweet spot00:25:48 Deployment flexibility and the control spectrum00:30:19 Common challenges in CIAM00:33:06 Build vs. buy for authentication00:36:00 Omni-channel authentication00:40:27 Why identity standards matter00:42:07 Risk-based MFA and intelligent challenges00:45:00 AI agents and the future of CIAM00:49:23 Closing thoughts00:51:35 Vacation roundupKeywords: IDAC, Identity at the Center, Jeff Steadman, Jim McDonald, Dan Moore, FusionAuth, CIAM, customer identity, authentication, access management, IAM, identity standards, MFA, risk-based authentication, progressive registration, OAuth, OIDC, SAML, AI agents, deployment flexibility, build vs buy, Sponsor Spotlight

In the first hour of the Chase & Big Joe Show, the guys talked about the Question of the Day: "Would you time travel to the past or the future?" Later in the hour, Connor Nute shared his disappointment about the Microtransactions within video games. In the second hour of the Chase & Big Joe Show, the guys talked about Mike Clay's NFL roster rankings and what teams are sleepers when it comes to the Super Bowl. Later in the hour, Casey Lee Veser joined the show and talked about the music event that is happening tonight at Barlines at the Omni tonight at 5 pm. In the third hour of the Chase & Big Joe Show, Nick Frazier ranted about the USMNT's performance against Belgium and whether or not Christian Pulisic is "the guy" of the USMNT. Later in the hour, the guys talked about the sponsorship deal between the Big 12 Conference and Monster Energy drink. To finish the hour, the guys competed in Where Are They Now?

In the second hour of the Chase & Big Joe Show, the guys talked about Mike Clay's NFL roster rankings and what teams are sleepers when it comes to the Super Bowl. Later in the hour, Casey Lee Veser joined the show and talked about the music event that is happening tonight at Barlines at the Omni tonight at 5pm. Listen to hear more. 

Omni Loop (2024) on The Atomic Cinema Experiment. This is a sci fi movie podcast. Omni Loop is directed by Bernardo Britto and stars Mary-Louise Parker, Ayo Edebiri, Carlos Jacott patreon: https://www.patreon.com/mildfuzztv all links: https://linktr.ee/mildfuzz discord: https://discord.gg/8fbyCehMTy Email: mftvquestions@gmail.com Audio version: https://the-ace-atomic-cinema-experime.pinecast.com

In this episode of Clocking In: Voices of NC Manufacturing, host Phil Mintz talks with Luke Taylor, owner of Omni Mold & Die, a high-precision machine shop in Winston-Salem, North Carolina, specializing in the design, machining, repair, and maintenance of plastic injection molds, metal stamping dies, and custom tooling. Luke's path into manufacturing was anything but traditional. Armed with degrees from Princeton University and the University of Chicago Booth School of Business, Luke entered manufacturing through entrepreneurship, purchasing Omni Mold & Die in 2020 after searching for the right small business opportunity. What began as a business acquisition quickly turned into a passion for manufacturing and problem-solving. LINKS NCMEP | IES | Omni Mold & Die About the Guest Luke Taylor is the owner of Omni Mold & Die, a high-precision tooling and machine shop based in Winston-Salem, North Carolina, specializing in plastic injection molds, metal stamping dies, and custom tooling repair. Since acquiring the company in 2020, Luke has led Omni through growth and modernization while preserving its 40+ year reputation for quality craftsmanship and responsive customer service. A graduate of Princeton University with an MBA from University of Chicago Booth School of Business, Luke took an unconventional path into manufacturing through entrepreneurship and small business acquisition. He is passionate about operational excellence, workforce development, and helping manufacturers solve complex tooling challenges with speed and precision. About the Host Dr. Phil Mintz is the Director of NC State University Industry Extension Services (IES). Through his leadership, IES supports manufacturers across the state with resources in innovation, process improvement, workforce development, and business growth. About NC State University Industry Extension Services NC State University Industry Extension Services (IES), established in 1955 as the outreach team for the NC State University College of Engineering, provides resources, tools, and customized training programs to help businesses survive, thrive, and grow. IES delivers comprehensive training and development programs tailored for a diverse range of sectors, including manufacturing, educational and research institutions, healthcare, pharmaceutical and medical device companies, defense contractors, aerospace, automotive, energy, and government agencies.  Your IES dedicated Regional Manager is available to assist in identifying and implementing customized solutions, tools, and resources designed to optimize your organization's productivity, efficiency, quality, and profitability.  ncstateies.com/RM

Welcome back to another episode where we're diving deep into the next big shift in Amazon advertising: Sponsored Prompts. You've probably seen those little blue AI questions popping up right under your product images, and guess what? Amazon is officially letting us track them with the new Prompt Report inside Campaign Manager. Today, I'm hanging out with Tyler from Pilot House to break down exactly what this means for your brand. We talk about how Amazon's AI shopping assistant is taking over customer sessions and how your ad budget might be bleeding into these prompts without you even knowing it. Tyler drops some amazing strategies on how to find the new report, pause the prompts that aren't working, and a killer hack using ChatGPT or Gemini to see exactly how AI reads your product images. You definitely want to get ahead of this curve before these new placements start eating up your budget. We'll see you in The PPC Den!

Carreras just posted an 80% profit surge and a $30 share price, but is a buyout next? Dr. Matthew Preston and Dr. Thaon Simms break down what's really driving the numbers, plus seven more JSE stocks worth watching this quarter. From Fesco's record fuel profits to Tropical Battery's failed capital raise, Omni's hurricane fueled growth, and why Elite Diagnostic just hit a fresh 52 week low. If you're holding JSE stocks, this recap tells you what actually matters.Chapters:00:00 Introduction and Market Roundup Kickoff01:51 CARRERAS: The 80% Profit Surge and $7 Billion Revenue Story19:23 Illicit Cigarettes, Vapes, and Could CARRERAS Go Private?33:27 LAB JAMAICA's Streaming Deals With Amazon, Apple TV and Tubi1:14:36 FESCO's Record Year as Middle East Tensions Spike Fuel Prices1:26:50 RPL's 75% Profit Jump and the Yadman LPG Acquisition1:32:34 OMNI INDUSTRIES Profits Double on Hurricane Melissa Rebuild1:46:00 ELITE DIAGNOSTIC Hits a 52 Week Low: What Went Wrong2:00:16 WOODCATS Down Over 20% Since Its IPO2:08:46 TROPICAL BATTERY's Failed $1.79 Billion Capital Raise

 This interview is disseminated on behalf of Virtuix.The landscape of possibilities starts with the Omni, Virtuix's premier brand of omni-directional treadmills that lets users move inside virtual and AI-generated environments.Virtuix (NASDAQ: VTIX) CEO Jan Goetgeluk discusses the company's recent entry into Meta Platforms, Inc.'s Made for Meta program, along with milestones it has achieved as a developer of full-body virtual reality systems, paving the way for what lies ahead. He also highlights how Virtuix plans to expand its reach beyond gaming into defense and medical applications, pushing the boundaries of immersive technology across multiple industries.Learn more: http://virtuix.com  Watch the full YouTube interview here: https://youtu.be/juFew4yGHbMAnd follow us to stay updated: https://www.youtube.com/GlobalOneMedia 

"Make sure customer touchpoints are cohesive and the data is clean. The average brand has 20+ apps and there's a lot of noise in that data. Removing that noise avoids wasted ad spend and drives more revenue."Episode summary:The major networks including Google & Meta are investing heavily in AI to automate campaign creative and targeting. As the capabilities accelerate, brands need to understand the role AI plays in marketing strategy.In this panel discussion, the CEO of Shopify datalayer specialist Littledata and marketing leaders from the brands using it, discuss the pre-requisites for using AI in marketing.The conversation covers:How to improve the quality of signal you're feeding the algorithms.What data matters most when enabling AI in marketing.Focusing on keeping data clean, reducing the noise from the apps and 3rd parties generating data points.Sensible adoption of AI in marketing, retaining a careful control on quality of execution.The panelists:Edward Upton, CEO at Littledata.Teddy Robinson, Chief of Staff at Grind Coffee.Dan Eales, Head of Growth at Omni.

Darrach Ó Duibh has such an interesting story, and I wanted to speak with him to discuss his incredible life journey and the profound, unexpected insights he gained while translating the Book of Mormon into the Irish language longhand. He's an ancient language researcher, polyglot, and textual critic based in Ireland. Having previously worked as a bibliographic researcher for FARMS (now the Maxwell Institute) under the legendary LDS scholar Hugh Nibley, Darrach started this translation project 20 years ago while bedridden after a honeymoon car crash. Adding another dimension to his life, Darrach is also a well-respected professional mixed martial arts (MMA) referee.We explore his unique personal interactions with Hugh Nibley, his background growing up with a father in military intelligence, and the powerful textual evidence for multiple, distinct prophetic voices that only becomes visible when translating the Book of Mormon text word by word.Some highlights from this episode include:Working with Hugh Nibley: Darrach shares first hand stories from his time sitting across the desk from Hugh Nibley at BYU, including Nibley's shocking command of ancient languages, his quiet patience with students, and a legendary family story involving a moving train in Utah.Growing Up in Military Intelligence: From learning languages at six years old to witnessing his father barter with guards in Russian at Checkpoint Charlie, Darrach explains how his childhood in Germany unlocked his lifelong love for language.The Ancient Egyptian Pattern in the Text: A fascinating deep dive into the very first verses of First Nephi, Enos, Omni, Mormon, and Moroni, revealing a complex, authentic ancient narrative pattern known as the narrative infinitive or "stacking" that Joseph Smith could not have easily fabricated.Hearing Distinct Voices: Darrach details how his translation work forced him to recognize completely different linguistic registers, vocabulary, and tones between Nephi, Jacob, and Mormon, providing stunning textual evidence for the book's authenticity.Faith, Revelation, and Suspension: A discussion on why the Lord purposely keeps archaeological and metaphysical proof in a state of suspension, allowing spiritual knowledge and true faith to flourish rather than letting it become dormant.You can find Darrach's Irish translation of the Book of Mormon at the following link:Amazon: An Leabhar Mhórmoin: Fianaise eile ar Íosa Críost (Irish Edition) https://www.amazon.com/Leabhar-Mh%C3%B3rmoin-Fianaise-Cr%C3%ADost-Irish/dp/B0H2W35RW4/Follow For All The Saints on social media for updates and inspiring content:www.instagram.com/forallthesaintspodhttps://www.facebook.com/forallthesaintspod/For All The Saints episodes are released every Monday on YouTube, Spotify, Apple Podcasts and more:https://www.youtube.com/watch?v=TVDUQg_qZIU&list=UULFFf7vzrJ2LNWmp1Kl-c6K9Qhttps://open.spotify.com/show/3j64txm9qbGVVZOM48P4HS?si=bb31d048e05141f2https://podcasts.apple.com/gb/podcast/for-all-the-saints/id1703815271If you have feedback or any suggestions for topics or guests, connect with Ben & Sean via hello@forallthesaints.org or DM on InstagramConversations to Refresh Your Faith.For All The Saints podcast was established in 2023 by Ben Hancock to express his passion and desire for more dialogue around faith, religious belief, and believers' perspectives on the topics of our day. Tune into For All The Saints every Monday on YouTube, Spotify, Apple Podcasts, and more.Follow For All The Saints on social media for daily inspiration.

This week on the Drive Thru, Jim looks at the top wrestlers in their 40s in 1984! Plus Jim answers YOUR questions about Sol Ruca, Ethan Page, Paul E. Dangerously's phone, The Omni, Triple H as a booker, Sting, Andre The Giant tagging with Bobby Eaton, the announcement of Kurt Angle's TNA signing, and much more! Also, Jim looks at an issue of Matwatch from March 1990! Thanks to our episode sponsors: RAYCON: Upgrade your dad’s everyday routine. Go to buyraycon.com/jce to get 15% off. Thanks Raycon for sponsoring! HEXCLAD: Find your forever cookware @hexclad and get 10% off at hexclad.com/JCE! #hexcladpartner Send in your question for the Drive-Thru to: CornyDriveThru@gmail.com Follow Jim and Brian on Twitter: @TheJimCornette @GreatBrianLast Merch! https://arcadianvanguard.com/ Join Jim Cornette's College Of Wrestling Knowledge on Patreon to access the archives & more! https://www.patreon.com/Cornette Subscribe to the Official Jim Cornette channel on YouTube! http://www.youtube.com/c/OfficialJimCornette Visit Jim's official site at www.JimCornette.com for merch, live dates, commentaries and more! You can listen to Brian on the 6:05 Superpodcast at 605pod.com or wherever you find your favorite podcasts!See omnystudio.com/listener for privacy information.

In Part 2 of this two-part podcast series, Rebecca and Vickie continue discussing their Florida educational tour on behalf of their travel agency. Building on the experiences shared in Part 1, they dive deeper into the highlights of the Disney Cruise ship and the Omni Convention Center, offering additional insights into guest experiences, event planning opportunities, and travel recommendations. They reflect on key takeaways from the tour, discuss how these experiences will benefit their clients, and share valuable tips for travelers considering a Disney cruise, group event, or Florida getaway. Follow us on all our social media accounts on Facebook and on Twitter at @Mousecapadespod. Thinking about being a guest on our show, or have a question or comment? Contact us anytime via text or phone at 636-373-4497. Have a magical day my friends!

The entire startup ecosystem is racing to build agent harnesses. Logan Kilpatrick, who leads Google AI Studio and the Gemini API, argues that scramble has a roughly 12-month shelf life. Models will absorb the scaffolding and run it natively, so the edge moves elsewhere. Google's own bet runs in parallel: a single agent harness, born from the Windsurf team and now called Antigravity, has become the connective tissue across search, the Gemini app, Cloud, and AI Studio — the role Gemini-the-model used to play. Logan makes the case that coding already feels like narrow superintelligence, and that "jagged" vertical superintelligence (in math, finance, and science) will arrive well before AGI. He argues Google's real goal is maximizing outcomes for users, not eyeball time. He unpacks Omni, the single model built to replace multiple separate systems Google once trained for text, audio, music, image, and video. His throughline: AI is an accelerant for human ambition, not a substitute for it. Hosted by Sonya Huang, Sequoia Capital

The Power of Functional Medicine: Finding the Root Cause of Chronic Health Problems In this episode of Stay Healthy Knoxville, Dr. John-Mark Chesney sits down with Emily Turner, PA-C, and Randy Martin, PharmD, of Omni Functional Medicine to discuss a different approach to healthcare—one focused on identifying and addressing the root causes of chronic symptoms rather than simply managing them. Together, they explore what functional medicine is, why so many people continue to struggle despite being told their labs are normal, and how factors such as hormones, gut health, stress, metabolism, and inflammation can impact overall health and well-being. Whether you're dealing with fatigue, weight gain, digestive issues, autoimmune disease, chronic pain, or simply aren't feeling your best, this conversation offers valuable insight into how a more personalized approach to healthcare may help uncover missing pieces of the puzzle. In This Episode, You'll Learn: ✅ What functional medicine is and how it differs from traditional healthcare ✅ Why patients can still feel unwell despite "normal" lab results ✅ The importance of looking for root causes instead of just treating symptoms ✅ How hormones, gut health, stress, and metabolism influence overall health ✅ Practical steps you can take to improve your health and energy About Our Guests Emily Turner, PA-C Bachelor's Degree in Dietetics – University of Kentucky Master's in Physician Assistant Studies – Sullivan University Advanced training through The Institute of Functional Medicine Advanced training through The American Academy of Anti-Aging Medicine Randy Martin, PharmD Doctor of Pharmacy – University of Tennessee Co-founder of Omni Functional Medicine Connect with Omni Functional Medicine Instagram: @omnifunctionalmedicine Website: omnifunctionalmedicine.com Enjoying the Podcast? Be sure to subscribe, leave a review, and share this episode with someone who may be searching for answers to ongoing health concerns. Stay healthy, Knoxville!

Kris Zellner is joined by Rob Naylor and Our Good Buddy Charles as we discuss the month of May 1991 in the world of World Championship Wrestling and pop culture at large. Topics of discussion include:The WWF trying to get dates at The Omni at a time when WCW was having some major issues drawing crowds at house shows.The wrestlers having a meeting about being overworked, hoping that their schedule will ease up soon.Rickey Henderson breaking the MLB stolen base record while Nolan Ryan throws his 7th no-hitter on the same day.TV season finales featuring “Night Court,” “A Different World,” “Beverly Hills 90210,” “Full House,” “In Living Color,” and the series finale of “Dallas.”The complete greatness of the video hyping up the Steiners vs. Lex Luger & Sting at SuperBrawl.Terry Funk appears on a wrestling themed episode of “Quantum Leap.”Reports of Hiroshi Hase & Kensuke Sasaki coming in…with manager Big Daddy Dink?!?!?Madonna's "Truth or Dare" and Bris Bosworth's “Stone Cold” hit the big screen.The TV ratings for WCW become dire, but they aren't alone in that.EMF, Seal, Smashing Pumpkins, and Jodeci all release their debut albums in the United States.President George Bush takes Queen Elizabeth to a baseball game.A full rundown of SuperBrawl, featuring the debuts of Johnny B. Badd, OZ, The Diamond Studd, and much more on a really fun PPV.This is just the tip of the iceberg, as we have so much going on during the month of May. I thought this was a tremendous show and I hope you agree!!!---To support the show and get access to exclusive rewards like special members-only monthly themed shows, go to our Patreon page at Patreon.com/BetweenTheSheets and become an ongoing Patron. Becoming a Between the Sheets Patron will also get you exclusive access to not only the monthly themed episode of Between the Sheets, but also access to our new mailbag segment, a Patron-only chat room on Slack, and anything else we do outside of the main shows!If you're looking for the best deal on a VPN service—short for Virtual Private Network, it helps you get around regional restrictions as well as browse the internet more securely—then Private Internet Access is what you've been looking for. Not only will using our link help support Between The Sheets, but you'll get a special discount, with prices as low as $1.98/month if you go with a 40 month subscription. With numerous great features and even a TV-specific Android app to make streaming easier, there is no better choice if you're looking to subscribe to WWE Network, AEW Plus, and other region-locked services.For the best in both current and classic indie wrestling streaming, make sure to check out IndependentWrestling.tv and use coupon code BTSPOD for a free 5 day trial! (You can also go directly to TinyURL.com/IWTVsheets to sign up that way.) If you convert to a paid subscriber, we get a kickback for referring you, allowing you to support both the show and the indie scene.To subscribe, you can find us on iTunes, Google Play, and just about every other podcast app's directory, or you can also paste Feeds.FeedBurner.com/BTSheets into your favorite podcast app using whatever “add feed manually” option it has.Advertising Inquiries: https://redcircle.com/brands

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

As the NCAA Division 1 men's golf participants descend on Omni La Costa in Carlsbad, CA, they can expect harder conditions with firmer and faster greens and grown up rough and fescue than previous years. Head Golf Professional of Resort and Tournament Golf, Robert Gogulich joins the Break80 Podcast to talk tournament setup and resort golf. He will discuss what he saw from the women's championship last week and what to expect when the men tee it up this week. Subscribe to the Break80 Podcast on Apple, Spotify and YouTube for weekly golf content. Learn more about your ad choices. Visit megaphone.fm/adchoices

You'll need a map, compass and legend to understand all the new AI Google announced at its I/O conference last week. (They literally wrote a blog post called, "100 things we announced at I/O 2026” and most of them were AI based.) Luckily for you, we spend hours each day going through the latest in AI to cut the fluff from the real. So on today's ‘AI Working Wednesdays' series, we break down 3 of Google's biggest AI updates you can use today: Google Omni, Gemini 3.5 Flash and Antigravity 2.0. What's new and how do they work? We'll show you the ins and outs live. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Gemini 3.5 Flash Model Hands-On DemoGemini 3.5 Flash Pricing and Token UsageBenchmarks: Gemini 3.5 Flash vs. 3.1 ProIntelligence vs. Cost in Gemini 3.5 FlashGemini 3.5 Flash for API and DevelopersGoogle Gemini Omni Flash Video Model ReviewOmni Anything-to-Anything Multimodal FeaturesGoogle Omni vs. Video Model CompetitorsAnti Gravity 2.0 Agent Desktop App OverviewAnti Gravity 2.0 Pros, Cons, and Use CasesUsage Limits in Google Gemini and Anti GravityChain of Thought Transparency in Gemini ModelsCanvas Mode Interactive Web App DemonstrationsTimestamps:00:00 Key AI updates from Google IO04:58 New Google AI updates discussed08:57 Google's anti gravity desktop use10:01 Touring Google's Anti Gravity App14:40 Testing a new AI prompt18:06 Critiquing vibe coding aesthetics21:28 Discussing Google's Gemini 3.1 Pro Model24:40 Comparing AI model performances and costs29:13 Google's advancements in video AI30:13 Future of Google's AI Technology33:58 Exploring Google Gemini features36:51 Google Gemini chain of thought feature42:02 Google Gemini's new model features44:23 River crossing puzzle gameplay48:25 Discussing Google Gemini 3.5 flash drawbacks51:10 Feedback on an AI releaseKeywords: Gemini 3.5 Flash, Google Gemini, AI updates, Google I/O 2026, Gemini Omni, Gemini Omni Flash, anti gravity 2.0, AI video model, hands-on AI demo, agentic coding, desktop AI app, benchmarking, AI model comparison, Gemini Spark, Gemini Pro 3.5, Gemini 3.1 Pro, token usage, API users, Google Workspace, always-on agent, AI cost efficiency, intelligent agents, world model, multimodal AI, generative video creation, video editing, scheduled tasks, Google Daily Brief, model usage limits, thinking steps, chain of thought, artificial analysis intelligence index, token inefficiency, cost to run AI, OpenAI GPT-5.5, Claude Sonnet, Claude Opus, open source AI models, AI-powered creativity, robotics, embodied AI, front-end AI tools, Canvas mode, conversational editing, interactive website builder, AI-powered app creation.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

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Google I/O 2026 just dropped Gemini Omni, a world-model AI that simulates physics, edits video, and might be the biggest leap since Seedance 2. But it's not perfect. Gavin and Kevin break down everything from Google I/O 2026, including the launch of Gemini Omni (Google's new world model), Gemini 3.5 Flash benchmarks against GPT-5.5 and Opus 4.7, the Gemini Spark personal agent, AskYouTube, Docs Live, new AI glasses, the first search box redesign in 25 years, and the shocking news that Andrej Karpathy is joining Anthropic. SHOW LINKS: Google I/O 2026 Full Keynote: https://www.youtube.com/live/wYSncx9zLIU?si=Nb881MfGTlf1Q0II Gemini Omni physics demos from Google DeepMind: https://x.com/GoogleDeepMind/status/2056786449312493669?s=20 Gemini Omni's incredible London knowledge (via fofrAI): https://x.com/fofrAI/status/2056789242274259242?s=20 Sundar Pichai and Demis Hassabis on Omni video editing: https://x.com/sundarpichai/status/2056524502746747048?s=20 Gavin's hands-on Gemini Omni experiments: https://x.com/gavinpurcell/status/2056762427879182692?s=20 Gemini Omni's character cameo feature (less impressive): https://x.com/gavinpurcell/status/2056772793539481830?s=20 Gemini Omni volleyball fail: https://x.com/flavioAd/status/2056771223359549645?s=20 Google's new Content Credentials Verification: https://x.com/Google/status/2056787498676658576?s=20 Genie 3 IRL — Google's world model now simulates real streets with Street View: https://techcrunch.com/2026/05/19/googles-genie-world-model-can-now-simulate-real-streets-with-street-view/ Bilawal Sidhu on Genie 3 IRL: https://x.com/bilawalsidhu/status/2056804315721843024?s=20 Gemini 3.5 Flash launches — official announcement: https://x.com/GeminiApp/status/2056788115893993701?s=20 Gemini Spark — Google's new personal coding agent: https://x.com/Google/status/2056791134295273554?s=20 Google's new AI glasses  https://x.com/backlon/status/2056807059707036050?s=20 Andrej Karpathy joins Anthropic to focus on recursive self-learning: https://www.axios.com/2026/05/19/anthropic-openai-karpathy-andrej-claude