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We start with the disturbing NYC subway stabbing case after the victim dies—and the reaction to the accused attacker sparks even more outrage. Then we head overseas as the U.S. strikes Iranian IRGC targets, Iran threatens retaliation, President Trump calls on the Iranian people to rise up, and Scott Bessent unloads on the IRGC, sanctions and the American media's coverage of Iran.Plus, Hamtramck's LGBTQ pride flag controversy erupts again, a congressional hearing on birth tourism gets heated, and a father delivers emotional testimony about losing his daughter in a crash involving an illegal immigrant.Milo Yiannopoulos complains about his treatment by ICE before getting grilled over his marriage and finances, Mark Carney asks the internet to stop making memes—which goes exactly as expected—and the House votes to denounce socialism.Then things get even stranger: Abdul El-Sayed drops an incredibly awkward campaign dating video, AOC reveals she wouldn't date a conservative, and leaked political recordings create headaches for candidates.We also break down the latest Lindsay Clancy trial developments after the jury becomes deadlocked. What happens after a hung jury, could there be a retrial, and could this case establish a troubling precedent surrounding accountability?SUPPORT OUR SPONSORS TO SUPPORT OUR SHOW!Give $26 Monthly to the Human Coalition. Be her lifeline. Create a life saving moment. Give today at https://HumanCoalition.org/ChicksSave an additional 10% off practical food for your pantry with the ReadyWise 4-Can Protein Bundle at https://ReadyWise.com with code CHICKS10.Don't change your dog's food—just add Ruff Greens. Get your FREE jumpstart trial bag (just cover shipping) with code CHICKS at https://RuffChicks.comSubscribe and stay tuned for new episodes every weekday!Follow us here for more daily clips, updates, and commentary:YoutubeFacebookInstagramTikTokXLocalsMore InfoWebsite
The American Radicals Podcast covers COVID malfeasance, election security efforts, college tuition, and Karen's reemergence. Check us out on Spotify! https://open.spotify.com/show/09AZ2WuYnWbZ2941wsb6jW?si=76c005605dc64dc1 https://signalscv.com/2026/08/fauci-told-aide-to-delete-email-about-risky-research/ https://www.news24.com.au/world/united-states/trumps-dhs-set-to-launch-voterfraud-crackdown-across-nine-states-ahead-of-midterms/video/1ff9bc2c5cf7453013767834d6cd6a9a https://lite.cnn.com/2026/08/31/politics/homeland-security-voter-fraud-investigations https://www.foxnews.com/politics/new-federal-grand-jury-subpoena-issued-former-james-comey-advisor-leak-investigation-sources https://www.newsweek.com/map-shows-rapid-covid-spread-schools-classes-canceled-12374696 https://zeale.co/news/articles/education-department-moves-to-strip-american-bar-association-of-law-school-accreditation?utm_campaign=129541854-The%20LOOP&utm_medium=email&_hsenc=p2ANqtz-_Wh2-QAHpjo58_AdQiVksM93umgbuT7hweb7EoJdE6GTOkW34qWt-6ADzYBcAoy0M212RE2h64NeUBZuZ2bI65I2gpsw&_hsmi=36061637&utm_content=36061637&utm_source=hs_email https://www.washingtonpost.com/education/2026/09/01/more-colleges-surpass-100k-price-tag-heres-how-you-can-afford-it/ https://www.npr.org/2026/09/01/nx-s1-5935431/college-reinvented-vermont-work-experience https://www.nytimes.com/2026/08/30/opinion/women-men-gender-quit.html
Today we are talking about something that I think we all need to know about… How to reset your dopamine. And before you think this is going to be one of those extreme episodes where I tell you to throw your phone into the ocean, stop drinking coffee, sit in a dark room for three days and eliminate everything fun from your life… That is absolutely not what we are doing. Because dopamine isn't bad. We need dopamine. Dopamine is part of what makes us want things. Pursue things. Work toward goals. Get up and go. The problem is that we are living in an environment where we can get enormous amounts of stimulation with almost zero effort. Open Instagram. Refresh your email. Check likes. Open TikTok. Eat something hyper-palatable. Buy something. Get a notification. Watch another episode. Click another video. Check your texts. Pick up your phone because you have 17 seconds with nothing to do. We have essentially eliminated boredom. And I think we need to talk about what that is doing to us. Because I don't think most people have a motivation problem. I think some people have made normal life too boring compared to the stimulation they consume all day long. Think about this. Working toward a goal is slow. Building a business is slow. Getting stronger is slow. Improving a relationship is slow. Reading a book is slow. Learning something difficult is slow. But your phone can give you novelty every half second. So what happens when your brain gets accustomed to incredibly stimulating rewards that require almost no effort? The things that actually build an awesome life can start feeling painfully boring. And THAT is what I want to change today. I've been going deep into what Andrew Huberman, Chris Williamson, Stanford addiction psychiatrist Anna Lembke, Peter Attia and Gary Brecka have discussed around dopamine, reward, pleasure, discomfort and motivation. And there is one massive theme that keeps coming up: We need to stop getting so many rewards without earning them. We need less cheap dopamine… And more earned dopamine. More accomplishment dopamine. More anticipation. More effort. More challenge. More doing hard things and feeling awesome afterward. So today we're going to talk about what dopamine actually does, why our modern environment can completely screw with our motivation, what people mean by a dopamine reset, and most importantly… How we actually do it. Not forever. Not perfectly. But intentionally enough that normal life starts feeling interesting again. Because imagine waking up and actually WANTING to work on your goals. Imagine sitting in your car without immediately opening your phone. Imagine finishing a workout and letting that accomplishment be the reward instead of instantly needing another stimulus. Imagine being fully engaged in dinner. Or a conversation. Or a walk. Or your kids. Or your work. That's what we're after. Not less enjoyment. More enjoyment from the things that actually matter. FIRST… WHAT IS DOPAMINE? Dopamine isn't simply the "pleasure chemical" This is probably the biggest misunderstanding. Dopamine plays a huge role in motivation, pursuit, learning, reinforcement and determining what your brain decides is worth pursuing again. Think less "this makes me happy" and more: "Go get that again." It's a molecule heavily involved in wanting and pursuing. Huberman explains dopamine in terms of baseline and peaks You have a baseline level of dopamine. Then certain experiences generate temporary increases above that baseline. The important part is what happens afterward. A big dopamine peak can be followed by a period where dopamine activity falls below where it was previously. This is part of why something incredible can sometimes leave you feeling strangely flat afterward. The higher and more frequently we chase enormous peaks, the more ordinary activities can start losing their appeal. Think about it like contrast If you eat incredibly sweet processed foods all day… A blueberry doesn't taste very sweet. There isn't anything wrong with the blueberry. Your reference point changed. The same general concept applies here. When your environment becomes filled with intense stimulation… Quiet feels boring. Reading feels boring. Work feels boring. Walking feels boring. A normal conversation feels boring. Your life didn't necessarily become boring. Your stimulation threshold changed. WHAT I MEAN BY "CHEAP DOPAMINE" I'm going to use the phrase cheap dopamine throughout this episode even though scientifically dopamine itself isn't cheap or expensive. What I mean is: A high-reward experience requiring very little effort. Things like: Endless short-form videos Constant social media refreshing Notifications Online shopping Gambling Highly processed foods Video games for hours and hours Constant snacking Drinking Continually checking texts Refreshing email Internet rabbit holes Watching episode after episode after episode Constant novelty And here's the key… None of those things has to be inherently evil. It's the dose. The frequency. And whether you've lost control over your consumption. There's a massive difference between choosing to watch a movie on Saturday night… And being literally unable to stand in an elevator for 25 seconds without checking your phone. That's the part I want us to notice. THE WORLD HAS REMOVED THE WAITING This might be one of the biggest problems. Almost everything used to have some friction. You wanted food? You cooked it. You wanted entertainment? You waited until your show came on. You wanted to talk to your friend? You called them. Maybe they weren't home. You wanted to buy something? You drove somewhere. You wanted information? You found a book. Now? Everything is immediate. Food. Entertainment. Shopping. Attention. Validation. Communication. Information. Novelty. Almost every human desire has an app attached to it. And our brains evolved in an environment where rewards generally required work. Now we can receive reward after reward while lying horizontally on the couch. That is a massive mismatch. THE PLEASURE-PAIN SEESAW One of my favorite explanations comes from Dr. Anna Lembke. She describes pleasure and pain almost like opposite sides of a balance. When we repeatedly push hard toward pleasure… Our brain tries to restore balance. And over time, you may need more of the stimulus just to experience the same effect. This is incredibly obvious with severe addiction. But I think it's useful to recognize much milder versions in ourselves. Have you ever: Opened Instagram for five minutes and 45 minutes disappeared? Finished a show and immediately needed another episode? Finished dessert and started looking for something else sweet? Checked one notification and then somehow checked four different apps? Picked up your phone without consciously deciding to? That automatic reaching is important. You aren't necessarily choosing anymore. You're responding. And I want us to get back into choosing. WHY NOTHING FEELS EXCITING ANYMORE Sometimes people say: "I'm unmotivated." "I don't feel like working." "I can't concentrate." "I'm bored." "I don't really feel excited about anything." Obviously there can be many medical, psychological and situational reasons for those feelings. But there is another question worth asking: How much stimulation are you consuming? Because if you're exposing yourself to novelty constantly… Your brain never gets a chance to be bored enough to create. Never bored enough to think. Never bored enough to solve a problem. Never bored enough to decide what you actually want. We're treating boredom like an emergency. I actually think boredom is incredibly productive. Some of my best ideas happen when there's nothing else coming into my brain. And we are filling every single empty pocket. DOPAMINE STACKING Huberman talks about something that I think is fascinating: stacking multiple dopamine-producing experiences together. Think about a workout. Exercise itself can be rewarding. But now we add: Pre-workout. Favorite playlist. Phone. Texts. Social media. Energy drink. Maybe stimulants. Maybe a reward meal afterward. Maybe we post the workout and check responses. We've taken one intrinsically rewarding activity and stacked reward upon reward upon reward. And his point isn't that you can never do those things. It's that you don't necessarily want every good experience in life turned up to 100 every single time. Sometimes train without the perfect playlist. Sometimes walk without the podcast. Sometimes drink the coffee without simultaneously scrolling Instagram. Sometimes eat without watching television. Separate the rewards. You don't have to eliminate pleasure. Stop supercharging every experience. LEARN TO LOVE EFFORT This might be my favorite part of the entire episode. We need to stop making the reward exclusively something that comes AFTER the work. "I'll work out and then I can have this." "I'll finish this project and then I'll reward myself." "I'll complete my work and then I'll scroll." There's nothing wrong with celebrating accomplishments. But we also want to train ourselves to associate the effort itself with something positive. Because if the entire purpose of doing something difficult is the reward waiting at the end… The work becomes something you're trying to escape. Instead: "This discomfort means I'm getting stronger." "This is hard because I'm growing." "I'm proud of myself for doing this when I don't feel like doing it." "This is what keeping promises to myself feels like." "This is who I am." That shift is huge. The workout becomes the win. Doing the work becomes the win. Keeping the promise becomes the win. USE DISCOMFORT ON PURPOSE This theme comes up across a lot of these conversations. We're living in an unbelievably comfortable world. Temperature-controlled houses. Food everywhere. Cars. Couches. Entertainment. Delivery. Escalators. Amazon. Everything is optimized to eliminate friction. But voluntary discomfort can be incredibly valuable. Exercise. Walking. Strength training. Running. Sauna. Cold exposure when appropriate and done safely. Doing something physically difficult. Getting outside when conditions aren't perfect. Taking the stairs. Having the difficult conversation. Doing the work you're avoiding. The idea isn't: "Suffering is good." The idea is: Stop organizing your entire life around avoiding discomfort. Because challenge followed by satisfaction is very different from pleasure followed by emptiness. One you earned. One you consumed. We need both fun and hard things. But modern life has heavily tilted us toward consumption. DON'T START YOUR DAY WITH OTHER PEOPLE'S DOPAMINE Here's something I think would change a lot of people's lives. Do not roll over and immediately hand your brain to your phone. Because instantly you've got: Texts. News. Instagram. Email. TikTok. Other people's emergencies. Other people's opinions. Other people's lives. Other people's accomplishments. Other people's bodies. Other people's vacations. Other people's businesses. Other people's problems. Before you've even decided what YOU want your day to look like. I would rather protect that first part of the morning. Wake up. Water. Sunlight. Walk. Workout. Journal. Think. Plan. Work. Whatever your routine is. Just don't immediately flood your brain with novelty. You should decide the direction of your day before an algorithm decides it for you. THE 2.0 DOPAMINE RESET Okay. Here is what I would actually do. Not some insane internet detox. A realistic reset. RULE #1: Identify YOUR cheap dopamine Ask yourself: What do I compulsively reach for? Not what does the internet tell me is bad. What has control over ME? Maybe yours is: Instagram. TikTok. Shopping. Sugar. Video games. News. YouTube. Email. Texting. Something else. Identify the top one or two. Don't try to become a monk overnight. RULE #2: Take a meaningful break from the biggest offender If something truly feels compulsive, consider taking a real period away from it rather than constantly trying to moderate it. Try deleting it for a week. Maybe two. Maybe 30 days. See what happens. You might be shocked by how often your hand reaches for an app that isn't there. That's valuable information. RULE #3: Stop filling every gap This is a HUGE one. We're bringing back empty space. Stand in line. Just stand there. Drive five minutes. No podcast. Walk around the block. No headphones. Sit outside. Do nothing. Take a shower without content playing. Cook dinner without a video. You are retraining yourself to tolerate a lower level of stimulation. And here's the funny thing… After a while it doesn't feel low stimulation. It feels peaceful. RULE #4: Create phone-free zones Pick places where your phone does not belong. Examples: Bedroom Dinner table Gym Family time Bathroom First hour of the morning Last hour before bed Don't rely purely on willpower. Change your environment. Leave the phone somewhere else. Make the bad habit inconvenient. RULE #5: Turn off almost every notification Notifications are someone else deciding when your attention moves. Think about that. Unless something legitimately requires immediate attention… Turn it off. RULE #6: Stop stacking everything Coffee + Instagram + television + food + texting? No. Pick one. Workout + podcast + scrolling between sets + energy drink + Instagram? Simplify it. Let an experience stand alone. Food can just be food. Coffee can just be coffee. A walk can just be a walk. A conversation can just be a conversation. RULE #7: Get your dopamine through pursuit This is the big replacement strategy. You can't just remove things. You need better things to move toward. Exercise. Building something. Learning. Working. Creating. Sports. Relationships. Adventure. Goals. Competition. Skill. Experiences. Meaningful challenges. Get obsessed with something difficult. That is completely different than being addicted to consumption. RULE #8: Do one hard thing every day I love this rule. Every day, intentionally do something that you'd rather avoid. Workout hard. Make the phone call. Send the proposal. Clean the closet. Have the conversation. Finish the project. Run the hill. Get into the cold water if that's safe for you. Walk when you'd rather sit. Do the thing you've been procrastinating. You start creating evidence: "I don't have to feel like doing something in order to do it." That is power. RULE #9: Prioritize sleep Your brain isn't operating independently of your body. If you're chronically sleep-deprived, everything becomes harder. Motivation. Impulse control. Training. Food choices. Emotional regulation. Focus. Peter Attia and Huberman both hammer sleep for a reason. If we're trying to optimize our brain while sleeping five hours… We're focusing on the wrong end of the problem. RULE #10: Morning sunlight and movement Huberman talks frequently about getting outdoor light early in the day to anchor your circadian system. I love pairing that with movement. Outside. Walk. Light. No Instagram. Let your nervous system wake up naturally. It's incredibly simple. And simple things done repeatedly tend to beat complicated hacks. THE 24-HOUR CHALLENGE Here is your challenge. For the next 24 hours: No phone for the first 30 minutes after waking. No social media until you've completed something productive. Turn off unnecessary notifications. No phone while eating. Take one walk without headphones. Do one difficult thing you've been avoiding. No double-screening. If you're watching TV, you're not also scrolling. Put your phone outside your bedroom tonight. Let yourself experience boredom. Notice every time you instinctively reach for stimulation. Don't judge yourself. Observe yourself. Because awareness is the beginning of changing anything. THEN TRY SEVEN DAYS Now let's level it up. For seven days: Reduce your biggest cheap-dopamine behavior dramatically. You don't need to eliminate all pleasure. In fact, please don't. Go to dinner. Laugh. Drink your coffee. Listen to music. Hang with friends. Enjoy your life. This isn't punishment. We're trying to reclaim our ability to enjoy NORMAL things. That's the goal. Less artificial stimulation. More real life. ASK YOURSELF THIS QUESTION Before you reach for something, ask: Am I choosing this, or am I escaping something? That question can completely change things. Why am I opening Instagram? Because I intentionally decided to spend 15 minutes enjoying it? Fine. Or because this task got difficult? Why am I eating this? Because I'm hungry and I want it? Fine. Or because I'm uncomfortable? Why am I drinking? Because I'm celebrating with friends? Or because I can't tolerate sitting with how I feel? Why am I checking email for the 47th time? Because I'm waiting for something important? Or because working on the project in front of me is harder? That pause between impulse and behavior… That's where your power is. CHEAP DOPAMINE VS EARNED DOPAMINE This is the easiest way I can summarize the episode. Cheap dopamine: Consume. Scroll. Click. Watch. Refresh. Snack. Buy. Escape. Earned dopamine: Build. Train. Create. Learn. Connect. Explore. Compete. Finish. Accomplish. The first category isn't evil. But if the balance of your life is overwhelmingly consumption… Don't be surprised when your motivation disappears. Shift the ratio. More creating. Less consuming. More doing. Less watching. More participating. Less observing. More real life. Less digital life. WE NEED ANTICIPATION BACK I think anticipation is disappearing. When everything is immediately available… There's nothing to look forward to. And looking forward to something is part of the fun. Plan the trip. Wait for Friday night. Save the amazing dessert for dinner. Don't buy everything the second you want it. Put the concert on the calendar. Build toward the race. Earn the reward. Delay things intentionally sometimes. The waiting isn't ruining the experience. The waiting IS part of the experience. YOUR LIFE SHOULD NOT NEED CONSTANT ENTERTAINMENT This might be the line I want you to remember. You should not need to be entertained every minute in order to enjoy your life. Your kids shouldn't either. We need boring car rides. We need afternoons with nothing scheduled. We need conversations without phones. We need walks. We need staring out the freaking window. We need thinking. We need daydreaming. We need opportunities for our brains to say: "Okay… what should we do?" Creativity often appears after the stimulation disappears. THE GOAL IS NOT LOWER DOPAMINE This is important. We're not trying to crush dopamine. We're not trying to become emotionless. We're not trying to avoid things that make us feel amazing. We're trying to protect our ability to feel amazing. That's different. We want the vacation to feel incredible. The concert. The accomplishment. The amazing dinner. The business win. The race. The adventure. The hug. The goal. The experience. We just don't need to manufacture an artificial mini-version of that feeling 800 times between breakfast and lunch. Leave some room. Let your baseline exist without constantly smashing the stimulation button. Here's what I think is so interesting about all of this. We have designed a world where almost everything that used to require effort can now be accessed instantly. And that sounds like freedom. But if we're not careful… Convenience becomes dependence. Entertainment becomes distraction. Pleasure becomes compulsion. And access becomes addiction. The answer isn't to reject modern life. I'm certainly not giving up my phone. I'm not giving up coffee. I'm not giving up Instagram. I'm not giving up amazing food or concerts or trips or all the fun things. I want MORE life. That's actually the entire point. I don't want to spend my life looking down at a screen getting tiny rewards when there are enormous rewards available by actually participating in life. I want the dopamine from doing the workout I didn't want to do. Building the business. Taking the trip. Making the memory. Trying something new. Getting really freaking good at something. Having an amazing conversation. Being completely present with my kids. Creating something I'm proud of. Doing something hard. That's the dopamine I want more of. And maybe the simplest takeaway from this whole episode is this: Stop making pleasure so easy and effort so optional. Let yourself work for things. Let yourself wait for things. Let yourself be bored. Let yourself struggle. Let yourself anticipate. Let yourself earn some of the really good feelings. Because when we stop trying to make every moment maximally stimulating… Something really cool happens. Normal life starts becoming awesome again. And that is how to be awesome at resetting your dopamine.
Shannon Sharpe, Chad “Ochocinco” Johnson and Iso Joe Johnson react to JJ McCarthy not playing in final preseason game, Saints beat Cowboys, Bengals beat Eagles and Tua gets boo’d! Subscribe to Nightcap presented by PrizePicks so you don’t miss out on any new drops! Download the PrizePicks app today and use code SHANNON to get $50 in lineups after you play your first $5 lineup! Visit https://prizepicks.onelink.me/LME0/NI... 00:00 - Introduction3:27 - Broncos beat Vikings16:25 - Saints beat Cowboys20:40 - Bengals beat Eagles36:15 - Falcons beat Dolphins (Timestamps may vary based on advertisements.) #ClubSee omnystudio.com/listener for privacy information.
I react to right-wing podcast star Tucker Carlson's latest wild claim that is going viral... and getting brutally debunked.Support My Show: https://linktr.ee/bradpolumboSee omnystudio.com/listener for privacy information.
FinTok is full of people giving confident financial advice… and a lot of it is dead wrong. This week, Jean sits down with CBS News Business Analyst Jill Schlesinger, and host of the new podcast Money Moves, to call out the worst financial trends going viral right now, answer real questions from HerMoney listeners, and share how to tell good financial advice from the bad. In this episode: Jean and Jill's list of “5 things that are always worth the money” Jill's top 3 worst pieces of financial advice circulating on TikTok right now, and what to do instead Jill's simple test for vetting any piece of financial advice before you act on it Learn more about your ad choices. Visit megaphone.fm/adchoices
Link Up w/The Morning Sickness Digitally All Over:Instagram: @hms_98_official, @bosskupd, @bretvesely, @dickToledoX/Twitter: @HMSon98, @DickToledo, @bretveselyFacebook: @HMSKUPDYouTube: @hmspodcast9320, @98kupdRequest/Call in/Wakeup Song line:(IN AZ) 602.585.9800More HMS: www.holmbergpodcast.com, www.98kupd.comEmail: dtoledo@98kupd.com, bvesely@98kupd.com, bbogen@98kupd.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Link Up w/The Morning Sickness Digitally All Over:Instagram: @hms_98_official, @bosskupd, @bretvesely, @dickToledoX/Twitter: @HMSon98, @DickToledo, @bretveselyFacebook: @HMSKUPDYouTube: @hmspodcast9320, @98kupdRequest/Call in/Wakeup Song line:(IN AZ) 602.585.9800More HMS: www.holmbergpodcast.com, www.98kupd.comEmail: dtoledo@98kupd.com, bvesely@98kupd.com, bbogen@98kupd.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
There is currently fierce backlash against vegetable oils. Sunflower, canola or soybean oil are said to be unhealthy. Even the universally praised olive oil has come under criticism. With nutritional therapist Eva-Maria Heikenwälder, we shed light on these myths. - Derzeit wird heftig gegen sogenannte Pflanzenöle Stimmung gemacht. Sonnenblumen-, Raps- oder Sojaöl sollen angeblich Entzündungen sowie chronische Krankheiten fördern und praktisch ungesund sein. Selbst das allseits gelobte Olivenöl ist in die Kritik geraten. Mit der Ernährungstherapeutin Eva-Maria Heikenwälder beleuchten wir diese Mythen.
Exhaustion is the one symptom every pregnant woman reports, and nobody investigates. In this solo episode, Dr. Renda Knapp explains why iron deficiency is the most common — and most under-treated — problem she sees in obstetrics, and why a "normal" hemoglobin doesn't mean your tank is full. Inside: the one test you're probably not getting, the symptoms that aren't just pregnancy (yes, including the ice chewing), how to take iron so it actually works, when IV iron beats pills, and why postpartum is the half of this conversation everyone abandons. Stay healthy, stay suspicious.
How AI Is Compressing Awareness, Consideration and Decision Into One ConversationFor decades, marketing followed a fairly predictable funnel.A prospective client discovered a problem. Searched Google. Read a few articles. Visited several websites. Downloaded a guide. Received a few nurture emails. Checked reviews. Compared firms.Eventually, they scheduled a consultation.AI is beginning to compress that entire journey.Today, a business owner can ask ChatGPT, Gemini, Perplexity or another AI assistant:“I own a construction company doing $4 million a year. My accountant handles my taxes but doesn't do much proactive planning. Am I missing opportunities? What should a proactive advisor do differently? What should that cost? And which firms near me work with businesses like mine?”One conversation can move that prospect through awareness, education, solution research, consideration, comparison and due diligence.The funnel hasn't disappeared psychologically.It's being compressed technologically.And that creates an important new reality for tax and accounting firms:The prospect can now self-nurture.In this episode of The Growth Minded Accountant, Lee Reams II and Rebekah Barton explore how AI is changing the traditional buyer journey, why website traffic may become a less complete measure of marketing success, and why firms increasingly need to think about whether they are understandable, provable and ready when a high-intent prospect appears.The bigger shift is simple:The funnel didn't die. Our control over it did.And the firms that adapt should focus on three things:Be Present. Be Provable. Be Ready.
Craig Carton called out Evan Roberts and Tiki Barber for supposedly ignoring the biggest question about Jaxson Dart returning to a preseason game after a blue-tent evaluation. There was just one problem: they had already debated it. Evan brings the receipts, while Tiki explains why a young quarterback like Dart still needs valuable preseason reps. The conversation also turns to Breece Hall's groin injury and why Tiki worries missed practice time could affect his Week 1 readiness. Plus, the Yankees face a difficult decision with Spencer Jones when Cody Bellinger returns, Evan makes the statistical case for batting your best hitter leadoff, and the Jets debate whether winning now matters more than chasing future draft position.
You have been doing the work. You understand your patterns. And you are still waking up unable to face the day. When the body decides survival is the priority, it stops converting thyroid hormone into the form your cells can use. It produces an inactive version instead, called reverse T3, and that version blocks the active hormone at the cell door. The result is a body running low and slow. It is the same physiology I call chronic functional freeze. Dr. Amie Hornaman ranks reverse T3 as the single most important thyroid marker, ahead of free T3, and it is a marker almost nobody orders. She also holds that stress and trauma on their own can raise it. This is Part 1 of a two part conversation. ➡️ Full show notes, research and timestamps: https://www.biologyoftrauma.com/post/can-a-thyroid-problem-keep-me-in-chronic-freeze WHAT YOU'LL LEARN: Why a normal TSH does not settle the question of whether your thyroid is involved What reverse T3 is, and why Dr. Hornaman ranks it above free T3 How stress and trauma can shift thyroid function directly Why the hibernation state and chronic functional freeze are the same physiology What happens to the thyroid in the year after an injury or accident Why you cannot think your way out of a decision the body made below thought RESOURCES: Take the free 2-Minute Assessment for Stored Trauma: https://biologyoftrauma.com/quiz Stress or Trauma Guide The Biology of Trauma: https://www.biologyoftrauma.com/book The Thyroid Fix by Dr. Amie Hornaman Part 2 releases August 20, 2026.
Send us Fan MailYou take the promotion because it looks like progress. You feel chosen, relieved, maybe even flattered, and you don't stop to ask yourself if it's actually right for you until you're already in it.That's the moment I keep coming back to. I built a career assuming someone else would tap me on the shoulder with the next opportunity, and by the time I noticed the pattern, I'd already handed the decision away.This episode is about catching that shift before it happens again, and what it actually takes to stop waiting and start choosing.In this episode, I'll cover:Recognize the difference between staying because it's easy and staying because it's rightAsk yourself the questions most people wait for someone else to askBuild the kind of career capital that keeps your options open, even when you're not looking
If you have switched to oat milk because you think it is healthier than dairy, this episode has some important information for you. And if you saw Miranda Kerr's diet advice doing the rounds in the media this week, Leanne and Susie have thoughts. This week the team does a full ranking of every milk on the market, from full cream dairy to oat, soy, almond, coconut, and the new high-protein dairy milks, covering protein, calcium, fortification, what to check on the label, and who each one is actually best suited to. Then they respond to Miranda Kerr's widely covered restrictive diet recommendations with the specific nutritional corrections the media cycle did not bother to include. In this episode: Every milk ranked by protein, calcium, and overall nutritional quality: dairy milk, soy, oat, almond, coconut, high-protein dairy blends, and why the answer to which is healthiest is not what most people assume Why oat milk is higher in carbohydrate, lower in protein, and often sweetened without people realising, and why the barista varieties from cafes are frequently not calcium-fortified at all Why soy milk is Leanne's personal choice and her preferred dairy alternative for clients, including the phytoestrogen angle for women in perimenopause and menopause The one thing to always check when buying any plant milk, why 300 milligrams of calcium per cup is the number to look for, and what most people are missing when they pick up almond milk thinking it is a nutritious swap Miranda Kerr's diet recommendations fact-checked: no eggs, no kale, no sushi, no salmon, no sourdough, venison for breakfast, and caviar as a protein source. What is actually wrong with this, why it is a concern when mainstream media amplifies it, and why the foods she is avoiding are among the most nutritionally valuable you can eat Why you can absolutely cook with extra virgin olive oil and always could, and the claim about salmon and mercury that is not supported by the evidence The new Philadelphia Protein Cream Cheese exposed: why the regular light Philadelphia and the protein version are nutritionally near-identical per 100 grams, and why the only meaningful difference is that the serving size has been increased How to find leaner and cheaper cuts of meat as beef prices continue to rise, why pork is underrated as a high-protein budget option, why slow cooker cuts are one of the best investments in your weekly food budget, and what to do if the supermarket sells you a fatty cut you cannot use See omnystudio.com/listener for privacy information.
Knuckleheads! this episode is a chaotic, hilarious ride: from ranting about gross protein bars and dreaming up waffle-cone spoons to me literally truckin' a phone-walking stranger on the C train. You get train drama, snack takes, and a whole lot of attitude. We also dig into cruise booking nightmares, brutal PTO policies, coworker horror stories (lunch thieves, microwaved fish, and more), and classic Florida-man antics — all with blunt, laugh-out-loud commentary. Pull up a seat and hang out with us. Follow Knuckleheadslab! Morning Star: The First Fall Dark Fantasy Novel Knuckleheadpodcast Merch! Check Out Lahna Turners website! Pounds of Power! Ralphie May Doc
A former Buckingham Palace press secretary reveals officials actually inspected the jail cells at Slough Magistrates' Court before Princess Anne appeared there in 2002, just in case the Princess Royal was sent to prison. Plus, King Charles strips ten people of British honours, Jennie Bond argues the royal line of succession has become “ridiculous,” James, Earl of Wessex takes a summer job driving tractors at Sandringham, and Lady Louise Windsor debuts a new look after graduating from Saint Andrews.Become a supporter of this podcast: https://www.spreaker.com/podcast/palace-intrigue-harry-meghan-the-crown-royal-family-news-and-gossip--4522904/support.Palace Intrigue is a daily British royal family podcast covering King Charles, Meghan Markle, Prince Harry, Kate Middleton and the House of Windsor. New episodes every day. Follow on Apple Podcasts, Spotify, or wherever you listen. Part of the Caloroga Shark Media network.
For years, reviews played a fairly predictable role in growing a tax or accounting firm.A prospect found your firm. Visited your website. Checked your reviews. And then decided whether to call.AI is beginning to rewrite that sequence.Today, prospects can ask ChatGPT, Gemini, Perplexity, or another AI assistant a much more valuable question:“Which accounting firm should I hire—and why?”That means discovery and due diligence are starting to happen inside the same conversation.Your reviews are no longer just something prospects read after they discover your firm. They are becoming part of the digital evidence that can help search engines, AI systems, and prospective clients understand who you serve, what you are good at, how clients experience working with you, and whether your firm deserves to be recommended.In this episode of The Growth Minded Accountant, Lee Reams II and Rebekah Barton explore the new ROI of reviews—and why reputation is becoming an increasingly important part of your search strategy.They explain why ten detailed, authentic client stories may communicate far more about a firm than ten generic five-star reviews; why review generation should become an ongoing “reputation heartbeat” instead of an occasional campaign; and why firms may need to rethink traditional marketing measurements built around clicks, traffic, and rankings.The bigger shift is simple:Stop optimizing only to get the click. Start building a firm that is easy to understand, easy to trust, and easy to recommend.Because your clients may already know how good your firm is.The market—and increasingly AI—only knows the evidence it can see.See How Recommendable Your Firm IsIf your ideal client asked AI today to recommend the perfect accounting firm for them, would your firm make the shortlist?Find out.Get your free Recommendability Report:https://recommendability.countingworks.appSee where your firm is strong, where your digital evidence may be creating gaps, and what you can improve to become easier for humans—and AI—to understand, trust, and recommend.
Have you ever gotten up from a chair with a groan and wondered why you feel ten years older than you should? Well, some people are opting to discover their body's biological age, using epigenetic tests. These tests analyse an individual's DNA to see if they are physically older or younger than their actual age.A range of them are available on the Irish market, so Newstalk Reporter Bairbre Holmes went to find out what they are, and how they work…
Cristina Gomez reviews the latest UFO / UAP news and covers Robert Bigelow's claim that AARO was built to squash UFO disclosure, his Kazakhstan crash and weapons claims and Secretary of State Marco Rubio's admission that he no longer controls the UAP file inside the current administration. To see the VIDEO of this episode, click or copy link - https://youtu.be/oJeSxBZh92UVisit my website with International UFO News, Articles, Videos, and Podcast direct links -www.ufonews.co00:00 - UFO Office Called A Fraud00:38 - Bigelow's UFO Warning01:30 - Inside The UFO Office02:17 - UFO Testimony Revealed03:55 - Secret UFO Weapon Claim05:43 - Rubio And The UFO File09:07 - Who Runs UFO Disclosure Become a supporter of this podcast: https://www.spreaker.com/podcast/strange-and-unexplained--5235662/support.
What could really improve your quality of life? I believe crossing off tasks - getting stuff fixed or jobs completed - really can remove stress and anxiety.There may be 10 small projects, but you never seem to have the time, the tools, or the skills, to "Git-R-Done!"Okay, not exactly one click, but www.jacksvictoria.ca can help. The platform built for and promoted by 21 year old Nolan Hupp and his partner, 23 year old Adam Vincent, basically shortens the distance between what your home needs and solutions. It allows contractors and handypersons to see Have a listen and learn how it works... how Jacks can improve your quality of life.
Nicholas "Harry" Callas expresses frustration with these two Pirates' pitchers, each who had a start in the series over the weekend against the New York Mets.
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In this episode, Zale sits down with pediatric ophthalmologist Dr. Christine Law to unpack when children should get their eyes checked and why that question is more nuanced than it seems. They break down the difference between basic vision screening and a full comprehensive eye exam, including what each can and cannot detect. Dr. Law shares practical guidance on common refractive issues in children like hyperopia, myopia, and astigmatism, and when glasses may be needed.The conversation also covers why early detection matters for preventing amblyopia and how parents can think about eye care as their kids grow.This episode is sponsored by Thea Pharma Canada. Learn more about them at https://theapharma.ca/who-we-areListen to our previous episode on the Myopia Epidemic with Dr. Rupa Wong at https://www.spreaker.com/episode/9-myopia-epidemic-dr-rupa-wong--56113340Become a supporter of this podcast: https://www.spreaker.com/podcast/blind-spot-the-eye-doctor-s-podcast--5819306/support.
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Bali is truly top of mind.4 Pines, a brewery born in Manly and enjoyed everywhere. Get their Japanese Lager available here: https://4pinesbeer.com.au/Neds: Smash out a same game multi in seconds and track it live as the action plays out. Use the Punter's Toolbox for extra value & protection. Get amongst it on the neds app. T&Cs apply see website for details https://www.neds.com.au/. You Win Some You Lose More.Good Day Multivitamin & Day Lyte Electrolytes, it's the least you can do. Use code 'dribblers' for 10% off your order here: https://gooddayaus.com.au/Join The Good Day Goers Facebook Group here.Bali TripManly v StormTrump's Japan CommentsMatt King Coaching NSWScratching An ItchScott McCreery's Instagram CommentJonah Hill Brazilian Jiu-JitsuKhabib's Sweaty GymBali BrainPool Etiquette Hosted on Acast. See acast.com/privacy for more information.
Suddenly social media algorithms and even mainstream slop media are promoting content suggesting women are begging men to start asking them out on dates again. Women in their late 30s are asking where the real men are and saying that they were sorry to have been taken advantage of by feminism. Assuming this were true, why should anyone accept such behavior as acceptable, or be empathetic, sympathetic, or compassionate? Shouldn't these people be shamed for poor choices, especially so young women know this is not acceptable behavior? Can shame actually be a positive motivator in society?*The is the FREE archive, which includes advertisements. If you want an ad-free experience, subscribe below.
Spain sends in its military as a massive wave of thousands of Moroccans floods its shores, CENTCOM denies Iranian claims of downing F-35s as the U.S. ramps up strikes, and a WNBA owner is suspended while star Sophie Cunningham stands her ground on women's sports. Reporting from Lynden Blake. Plus, we speak with the host of GB News, Bev Turner, and the acting director of the Heritage Foundation Center for National Security, Robert Peters. Get the facts first with Morning Wire. - - - Ep. 3014 - - - Wake up with new Morning Wire merch: https://bit.ly/4lIubt3 - - - Today's Sponsor: Alliance Defending Freedom - Learn more about how YOU can support free speech by texting WIRE to 83848 or going to https://JoinADF.com/WIRE - - - Privacy Policy: https://www.dailywire.com/privacy morning wire,morning wire podcast,the morning wire podcast,Georgia Howe,John Bickley,daily wire podcast,podcast,news podcast Learn more about your ad choices. Visit podcastchoices.com/adchoices
TUESDAY HR 1 Funky sleeping. Dealing with parents that are getting older. Russ comes clean about his dermatology visit. Drivers test.
TUESDAY HR 1 Funky sleeping. Dealing with parents that are getting older. Russ comes clean about his dermatology visit. Drivers test. See omnystudio.com/listener for privacy information.
Rob and Teresa Heckenlively never set out to own a haunted hotel. During a family vacation to Osceola, Missouri, they called a realtor simply because they wanted to see inside the long-vacant Commercial Hotel. They weren't looking to buy a historic landmark—but by the end of the tour, that's exactly what they wanted to do.Built in 1867 after the Civil War's Sacking of Osceola destroyed an earlier hotel on the property, the Commercial Hotel has welcomed famous guests including President Harry Truman and brothers Jesse and Frank James. Today, many believe not every guest has ever truly checked out.As Rob and Teresa began bringing the historic hotel back to life, they discovered they weren't the only ones invested in its future. Before the restoration was even complete, Rob had an encounter so unsettling it kept him away from the hotel for months. Since then, guests have reported heavy footsteps, voices in empty hallways, shadow figures, and other unexplained encounters that continue to fuel the hotel's haunted reputation.Today on The Grave Talks, Rob and Teresa share the remarkable story of rescuing the Commercial Hotel—and what it's like living with whatever may have never left.For more information, just search “Haunted Commercial Hotel” on Facebook.#TheGraveTalks #HauntedCommercialHotel #OsceolaMissouri #HauntedHotel #MissouriHauntings #GhostStories #ParanormalPodcast #HistoricHotels #HauntedHistory #ParanormalInvestigationLove real ghost stories? Don't just listen—join us on YouTube and be part of the largest community of real paranormal encounters anywhere. Subscribe now and never miss a chilling new story:
Hidden Killers With Tony Brueski | True Crime News & Commentary
Lindsay Clancy sent her husband out to grab dinner, and prosecutors say she used that window like a stopwatch. In Duxbury, Massachusetts, in January 2023, Lindsay Clancy killed her three children: Cora, five, Dawson, three, and Callan, eight months. She isn't disputing that she did it. The fight in Plymouth Superior Court is over what was happening in her mind when she did. Prosecutors say Lindsay checked Apple Maps before Patrick left the house, working out exactly how long a dinner run would take him. Her fitness tracker recorded her climbing three flights of stairs the moment she got off the phone with him. To the state, that's not a woman lost in psychosis, that's premeditation. Her defense tells a different story. In the months before the killings, Lindsay was prescribed at least ten different medications, eight of them crammed into a single three-week stretch. She hospitalized herself three times. She called a crisis hotline and was turned away. She told her providers, in plain language, that she was having thoughts of hurting her kids. A hospital assessment ruled out bipolar disorder at the time, the same assessment two separate lawsuits now call negligently wrong. After the killings, a forensic evaluation diagnosed her with Bipolar Disorder I with psychosis and postpartum onset. Her husband Patrick found the children. He's also suing her healthcare providers for wrongful death, and he's on the defense's witness list, not the prosecution's. Her attorney even offered to admit she killed the children if the judge would split the trial into separate guilt and sanity phases. The judge refused. Some of Lindsay's psychiatric appointments in that stretch were virtual and lasted as little as seventeen minutes. Jury selection begins July 20. Whatever that fifty-four-minute window really meant, twelve strangers are about to decide. Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/ Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1 Instagram https://www.instagram.com/hiddenkillerspod/ Facebook https://www.facebook.com/hiddenkillerspod/ Tik-Tok https://www.tiktok.com/@hiddenkillerspod X Twitter https://x.com/TrueCrimePod This publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice. Hashtags #LindsayClancy #PatrickClancy #HiddenKillers #TrueCrime #AppleMaps #PostpartumPsychosis #DuxburyMA #MurderTrial #WrongfulDeath #JurySelection
A heated livestream debate erupts when a young single mother challenges Marquett's views on single motherhood, accountability, family structure, relationships and personal responsibility.Marquett questions her decisions, pushes back on her definition of a single mother and challenges her claims about whether women can control becoming single parents. The conversation quickly expands into a tense discussion about dating, choosing partners, stepfatherhood, family protection, adulthood and the consequences of personal choices.The caller refuses to back down—but Marquett continues pressing every contradiction.Do you think Marquett made valid points, or did he go too far?Comment your thoughts below.
7/24: Hosts Josh Silver & Larry Hott Max Page, Former President of the Massachusetts Teachers Association: Billionaire tax and newly freed up millions for public education, but when will it get to the schools? David Bollier, Author of the book “How to Think Like a Commoner”. The commons, a parallel social economy helping millions take charge and escape the predatory state order. Abbe Spector & Jess Dawson of the Northampton Information Hub: Get real, honest, true, fact-checked information on everything to do with Northampton's Government. Evan Goodchild: Tells prospective podcasters all they need to know to record, produce, & reach millions of listeners with a compelling show. Artbeat w/ Donnabelle Casis: Nikki Bassette & Dan Jones owners of Abracadabra Film Lab & Trevor Powers Artist and wrestling fan and the return of analog photography.
If you've been enjoying The Independent Advisors podcast for a while now and want to take the next step in your financial journey, I'd encourage you to head to our website, jessupwealthmanagement.com (https://www.jessupwealthmanagement.com/) . Matt offers a 15-minute initial call where you can discuss your financial goals and see if JWM is a good fit for your needs. Scheduling is easy—once you land at jessupwealthmanagement.com (https://www.jessupwealthmanagement.com/) just click “Schedule Initial Call” and select a time that works best for you! There's a quick survey to fill out that will help guide the conversation and ensure your time is used efficiently. If you're ready to learn more, visit jessupwealthmanagement.com (https://www.jessupwealthmanagement.com/) and book your call today! Take advantage of our partnership with LifeLock and get discounts using our link: https://lifelock.norton.com/offers?expid=LLONEYEAR&promocode= JSPW24&VENDORID= _JESSUPWM&om_ext_cid=ext_partner_ JSPW24_Productpage $) Topics: Sector Rotation & Tech "Bubble" Narrative — Energy vs. Technology leadership data (12:09) Earnings Season Strength — beat rates, guidance trends (02:51) Volatility Seasonality & Q3 Risk — VIX trends, historical patterns (07:17) Seasonal & Historical Market Patterns — August/September weakness, strong Q4 outlook (05:34) Global Revenue & Currency Impact — S&P 500 foreign revenue share (14:55) Retirement & Financial Planning Insights — advisor engagement, retirement income shifts, common regrets (16:50)Hosts: Mark McEvily - Chief Investment Officer and Managing Partner Matthew Jessup – Chief Executive Officer, Chief Compliance Officer, and Managing Partner Address: 35 Park Ave. Dayton, OH 45419 Phone: 937-938-9105 https://www.jessupwealthmanagement.com/ Social Media: Facebook: @JessupWealthManagement LinkedIn: @JessupWealthManagement Twitter: @jessupwealth Instagram: @jessupwealth https://www.jessupwealthmanagement.com/disclosures-page
An Alabama custody death is drawing widespread attention after disturbing footage surfaced showing Amarin Tunstall being dragged by police officers moments before he later became unresponsive.In this video, Marquett examines the publicly available footage, compares it with official police statements, and discusses the unanswered questions surrounding the incident. The conversation also covers eyewitness accounts, the timeline of events, and why many community members are calling for a full investigation.The goal of this discussion is to analyze the available information and encourage viewers to think critically as more facts emerge.Topics Covered:* Alabama custody death investigation* Amarin Tunstall case* Police body camera questions* Eyewitness testimony* Official police statement* Timeline analysis* Community reaction* Justice and accountabilityWhat are your thoughts after watching the footage?Join the discussion respectfully in the comments.⸻
Multiple callers join Marquett live expecting a debate—but the conversations quickly turn into lessons on accountability, masculinity, relationships, leadership, financial success, and critical thinking.Throughout the stream, Marquett responds to questions about Kevin Samuels, marriage, modern dating, single motherhood, identity, financial literacy, and personal responsibility, challenging callers while explaining the reasoning behind his views.Topics Covered* Marquett debates multiple live callers* Kevin Samuels discussion* Masculinity vs. attention-seeking behavior* Marriage and modern relationships* Single motherhood* Leadership and accountability* Financial literacy and success* Identity, culture, and self-improvement* Why earning respect mattersWhether you agree or disagree, these debates are packed with strong opinions, tough questions, and thought-provoking conversations.Which debate stood out to you the most? Let us know in the comments.⸻
In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan
This week on Purple Political Breakdown, Radell Lewis breaks down a night where every major American institution seemed to bend under pressure at once. The Iran war didn't end, it got worse. After the June ceasefire collapsed, the US and Iran have traded strikes across the region, eighteen American service members have died since February, and Congress is now being asked to approve $87.6 billion more to keep funding it, all while Trump quietly approved a 30-year nuclear deal with Saudi Arabia in the middle of the fighting. Back home, attorney general nominee Todd Blanche confirmed a settlement that permanently bars the IRS from auditing Trump's own tax returns, Mahmoud Khalil filed a lawsuit accusing senior administration officials of using an 1871 law written to stop the Klan to target protesters, and a federal appeals court ended 60 years of desegregation oversight in Louisiana at the DOJ's own request. Then there's the elections themselves. The Election Assistance Commission just lost its entire bipartisan leadership four months before the midterms, and Trump gave a primetime speech claiming newly declassified documents prove foreign election interference. Radell read the actual documents and breaks down what they really say, and don't say, about China, Russia, and the 2020 election. Plus new Senate developments out of Maine and South Carolina, a fresh look at Arizona's governor race, a deep dive into why the public trusts both political parties less than ever, and why the conservative backlash to Christopher Nolan's The Odyssey turned out to have nothing to do with what audiences actually thought when they showed up. We close things out, as always, with some good news: a record-breaking rescue operation in China, a promising new Alzheimer's treatment, 3D-printed organs manufactured in space, and humpback whales making a stunning comeback off the coast of Brazil. 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The character Brooke is channeling in today’s Phone Tap is so off putting, it almost made us LOSE our LUNCH when we first heard it.See omnystudio.com/listener for privacy information.
The character Brooke is channeling in today’s Phone Tap is so off putting, it almost made us LOSE our LUNCH when we first heard it.See omnystudio.com/listener for privacy information.
People who want to lose weight often count calories. But how practical is that really long-term? In this episode of “Nutrition myths fact-checked” nutritional therapist Eva-Maria Heikenwalder gives tips for sustainable weight management and explains which role metabolism and lifestyle play. Also: how useful are the social media trends intermittent fasting, meal replacement products and subscription models for meals? - Wer abnehmen will, zählt oft Kalorien. Doch wie alltagstauglich ist das wirklich? In dieser Folge von „Ernährungsmythen im Faktencheck“ gibt die Ernährungstherapeutin Eva-Maria Heikenwälder Tipps für eine nachhaltige Gewichtskontrolle und erklärt, welche Rolle der Stoffwechsel und der Lebensstil spielen. Außerdem: Wie sinnvoll sind die Social-Media-Trends Intervallfasten, Mahlzeitenersatzprodukte und Abo-Modelle für Mahlzeiten?
This is Part Two of our conversation.Rob and Teresa Heckenlively never set out to own a haunted hotel. During a family vacation to Osceola, Missouri, they called a realtor simply because they wanted to see inside the long-vacant Commercial Hotel. They weren't looking to buy a historic landmark—but by the end of the tour, that's exactly what they wanted to do.Built in 1867 after the Civil War's Sacking of Osceola destroyed an earlier hotel on the property, the Commercial Hotel has welcomed famous guests including President Harry Truman and brothers Jesse and Frank James. Today, many believe not every guest has ever truly checked out.As Rob and Teresa began bringing the historic hotel back to life, they discovered they weren't the only ones invested in its future. Before the restoration was even complete, Rob had an encounter so unsettling it kept him away from the hotel for months. Since then, guests have reported heavy footsteps, voices in empty hallways, shadow figures, and other unexplained encounters that continue to fuel the hotel's haunted reputation.Today on The Grave Talks, Rob and Teresa share the remarkable story of rescuing the Commercial Hotel—and what it's like living with whatever may have never left.For more information, just search “Haunted Commercial Hotel” on Facebook.#TheGraveTalks #HauntedCommercialHotel #OsceolaMissouri #HauntedHotel #MissouriHauntings #GhostStories #ParanormalPodcast #HistoricHotels #HauntedHistory #ParanormalInvestigationLove real ghost stories? Don't just listen—join us on YouTube and be part of the largest community of real paranormal encounters anywhere. Subscribe now and never miss a chilling new story:
Handel on the Law. Marginal Legal Advice.See omnystudio.com/listener for privacy information.
This is Part Two of our conversation.Rob and Teresa Heckenlively never set out to own a haunted hotel. During a family vacation to Osceola, Missouri, they called a realtor simply because they wanted to see inside the long-vacant Commercial Hotel. They weren't looking to buy a historic landmark—but by the end of the tour, that's exactly what they wanted to do.Built in 1867 after the Civil War's Sacking of Osceola destroyed an earlier hotel on the property, the Commercial Hotel has welcomed famous guests including President Harry Truman and brothers Jesse and Frank James. Today, many believe not every guest has ever truly checked out.As Rob and Teresa began bringing the historic hotel back to life, they discovered they weren't the only ones invested in its future. Before the restoration was even complete, Rob had an encounter so unsettling it kept him away from the hotel for months. Since then, guests have reported heavy footsteps, voices in empty hallways, shadow figures, and other unexplained encounters that continue to fuel the hotel's haunted reputation.Today on The Grave Talks, Rob and Teresa share the remarkable story of rescuing the Commercial Hotel—and what it's like living with whatever may have never left.For more information, just search “Haunted Commercial Hotel” on Facebook.#TheGraveTalks #HauntedCommercialHotel #OsceolaMissouri #HauntedHotel #MissouriHauntings #GhostStories #ParanormalPodcast #HistoricHotels #HauntedHistory #ParanormalInvestigationLove real ghost stories? Don't just listen—join us on YouTube and be part of the largest community of real paranormal encounters anywhere. Subscribe now and never miss a chilling new story:
A fatal shooting in Maine sparks anti-ICE protests and a significant shift in federal policy, heightened security threats prompt rare congressional testimony by Supreme Court justices as well as a new approach to the White House Correspondents Dinner, and we look at the numbers most impacting Americans' wallets. Reporting from Jennie Taer & Ben Domenech. Plus, we speak with EJ Antoni. Get the facts first with Morning Wire.- - -Ep. 2892- - -Wake up with new Morning Wire merch: https://bit.ly/4lIubt3- - -Today's Sponsors:Alliance Defending Freedom - Go to https://JoinADF.com/WIRE, or text WIRE to 83848, to give today. With Alliance Defending Freedom, your gift defends religious freedom for all.Factor - Head to https://FactorMeals.com/morningwire50off and use code morningwire50off to get 50% off and free daily greens per box. Vanta - Whether you're a fast-growing startup or a global enterprise, Vanta is here to help you automate your security and compliance, and earn and prove trust. Get started today at https://vanta.com/morningwire. - - -Privacy Policy: https://www.dailywire.com/privacymorning wire,morning wire podcast,the morning wire podcast,Georgia Howe,John Bickley,daily wire podcast,podcast,news podcast Learn more about your ad choices. Visit podcastchoices.com/adchoices
Rob and Teresa Heckenlively never set out to own a haunted hotel. During a family vacation to Osceola, Missouri, they called a realtor simply because they wanted to see inside the long-vacant Commercial Hotel. They weren't looking to buy a historic landmark—but by the end of the tour, that's exactly what they wanted to do.Built in 1867 after the Civil War's Sacking of Osceola destroyed an earlier hotel on the property, the Commercial Hotel has welcomed famous guests including President Harry Truman and brothers Jesse and Frank James. Today, many believe not every guest has ever truly checked out.As Rob and Teresa began bringing the historic hotel back to life, they discovered they weren't the only ones invested in its future. Before the restoration was even complete, Rob had an encounter so unsettling it kept him away from the hotel for months. Since then, guests have reported heavy footsteps, voices in empty hallways, shadow figures, and other unexplained encounters that continue to fuel the hotel's haunted reputation.Today on The Grave Talks, Rob and Teresa share the remarkable story of rescuing the Commercial Hotel—and what it's like living with whatever may have never left.For more information, just search “Haunted Commercial Hotel” on Facebook.#TheGraveTalks #HauntedCommercialHotel #OsceolaMissouri #HauntedHotel #MissouriHauntings #GhostStories #ParanormalPodcast #HistoricHotels #HauntedHistory #ParanormalInvestigation Love real ghost stories? Don't just listen—join us on YouTube and be part of the largest community of real paranormal encounters anywhere. Subscribe now and never miss a chilling new story:
Chris McMonigle and Craig Carton break down the growing hype surrounding Giants quarterback Jaxson Dart as fans start comparing him to NFL superstars like Lamar Jackson and Patrick Mahomes. Is the excitement justified, or are expectations getting way too high after a small sample size? The guys debate Dart's rushing ability, his potential as a dual-threat quarterback, whether the Giants should use him in short-yardage situations, and why Lamar Jackson has become one of the NFL's best pocket passers. Plus, Craig and Big Mac get into some hilarious food debates, cheesesteaks, and take your calls on WFAN.
Chelsea found her new fav spot in the city!See omnystudio.com/listener for privacy information.
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