Podcasts about harvard

In this eye-opening interview, conservative influencer Kaizen Asiedu and Dr. Phil dissect the fatal Minneapolis ICE shooting of Renée Nicole Good and explain why Americans must stop choosing sides based on pure emotion and start thinking clearly and critically.At a time when legacy media, tribal identity, and algorithm-driven outrage fuel our beliefs, Kaizen, a Harvard philosophy graduate turned social media star, tackles some of the most divisive issues facing Americans, including the difference between true racism and the political-motivated race card. This conversation isn't about left vs. right, it's clarity vs. chaos.Thank you to our sponsor: Preserve Gold - text "ASK PHIL" to 50505 and go to https://DrPhilGold.comJoin the conversation with Kaizen:Instagram: @thatskaizen https://www.instagram.com/thatskaizen/ YouTube: https://www.youtube.com/@KaizenAsiedu1 Ground News: https://ground.news/landingV8/thatskaizen?Substack: https://thatskaizen.substack.com/ See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, you're going to receive the best dating advice no one ever told you. Whether you're single, dating, in a relationship, or listening because you're looking for advice for someone you love - this episode will change how you think about dating and love.If you're feeling discouraged, burned out, or you're starting to wonder, “Am I ever going to find the right person?” or “Am I with the right person?” you're not alone - and this episode is for you.Today, Mel sits down with Logan Ury, who is a Harvard-trained behavioral scientist, world-renowned researcher, dating expert, bestselling author of How to Not Die Alone: The Surprising Science That Will Help You Find Love, and one of the most trusted voices on modern dating and relationships. Logan is a fan-favorite and Mel has brought her back on the show for a brand new episode all about dating.She is going to give you the data, the psychology, and the tools you need to stop spiraling and start dating with confidence.Logan breaks down why dating feels so hard in today's world - and how to stop repeating patterns that keep you stuck. She will also cover topics like rejection, attachment, and commitment. After today, you will know:-The 8 questions that instantly help you choose a better partner -What to text instead of ghosting - including a simple rejection text you can copy/paste-How to have the “What are we?” conversation without begging, negotiating, or abandoning yourself-How to break the anxious-avoidant loop that makes dating feel like chaos -What “the ick” really is - and how it keeps you single when you say you want love-How to stop dating burnout by dating more sustainably After this episode, you'll know how to build deeper connections, find and keep true love, and show up as a better partner. For more resources related to today's episode, click here for the podcast episode page.  If you liked the episode, check out Logan's first appearance on The Mel Robbins Podcast: The Best Relationship Advice No One Ever Told YouFor more dating and relationship advice, listen to this episode with Matthew Hussey: The Brutal Truth About Relationships You Need to HearConnect with Mel:   Order Mel's new product, Pure Genius ProteinGet Mel's newsletter, packed with tools, coaching, and inspiration.Get Mel's #1 bestselling book, The Let Them TheoryWatch the episodes on YouTubeFollow Mel on Instagram The Mel Robbins Podcast InstagramMel's TikTok Subscribe to SiriusXM Podcasts+ to listen to new episodes ad-freeDisclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Listen to the rest of this premium episode by subscribing at patreon.com/knowyourenemy.This episode is about Shattered Glass, the 2003 movie portraying former New Republic writer Stephen Glass's fall from the heights of magazine journalism after he was exposed as a serial fabulist who routinely made up quotes, sources, key details, and more in his stories. We've both loved this movie for years, and thought discussing it would serve as a companion of sorts to our interview with Jason Zengerle about Tucker Carlson—and, of course, as a chance for us to geek out about it. After describing the basics of the plot and introducing the main characters, we explore the history of the New Republic under its then-owner and editor in chief Marty Peretz; its string of young, Harvard educated editors during the Peretz Era, who often had short, turbulent stints in that role; fact-checking and the mythos of objective journalism; the relationship between elite magazine writing and celebrity culture during "the end of history"; and more.Sources:Shattered Glass (2003)Buzz Bissinger, "Shattered Glass," Vanity Fair, Sept 1998Howard Kurtz, "Stranger Than Fiction: The Cautionary Tale of Magazine Writer Stephen Glass," Washington Post, May 12, 1998Jonathan Last, "Stopping Stephen Glass," Weekly Standard, Oct 30, 2003Pete Croatto, "Why ‘Shattered Glass' Endures," Poynter, Jan 24, 2024Martin Peretz, The Controversialist: Arguments with Everyone, Left Right and Center (2023)Benjamin Wallace-Wells, "Peretz in Exile," New York, Dec 23, 2010John Cook, "Why Won't Anyone Tell You That Marty Peretz Is Gay?" Gawker, Jan 25, 2011David Klion, "Everybody Hates Marty," The Baffler, Sept 13, 2023Andrew Sullivan, Virtually Normal: An Argument About Homosexuality (1996)— "The Tao of Marty," The Weekly Dish, July 21, 2023Alex Shultz, "Nobody Wants To Talk About John Fetterman And Buzz Bissinger's Pricey Memoir Project," Defector, June 23, 2025

How is Tiktok rewiring your brain? Social psychologist Jonathan Haidt and Harvard physician Dr Aditi Nerurkar reveal how tech addiction and short-form video are ROTTING your brain, and why AI chatbots could cause the next global addiction CRISIS!  Jonathan Haidt is a social psychologist at NYU Stern and the author of the #1 New York Times bestseller The Anxious Generation. Dr. Aditi Nerurkar is a world-renowned expert in stress, burnout, and mental health, and best-selling author of ‘The 5 Resets'.  They explain: ◼️The "brain hacking" secrets tech companies use to hook you ◼️Why short-form video is shattering the global attention span ◼️The link between phone-based childhoods and the teen mental health crisis ◼️How TikTok causes a 40% drop in memory accuracy ◼️Why you must delete addictive slot-machine apps to reclaim your focus Enjoyed the episode? Share this link and earn points for every referral - redeem them for exclusive prizes: https://doac-perks.com  00:00 Intro 02:26 The Largest Threat To Humanity Right Now—And Why No One Wants To Admit It 06:31 How Short-Form Videos Are Rewiring Your Brain For The Worse 09:26 What Your Phone Is Doing To Your Sleep, Heart, And Stress Levels 16:15 Why Short-Form Content Is Quietly Killing Deep Thinking 19:07 What's Really Happening In Your Brain When You Scroll 26:24 What Happens When You Quit Social Media—And Take Back Control 30:00 The Real Danger Behind Meta, Snapchat, And TikTok 36:05 The Dark Side Of Snapchat: Cyberbullying And Predators Exposed 41:23 Oxytocin, AI Chatbots, And What This Means For Your Brain 55:20 What If Your Business Depends On Social Media—Is There Another Way? 01:00:28 Why So Many People Feel Lost—And How Tech Plays A Role 01:06:16 Ads 01:07:17 The Simple Test To Know If You're Addicted To Your Phone 01:26:08 What Is “Popcorn Brain”—And Do You Have It? 01:28:04 Brain Rot: Why Adults Can Recover—But Teens Might Not 01:31:45 Why Australia Banned Social Media For Under-16s—And What Happens Next 01:43:16 Ads 01:45:33 Why Parents Can't Sue Social Media Companies—And What This Law Protects 02:04:29 How Technology Is Eroding Our Sense Of Meaning 02:08:39 How To Reclaim Meaning And Joy In A Hyper-Digital World 02:14:14 The 3-Second Brain Reset That Breaks The Scroll Cycle Follow Dr Aditi: Instagram - https://linkly.link/2aYTX  Website - https://linkly.link/2aYTZ You can purchase Aditi's book, ‘The 5 Resets', here: https://linkly.link/2aYTd  Follow Jonathan: X - https://linkly.link/2aYTq Website - https://linkly.link/2aYTs  You can purchase Jonathan's book, ‘The Amazing Generation', here: https://linkly.link/2aYU7  Independent research: https://stevenbartlett.com/wp-content/uploads/2026/02/DOAC-Attention-Discussion-Independent-Research-further-reading.pdf The Diary Of A CEO: ◼️Join DOAC circle here - https://doaccircle.com/  ◼️Buy The Diary Of A CEO book here - https://smarturl.it/DOACbook  ◼️The 1% Diary is back - limited time only: https://bit.ly/3YFbJbt  ◼️The Diary Of A CEO Conversation Cards (Second Edition): https://g2ul0.app.link/f31dsUttKKb  ◼️Get email updates - https://bit.ly/diary-of-a-ceo-yt  ◼️Follow Steven - https://g2ul0.app.link/gnGqL4IsKKb  Sponsors: Wispr - Get 14 days of Wispr Flow for free at https://wisprflow.ai/DOAC   Function Health - https://Functionhealth.com/DOAC to sign up for $365 a year. One dollar a day for your health Pipedrive - https://pipedrive.com/CEO

Jillian is joined by Harvard psychologist Dr. Ellen Langer, the pioneer of mindfulness research, for a conversation that challenges everything you think you know about stress, healing, and being present. Dr. Langer explains why mindfulness isn't about meditation—it's about noticing. They unpack how our mindset shapes our relationships, our health, and even how we age. From letting go of labels to rethinking forgiveness, this episode is a masterclass in conscious living. Buy Dr. Ellen Langer's book, The Mindful Body Download Jillian's FREE limerence workbook, http://jillianturecki.com/workbook  Join my community and membership, The Conscious Woman Submit your relationship question for Jillian at https://forms.gle/FbtgkGTwfnrjvHwW7  Order Jillian's book It Begins with You: The 9 Hard Truths About Love That Will Change Your Life at https://www.jillianturecki.com/book ~~ Follow the show on: Instagram: @jillianonlove Email the show at hello@jillianonlove.com  Subscribe to Jillian on Love+ on Apple Podcasts or Patreon ~~ Follow Jillian Turecki on: Instagram: @jillianturecki TikTok: @jillian.turecki X: @JillianTurecki Visit her website at jillianturecki.com ~~ Jillian On Love is brought to you by QCODE. To advertise on the show, contact us! Learn more about your ad choices. Visit podcastchoices.com/adchoices

From uncovering the belief that's quietly holding you back… to mapping your path to the C-suite… to recovering after a career-defining mistake — executive coach Muriel Wilkins is back with real, unscripted coaching conversations that take you inside the lives of leaders at pivotal moments. And for the first time ever, you'll get new episodes twice a month, all year long - no more waiting between seasons.Listen in as leaders wrestle with ambition, visibility, competition, fulfillment, and the hard challenges that come with the job. Plus, check out a brand-new monthly series — Ask Muriel Anything — where Muriel answers the toughest leadership questions straight from listeners.The new season of Coaching Real Leaders drops March 2. Connect with Muriel:Website: murielwilkins.comLinkedIn: @Muriel Maignan Wilkins Instagram: @CoachMurielWIlkins Join the Coaching Real Leaders Community: coachingrealleaderscommunity.comRead Muriel's book: LeadershipUnblocked.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode Andrea Samadi revisits Season 15's foundation with Dr. Bruce Perry to explore how safety, regulation, and patterned experience shape the brain's capacity to learn and create. We examine why potential must be activated through repetition, rhythm, and low-threat environments, and how trauma, stress, or dysregulation block learning. Takeaways include practical steps for educators, parents, and leaders: prioritize nervous-system safety before instruction, use micro-repetition to build skills, and employ storytelling to make scientific ideas stick. This episode anchors Phase 1 of the season: regulation, rhythm, repetition, and relational safety as the prerequisites for sustainable performance and lasting change. This week, Episode 385—based on our review of Episode 168 recorded in October 2021—we explore: ✔ 1. Genetic Potential vs. Developed Capacity We are born with extraordinary biological potential. But experience determines which neural systems become functional. The brain builds what it repeatedly uses. ✔ 2. The Brain Is Use-Dependent Language, emotional regulation, leadership skills, motor precision— all are wired through patterned, rhythmic repetition. ✔ 3. Trauma, Regulation & Learning A dysregulated nervous system cannot efficiently learn. Safety, rhythm, and relational connection come before strategy. ✔ 4. “What Happened to You?” vs. “What's Wrong with You?” Shifting from judgment to curiosity changes how we approach: Children Students Teams Ourselves ✔ 5. Early Experience Shapes Long-Term Expression Developmental inputs—especially patterned, early ones— determine which capacities are strengthened. ✔ 6. Repetition Builds Confidence Confidence is not a personality trait. It is neural circuitry built through structured repetition in safe environments. ✔ 7. Story Makes Science Stick From Dr. Perry's experience writing with Oprah: You can't tell everybody everything you know. Impact comes from: One core idea Wrapped in story Delivered with restraint ✔ 8. Information Overload Weakens Learning Depth > Volume Clarity > Density Retention > Impressive Data ✔ 9. Regulation Comes Before Motivation Before goals. Before performance. Before achievement. The nervous system must feel safe. ✔ 10. Season 15's Foundational Question Is the nervous system safe enough to learn? Welcome back to Season 15 of the Neuroscience Meets Social and Emotional Learning Podcast. I'm Andrea Samadi, and here we bridge the science behind social and emotional learning, emotional intelligence, and practical neuroscience—so we can create measurable improvements in well-being, achievement, productivity, and results. When we launched this podcast seven years ago, it was driven by a question I had never been taught to ask— not in school, not in business, and not in life: If results matter—and they matter now more than ever—how exactly are we using our brain to make these results happen? Most of us were taught what to do. Very few of us were taught how to think under pressure, how to regulate emotion, how to sustain motivation, or even how to produce consistent results without burning out. That question led me into a deep exploration of the mind–brain–results connection—and how neuroscience applies to everyday decisions, conversations, and performance. That's why this podcast exists. Each week, we bring you leading experts to break down complex science and translate it into practical strategies you can apply immediately. If you've been with us through Season 14, you may have felt something shift. That season wasn't about collecting ideas. It was about integrating these ideas into our daily life, as we launched our review of past episodes. Across conversations on neuroscience, social and emotional learning, sleep, stress, exercise, nutrition, and mindset frameworks—we heard from voices like Bob Proctor, José Silva, Dr. Church, Dr. John Medina, and others—one thing became clear: These aren't separate tools that we are covering in each episode. They're parts of one operating system. When the brain, body, and emotions are aligned, performance stops feeling forced—and starts to feel sustainable. Season 14 showed us what alignment looks like in real life. We looked at goals and mental direction, rewiring the brain, future-ready learning and leadership, self-leadership, which ALL led us to inner alignment. And now we move into Season 15 that is about understanding how that alignment is built—so we can build it ourselves, using predictable, science-backed principles. Because alignment doesn't happen all at once. It happens by using a sequence. And when we understand the order of that sequence — we can replicate it. By repeating this sequence over and over again, until magically (or predictably) we notice our results have changed. So Season 15 we've organized as a review roadmap, where each episode explores one foundational brain system—and each phase builds on the one before it. Season 15 Roadmap: Phase 1 — Regulation & Safety Phase 2 — Neurochemistry & Motivation Phase 3 — Movement, Learning & Cognition Phase 4 — Perception, Emotion & Social Intelligence Phase 5 — Integration, Insight & Meaning PHASE 1: REGULATION & SAFETY Staples: Sleep + Stress Regulation Core Question: Is the nervous system safe enough to learn? Anchor Episodes Episode 384 — Baland Jalal How learning begins: curiosity, sleep, imagination, creativity Bruce Perry “What happened to you?” — trauma, rhythm, relational safety Sui Wong Autonomic balance, lifestyle medicine, brain resilience Rohan Dixit HRV, real-time self-regulation, nervous system literacy Last week we began with Phase One: Regulation and Safety as we revisited Dr. Baland Jalal's interview from June 2022. EP 384 — Dr. Baland Jalal[i] Dr. Baland Jalal This episode sits at the foundation of Season 15. Dr. Baland Jalal is a Harvard neuroscientist whose work explores how sleep, imagination, and curiosity shape the brain's capacity to learn and create. What stood out to me then — and even more now — is that learning doesn't begin with effort. It begins when the brain is rested, regulated, and free to explore possibility. This conversation reminds us that creativity isn't added later — it's built into the brain when conditions are right. It's here we remember that before learning can happen, before curiosity can emerge, before motivation or growth is possible— the brain must feel safe. And what better place to begin with safety and the brain, than with Dr. Bruce Perry, who we met October of 2021 on EP 168.[ii] EP 385 — Dr. Bruce Perry Dr. Bruce Perry (Episode 168 – October 2021) Dr. Bruce Perry, Senior Fellow of the Child Trauma Academy in Houston, Texas, and Adjunct Professor of Psychiatry and Behavioral Sciences at the Feinberg School of Medicine in Chicago, joined the podcast to help us better understand how traumatic experiences shape the developing brain. At the time, I was deeply concerned about the generational impact of the COVID-19 pandemic. In one of Dr. Perry's trainings, he referenced research conducted after Hurricane Katrina in 2005, which showed that families exposed to prolonged stress experienced increased rates of substance abuse — not only in those directly affected, but in the next generation as well. As I began hearing reports of rising depression, anxiety, and substance use during the pandemic, I wondered: What could we do now to reduce the long-term neurological and emotional impact on our children, our schools, and future generations? Dr. Perry agreed to come on the show to share insights from his work and to discuss his book, co-authored with Oprah Winfrey: What Happened to You: Conversations on Trauma, Resilience and Healing.[iii] Dr. Bruce Perry challenges one of the most common questions we ask in education, leadership, and parenting. Instead of asking, “What's wrong with you?” he asks, “What happened to you?” In this conversation, we explored how early experiences shape the brain, how trauma disrupts regulation, and why healing begins with rhythm, safety, and connection. You can find a link to our full interview in the resource section in the show notes. This episode anchors Season 15 by reminding us: a dysregulated brain cannot learn — no matter how good the strategy. Let's go to our first clip with Dr. Bruce Perry, and look deeper at how we are all born with potential, but our experience builds the rest.

Send us a message!Ever wondered what it is like to start a group RD practice? In this episode Dana interviews Christina Vosbikian about how she went from finance to Harvard's MBA program to starting Coord Health!Christina Vosbikian is the founder and CEO of Coord Health, a tech-enabled healthcare startup supporting patients and providers across women's healthcare settings by delivering virtual interventions between in-person visits. Their first service line is virtual nutrition counseling from a team of Registered Dietitians. Before starting Coord, Christina had a background in operating (Planned Parenthood, Allara Health), private equity investing at Berkshire Partners, and investment banking at Goldman Sachs. Christina holds an MBA from Harvard and did her undergrad in public policy at Princeton University. She is a recipient of the Robert F. Jasse Award for entrepreneurship at Harvard Business School and Harvard Business School's Blavatnik Fellowship for Life Science Entrepreneurship.Coord Health is a women's health startup out of Harvard built around the idea that lifestyle care – nutrition, movement, sleep, and stress – is key a key part of healthcare. We partner directly with OBGYN practices to provide evidence-based virtual nutrition counseling for women across every stage of life.

In this event, Kent Walker, President of Global Affairs for Google, discusses the importance of Ireland as a digital economy hub in Europe and the role of the digital economy in ensuring Ireland's future prosperity. Mr Walker also discusses how Ireland can harness its upcoming presidency of the Council of the EU, starting in July 2026, to push for the measures that are needed to unleash Europe's digital competitiveness and to secure Europe's digital resilience. In his remarks, Mr Walker examines the debate about Europe's regulatory framework, the growing role of AI, and how to ensure Europe's resilience against digital threats. This event is organised as a collaboration between the IIEA and Google. Kent Walker is President of Global Affairs at Google and Alphabet, overseeing content policy, government and regulatory affairs, and legal, risk, and compliance matters. With a 30+ year career at the intersection of technology, law, and policy, he has led Google's advocacy on key issues and served as the first chair of the Global Internet Forum to Combat Terrorism. A Harvard and Stanford Law graduate, Kent was previously an Assistant U.S. Attorney and held executive positions at Netscape, AOL, and eBay. He serves on TechNet's executive committee and the Council on Foreign Relations.

Steve and Jeremy talk to Mark and Todd Stricker, two of the co-founders of Rush Creek Distilling in Harvard, Illinois. The Bourbon Show music (Whiskey on the Mississippi) is by Kevin MacLeod (incompetech.com).   Important Links: YouTube: https://bit.ly/3kAJZQz Our Club: https://www.abvnetwork.com/club Patreon: https://www.patreon.com/theabvnetwork Check us out at: abvnetwork.com. Join the revolution by adding #ABVNetworkCrew to your profile on social media.

Host Piya Chattopadhyay speaks with Gisèle Pelicot about her public rape trial and her thoughts on becoming a feminist heroLaw professor Elaine Craig breaks down the intersection of sexual assault, law and culture in Canada -- and why the courts alone can't address society-wide issuesProfessor Emeritus of Social Work at the University of British Columbia Edward Taylor unpacks the mental health effects of mass violence following the deadly shooting in Tumbler Ridge, B.C.Harvard professor Jarvis R. Givens explains why on the 100th anniversary of Black History Month, the occasion is as big a cultural flashpoint as ever

Brian Petrovek is a longtime community leader and former sports and entertainment executive whose career spans elite athletics, business leadership, and civic engagement. In this episode of Radio Maine, Petrovek joins host Dr. Lisa Belisle to reflect on his journey from high-level hockey at Hotchkiss and Harvard to decades of leadership in professional sports and live entertainment. Known in Maine for bringing elevated sports experiences to the state, Petrovek shares how those years shaped his belief in hospitality, risk-taking, and creating meaningful experiences by choice—not necessity. Now in a new chapter, he is focused on service, arts, education, and strengthening community life in Portland, from board leadership to cultural institutions and mentoring future leaders. This conversation weaves together family, Maine's youth sports culture, and Petrovek's guiding philosophy of “first principles”—simplifying challenges to their core. Thoughtful and forward-looking, this episode offers insight into leadership, creativity, and giving back. Join our conversation with Brian Petrovek today on Radio Maine—and be sure to subscribe to the channel. 

The newest tranche of documents from the U.S. Department of Justice's Epstein Files shows that Jeffrey Epstein's reach into academia was wider than previously understood, revealing communications and interactions between the disgraced financier and faculty, administrators, and fundraisers at major universities. Emails and records include discussions about potential donations, academic projects, and introductions to other scholars, with figures at institutions such as Harvard, Yale, and Bard College appearing in the files. At Harvard, for example, correspondence shows some faculty and leaders engaging with Epstein even after his 2008 conviction, while at Yale, two professors were named — one of whom has been removed from teaching while the university reviews his contact with Epstein. The documents illustrate how Epstein positioned himself as a potential benefactor to researchers and institutions, often offering a quicker route to funding than federal grants and prompting criticism about ethical compromises made in pursuit of private money.At Bard College, longtime president Leon Botstein's name appears extensively in the files, with emails showing repeated contact with Epstein over several years regarding fundraising and events; these revelations have sparked student dismay and scrutiny of how the college handled the relationship. Other universities and scholars mentioned in the broader Epstein Files — including faculty ties at Ohio State University indirectly through connections like donors or trustees — reflect the broader trend of elite academic figures maintaining some form of correspondence with Epstein, sometimes long after his criminal conduct was public. Collectively, the disclosures raise questions about the influence of wealthy private donors on higher education and the oversight universities exercised when engaging with Epstein and his network.to contact  me:bobbycapucci@protonmail.comsource:Colleges face scrutiny over Epstein connectionsBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-moscow-murders-and-more--5852883/support.

In this insightful interview, Pastor Jack King sits down with Stan Jones, the longtime Associate Head Coach for Florida State University's men's basketball team. With over two decades in Tallahassee and a career spanning the University of Miami and the Washington Wizards, Coach Jones shares how his career was less about "climbing the ladder" and more about walking through doors opened by God. From the pressure of the ACC tournament to the quiet sacrifices of a family man, this conversation reveals the character behind the coach. -- The Calling of Coaching: How a gift of John Wooden's book at age 17 shifted Stan's path from business management to the basketball court. -- Divine Timing: The incredible story of how Coach Jones transitioned from a private high school in Mississippi to the University of Miami without ever seeking the job. -- Leadership Secrets: Insights into the "emotional IQ" required to manage high-pressure games and the importance of filling in the "leadership gaps" for a head coach. -- The Power of Loyalty: Why Stan chose to stay with Leonard Hamilton for 29 seasons instead of chasing head coaching titles elsewhere. -- Faith and Family: A moving account of how a job change provided the exact health insurance needed just days before a family medical crisis. -- Managing Success and Failure: Reflections on the 2020 "National Championship" that never was due to COVID-19 and the lessons learned from tough losses to Princeton and Harvard. 5. Scriptures for Further Study -- Colossians 3:23-24 -- Proverbs 3:5-6 -- Psalm 37:5 This is episode 1263. ******* This is the radio program with the music removed. By the way, I have written a new book, and you can find it here: https://www.amazon.com/Dreams-Visions-Stories-Faith-Pastor/dp/161493536X

In March of 2015, Denise Huskins was kidnapped, drugged, sexually assaulted, and held for 48 hours. When she was released, police called it a hoax and demanded that she apologize for wasting resources. The media dubbed it the "Gone Girl" case and death threats started flooding in. Except it wasn't a hoax at all. It was a Harvard-educated serial rapist named Matthew Muller who'd been terrorizing California for years. In this episode, we'll go through the kidnapping, the police misconduct that revictimized the survivors, Detective Misty Carausu's brilliant investigative work that finally caught Muller, and how Denise and Aaron turned trauma into national advocacy. From victims to suspects to survivors...their story changed how law enforcement handles sexual assault cases across America.For Survivors of Sexual Violence:- RAINN National Sexual Assault Hotline: 1-800-656-HOPE (4673)- RAINN Online Chat:https://hotline.rainn.org/online- Crisis Text Line: Text HOME to 741741- National Sexual Violence Resource Center:https://www.nsvrc.org/For Victims of Police Misconduct:- ACLU:https://www.aclu.org/- National Police Accountability Project:https://www.nlg-npap.org/- Innocence Project:https://innocenceproject.org/Mental Health Support:- National Suicide Prevention Lifeline: 988- SAMHSA National Helpline: 1-800-662-4357- Psychology Today Therapist Finder:https://www.psychologytoday.com/us/therapistsSources:San Francisco Chronicle (Henry K. Lee's Reporting):- https://www.sfchronicle.com/ (Search "Denise Huskins" for extensive archive)Major National News Outlets:- https://abcnews.go.com/ - https://www.nbcnews.com/ - https://www.cnn.com/ - https://www.nytimes.com/ - https://www.latimes.com/ - https://www.usatoday.com/ Bay Area Local News:- https://www.ktvu.com/ - https://www.kron4.com/ - https://www.mercurynews.com/ - https://www.sfgate.com/ - https://www.timesheraldonline.com/ People Magazine & Entertainment:- https://people.com/ (Search "Denise Huskins" for features)American Nightmare (2024):- https://www.netflix.com/title/81456520 "Victim F: From Crime Victims to Suspects to Survivors" (2021):- https://www.amazon.com/Victim-Crime-Victims-Suspects-Survivors/dp/1538720558Federal Court Case:- https://www.justice.gov/usao-edca - Case: USA v. Matthew Daniel Muller, Case No. 2:15-cr-00242-TLN- https://www.pacer.gov/ State Court Cases:- https://www.solano.courts.ca.gov/ - https://www.santaclaracourt.org/ - https://www.cc-courts.org/ Defamation Lawsuit:- Huskins v. City of Vallejo - Settled March 2018 for $2.5 millionDenise Huskins' Attorneys:- Doug Rappaport- https://www.rappaportlaw.com/ Aaron Quinn's Attorneys:- Daniel Russo- https://russoandrusso.com/ Law Enforcement Training:- The case is now taught at police academies nationwide- Featured in FBI training materials on sexual assault investigations- https://www.fbi.gov/services/training-academy Criminal History & Background:- https://www.bop.gov/inmateloc/ (Federal Bureau of Prisons Inmate Locator)- Search: Matthew Daniel Muller, Register Number: 04664-111California State Bar:- https://www.calbar.ca.gov/ - Search for Matthew Muller's disciplinary records and disbarmentYouTube:- https://www.youtube.com/@ABCNews - https://www.youtube.com/@DatelineNBC - https://www.youtube.com/@netflix 2015 News Archives:- https://www.newspapers.com/ - https://news.google.com/newspapers Articles Analyzing the Case:- https://www.vulture.com/ (Vulture - entertainment analysis)- https://www.rollingstone.com/ (Rolling Stone features)- https://www.vanityfair.com/ (Vanity Fair long-form)"Gone Girl" Film (2014):- https://www.imdb.com/title/tt2267998/ Denise & Aaron's Advocacy Work:- They've trained law enforcement agencies nationwide- Spoken at conferences on sexual assault investigation best practices- Worked with prosecutors on Muller's cold casesCalifornia Prosecutors' Recognition:- 2025: Named "Witnesses of the Year" by California prosecutors- https://www.cdaa.org/California District Attorneys Association:- https://www.cdaa.org/ (2025 Witnesses of the Year announcement)Snopes:- https://www.snopes.com/ (Search "Denise Huskins" for fact-checking)FBI Press Releases:- https://www.fbi.gov/news/press-releases (Search "Matthew Muller")U.S. Attorney's Office:- https://www.justice.gov/usao-edca/pr (Press releases on Muller's prosecution)Vallejo Police 2021 Apology:- Issued by Chief Shawny Williams on August 25, 2021- Archived in news articles and official city records$2.5 Million Settlement (March 2018):- City of Vallejo settled defamation lawsuit- No admission of wrongdoing required by settlement terms- Covered extensively in news mediaDenise & Aaron's Media Appearances:- ABC News 20/20- Dateline NBC- Various podcast interviews- Law enforcement training events- Public policy panelsBecome a supporter of this podcast: https://www.spreaker.com/podcast/reverie-true-crime--4442888/support.Keep In Touch:Twitter: https://www.twitter.com/reveriecrimepodInstagram: https://www.instagram.com/reverietruecrimeTumblr: https://reverietruecrimepodcast.tumblr.comFacebook: https://www.facebook.com/reverietruecrimeContact: ReverieTrueCrime@gmail.com Intro & Outro by Jahred Gomes: https://www.instagram.com/jahredgomes_official 

The newest tranche of documents from the U.S. Department of Justice's Epstein Files shows that Jeffrey Epstein's reach into academia was wider than previously understood, revealing communications and interactions between the disgraced financier and faculty, administrators, and fundraisers at major universities. Emails and records include discussions about potential donations, academic projects, and introductions to other scholars, with figures at institutions such as Harvard, Yale, and Bard College appearing in the files. At Harvard, for example, correspondence shows some faculty and leaders engaging with Epstein even after his 2008 conviction, while at Yale, two professors were named — one of whom has been removed from teaching while the university reviews his contact with Epstein. The documents illustrate how Epstein positioned himself as a potential benefactor to researchers and institutions, often offering a quicker route to funding than federal grants and prompting criticism about ethical compromises made in pursuit of private money.At Bard College, longtime president Leon Botstein's name appears extensively in the files, with emails showing repeated contact with Epstein over several years regarding fundraising and events; these revelations have sparked student dismay and scrutiny of how the college handled the relationship. Other universities and scholars mentioned in the broader Epstein Files — including faculty ties at Ohio State University indirectly through connections like donors or trustees — reflect the broader trend of elite academic figures maintaining some form of correspondence with Epstein, sometimes long after his criminal conduct was public. Collectively, the disclosures raise questions about the influence of wealthy private donors on higher education and the oversight universities exercised when engaging with Epstein and his network.to contact  me:bobbycapucci@protonmail.comsource:Colleges face scrutiny over Epstein connections

What happens when AI-generated video becomes indistinguishable from reality — and it's cheaper than lunch?This week, the AI race took a dramatic turn. China didn't just catch up — in some areas, it leaped ahead. And with video models that generate flawless visuals and synchronized audio in real time, we've entered a new era where “seeing is believing” no longer applies.For business leaders, this isn't just geopolitical theater. It's a strategic inflection point. From AI-generated fraud and deepfake manipulation to workforce burnout driven by productivity acceleration, the rules of competition — and trust — are changing faster than most organizations can process.In this episode, we break down what China's AI surge really means, why deepfake technology is now a board-level issue, and how AI may be making your top performers more productive… and more exhausted.In this session, you'll discover:Why China's latest AI releases signal a shift in the global AI power balanceWhat makes ByteDance's new video model fundamentally different from U.S. competitorsHow AI-generated video with synchronized audio changes the fraud landscapeThe growing legal and regulatory backlash from Hollywood and governmentsWhy “Deepfake-as-a-Service” is becoming a criminal business modelThe real financial cost of AI-enabled fraud — and why it's acceleratingMicrosoft's legal action against synthetic abuse networksHow governments are attempting (and struggling) to regulate synthetic mediaWhy AI may increase burnout instead of reducing workloadThe “productivity treadmill” effect inside AI-enabled organizationsHow AI agents are transforming coding, databases, and knowledge workWhy only a tiny percentage of employees are true AI power usersWhat business leaders must do now to prepare for the next waveAbout Leveraging AI The Ultimate AI Course for Business People: https://multiplai.ai/ai-course/ YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/ Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events If you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Former CNN anchor Don Lemon pleads "not guilty" to federal charges, The White House fires off another round in its battle with Harvard, and Patriots receiver Stefon Diggs made his first court appearance on allegations he assaulted his personal chef. Stay in "The Loop" with WBZ NewsRadio. See omnystudio.com/listener for privacy information.

A State Trooper is seriously hurt after a car crashes into his cruiser in Salisbury, a woman has died after falling through the ice in Eastham, and Harvard's Hasty Pudding Theatricals honors actress Rose Byrne. Stay in "The Loop" with WBZ NewsRadio.See omnystudio.com/listener for privacy information.

#689: Most people think forgetting a name means their brain is failing.  Dr. Majid Fotuhi, a neurologist who taught at Johns Hopkins and Harvard, sees thousands of patients convinced they have Alzheimer's – only to discover they're dealing with poor sleep or stress. Dr. Fotuhi joins us to break down the difference between cognitive decline, dementia and Alzheimer's disease. He explains why chronic stress physically shrinks your hippocampus — the thumb-sized memory center in your brain — and how twelve weeks of lifestyle changes reversed cognitive decline in 84 percent of his patients. We talk about the five hidden taxes draining your brain: sedentary lifestyle, poor sleep, junk food, chronic stress and mental laziness. Scrolling social media after work counts as mental laziness, even if your day job involves intense focus. Dr. Fotuhi offers a different framework: five pillars that compound over time. Exercise ranks first because it multiplies mitochondria in your brain cells, reduces inflammation and generates new neurons in your hippocampus. Walking 10,000 steps daily cuts Alzheimer's risk by 50 percent. Sleep comes second. Your brain rinses itself during deep sleep, flushing out amyloid — the core protein in Alzheimer's disease. One night of poor sleep increases amyloid in your brain. We cover nutrition (skip the junk food debate), mindset (heart rate variability breathing reduces Alzheimer's footprints) and brain training. Dr. Fotuhi memorizes 70 names in a single lecture and explains his technique for remembering credit card numbers using mental imagery. The conversation covers London taxi drivers who grew their hippocampus by memorizing 10,000 streets, why stress management beats supplements, and how Swedish students learning Arabic increased their brain volume in three months. Timestamps: Note: Timestamps will vary on individual listening devices based on dynamic advertising segments. The provided timestamps are approximate and may be several minutes off due to changing ad lengths. (00:00) Defining cognitive decline, dementia and Alzheimer's disease (05:19) Why cognitive issues don't always mean Alzheimer's (07:24) Thinking of your brain as an asset to manage (07:51) The five hidden taxes draining your brain (10:45) How poor sleep prevents brain rinsing and causes inflammation (14:20) Oral health and brain health connection (16:40) Brain plasticity and the Broca lobe (27:02) The five pillars of brain health (35:23) Cardiovascular fitness versus strength training for brain health (38:51) Sleep as the second pillar of brain health (48:05) When exercise beats sleep (51:33) Different types of intelligence beyond IQ tests (1:03:53) Reversing brain damage from decades of bad habits (1:10:25) Nutrition and avoiding junk food (1:25:09) Mindset and stress management as pillar four (1:33:35) Breathing exercises for stress reduction (1:39:24) Brain training as the fifth pillar (1:51:52) Memory techniques for names and numbers (2:02:46) Nootropics and supplements for brain health Learn more about your ad choices. Visit podcastchoices.com/adchoices

Newly released Epstein files reveal a $30 million donation to Harvard's Program for Evolutionary Dynamics, prompting scrutiny over how evolution is used as more than just science. The discussion frames Darwinism as a philosophical weapon, one that replaces God with randomness and erodes moral responsibility, aligning with elite interests that benefit from a godless worldview. Scientific challenges to neo-Darwinism are explored, from frauds like Piltdown Man to modern critiques of genetic information and irreducible complexity. The episode warns that dissenting voices in academia are suppressed to maintain the illusion of consensus.HELP SUPPORT WORK LIKE THIS: https://give.lifesitenews.com/?utm_source=SOCIAL U.S. residents! Create a will with LifeSiteNews: https://www.mylegacywill.com/lifesitenews ****PROTECT Your Wealth with gold, silver, and precious metals: https://sjp.stjosephpartners.com/lifesitenews +++SHOP ALL YOUR FUN AND FAVORITE LIFESITE MERCH! https://shop.lifesitenews.com/ ****Download the all-new LSNTV App now, available on iPhone and Android!LSNTV Apple Store: https://apps.apple.com/us/app/lsntv/id6469105564 LSNTV Google Play: https://play.google.com/store/apps/details?id=com.lifesitenews.app +++Connect with John-Henry Westen and all of LifeSiteNews on social media:LifeSite: https://linktr.ee/lifesitenewsJohn-Henry Westen: https://linktr.ee/jhwesten Hosted on Acast. See acast.com/privacy for more information.

This episode's got a mix of current events and inspiring stories. We're covering the latest news, including a search for a missing person in Arizona, a NASA launch, and a Boston Marathon runner's incredible story. We're also talking about a Patriots player facing charges, the Olympic Games, and a Harvard event honoring an actress. Plus, we're sharing updates on a movie release, NBA All-Star Weekend, and a concert at Fenway Park. It's a packed episode with something for everyone.See omnystudio.com/listener for privacy information.

February 13, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Erewhon partners with Ject to offer lip flips and brow lifts at exclusive LA event, marking second move into med spa space Harvard study of 100K+ Americans over 30 years finds exercise variety predicts lifespan better than single modality, with 19% lower all-cause mortality Nike Strength rolls out equipment across 25 Everlast Gyms in UK and Ireland, expanding commercial partnerships beyond consumer training gear More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

Join Simtheory: https://simtheory.aiRegister for the STILL RELEVANT tour: https://simulationtheory.ai/16c0d1db-a8d0-4ac9-bae3-d25074589a80GLM-5 just dropped and it's trained entirely on Huawei chips – zero US hardware dependency. Meanwhile, we're having existential crises about whether we're even needed anymore. In this episode, we break down China's new frontier model that's competing with Opus 4.6 and Codex at a fraction of the price, why agentic loops are making 200K context windows the sweet spot (sorry, million-token dreams), and the very real phenomenon of AI productivity psychosis. We dive into why coding-optimized models are secretly winning at everything, the Harvard study confirming AI doesn't reduce work – it intensifies it, and the exodus of safety researchers from XAI, Anthropic, and OpenAI (spoiler: they're not giving back their shares). Plus: Mike's arm is failing from too much mouse usage, we debate whether the chatbot era is actually fading, and yes – there's a safety researcher diss track called "Is This The End?"CHAPTERS:0:00 Intro - Is This The End? (Song Preview)0:11 Still Relevant Tour Update & NASA Listener Callout1:42 AI Productivity Psychosis: The Pressure of Infinite Capability4:25 GLM-5 Breakdown: China's New Frontier Model on Huawei Chips7:24 First Impressions: GLM-5 in Agentic Loops9:48 Why Cheap Models Matter & The New Model War14:09 Codex Vibe Shift: Is OpenAI Winning?16:24 Does Context Window Size Even Matter Anymore?22:27 The Parallelization Problem & Cognitive Overload27:27 Mike's Arm Injury & The Voice Input Pivot31:17 Single-Threaded Work & The 95% Problem35:06 UX is Unsolved: Rolling Back Agentic Mistakes38:45 Harvard Study: AI Doesn't Reduce Work, It Intensifies It44:01 How AI Erodes Company Structure & Why Adoption Takes Years50:14 My AI vs Your AI: Household Debates50:43 The Safety Researcher Exodus: XAI, Anthropic, OpenAI56:49 Final Thoughts: Are We All Still Relevant?59:04 BONUS: Full "Is This The End?" Diss TrackThanks for listening. Like & Sub. Links above for the Still Relevant Tour signup and Simtheory. GLM-5 is here, your productivity psychosis is valid, and the safety researchers are becoming poets. xoxo

In this episode of Sisterhood of S.W.E.A.T., Linda Mitchell sits down with Dr. John Osborne, board-certified cardiologist, lipidologist, and founder of Clear Cardio, to challenge conventional thinking around cholesterol and heart disease. Dr. Osborne explains why LDL cholesterol alone is not a reliable standalone marker for cardiovascular disease and why focusing only on traditional lab numbers can create a false sense of security. He shares how plaque can quietly develop for decades before symptoms appear — and why the first symptom for many people is a heart attack or sudden cardiac event. The conversation explores the evolution of cardiac imaging, the role of ApoB and lipoprotein(a) in risk assessment, and how advanced cardiac CT combined with artificial intelligence now allows physicians to detect, measure, and track plaque in ways that were previously impossible. This episode reframes heart disease as something that can be identified early — and potentially prevented — when the right tools are used. What We Talk About in This Episode Why LDL cholesterol alone does not tell the full story The difference between risk factors and actual disease How ApoB improves cardiovascular risk assessment Why lipoprotein(a) is genetic and should be tested at least once The limits of traditional stress testing How plaque forms in the arterial wall decades before symptoms Calcium scoring versus full cardiac CT imaging How AI is transforming plaque detection and measurement Whether arterial plaque can be slowed or reversed The real role of statins and other cholesterol-lowering tools Why you cannot out-train genetics The one scan adults over 40 should consider Quotes from This Episode Cholesterol floating in your bloodstream does not tell me if it is sticking. Risk is not disease. The first question should be: do you have plaque? Half of men and two-thirds of women, their first symptom of heart disease is a heart attack or death. The problem is not that we cannot treat plaque. The problem is that we are not looking for it early enough. Early detection for heart disease should be as routine as screening for cancer. Connect with Dr. John Osborne Clear Cardio https://clearcardio.com Clear Cardio – Powers of Prevention YouTube Channel https://www.youtube.com/@ClearCardio Learn more about Cardiac CT and AI plaque analysis https://clearcardio.com/services/ Contact and Locations (Texas, Chicago, expanding to New York) https://clearcardio.com/contact/  

From Washington, D.C. to the Brooklyn waterfront, Lindsay Green shares how a career in finance evolved into a mission to transform industrial spaces into engines of opportunity for underserved communities.Lindsay Green is the President and CEO of the Brooklyn Navy Yard, where she leads one of New York City's most ambitious models for inclusive economic development. With more than 550 businesses and 11,000 employees on site, the Navy Yard is not simply a real estate portfolio but a living ecosystem designed to create quality jobs and connect local residents to meaningful careers. Her work blends business strategy, workforce development, and community engagement into a powerful example of how cities can rethink the purpose of former industrial spaces.Her journey began in Washington, D.C., where daily exposure to economic disparities shaped her desire to work at the intersection of business and community impact. After studying economics at Harvard and starting her career in investment banking at Goldman Sachs, she discovered urban development through the Urban Investment Group under Alicia Glen. Mentorship from leaders like Glen and MIT professor Phil Thompson helped her shift from traditional finance to mission driven economic development. A detour into the food industry after Yale School of Management eventually led her back to this work, culminating in her leadership at the Navy Yard in 2022.Lindsay explains how the Brooklyn Navy Yard goes beyond affordable real estate to support small, women owned, and minority owned businesses with mentorship, capital access, and technical advisory services. She highlights the Brooklyn STEAM Center, a public high school that gives 600 students hands on training with industry grade equipment, as well as new adult reskilling programs that recognize the value of both digital and analog problem solving. Through initiatives like the Micro Business Accelerator Program, she is building pathways for entrepreneurs to start small, grow, and scale within a supportive ecosystem.This conversation explores leadership, economic mobility, and the importance of early exposure to career possibilities. Lindsay's work demonstrates that revitalizing industrial spaces can do more than preserve history. It can create futures. Tune in to hear how thoughtful economic development can reshape communities and expand opportunity for the next generation.Chapters:00:00

The newest tranche of documents from the U.S. Department of Justice's Epstein Files shows that Jeffrey Epstein's reach into academia was wider than previously understood, revealing communications and interactions between the disgraced financier and faculty, administrators, and fundraisers at major universities. Emails and records include discussions about potential donations, academic projects, and introductions to other scholars, with figures at institutions such as Harvard, Yale, and Bard College appearing in the files. At Harvard, for example, correspondence shows some faculty and leaders engaging with Epstein even after his 2008 conviction, while at Yale, two professors were named — one of whom has been removed from teaching while the university reviews his contact with Epstein. The documents illustrate how Epstein positioned himself as a potential benefactor to researchers and institutions, often offering a quicker route to funding than federal grants and prompting criticism about ethical compromises made in pursuit of private money.At Bard College, longtime president Leon Botstein's name appears extensively in the files, with emails showing repeated contact with Epstein over several years regarding fundraising and events; these revelations have sparked student dismay and scrutiny of how the college handled the relationship. Other universities and scholars mentioned in the broader Epstein Files — including faculty ties at Ohio State University indirectly through connections like donors or trustees — reflect the broader trend of elite academic figures maintaining some form of correspondence with Epstein, sometimes long after his criminal conduct was public. Collectively, the disclosures raise questions about the influence of wealthy private donors on higher education and the oversight universities exercised when engaging with Epstein and his network.to contact  me:bobbycapucci@protonmail.comsource:Colleges face scrutiny over Epstein connectionsBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-epstein-chronicles--5003294/support.

In this Mission Matters episode, Adam Torres interviews Kevin McGovern, Chairman of McGovern Capital LLC, about his journey from investing in the stock market in seventh grade to building global brands like SoBe and mentoring the next generation of entrepreneurs through purpose-driven investing. About Kevin McGovern Kevin McGovern is Chairman and CEO of McGovern Capital, a New York–based single-family office that invests globally in innovative companies and strategic opportunities. Through McGovern Capital and its affiliates, he has co-founded over 25 companies, six of which became world or category leaders, and has served as lead negotiator in more than 15 international joint ventures across approximately 80 countries. McGovern was a founder of SoBe Beverages, helped popularize advanced water filtration technologies, and licensed skincare innovations used in ~40 % of products worldwide. He also serves on public and private boards, speaks internationally at academic and family office events, and has taught “Global Innovation and Commercialization” at top universities including Cornell, Stanford, MIT, and Harvard. About McGovern Capital LLC McGovern Capital LLC is a leading private investment firm and intellectual property strategist based in New York City, Greenwich, CT, and Miami, Florida. The firm originates, funds, structures, and implements capital formation as well as domestic and international joint ventures and business alliances, and provides early-stage capital and facilitative services to its portfolio companies. Through its global network, McGovern Capital and its affiliates have co-founded over 25 companies, including six category leaders such as SoBe Beverages and KX Industries, and have conducted business in approximately 80 countries. The firm specializes in go-to-market strategies for disruptive technologies and consumer-focused innovations, helping founders assess, strategize, and monetize intellectual property and business opportunities worldwide. Watch Full Episode On Youtube --- Follow Adam on Instagram at https://www.instagram.com/askadamtorres/ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: https://missionmatters.lpages.co/podcastguest/ Visit our website: https://missionmatters.com/ More FREE content from Mission Matters here: https://linktr.ee/missionmattersmedia Learn more about your ad choices. Visit podcastchoices.com/adchoices Learn more about your ad choices. Visit podcastchoices.com/adchoices

President Trump says regime change is the best thing that could happen in Iran. Journalist Don Lemon pleads not guilty to charges stemming from a Minnesota church protest. The Trump administration sues Harvard. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

As we move through midlife, many of us start noticing subtle shifts in our brains — maybe it takes longer to find a word, focus feels harder than it used to, or multitasking suddenly feels overwhelming. It's easy to assume these changes are just “part of aging,” but the truth is far more hopeful: our brains are far more adaptable than we've been led to believe. That's one reason this episode feels especially important. For so many years we were told that cognitive decline was inevitable, that once certain abilities slowed down there was little we could do. But today's research on neuroplasticity tells a very different story — one where the brain can continue to strengthen, retrain, and improve well into later life when given the right kind of stimulation. My guest today, Dr. Tere Linzey, is a Licensed Educational Psychologist and founder of BrainMatterZ, a program dedicated to optimizing core cognitive skills like processing speed, attention, memory and executive functioning. With more than 30 years in education and research at institutions including Harvard and UC Berkeley, she has focused her work on helping people understand how the brain can be trained to function more efficiently at any age. Together, we talk about what's actually happening in the brain during midlife, how to tell the difference between normal aging and cognitive skills that can be improved with training, and why strengthening foundational brain functions can have such a powerful effect on confidence, focus, and everyday performance. Dr. Linzey also explains how menopause, stress and modern lifestyle pressures influence cognitive health — and what we can begin doing today to support our brains for the long term. A few things we discuss: • What neuroplasticity really means and how it allows the brain to improve at any age• How to distinguish normal aging changes from cognitive skills that can be strengthened• The impact of menopause, stress and lifestyle factors on focus, memory and processing speed• Practical steps to support long-term brain health and cognitive resilience If you've ever wondered whether the changes you're noticing are simply aging — or something you can actually improve — this conversation will leave you feeling informed, empowered, and far more hopeful about what's possible.

The Trump administration is taking legal action against Harvard. AP correspondent Mike Hempen reports.

The Homeland Security Department is on the brink of shutting down. A fast arraignment for Patriots receiver Stefon Diggs. Harvard calls the government's new lawsuit against it "retaliatory." Stay in "The Loop" with WBZ NewsRadio.See omnystudio.com/listener for privacy information.

we dive deep into Nas' groundbreaking Nasir Jones Hip-Hop Fellowship at Harvard University — the first academic fellowship ever named after a hip-hop artist. We break down its mission, how it empowers global scholars and creators, and why hip-hop's influence on academia is more powerful than ever.We also explore how universities, cultural institutions, and creators worldwide are elevating hip-hop as a serious field of study — from music history and social commentary to fashion, activism, and community storytelling.Whether you're an artist, a scholar, or a fan of hip-hop culture, this episode gives you a full breakdown of how the genre continues shaping education, identity, and global youth culture.

The Dean's List with Host Dean Bowen – Hegseth's move away from Harvard marks a significant turning point in closing the chapter of Wokeism in higher education as other universities also end the destructive ideology. “We are now seeking One Billion Dollars in damages, and want nothing further to do, into the future, with Harvard University,” the president said in...

You may know people who are seemingly unflappable. Steadfast. As if they've somehow been inoculated with antibodies to render them able to survive, strive, and even thrive through major life stressors and transitions. What's different about these people? Are they born with resilient genes, or does culture play a role? Do they have certain outlooks or behaviors that make them able to manage life with grace and confidence?If so, are these skills that everyone can learn?Today's guest, Stanford and Harvard trained psychiatrist, medical anthropologist, and humanitarian adviser, Dr. Suzan Song,has been exploring these questions for decades. Working with people facing everything from everyday instability to profound human rights violations,She's witnessed first-hand the limits of routine Western approaches to adversity. And now, in her highly anticipated debut book, Why We Suffer and How We Heal, Suzan shows us that resilience isn't inherited or taught in isolation—it emerges from the stories we tell, the rituals we keep, and the connections we depend on. In today's conversation we'll unpack some of Dr. Song's hard-earned wisdom, Her insights about what helps most to weather life's storms, And the groundbreaking path she's uncovered that can lead to deep healing, thriving in spite of challenges, and feeling fully alive again.  Perhaps most importantly though, she reminds us that this path Is open to us all.For more on Dr. Song's extraordinary work, speaking, and her new book (which if you enjoy this conversation I can't recommend highly enough) please visit Suzansong.comEnjoying the show? Please rate it wherever you listen to your podcasts!Did you find this episode inspiring? Here are other conversations we think you'll love:On Curiosity, Presence, and Love | Dr. Jacob HamOn Choosing Love | Mark NepoOn Unlocking Our Primal Intelligence | Angus FletcherThanks for listening!Support the show

Welcome to part two of our deathbed regrets series. Last week I covered the first four regrets—this week I'm finishing with the final six, and these might hit even harder because they're about living on autopilot, postponing joy, and holding grudges.   Regret #5: Not choosing happiness. Happiness isn't something that happens to you—it's a daily decision.  Regret #6: Not taking the risk. People don't regret what they tried and failed at—they regret what they never tried.  Regret #7: Not prioritizing self-care. Not bubble baths—actual care. Meeting your needs, protecting your energy, honoring your body.  Regret #8: Not taking the vacation. Both literally and metaphorically. People regret not traveling while they had their health, but this is also about not postponing joy.  Regret #9: Not living in the present. Harvard research found we spend 47% of our waking hours thinking about something other than what we're doing—and it makes us less happy. Presence isn't passive, it's a practice. Regret #10: Not forgiving. Both others and yourself. Forgiveness research shows lower stress, better cardiovascular health, better sleep.    You have enough history to know where your regret lies. Do you have enough courage to stop rehearsing it and start rewriting it?

In this episode of the Epigenetics Podcast, we talked with Srinjan Basu from Imperial College London to talk about his work on how chromatin architecture and epigenetic mechanisms orchestrate developmental gene expression programs. We begin by exploring Dr. Basu's early work at Harvard which involved pioneering Raman-based label-free imaging, allowing the study of chromatin dynamics in live tissue. Here, he tackles technical challenges faced in visualizing DNA interactions, emphasizing the shift from 2D to 3D analysis and the importance of real-time observation of chromatin behavior under various conditions. This segues into his groundbreaking research on single transcription factors interacting with chromatin, revealing subtle but significant changes in the dynamics of gene regulation. We transition into the complexities of chromatin architecture as Dr. Basu recounts his efforts in mapping the entire mouse genome in single pluripotent cells, unearthing unexpected heterogeneity among cells. This heterogeneity raises intriguing questions about its impact on cellular function, prompting ongoing investigations into chromatin dynamics and the role of remodeling complexes like NuRD in cell fate transitions. Dr. Basu elucidates how recent studies have begun to bridge the gaps in understanding how transcription factors and chromatin dynamics interact during cellular decisions, particularly emphasizing the influence of mechanical signals and the intrinsic properties of cells. His research underscores the idea that stem cells undergo a preparatory phase for differentiation, highlighting the critical balance of intrinsic and extrinsic factors that govern genetic expression and cellular outcomes. We also talk about Dr. Basu's current research trajectory, focusing on enhancing imaging techniques to study gene dynamics in tissue contexts relevant to developmental biology and disease states. He illustrates a vision for future projects that integrate advanced imaging tools to investigate transcription factor dynamics and chromatin interactions in live cells and embryos, furthering the understanding of decision-making processes in cellular contexts. References Stevens TJ, Lando D, Basu S, et al. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature. 2017 Apr;544(7648):59-64. DOI: 10.1038/nature21429. PMID: 28289288; PMCID: PMC5385134. Basu S, Needham LM, Lando D, et al. FRET-enhanced photostability allows improved single-molecule tracking of proteins and protein complexes in live mammalian cells. Nature Communications. 2018 Jun;9(1):2520. DOI: 10.1038/s41467-018-04486-0. PMID: 29955052; PMCID: PMC6023872. Related Episodes Advanced Optical Imaging in 3D Nuclear Organisation (Lothar Schermelleh) Analysis of 3D Chromatin Structure Using Super-Resolution Imaging (Alistair Boettiger) Single-Molecule Imaging of the Epigenome (Efrat Shema) Contact Epigenetics Podcast on Mastodon Epigenetics Podcast on Bluesky Dr. Stefan Dillinger on LinkedIn Active Motif on LinkedIn Active Motif on Bluesky Email: podcast@activemotif.com

La borrasca Nils suspende clases en Cataluña y Castilla-La Mancha por vientos, con avisos en otras zonas. Se recomienda teletrabajo. El Congreso debate una ley contra la multirreincidencia. Sánchez admite carencias ferroviarias y anuncia mejoras, ante futuras acciones legales de Feijóo y Abascal. Adif no tiene fecha para la reapertura Madrid-Sevilla. Madrid y Barcelona presentan la vivienda más cara de España (más de 10.000€/m²). El 34% paga con el móvil y el 70% consulta IA para compras. Un estudio de Harvard revela que el 70% de los empleados cree que las reuniones dificultan el trabajo, siendo las de pie un 40% más cortas. Oyentes comparten experiencias de reuniones absurdas. Se destacan novedades musicales de Aitana, Shakira, Juanes, David Guetta, John Legend y Rosalía.

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

Send a textAutonomy sounds like progress until the system turns your choices against you. We dive into how AI agents change the risk equation, why “don't trust, verify” now beats “trust but verify,” and what to do when the update button itself becomes the attack vector.We start with the Ivy League leak tied to Harvard and UPenn, where attackers exposed admissions hold notes that map influence rather than credit cards. That context turns routine records into leverage for extortion, social pressure, and geopolitical targeting. From there, we trace the surge of agentic AI in the workplace as employees paste code, legal docs, and sensitive files into chat interfaces. The real accelerant is MCP, the model context protocol that standardizes connections across Google Drive, Slack, databases, and more. Like USB for AI, MCP makes integration simple and powerful, but a single prompt injection can pivot across everything the agent can reach.Security gets messier with supply chain compromise. A China‑nexus campaign allegedly hijacked the Notepad++ update mechanism, handing a bespoke backdoor to developers who did the right thing. We unpack how to keep patching while reducing risk: signed updates, independent checksum checks, tight egress policies for updaters, and strong monitoring around update flows. On the policy front, Rhode Island's vendor transparency rule forces companies to name who buys data. It is a nutrition label for privacy, and it lets users and watchdogs finally connect the dots between friendly interfaces and aggressive brokers.We close with concrete defenses that raise the floor. Move high‑value accounts to FIDO2 hardware keys or platform passkeys to block phishing at the protocol level. Scope agent permissions narrowly, isolate MCP connectors by function, and require explicit approvals for sensitive actions. Log everything an agent touches and review those trails. Autonomy should be earned, minimal, and observable. If AI is going to act on your behalf, it must prove itself at every step.If this conversation helps you think differently about agents, influence mapping, and how to lock down your stack, subscribe, share with a teammate, and leave a quick review telling us the one control you plan to implement this week.Support the show

Guest: Larry Cheng is the Co-Founder and Managing Partner of Volition CapitalWebsite: https://www.volitioncapital.com/. AUM: Volition Capital has $1.8 Billion AUM on their 5th FundLarry's BioLarry Cheng is the Co-Founder and Managing Partner of Volition Capital, a growth equity firm focused on supporting founders building capital-efficient technology businesses. With over 25 years of investing experience, Larry has led investments in dozens of companies across Internet, e-commerce, software, and consumer sectors. Most notably, Larry was the first investor in Chewy, which became the most valuable e-commerce acquisition in history. He currently serves on public company boards such as GameStop and Grove Collaborative as well as several private company boards such as US Mobile, Rounds, Levanta and several others. Earlier in his career, he led investments at Fidelity Ventures and began in venture capital at Bessemer Venture Partners.Larry's entrepreneurial journey began early when he became Apple's youngest certified technician at age 13. While at Harvard, he launched a $400,000 laundry business and later became President of Harvard Student Agencies, a $4 million student-run company serving the greater Harvard community. He graduated with a B.A. in Psychology and played football for the Crimson. Larry is a frequent guest lecturer at institutions including Harvard Business School, MIT Sloan, and USC Marshall School of Business.

Market Correction vs. Collapse: Analysis of why the "SaaS is dead" narrative is likely an exaggeration of a necessary shift, where the real threat lies in "sleepy" companies failing to adapt to rapid technological transitions.The Productivity Paradox: Exploration of recent Harvard research showing AI often intensifies workloads rather than reducing them, leading to expanded job scopes, "vibe-coding," and increased cognitive load.The $700 Billion Infrastructure Gamble: Breakdown of unprecedented AI capital expenditures from Big Tech giants like Amazon and Google, and the resulting strain on free cash flow and debt levels.High-Stakes Influencer Marketing: Discussion on the billion-dollar digital ad surge and $600,000 influencer deals used to drive AI adoption, questioning if revolutionary tech should require such aggressive paid promotion.OpenAI's Financial Projections: A look at OpenAI's projected $14 billion loss in 2026 and the implications of its massive burn rate for the future of the industry.Credit: @Ric_RTP on X

We kicked off the program with four news stories and different guests on the stories we think you need to know about!The nonprofit Cancer Kids First, started by Harvard grad Olivia Zhang when she was 14, after losing her grandfather and a beloved teacher to cancer. The youth-led nonprofit has helped 10,000+ patients across 22 countries. -Olivia also wrote a book called: YOUth: The Young Person’s Guide to Starting a Nonprofit Guest: Olivia Zhang – Founder of Cancer Kids First - the youngest 2025 L’Oréal Paris Women of Worth, a Diana Legacy Award recipient, and the youngest 2026 Forbes 30 Under 30 Social Impact honoree Family of Spies: A World War II Story of Nazi Espionage, Betrayal, and the Secret History Behind Pearl Harbor…this is the author’s real-life story finding out her family’s horrendous family secret kept hidden for half a century. Guest: Christine Kuehn – author and former journalist How much gold is in an Olympic gold medal, and how much is it worth? Some Olympians have been complaining about their medals breaking and deteriorating…-There’s also a current Olympic Auction going on right now at RR Auction… Guest: Bobby Eaton - Olympic Specialist at RR Auction The latest jobs report released Wednesday shows Employers added 130,000 jobs in January, blowing past expectations… Guest: Dan Varroney - Economic Strategist & Founder and CEO of Potomac Core, a strategic planning firm that advises business leaders, trade associations, and policymakers. See omnystudio.com/listener for privacy information.

"I will say that QAnon was right and I was wrong." — Pepper CulpepperFrom Bannon and Trump to Summers, Gates, Blavatnik and Chomsky, the Epstein scandal has revealed elites of all ideological stripes behaving shamefully together. The Oxford political scientist Pepper Culpepper argues this is exactly the kind of corporate scandal that can save democracy—not despite its ugliness, but because of it. His new co-authored book, Billionaire Backlash, shows how scandals activate "latent opinion," bringing long-simmering public concerns to the surface and triggering society-wide demand for regulation. We discuss why Cambridge Analytica led to California privacy law, how Samsung's bribery scandal sparked Korea's Candlelight Protests, and why China's authoritarian approach to corporate malfeasance actually undermines trust.Culpepper, himself the Blavatnik Professor of Government at Oxford's Blavatnik School, acknowledges an uncomfortable truth. "I would say that QAnon was right," he admits, "and I was wrong." The specifics might have been fantasy, but the underlying suspicion about elite corruption was justified. And policy entrepreneurs—obsessive individuals who channel public outrage into actual legislation—matter more than we think. For Culpepper, billionaire backlash isn't a threat to democracy—it might actually be what saves it.About the GuestPepper Culpepper is Vice Dean of the Blavatnik School of Government at the University of Oxford. He is the co-author, with Taeku Lee of Harvard, of Billionaire Backlash: The Age of Corporate Scandal and How It Could Save Democracy (2026).ReferencesScandals discussed:●      The Epstein scandal revealed that elites across politics, finance, and academia were connected to Jeffrey Epstein's network of abuse—vindicating populist suspicions that "the system is broken."●      Cambridge Analytica (2018) exposed how Facebook leaked data on 90 million users, leading to the Digital Markets Act and Digital Services Act in the EU, and California's privacy regulations.●      The Samsung bribery scandal in South Korea led to the Candlelight Protests and President Park Geun-hye's resignation, demonstrating how corporate scandals can strengthen civil society.●      The 2008 Chinese milk scandal killed six infants due to melamine contamination; the government's cover-up during the Beijing Olympics destroyed public trust in domestic food safety.●      Volkswagen's Dieselgate scandal showed how companies cheat on regulations, bringing latent concerns about corporate behavior to the surface.Policy entrepreneurs mentioned:●      Carl Levin was a US Senator from Michigan who shepherded the Goldman Sachs hearings and contributed to the Dodd-Frank Act.●      Margrethe Vestager served as EU Competition Commissioner and pushed for the Digital Markets Act and Digital Services Act.●      Max Schrems is an Austrian privacy activist who, as a student, discovered Facebook retained his deleted messages and eventually brought down the US-EU data transfer agreement.●      Alastair Mactaggart is a California property developer who pushed through the state's privacy regulations when federal action proved impossible.●      Zhao Lianhai was a Chinese activist who tried to organize parents after the 2008 milk scandal; the government arrested and imprisoned him.Concepts discussed:●      Latent opinion refers to concerns people hold in the back of their minds that aren't front-of-mind until a scandal brings them to the surface.●      The Thermidor reference is to the French Revolutionary period when the radical Jacobins were overthrown—Culpepper suggests a controlled version might benefit democracy.●      The muckrakers were Progressive Era journalists whose exposés led to reforms like the Food and Drug Administration.Also mentioned:●      Michael Sandel is a Harvard political philosopher known for arguing that "there shouldn't be a price on everything."●      Patrick Radden Keefe wrote Empire of Pain, the definitive account of the Sackler family and the opioid epidemic.●      Lee Jae-yong is the heir apparent to Samsung, implicated in the bribery scandal.●      Parasite, Squid Game, and No Other Choice are Korean cultural works that critique the country's relationship with its conglomerates.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States—hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotifyChapters:(00:00) - (00:22) - The Epstein opportunity (01:21) - Elite overreach exposed (03:12) - Scandals without partisan charge (05:04) - The Vice Dean's credibility problem (06:21) - Latent opinion explained (09:39) - Is there anything wrong with being a billionaire? (11:47) - American vs. European scandals (14:48) - Saving democracy vs. saving capitalism (17:05) - Corporate scandals and economic vitality (18:33) - Policy entrepreneurs: Carl Levin and Margrethe Vestager (19:54...

Ian Coss of the Big Dig and John Bullard, former New Bedford mayor and Sustainable Development director at NOAA, discuss season three of the podcast, "Catching the Codfather."Harvard national security expert Juliette Kayyem on security at the Olympics and the Super Bowl, plus the crypto currency grift within the Trump family.Naturalist and author Sy Montgomery zooms in to discuss inter-species communication between dogs and the humans who give them buttons. Plus, the Indigenous-led declaration recognizing whales as legal persons.And, Joe Hanson, host of High School Quiz Show, checks in ahead of the new season.

If your skin suddenly feels dry, dull, unpredictable, or just not like you anymore in your 40s, this episode is a must-listen. I'm joined by dermatologist Dr. Mary Alice Mina to break down what's really happening to your skin during perimenopause, and why slapping on more products isn't the answer. We cut through the noise on aging skin, talk hormones vs. hype, and get her honest takes on Botox, sunscreen, lymphatic drainage, glow-boosting treatments, and what's actually worth your money. No fear-mongering, no trends — just real, grounded advice to help you feel confident in your skin again. Dr. Mary Alice Mina is a Harvard-trained, double board-certified dermatologist, international speaker, author, and host of The Skin Real, a podcast ranked in the top 2% globally. She is the founder of The Skin Real Serenbe, a bespoke dermatology practice dedicated to bringing humanity back to medicine through personalized, transparent, and human connection. Dr. Mina blends science-backed expertise with nearly two decades of clinical experience to help women look in the mirror and love what they see—through empowerment, clarity, and alignment. She believes that how you feel in your skin affects how you show up in the world. A sought-after speaker, she delivers impactful keynotes and leads workshops and retreats that reframe aging, aesthetics, and skin health. Her approachable expertise has been featured in People Magazine, The Wall Street Journal, Glamour, and HuffPost. Social Media & Website Instagram TikTok LinkedIn Website Podcast YouTube Apple Podcasts  Spotify  Affiliate Links RegimenPro Aire Health The Skin Real Store Elm Biosciences Free Resource 5 Day Perimenopause Challenge In My Perimenopause Era  

Larry Summers and Jeffrey Epstein were connected through overlapping elite academic, financial, and political networks rather than any formally acknowledged partnership, but the relationship has raised persistent ethical and reputational questions. Epstein cultivated proximity to power by attaching himself to influential figures, and Summers—then a central node in global economics as former U.S. Treasury Secretary and later president of Harvard—was part of the world Epstein aggressively courted. Epstein donated money connected to Harvard-linked initiatives during and after Summers' tenure, and he leveraged those institutional ties to maintain legitimacy even after his 2008 sex-crime conviction. Critics argue that Summers' broader ecosystem helped normalize Epstein's continued access to elite spaces, particularly as Epstein sought to launder his reputation through academia and intellectual patronage.to contact me:bobbycapucci@protonmail.com

Renue Healthcare https://Renue.Healthcare/ToddYour journey to a better life starts at Renue Healthcare. Visit https://Renue.Healthcare/Todd Bulwark Capital https://KnowYourRiskPodcast.comBe confident in your portfolio with Bulwark! Schedule your free Know Your Risk Portfolio review. Go to KnowYourRiskPodcast.com today. Alan's Soaps https://www.AlansArtisanSoaps.comUse coupon code TODD to save an additional 10% off the bundle price.Bonefrog https://BonefrogCoffee.com/ToddGet the new limited release, The Sisterhood, created to honor the extraordinary women behind the heroes. Use code TODD at checkout to receive 10% off your first purchase and 15% on subscriptions.LISTEN and SUBSCRIBE at:The Todd Herman Show - Podcast - Apple PodcastsThe Todd Herman Show | Podcast on SpotifyWATCH and SUBSCRIBE at: Todd Herman - The Todd Herman Show - YouTubeThe New York Times is pushing something you would never expect them to push. They are effectively promoting schizophrenia. I'll explain how…Episode Links:RFK, Jr on food cures for schizophrenia BREAKING: The New York Times just called RFK Jr.'s claim that diet can treat schizophrenia "unfounded."Columbia's Dr. Appelbaum said there's "no credible evidence." One problem: There are 75+ years of published research — from Harvard, Stanford, McLean, Oxford, Duke, and the journal

Lawmakers to View Unredacted Epstein Files. Hegseth vs The Boy Scouts and Harvard. JD Vance Booed at Olympics. US Olympians Speak Out Against ICE. Super Bowl Monday Should be a National Holiday.  It's Super Bowl Monday and Independent Americans host Paul Rieckhoff is unpacking a wild 24 hours in America—from a politically charged halftime show and Seattle's gritty win to dangerous ICE raids, new 9/11 revelations, and the most political Olympics yet. Every episode of Independent Americans with Paul Rieckhoff (@PaulRieckhoff) breaks down the most important news stories and offers light to contrast the heat of other politics and news shows. It's independent content for independent Americans in a time when trusted news, politics, inspiration, and hope are in short supply. In this all‑new solo “Manosphere Monday” episode, Paul ties together Bad Bunny's historic halftime performance and its “the only thing more powerful than hate is love” message, Trump-world backlash, and the NFL's bet on Latino and Puerto Rican culture as a preview of America's demographic future. He launches “Manosphere Monday” with real talk on male leadership, raising boys, and men's health—spotlighting prostate screenings via that unforgettable “Relax Your Tight End” Novartis ad. Along the way he exposes chilling ICE abuses, honors murdered Minneapolis VA hero Alex Preti, and reveals a newly surfaced 9/11 memo showing New York City officials quietly worried about toxic air and legal liability while first responders and residents were told it was safe.​ Paul also tracks Trump's war on the free press, Pentagon stonewalling, Pete Hegseth's escalating culture war against the Boy Scouts and Harvard, JD Vance getting booed at the Olympics, and why Ukraine's athletes are now the spiritual center of the Games. He highlights the growing movement for open primaries, new polling showing Americans are fed up with partisan primaries, and why veterans and independents are leading the charge to reclaim our democracy—before closing with some sports hope in college hoops, March Madness, and his surging St. John's Johnnies. If you're exhausted by partisan spin, corporate media, and performative “manosphere” grifters, this is your alternative, independent briefing on Super Bowl Monday—packed with politics, culture, sports, and honest conversation about health, masculinity, and American leadership. Because every episode of Independent Americans with Paul Rieckhoff breaks down the most important news stories--and offers light to contrast the heat of other politics and news shows. It's independent content for independent Americans. In these trying times especially, Independent Americans is your trusted place for independent news, politics, inspiration and hope. The podcast that helps you stay ahead of the curve--and stay vigilant. -WATCH video of this episode on YouTube now. -Learn more about Paul's work to elect a new generation of independent leaders with Independent Veterans of America. -Join the movement. Hook into our exclusive Patreon community of Independent Americans. Get extra content, connect with guests, meet other Independent Americans, attend events, get merch discounts, and support this show that speaks truth to power.  -Check the hashtag #LookForTheHelpers. And share yours.  -Find us on social media or www.IndependentAmericans.us.  -And get cool IA and Righteous hats, t-shirts and other merch now in time for the new year.  -Check out other Righteous podcasts like The Firefighters Podcast with Rob Serra, Uncle Montel - The OG of Weed and B Dorm.  Independent Americans is powered by veteran-owned and led Righteous Media.  And now part of the BLEAV network!  Ways to listen: Spotify • Apple Podcasts • Amazon Podcasts  Ways to watch: YouTube • Instagram  Social channels: X/Twitter • BlueSky • Facebook    Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Watch The X22 Report On Video No videos found (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:17532056201798502,size:[0, 0],id:"ld-9437-3289"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="https://cdn2.decide.dev/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs");pt> Click On Picture To See Larger Picture The Fake News lost the narrative on the climate hoax. Trump bringing back the fishing industry in Maine. Everything is being reverse, jobs are coming back. Trump is moving the pieces on the board and preparing the country to move back to sound money and the is using the market as a weapon. The [DS] cannot keep the country divided anymore. The people are awake and they are seeing the true enemy through the fog. Trump is pushing everything to win the Midterms. We are watching the final countdown. Trump is exposing the system and the election cheating system to force the RINOS to pass the save act. Once this is done it is game over.   Economy https://twitter.com/ChrisMartzWX/status/2020341736896360591?s=20 (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:18510697282300316,size:[0, 0],id:"ld-8599-9832"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="https://cdn2.decide.dev/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs");   foolishly reinstated them. Since Day One, I have taken historic action to END these disastrous policies and, today, I signed a Presidential Proclamation to UNLEASH Commercial Fishing in the Atlantic Ocean, advancing the America First Fishing Policy! I am restoring nearly 5,000 square miles of Fishing access off the Coast of New England, which will revitalize our Fishing Industry, and STRENGTHEN our Booming Economy. Congratulations to all of our Great Fishermen. Please remember I did this for you, against strong Democrat opposition, and VOTE REPUBLICAN IN THE MIDTERMS! PRESIDENT DONALD J. TRUMP  https://twitter.com/unusual_whales/status/2020181009124192563?s=20   https://twitter.com/Bobby1_x/status/2020284867708350837?s=20  house: 614 oz gold Now: 82 oz 1971 Car: 86 oz gold Now: 9 oz 1971 Harvard: 63 oz gold Now: 11 oz 1971 Gas: 1 oz gold = 113 gallons Now: 1 oz gold = 1736 gallons If you saved in dollars your value inflated away to almost nothing But if you saved in gold you INCREASED your real world purchasing power MASSIVELY You didn’t see inflation, you saw deflation And you never even had to do so much as sell as stock or learn about bonds and interest rates All you had to do was save in gold Gold is and always will be the ultimate store of value https://twitter.com/KobeissiLetter/status/2020229075487322323?s=20  By comparison, the 2020 high and 2012 peak were 40.9 million and 43.4 million, respectively. Meanwhile, ETFs of gold and other precious metals attracted +$4.39 billion in inflows in January, posting their 8th consecutive monthly intake. Furthermore, investors have invested a net +$3.62 billion in gold miner ETFs, the most since at least 2009. Demand for gold investments remains robust. https://twitter.com/MrPool_QQ/status/2020219515615793465?s=20  Reserve nominee if he doesn’t lower rates. “It was a joke.” No. It was a WARNING. The Fed’s days are numbered. MOVE 3: Pentagon CUT ALL TIES with Harvard. Military training. Fellowships. Programs. ALL GONE. The Ivy League pipeline to power is DEAD. MOVE 4: Launched TrumpRx. 43 medications. Ozempic included. Big Pharma’s monopoly: BROKEN. They charged you $1,000. He’s giving it for $300. MOVE 5: DHS funding expires February 13th. 6 days from now. Controlled shutdown incoming. Why? Because you can’t RESTRUCTURE what’s still running. Connect the dots: Iran tariffs = END of petrodollar Fed threat = END of central banking control Harvard cut = END of Deep State recruitment TrumpRx = END of Big Pharma monopoly DHS shutdown = RESTRUCTURING of homeland security This isn’t chaos. This is a DEMOLITION. Piece by piece. System by system. Pillar by pillar. The old world is being dismantled in REAL TIME. And the new one is being built while you watch.  DARK TO LIGHT   Political/Rights https://twitter.com/ICEgov/status/2019804241343234265?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2019804241343234265%7Ctwgr%5Ea4849f0e923af3c8c6337a4af454066151ac3a71%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2026%2F02%2Fsupposedly-autistic-womans-tale-being-abused-arrested-ice%2F   the location, continued to impede our officers, and found out the hard way. 18 U.S.C. § 111 criminalizes impeding or interfering with federal officers. Team Trump Catches Gavin Newsom in a HUGE Lie During Back-and-Forth as California Governor Releases Thousands of Violent Criminal Illegals Back into Society https://twitter.com/KristiNoem/status/2019831108511158481?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2019831108511158481%7Ctwgr%5Ed4914c3e3e7d1872b32b0c54f58216356aecffd0%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2026%2F02%2Fteam-trump-catches-gavin-newsom-huge-lie-during%2F https://twitter.com/CAgovernor/status/2019876274798567749?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2019876274798567749%7Ctwgr%5Ed4914c3e3e7d1872b32b0c54f58216356aecffd0%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2026%2F02%2Fteam-trump-catches-gavin-newsom-huge-lie-during%2F https://twitter.com/USAttyEssayli/status/2019883966355107911?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2019883966355107911%7Ctwgr%5Ed4914c3e3e7d1872b32b0c54f58216356aecffd0%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2026%2F02%2Fteam-trump-catches-gavin-newsom-huge-lie-during%2F   The law in question is the California Values Act (SB 54), signed into law in 2017 by then-Governor Jerry Brown. The legislation bars state and local resources from being used to assist federal immigratio Source: thegatewaypundit.com https://twitter.com/liz_churchill10/status/2020347917962473789?s=20 https://twitter.com/elonmusk/status/2020451356562096282?s=20 https://twitter.com/KanekoaTheGreat/status/2020249786017095995?s=20   https://twitter.com/Kimberlyrja8/status/2019799463129133362?s=20  , Savannah stated, “[Nancy] is full of kindness and knowledge. Talk to her, and you'll see.” Many have noticed that the phrasing is nearly identical to the line from the famous thriller, when Sen. Ruth Martin addresses the kidnapper of her daughter, Catherine, saying, “Catherine is very gentle and kind. Talk to her, and you'll see.” https://twitter.com/IENouwen/status/2020088584964125145?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2020088584964125145%7Ctwgr%5E35d5b78a17a39c8933cea82db5535043ef4b09ff%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2026%2F02%2Fwatch-savannah-guthrie-echoed-iconic-silence-lambs-line%2F TAKE A LISTEN   https://twitter.com/RyanSaavedra/status/2019972293032833214?s=20 https://twitter.com/drawandstrike/status/2020283785451806956?s=20   is coming. Remember immediately after that last tranche of documents were released, all of a sudden our international elite class of baby-farming, baby-eating kid fucking criminals were in an increasingly untenable position, where some of ’em had to resign from important positions, and others were being forced into exceedingly awkward explanations/apologies? Well how do you stop the train? How do you arrest the progress of the exposure of your baby-eating/kid fucking activities? Wouldn’t you try to come up with a way to do damage control where you make as VERY PROMINENT PUBLIC WARNING to the mainstream media: You do NOT really want to GO THERE and keep asking us awkward questions. BACK THE FUCK OFF. It could be YOUR mother next…TAKE THE HINT… Now… Who is she? Who is she pictured with? Where was the picture taken? Will Bill Clinton be asked on February 28 who she is and why he was with her on Epstein’s plane? Stay tuned for developments. https://twitter.com/ByronYork/status/2020107433612288444?s=20   BREAKING: Pam Bondi Announces Arrest of Key Suspect in the 2012 Benghazi Attack (VIDEO)  Attorney General Pam Bondi announced on Friday morning that the FBI arrested one of the key players behind the deadly terrorist attack against the US Consulate in Benghazi. Islamic terrorists attacked the US Consulate in Benghazi on September 11, 2012, eleven years after the attacks on the World Trade Center. As noted previously, the Libyan nightmare was the result of a war that President Obama and Hillary Clinton started.  They never should have started the war in Libya, never should have placed Americans there unprotected, and when the Americans in Benghazi were under attack on 9-11, 2012 they should have provided help.  Instead, four Americans died in Benghazi as was famously portrayed in the movie 13 Hours. For days after the attack on Benghazi, President Obama and Hillary Clinton blamed the attack in Benghazi on a made up story about a US citizen who incited protests in Benghazi from a YouTube video about Islam. They continued with the story as the caskets of the four dead Americans, including Ambassador Chris Stevens, were shipped back to the US. Barack Obama and Hillary Clinton left the Consulate to fend for itself and never sent military support to rescue the men trapped at the Consulate. Attorney General Pam Bondi: On September 11th, 2012, Americans watched horrified as our embassy in Bengasi came under a vicious terror attack. We lost four American lives that day: Ambassador Chris Stevens, Sean Smith with the State Department, and two CIA contractors, Glenn Dordy and Tyrone Woods. We have never forgotten those heroes, and we have never stopped seeking justice for that crime against our nation. In fact, from day one, Cash and Dan would sit in meetings and say, We're going to get them, and they did. Today, I'm proud to announce that the FBI has arrested one of the key participants behind the Bengasi attack. Zubar Albaqash landed at Andrews Air Force Base at 03: 00 AM this morning. He is in our custody. He was greeted by Director Director Patel and US attorney Jeanine Piero. Source: thegatewaypundit.com   DOGE Geopolitical https://twitter.com/GuntherEagleman/status/2020137645339226362?s=20   supposed to GUARANTEE freedom, not RESTRICT it!” Poland standing tall against Brussels' Big Brother nonsense. This is what real leadership looks like. No bowing to globalist overlords. Poland remains a STRONG ally of the USA and a fighter for liberty.   Illegal Migrants and Gang Members out of the United States. We discussed many other issues, including Investment and Trade between our two Countries. He loves the people of Honduras, and is focused on their Health, Well-being, Education, and Economic Prosperity. I look forward to welcoming President Asfura back to the United States. Tito: Congratulations on your Great Victory! PRESIDENT DONALD J. TRUMP War/Peace https://twitter.com/BuzzPatterson/status/2020388749834965399?s=20 https://twitter.com/Osint613/status/2020238386108543128?s=20 Security Alert: Land Border Crossings (February 5, 2026) Location:  Iran, countrywide Event:  Increased security measures, road closures, public transportation disruptions, and internet blockages are ongoing. The Government of Iran continues to restrict access to mobile, landline, and national internet networks. Airlines continue to limit or cancel flights to and from Iran. U.S. citizens should expect continued internet outages, plan alternative means of communication, and, if safe to do so, consider departing Iran by land to Armenia or Türkiye. Actions to Take: Leave Iran now. Have a plan for departing Iran that does not rely on U.S. government help. Flight cancellations and disruptions are possible with little warning. Check directly with your airlines for updates. If you cannot leave, find a secure location within your residence or another safe building. Have a supply of food, water, medications, and other essential items. Avoid demonstrations, keep a low profile, and stay aware of your surroundings. Monitor local media for breaking news. Be prepared to adjust your plans. Keep your phone charged and maintain communication with family and friends to inform them of your status. Enroll in the Smart Traveler Enrollment Program (STEP)  to receive the latest updates on security in Iran. If You Plan to Leave Iran:  U.S.-Iranian dual nationals must exit Iran on Iranian passports.  The Iranian government does not recognize dual nationality  and will treat U.S.-Iranian dual nationals solely as Iranian citizens. U.S. nationals are at significant risk of questioning, arrest, and detention in Iran. Showing a U.S. passport or demonstrating connections to the United States can be reason enough for Iranian authorities to detain someone.  U.S. citizens who do not have a valid U.S. passport in their possession should apply for one at the nearest U.S. embassy or consulate after departing Iran. The U.S. government cannot guarantee your safety if you choose to depart using the following options. You should leave only if you believe it is safe to do so. As of Thursday, February 5:   Source:    Medical/False Flags China Bombshell: Patel says Biden-era FBI ‘buried' truth about CCP's ties to biolab on US soil  FBI Director Kash Patel says his agency has resumed an aggressive counterintelligence offensive against China and its Communist Party (CCP) that had been sidelined during the Biden presidency but is concerned the prior administration may have “buried” the truth about dangerous biolabs on U.S. soil tied to Beijing. The FBI boss said the renewed efforts have already resulted in a 40% increase in Chinese espionage arrests in the first year of the second Trump administration. Source: justthenews.com  [DS] Agenda ICE Humilates Far-Left Boston Mayor Michelle Wu in EPIC Fashion After She Signs Executive Order Barring Agency from Conducting “Unconstitutional and Violent” Operations ICE agents delivered a humiliating and richly deserved blow to Boston Mayor Michelle Wu's ego on Friday, one day after she tried to hamstring them for doing their jobs. As WHDH reported, Wu signed an “An Executive Order To Protect Bostonians From Unconstitutional and Violent Federal Operations.” Specifically, the order bans federal officials, including ICE, from using city property for immigration enforcement operations. Wu's office says the order is designed to “protect residents from illegal federal overreach, prioritizing de-escalation, and reaffirms that Boston will hold anyone accountable who commits violence, property damage, or any criminal conduct in the City, including federal officials.” Source: thegatewaypundit.com https://twitter.com/libsoftiktok/status/2020487139377443327?s=20 https://twitter.com/WallStreetApes/status/2019900883082031120?s=20   https://twitter.com/Badhombre/status/2019488291263823960?s=20    “People for the American Way” and Brian Tyler Cohen's “Chorus.” People for the American Way receives most of its funding from George Soros' Open Society Foundations. Brian Tyler Cohen @briantylercohen was recently exposed in a scandal for receiving dark money from the Sixteen Thirty Fund and paying up to $8,000 a month to influencers like Olivia Julianna, David Pakman, JoJo From Jerz, and Leigh “Politics Girl” McGowan to amplify coordinated content. The Sixteen Thirty Fund, managed by Arabella Advisors, receives its funding from three major sources: – Berger Action Fund (Swiss billionaire Hansjörg Wyss) – Open Society Policy Center (Hungarian Billionaire George Soros) – Democracy Fund Voice (French-born eBay founder Pierre Omidyar). Twelve people run the “HQ” account full-time. This is yet another coordinated propaganda campaign funded by leftist billionaires attempting to push their globalist agenda and sow division. Nothing organic or truly Gen-Z about it beyond the faces used to represent it. https://twitter.com/visegrad24/status/2020289816882024790?s=20 President Trump's Plan https://twitter.com/Rightanglenews/status/2020293934413680968?s=20 NBC CAUGHT IN ANOTHER LIE: VP Vance and Wife Were Not Booed at Olympics – It Was Quite the Opposite Vice President J.D. Vance, his with Usha and three children are representing the United States this week at the Winter Olympics.   J.D. was a hit at the Olympics venue.  On Friday night during the opening ceremonies NBC claimed the crowd was booing when J.D. Vance and his wife were pictured on the big screen. What disgusting people. Of course, this lie was quickly exposed by several fact-checkers online. Ovation Eddie 2 caught the media in their latest disgusting lie: https://twitter.com/EricLDaugh/status/2020155556158136778?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2020155556158136778%7Ctwgr%5Ed35db378d07d7f30cba1d9449c0da87c52040e2a%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2026%2F02%2Fnbc-caught-another-lie-vp-vance-wife-were%2F   Remember: You can never trust a single word coming from the anti-Trump, Anti-American legacy media. Source: thegatewaypundit.com   https://twitter.com/MrAndyNgo/status/2020310461267202235?s=20   https://twitter.com/CynicalPublius/status/2020285717713453058?s=20   that out. Democrats Cry As Trump Makes It Easier to Fire Federal Workers The Trump administration is planning to make it easier to discipline—and potentially fire—career officials in senior positions across the government, a move that would affect roughly 50,000 federal workers. The U.S. Office of Personnel Management, which oversees the federal workforce, issued a final rule on Thursday that creates a category of worker for high-ranking career employees whose work focuses on executing the administration's policies. Workers who fall into that category would no longer be subject to rules that for decades have set a high bar for firing federal employees.  The Trump team, however, characterizes the move as one that gives the executive branch the ability to better shape the bureaucracy to help serve its agenda, instead of allowing it to clandestinely thwart it: The administration has been clear that the goal of the rule is to more easily fire workers they argue are hindering Trump policies — a nod to the president's claims of a “Deep State” within the federal government trying to undermine him. “This is not about people's views or ideas. This is about whether they are refusing to actually affect their duties on behalf of the American people consistent with the objectives of this administration,” said Scott Kupor, director of the Office of Personnel Management (OPM), which promulgated the rule.   Source: redstate.com https://twitter.com/drawandstrike/status/2020298873923567783?s=20   doesn’t agree with the 5th Circuit’s ruling. How in the world would you REINSTATE a policy where an illegal who successfully evaded detection at a port of entry has legal recourse to bond when those illegals detected at a port of entry do not? The 5th just rightfully found that NEITHER kind of illegal should have recourse to bond – whether they are detected at a port of entry or they successfully sneak into the country and are here for months/years before being caught. The fact this absurd situation persisted for decades shows you the system was rigged to allow human trafficking and to create a literal legal industry to facilitate it.      Trump can “legally” mass deport ALL illegals, whether they have committed a crime or not. “A federal appeals court ruled that Trump administration can lock up the vast majority of people it is seeking to deport without offering a chance for bond, even if they have no criminal records and have resided in the country for decades.        https://twitter.com/alexahenning/status/2020196173663867144?s=20 https://twitter.com/HansMahncke/status/2020253940374245522?s=20   https://twitter.com/DNIGabbard/status/2020227805976678574?s=20  control of the Whistleblower's complaint, so I obviously could not have “hidden” it in a safe. Biden-era IC Inspector General Tamara Johnson was in possession of and responsible for securing the complaint for months. – The first time I saw the whistleblower complaint was 2 weeks ago when I had to review it to provide guidance on how it should be securely shared with Congress. – As Vice Chair of the Senate Intelligence Committee, Senator Warner knows very well that whistleblower complaints that contain highly classified and compartmented intelligence—even if they contain baseless allegations like this one—must be secured in a safe, which the Biden-era Inspector General Tamara Johnson did and her successor, Inspector General Chris Fox, continued to do. After IC Inspector General Fox hand-delivered the complaint to the Gang of 8, the complaint was returned to a safe where it remains, consistent with any information of such sensitivity. – Either Senator Warner knows these facts and is intentionally lying to the American people, or he doesn't have a clue how these things work and is therefore not qualified to be in the U.S. Senate—and certainly not the Vice Chair of the Senate Intelligence Committee. Here is a detailed chronology of the situation: – June 2025, I became aware that a whistleblower made a complaint against me that after further investigation, neither Biden-era IC Inspector General Tamara Johnson nor current IC Inspector General Chris Fox found the complaint to be credible. – The complaint required special handling and storage in a safe because the complainant chose to include highly sensitive information within the complaint itself rather than referencing the sensitive reporting and leaving the complaint at a lower level of classification. – Security standards for complaints that include such sensitive intelligence required the Inspector General to keep the complaint and the intelligence referenced secured in a safe from the time the complaint was made, until now. – In June 2025 after Biden-era Inspector General Tamara Johnson completed her review of the complaint, no further oversight or investigative activity took place. – Biden-era Inspector General Johnson had communicated with me directly throughout the course of her investigation into this complaint, yet neither she nor anyone from her office informed me that the Whistleblower chose to send the complaint to Congress which would require me to issue security instructions. – When a complaint is not found to be credible, there is no timeline under the law for the provision of security guidance. The “21 day” requirement that Senator Warner alleges I did not comply with, only applies when a complaint is determined by the Inspector General to be both urgent AND apparently credible. That was NOT the case here. – I was made aware of the need to provide security guidance by IC Inspector General Chris Fox on December 4, 2025, which he detailed in his letter to Congress. – I took immediate action to provide the security guidance to the Intelligence Community Inspector General who then shared the complaint and referenced intelligence with relevant members of Congress last week. Senator Warner’s decision to spread lies and baseless accusations over the months for political gain, undermines our national security and is a disservice to the American people and the Intelligence Community. https://twitter.com/seanmdav/status/2020151219210137711?s=20   https://twitter.com/AlecLace/status/2019802427487027667?s=20 https://twitter.com/WallStreetMav/status/2020150184374681890?s=20 https://twitter.com/AlecLace/status/2019849309148311983?s=20 https://twitter.com/TheStormRedux/status/2019941561367191842?s=20 https://twitter.com/Rasmussen_Poll/status/2020183096667128211?s=20  2. ALL VOTERS MUST SHOW PROOF OF UNITED STATES CITIZENSHIP TO REGISTER FOR VOTING.   3. NO MAIL-IN BALLOTS (EXCEPT FOR ILLNESS, DISABILITY, MILITARY, OR TRAVEL!). https://twitter.com/WarClandestine/status/2020314452483342609?s=20     (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:13499335648425062,size:[0, 0],id:"ld-7164-1323"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="//cdn2.customads.co/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs");