POPULARITY
Categories
54 episodes with Validation
129 episodes with Validation
80 episodes with Validation
95 episodes with Validation
33 episodes with Validation
19 episodes with Validation
13 episodes with Validation
15 episodes with Validation
35 episodes with Validation
19 episodes with Validation
16 episodes with Validation
10 episodes with Validation
7 episodes with Validation
8 episodes with Validation
9 episodes with Validation
13 episodes with Validation
10 episodes with Validation
9 episodes with Validation
11 episodes with Validation
5 episodes with Validation
12 episodes with Validation
5 episodes with Validation
13 episodes with Validation
20 episodes with Validation
11 episodes with Validation
7 episodes with Validation
8 episodes with Validation
10 episodes with Validation
9 episodes with Validation
12 episodes with Validation
8 episodes with Validation
6 episodes with Validation
14 episodes with Validation
5 episodes with Validation
4 episodes with Validation
7 episodes with Validation
7 episodes with Validation
5 episodes with Validation
7 episodes with Validation
5 episodes with Validation
5 episodes with Validation
15 episodes with Validation
14 episodes with Validation
10 episodes with Validation
6 episodes with Validation
5 episodes with Validation
5 episodes with Validation
4 episodes with Validation
6 episodes with Validation
4 episodes with Validation
6 episodes with Validation
4 episodes with Validation
3 episodes with Validation
4 episodes with Validation
6 episodes with Validation
11 episodes with Validation
6 episodes with Validation
12 episodes with Validation
5 episodes with Validation
3 episodes with Validation
4 episodes with Validation
6 episodes with Validation
5 episodes with Validation
3 episodes with Validation
6 episodes with Validation
5 episodes with Validation
5 episodes with Validation
6 episodes with Validation
6 episodes with Validation
3 episodes with Validation
8 episodes with Validation
4 episodes with Validation
5 episodes with Validation
3 episodes with Validation
3 episodes with Validation
5 episodes with Validation
4 episodes with Validation
4 episodes with Validation
3 episodes with Validation
5 episodes with Validation
3 episodes with Validation
3 episodes with Validation
2 episodes with Validation
3 episodes with Validation
4 episodes with Validation
5 episodes with Validation
3 episodes with Validation
6 episodes with Validation
6 episodes with Validation
4 episodes with Validation
4 episodes with Validation
2 episodes with Validation
5 episodes with Validation
3 episodes with Validation
8 episodes with Validation
5 episodes with Validation
4 episodes with Validation
3 episodes with Validation
4 episodes with Validation
4 episodes with Validation
3 episodes with Validation
3 episodes with Validation
5 episodes with Validation
3 episodes with Validation
4 episodes with Validation
4 episodes with Validation
3 episodes with Validation
4 episodes with Validation
3 episodes with Validation
2 episodes with Validation
2 episodes with Validation
3 episodes with Validation
3 episodes with Validation
6 episodes with Validation
6 episodes with Validation
6 episodes with Validation
4 episodes with Validation
3 episodes with Validation
3 episodes with Validation
2 episodes with Validation
4 episodes with Validation
9 episodes with Validation
3 episodes with Validation
4 episodes with Validation
3 episodes with Validation
2 episodes with Validation
4 episodes with Validation
3 episodes with Validation
5 episodes with Validation
2 episodes with Validation
4 episodes with Validation
4 episodes with Validation
3 episodes with Validation
3 episodes with Validation
2 episodes with Validation
4 episodes with Validation
3 episodes with Validation
3 episodes with Validation
3 episodes with Validation
5 episodes with Validation
6 episodes with Validation
4 episodes with Validation
3 episodes with Validation
3 episodes with Validation
4 episodes with Validation
3 episodes with Validation
3 episodes with Validation
3 episodes with Validation
4 episodes with Validation
![Being [at Work]](https://ivyfm.s3.amazonaws.com/i320/636094.jpg)
3 episodes with Validation
3 episodes with Validation
7 episodes with Validation
3 episodes with Validation
4 episodes with Validation
4 episodes with Validation
4 episodes with Validation
3 episodes with Validation
2 episodes with Validation
3 episodes with Validation
3 episodes with Validation
3 episodes with Validation
7 episodes with Validation
This episode of WarDocs features Dr. David Tate, a clinical neuropsychologist and lead author of the 2025 Military Medicine Article of the Year. The discussion centers on a groundbreaking study utilizing the LIMBIC-CENC cohort—a massive data set of over 3,000 participants—to investigate persistent brain changes in mild traumatic brain injury (mTBI). Dr. Tate explains that traditional MRI scans often show normal results in patients with invisible symptoms because researchers often oversimplify patient groupings. By digging into more refined clinical characteristics, such as the mechanism of injury and number of exposures, his team identified unique physical signatures in the brain. Specifically, blast exposures were linked to changes in central white matter, while repetitive traumatic hits impacted more peripheral gray matter structures. The conversation highlights the critical importance of neuroimaging techniques like diffusion tensor imaging, which is more sensitive to structural white matter changes than standard hospital sequences. Dr. Tate emphasizes that these findings provide vital validation for service members and veterans, demonstrating that their ongoing symptoms are rooted in physical, biological changes rather than purely psychological or "imagined". For clinicians, the episode serves as a call to action to move beyond simplistic interpretations of "normal" imaging and to prioritize exhaustive injury histories that include the physics of every exposure event. By combining a deep dive into advanced neuroimaging with a focus on personalized medicine, this episode provides a comprehensive look at the future of TBI diagnosis and treatment. Listeners will learn how high-resolution volumetric data and detailed clinical info—including loss of consciousness and post-traumatic amnesia markers—are used to improve prognostic accuracy. Ultimately, Dr. Tate's work demonstrates that injury history matters even years later, pointing researchers and clinicians toward a more precise approach to studying and treating the diverse landscape of mild traumatic brain injuries in the military population. Chapters (00:00-01:30) Introduction to the 2025 Military Medicine Article of the Year (01:30-06:17) Dr. David Tate's Professional Background and Career Evolution (06:17-08:04) Understanding the LIMBIC-CENC Cohort and Consortium Research (08:04-12:44) Methodology: Advanced Neuroimaging and Detailed Clinical Variables (12:44-17:03) Key Findings: Heterogeneity of mTBI and Mechanism-Specific Signatures (17:03-22:15) The Bottom Line: Validating Veteran Experiences and Clinical Takeaways Chapter Summaries (00:00-01:30) Introduction to the 2025 Military Medicine Article of the Year MG(R) Jeff Clark introduces guest Dr. David Tate and recognizes his team for winning the 2025 Military Medicine Article of the Year. The article focuses on persistent MRI findings unique to blast and repetitive mild traumatic brain injury within the LIMBIC-CENC cohort. (01:30-06:17) Dr. David Tate's Professional Background and Career Evolution Dr. Tate shares his journey from growing up on a farm in Mississippi to becoming a leading researcher in academic neuropsychology. He discusses his mentorship under Erin Bigler and his favorite career experiences working directly with service members at Brooke Army Medical Center. (06:17-08:04) Understanding the LIMBIC-CENC Cohort and Consortium Research The discussion explores the advantages of using a large consortium dataset that includes over 3,000 participants across the United States. This prospective study enables leading scientists and clinicians to collaborate on well-characterized, long-term functional outcomes following brain injury. (08:04-12:44) Methodology: Advanced Neuroimaging and Detailed Clinical Variables Dr. Tate explains the use of high-resolution volumetric MRI data and diffusion tensor imaging to map brain structural connections. Researchers combined these images with a plethora of clinical data, including lifetime exposure histories, demographics, and specific injury markers like loss of consciousness. (12:44-17:03) Key Findings: Heterogeneity of mTBI and Mechanism-Specific Signatures The study reveals that mild TBI is extremely heterogeneous and simplistic group comparisons often obscure meaningful findings. Findings showed that blast exposures leave signatures in central white matter, while repetitive traumatic injuries more specifically affect gray matter structures. (17:03-22:15) The Bottom Line: Validating Veteran Experiences and Clinical Takeaways The bottom line is that persistent brain changes can be detected if clinicians look at the right variables and mechanism of injury. This research validates the lived experiences of veterans, proving their symptoms are not imagined and emphasizing the need for detailed injury histories. Article Reference Persistent MRI Findings Unique to Blast and Repetitive Mild TBI: Analysis of the CENC/LIMBIC Cohort Injury Characteristics Open Access David F Tate, PhD , Benjamin S C Wade, PhD , Carmen S Velez, MS , Erin D Bigler, PhD , Nicholas D Davenport, PhD , Emily L Dennis, PhD , Carrie Esopenko, PhD , Sidney R Hinds, MD , Jacob Kean, PhD , Eamonn Kennedy, PhD Military Medicine, Volume 189, Issue 9-10, September/October 2024, Pages e1938–e1946, https://doi.org/10.1093/milmed/usae031 Take Home Messages Heterogeneity of Mild TBI: Mild traumatic brain injury is not a single, uniform condition, and simplistic groupings can obscure meaningful characteristics of an injury. Clinicians must recognize that "if you've seen one mild TBI, you've seen one mild TBI," requiring a more personalized approach to diagnosis. Mechanism-Specific Signatures: The physical signature left on the brain depends heavily on the mechanism of injury, with blast exposures typically affecting central white matter and repetitive traumatic hits impacting peripheral gray matter. Understanding these distinctions helps explain why different patients experience different functional outcomes even with the same diagnosis. Sensitivity of Advanced Neuroimaging: Standard MRI sequences often fail to detect injuries in mTBI patients, but advanced techniques like diffusion tensor imaging are highly sensitive to structural white matter changes. Relying solely on basic imaging can lead to an over-simplistic interpretation that overlooks persistent brain changes. Validation of Lived Experiences: Research into persistent brain changes provides vital biological validation for veterans and service members who struggle with ongoing symptoms. These findings support the idea that invisible wounds have a physical basis and are not simply psychological or imagined. Importance of Detailed Injury Histories: For clinicians, the most critical takeaway is the necessity of capturing a detailed lifetime injury history, including the number of exposures and specific physics of each event. This detailed clinical information is essential for improving prognostic accuracy and understanding a patient's long-term health trajectory. Episode Keywords Military Medicine, WarDocs Podcast, Traumatic Brain Injury, TBI Diagnosis, Blast Exposure, Neuropsychology, Persistent MRI Findings, Veteran Healthcare, Brain Imaging, Mild TBI, LIMBIC-CENC Cohort, Neuroimaging Research, AMSUS, Combat Injury, White Matter Change, Brain Health, Dr. David Tate, Military Health System, Invisible Injuries, Medical Podcast, Concussion Recovery, Gray Matter, MRI Scans, AMSUS Article of the Year, Veteran Support, Brain Mapping Hashtags #MilitaryMedicine, #WarDocs, #BrainHealth, #Veterans, #Neuroscience, #MildTBI, #BlastInjury, #MedicalResearch Honoring the Legacy and Preserving the History of Military Medicine The WarDocs Mission is to honor the legacy, preserve the oral history, and showcase career opportunities, unique expeditionary experiences, and achievements of Military Medicine. We foster patriotism and pride in Who we are, What we do, and, most importantly, How we serve Our Patients, the DoD, and Our Nation. Find out more and join Team WarDocs at https://www.wardocspodcast.com/ Check our list of previous guest episodes at https://www.wardocspodcast.com/our-guests Subscribe and Like our Videos on our YouTube Channel: https://www.youtube.com/@wardocspodcast Listen to the “What We Are For” Episode 47. https://bit.ly/3r87Afm WarDocs- The Military Medicine Podcast is a Non-Profit, Tax-exempt-501(c)(3) Veteran Run Organization run by volunteers. All donations are tax-deductible and go to honoring and preserving the history, experiences, successes, and lessons learned in Military Medicine. A tax receipt will be sent to you. WARDOCS documents the experiences, contributions, and innovations of all military medicine Services, ranks, and Corps who are affectionately called "Docs" as a sign of respect, trust, and confidence on and off the battlefield,demonstrating dedication to the medical care of fellow comrades in arms. Follow Us on Social Media Twitter: @wardocspodcast Facebook: WarDocs Podcast Instagram: @wardocspodcast LinkedIn: WarDocs-The Military Medicine Podcast YouTube Channel: https://www.youtube.com/@wardocspodcast
F-Stop Collaborate and Listen - A Landscape Photography Podcast
In this episode of F-Stop Collaborate and Listen, Matt Payne sits down with amateur photographer Colleen Parker for an open, insightful chat about staying inspired, steering clear of creative ruts, and enjoying a personal and meaningful photographic journey. Colleen Parker, a retired radiologist, discusses how her scientific background intersects with her artistry and how letting go of expectations—both internal and external—has allowed her creativity to flourish. The conversation delves into the pressures of social media, the importance of personal growth over style conformity, the pitfalls and benefits of seeking validation, finding purpose in photography (from conservation to simply bringing joy), and how to move from imitation to authentic self-expression. Whether you're just starting out or decades into your craft, this episode offers practical wisdom on making photography a fulfilling, lifelong pursuit. P.S. don't miss our insightful and fun bonus episode on Patreon! Links and Resources: Colleen Parker Support the show on Patreon Matt Payne's Book, The Colorado Way Natural Landscape Photography Awards Art Wolfe Alex Noriega Rachel Talibart Paul Nicklen Cristina Mittermeier Alex Rohde April Norman Becky Kuperstein Nader Daii Ambarish Goswami (naturewithambarish) Maria Ruggieri Feli Hansen, “Guilty Trashures” Project (NLPA)
In this episode of the Ageless Future podcast, host Cade Archibald sits down with Dr. Brandon Burke to explore his journey from building multiple successful orthodontic practices to stepping into a new chapter focused on coaching and personal development. Brandon shares how growing up in a small Utah town shaped his drive, what it looked like to build a practice from scratch during the recession, and the moment he realized his achievements weren't the same as fulfillment. The conversation dives into themes of identity, burnout, resilience, and “unbecoming”—letting go of external validation to reconnect with purpose, emotional awareness, and grounded leadership in family and community. Brandon closes with a message about choosing to live as a creator rather than a victim, and how his most difficult season became the turning point that helped him realign his life.BRANDON BURKE:Websites: gettinlostisbeingfound.com and more-than-love.comIG: https://www.instagram.com/gettinlostisbeingfound/FB: @safetobeseenTikTok: https://www.tiktok.com/@gettinlostisbeing/YouTube: Safe To Be Seen~ https://youtube.com/@safetobeseenPodcast: https://podcasts.apple.com/us/podcast/safe-to-be-seen/id1817823527AGELESS FUTURE:Book Comprehensive Labs: https://agelessfuture.com/longevity-labs/FREE copy of The Peptide Blueprint: https://agelessfuture.com/blueprintSign up for future Health Accelerator Challenges calls LIVE! https://us02web.zoom.us/webinar/register/WN_YZsiUMOzSyqcE8IinC5YEQ#/registrationBooks: https://www.amazon.com/Books-Regan-Archibald/s?rh=n%3A283155%2Cp_27%3ARegan%2BArchibaldArticles: https://medium.com/search?q=Regan+ArchibaldLIKE/FOLLOW/SUBSCRIBE:YouTube -https://www.youtube.com/@ReganArchibald / https://www.youtube.com/@Ageless.FutureLinkedIn: https://www.linkedin.com/in/regan-archibald-ab70b813Instagram: https://www.instagram.com/ageless.future/Facebook: https://www.facebook.com/AgelessFutureHealth/DISCLAIMER: This video is for educational purposes only and does not provide medical advice, diagnosis, or treatment. Many of the molecules discussed in this video are research compounds and are not approved by the U.S. Food and Drug Administration (FDA) for any specific medical use, indication, or condition. They are mentioned only in the context of existing scientific literature and ongoing research and are not being recommended, prescribed, sold, or offered through this video. This content does not endorse or recommend any specific tests, products, procedures, or treatment protocols.References to our clinic are for general educational context only; investigational or non‑approved products are not available for direct ordering or prescribing based solely on viewing this content. Do not start, stop, or change any medication, peptide, or supplement based on this video. All medical decisions must be made with a licensed prescribing clinician after a proper evaluation. No provider–patient relationship is created by viewing this content or contacting our clinic. Regan Archibald is a Licensed Acupuncturist and longevity coach. He is not a medical doctor. Cade Archibald is COO and Co-Founder of Ageless Future, also not a medical doctor. All medical decisions, lab ordering, and prescribing in our clinic are performed only by our licensed medical team (MD, APRN, PA). Viewers should follow the guidance of their own licensed clinicians and local health authorities regarding diagnosis and treatment decisions.
What actually separates people who say they want hard things from those who follow through for years?In this episode of Common Denominator, I sit down with endurance athlete Mark Dowdle for a deep conversation on discipline, faith, suffering, and what it really takes to do difficult things over long periods of time.Mark shares where his mindset comes from, why consistency—not motivation—is the real differentiator, and how his relationship with faith reshaped his identity after chasing validation through extreme physical challenges. He opens up about running the Calendar Club challenge for an entire year, racing in life-threatening conditions at Arrowhead 135, and the moment he realized that external praise was never going to fill the void.Mark and I explore the psychology of quitting, the inner dialogue that convinces people to stop just before a breakthrough, and why no one accomplishes hard things alone. From the importance of choosing the right life partner to the role of accountability, truth-telling, and surrender, this conversation is a grounded look at what sustained excellence actually requires.This episode is a reminder that discipline isn't about feeling ready — it's about showing up anyway.In This Episode, You'll Learn:- Why some people are drawn to hard things—and how that mindset is shaped early- How redefining failure changes everything- The difference between real danger and mental excuses- Why external validation eventually collapses- How faith reframes suffering, purpose, and discipline- Why motivation is unreliable—and discipline is required- The role of accountability, partners, and “truth tellers”- Why consistency is the true common denominator of high performersTimestamps:02:39 – Redefining Failure03:47 – Validation, High Goals & Dropout Points04:47 – Shifting Identity Away from External Praise06:00 – Faith, Purpose & Olympic-Level Emptiness08:23 – Calendar Club Challenge & Expectation Collapse10:15 – Surrender, Entitlement & Freedom12:24 – Pushing the Line Between Discomfort and Danger14:28 – Inner Dialogue, Fear & Presence16:00 – Accountability, Marriage & Not Quitting18:11 – Love, Truth & Saying the Hard Thing21:12 – Discipline vs Motivation23:22 – Confidence Through Evidence25:10 – The True Common Denominator: Consistency26:19 – Final Reflections & Sign-OffLike this episode? Leave a review here:https://ratethispodcast.com/commondenominator
Stop the Guessing Game: Is Your Teen's "Broken" Brain Actually a Superpower?
In this episode of The Behavioral View, Nissa Van Etten, Olivia Teal, Elizabeth Barajas, and Yagnesh Vadgama discuss the evolution of outcomes-based care within applied behavior analysis (ABA). Drawing from extensive experience in both clinical practice and payer systems, Vadgama outlines the differences between traditional fee-for-service models and outcomes-based care frameworks. The panel explores how standardized assessments, aggregate data analysis, and empirically supported dosing recommendations can create greater alignment between providers and payers while maintaining individualized clinical decision-making. The discussion addresses administrative burden, prior authorization processes, value-based payment arrangements, caregiver involvement, social determinants of health, and interdisciplinary collaboration. Emphasis is placed on transparency, data-driven decision making, and protecting the integrity of behavior analytic practice while demonstrating measurable outcomes at both the individual and population levels. This course provides practical insight into how outcomes-based care models may shape the future of ABA service delivery. To earn CEUs for listening, click here, log in or sign up, pay the CEU fee, + take the attendance verification quiz to generate your certificate! Don't forget to subscribe and follow and leave us a rating and review. Show Notes: References Frazier, T. W., Youngstrom, E. A., Speer, L., Embacher, R., Law, P., Constantino, J., Findling, R. L., Hardan, A. Y., & Eng, C. (2014). Validation of proposed DSM-5 criteria for autism spectrum disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 53(1), 28–40. https://doi.org/10.1016/j.jaac.2013.10.012 Frazier, T. W., Klingemier, E. W., Beukemann, M., Speer, L., Markowitz, L., Parikh, S., & Strauss, M. S. (2021). Development and validation of the Autism Impact Measure (AIM). Journal of Autism and Developmental Disorders, 51, 3407–3421. https://doi.org/10.1007/s10803-020-04795-1 Smith, P. C., Sagan, A., Siciliani, L., & Figueras, J. (2023). Building on value-based health care: Towards a health system perspective. Health Policy, 138, 104918. https://doi.org/10.1016/j.healthpol.2023.104918 AI.Measures Scientific Support Ferguson, E. F., Frazier, T. W., Hardan, A. Y., & Uljarević, M. (2025). Challenging behavior domains in individuals with neurodevelopmental genetic syndromes: The role of psychological features. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 0(1), 1-12 Frazier, T. W., Huba, K., Frazier, A. R., Womack, R. A., Youngstrom, E. A., Chetcuti, L., Hardan, A. Y., & Uljarevic, M. (2025). Maximizing accurate detection of divergence from normative expectation in behavioral intervention outcome assessment. Research in Autism, 126, 202646. Frazier, T. W., Youngstrom, E. A., Frazier, A. R., & Uljarevic, M. (2025). A critical appraisal of the measurement of adaptive social communication behaviors in the behavioral intervention context. Behavioral Sciences, 15(6), 722 Frazier, T.W., Helton, M., Akouri, C., Chetcuti, L., Uljarevic, M. (2025) Identifying Reliable Change In Outcome Assessments for Behavioral Intervention. Behavioral Interventions. Frazier, T. W., Dimitropoulos, A., Abbeduto, L., Armstrong-Brine, M., Kralovic, S., Shih, A., Hardan, A. Y., Youngstrom, E. A., Uljarevic, M., Verbal Beginnings, T. (2024). Psychometric evaluation of the Autism Symptom Dimensions Questionnaire. Developmental Medicine and Child Neurology. Frazier, T. W., Busch, R. M., Klaas, P., Lachlan, K., Jeste, S., Kolevzon, A., Loth, E., Harris, J., Speer, L., Pepper, T., Anthony, K., Graglia, J. M., Delagrammatikas, C., Bedrosian-Sermone, S., Beekhuyzen, J., Smith-Hicks, C., Sahin, M., Eng, C., Hardan, A. Y., & Uljarevic, M. (2023). Development of informant-report neurobehavioral survey scales for PTEN hamartoma tumor syndrome and related neurodevelopmental genetic syndromes. Am J Med Genet A, 191(7), 1741-1757. https://doi.org/10.1002/ajmg.a.63195 Frazier, T. W., Crowley, E., Shih, A., Vasudevan, V., Karpur, A., Uljarevic, M., & Cai, R. Y. (2022). Associations between executive functioning, challenging behavior, and quality of life in children and adolescents with and without neurodevelopmental conditions. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2022.1022700 Frazier, T. W., Dimitropoulos, A., Abbeduto, L., Armstrong-Brine, M., Kralovic, S., Shih, A., Hardan, A. Y., Youngstrom, E. A., Uljarevic, M., & Quadrant Biosciences - As You Are Team. (2023). The Autism Symptom Dimensions Questionnaire: Development and psychometric evaluation of a new, open-source measure of autism symptomatology. Developmental Medicine and Child Neurology. https://doi.org/10.1111/dmcn.15497 Frazier, T. W., Dimitropoulos, A., Abbeduto, L., Armstrong-Brine, M., Kralovic, S., Shih, A., Hardan, A. Y., Youngstrom, E. A., Uljarevic, M., Womack, R., Wolf, D., Chappell, N., & Verbal Beginnings Team. (2024). Psychometric Evaluation of the Autism Symptom Dimensions Questionnaire (ASDQ). Developmental Medicine and Child Neurology. Frazier, T. W., Hyland, A. C., Markowitz, L. A., Speer, L. L., & Diekroger, E. A. (2020). Psychometric evaluation of the revised child and family quality of life questionnaire (CFQL-2). Research in Autism Spectrum Disorders, 70. https://doi.org/https://doi.org/10.1016/j.rasd.2019.101474 Frazier, T. W., Khaliq, I., Scullin, K., Uljarevic, M., Shih, A., & Karpur, A. (2022). Development and psychometric evaluation of the open-source challenging behavior scale. Journal of Autism and Developmental Disabilities. https://doi.org/https://doi.org/10.1007/s10803-022-05750-5 Frazier, T. W., Krishna, J., Klingemier, E., Beukemann, M., Nawabit, R., & Ibrahim, S. (2017). A Randomized, Crossover Trial of a Novel Sound-to-Sleep Mattress Technology in Children with Autism and Sleep Difficulties. J Clin Sleep Med, 13(1), 95-104. https://doi.org/10.5664/jcsm.6398 Frazier, T. W., Busch, R. M., Klass, P., Crowley, E., Lachlan, K., Jeste, S., Kolevzon, A., Loth, E., Harris, J., Pepper, T., Anthony, K., Graglia, J. M., Helde, K., Delagrammatikas, C., Bedrosian-Sermone, S., Smith-Hicks, C., Sahin, M., Eng, C., Hardan, A. Y., . . . Uljarevic, M. (2024). Quantifying Neurobehavioral Profiles across Neurodevelopmental Genetic Syndromes and Idiopathic Neurodevelopmental Disorders. Developmental Medicine and Child Neurology. https://doi.org/https://doi.org/10.1111/dmcn.16112 Uljarevic, M., Cai, R. Y., Hardan, A. Y., & Frazier, T. W. (2022). Development and validation of the Executive Functioning Scale. Front Psychiatry, 13, 1078211. https://doi.org/10.3389/fpsyt.2022.1078211 Uljarevic, M., Spackman, E. K., Cai, R. Y., Paszek, K. J., Hardan, A. Y., & Frazier, T. W. (2022). Daily living skills scale: Development and preliminary validation. Frazier, T. W., Helton, M., Akouri, C., Chetcuti, L., & Uljarevic, M. (2025). Identifying reliable change in outcome assessments for behavioral interventions. Behavioral Interventions, 40, e70007. https://doi.org/https://doi.org/10.1002/bin.70007 Resources CentralReach. (n.d.). AI Measures (AIM). https://centralreach.com
Chris Cieslak, CEO of BladeBug, joins the show to discuss how their walking robot is making ultrasonic blade inspections faster and more accessible. They cover new horizontal scanning capabilities for lay down yards, blade root inspections for bushing defects, and plans to expand into North America in 2026. Sign up now for Uptime Tech News, our weekly newsletter on all things wind technology. This episode is sponsored by Weather Guard Lightning Tech. Learn more about Weather Guard’s StrikeTape Wind Turbine LPS retrofit. Follow the show on YouTube, Linkedin and visit Weather Guard on the web. And subscribe to Rosemary’s “Engineering with Rosie” YouTube channel here. Have a question we can answer on the show? Email us! Welcome to Uptime Spotlight, shining Light on Wind. Energy’s brightest innovators. This is the Progress Powering Tomorrow. Allen Hall: Chris, welcome back to the show. Chris Cieslak: It’s great to be back. Thank you very much for having me on again. Allen Hall: It’s great to see you in person, and a lot has been happening at Blade Bugs since the last time I saw Blade Bug in person. Yeah, the robot. It looks a lot different and it has really new capabilities. Chris Cieslak: So we’ve continued to develop our ultrasonic, non-destructive testing capabilities of the blade bug robot. Um, but what we’ve now added to its capabilities is to do horizontal blade scans as well. So we’re able to do blades that are in lay down yards or blades that have come down for inspections as well as up tower. So we can do up tower, down tower inspections. We’re trying to capture. I guess the opportunity to inspect blades after transportation when they get delivered to site, to look [00:01:00] for any transport damage or anything that might have been missed in the factory inspections. And then we can do subsequent installation inspections as well to make sure there’s no mishandling damage on those blades. So yeah, we’ve been just refining what we can do with the NDT side of things and improving its capabilities Joel Saxum: was that need driven from like market response and people say, Hey, we need, we need. We like the blade blood product. We like what you’re doing, but we need it here. Or do you guys just say like, Hey, this is the next, this is the next thing we can do. Why not? Chris Cieslak: It was very much market response. We had a lot of inquiries this year from, um, OEMs, blade manufacturers across the board with issues within their blades that need to be inspected on the ground, up the tap, any which way they can. There there was no, um, rhyme or reason, which was better, but the fact that he wanted to improve the ability of it horizontally has led the. Sort of modifications that you’ve seen and now we’re doing like down tower, right? Blade scans. Yeah. A really fast breed. So Joel Saxum: I think the, the important thing there is too is that because of the way the robot is built [00:02:00] now, when you see NDT in a factory, it’s this robot rolls along this perfectly flat concrete floor and it does this and it does that. But the way the robot is built, if a blade is sitting in a chair trailing edge up, or if it’s flap wise, any which way the robot can adapt to, right? And the idea is. We, we looked at it today and kind of the new cage and the new things you have around it with all the different encoders and for the heads and everything is you can collect data however is needed. If it’s rasterized, if there’s a vector, if there’s a line, if we go down a bond line, if we need to scan a two foot wide path down the middle of the top of the spa cap, we can do all those different things and all kinds of orientations. That’s a fantastic capability. Chris Cieslak: Yeah, absolutely. And it, that’s again for the market needs. So we are able to scan maybe a meter wide in one sort of cord wise. Pass of that probe whilst walking in the span-wise direction. So we’re able to do that raster scan at various spacing. So if you’ve got a defect that you wanna find that maximum 20 mil, we’ll just have a 20 mil step [00:03:00] size between each scan. If you’ve got a bigger tolerance, we can have 50 mil, a hundred mil it, it’s so tuneable and it removes any of the variability that you get from a human to human operator doing that scanning. And this is all about. Repeatable, consistent high quality data that you can then use to make real informed decisions about the state of those blades and act upon it. So this is not about, um, an alternative to humans. It’s just a better, it’s just an evolution of how humans do it. We can just do it really quick and it’s probably, we, we say it’s like six times faster than a human, but actually we’re 10 times faster. We don’t need to do any of the mapping out of the blade, but it’s all encoded all that data. We know where the robot is as we walk. That’s all captured. And then you end up with really. Consistent data. It doesn’t matter who’s operating a robot, the robot will have those settings preset and you just walk down the blade, get that data, and then our subject matter experts, they’re offline, you know, they are in their offices, warm, cozy offices, reviewing data from multiple sources of robots. And it’s about, you know, improving that [00:04:00] efficiency of getting that report out to the customer and letting ’em know what’s wrong with their blades, actually, Allen Hall: because that’s always been the drawback of, with NDT. Is that I think the engineers have always wanted to go do it. There’s been crush core transportation damage, which is sometimes hard to see. You can maybe see a little bit of a wobble on the blade service, but you’re not sure what’s underneath. Bond line’s always an issue for engineering, but the cost to take a person, fly them out to look at a spot on a blade is really expensive, especially someone who is qualified. Yeah, so the, the difference now with play bug is you can have the technology to do the scan. Much faster and do a lot of blades, which is what the de market demand is right now to do a lot of blades simultaneously and get the same level of data by the review, by the same expert just sitting somewhere else. Chris Cieslak: Absolutely. Joel Saxum: I think that the quality of data is a, it’s something to touch on here because when you send someone out to the field, it’s like if, if, if I go, if I go to the wall here and you go to the wall here and we both take a paintbrush, we paint a little bit [00:05:00] different, you’re probably gonna be better. You’re gonna be able to reach higher spots than I can. Allen Hall: This is true. Joel Saxum: That’s true. It’s the same thing with like an NDT process. Now you’re taking the variability of the technician out of it as well. So the data quality collection at the source, that’s what played bug ducts. Allen Hall: Yeah, Joel Saxum: that’s the robotic processes. That is making sure that if I scan this, whatever it may be, LM 48.7 and I do another one and another one and another one, I’m gonna get a consistent set of quality data and then it’s goes to analysis. We can make real decisions off. Allen Hall: Well, I, I think in today’s world now, especially with transportation damage and warranties, that they’re trying to pick up a lot of things at two years in that they could have picked up free installation. Yeah. Or lifting of the blades. That world is changing very rapidly. I think a lot of operators are getting smarter about this, but they haven’t thought about where do we go find the tool. Speaker: Yeah. Allen Hall: And, and I know Joel knows that, Hey, it, it’s Chris at Blade Bug. You need to call him and get to the technology. But I think for a lot of [00:06:00] operators around the world, they haven’t thought about the cost They’re paying the warranty costs, they’re paying the insurance costs they’re paying because they don’t have the set of data. And it’s not tremendously expensive to go do. But now the capability is here. What is the market saying? Is it, is it coming back to you now and saying, okay, let’s go. We gotta, we gotta mobilize. We need 10 of these blade bugs out here to go, go take a scan. Where, where, where are we at today? Chris Cieslak: We’ve hads. Validation this year that this is needed. And it’s a case of we just need to be around for when they come back round for that because the, the issues that we’re looking for, you know, it solves the problem of these new big 80 a hundred meter plus blades that have issues, which shouldn’t. Frankly exist like process manufacturer issues, but they are there. They need to be investigated. If you’re an asset only, you wanna know that. Do I have a blade that’s likely to fail compared to one which is, which is okay? And sort of focus on that and not essentially remove any uncertainty or worry that you have about your assets. ’cause you can see other [00:07:00] turbine blades falling. Um, so we are trying to solve that problem. But at the same time, end of warranty claims, if you’re gonna be taken over these blades and doing the maintenance yourself, you wanna know that what you are being given. It hasn’t gotten any nasties lurking inside that’s gonna bite you. Joel Saxum: Yeah. Chris Cieslak: Very expensively in a few years down the line. And so you wanna be able to, you know, tick a box, go, actually these are fine. Well actually these are problems. I, you need to give me some money so I can perform remedial work on these blades. And then you end of life, you know, how hard have they lived? Can you do an assessment to go, actually you can sweat these assets for longer. So we, we kind of see ourselves being, you know, useful right now for the new blades, but actually throughout the value chain of a life of a blade. People need to start seeing that NDT ultrasonic being one of them. We are working on other forms of NDT as well, but there are ways of using it to just really remove a lot of uncertainty and potential risk for that. You’re gonna end up paying through the, you know, through the, the roof wall because you’ve underestimated something or you’ve missed something, which you could have captured with a, with a quick inspection. Joel Saxum: To [00:08:00] me, NDT has been floating around there, but it just hasn’t been as accessible or easy. The knowledge hasn’t been there about it, but the what it can do for an operator. In de-risking their fleet is amazing. They just need to understand it and know it. But you guys with the robotic technology to me, are bringing NDT to the masses Chris Cieslak: Yeah. Joel Saxum: In a way that hasn’t been able to be done, done before Chris Cieslak: that. And that that’s, we, we are trying to really just be able to roll it out at a way that you’re not limited to those limited experts in the composite NDT world. So we wanna work with them, with the C-N-C-C-I-C NDTs of this world because they are the expertise in composite. So being able to interpret those, those scams. Is not a quick thing to become proficient at. So we are like, okay, let’s work with these people, but let’s give them the best quality data, consistent data that we possibly can and let’s remove those barriers of those limited people so we can roll it out to the masses. Yeah, and we are that sort of next level of information where it isn’t just seen as like a nice to have, it’s like an essential to have, but just how [00:09:00] we see it now. It’s not NDT is no longer like, it’s the last thing that we would look at. It should be just part of the drones. It should inspection, be part of the internal crawlers regimes. Yeah, it’s just part of it. ’cause there isn’t one type of inspection that ticks all the boxes. There isn’t silver bullet of NDT. And so it’s just making sure that you use the right system for the right inspection type. And so it’s complementary to drones, it’s complimentary to the internal drones, uh, crawlers. It’s just the next level to give you certainty. Remove any, you know, if you see something indicated on a a on a photograph. That doesn’t tell you the true picture of what’s going on with the structure. So this is really about, okay, I’ve got an indication of something there. Let’s find out what that really is. And then with that information you can go, right, I know a repair schedule is gonna take this long. The downtime of that turbine’s gonna be this long and you can plan it in. ’cause everyone’s already got limited budgets, which I think why NDT hasn’t taken off as it should have done because nobody’s got money for more inspections. Right. Even though there is a money saving to be had long term, everyone is fighting [00:10:00] fires and you know, they’ve really got a limited inspection budget. Drone prices or drone inspections have come down. It’s sort, sort of rise to the bottom. But with that next value add to really add certainty to what you’re trying to inspect without, you know, you go to do a day repair and it ends up being three months or something like, well Allen Hall: that’s the lightning, Joel Saxum: right? Allen Hall: Yeah. Lightning is the, the one case where every time you start to scarf. The exterior of the blade, you’re not sure how deep that’s going and how expensive it is. Yeah, and it always amazes me when we talk to a customer and they’re started like, well, you know, it’s gonna be a foot wide scarf, and now we’re into 10 meters and now we’re on the inside. Yeah. And the outside. Why did you not do an NDT? It seems like money well spent Yeah. To do, especially if you have a, a quantity of them. And I think the quantity is a key now because in the US there’s 75,000 turbines worldwide, several hundred thousand turbines. The number of turbines is there. The number of problems is there. It makes more financial sense today than ever because drone [00:11:00]information has come down on cost. And the internal rovers though expensive has also come down on cost. NDT has also come down where it’s now available to the masses. Yeah. But it has been such a mental barrier. That barrier has to go away. If we’re going going to keep blades in operation for 25, 30 years, I Joel Saxum: mean, we’re seeing no Allen Hall: way you can do it Joel Saxum: otherwise. We’re seeing serial defects. But the only way that you can inspect and or control them is with NDT now. Allen Hall: Sure. Joel Saxum: And if we would’ve been on this years ago, we wouldn’t have so many, what is our term? Blade liberations liberating Chris Cieslak: blades. Joel Saxum: Right, right. Allen Hall: What about blade route? Can the robot get around the blade route and see for the bushings and the insert issues? Chris Cieslak: Yeah, so the robot can, we can walk circumferentially around that blade route and we can look for issues which are affecting thousands of blades. Especially in North America. Yeah. Allen Hall: Oh yeah. Chris Cieslak: So that is an area that is. You know, we are lucky that we’ve got, um, a warehouse full of blade samples or route down to tip, and we were able to sort of calibrate, verify, prove everything in our facility to [00:12:00] then take out to the field because that is just, you know, NDT of bushings is great, whether it’s ultrasonic or whether we’re using like CMS, uh, type systems as well. But we can really just say, okay, this is the area where the problem is. This needs to be resolved. And then, you know, we go to some of the companies that can resolve those issues with it. And this is really about played by being part of a group of technologies working together to give overall solutions Allen Hall: because the robot’s not that big. It could be taken up tower relatively easily, put on the root of the blade, told to walk around it. You gotta scan now, you know. It’s a lot easier than trying to put a technician on ropes out there for sure. Chris Cieslak: Yeah. Allen Hall: And the speed up it. Joel Saxum: So let’s talk about execution then for a second. When that goes to the field from you, someone says, Chris needs some help, what does it look like? How does it work? Chris Cieslak: Once we get a call out, um, we’ll do a site assessment. We’ve got all our rams, everything in place. You know, we’ve been on turbines. We know the process of getting out there. We’re all GWO qualified and go to site and do their work. Um, for us, we can [00:13:00] turn up on site, unload the van, the robot is on a blade in less than an hour. Ready to inspect? Yep. Typically half an hour. You know, if we’ve been on that same turbine a number of times, it’s somewhere just like clockwork. You know, muscle memory comes in, you’ve got all those processes down, um, and then it’s just scanning. Our robot operator just presses a button and we just watch it perform scans. And as I said, you know, we are not necessarily the NDT experts. We obviously are very mindful of NDT and know what scans look like. But if there’s any issues, we have a styling, we dial in remote to our supplement expert, they can actually remotely take control, change the settings, parameters. Allen Hall: Wow. Chris Cieslak: And so they’re virtually present and that’s one of the beauties, you know, you don’t need to have people on site. You can have our general, um, robot techs to do the work, but you still have that comfort of knowing that the data is being overlooked if need be by those experts. Joel Saxum: The next level, um, commercial evolution would be being able to lease the kit to someone and or have ISPs do it for [00:14:00] you guys kinda globally, or what is the thought Chris Cieslak: there? Absolutely. So. Yeah, so we to, to really roll this out, we just wanna have people operate in the robots as if it’s like a drone. So drone inspection companies are a classic company that we see perfectly aligned with. You’ve got the sky specs of this world, you know, you’ve got drone operator, they do a scan, they can find something, put the robot up there and get that next level of information always straight away and feed that into their systems to give that insight into that customer. Um, you know, be it an OEM who’s got a small service team, they can all be trained up. You’ve got general turbine technicians. They’ve all got G We working at height. That’s all you need to operate the bay by road, but you don’t need to have the RAA level qualified people, which are in short supply anyway. Let them do the jobs that we are not gonna solve. They can do the big repairs we are taking away, you know, another problem for them, but giving them insights that make their job easier and more successful by removing any of those surprises when they’re gonna do that work. Allen Hall: So what’s the plans for 2026 then? Chris Cieslak: 2026 for us is to pick up where 2025 should have ended. [00:15:00] So we were, we were meant to be in the States. Yeah. On some projects that got postponed until 26. So it’s really, for us North America is, um, what we’re really, as you said, there’s seven, 5,000 turbines there, but there’s also a lot of, um, turbines with known issues that we can help determine which blades are affected. And that involves blades on the ground, that involves blades, uh, that are flying. So. For us, we wanna get out to the states as soon as possible, so we’re working with some of the OEMs and, and essentially some of the asset owners. Allen Hall: Chris, it’s so great to meet you in person and talk about the latest that’s happening. Thank you. With Blade Bug, if people need to get ahold of you or Blade Bug, how do they do that? Chris Cieslak: I, I would say LinkedIn is probably the best place to find myself and also Blade Bug and contact us, um, through that. Allen Hall: Alright, great. Thanks Chris for joining us and we will see you at the next. So hopefully in America, come to America sometime. We’d love to see you there. Chris Cieslak: Thank you very [00:16:00] much.
In this episode of the Love Your Life Show, I teach one of the most important relationship skills we were never taught: emotional validation. If you often feel the urge to fix, explain, or make things better when someone you love is struggling, this episode is for you. I have found that in learning the skills of emotional validation and emotional intelligence, the people I love (my kids, my husband, my friends, etc) feel so much more love from me. Versus, when I didn't know these skills, our conversations and my efforts to support were wonky and they were left feeling disconnected, or worse, incompetent or shamed. Learning how to validate is how we support others without taking on their emotions or trying to change their experience, and it will change your parenting, marriage, and every close relationship you have. This episode builds directly on last week's conversation about emotional regulation. When you can stay calm inside yourself while someone else is having big feelings, validation becomes possible. I explain what emotional validation actually is, what it is not, and why validation does not mean agreement, approval, or fixing the problem. We talk about why emotions need soothing, not solving, how to stop minimizing or invalidating feelings without realizing it, and what to say when you want someone to feel seen, heard, and supported. I share practical phrases you can use with your kids, your partner, and the people you care about most, along with guidance on how to stay present without overfunctioning or taking responsibility for someone else's emotions. If you are a parent, partner, helper, or chronic fixer, this episode will help you build healthier emotional boundaries, deeper connection, and more emotionally safe relationships. Spoiler Alert: When they feel better in relationship with you, YOU feel better too!
If you have received enough external validation then it's likely because you are self-validating. This is a milestone in your self-love journey and in reparenting yourself. I'll share more on how to move forward after metabolizing validation. Thanks for tuning in! Share your support as a comment or rating the podcast. Book time with me if you're going through spiritual awakening and would benefit from support. Meet with me 1-1: https://calendly.com/anisabenitezMy website: https://www.anisabenitez.com/podcastFollow me on…YouTube: https://www.youtube.com/@anisabenitezInstagram: https://www.instagram.com/anisabenitezTikTok: https://www.tiktok.com/@anisabenitezSubstack: https://substack.com/@anisabenitezListen to the podcast…Spotify: https://open.spotify.com/show/3o4HTSBzZHmYUwLzDCE46KApple: https://podcasts.apple.com/us/podcast/create-to-liberate/id1502449035
Darshan H. Brahmbhatt, Podcast Editor of JACC: Advances, discusses a recently published original research paper on Multisite, External Validation of an AI-Enabled ECG Algorithm for Detection of Low Ejection Fraction.
In this episode of Get Plugged In – AI Insights, Dale Hall (Managing Director, Society of Actuaries Research Institute) sits down with Ronald Poon Affat, Independent Board Director & Cross-Continental Actuary, joining live from São Paulo, Brazil, to explore how NIST is shaping the standards that will define trustworthy AI—and why that matters for actuaries. They discuss what the NIST AI Consortium is, why the SOA is actively contributing through its AI Safety Working Group, and what it's like collaborating with leading voices across technology, academia, and public policy. The conversation also dives into the next major focus area: TEVV (Testing, Evaluation, Verification, and Validation)—a practical "quality assurance" approach to ensure AI models are fair, explainable, reliable, and ready for regulatory scrutiny. Listen in for a clear, actuarial lens on where AI governance is headed in insurance—and how actuaries can lead by asking the right risk questions.
How To Talk To Your Spouse About Problems Without Starting A Fight When every attempt to bring up a problem turns into defensiveness, arguing, or shutdown, it's easy to stop trying or to push harder and make things worse. Common “clear communication” tactics can backfire in a strained relationship because they feel like criticism or control, even when they're meant to help. In this episode, Coach Jack explains a calmer, more effective way to raise issues while protecting emotional connection and increasing cooperation over time.What You'll LearnHow to bring up a problem in a way that reduces defensiveness and keeps your spouse emotionally engagedHow to prepare the relationship so requests land better and don't trigger a fightHow to choose the right timing and wording so the conversation feels natural instead of threateningHow to use a simple win-win method (and a Plan B) so problems actually get solved instead of repeatedWant to Work With Coach Jack?If you want step-by-step help applying this approach to your specific situation, Coach Jack can help you build healthier connection, improve communication, and address hard issues without escalating conflict. The best starting point is the Difficult Partner Coaching Package, which focuses on ending a spouse's damaging behavior and building respect.Key TakeawaysDirect “I statements” can still trigger defensiveness when the relationship is strained.Strengthening everyday connection often needs to happen before problem talks.Talk about problems when both of you are relaxed, not while the issue is happening.Lead with validation and keep the conversation natural and low-pressure.Solve one issue at a time using a win-win plan, and use boundaries when discussion won't work.Additional ResourcesOvercome Neediness and Get the Love You Want, by Jack Ito PhDConnecting Through "Yes!" by Jack Ito PhDLove Language Quiz12 Ways to Revive Your Love for Your SpouseWork one-on-one with Coach Jack to repair your relationship using small, easy steps that rebuild connection quickly. Visit CoachJackIto.com to learn more about relationship coaching.
Send a textSometimes a paper comes out that's so practical and relevant to what we do in digital pathology that I know we have to talk about it.In this episode, I dive into “A Guide for the Deployment, Validation and Accreditation of Clinical Digital Pathology Tools” from Geneva University Hospital (HUG) — one of the most useful, real-world frameworks I've seen for bringing digital pathology tools safely into clinical practice.If you've ever built an AI model and wondered, “Now what?”, this episode is for you. Because building the model is often the easy part — deployment is where things get complex.This guide breaks the process into four practical phases every lab can follow:1️⃣ Pre-Development – Define your clinical need, project scope, and validation plan before writing a single line of code. 2️⃣ Development – Build and integrate the algorithm in a production-ready environment. 3️⃣ Validation & Hardening – Turn your research code into a reliable, secure, and compliant clinical tool. 4️⃣ Production & Monitoring – Keep the tool validated and performing consistently over time.We also discuss what makes qualification, validation, and accreditation different — and why that order really matters. You'll hear about the multidisciplinary team behind these deployments, especially the deployment engineer (DE) — the technical linchpin who turns AI research into clinical reality.I share the story of HUG's H. pylori detection tool, which cut diagnostic time by 26% while maintaining a 0% false negative rate. The team's secret? Careful planning, quality control, and continuous user feedback — not just great code.Other highlights include:Why integration often takes longer than building the AI model itselfHow to avoid invalidating your validation dataWhat continuous performance monitoring looks like in real labsAnd why every lab still needs to do local validation, even with proven toolsIf you're working on digital or computational pathology tools — or just want to understand how AI safely moves from research to routine diagnostics — this episode will give you a roadmap grounded in real experience.
Discover how unconscious needs for validation can quietly poison your sexual relationship and lead to a dead bedroom or sexless marriage. This episode breaks down the common validation traps men fall into, including attraction validation and performance validation, and how those habits undermine desire. You will learn how approval seeking creates tension, why outcome based intimacy feels forced, and how removing hidden validation needs restores authenticity and polarity. This is about building a marriage where desire is natural, not negotiated.VIDEOS TO WATCH NEXT:Watch this playlist to figure out how to fix your failing marriage:https://www.youtube.com/playlist?list=PLEXcvFDdRqPuu_G8-sTLS7eXT7myvidMFWatch this playlist to help you get over your ex for good:https://www.youtube.com/playlist?list=PLEXcvFDdRqPsZ9JCTSAIkin-oMnavqNJZWatch this playlist to develop an unshakable frame and take control of your life:https://youtube.com/playlist?list=PLEXcvFDdRqPvgN8idHfGfOp3gA8Y0tMxT&si=NccZ6koKYz3hSuUz--------------------------------------------BOOKS THAT WILL CHANGE YOUR LIFE➡️ Want to learn the life lessons I wish I knew when I was 18? Click here to get started:https://mybook.to/EIWIKWIW18➡️ Want to master your mindset and build an unshakable masculine presence? Click here now:https://mybook.to/psychology-paradigm➡️ Get your wife to bang you again:https://mybook.to/GHTFYA➡️ Move on from your ex FOR GOOD:https://mybook.to/FTB➡️ Keep your woman FOREVER:https://mybook.to/KeepYourB-tch➡️ This Little Book Will Change Your Life:https://mybook.to/littlebook--------------------------------------------FOLLOW MEFollow on TikTok:https://www.tiktok.com/@comeonmanpodFollow on Instagram:https://www.instagram.com/comeonmanpodcast/Follow on X:https://x.com/bestmenspodFollow on Facebook:https://www.facebook.com/comeonmanpodcast--------------------------------------------COMMUNITIES➡️ Join The W.O.L.F. Pack:https://wolf.comeonmanpod.com/➡️ Become a Spotify Channel Subscriber:https://podcasters.spotify.com/pod/show/comeonman/subscribe--------------------------------------------
Had an AHA or Insight? Share it:Why Prosperity Is an Internal System, Not a NumberMoney is believed to solve many, if not all, issues.Most of us don't consciously reject wealth. We say we want it. We work for it. We chase growth. We build teams, products, and systems. And yet income plateaus. There's a barrier we can't seem to break through, even though our capability clearly exceeds our current results.Then it shifts.The deal lands.The raise comes through.The windfall hits.For a moment, there is relief. Validation. Proof.And then something else surfaces.The inherited beliefs. The subtle conditioning. The narratives we absorbed about money, power, and what kind of person wealthy people are. Questions start to move in: Did I just get lucky? Can I sustain this? Do I deserve this level of success? Why does this not feel the way I imagined it would?That is why I invited Randy Gage onto the show.Randy has spent decades studying prosperity through the lens of our internal operating system. His own life forced him into that inquiry. He was arrested and jailed at 15. At 30, the IRS seized his business. He eventually confronted a difficult truth: he wanted money consciously, but subconsciously held beliefs that made prosperity incompatible with who he thought he was. As long as that contradiction existed, he sabotaged himself.In this episode of The Business Growth Architect Show: Founders of the Future, he shares the belief that kept him stuck and the work required to dismantle it. We talk about programming, identity, and why income ceilings are often belief ceilings.If you sense you have a money story that limits your ability to create joyful and unapologetic wealth, this conversation will challenge you to go deeper than hustle and tactics.Wealth without apology begins with examining the system that produces your results.Whether you are pushing against an old story and struggling financially, just beginning your journey, or already sitting on significant success, this episode is worth your time.#WealthWithoutApology #FoundersOfTheFuture #Prosperity #MoneyStoryRandy Gage Resources: Website | LinkedIn | Instagram | YouTube_____________________We appreciate you, thank you for listening. Let us know in the comments what resonated in this episode, we want to hear from you. Leave a comment, like, share with one person who needs to hear the message our guest shared. Take our QUIZ and find out what your talent is worth in this market: What's Your Talent Worth (http://WhatsYourTalentWorth.com)Follow us on Instagram:Check us out on Tik Tok: Work With Us
The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch
Alexander Embiricos is the Head of Codex at OpenAI, leading the development of the company's flagship AI coding systems that power automated software generation, debugging and developer workflows. Under his leadership, Codex has become one of the most widely adopted AI developer platforms. AGENDA: 05:13 Will Coding Be Automated? Why AI Could Create More Engineers, Not Fewer 07:17 Do We Need PMs? The "Undefined" Product Role and When It Matters 08:06 The Real AGI Bottleneck: Human Prompting, Validation, and "Too Much Effort" 13:04 Three Phases of Agents: Coding → Computer Use → Productized Workflows 13:52 Enterprise Reality Check: Security, Permissions, and Safe Agentic Browsing 17:57 Is Inference the New Sales and Marketing? 18:49 What % of Codex Was Written by AI? 21:33 Do OpenAI Use AI for Code Review? 23:31 Is there any stickiness to AI coding tools? 28:22 What Does "Winning" Mean at OpenAI? Mission, Competition, and Moats 32:04 The Future UI: Chat or Voice 34:10 Agent-to-Agent Workflows: Designing for Approvals, Compliance, and Automation 35:39 Do Coding Models Have a Data Moat? 36:50 How does Codex View Data: Will They Build Their Own Mercor and Turing? 37:27 How Does Codex View Consumer: Will They Compete with Lovable? 41:56 Benchmarks vs "Vibes": How People Actually Judge Models 42:43 Cursor's Edge and the Case for Building Your Own Models 47:37 Is SaaS Dead? What Still Defends Value (Humans + Systems of Record) 51:28 Talent Wars and Career Advice for New Engineers in the AI Era 01:01:03 Guardrails, the Fully AI-Managed Stack, and a 10-Year Vision for Everyone
In this episode of 2 Be Better, Chris and Peaches break down what self validation actually means, why your own voice matters, and how chasing approval keeps you stuck in reaction mode, people pleasing, and shaky confidence. You'll hear a straight talk walkthrough of the emotional fallout that comes from ignoring your inner compass, over apologizing, decision paralysis, self betrayal, and losing your sense of identity, plus how external validation and social media “likes” can quietly hijack your self worth and choices. You can expect practical, repeatable tools for building self validation and self care into daily life, pausing before you look outward, naming feelings without shame, rewriting harsh inner talk, celebrating effort, and creating simple rituals like journaling prompts, mirror work, anchoring phrases, micro breaks, breathwork, meditation, sound baths, and boundary choices that help your nervous system feel safe. If you're working through codependency, anxiety, guilt, burnout, or relationship patterns that make you abandon yourself to keep the peace, this conversation gives you language, examples, and a clear path to reclaim agency, hold your no, and live with more clarity in your marriage, family, and life.Disclaimer: We are not professionals. This podcast is opinioned based and from life experience. This is for entertainment purposes only. Opinions helped by our guests may not reflect our own. But we love a good conversation.Become a supporter of this podcast: https://www.spreaker.com/podcast/2-be-better--5828421/support.
If you've ever wondered:'Who am I without alcohol?''Why do I still want it even though it's hurting me?''Is it possible to feel genuinely good without it?'This episode is for you.In episode 239 of The Alcohol ReThink Podcast, Patrick sits down with fitness coach and sobriety advocate Sam LaPage for a raw, honest, and surprisingly uplifting conversation about identity, injury, shame, and learning how to actually love yourself.Sam grew up in a small town where drinking in the park wasn't just something you did… it was who you were. Alcohol became confidence. Belonging. Fun. Validation.At the same time, he was living a double life, chasing excellence in rowing while quietly numbing feelings of not being 'enough.'When a back injury ended his rowing career in the US, it didn't just take away a sport. It took away an identity. What followed was years of drinking driven by failure, shame, and a deep sense of not measuring up.And then something changed.Awesome topics covered in this episode:Why his drinking was never really about alcoholHow shame became the real cycle keeping him stuckThe unexpected power of fitness in building sobrietyWhat it actually means to 'love yourself' (and how awkward that felt at first)Why early sobriety can feel flat… and why that's normalThe difference between chemical highs and genuine joyHow he rebuilt his identity without alcoholPatrick and Sam also dive into vulnerability, masculinity, and why being 'radically human' means feeling everything, not escaping it.Connect with Sam LaPageInstagram: sam_lapage_coachWork with Patrick:Discover how coaching can support your goals in rethinking alcohol.
Send a textWhat actually needs to be in place before digital pathology can replace the microscope?In this episode of DigiPath Digest, I walk through the 2026 Polish Society of Pathologists guidelines and translate them into practical steps for real pathology labs. This isn't theory. It's about hardware fidelity, data integrity, validation, and AI integration — and what each of these actually requires in daily workflow.We talk about scanner resolution standards (≤0.26 μm per pixel), 4K monitor calibration, visually lossless compression (20:1), scalable storage, pathologist-driven validation, and what “non-inferiority” truly means.Digital pathology is not just a change of medium. It's an operational shift.Episode Highlights[00:02] Community & growth 1,600+ new newsletter subscribers, 10,000+ Facebook members, and free Digital Pathology 101 book access.[07:20] The 4 pillars of adoption Hardware fidelity · Data integrity · Clinical validation · Future integration.[08:30] Hardware requirements 40x equivalent scanning (≤0.26 μm/px), 4K monitors, >300 cd/m² luminance, 10-bit color depth.[12:00] Workflow & throughput 200–300 slides/day per scanner, automated focus control, urgent case prioritization.[17:25] Storage & archiving ~1 GB per slide. Active archive (6–24 months). Long-term retention (10–20 years). GDPR compliance & TLS encryption.[23:09] Validation philosophy Pathologist-centered validation. Two phases: • Familiarization (~20 retrospective cases) • Dual review with discrepancy tracking Goal: digital must be non-inferior to glass.[29:03] AI in digital pathology AI supports quantification (Ki-67, HER2, ER/PR, PD-L1), tumor detection, and future multimodal predictions — but pathologists remain central.[33:26] Intraoperative telepathology
Whether it's a partner, family member, or close friend, loving someone with OCD can feel confusing at times, especially when you want to be supportive but aren't sure how to respond to intrusive thoughts, anxiety, or repeated reassurance-seeking. In this solo episode, I talk more about how OCD can show up in relationships, the difference between validating feelings and reinforcing compulsions, why reassurance can keep the cycle going, what supportive language can look like, and how to stay compassionate without becoming a part of the OCD loop. Also, I touch on the importance of self-care for those supporting loved ones with OCD. Your mental wellbeing and your needs matter too. STAY CONNECTED:INSTA: @trustandthriveTIKOK: @trustandthriveEMAIL: trustandthrive@gmail.com
Self-led digital practices for emotional resilience, inner growth mindset development, and steady living through uncertainty — and beyond.Designed for people who choose self-responsibility, emotional maturity, and inner authority as a way of living.✨ Featured BundleIf you're moving through uncertainty and want to build steadiness from within — without bypassing emotions or forcing clarity — the Uncertainty to Steadiness Inner Growth Mindset Practice Bundle offers self-led practices designed to support emotional resilience, nervous system safety, and intentional living over time.
In this episode of Acta Non Verba, Marcus Aurelius Anderson sits down with virtuoso guitarist Angel Vivaldi to explore the intersection of artistry, authenticity, and perseverance. Angel shares insights from his recent tour with legendary guitarist Steve Morse, discusses his creative process behind concept albums like "Synapse," and reveals how he balances being 65% artist and 35% business. The conversation dives deep into topics ranging from working with difficult people and learning from enemies, to the role of AI in music, the importance of vulnerability, and why the only thing worse than living with regret is dying with it. This is a masterclass in commitment, creativity, and staying true to yourself in an industry that constantly demands compromise. Episode Highlights [2:14] Learning from Steve Morse's Humility and Reinvention - Angel describes touring with guitar legend Steve Morse and witnessing him reinvent his playing technique due to arthritis. Despite being one of the greatest guitarists alive, Morse remained humble enough to learn legato and tapping techniques from Angel, demonstrating that true mastery includes the willingness to continuously evolve. [20:59] The Muse and Discipline: Speaking Her Language - Angel shares his philosophy on creativity and the muse: "She has a lot of people to visit and she's gonna favor those who know how to speak her language. What is her language? Music." He explains why showing up consistently to practice—even without inspiration—is essential, because you're refining how you speak music so the muse can work through you. [39:44] The Synapse Album: Painting Studios and Neurotransmitters - Angel reveals the extreme creative process behind his concept album "Synapse," where each song represents a different neurotransmitter. He painted his studio a different color for each song (red for adrenaline, green for serotonin), changed scents, and even wrote at specific times of day to embody each neurochemical state—a process that nearly broke him but resulted in some of his most authentic work. [82:13] Learning from Your Enemies: Unfiltered Feedback - Angel offers a provocative perspective: "Your enemies have no stake in you liking them or them liking you. If you want unfiltered, uncensored, direct feedback on your flaws as a human being, look to your enemies." He explains how to parse criticism from adversaries to find genuine insights while filtering out projection and insecurity. Angel Vivaldi is an American virtuoso guitarist, songwriter, and producer who has been pushing the boundaries of instrumental guitar music since beginning his solo career in 2003. Self-taught from age 15, Angel has released multiple concept albums including "Universal Language," "Away With Words Parts 1 & 2," and "Synapse," each showcasing his unique blend of progressive metal, fusion, and melodic sensibility. Beyond his solo work, Angel is a multifaceted creative force—he's a cinematographer, fashion enthusiast, interior designer, and entrepreneur who founded Zenith Council, an artist services company helping musicians with branding, marketing, and creative vision. Recently, he toured as a guest guitarist with legendary Steve Morse, managing Morse's career while contributing rhythm guitar and content creation. Angel's approach to music and life embodies his belief that authenticity and vulnerability are the keys to creating art that truly resonates. Learn more about the gift of Adversity and my mission to help my fellow humans create a better world by heading to www.marcusaureliusanderson.com. There you can take action by joining my ANV inner circle to get exclusive content and information.See omnystudio.com/listener for privacy information.
In this episode of the Unbound Writer's Club, Nicola's in conversation with Holly Copeland about ‘Inner Rewilding: A Scientist's Journey Into Being'. It's a guide for the “spiritually weary, climate-anxious, and healers who've forgotten to tend their own hearts”. Holly's going to create a course from the book, so watch this space.In this Episode:What makes Holly an unbound writer?Each Unbound Press author has a unique essenceThe energy and spirit of a book – quite distinct from the authorBooks change you, and trusting the processThe four-year process of Holly and the Unbound Press working togetherHow did Holly feel the initial call to write her book?Moments when there's a new layer of clarityHow did Holly's book change and evolve?What fears and doubts came up for Holly throughout the process?Divine timing: things unfolding just as they're meant toHow does Holy feel now Inner Rewilding is out in the world?The book-writing and birthing process is huge (and terrifying)Validation signs coming in from the universeWhat advice would Holly give someone called to write a book?Honouring the spirit of the book that wants to come through youLinks:Interested in becoming an author with us at The Unbound Press? To access our info pack, click here.Ready to write, finish + publish your book? Come join the Unbound Writer's Collective - our vibrant community + membership for soul-led writers.Access Nicola's free guide - The 3 Things I Wish I'd Known Before I Wrote my Most Transformational Book here.Connect with Nicola on Instagram, and The UNBOUND Press on Instagram or Facebook.Connect with Holly Copeland on her website and Substack.Purchase Holly's book via the usual outlets.Music Credit: Joseph McDade.We'd love you to share this episode with your friends, community and anyone you think would enjoy it.
What happens when your greatest strengths—your empathy, your willingness to self-reflect, your sensitivity—become the very tools someone uses to convince you everything is your fault? In this crossover episode with therapist Angela De Hoyos, ALC, Tony explores why validation feels like survival when you were raised in an emotionally unpredictable home. You learned that love could vanish without warning—so you became hypervigilant, endlessly working to secure a connection that was never yours to earn. Now you may find yourself starving for validation from the one person who can't hold it steadily. You can learn more about Angela by visiting her website https://www.findingbalancecounseling.com/ and subscribe to her podcast “Finding Balance with Mental Health and Spirituality” here https://www.findingbalancecounseling.com/podcast EPISODE HIGHLIGHTS: Understand the origins of validation: why we learn we exist through others' responses—and how that wiring gets exploited Discover why "pathologically kind" people attract emotionally immature partners—and keep trying harder when it doesn't work Recognize the trap of "if it's my fault, I can fix it"—and why that belief keeps you chasing validation instead of building self-trust Learn the crucial difference between validation and agreement—you can acknowledge someone's experience without abandoning your own Build a 90% solid sense of self so you stop outsourcing your worth to people who use it against you 00:00 Introduction and Episode Overview 01:25 Guest Introduction: Angela de Hoyos 03:16 The Magnetic Marriage Course Pitch 06:20 Understanding Validation and Emotional Immaturity 08:15 Therapeutic Insights and Parenting Dynamics 20:46 The Concept of Co-Regulation 28:40 Exploring the Concept of Existence and Value 29:05 The Story of Jill: Unpredictable Childhood 30:33 Understanding Validation and Recognition 33:50 The Role of Self-Validation 40:59 Spiritual Perspectives on Validation 51:25 Final Thoughts and Reflections Get on the waitlist today for Tony's upcoming Magnetic Marriage live course! Head to https://tonyoverbay.com/magnetic If you are interested in joining Tony's private Facebook group for women in narcissistic or emotionally immature relationships of any type, please reach out to him at contact@tonyoverbay.com or through the form on the website, HTTP://www.tonyoverbay.com If you are a man interested in joining Tony's "Emotional Architects" group to learn how to better navigate your relationship with a narcissistic or emotionally immature partner or learn how to become more emotionally mature yourself, please reach out to Tony at contact@tonyoverbay.com or through the form on the website, HTTP:www.tonyoverbay.com
Stop the struggle with difficult dementia behaviors by uncovering the "hidden history" your loved one can no longer express. In this breakthrough episode, we reveal why "random" symptoms like wandering, repetitive questions, and agitation are often deeply rooted in a person's personal history. Whether it's a past career as an architect or a childhood role as the eldest sibling, these memories don't disappear—they manifest as behaviors. We provide actionable caregiving strategies to help you move from frustration to empathy by "detecting" the life stories behind the diagnosis. If you are facing caregiver burnout or feeling like you've tried everything to manage dementia symptoms, this personal history approach offers a transformative shift in perspective. Learn how to validate their reality, reduce triggers, and even heal old family wounds during this difficult journey. Understanding their unique personal history is the ultimate key to personalized, compassionate care. ⏳ Episode Timestamps (SEO-Linked) 21:10 – Final Strategy: What to write down now in case you get dementia later. 00:00 – Why your loved one's personal history is the "missing key" to care. 01:45 – Meet Tammy Anastasia: Navigating the shift from wellness to dementia care. 03:12 – The Architect Story: Why staring at a wall isn't a random symptom. 05:30 – Routine & Resistance: Why dementia patients fight changes in their day. 07:45 – [AD BREAK] Practical tools for caregivers. 08:15 – Unfiltered Emotions: How childhood trauma resurfaces in dementia. 11:20 – The "Best Friend" Shift: Handling the pain when they forget you're their child. 14:40 – Validation vs. Redirection: The common mistake that fuels agitation. 17:05 – Healing the Caregiver: Using this journey to resolve your own past history. Our Guest: Tami Anastasia Tami Anastasia is an Alzheimer's and dementia counselor and educator, providing one-on-one caregiver support, guidance and strategies to help make the dementia journey easier on the caregiver. Tami holds a Master's Degree in Counseling and has Certificates in Gerontology and End-of-Life. She is the author of the new book Dementia, Caregiving, and Personal History: How to Help, Cope, Connect, and Heal In addition to her work as a dementia counselor and consultant, Tami facilitates dementia caregiver support groups and conducts educational workshops and personalized one-on-one educational sessions. She also works with people with dementia and provides cognitive and physical stimulation. She is a frequent speaker at professional and community organizations, senior retirement communities, memory care and assisted living communities, health and wellness conferences, local colleges, and public health libraries. Tami has been a guest on local television and radio shows and has published several articles on health and wellness. ++++++++++++++++++++++++++++++++++++++++ Related Episodes: Dementia Challenges - Avoiding Triggers Dementia Care Conversations: Unveiling the Four Essentials ++++++++++++++++++++++++++++++++++++++++ Sign Up for more Advice & Wisdom - email newsletter. ++++++++++++++++++++++++++++++++++++++++ Please Support Our Sponsors So We Can Continue To Bring The Show to You For Free ++++++++++++++++++++++++++++++++++++++++ Make Your Brain Span Match Your LifeSpan Relevate from NeuroReserve With Relevate nutritional supplement, you get science-backed nutrition to help protect your brain power today and for years to come. You deserve a brain span that lasts as long as your lifespan. ++++++++++++++++++++++++++++++++++++++++ Please help us keep our show going by supporting our sponsors. Thank you. Stop 100% of Unwanted Calls with imp. Did you know people with Alzheimer's can receive nearly 200 spam calls a week? You can put a stop to those now. ++++++++++++++++++++++++++++++++++++++++ Join Fading Memories On Social Media! If you've enjoyed this episode, please share this podcast with other caregivers! You'll find us on social media at the following links. Instagram Twitter LinkedIn Facebook Contact Jen at hello@fadingmemoriespodcast.com or Visit us at www.FadingMemoriesPodcast.com
Send a textSupport the showBreakfast With Tiffany Show Official Facebook Page ~ https://www.facebook.com/breakfastwithtiffanyshow Tiffany's Instagram Account ~ https://www.instagram.com/tiffanyrossdaleofficial/ Breakfast With Tiffany Show Youtube Channel ~ https://bit.ly/3vIVzhE Breakfast With Tiffany Show Official Page ~ https://www.tiffanyrossdale.com/podcast For questions, requests, collaborations and comments, feel free to reach us via our e-mail ~ breakfastwithtiffanyshow@outlook.com SUBSCRIBE and SUPPORT us here ~ https://www.buzzsprout.com/1187534/supporters/new
What happens when your greatest strengths—your empathy, your willingness to self-reflect, your sensitivity—become the very tools someone uses to convince you everything is your fault? In this crossover episode with therapist Angela De Hoyos, ALC, Tony explores why validation feels like survival when you were raised in an emotionally unpredictable home. You learned that love could vanish without warning—so you became hypervigilant, endlessly working to secure connection that was never yours to earn. Now you may find yourself starving for validation from the one person who can't hold it steadily. You can learn more about Angela by visiting her website https://www.findingbalancecounseling.com/ and subscribe to her podcast “Finding Balance with Mental Health and Spirituality” here https://www.findingbalancecounseling.com/podcast EPISODE HIGHLIGHTS: Understand the origins of validation: why we learn we exist through others' responses—and how that wiring gets exploited Discover why "pathologically kind" people attract emotionally immature partners—and keep trying harder when it doesn't work Recognize the trap of "if it's my fault, I can fix it"—and why that belief keeps you chasing validation instead of building self-trust Learn the crucial difference between validation and agreement—you can acknowledge someone's experience without abandoning your own Build a 90% solid sense of self so you stop outsourcing your worth to people who use it against you 00:00 Introduction and Episode Overview 01:25 Guest Introduction: Angela de Hoyos 03:16 The Magnetic Marriage Course Pitch 06:20 Understanding Validation and Emotional Immaturity 08:15 Therapeutic Insights and Parenting Dynamics 20:46 The Concept of Co-Regulation 28:40 Exploring the Concept of Existence and Value 29:05 The Story of Jill: Unpredictable Childhood 30:33 Understanding Validation and Recognition 33:50 The Role of Self-Validation 40:59 Spiritual Perspectives on Validation 51:25 Final Thoughts and Reflections Get on the waitlist today for Tony's upcoming Magnetic Marriage live course! Head to https://tonyoverbay.com/magnetic If you are interested in joining Tony's private Facebook group for women in narcissistic or emotionally immature relationships of any type, please reach out to him at contact@tonyoverbay.com or through the form on the website, HTTP://www.tonyoverbay.com If you are a man interested in joining Tony's "Emotional Architects" group to learn how to better navigate your relationship with a narcissistic or emotionally immature partner or learn how to become more emotionally mature yourself, please reach out to Tony at contact@tonyoverbay.com or through the form on the website, HTTP:www.tonyoverbay.com
On Healthy Mind, Healthy Life, hosted by Sayan, theatre maker and arts advocate Jessie Fahay unpacks what fulfillment really means when the applause ends—and how to stop confusing recognition with inner wholeness. This episode is for anyone who looks “successful” on paper but feels disconnected inside—creatives, leaders, parents, and everyday professionals. Jessie shares a practical way to shift from scarcity, comparison, and performative living toward meaning, contribution, and a steadier inner life. About the Guest: Jessie Fahay is a theatre maker, speaker, and advocate based in New York. She founded the nonprofit Ripple Effect Artists and uses art to spark empathy, dialogue, and real-world action. Episode Chapter: 00:08:39 – The quiet question behind fulfillment when the noise fades 00:10:25 – “Fulfilled” as full + filled: connection, contribution, love 00:12:09 – The visibility trap: recognition vs. real fulfillment 00:17:24 – When “having it all” still hurts: the spiral of disconnection 00:20:13 – Orienting life toward community (without self-neglect) 00:24:10 – The “context” practice: same life, different meaning 00:31:13 – Keep sharing your vision: how Jessie built Ripple Effect Artists Key Takeaways: Redefine fulfillment as connection + contribution, not applause or metrics. Notice when you're chasing visibility and gently return to what matters. Create a daily context statement for your work: “This is what I'm here for today.” Reframe your role: the same task can feel empty or meaningful depending on context. Share your vision often—unexpected people can become allies and connectors. How to Connect With the Guest: Website: https://www.rippleeffectartists.com/ Jessie Fahay Want to be a guest on Healthy Mind, Healthy Life? DM on PM - Send me a message on PodMatch DM Me Here: https://www.podmatch.com/hostdetailpreview/avik Disclaimer: This video is for educational and informational purposes only. The views expressed are the personal opinions of the guest and do not reflect the views of the host or Healthy Mind By Avik™️. We do not intend to harm, defame, or discredit any person, organization, brand, product, country, or profession mentioned. All third-party media used remain the property of their respective owners and are used under fair use for informational purposes. By watching, you acknowledge and accept this disclaimer. Healthy Mind By Avik™️ is a global platform redefining mental health as a necessity, not a luxury. Born during the pandemic, it's become a sanctuary for healing, growth, and mindful living. Hosted by Avik Chakraborty, storyteller, survivor, and wellness advocate. With over 6000+ episodes and 200K+ global listeners, we unite voices, break stigma, and build a world where every story matters.
What happens when the “million-dollar months” stop feeling aligned?In this unfiltered, high-level conversation, I sit down with Cynthia Stant — widely known as the Queen of Sales — to talk about what no one in the online space wants to admit:The identity crisis after big successThe bus moment when revenue dipsWhy low-ticket overload is killing brandsThe truth about DM sellingThe difference between gross and netWhy validation must die for legacy to riseCynthia shares how she went from food stamps and bankruptcy… to self-made millionaire… to rebuilding her entire business in 2024 — and somehow netting more than ever while letting her old identity burn.We talk about:✔️ Why money always comes from people✔️ Why following up in DMs is feminine leadership — not icky✔️ The pendulum swing from low-ticket overload to premium proximity✔️ Why discernment has changed buyer psychology✔️ What happens when you build validation-based success✔️ The death of ego and rebirth of legacy✔️ The power of getting in the roomThis episode is for the woman who:Had six-figure or multi-six-figure years… and now feels the dipIs questioning her pricingIs exhausted from trying to “look successful”Knows she's being called to something deeperRefuses to quit — but needs clarityConnect with Cynthia on Facebook HERE
Functional medicine testing is everywhere. It is often marketed as “test, don't guess,” and just as often dismissed as invalidated or unscientific. So what is the truth?In this episode of The Trip Lab, we take a deep dive into what functional medicine testing actually is, how it differs from traditional laboratory testing, and what clinicians really mean when they say these tests are not “validated.” We explore why some advanced tests can be genuinely helpful when used thoughtfully, where their limitations lie, and why more testing does not always lead to better care.We walk through several commonly used functional medicine tests that I actually do use in my practice, including DUTCH, GI-MAP, and Organic Acids Testing (OAT), breaking down what each test measures, when it can add value, and … when it might not be helpful as well. We also discuss why I typically don't recommend mold or environmental toxin testing, and why exposure history and foundational interventions often matter more than identifying a specific toxin.
In this throwback episode of the Awake & Winning Podcast, Kaylor sits down with Mark Groves for a raw, high-level conversation about social media, nervous system health, and what it actually takes to stay authentic when the internet is trying to hijack your brain. Mark breaks down why he left Instagram (and why he came back), how validation and family dynamics can quietly shape our online behavior, and why modern platforms keep the body stuck in a constant state of social "threat." They dive into addiction, boundaries, oversharing vs real vulnerability, and the uncomfortable truth: you can't be free if you can't choose "no." If you've ever felt drained, reactive, or dependent on metrics—this episode will help you reset your relationship with attention, identity, and integrity. Episode Highlights: social media addiction, nervous system regulation, validation wounds, mother-son dynamics, boundaries vs walls, oversharing vs vulnerability, authenticity with strategy, dopamine loops, blue light and sleep, online criticism resilience, identity and freedom, choosing "no" Takeaways: Social media can trigger survival wiring, not just "bad habits" Validation seeking often masks a deeper relational wound Your body treats social threat like physical threat Build boundaries by starting with "walls" then refining Oversharing asks others to hold what they didn't earn Vulnerability shares the lesson, not the emotional dump Freedom comes from choice, not dependence If this episode lit a fire under you, don't keep it to yourself. Screenshot it, throw it up on Instagram, and tag @thekaylorbetts or @bettsnation so we can share the love. And hey, if you're vibing with the show, take 30 seconds to drop us a 5-star review, it helps us reach more freedom-loving legends like you. _____________________________ RESOURCES & LINKS MENTIONED IN THIS EPISODE: Instagram | https://www.instagram.com/createthelove/ X | https://x.com/itsmarkgroves/ YouTube | https://www.youtube.com/channel/UClgLCOnztdrdu6qAOH-PVdA Facebook | https://www.facebook.com/createthelove Websites | https://markgroves.com/ Book | https://amzn.to/4cW3eyR Podcast | https://open.spotify.com/show/6MvcXSwmkNnnfiWXdYoPT8#login _____________________________ SPONSORS: Truly Tallow | https://www.trulytallow.com/ Use code "SUNNYBALLS10" at checkout for 10% off your order _____________________________ IMPORTANT UPDATES: Join the Betts Nation | https://bettsnation.ca/biz-kb/ Follow Kaylor on Instagram | https://www.instagram.com/thekaylorbetts/ Follow Betts Nation on Instagram | https://www.instagram.com/bettsnation/ Join Kaylor's Newsletter | https://awakeandwinning.lpages.co/optin/ _____________________________ CHAPTERS: 00:00 Intro + reconnecting 01:02 Why he left Instagram 03:39 Podcast algorithm changes 04:42 The "mom validation" insight 07:28 Nervous system + social media 08:04 "MKUltra by smartphones" 09:40 Blood sugar spike from stress 11:14 Why he came back 14:35 Boundaries, people-pleasing, self-abandonment 36:36 Oversharing vs vulnerability 45:54 What COVID revealed 57:49 Simulation + meaning 01:18:23 Advice to his 2020 self 01:23:04 Where to find Mark
As part of this Valentine's Day Special — From the Archives, the SexyCoolLounge Podcast™ revisits a meaningful and uplifting conversation with Jess Marie, host of the Unapologetically Overcoming Podcast.This featured episode centers on themes of self-love, self-compassion, and emotional well-being, highlighting the importance of validating yourself and embracing the relationship you have with yourself. The discussion blends reflection, humor, and authentic dialogue — including a lighthearted exchange on celebrity crushes.This Valentine's Day archive feature serves as a reminder that the most meaningful relationship you will ever have is the one you build within.“The best love you can receive is the one you give to yourself.” SHOW LINKS:About The AuthorBooksInstagramFacebookApple PodcastVoyage Baltimore Magazine Highlight Series 2nd. Return InterviewStitcherSpotifyAudibleYouTubeVoyage Baltimore Magazine 1st. Interview
If it isn't obvious…the boys are lonely. In this episode, Beatrice Oshodi, your new favorite Gemini and Jill of all trades, joins me to explore masculinity, emotional homes, and the contradictions —power and tenderness, silence and expression—that shape male loneliness and connection.
Join the dialogue - text your questions, insights, and feedback to The Dignity Lab podcast.In this episode of the Dignity Lab, Jennifer Griggs explores the concept of dignity in the context of forgiveness and its alternatives. She discusses how understanding dignity can aid in healing from past hurts, emphasizing the importance of validating one's own experiences and recognizing the elements of dignity that may have been violated. She also covers the ways in which taking accountability can, if applicable, can further healing.TakeawaysDignity is your inherent worth or value.Understanding dignity aids in healing even if forgiveness does not appeal.Dignity is vulnerable to harm and trauma.Naming dignity elements helps validate personal pain.Validating experiences confirms their authenticity.Accountability is a key element of dignity.Recognizing personal agency can empower healing.Accountability helps make sense of personal hurt.Exploring what it means to live and lead with dignity at work, in our families, in our communities, and in the world. What is dignity? How can we honor the dignity of others? And how can we repair and reclaim our dignity after harm? Tune in to hear stories about violations of dignity and ways in which we heal, forgive, and make choices about how we show up in a chaotic and fractured world. Hosted by physician and coach Jennifer Griggs.For more information on the podcast, please visit www.thedignitylab.com.For more information on podcast host Dr. Jennifer Griggs, please visit https://jennifergriggs.com/.For additional free resources, including the periodic table of dignity elements, please visit https://jennifergriggs.com/resources/.The Dignity Lab is an affiliate of Bookshop.org and will receive 10% of the purchase price when you click through and make a purchase. This supports our production and hosting costs. Bookshop.org doesn't earn money off bookstore sales, all profits go to independent bookstores. We encourage our listeners to purchase books through Bookshop.org for this reason.
The episode features Nate Samuels, a well-known club promoter who gained viral fame on TikTok for his videos about nightlife, club dynamics, and personal stories, including a publicized incident where his girlfriend cheated on him with another promoter. Nate relocated to Miami and started working as a club promoter, building a large following and deep insights into club culture, social dynamics, and influencer marketing. 00:00 - Intro 00:32 - The Andrew Tate Club Incident 02:45 - Streaming Culture in Nightclubs 05:43 - Jewish Education and Community Resolution 08:55 - The Decline of Traditional Nightclubs 10:32 - Social Media's Impact on Validation 12:40 - The Importance of Social Capital 14:46 - Managing Client Rotations and Trust 17:40 - The Reality of Pre-selection 21:04 - Promoting with a Long-term Mindset 24:22 - Solving for Revenue vs. Experience 27:09 - The Business Strategy of Friend-zoning 29:49 - Exclusivity and Blacklisting Guidelines 33:10 - Maintaining Professional Discretion and Optics 39:42 - Aura Maxing vs. Looks Maxing 42:30 - Venue Standards and Entry Logistics 45:22 - Mastering Girls and High-Value Clients 49:36 - Personal Background and Cheating Viral Story 52:35 - Cheating Realities in Miami Nightlife 54:08 - Predatory OnlyFans Management Agencies 57:46 - Screening Bad Clients and Interactions 59:37 - Building Automated Lead Generation Funnels 1:04:12 - Data Filtering and Outreach Systems 1:07:18 - Dealing with Volatile Crypto Clients 1:12:07 - Miami Club Tiers and Filler Economics 1:15:26 - Coaching Differences and Service Tiers 1:21:18 - Learning Through Free Side Events 1:24:21 - Comparative Theories on Pre-selection 1:29:05 - The Psychology of Female Choice 1:35:02 - Content Creation and Social Proof ————————————————————
Executive leader and transformation strategist Victoria Pelletier joins me to talk about what happens when success stops feeling like success — and why growth requires stepping into discomfort intentionally.Most career narratives celebrate upward mobility, titles, and financial wins. This episode looks underneath that surface. Victoria and I unpack the transition from chasing validation and status to building a life anchored in meaning, resilience, and conscious choice.Victoria shares how a traumatic childhood, adoption, and early exposure to scarcity drove her relentless pursuit of achievement. Becoming an executive at 24, climbing the corporate ladder, accumulating status and material markers of success — all of it was within her control. And all of it was tied to external validation.Then life intervened.Motherhood shifted priorities. Loss reshaped perspective. Reflection redefined what mattered.From there, our conversation expands into resilience, self-awareness, and the uncomfortable but necessary process of recalibrating identity. We talk about bankruptcy, layoffs, corporate politics, performative leadership, toxic top performers, and why discomfort — when processed deliberately — becomes a catalyst instead of a crisis.This isn't a motivational episode about “pushing through.”It's a conversation about processing adversity, choosing discomfort strategically, and designing growth rather than defaulting to reaction.The lesson isn't to reject ambition.It's to anchor it in alignment rather than approval.TL;DR* External validation can masquerade as success.* Trauma often fuels achievement — but doesn't define fulfillment.* Resilience isn't brute force; it requires reflection and processing.* Discomfort is where growth happens — if approached consciously.* Surround yourself with people who challenge without destabilizing.* Toxic top performers erode culture, even if they hit numbers.* Performative leadership creates long-term organizational decay.* Real reinvention begins when identity shifts, not just strategy.Memorable Lines* “Everything you've ever wanted lives on the other side of fear.”* “Resilience isn't shouldering everything — it's processing it.”* “Discomfort is the price of clarity.”* “Validation can look like success — until it doesn't.”* “If you want growth, step into the room that scares you.”GuestVictoria Pelletier — Executive leader and transformation strategistSpecializing in the intersection of human performance, leadership, and technology-driven transformation. Known for candid conversations around resilience, culture, and creating environments where people actually thrive.
In this episode of the Asking Why podcast, host Clint Davis welcomes back Rosalie Elliott, a YouTube content creator known for her psychological takes on music reactions. They discuss Rosalie's journey in expanding her platform from reaction videos to a more holistic approach that includes education, coaching, and personal growth. The conversation delves into the challenges of navigating the digital landscape, the importance of authenticity, and the impact of personal stories on audience engagement. They also explore the responsibility that comes with influence, the role of pain in society, and the need for self-regulation in a fast-paced world. The episode concludes with reflections on the ripple effect of their work and the importance of relationships in healing. Rosalie Elliott is a teacher, counselor, singer-songwriter, producer, and podcast creator whose work bridges world music, psychology, and philosophy. She's passionate about encouraging others through stories of hope, healing, and freedom, and hosts The Rosalie Elliott Show, where she explores culture, creativity, human behavior, and meaning through thoughtful conversations. Rosalie brings a whole-hearted perspective shaped by her journey as a wife, mom, artist, and lifelong learner — inviting listeners to reflect deeply, grow intentionally, and engage the world with curiosity and joy. Social Media: youtube.com/@rosalieelliottofficial instragram.com/rosalieelliottofficial facebook.com/@rosalieelliottofficial Tiktok.com/@rosalieelliottofficial Chapters 00:00 Introduction and Background 00:50 Evolving Content and Purpose 03:44 Navigating Trends and Authenticity 08:51 Engagement and Community Impact 11:44 The Weight of Responsibility 16:43 Understanding Power and Identity 20:44 The Role of God and Faith 24:53 Therapeutic Relationships and Coaching 30:36 Finding Balance in Therapy 32:03 The Impact of Therapist-Client Relationships 34:33 Real-World Experience vs. Textbook Learning 36:27 The Value of Unique Contributions 37:59 Navigating Pain and Polarization in Society 40:34 The Role of Validation in Healing 43:03 Encouragement to Take Action 43:59 Addressing Current Mental Health Climate 49:11 The Dangers of Surface-Level Validation 54:04 The Ripple Effect of Genuine Connection
After 14 years as a licensed therapist, Hilary walked away from her practice because she stopped believing in the long-term model. Therapy, she argues, is powerful when you are in a true crisis or dealing with real mental illness. It can pull you out of the hole. But once you are stable, why are so many smart, capable women still sitting on the couch week after week talking about the same problems? At some point you have to face the fact that therapy isn't moving you forward. This conversation challenges the culture of endless processing, over-identifying with labels, and outsourcing your clarity to a paid professional. Insight feels good. Validation feels even better. But are you being challenged? Are you building skills? Is there a clear endpoint? Hilary makes the case that awareness without action keeps you looping in the same patterns, and that real evolution requires structure, accountability, and someone strong enough to tell you the truth. The question she leaves you with is simple and a little uncomfortable: are you actually moving forward, or are you just really good at talking about why you are stuck? Episode Highlights: When therapy is absolutely the right tool and when it is not How long-term therapy is actually a problem Why insight alone rarely creates real change The cultural obsession with labels and attachment styles What to look for if you want growth instead of endless processing Episode Breakdown: 00:00 Why I Walked Away From Therapy 03:02 When Therapy Actually Works 06:12 How Therapy Creates Dependence Instead of Self-Trust 11:53 Why Insight Alone Does Not Create Change 15:10 The Problem With Endless Therapy and No Clear Endpoint 17:53 Coaching vs Therapy for Real Personal Growth ✨ I'm Hilary Silver, LCSW, former psychotherapist turned master coach and founder of Ready for Love. I help high-achieving women show up in love as confidently as they do in their careers.
James Allcott is joined by Clive Palmer (@Clivepafc) and Jacob Hawley (@hawleyjacob) to discuss Arsenal's season in depth. The trio talk about the ins and outs that have gotten Arsenal to this point, whilst taking a look at why Mikel Arteta needs this Premier League title more than anyone else.Host: James AllcottGuests: Clive Palmer and Jacob HawleyProducer: Cai JonesEditor: Finn McSkimmingAdditional Production: Patris Gordon Learn more about your ad choices. Visit podcastchoices.com/adchoices
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
Leadership advisor and author Greg Morley joins me to unpack what it actually takes to rebound from setbacks—and why resilience isn't an individual trait as much as a relational one.Most conversations about adversity focus on grit, mindset, or personal toughness. This episode doesn't. Greg and I explore what happens after layoffs, career pivots, health crises, and identity shifts—and why the people who rally fastest are rarely the ones who go it alone.Drawing from over 30 years in global HR leadership, and from interviews conducted for his upcoming book Rally, Greg shares lessons from individuals who endured job loss, serious illness, organizational upheaval, and even genocide. The common thread isn't bravado. It's perspective, learning velocity, and community depth.We discuss why layoffs feel existential, how high burn rates trap professionals in fragile career paths, and why optionality comes from lowering fixed costs—both financial and psychological. We also examine the hidden tension between success and validation, and why redefining what “winning” means is often the first step toward rebuilding.This isn't a conversation about avoiding setbacks. It's about designing a life resilient enough to absorb them.The lesson isn't endurance for its own sake.It's adaptability, self-reflection, and tending the relationships that hold when titles fall away.TL;DR* Resilience is less about toughness and more about future orientation* Recovery speed determines long-term trajectory* Community acts as long-term insurance against career shocks* High fixed costs limit professional flexibility* Continuous learning expands rebound opportunities* Validation through status or possessions creates fragile identity* Simplicity increases adaptability* Listening across differences builds durable relationshipsMemorable Lines* “Rally isn't about pretending nothing happened—it's about moving forward with what you learned.”* “Your network is a long-term investment, not a short-term transaction.”* “Lower the bar you have to step over, and the world opens up.”* “You can't control the shock—but you can control the response.”* “Resilience lives in community, not isolation.”GuestGreg Morley — Leadership advisor, former global HR executive, and authorAuthor of Bond: Belonging and the Keys to Inclusion and Connection and the forthcoming Rally, focused on resilience, recovery, and leadership through adversity.
In this episode we're joined by Midlife Reinvention Architect and host of the Reinvention Rebels Podcast, Wendy Battles. Wendy reveals why even the most accomplished women still find themselves waiting for permission to take their next bold step and shares how we can move from a peacekeeping mindset to a state of internal authority.Tune in to learn:Why authority isn't something that's granted by others—it's something you claim for yourself from the inside out.The secrets to escaping the "Matrix-like" trap of waiting for an okay from bosses, family, or society before moving forward.How to navigate the transition from chronic people-pleasing to leading with the deep conviction you've spent decades cultivating.Practical ways to build your "Reinvention Dream Team" and why speaking your tiny nuggets of ideas out loud is the key to building real momentum.It's time to stop shrinking and start owning the brilliance of your actual, evolving life!Free Gift: Do It Scared GuideDo It Scared gives you a mindset roadmap for turning fear into forward momentum. Through three powerful shifts and reflective prompts, it helps you recognize your courage, take meaningful action despite uncertainty, and build self-trust — so you can step into your next chapter with confidence and clarity.Wendy's Giveaway Contribution: Midlife Reinvention from the Inside OutMidlife Reinvention from the Inside Out is an audio-based mindset experience designed to help women move from self-doubt and uncertainty to clarity, confidence, and self-trust. Through eight powerful lessons, Wendy guides listeners to release limiting beliefs, reconnect with their values and voice, and create meaningful change from the inside out.Connect with Wendy: Website | Podcast | Instagram---Enter the Book Launch Celebration Giveaway!
SPONSORMedboard: https://www.medboard.com/EUROPE New Harmonization Standards - Implementing Decision 2026/193: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L_202600193Neurosurgical implantsEN ISO 14155:2020 on clinical investigationsEN ISO 18562 series on Biocompatibility for Breathing gas pathways Germany: Transition from DMIDS to EUDAMED - March 19, 2026:https://www.bfarm.de/DE/Aktuelles/Veranstaltungen/Termine/2026-03-19-registrierung-mp.html?nn=986770EUDAMED mandatory by May 28th, 2026 High-Level Conference on Medical Devices - March 16th, 2026 - Brussels:https://health.ec.europa.eu/events/high-level-conference-medical-devices-innovation-and-patient-safety-16-march-2026-brussels-belgium-2026-03-16_enThe conference will feature three breakout sessions focusing on:Enhanced predictability for conformity assessments: combining certainty with flexibilityClinical evidence at EU level to support the regulatory framework: the key role of Expert PanelsBreakthrough technologies for better care: turning guidance into realityTeam-NB: Letter on Cybersecurity - MDR and IVDR proposal draft version:https://www.team-nb.org/wp-content/uploads/2026/02/Team-NB-Letter-on-cybersecurity-20260205.pdfSwitzerlandSwissmedic inspection on Importers -30 importers, 232 product samples, RESULT?:https://www.swissmedic.ch/dam/swissmedic/en/dokumente/medizinprodukte/infos/smc-ueberprueft-ch-importeure-2025.pdf.download.pdf/md-schwerpunktaktion-importeure-2025_en.pdf Swissdamed Webinar - May 28th, 2026:https://www.swissmedic.ch/swissmedic/en/home/services/veranstaltungen/swissdamed-webinar.htmlUKUK to exempt Health Institution - Not a priority for nowhttps://www.gov.uk/government/publications/health-institution-exemption-for-general-medical-devicesTrainingTeam-NB: Training on MDR technical Documentation - For manufacturers on April 19th, 2026:https://www.team-nb.org/new-session-mdr-technical-documentation-training-for-manufacturers/EasyIFUCreate eIFU and Labels easily - Compliance to EU MDR/IVDR:Https://easyifu.comRoWNorth AmericaFDA: General Wellness devices - Guidance by the FDA:https://www.fda.gov/regulatory-information/search-fda-guidance-documents/general-wellness-policy-low-risk-devicesFDA: Cybersecurity in Medical Devices - QMS and Pre-market submission:https://www.fda.gov/regulatory-information/search-fda-guidance-documents/cybersecurity-medical-devices-quality-management-system-considerations-and-content-premarketAPACMalaysia: Electronic Certificates issued by MDA - For FSC, Manufacturing Certificates and Export Certificateshttps://portal.mda.gov.my/index.php/announcement/1746-implementation-of-electronic-certificates-for-certificate-of-free-sale-manufacturing-certificate-and-export-certificate-issued-by-medical-device-authority-mda-malaysiaIndia: Import of IVD - Online Portal: https://cdsco.gov.in/opencms/opencms/system/modules/CDSCO.WEB/elements/download_file_division.jsp?num_id=MTM4NDE%3DAfricaEgypt: Database for Product Registration -Online Electronic Service: https://eservicesdata.edaegypt.gov.eg/MedicalDevicesMiddle EastSaudi Arabia: SFDA inspection of QMS requirements - Process that would be followed:https://www.sfda.gov.sa/sites/default/files/2026-01/MDS-REQ10E.pdfPodcastEpisode 372 - How to remediate a Design History File (DHF): https://podcast.easymedicaldevice.com/372-2/Episode 373 - QMSR is coming: Why FDA inspections with change completely in 2026: https://podcast.easymedicaldevice.com/373-2/Episode 374 - Validation & Supplier Management in MedTech: https://podcast.easymedicaldevice.com/374-2/ServicesConsulting support: info@easymedicaldevice.comAuthorized Representative: EO@easymedicaldevice.comSocial Media to followMonir El Azzouzi Linkedin: https://linkedin.com/in/melazzouziTwitter: https://twitter.com/elazzouzimPinterest: https://www.pinterest.com/easymedicaldeviceInstagram: https://www.instagram.com/easymedicaldeviceThis podcast is powered by Podcastics, the easiest platform to create and publish your podcast.
What if I told you that making art literally changes your DNA? In a world that often treats creativity as a luxury or a hobby, the data is finally catching up to what artists have always felt: art is essential medicine. In this episode, Kat sits down with Daisy Fancourt, a professor of psychobiology and epidemiology at University College London, whose groundbreaking research provides the "validation ammunition" every creative needs. We dive into her new book, Art Cure, which presents decades of evidence showing that arts engagement is a vital clinical intervention.From reducing stress hormones like cortisol to slowing biological aging and influencing gene expression, we explore why creative engagement should be recognized as the Fifth Pillar of Health alongside nutrition, exercise, sleep, and stress management. In this episode, we discuss: The "Fifth Pillar" Concept: Why creative engagement is as vital to your longevity as diet and exercise. The Biology of Art: How making and viewing art creates measurable health benefits that accumulate over time. DNA & Gene Expression: The fascinating science behind how creativity affects our bodies at a cellular level. Validation for Artists: Why your work is a necessity for your collectors and the world, especially during turbulent times. The Psychobiology of Art: Daisy's journey from professional pianist to leading researcher at UCL. Resources & Links Mentioned: The Book: Art Cure by Daisy Fancourt Daisy's Research Group: SBPR Research Create! Magazine: www.createmagazine.co Newsletter: Join the Weekly Newsletter Community: Follow Create! Magazine on Instagram Connect with the Guest: Daisy Fancourt is Professor of Psychobiology and Epidemiology at University College London where she heads the Social Biobehavioural Research Group, and Director of the World Health Organisation Collaborating Centre on Arts and Health. She has published 300 scientific papers, won over two dozen academic prizes and is listed as one of the most highly cited scientists in the world. Daisy is also a multi-award-winning science communicator and has been named a World Economic Forum Global Shaper and BBC New Generation Thinker.
I'm sharing the two best, and least used, relationship tools I know. We didn't get a class on this, so most of us are winging it. And no, I'm not teaching voodoo Jedi mind tricks to change your partner. This is about making it easier to be in relationship with you. The first tool is validating your partner's experience, even when it's different from yours. That builds trust and safety. Validation is not agreement. It's simply, “I can see how you'd see it that way.” The second tool is taking 100% accountability for your thoughts, feelings, and actions. That's where your power lives. As I say in the episode, “I almost got a tattoo that said, ‘It's all me.'” Use these two tools and you'll feel the shift, starting with you.
Sex Addiction, Pornography, and Sexual Purity -- Castimonia.org
What does The Hunt for Red October have in common with helping your wife through her pain? In this episode, we explore what it actually means to sit with your wife's pain after betrayal, and why most men struggle to do it well. Using the framework of CVE: Compassion, Validation, and Empathy, we look at why pain isn't […] The post Castimonia Purity Podcast Episode 133 – How to Sit With Your Wife's Pain appeared first on CASTIMONIA.
Bitcoin Core doesn't stand still even if consensus rules don't change. In this episode, Stéphan (Core Developer at Brink) explains how the Kernel and multiprocess projects are reshaping Bitcoin Core for long-term reliability. From modular validation logic to safer development workflows, this conversation shows why maintenance work matters. Hosted by Shinobi of Bitcoin Magazine.#BitcoinCore #BitcoinDevelopment #BitcoinKernel ⭐️⚔: SIGN UP WITH DUELBITS TODAY FOR A CHANCE TO WIN UP TO 2 BTC:
Send me a DM here (it doesn't let me respond), OR email me: imagineabetterworld2020@gmail.comToday we are welcomed back by podcast guest regular: Rape, trafficking, and abduction survivor, overcomer and warrior, devoted wife and loving mother, podcast host, content creator, author and blogger, singer and performer, vocal coach, entrepreneur, Epstein, Trump, and Ghislane Maxwell Recruit, beauty queen - past and present, and someone I've been honored to call a long-term dear friend, Molly Skye Brown. A little bit about Molly if you are new to her story, and what we will be discussing today.Born in the sun-soaked suburbs of California, Molly Skye Brown entered the world with a voice destined to echo far beyond her years. From a young age, she was a prodigy: an honor student, classically trained vocalist, and aspiring lawyer, with dreams as vast as the Atlantic horizon. But beneath this promising facade lurked early shadows of horror. At just nine years old, Molly endured a brutal rape, the first in a series of assaults that would test her resilience to its core. The first brush with the Epstein-Maxwell web came in 1992, when Molly was only 14 or 15, working at a local gym in Jupiter to earn a free membership. Dressed modestly in a sports bra and shorts after her shift, she caught the eye of Ghislaine Maxwell, who approached her with a business card and a predatory gleam. "You could easily pass for 18," Maxwell said, dangling offers of Victoria's Secret modeling gigs complete with international travel and massages. Trauma compounded in Molly's late teens and early twenties. Abducted and raped by a producer at an MTV event in 2000, she spiraled into depression, flunking out of school and hiding her pain. A year later, haunted by suicidal ideations on the assault's anniversary, she checked into Columbia Hospital in Palm Beach for help. There, fate twisted cruelly: her roommate was Lisa Villanu, a 19-year-old recruiter born into the trafficking world, who outranked her own parents in their shadowy hierarchy. Lisa befriended Molly, learning of her vulnerabilities, and soon lured her into the Epstein orbit under the guise of friendship.In late 2001, shortly after 9/11, Lisa drove Molly to Epstein's Palm Beach mansion for what was pitched as a casual holiday gathering. Instead, it was a meticulously orchestrated "matchmaking" event - a marketplace of exploitation where young women were paraded before wealthy men. Repulsed, Molly resisted, noting the red flags - the drug offers, the private "meetings," and the young girl asleep in a red-lit room amid making-out adults.But the past resurfaced in 2019 when news of Epstein's arrest triggered memories. Molly pieced together the puzzle: the gym recruiter was Maxwell, the party was a trafficking hub. Speaking out on social media and podcasts, she faced vicious backlash - and endless harassment from alleged Epstein victims and survivors - which is what we will be talking about and exposing today along with her revelations about Trump, Maxwell and Epstein and the information that has been deliberately suppressed and covered up through the limited hangout ‘Epstein Files' and narrative controlled by mainstream media and mainstream ‘alt media'. CONNECT WITH MOLLY:Website: https://www.mollyskyebrown.com/YouTube: https://youtube.com/@mollyskyebrown?si=CHWL7qVpywM0cDYmCONNECT WITH EMMA:YouTube: https://www.youtube.com/@imaginationpodcastofficialRumble: https://rumble.com/c/TheImaginationPodcastEMAIL: imagineabetterworld2020@gmail.com OR standbysurvivors@protonmail.comMy SubstackSupport the show
Don't get to the end of this year wishing you had taken action to change your business and your life.Click here to schedule a free discovery call for your business: https://geni.us/IFORABEDon't miss an upcoming event with The Institute: https://geni.us/InstituteEvents2026Shop-Ware gives you the tools to provide your shop with everything needed to become optimally profitable.Click here to schedule a free demo: https://info.shop-ware.com/profitabilityTransform your shop's marketing with the best in the automotive industry, Shop Marketing Pros!Get a free audit of your shop's current marketing by clicking here: https://geni.us/ShopMarketingProsShop owners, are you ready to simplify your business operations? Meet 360 Payments, your one-stop solution for effortless payment processing.Imagine this—no more juggling receipts, staplers, or endless paperwork. With 360 Payments, you get everything integrated into a single, sleek digital platform.Simplify payments. Streamline operations. Check out 360payments.com today!In this episode, Lucas and David are joined by Andy Tirado, co-founder of the ADAS Network. Andy discusses the challenges of delivering relevant, effective technical training across the automotive industry, highlighting the critical need for trainers to adapt content to local cultures and shop-specific needs. The conversation also explores the complexities of ADAS calibrations, from misinformation in service procedures to pressure from insurance companies.00:00 Struggles with Local Training Participation08:25 Improving Training Accessibility and Retention10:24 "Training, Culture, and Communication"20:09 "Missed Details in Calibration Procedure"23:44 "ADAS Resource and Referral Network"28:45 Greed, Insurance, and Right to Repair33:43 "Taking Control of Business Processes"40:54 Braking System Safety Concerns46:09 Counterfeit Parts and Industry Issues52:21 Car Alignment and Calibration Costs53:56 "Trusting My Chosen Shop"01:00:05 "ADAS Insights and Validation"01:03:33 "Aligned for Selfless Collaboration"
The meaning we find in scripture often reveals more about our motive than the text itself. This episode explores the uncomfortable truth: that we don't just read scripture—we often recruit it. Nearly any philosophy can be bent in the direction we're already leaning. Raghunath and Kaustubha examine how two people can read the same teaching and walk away with completely opposite conclusions, why real growth begins with examining our motives rather than merely quoting sources, the difference between being transformed by sacred texts and being justified by them, and how compassion may be the clearest measure of whether a teaching has truly been understood. ******************************************************************** LOVE THE PODCAST? WE ARE COMMUNITY SUPPORTED AND WOULD LOVE FOR YOU TO JOIN! Go to https://www.wisdomofthesages.com WATCH ON YOUTUBE: https://youtube.com/@WisdomoftheSages LISTEN ON ITUNES: https://podcasts/apple.com/us/podcast/wisdom-of-the-sages/id1493055485 CONNECT ON FACEBOOK: https://facebook.com/wisdomofthesages108 *********************************************************************
© 2020-2026 Ivy Podcast Discovery LLC
