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Research Questions the Accuracy of Oral Health Advice Givenby Non-ProfessionalsBy Today's RDH ResearchOriginal article published on Today's RDH: https://www.todaysrdh.com/research-questions-the-accuracy-of-oral-health-advice-given-by-non-professionals/Need CE? Start earning CE credits today at https://rdh.tv/ce Get daily dental hygiene articles at https://www.todaysrdh.com Follow Today's RDH on Facebook: https://www.facebook.com/TodaysRDH/Follow Kara RDH on Facebook: https://www.facebook.com/DentalHygieneKaraRDH/Follow Kara RDH on Instagram: https://www.instagram.com/kara_rdh/
On this episode of the Acceptable Accuracy Podcast, Kyle and Philip sit down with GM USPSA shooter Ryan Fowler, who accomplished the incredible feat of reaching GM in just two years. Ryan shares his shooting background, what brought him into USPSA, his competition setup, and practical tips for progressing toward GM. We also discuss preparing for major matches, Fowler Defense, and the always-important question: What's your dream gun?
What happens when two people in a neurodiverse relationship remember the same conversation completely differently? This week, Mona and I explore the memory and communication disconnects that can put neurodiverse couples in what I call “the boxing ring”—where partners become opponents, each trying to prove who's right and who's wrong. From the partner who remembers every word but misses the meaning to the one who remembers the feeling but not the details, we break down why this happens—and why it doesn't have to be a deal breaker. We also talk about executive functioning in daily life, the cereal box story, and important questions every couple should ask before moving in together.
Book Alan's Business Breakthrough Session. Your first 30-minute coaching call is FREE. Learn how to prioritize success and let your quality of life become the byproduct. - https://calendly.com/alanlazaros/30-minute-breakthrough-sessionFitness is forever, it's a lifestyle. Get jacked and join the Next Level Fitness Accountability Group. This group is invite-only. Reach out to Kevin or Alan on Instagram:Kevin: https://www.instagram.com/neverquitkid/Alan: https://www.instagram.com/alazaros88/_______________________In today's episode of Next Level University, hosts Kevin Palmieri and Alan Lazaros examine the connection between belief and behavior. What you believe about yourself, other people, and what is possible affects what you notice, what you attempt, and how you respond when results fall short. Kevin and Alan discuss why accuracy matters more than blind optimism, how external messages can influence performance, and why your current beliefs should be tested through real action.They also break down the difference between using evidence wisely and allowing statistics, doubt, or comforting stories to limit your potential. This episode offers a direct look at self-awareness, personal responsibility, and the practical feedback loop that helps you build stronger beliefs through consistent behavior._______________________NLU is more than a podcast. From the Next Level Dreamliner to Group Coaching, we provide tools and communities to help you grow with more clarity, consistency, and accountability.Visit our website and socials through the links below.
Women Leaders Psychological Safety: How Kerry Siggins Turned "You're Erratic" Into Her Best Leadership Lesson Last updated: September 3, 2026 Trusted by leaders at Stanford University, Ernst & Young, Autodesk, MGM Grand Resorts, and Farmers Insurance Executive Summary: The word that stung most in StoneAge CEO Kerry Siggins' toughest feedback moment was the word that was true. Here's her litmus test for real feedback, how she builds psychological safety without losing accountability, and how women leaders navigate the double bind. Quick Takeaways: If feedback "stings," there's probably truth in it worth examining Psychological safety and accountability aren't opposites — done right, they build each other The double bind is real: women who are direct risk being called aggressive Influence matters more than being right — and it changes how feedback lands U.S. employee engagement hit a decade-low of 31% in early 2026 Jump to:The Feedback Story · Spotting Valid Feedback ·Safety vs. Accountability · The Double Bind ·Action Plan · FAQ As an executive leadership coach with over 30 years of experience (MA, MFT, PCC) working with Fortune 50–1000 companies, I've watched brilliant women leaders freeze in two different ways: some can't tell which feedback to take seriously and which to release, and others have been burned so badly by being called "too direct" that they've gone quiet. Both patterns erode psychological safety — theirs, and their team's. This is Part Two of my conversation with Kerry Siggins, CEO of StoneAge and author of Talk With Trust: How to Own Feedback, Spark Growth, and Build Stronger Teams. In Part One, we walked through her SHARES framework for difficult conversations. Here, we go deeper into the moment that started it all — the day a trusted colleague told her she was "erratic" — and what she's learned since about telling real feedback from noise, building psychological safety without losing accountability, and the double bind that so many women leaders still navigate every day. The Feedback That Almost Cost Kerry Siggins Her COO — And Changed Her Leadership Kerry had spent a year having what she thought were deep, honest conversations with her COO. Then, in a discussion about his role and growth path, it came out all at once: "You think you're this inspiring CEO who disrupts the industry, but really what you are is erratic, and you're really hard to work for. You're the problem." Kerry's first instinct was to fire him. She paused instead — because, as she told me, "I don't want to react in the moment." That pause is the whole story. Kerry went home, sat with the feedback, and asked herself a simple question: what's true about this? She landed on one word — erratic — and traced it back to something real: her fast pace and love of new ideas could look, from the outside, like constant direction-changing. She decided, in that moment, to become a "cool, calm, and collected, unflappable leader." It changed how she led. It also cost her the relationship — the way the feedback was delivered damaged the trust between them beyond repair, and her COO eventually left the company. Both things were true at once: the feedback was a gift, and the delivery broke something. That tension is exactly why psychological safety matters — not as comfort, but as the structure that lets hard truths get said before they explode. How to Tell Valid Feedback From Noise Kerry's litmus test is disarmingly simple: if it stings, there's probably some truth to it. She described being called "disingenuous" by someone recently and letting it go completely — because it didn't sting. She knows who she is on that dimension. But "erratic" stung, because some part of her recognized it. That sting is data. The follow-up question matters just as much: is this feedback about who I am, or about someone else's perception and unmet need? "Just because something hits your identity," Kerry said, "doesn't mean that person is right or wrong — it just means they're seeing things differently." Her advice for women leaders receiving feedback that challenges their identity: get curious, ask for examples, and then decide. "Do you want to be right, or do you want to be accurate?" Accuracy requires asking questions. Being right just requires defending your position. She also named something every leader has lived: most of what reaches you has already been filtered. The brain can only process a tiny fraction of available information, and the person giving you feedback is filtering it further through their own fear of your reaction. The fix is direct: ask "what are you not saying right now?" and genuinely invite the rest. Psychological Safety vs. Accountability: Why They're Not Opposites Most leaders treat psychological safety and accountability as a tradeoff — soft versus firm, safe versus serious. Kerry rejects that framing entirely. "There can be accountability in how you're addressing any kind of challenge through dialogue," she said. Her practice: after a hard conversation, put recurring check-ins on the calendar — not as a punishment, but as a standing space to talk about progress, struggles, and wins. "My manager really cares about me" and "I'm being held accountable" become the same sentence, not opposite ones. Accountability only becomes destructive to psychological safety when it's used as a weapon instead of a rhythm. This is the same territory I mapped out recently in a LinkedIn post on the three pillars of trust — and two of them apply directly here. Trust isn't built in one big moment. It's built in the small moments people learn they can count on you. — Sabrina Braham, MA, MFT, PCC Authenticity breaks when a leader hides a mistake to protect their image instead of admitting when they're wrong — exactly what Kerry avoided by sitting with "erratic" instead of dismissing it. And reliability breaks when accountability only shows up when something goes wrong instead of on a consistent rhythm — which is precisely why Kerry's recurring check-ins work: the consistency itself is what makes accountability feel like care instead of a threat. The Double Bind: When Direct Reads as Aggressive I asked Kerry directly: women leaders often face a double bind — direct enough to be respected reads as aggressive; warm enough to be liked reads as weak. How do you navigate it? Her answer came from experience: a fellow board member once told her, in front of a long-time customer, that she was "aggressive" and an "overachiever" — framed as a flaw. "There's no way he would have ever said that to a man," she said. This isn't just anecdote. New 2026 research out of INSEAD confirms the bind is still fully intact: promotion-focused language — bold, action-oriented, opportunity-framed — tends to clash with gendered expectations for women leaders, while prevention-focused, risk-aware language can reinforce perceptions of low competence, a tension that gets sharper under crisis or high pressure. Separate research on women serving as corporate board directors found something even more troubling: the constant work of switching between "warmth" tactics and "competence" tactics to avoid backlash is itself a driver of the disproportionate burnout many women in leadership report — the double bind isn't just unfair, it's exhausting in a measurable way. Kerry's shift wasn't to soften herself — it was to change what she was optimizing for. "I changed my perspective: do I want to be right, or do I want to influence?" When someone in a meeting takes her idea and repeats it as their own, she no longer takes it personally. "That's a great idea, let's build on that" gets her further than proving the idea was hers first. Influence, not being right, became her north star — and it's a reframe available to any woman leader caught in the double bind. You don't have to shrink to be trusted. The FREE Leading Before You're Ready Playbook gives you language and a framework for navigating exactly this — leading with directness and building the trust that gets you promoted. GET THE FREE PLAYBOOK Implement This Week: Building Psychological Safety Without Losing Accountability (10 minutes) Recall the last piece of feedback that stung. Write down exactly why it stung — what did it touch that you believe about yourself? (15 minutes) Separate what's accurate from what's someone else's unmet expectation. Ask: is this something I need to work on, or a relationship I need to manage? (5 minutes) Identify one relationship where you suspect you're only getting filtered information. Plan to ask directly: "What are you not telling me?" (20 minutes) Put one recurring check-in on the calendar with a direct report — framed around progress and support, not correction. (This week) Bring one piece of feedback to your own team: "I'm working on X, and I'd love one honest thing I could do better." Modeling receptivity is the fastest way to build a feedback culture. (Ongoing) The next time you're told you're "too direct," ask for a specific example before deciding whether to change anything. Success metric: you leave with clarity, not just a feeling. Common Mistakes Leaders Make With Feedback and Safety Treating every sting as truth. Not all feedback that hurts is accurate — some reflects the other person's ego or fear, not your reality. Examine it before you internalize it. Defending instead of asking. Over-explaining your intentions in the moment shuts down the other person's willingness to keep talking, even if it doesn't feel defensive to you. Using accountability as punishment. If check-ins only happen when something goes wrong, people stop seeing them as safe — and stop bringing you the truth early. Softening yourself to avoid the double bind. Shrinking your directness doesn't resolve the bind — it just trades one cost for another, and the research suggests the constant self-regulation itself carries a burnout cost. Shift toward influence instead of retreat. Reading body language as neutral....
A second ESC recap plus two breaking stories on the failure of an investigational Lp(a) reducing drug and a new FDA approval of an AI-ECG app are covered by John Mandrola, MD This podcast is intended for healthcare professionals only. To read a partial transcript or to comment, visit: https://www.medscape.com/twic I Lp(a) Disappointment HORIZON https://clinicaltrials.gov/study/NCT04023552 Press Release https://www.novartis.com/news/media-releases/novartis-announces-lpahorizon-phase-iii-topline-results-pelacarsen-patients-elevated-lpa-and-established-cardiovascular-disease-cvd Rationale paper https://doi.org/10.1016/j.ahj.2025.03.019 II FDA Clears AI-ECG for Acute MI Detection Accuracy of cath lab activation decisions for STEMI-equivalent and mimic ECGs: Physicians vs. AI (Queen of Hearts by PMcardio) 10.1016/j.ajem.2025.07.061 AI-Enabled ECG Analysis Improves Diagnostic Accuracy and Reduces False STEMI Activations: A Multicenter U.S. Registry https://www.jacc.org/doi/10.1016/j.jcin.2025.10.018 Queen of Hearts™ AI ECG App https://www.powerfulmedical.com/pmcardio-stemi/ III TARGET CTCA Trial Targeted Use of Computed Tomographic Coronary Angiography in Acute Chest Pain https://www.nejm.org/doi/10.1056/NEJMoa2608903 IV CorCal trial Press release https://www.escardio.org/news/press/press-releases/new-data-presented-on-the-use-of-coronary-artery-calcium-for-cardiovascular-risk-assessment/ CorCal Outcomes: A randomized trial using the pooled cohort equation or coronary artery calcium to select statin therapy in primary prevention patients. Study design and baseline characteristics https://doi.org/10.1016/j.ahj.2026.107473 V AMUNDSEN Trial LDL Cholesterol Lowering With Evolocumab Before Percutaneous Coronary Intervention for Acute Myocardial InfarctionThe AMUNDSEN Randomized Clinical Trial https://jamanetwork.com/journals/jama/fullarticle/2853274 VI TRIC-I-HF trial Tricuspid Repair Cuts Death, HF Hospitalization in Severe TR https://www.medscape.com/viewarticle/tricuspid-repair-cuts-death-hf-hospitalization-severe-tr-2026a1000wvk Tricuspid-Valve Intervention in Heart Failure https://www.nejm.org/doi/full/10.1056/NEJMoa2606934 You May Also Like: The Bob Harrington Show with the Stephen and Suzanne Weiss Dean of Weill Cornell Medicine, Robert A. Harrington, MD. https://www.medscape.com/author/bob-harrington Questions or feedback, please contact news@medscape.net
Anku Rani, a doctoral researcher at MIT and an MIT Tata Center fellow, joins Gabriella Mirabelli to challenge a working assumption in AI video production: that a better-looking clip is automatically a better clip. Rani built a culture score that separates identity, behavior, and context, then tested it against leading video generation models and against real viewers from nine countries. The results complicate how brands should think about choosing a video model for global campaigns, especially once cinematic polish and cultural accuracy start pulling in opposite directions.
So, here is a not uncommon situation: A new OB patient presents for initial prenatal care at 28 weeks by “sure LMP” but her ultrasound EGA is just over 3 weeks behind. Simple redating, right? How can we be assured that we are not missing FGR? That is where the “ancillary” use of transcerebellar diameter (TCD) plays a role. In this episode, we will highlight the recent JUNE 2026 data on this and review the 4 scenarios when this supplemental ultrasound finding can play a vital role. 1. Fetal Transcerebellar Diameter Measurement With Particular Emphasis in the Third Trimester: A Reliable Predictor of Gestational Age. AJOG. 2004. 2. Elgadi A, Wagealla M, Idris E, Eissa AYH, Altraifi S, Altraifi H, Abdallah E, Noorallah T, AbdAlla E. Accuracy of Ultrasonographic Transcerebellar Diameter for Gestational Age Estimation: A Systematic Review and Meta-Analysis. J Clin Ultrasound. 2026 Jun 17.3. ACOG Committee on Obstetric Practice. Committee Opinion No 700: Methods for Estimating the Due Date.Obstetrics and Gynecology. 2017. 4. Arzik IG, Golbasi H, Can ST, Aktas HA, Cakir ZE, Purut CS, Torun R, Toka I, Oztataroglu C, Ekin A. Role of Transcerebellar Diameter in Estimating Gestational Age in the Third Trimester: A Comparative Analysis in Fetuses With Different Growth Patterns. J Ultrasound Med. 2026 Apr;45(4):895-903.
Send us Fan MailThis members-only episode is a blunt, no-filter conversation between Peaches and Trent—and it goes exactly where it needs to. They bounce from Operator Training Summit updates and deliberate training to beards, uniforms, recruiting hype, Special Reconnaissance misinformation, and why accuracy matters more than motivation. They break down why scale matters in the U.S. military, why influencers selling fantasy jobs are doing real damage, and why being an extension of a capability isn't the same as being the capability. Add in AI, psyops, SMUs, deterrence, and why some truths will never be public—and you get a classic Ones Ready reminder: the job is harder, messier, and more professional than the internet wants it to be.⏱️ Revised Timestamps (unchanged, attribution corrected):00:00 Ones Ready intro and attributes-based reality02:30 OTS updates and deliberate training explained06:30 “Hazing” vs skill building09:00 Scale problems: U.S. vs partner nations13:40 Beards, uniforms, and morale myths18:50 Fitness standards and readiness23:30 Recruiting hype and false expectations28:40 Special Reconnaissance misinformation34:30 Accuracy vs motivation reels40:30 SMUs, CQB, and why you won't see it46:00 Venezuela, psyops, and deterrence51:30 AI, Grok, and truth vs narrative57:30 Government contracts and incentives01:03:00 Why transparency has limits01:08:30 Final thoughts for membersSupport the showJoin this channel to get access to perks: HEREBuzzsprout Subscription page: HERERegister for our Operator Training Summit: OperatorTrainingSummit.comFind an Air Force Recruiter: AirForce.comCollabs:Ones Ready - OnesReady.com 18A Fitness - Promo Code: ONESREADY ATACLete - Follow the URL (no promo code): ATACLeteDanger Close Apparel - Promo Code: ONESREADYDFND Apparel...
All remaining classes for 2026 will include online mental management at no additional charge, include the 3 with Kita Busse! sign up at andersonshooting.com
While it is true that an August apple forecast was not put out by the Washington State Tree Fruit Association this year, you may have seen one from the US Apple Association.
Ever wondered how the ancient Egyptians built the pyramids with mind-blowing precision?
⚖️ What Happens If Your Spouse Doesn't Respond to Divorce Papers in California? | Los Angeles Divorce
AI can score highly on medical exams, identify conditions in written cases, and still perform poorly when real people use it for their health. In this episode, I break down new research that tested what happens when members of the public use AI tools to interpret medical scenarios and decide where to seek care. The results point to a problem that has less to do with the model's knowledge and more to do with the human interaction around it. Topics discussed: - The Hemorrhage Case Study- The Nature Medicine Study Design- 95% to 35% Accuracy Drop- The Control Group Outperformed AI- Where the AI-Human Handoff Fails- Reading Confidence as Competence- The 1 in 6 Adults Using Health Chatbots---------- My Live Program for Coaches: The Functional Nutrition and Metabolism Specialization www.metabolismschool.com---------- [Free] Metabolism School 101: The Video Serieshttp://www.metabolismschool.com/metabolism-101----------Subscribe to My Youtube Channel: https://youtube.com/@sammillerscience?si=s1jcR6Im4GDHbw_1----------Grab a Copy of My New Book - Metabolism Made Simple---------- Stay Connected: Instagram: @sammillerscienceYoutube: SamMillerScience Facebook: The Nutrition Coaching Collaborative CommunityTikTok: @sammillerscience----------“This Podcast is for general informational purposes only and does not constitute the practice of medicine, nursing or other professional health care services, including the giving of medical advice, and no doctor/patient relationship is formed. The use of information on this podcast and the show notes or the reliance on the information provided is to be done at the user's own risk. The content of this podcast is not intended to be a substitute for professional medical advice, diagnosis, or treatment and is for educational purposes only. Always consult your physician before beginning any exercise program and users should not disregard, or delay in obtaining, medical advice for any medical condition they may have and should seek the assistance of their health care professionals for any such conditions. By accessing this Podcast, the listener acknowledges that the entire contents and design of this Podcast, are the property of Oracle Athletic Science LLC, or used by Oracle Athletic Science LLC with permission, and are protected under U.S. and international copyright and trademark laws. Except as otherwise provided herein, users of this Podcast may save and use information contained in the Podcast only for personal or other non-commercial, educational purposes. No other use, including, without limitation, reproduction, retransmission or editing, of this Podcast may be made without the prior written permission of Oracle Athletic Science LLC, which may be requested by contacting the Oracle Athletic Science LLC by email at operations@sammillerscience.com. By accessing this Podcast, the listener acknowledges that Oracle Athletic Science LLC makes no warranty, guarantee, or representation as to the accuracy or sufficiency of the information featured in this Podcast."
Adam Guillette from Accuracy in Media joins Marc Cox to reveal undercover footage from the University of Missouri showing university staff admitting that banned DEI initiatives are still active under disguised department names. Guillette outlines how conservative lawmakers and board of governors members must enforce strict financial penalties and eliminate bloated administrative departments to end taxpayer-funded political activism on campus. Listen to 97.1 FM Talk live in St. Louis or stream worldwide on the free Audacy app!
Hour 4 begins with Adam Guillette from Accuracy in Media revealing undercover footage of university administrators secretly keeping DEI programs active under renamed departments. Joe Steiger from the St. Louis Police Officers Association pushes back against city proposals regarding officer pay and benefits, and Joe Parisi details a new global streaming agreement for the annual Guns & Hoses event benefiting Backstoppers. Listen to 97.1 FM Talk live in St. Louis or stream worldwide on the free Audacy app!
The Marc Cox Morning Show breaks down major news hitting St. Louis and the nation, from local police officers confronting proposed benefit cuts to Senator Josh Hawley demanding answers on nationwide AI surveillance networks. The broadcast features foreign policy insight from Victoria Coates on global energy security, congressional updates from Representative Bob Onder, undercover reports on university DEI rebrandings from Accuracy in Media, and big streaming updates for Guns & Hoses. Listen to 97.1 FM Talk live in St. Louis or stream worldwide on the free Audacy app!
All Home Care Matters and our host, Lance A. Slatton were honored to welcome Michelle Cera, PhD as guest to the show. About Michelle Cera, PhD: Michelle is an investigative reporter with Hunterbrook Media. Her work covers the nursing home industry, environmental disasters, medical devices, real estate, and corporate wrongdoing of all kinds. Michelle received her Ph.D. in Sociology from New York University, building on her Bachelor of Arts degree with Highest Honors from the University of California, Berkeley. Michelle is based in Brooklyn. About Hunterbrook Media: Hunterbrook Media publishes investigative and global reporting with no ads or paywalls. Our mission is to bring visibility to under covered areas and accountability to under scrutinized sectors. Accuracy and trust are key. Hunterbrook articles include detailed references for verification by journalists, litigators, investors, policy-makers, advocates, consumers, regulators, and other readers. Investigations are rigorously fact-checked, copy-edited, and legally vetted. The mission of our reporting at Hunterbrook Media is to make an impact — so as we are conducting investigations, we must always be considering which levers of change we are attempting to pull. Whether that is breaking stories with market relevance, uncovering scams so the victims can seek restitution, or highlighting practices ripe of regulatory intervention, our goal is not to identify an issue but attempt to rectify it. Official Website: https://hntrbrk.com Original Article: www.hntrbrk.com/ensign Follow-up Article on Hunger: www.hntrbrk.com/ensign-hunger
Adam Guillette, President of Accuracy in Media, On Undercover D.E.I. Videos at UMKC Campus | 8-26-26See omnystudio.com/listener for privacy information.
Rushed methodology changes implemented mid-season by Nielsen have severely shaken the NFL's confidence in standard television ratings. NFL Chief Data Officer Paul Ballew explains how media fragmentation is forcing leagues and networks to demand radical transparency or switch to new transactional metrics. Key Highlights
In this episode, we explore essential pre-season hunting preparation tips, equipment checks, practice routines, and safety measures to ensure a successful and safe hunting experience.Key topicsImportance of equipment inspection before hunting seasonEffective practice routines for accuracy and confidenceSafety measures for tree stand and equipment useTuning bows and broadheads for optimal performancePlanning and organizing hunting gear for quick accessChapters00:00Introduction to Hunting Season Prep03:57Inspecting and Maintaining Your Bow and Arrows07:57Practice Routines for Accuracy and Confidence15:04Practicing in Realistic Hunting Conditions20:04Safety Tips for Tree Stand and Equipment Use30:12Preparing Your Hunting Gear and Checklist39:59Dealing with Last-Minute Equipment Issues45:09Mental Preparation and Practice Discipline50:03Final Tips and Event Details for Pre-Season ChecksOur next event at the shop will be Sunday August 30th. We will be setting up sight tapes using precision cut and the lab radar absolutely free, as well as doing some coaching and lots of other one on one instruction. Email or call the shop for more information:SilverBirchArchery - (570) 491-8166113 Rt 6, Milford, Pennsylvania 18337Hours: Tuesday - Saturday: 10am - 6:30pmSunday: 12pm - 5pmDon't forget to subscribe and leave comments. Your progress begins with deliberate practice and a calm mind. Reach out for coaching, training, or just to share your journey!Here are some links to stay up to date with us:Our YouTube Channel: https://youtube.com/highpowerarcheryMy facebook link: https://www.facebook.com/AngelGarciaArcheryCoachHigh Power Archery Instagram: https://www.instagram.com/highpowerarchery/Jonathan's Instagram:@silverbirch_viking
In the Best of the Bears this week, Laurence Holmes and Carmen Vitali discussed how Bears quarterback Caleb Williams remains focused on improving his accuracy; Score reporter Chris Emma joined the Spiegel & Holmes Show to share his observations from the Bears' joint practice with the Bengals; and Matt Spiegel and Laurence Holmes discussed the Bears' ongoing left tackle competition.
In the Best of the Bears this week, Laurence Holmes and Carmen Vitali discussed how Bears quarterback Caleb Williams remains focused on improving his accuracy; Score reporter Chris Emma joined the Spiegel & Holmes Show to share his observations from the Bears' joint practice with the Bengals; and Matt Spiegel and Laurence Holmes discussed the Bears' ongoing left tackle competition.
In the Best of the Bears this week, Laurence Holmes and Carmen Vitali discussed how Bears quarterback Caleb Williams remains focused on improving his accuracy; Score reporter Chris Emma joined the Spiegel & Holmes Show to share his observations from the Bears' joint practice with the Bengals; and Matt Spiegel and Laurence Holmes discussed the Bears' ongoing left tackle competition.
Mark Potash talks Caleb Williams' need to improve accuracy (Hour 2) full 2926 Sat, 22 Aug 2026 19:40:00 +0000 eZ7qeSWB1z226EL0jPpoJ8q2UIPC4J2i sports Steve Rosenbloom Show sports Mark Potash talks Caleb Williams' need to improve accuracy (Hour 2) Former Chicago Tribune writer and columnist Steve Rosenbloom brings his witty, at-times snarky sports commentary to the airwaves on Saturdays at 11 a.m. to lead a show that he and listeners affectionately call Saturday Suckage. Follow him on Twitter @SteveRosenbloom. 2024 © 2021 Audacy, Inc. Sports https://player.amperwavepod
GPS sports watches are impressive little devices that are able to track and measure more and more things each year. But how do these devices actually work? How accurate are they? And what are the important differences among all the options? Today, Darian Allberry from COROS joins us and provides an outstanding overview of this entire product category.Note: We Want to Hear From You!Please share with us the questions, topics, or stories you'd like us to cover on GEAR:30. You can email those to us here.RELATED LINKS: Momentous: livemomentous.com use code: BlisterOneSkin: oneskin.co/BLISTERGet Yourself Covered: BLISTER+Order our 26/27 Winter Buyer's GuideEnter Our Free Weekly Gear GiveawaysCHECK OUT OUR YOUTUBE CHANNELS:Blister Studios (our new channel)Blister Review (our original channel)TOPICS & TIMES: What Should We Call This Product Category?? (3:08)How Hardware Differs (4:31)COROS History (10:26)Accuracy (16:57)Over-the-Air Updates (10:20)Displays (25:24)OLED vs AMOLED Displays (29:19)Darian's Background (32:20)Data: How Much is Too Much? (34:53)Prices / Price Ranges (41:03)Size (46:06)What Feature is Most Underutilized? (49:35)A.I. (53:53)Durability & Care (58:26)What's Next at COROS? (1:02:31)CHECK OUT OUR OTHER PODCASTS:The Blister VaultBlister CinematicCRAFTEDBikes & Big IdeasBlister Podc Hosted on Acast. See acast.com/privacy for more information.
Get Up resumes with CJ Stroud's bank account on the line! The Texans starter heads into the final year of his rookie deal with immense pressure to perform. The Texans boast the league's top defense, and could find themselves in ESPN's Super Bowl with just average QB play! Plus, with a big year, Stroud gains generational wealth! Meanwhile - Ben Johnson praised Caleb Williams' improved accuracy. How could on target passes raise the ceiling of an already exquisite offense? Then - if Jalen Hurts plays to his normal mediocre standards, sailing balls over receivers' heads down the middle of the field, unable to throw on the run, should Philly look for their next QB? Learn more about your ad choices. Visit podcastchoices.com/adchoices
In the second hour, Laurence Holmes and Carmen Vitali discussed Bears quarterback Caleb Williams' accuracy, with Vitali sharing metrics and context on his numbers and detailing what a realistic progression in 2026 would look like. After, that Matt Snyder of CBS Sports joined the show to discuss Cubs star Pete Crow-Armstrong's sensational season.
When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro
Laurence Holmes and Carmen Vitali discussed Bears quarterback Caleb Williams' accuracy, with Vitali sharing metrics and context on his numbers and detailing what a realistic progression in 2026 would look like.
Bears' Ben Johnson talks improvement in QB Caleb Williams' accuracy full 572 Thu, 20 Aug 2026 18:39:13 +0000 QmOviAVGbtpMyXssxJ7HTxDX17vMcqTA nfl,chicago bears,sports Rahimi & Grote Show nfl,chicago bears,sports Bears' Ben Johnson talks improvement in Caleb Williams' accuracy Leila Rahimi and Mark Grote bring a thoughtful, fast-moving approach to Chicago sports as they break down the day's biggest stories across the NFL, MLB, NBA and college sports, with Chicago always leading the conversation — from the Bears and Cubs to the Bulls, White Sox and more. Known for smart analysis, lively discussion and caller interaction, the show offers Chicago fans an informed perspective on everything happening in the city's sports landscape. Catch the Rahimi & Grote Show live Monday through Friday from 10 a.m. to 2 p.m. on 104.3 The Score or on the Audacy app. © 2026 Audacy, Inc. Sports https://player.amperwavepodcas
Kyle and Philip are joined by Casey to recap the recent Brutal 2Gun match at Barry County Conservation Club. This unique match combines shooting with serious physical and mental challenges, featuring 5 shooting stages and 5 additional stages that included a weighted ruck, physical challenges, an obstacle course, and trivia. The guys break down the match, the challenges, and what makes Brutal 2Gun such a uniquely difficult competition.
In Episode 77 of the Teaching Literacy Podcast, guest host Dr. Laura Tortorelli sits down with Dr. Jake Downs to unpack a recent grade 3 intervention study published in Reading Research Quarterly with co-authors Freddy Hiebert, Kristin Conradi Smith, and Katie Martz. Together, they explore the demands of reading long, complex words and a straightforward blending routine for supporting multisyllabic word reading. They also discuss the need for supporting accuracy and automaticity in complex text and how to use the multisyllabic blending strategy with the repeated reading Read Like Us protocol. Plenty of takeaways for any upper elementary teacher, reading coach, or literacy support professional. Read the article here! No paywall!Downs, J., Hiebert, E., Conradi Smith, K., & Martz, K. (2026). Promoting reading accuracy and fluency outcomes with complex texts: A Grade 3 intervention comparison. Reading Research Quarterly. Advance online publication. https://doi.org/10.1002/rrq.70117 Episode Outline:0:00 Intro — Guest host Laura Tortorelli introduces the episode1:44 Study overview — Jake Downs et al. study comparing simplified vs. complex texts in Reading Research Quarterly4:24 Welcome, Jake — Jake joins the conversation4:48 Complex text effects — Accuracy, fluency, and comprehension decrease with harder text3:54 Why accuracy matters — Accuracy as the “floor of good reading”4:58 Why harder texts? — Vocabulary density, idea sophistication, word automaticity7:24 Teachers’ quandary — Grade-level texts cause accuracy and fluency to plummet8:00 What can we do? — Scaffold rather than water down text9:32 RLU 1.0 vs. new study — Adding long word blending to improve accuracy10:09 Multisyllabic approaches — Syllable types vs. morphology; limitations of each12:48 Hybrid peel-off approach — Combining morphology with syllabication15:58 Curricular design — 80/20 split: text reading vs. word blending17:36 Text design vs. scope & sequence — Pull words from strong texts, not vice versa21:56 Texts in upper grades — Building vocabulary, knowledge, and comprehension22:38 Word repetition — ~25 encounters per target word through repeated readings24:57 A day in Read Like Us — Step-by-step blending routine and repeated reading protocol28:48 Study parameters — 60 sessions, 12 topics, 5 texts each from ReadWorks.org29:38 Comparison condition — A validated decoding intervention, not a weak control31:36 Key differences — Text complexity, word complexity, and word-text alignment34:04 Findings: Accuracy — RLU students 2.79x more likely to reach benchmark35:49 Findings: Fluency — Comparable growth; no cost from harder texts37:33 Vocabulary hypothesis — Likely benefits from text volume and topic diversity37:57 Who benefits? — Students at ~95%+ accuracy; not below 90%40:14 Advice for teachers — Scaffolding is essential; payoff is compound interest43:00 Selecting words — Pick hard, text-meaningful words; 3 words, 5–7 minutes45:41 Challenging deficit views — Build on what students can do; scaffold the edge48:08 What’s Jake optimistic about — Public-facing research and free resources49:38 Laura’s take: Rethinking ZPD — Scaffolding should match text challenge, not avoid it
Jon Seaton, founding Partner at Echo Canyon Consulting, has developed a platform to reshape how data and AI can improve the ground game of field canvassing. Field data, often manually audited and slowly made available, is a key source of information for campaigns, and intentional inaccuracies or accidental errors can quickly contaminate datasets downstream. While many AI solutions focus on generating content, this solution focuses on verifying that a human has actually conducted the work. We talk about: Previous struggles with data reliability from field canvassers leading to poor strategic campaign decisions Fraud detection and what canvassers think they can get away with How near real-time reporting allows for corrective actions and feedback to canvassers Why door-to-door canvassing remains the most effective campaign tool #EchoCanyonConsulting #Astralis #PoliticalCampaigns #FieldOperations #CampaignTech #GroundGame #AIinPolitics #DataIntegrity #GetOutTheVote #CampaignStrategy EchoCanyonConsulting.com
TABLE OF CONTENTS, THE JOHN BATCHELOR SHOW, 8-17-2026.Bill Roggio reflects on the five-year anniversary of the 2021 Afghanistan withdrawal, highlighting the accuracy of the maps he built that predicted the Taliban's takeover. He describes the withdrawal as disorganized, noting there was zero plan for the Afghan government or military equipment. Roggio specifically criticizes the abandonment of Bagram Air Base, which he believes was the superior evacuation site. He also addresses current Middle East policy, dismissing proposed land blockades against Iran as "fanciful" and impractical. He warns that current strategic failures mirror the indifference seen in Afghanistan. (1)Bridget Toomey analyzes the reawakening of the Yemeni Civil War, noting that the Houthis are launching fresh offensives to seize the Red Sea coast and the Bab el-Mandeb choke point. While the Houthis claim to target Saudiinterests, their primary focus has shifted to defeating government-backed ground troops. Toomey highlights the extensive use of drone technology by both sides, with the Houthis demonstrating sophisticated capabilities while the Yemeni government adopts modified Chinese systems. She notes that Saudi Arabia remains hesitant to fully re-initiate a large-scale military intervention despite continued Houthi aggression. (2)Janatyn Sayeh explores the "secret plan" of Iranian hardliners to escalate regional conflict and raise costs for the United States. He notes that while the Iranian economy is an existential vulnerability, the regime is willing to ride out sanctions to maintain its Islamist agenda. Sayeh critiques the viability of a U.S. land blockade, citing the difficulty of gaining cooperation from neighbors like Afghanistan and Pakistan. He argues that power is concentrated within the IRGCsecurity establishment rather than just the Supreme Leader, and that internal rifts are secondary to the elite's collective survival. (3)Malcolm Hoenlein provides an overview of the October 27th Israeli election, explaining the complex "list" voting system and Netanyahu's challenges. He discusses the "Board of Peace" negotiations, stressing that the central hurdle remains Hamas's refusal to disarm before Israeli withdrawal. Hoenlein also touches on regional tensions, including Hezbollah's rearming and internal unrest in Iran fueled by economic desperation. Finally, he notes Turkey's significant lobbying efforts in Washington through a new headquarters and a "glorious new front" of diplomatic normalization between Israel and several South American nations including Colombia. (4)Edmund Fitton-Brown argues that the U.S. is "running out of ideas" regarding economic pressure on Iran, labeling a land blockade as far-fetched due to uncooperative neighbors. He suggests that secondary sanctions on Chinese "teapot" refineries are a more practical tool. Regarding Yemen, Fitton-Brown observes that the Houthis have become emboldened by U.S. irresolution and are pushing south to capture the Bab el-Mandeb. He expresses cautious optimism regarding Jared Kushner's diplomatic delegation, noting that experienced figures like Tony Blair are unlikely to be "dupes" for Hamas during negotiations. (5)John Hardie reports on the Russia-Ukraine conflict, specifically Russia's rejection of a Black Sea ceasefire in favor of striking Ukrainian food supplies. He notes that while Russia maintains a maritime blockade advantage, Ukraine is retaliating with high-precision long-range drone strikes against Russian oil refineries and defense plants. Hardie suggests that the Kremlin may be receptive to a moratorium on deep-strike operations because their own infrastructure is increasingly vulnerable. He concludes that Ukraine currently holds a tactical edge in drone technology, aided by Western intelligence to bypass Russian air defenses. (6)David Daoud details the escalating tensions between Israel and Hezbollah in Lebanon, noting that Hezbollah's refusal to disarm has prompted the IDF to prepare for a major operation. Israel is currently conducting "clearing operations" to destroy Hezbollah installations near the Al-Aishiyeh Ridge. Daoud highlights the recent deaths of senior commanders from the elite Radwan and Badr units, which indicates that Hezbollah's operational security remains compromised. Despite these losses, Hezbollah is working with Iran to rebuild its command structure and has attempted to smuggle advanced weaponry, including cruise missiles, into Lebanon. (7)Kamran Bokhari examines the evolution of the Iranian regime, arguing that the Supreme Leader has become a figurehead for the IRGC and security establishment. He suggests that the war has accelerated a process of internal fragmentation, though the elite remains united by a shared fear of collapse. While public uprisings have gone "underground," the regime continues to conduct mass arrests to prevent a critical mass of resistance. Bokhari describes a "post-clergy" era where the military dominates political life, noting that the regime has evolved into a different entity than it was decades ago. (8)Alan Tonelson exposes a massive Chinese tariff evasion scheme, where goods are rerouted through countries like Cambodia and Mexico to bypass U.S. duties. This "scam" is estimated to cost the U.S. up to $26 billion in annual revenue. While the White House is developing AI-based systems to strengthen customs enforcement, there is criticism regarding the lack of direct penalties against Beijing. Tonelson notes that 40 countries are currently complicit in these practices, harming the U.S. manufacturing base. He predicts only modest changes to these trade dynamics through the remainder of the current administration. (9)Samuel Ben-Ur discusses the ongoing conflict with Hamas in Gaza, focusing on Jared Kushner's meeting with Hamas leadership to discuss a 15-point peace plan. Ben-Ur explains that Israel rejected the plan because it lacked a verifiable mechanism for Hamas's disarmament before Israeli withdrawal. He dismisses reports of famine in Gaza as propaganda, noting that international bodies currently view aid levels as acceptable. The conversation emphasizes that Hamas remains committed to the destruction of Israel, making voluntary disarmament a "fiction," while Israeli politics remain divided over the war's prosecution. (10)Richard Epstein critiques Senator Bernie Sanders' characterization of Israeli politicians as "extremists," viewing it as a provocative attempt to shift American Democratic policy. Epstein argues that a two-state solution is fundamentally unstable because an autonomous Palestinian region cannot be prevented from remilitarizing. He contends that proponents of this solution often act in bad faith, ignoring Hamas's history of violated ceasefires. Furthermore, Epstein describes the Democratic Socialists of America as isolationists who focus disproportionately on Israel while ignoring other global aggressors like Vladimir Putin in Ukraine. (11)Jeremy Zakis reports on the looming weather crisis in Australia, where meteorologists predict a "super El Niño" combined with a positive Indian Ocean Dipole. This rare combination is expected to cause extreme drought and record-breaking temperatures in New South Wales and Victoria. Zakis warns that a wet winter has fueled rampant eucalyptus growth, creating a massive volume of flammable oils. These conditions have "primed" the landscape for catastrophic wildfires. Residents are being urged to clear undergrowth immediately, as the upcoming fire season could surpass the severity of the devastating 2020–2021 bushfires. (12)One small fix beyond the log: Badr unit (source had "Bader," segment 7) — the standard rendering of Hezbollah's Badr unit. Janatyn Sayeh applied per your confirmed spelling.
Bill Roggio reflects on the five-year anniversary of the 2021 Afghanistan withdrawal, highlighting the accuracy of the maps he built that predicted the Taliban's takeover. He describes the withdrawal as disorganized, noting there was zero plan for the Afghan government or military equipment. Roggio specifically criticizes the abandonment of Bagram Air Base, which he believes was the superior evacuation site. He also addresses current Middle East policy, dismissing proposed land blockades against Iran as "fanciful" and impractical. He warns that current strategic failures mirror the indifference seen in Afghanistan. (1)
In this episode of Leap Forward with Riesterer & Schnell, host Nate Soltvedt visits with Technology Product Specialist Wyatt Davidson in Central Wisconsin to explore how John Deere AutoPath is helping growers and applicators improve accuracy, efficiency, and crop protection.The two discuss how AutoPath differs from traditional AB guidance lines by documenting the exact location of every planted row and carrying that data through future field operations. From navigating hills, waterways, and curved headlands to reducing crop damage during spraying and fungicide applications, AutoPath is helping operators stay on the intended path all season long.Nate and Wyatt also cover:How AutoPath improves sprayer performance in challenging field conditionsThe advantages of integrating AutoPath with turn automationWhy applicators are confidently running higher speeds through tall cornThe role of RTK accuracy and Operations Center connectivityReal-world customer feedback and adoption experiencesHow AutoPath benefits both farmers and custom operatorsWhether you're applying fungicide, harvesting corn, or looking to maximize efficiency across every field pass, this episode provides valuable insight into one of John Deere's most impactful guidance technologies.➡️ Let's ConnectTikTokInstagramFacebookLinkedInTwitterWhy Riesterer & Schnell?Riesterer & Schnell, a progressive locally-owned John Deere dealership, has proudly been serving Wisconsin communities since 1931. Because you are committed to your land, we are determined to provide you with the very best in equipment and service. Our specialties are tractors, farm equipment, zero turns, riding lawn mowers, precision farming technology, parts and service.www.rands.com
How should finance leaders measure AI ROI when adoption has slowed and the cost of models, tokens and disconnected tools remains difficult to predict? In this episode of Tech Talks Daily, I welcome Jeremy Ung, Chief Technology Officer at BlackLine, back to the podcast to discuss how businesses can move from finance AI experimentation to operational deployment. Figures supplied for the interview show AI adoption in finance rising from 37% in 2023 to 58% in 2024, before moving only slightly to 59% in 2025. Jeremy argues that this apparent plateau reflects several pressures, including uncertainty around cost, regulatory requirements, auditability and the continuing debate over whether companies should build their own AI capabilities or purchase them through established platforms. Token spending is part of the problem. Unlike traditional software costs, model usage can be difficult to predict and allocate. Finance leaders want to understand whether applying AI to a workflow will produce enough value to justify that uncertainty. Jeremy believes companies should avoid creating artificial AI ROI metrics. The business measurements already exist. Is AI helping the company close its books faster? Is transaction matching becoming more accurate? Are collections improving? Is the work being completed faster or with fewer manual steps? We discuss what operationalizing AI in finance looks like in practice. Many processes still require employees to contact vendors, collect information, reconcile data and coordinate with other departments. Traditional software struggled with the variation found in these workflows, while AI can adapt to different processes and communication requirements. Accuracy and oversight remain necessary. Jeremy explains why companies need visibility into the prompts, reasoning, models, data, tools and permissions used by every AI agent. That information creates an operating record that finance teams, auditors and regulators can examine later. His analogy with food labeling provides a useful way to understand AI auditability. Consumers can inspect ingredients, calories and sourcing information before buying food. Finance leaders should expect comparable information about the models and data involved when an agent performs financial work. We also discuss the problem of fragmented data. Jeremy acknowledges the familiar rule of garbage in, garbage out, but argues that AI can help connect legacy platforms and mainframe systems that businesses previously found difficult to integrate. The role of finance professionals will change as agents perform additional work. Employees may spend less time completing individual tasks and more time setting goals, reviewing results, approving actions and directing teams of agents. Should CFOs continue buying additional AI tools, or concentrate on embedding existing investments into the financial workflows that determine business performance? Please share your thoughts with me.
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It's only mid-August, and Watch Duty CEO John Mills says it's only going to get worse. With British Columbia under a province-wide state of emergency and Spokane still reeling from the most destructive wildfire in Washington state history, Mills joins Rapid Response to take us inside the most catastrophic fire season his team has ever faced. Since the LA wildfires made Watch Duty a household name, the nonprofit has doubled its headcount and revenue — growth Mills describes as a little too wild for comfort. He talks about the decision to expand into flood monitoring across all 50 states, the controversial partnership with Ring cameras, and what it actually takes to scale a life-saving operation without investors, a cap table, or the luxury of getting it wrong. Visit the Rapid Response website here: https://www.rapidresponseshow.com/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Ben and Nathan advise a listener to stay the course on their accuracy-first approach. Read more on our website. Email daily@lsatdemon.com with questions or comments. Watch this episode on YouTube!More LSAT Demon Resources.
Sam Roberts is the longtime obituary reporter for The New York Times, a job that requires writing mini-biographies with speed and accuracy. Roberts discusses his new book about that art, Are They Dead Yet?: The Art of the Obit. Cover art courtesy of the publisher Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
People spend enormous amounts of money on supplements, organic food, specialty diets, and the latest wellness technology. But what if one of the most overlooked ways to support digestion, satisfaction, and a healthier relationship with food has been hiding in plain sight all along? In this episode of The Body Wisdom Podcast, Jamie Belz reveals a shockingly simple wellness practice that is available almost anywhere, works with nearly every dietary philosophy, and costs absolutely nothing. Jamie explores dopamine, food cravings, reward pathways, sensory-specific satiety, distracted eating, and the modern "gimme, gimme" cycle that can keep people reaching for more. She explains the biological signals that help the body recognize hunger, satisfaction, and fullness. The episode then overviews digestion from the top down, revealing that the process begins long before food reaches the stomach. The brain, senses, vagus nerve, digestive secretions, oral processing, and gut-brain connection are all part of a highly coordinated conversation. When meals are rushed, distracted, or consumed on autopilot, an important part of that conversation can be missed. The solution is not another prescription, supplement, powder, gadget, or complicated biohack. It is something extraordinarily ordinary that could change the way listeners experience their very next meal. In this episode, Jamie explores: • Dopamine, anticipation, and the difference between wanting and liking • Why the first bite or first sip feels especially rewarding • Sensory-specific satiety and why variety can encourage overeating • Food cues, emotional eating, habitual eating, and the reward cycle • The gut-brain connection and natural satiation signaling • The cephalic phase of digestion and the role of the vagus nerve • Saliva, salivary amylase, lingual lipase, and oral processing • GLP-1, CCK, PYY, hunger cues, fullness cues, and meal satisfaction • Why distracted or rushed eating can work against mindful digestion • How a food and mood journal can reveal personal patterns and triggers • The difference between stopping satisfied and stopping stuffed • A free mindful-eating experiment to try with the next meal >>> THE SHOCKINGLY SIMPLE AND FREE WELLNESS TIP THAT COULD CHANGE YOUR LIFE! This is not about deprivation, dieting, or forcing anyone to chew every bite a prescribed number of times. It is about restored communication, not restriction. Jamie challenges listeners to sit down, look at their food, smell it, express gratitude, take a reasonable bite, set down the fork, taste the food, and chew until it is thoroughly broken down before reaching for the next bite. No one can personally control every stage of digestion, absorption, or assimilation. But everyone can control the part assigned to the mouth. Accuracy note: Chewing supports digestion, nutrient accessibility, oral processing, and satiation signaling. It does not guarantee absorption or assimilation, which also depend on stomach acid, pancreatic enzymes, bile, intestinal health, nutrient status, medications, disease states, food matrix, and other bioindividual factors. Rapid Replay Episodes Mentioned: S1E80: Digestive Hell S1E79: Optimal Digestion Connect with Jamie on Instagram: https://www.instagram.com/jamiebelzfntp/ https://www.instagram.com/stories/thebodywisdompodcast/ Work with a Nutritional Therapy Practitioner (NTP): NTP in Private Practice NTP at NTA Health Check out the Nutritional Therapy Association Join a Nutritional Therapy Practitioner Program webinar! Please SUBSCRIBE + leave a review. :-)
Would you trust AI at only 80% accuracy? What about for your enterprise? KPMG Chief Digital Officer Kelle Fontenot explains why AI success cannot be measured by headcount reduction alone - and how synthetic data can help companies test agents safely, protect sensitive information, and focus on the moments that matter most to employees. -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Are wearable devices like Fitbits, Apple Watches, and fitness trackers really worth it? In this episode of the “NASM CPT Podcast,” host Rick Richey dives deep into the science and practical truth behind modern wearable tech.
In a shocking expose, a leading mental health organization is pushing divisive and radical ideologies on its members, leaving many questioning the true purpose of their work. This episode delves into the disturbing practices of the American Psychological Association, where professionals are being taught to use cognitive behavioral therapy and dialectical behavioral therapy to advance DEI and combat racism. But is this really what mental health professionals should be focusing on? The speaker is joined by Adam Gillette, President of Accuracy in Media, who shares his organization's findings on the APA's annual conference. The conference featured a rally-like atmosphere where attendees were encouraged to use language that promotes anti-racism and anti-colonialism. Adam explains how this kind of language is not only divisive but also damaging to mental health professionals and their patients. He also highlights the alarming lack of focus on actual mental health issues and the emphasis on promoting DEI and anti-racism instead. The conversation touches on the worrying trend of DEI being pushed in various institutions, including schools, universities, and even the Department of Justice. Adam shares his concerns about the impact of this ideology on society and the need for conservatives to fight back. He also discusses the importance of holding institutions accountable and the need for states to enact laws that prohibit the promotion of identity politics in education. If you're concerned about the state of our mental health system and the ideologies being pushed on our children, tune in to this episode to hear the full conversation and learn more about the disturbing practices of the American Psychological Association. Follow Carl Jackson:Facebook: https://www.facebook.com/carljacksonradioX/Twitter: https://twitter.com/carljacksonshowInstagram: https://www.instagram.com/thecarljacksonshowWebsite: http://www.TheCarlJacksonShow.comStore: https://CarlJacksonStore.comSee omnystudio.com/listener for privacy information.
In a shocking expose, a leading mental health organization is pushing divisive and radical ideologies on its members, leaving many questioning the true purpose of their work. This episode delves into the disturbing practices of the American Psychological Association, where professionals are being taught to use cognitive behavioral therapy and dialectical behavioral therapy to advance DEI and combat racism. But is this really what mental health professionals should be focusing on? The speaker is joined by Adam Gillette, President of Accuracy in Media, who shares his organization's findings on the APA's annual conference. The conference featured a rally-like atmosphere where attendees were encouraged to use language that promotes anti-racism and anti-colonialism. Adam explains how this kind of language is not only divisive but also damaging to mental health professionals and their patients. He also highlights the alarming lack of focus on actual mental health issues and the emphasis on promoting DEI and anti-racism instead. The conversation touches on the worrying trend of DEI being pushed in various institutions, including schools, universities, and even the Department of Justice. Adam shares his concerns about the impact of this ideology on society and the need for conservatives to fight back. He also discusses the importance of holding institutions accountable and the need for states to enact laws that prohibit the promotion of identity politics in education. If you're concerned about the state of our mental health system and the ideologies being pushed on our children, tune in to this episode to hear the full conversation and learn more about the disturbing practices of the American Psychological Association. Follow Carl Jackson:Facebook: https://www.facebook.com/carljacksonradioX/Twitter: https://twitter.com/carljacksonshowInstagram: https://www.instagram.com/thecarljacksonshowWebsite: http://www.TheCarlJacksonShow.comStore: https://CarlJacksonStore.comSee omnystudio.com/listener for privacy information.
What if telepathy is real and neuroscience has been looking at it the wrong way all along?In this episode of Mayim Bialik's Breakdown, Dr. Diane Hennacy, MD (neuropsychiatrist, neuroscientist, consciousness researcher known for her groundbreaking work with autistic savants and other neurodivergent individuals) shares the astonishing evidence that led her to investigate telepathy, precognition, consciousness, and the hidden capabilities of the human mind.Dr. Hennacy reveals the unbelievable things she witnessed while testing nonspeaking autistic individuals, why statistically significant psi phenomena continue to challenge mainstream scientific assumptions, and what it would actually take to prove telepathy scientifically. She explains why scientists struggle to recreate psi abilities in laboratory settings, how skeptics critique her work, and why researchers in the field believe the word "telepathy" itself may be preventing serious scientific engagement.We explore groundbreaking questions about consciousness, including whether telepathic abilities could help explain how babies acquire language so effortlessly, why Dr. Hennacy believes all humans may possess latent telepathic abilities, and how modern society may have caused many of us to lose access to them. She also explains why telepathy cannot be explained by quantum computation in the brain, even while discussing how savant abilities may reveal previously unknown computational capacities of the human mind.Dr. Hennacy teases her emerging model of how the brain actually works, shares extraordinary case studies involving verified precognitive dreams, and discusses where consciousness goes during dream states. She explains why dreams may provide a window into the unconscious, how they reveal our interconnectedness with others, which dreams deserve special attention, and why reports of precognition continue to fascinate both researchers and experiencers alike.We also dive into why psychedelic experiences so often include reports of telepathy, the shared physiological traits found among individuals who report psychic abilities, and the mystical experiences with a psychic that first sparked Dr. Hennacy's interest in studying these controversial phenomena scientifically.The conversation expands into autism research, exploring how autistic individuals process information differently, why so many nonspeaking autistic people experience synesthesia, reports of seeing auras, and what the autism community can teach the rest of humanity about cognition, perception, communication, and consciousness itself.Dr. Hennacy discusses the profound impact of The Telepathy Tapes podcast, why it resonated so deeply with autistic individuals and their families, and how it helped many people feel seen, heard, and understood for the first time.We also explore acquired savant syndrome and its most mind-blowing documented cases, potential causes behind the apparent rise in autism, and how environmental pressures may be contributing to increasing diversity in human cognitive styles. Are humans evolving toward greater cognitive specialization, or losing something we once possessed?Other topics include:- NDE-adjacent experiences from Dr. Hennacy's youth and how later discoveries in physics shaped her interpretation of those events- Brain function, consciousness, & the limits of current neuroscience- Anxiety, OCD, & emotional dysregulation in nonspeaking autistic individuals- Scientific challenges of measuring telepathy quantitatively- Why more research into the gut microbiome could revolutionize psychiatry- Implications of telepathy research for human cognitive development- Why we should operate from a higher level of consciousness when interacting with AI- How evolving environments shape cognitive diversity across humanityIf consciousness is far stranger than we imagine, this conversation may completely change how you think about autism, dreams, telepathy, AI, neuroscience, and what it means to be human!Learn more about The Hennacy Institute: https://hennacyinstitute.org/Follow us on Substack for Exclusive Bonus Content: https://bialikbreakdown.substack.com/BialikBreakdown.comYouTube.com/mayimbialikSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
What if telepathy is real and neuroscience has been looking at it the wrong way all along?In this episode of Mayim Bialik's Breakdown, Dr. Diane Hennacy, MD (neuropsychiatrist, neuroscientist, consciousness researcher known for her groundbreaking work with autistic savants and other neurodivergent individuals) shares the astonishing evidence that led her to investigate telepathy, precognition, consciousness, and the hidden capabilities of the human mind.Dr. Hennacy reveals the unbelievable things she witnessed while testing nonspeaking autistic individuals, why statistically significant psi phenomena continue to challenge mainstream scientific assumptions, and what it would actually take to prove telepathy scientifically. She explains why scientists struggle to recreate psi abilities in laboratory settings, how skeptics critique her work, and why researchers in the field believe the word "telepathy" itself may be preventing serious scientific engagement.We explore groundbreaking questions about consciousness, including whether telepathic abilities could help explain how babies acquire language so effortlessly, why Dr. Hennacy believes all humans may possess latent telepathic abilities, and how modern society may have caused many of us to lose access to them. She also explains why telepathy cannot be explained by quantum computation in the brain, even while discussing how savant abilities may reveal previously unknown computational capacities of the human mind.Dr. Hennacy teases her emerging model of how the brain actually works, shares extraordinary case studies involving verified precognitive dreams, and discusses where consciousness goes during dream states. She explains why dreams may provide a window into the unconscious, how they reveal our interconnectedness with others, which dreams deserve special attention, and why reports of precognition continue to fascinate both researchers and experiencers alike.We also dive into why psychedelic experiences so often include reports of telepathy, the shared physiological traits found among individuals who report psychic abilities, and the mystical experiences with a psychic that first sparked Dr. Hennacy's interest in studying these controversial phenomena scientifically.The conversation expands into autism research, exploring how autistic individuals process information differently, why so many nonspeaking autistic people experience synesthesia, reports of seeing auras, and what the autism community can teach the rest of humanity about cognition, perception, communication, and consciousness itself.Dr. Hennacy discusses the profound impact of The Telepathy Tapes podcast, why it resonated so deeply with autistic individuals and their families, and how it helped many people feel seen, heard, and understood for the first time.We also explore acquired savant syndrome and its most mind-blowing documented cases, potential causes behind the apparent rise in autism, and how environmental pressures may be contributing to increasing diversity in human cognitive styles. Are humans evolving toward greater cognitive specialization, or losing something we once possessed?Other topics include:- NDE-adjacent experiences from Dr. Hennacy's youth and how later discoveries in physics shaped her interpretation of those events- Brain function, consciousness, & the limits of current neuroscience- Anxiety, OCD, & emotional dysregulation in nonspeaking autistic individuals- Scientific challenges of measuring telepathy quantitatively- Why more research into the gut microbiome could revolutionize psychiatry- Implications of telepathy research for human cognitive development- Why we should operate from a higher level of consciousness when interacting with AI- How evolving environments shape cognitive diversity across humanityIf consciousness is far stranger than we imagine, this conversation may completely change how you think about autism, dreams, telepathy, AI, neuroscience, and what it means to be human!Watch the Stuart Fails to Save the Universe Official Podcast on HBO Max, the HBO Max YouTube channel, or listen wherever you get your podcasts. https://link.mgln.ai/StuartHead to https://forkfulmeals.com/BREAKDOWN for 50% off your first order today!Machine Washable Rugs, Made Better. For a limited time only, our listeners get 10% off + free shipping at https://www.tumbleliving.com/BREAK #Tumble #adHead to https://www.Superpower.com and use code BREAK at checkout for $20 off your membership. Unlock your new health intelligence. 100+ biomarkers. Every year. Detects early signs of 1,000+ conditions. #superpowerpodMake your summer wardrobe feel easier. Go to https://www.quince.com/breakdown for free shipping on your order and 365-day returns.Go to https://helixsleep.com/breakdown to receive 20% off sitewide, 25% of Luxe Mattresses and 30% off Elite Mattresses.Upgrade your kitchen with Our Place today. Visit fromourplace.com/BREAK and use code BREAK for 10% off sitewide.Learn more about The Hennacy Institute: https://hennacyinstitute.org/Follow us on Substack for Exclusive Bonus Content: https://bialikbreakdown.substack.com/BialikBreakdown.comYouTube.com/mayimbialikSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Dr. Alan Castel, PhD, is a professor of psychology at the University of California, Los Angeles (UCLA) and one of the world's foremost experts on human memory and cognitive aging. We discuss what science actually tells us about how to improve our learning ability and memory at any age. We also discuss how memory works, why all planning and imagination about the future is based on the past, false memories, and how to leverage curiosity, emotion, and self-testing retrieval practice to stamp in memories for the long term. We discuss what "superagers"—people who actually improve their cognitive capacity with age—do differently than everyone else. This episode is for anyone interested in the science of memory and tools to maintain and improve your memory across the lifespan. Thank you to our sponsors AG1: https://drinkag1.com/huberman Wealthfront*: https://wealthfront.com/huberman Helix Sleep: https://helixsleep.com/huberman Function: https://functionhealth.com/huberman Lingo: https://hellolingo.com/huberman Timestamps (00:00:00) Dr. Alan Castel (00:02:41) What Is Memory?, Reconstruction & Metacognition (00:04:49) Mnemonics, Remembering Names & Deeper Learning (00:08:22) The Penny & Apple Logo, Noticing vs Seeing, Learning Through Mistakes (00:10:43) Sponsors: Wealthfront & Helix (00:14:05) Neuroplasticity, Frustration, Curiosity & Mindset (00:17:42) Maintaining vs Learning New Things, Habits, Novelty & Emotional Memory (00:24:28) "Mental Photographs," Photo-Taking & Imagining the Future (00:29:28) Eyewitness Memory, the Ronald Cotton Case, Confidence vs Accuracy (00:35:07) Medium-Term & Prospective Memory, Hotel Fire Exits (00:40:28) Sponsor: AG1 (00:41:47) When Habits Turn Lethal, Aviation & Human Error (00:49:01) Why Memory Changes With Age; Alzheimer's & the Nun Study (00:52:34) Exercise & Hippocampal Volume, Falls & Balance (00:57:14) SuperAgers & Athletes; Regret, Balance & Being Driven (01:12:08) Sponsor: Function (01:13:45) Age Stereotypes, Subjective Age & Positive Age Beliefs (01:20:02) Goals & Plans, Scams; Anterior Midcingulate Cortex & SuperAgers (01:26:23) Culture, Resilience, Blue Zones & COVID (01:29:18) Adversity, the Positivity Effect & Intergenerational Learning (01:36:31) Sponsor: Lingo (01:38:00) Limitations & Purpose; Time, Family & Connection (01:44:58) Deliberately Building Memories; the ABCs of Successful Aging (01:51:02) Following Your Interests; Castel's Path & Older Adults (01:57:16) Mental Simulations, Curiosity Studies & Selectivity (02:01:19) Socioemotional Selectivity Theory; Steve Jobs & Lifespan (02:07:10) The Secret to Successful Aging; State vs Trait Curiosity (02:11:04) Scams & AI Voice Cloning (02:14:31) John Wooden, Wisdom, Love & Balance (02:17:41) Learning Through Mistakes; Does the Brain Get Better With Age? (02:25:00) Conclusion, Better With Age (02:26:00) Zero-Cost Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Sponsors, Protocols Book, Social Media, Neural Network Newsletter Disclaimer & Disclosures *This experience may not be representative of other Wealthfront clients, and there is no guarantee of future performance or success. Experiences will vary. Andrew Huberman receives cash compensation from Wealthfront Brokerage for paid testimonials in his podcast, creating a conflict of interest. The Cash Account, which is not a deposit account, is offered by Wealthfront Brokerage LLC, member FINRA/SIPC. Wealthfront Brokerage is not a bank. The base APY is 3.30% on cash deposits as of January 30, 2026, is representative, subject to change, and requires no minimum. If eligible for the overall boosted rate of 4.05% offered in connection with this promo, your boosted rate is also subject to change if the base rate decreases during the 3 month promo period. Additional terms and conditions apply, which can be found on Wealthfront.com/Huberman. Funds in the Cash Account are swept to program banks, where it earns the variable APY. Same-day withdrawal or instant payment transfers may be limited by destination institutions, daily transaction caps, and by participating entities such as Wells Fargo, the RTP® Network, and FedNow® Service. New Cash Account deposits are subject to a 2-4 day holding period before becoming available for transfer. Investment advisory services are provided by Wealthfront Advisers LLC, an SEC-registered investment adviser. Securities investments: not bank deposits, bank-guaranteed or FDIC-insured, and may lose value. Learn more about your ad choices. Visit megaphone.fm/adchoices
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