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Gus Serino, President of I&C Secure, joins the Nexus Podcast to discuss the formation and evolution of an ongoing water utility cybersecurity collaborative that leans on a collective defense model to share resources and threat intelligence among six water & wastewater companies in the commonwealth. Serino helped put together this coalition, and he explains how a feasibility study came together resulting in a shared grant that member utilities can use to improve their security programs. Gus talks about the origins of this collaborative and how other utilities can follow this model to improve cybersecurity in this struggling, resource-strapped critical infrastructure sector.Subscribe and listen to the Nexus Podcast here.
Whether you're navigating a high-conflict divorce or trying to prevent everyday burnout in your marriage, award-winning family lawyer and co-parenting expert Gabriella Pomari has advice for you. In this episode, we're sitting down to explore why so many successful women fall into the trap of over-functioning. Plus, how to reclaim your power through cleaner boundaries. Read the show notes for today's episode at terricole.com/812
Dr. Kurt Gray, social psychologist, award-winning researcher and teacher, and author of Outraged, joins me on this episode. Kurt is the Weary Foundation Endowed Chair in the Social Psychology of Polarization and Misinformation at The Ohio State University, where he directs the Deepest Beliefs Lab and the Center for the Science of Moral Understanding. He was also appointed to lead the university's Collaborative for the Science of Polarization and Misinformation (C-SPAM). He earned his PhD from Harvard University. In this fascinating conversation, we explore the psychology behind outrage, conflict, and why disagreements escalate so quickly in today's world.
For 70 years, a simple idea has shaped efforts to reduce prejudice: put people from different groups together under the right conditions, and contact reduces prejudice. Gordon Allport proposed it in 1954. A landmark 2006 meta-analysis of 515 studies seemed to confirm it, reporting an average effect of 0.4 standard deviations on prejudice measures. That paper has been cited more than 14,000 times. The credibility revolution has undermined this evidence, by correcting for publication bias that meant null results were seldom published. Matt Lowe of the Vancouver School of Economics has published a new review of 41 pre-registered studies, and he finds the average effect is one-tenth of a standard deviation. Those 41 pre-registered intergroup contact experiments cover nearly 40,000 participants across a wide range of countries, roughly half of them in the Global South. He tells Tim Phillips that the effects are real, consistently positive … but consistently small. Contact interventions are a waste of time. Costs can be low, and the alternatives have not yet been held to the same rigorous standard. But the gap between what the old literature promised and what careful experiments deliver is large enough to matter for anyone designing programmes to reduce prejudice between groups.The research behind this episode:Lowe, Matt. 2025. "Has Intergroup Contact Delivered?" Annual Review of Economics 17.To cite this episode:Phillips, Tim. 2026. "Has Intergroup Contact Delivered?" VoxDev Talk (podcast). Assign this as extra listening: the citation above is formatted and ready for a reading list or VLE.About Matt LoweMatt Lowe is an assistant professor at the Vancouver School of Economics at the University of British Columbia, a CIFAR Azrieli Global Scholar, and a J-PAL faculty affiliate whose research spans intergroup relations, development, and political economy. His website is at mattjlowe.github.io. He has previously been published in VoxDev discussing his field experiment on collaborative and adversarial caste integration through cricket leagues in India.Research cited in this episodeAllport, Gordon W. 1954. The Nature of Prejudice. Addison-Wesley. The founding text of intergroup contact theory, which proposed that contact between groups reduces prejudice when it meets four conditions: equal status, common goals, intergroup cooperation, and support from authorities.Pettigrew, Thomas F., and Linda R. Tropp. 2006. "A Meta-Analytic Test of Intergroup Contact Theory." Journal of Personality and Social Psychology 90 (5). The 515-study meta-analysis that established the 0.4 standard deviation benchmark for contact effects and became the dominant reference point for the field.Paluck, Elizabeth Levy, Roni Porat, Chelsey S. Clark, and Donald P. Green. 2021. "Prejudice Reduction: Progress and Challenges." Annual Review of Psychology 72. A review of 418 experiments on prejudice reduction from 2007 to 2019, identifying troubling signs of publication bias and finding that most studies evaluate light-touch, small-scale interventions with uncertain long-term effects.Scacco, Alexandra, and Shana S. Warren. 2018. "Can Social Contact Reduce Prejudice and Discrimination? Evidence from a Field Experiment in Nigeria." American Political Science Review 112 (3). A randomised field experiment mixing Christian and Muslim young men in a vocational training programme in Kaduna, Nigeria. Contact reduced discriminatory behaviour but did not change attitudes.Mousa, Salma. 2020. "Building Social Cohesion between Christians and Muslims through Soccer in Post-ISIS Iraq." Science 369 (6505). Randomly assigned Iraqi Christian displaced persons to football teams with Muslim teammates. Effects were positive on behaviours within the intervention but did not generalise to interactions with Muslim strangers outside it.Chakraborty, Anujit, Arkadev Ghosh, Matt Lowe, and Gareth Nellis. 2024. "Learning About Outgroups: The Impact of Broad Versus Deep Interactions." SSRN Working Paper. A field experiment in India finding that broad contact (meeting many different outgroup members) corrects misperceptions about outgroups, while deep contact (sustained interaction with one person) builds social and economic ties. Neither type generalises fully to the wider outgroup.Lowe, Matt. 2021. "Types of Contact: A Field Experiment on Collaborative and Adversarial Caste Integration." American Economic Review 111 (6). Randomly assigned Indian men from different castes to cricket teams or control groups, finding that collaborative contact increased cross-caste friendships and efficiency in trade while adversarial contact reduced them.More VoxDev Talks on this topicPromoting national integration in Nigeria: Tim Phillips talks to Oyebola Okunogbe about her research on the Nigerian National Youth Service Corps, which posts university graduates to states other than their own to promote national integration through intergroup contact.Peacemaking, peacebuilding and post-war reconstruction: Salma Mousa and Lisa Hultman discuss what the evidence shows about building peace and social cohesion after conflict, including which interventions hold up and which do not.Building social cohesion in ethnically mixed schools: an intervention in Turkey: Sule Alan discusses a programme designed to build cohesion between children from different ethnic backgrounds in Turkish schools, with effects on peer violence, reciprocity, and interethnic friendships.Related reading on VoxDevHow competition between villages helped divided communities in Indonesia: in ethnically diverse or divided settings, shared efforts towards a collective external goal can help bridge internal divides and build a shared identity.Reducing prejudice towards forced migrants through perspective taking: evidence on how perspective-taking interventions affect attitudes towards refugees and displaced populations.How a documentary film fostered interethnic harmony in Bangladesh: a media-based approach to reducing intergroup prejudice, examining what content and delivery can shift attitudes at scale.
In this episode, Barb talks with Kurtis Hewson about what makes collaborative teams truly effective and how principals can build a culture where teachers solve problems together. Kurtis shares simple meeting structures that increase psychological safety and shared ownership, like clear norms, roles, and a predictable agenda. He breaks down the “Collaborative Team Meeting” format that helps teams focus on key issues, swap strategies, and leave with one clear action to try. You'll also learn how this approach connects PLCs and MTSS and reduces reactive meetings over time.Connect with Kurtis at Jigsaw LearningDownload the Free Toolkit for Collaborative TeamsCheck out Barb's resource for Navigating Challenging Team Dynamics Learn more about today's sponsors, Playworks and IXL:We're proud to be sponsored by Playworks, a 501(c)3 nonprofit organization with evidence-based practices that help schools improve the health and well-being of children by increasing opportunities for physical activity and safe, meaningful play.If you're a school or district leader struggling with the challenge of chronic absenteeism, as so many are across the U.S., you may not realize that structured recess is a research-backed approach to keep kids in school. In fact, a UC Berkeley study of Title I schools found that those partnering with Playworks had significantly lower chronic absenteeism rates. Further, Mathematica research demonstrated that Playworks schools spent 27% less time transitioning from recess back to learning, saving teachers valuable instructional time. These results are possible for your students, too. Learn how Playworks can help you improve student-educator relationships, belonging, and attendance by signing up for a quick no-obligation conversation. We're also thrilled to be sponsored by IXL. IXL's comprehensive teaching and learning platform for math, language arts, science, and social studies is accelerating achievement in 95 of the top 100 U.S. school districts. Loved by teachers and backed by independent research from Johns Hopkins University, IXL can help you do the following and more:Simplify and streamline technologySave teachers' timeReliably meet Tier 1 standardsImprove student performance on state assessments
Ron Ivey studies one of the quiet drivers behind many of today's challenges for young people: loneliness. In this episode of JOY over Happiness, Dr. Jon Eckert talks with the Harvard Human Flourishing Program research fellow and founder of Noēsis Collaborative, an initiative launched in 2025 to build an ecosystem of leaders researching and shaping technologies that advance human flourishing, about why belonging and meaningful relationships are essential to a joyful life.Drawing on research about technology, culture, and connection, Ron explains how rising isolation is reshaping how young people seek belonging. This conversation reminds us that joy is not found in comfort or convenience. It grows through real relationships, shared struggle, and the deep connections that help us become who we are created to be.JOY over Happiness is brought to you by the Baylor Center for School Leadership.Lean into the struggle. Live with joy.Mentioned:Connect with us:Center for School Leadership at Baylor UniversityLinkedInBaylor MA in School Leadership
In this episode, I sit down with Roberto Rios aka Peruvian Bull, and he didn't come to play it safe. Fresh off breaking news the morning of recording, we kick things off with the Jane Street manipulation lawsuit and what it might mean for Bitcoin price action. We cover the terrifying thesis of The Great Taking, why you may not actually own the stocks in your brokerage account, China's secret gold accumulation strategy and what it signals about the dollar's future, the slow-motion collapse of Japan's bond market, and how silver is finally breaking free from decades of paper price suppression. ––– Offers & Discounts –––
In this engaging conversation, Tom Antion shares his journey from being an entrepreneur to becoming a filmmaker, emphasizing the importance of continuous learning and adaptability in business. He discusses how to identify marketable opportunities, create digital products, and the value of collaboration in entrepreneurship. The conversation highlights the mindset needed for success and offers practical advice for aspiring entrepreneurs. Takeaways Tom Antion emphasizes the importance of being in charge of your own life. Entrepreneurship can change lives and provide freedom. Continuous learning is crucial for entrepreneurs. Identifying market demand is key to business success. Creating digital products minimizes risk and investment. Collaborative ventures can enhance business opportunities. Understanding your audience is essential for product development. Pain points can drive sales for products and services. Networking and joint ventures can lead to success. Don't fear failure; view it as a learning opportunity. Chapters 00:00 Introduction to Entrepreneurship and Film 03:02 The Journey of an Entrepreneur 05:53 The Importance of Continuous Learning 08:58 Identifying Entrepreneurial Opportunities 12:05 Creating Marketable Products 14:59 Collaborative Ventures and Networking 17:47 Final Thoughts and Contact Information More Information Find Tom Antion's podcast and resources at screwthecommute.com. Learn more about your ad choices. Visit megaphone.fm/adchoices
Join me as I speak with Sean Guerre, who is the the Managing Director of the Energy Drone and Robotics Coalition. In this episode, we talk about building an energy and utility centric approach to robotics technology adoption and deployment.If you are looking for the premiere event in robotics, drones, and data for the energy sector, look no further than the Energy Drone and Robotics Summit in the Woodlands, TX in June.Liked this episode and want to see or hear more? Please subscribe to Collaborative with Spencer Krause today. You'll get notified every time a new episode releases, and it's the best way to support the channel! Businesses looking to outsource difficult robotics engineering problems should consider SKA Robotics. They sponsor this podcast and solve some of the most difficult robotics engineering problems in the world. Companies looking for premium space in Pittsburgh should consider renting in Rockwell Park. Located in Pittsburgh's hip Point Breeze Neighborhood, Rockwell Park features over 800,000 square feet of high-end industrial, retail, and office space. Contact Icon Development Group to learn more.
Join me as I speak with Sean Guerre, who is the the Managing Director of the Energy Drone and Robotics Coalition. In this episode, we talk about building an energy and utility centric approach to robotics technology adoption and deployment.If you are looking for the premiere event in robotics, drones, and data for the energy sector, look no further than the Energy Drone and Robotics Summit in the Woodlands, TX in June.Liked this episode and want to see or hear more? Please subscribe to Collaborative with Spencer Krause today. You'll get notified every time a new episode releases, and it's the best way to support the channel! Businesses looking to outsource difficult robotics engineering problems should consider SKA Robotics. They sponsor this podcast and solve some of the most difficult robotics engineering problems in the world. Companies looking for premium space in Pittsburgh should consider renting in Rockwell Park. Located in Pittsburgh's hip Point Breeze Neighborhood, Rockwell Park features over 800,000 square feet of high-end industrial, retail, and office space. Contact Icon Development Group to learn more.
In this episode, I chat with Kim Marie Kefalas, an elementary technology teacher and owner of Kimmersive Technology, about designing meaningful schoolwide projects and using collaborative platforms to build community and student voice. You'll also hear practical strategies for scaffolding collaboration with young learners, including student-led roles, clear expectations, and creative ways to connect classrooms across a school. If you want to create engaging, inclusive schoolwide projects that strengthen collaboration and independence, this episode has you covered! Show notes: https://classtechtips.com/2026/03/06/schoolwide-projects-bonus/ Sponsored by Jotform: https://jotform.com/enterprise/education/ Follow Kim Marie Kefalas on social: https://x.com/kefalastech Follow Monica on Instagram: https://www.instagram.com/classtechtips/ Take your pick of free EdTech resources: https://classtechtips.com/free-stuff-favorites/
A lifelong devotion to movement can shape not just an artist's career, but an entire community. Today, choreographer, educator, and Artistic Director Jessica Gaynor shares how her path began in gymnastics before finding her true calling in modern dance. From her early days studying with the legendary Alice Teirstein to becoming a mentor and leader in her own right, Jessica's story is one of passion, resilience, and creativity. In our conversation, Jessica reflects on the freedom and self-expression she discovered through improvisation, her formative years studying dance at Brown and CalArts, and the drive that led her to form her own company back in New York. She recalls milestone performances, collaborations with composers, and the challenges of sustaining a company while teaching full-time. Today, her work as Artistic Director of the Young Dancemakers Company continues to empower teens to create original work in partnership with professional composers, carrying forward the legacy of her mentor. Tune in to hear how Jessica has built a multifaceted career in dance, and why she remains committed to creating, teaching, and inspiring the next generation. Key Points From This Episode: Jessica's upbringing in NYC and her shift from gymnastics to ballet and modern dance. How she found mentorship with Alice Teirstein at the Fieldston School. Her love of dance as a form of freedom, self-expression, and community through movement. Early influences on her dance career: Pilobolus and Bill T. Jones/Arnie Zane Company. Studying at Brown, while doing an academic major, repertory works, choreography, and more. Graduate training at CalArts, focusing on choreography, performance, and collaboration. What it was like returning to NYC and forming a company with fellow dancers. Creating evening-length works at Triskelion Arts and touring performances. Collaborative works with composers, exploring structure and form. Joining the Young Dancemakers Company (YDC) and becoming Artistic Director. Leading YDC through the pandemic with Zoom dance films and outdoor shows. Jessica's love of teaching and helping teens create original work. Rediscovering performance and collaborating with her musician husband. Reflections on highlights and challenges while sustaining a career in dance. For more on the episode: Movers & Shapers: A Dance Podcast Follow the podcast on Instagram & Facebook
This is the fifty-seventh episode in the Crypto Hipster's Curtain Calls Series, which includes 3–4-minute clips from Seasons 6-8. This compilation draws upon my conversations with:Bunny, co-founder and CEO @ DORA (9/14/2024, Season 8)Alison Haire, CEO and founder @ Lilypad (6/27/2024, Season 7)Josh Benaron, CEO and founder @ Irys (9/7/2024, Season 8)Sebastian Pfeiffer, Managing Director @ Impossible Cloud Network (2/18/2025, Season 8)
To read the complete transcript and watch the podcast video, visit the episode blog.In this episode of the Successful Musicians Podcast, Jason Tonioli sits down with pianist, composer, and arranger Anne Britt. With nearly 15 piano books, multiple album recordings, and a growing catalog of intermediate-level piano duet arrangements, Anne shares how she built a publishing career rooted in collaboration and communication, and how she builds meaningful piano duets and hymn arrangements that connect with listeners.From earning a math degree to studying composition later in life, Anne's journey proves that it's never too late to develop your creative voice. This episode is especially relevant for pianists, hymn arrangers, and musicians who want to publish their own sheet music but struggle with self-doubt.What You'll Learn- Why Anne believes music is a language- How to define success as communication rather than comparison- Why starting composition later in life is not a limitation- The power of mentorship in developing musical confidence- How to overcome the belief that you are “not creative enough”- Why intermediate-level piano music fills an important need- What makes piano duets uniquely collaborative- How sharing your music builds courage over time- Why feedback from listeners matters more than online criticism- How small, meaningful moments define real musical successTopics Covered in This Episode- Growing up in a musical family- Studying math at BYU while continuing piano performance- Becoming a collaborative pianist and staff accompanist-The turning point during a high school production of Guys and Dolls- Studying music theory and composition in her 30s- Publishing her first CD and songbook- Writing and publishing hymn arrangements- Creating intermediate piano duet collections- Contributing to the Hymn Sampler collaboration- Upcoming holiday piano duet projectsWho This Episode Is For- Pianists who want to publish sheet music- Church musicians and hymn arrangers- Intermediate-level piano players- Musicians who feel like they started late- Creative professionals overcoming self-doubt- Piano teachers looking for duet repertoire- LDS musicians building niche catalogs- Collaborative pianists- Arrangers developing their own voice- Anyone who believes music should communicate meaningConnect with Anne BrittWebsiteFacebookYouTubeConnect with Jason TonioliWebsite FacebookYouTube InstagramSpotifyPandoraAmazon MusicApple Music
Doing Divorce Different A Podcast Guide to Doing Divorce Differently
Collaborative divorce offers a respectful, structured alternative to litigation for couples who want to avoid court battles and create agreements that protect children and finances. In this episode, Lesa Koski sits down with Minnesota collaborative professionals Jana Hefty and Michelle Leason to explain how collaborative divorce works, who it's best for, and why collaborative divorce can save time, reduce conflict, and support healthier co-parenting.You'll learn the key differences between collaborative divorce vs mediation vs litigation, how financial neutrals help organize assets and cashflow planning, and what happens if the collaborative divorce process doesn't work. This conversation is especially helpful for parents considering divorce who want a better way to divorce with respect.Divorce doesn't have to be a fight. Collaborative divorce can be a better way.Timestamps:(00:00) Welcome + why divorce doesn't have to be a fight(01:30) Introducing Jana Hefty and Michelle Leason(03:15) Jana's path to law + staying calm in crisis(06:45) Michelle's background as a Certified Divorce Financial Analyst and mediator(10:10) What collaborative divorce is and how it works(13:40) The collaborative divorce team: attorneys, financial neutral, child specialist(17:10) Why finances feel scary and how to create clarity early(21:30) What happens if collaborative divorce doesn't work(25:10) Cost and timeline: collaborative divorce vs litigation(28:30) Divorce With Respect Week (March 1–8) + how to find resources(30:10) Closing thoughts + next stepsKey Takeaways:• Collaborative divorce helps couples avoid court and make decisions together• A team approach supports finances, parenting plans, and communication• Financial neutrals reduce duplicated work and improve clarity• Collaborative divorce can be less expensive and faster than litigation• If collaborative fails, agreements can still be used—progress isn't lostGuest Bio:Jana Hefty is a Minnesota collaborative family law attorney who helps couples navigate divorce with respect, structure, and reduced conflict.Michelle Liesen is a Certified Divorce Financial Analyst (CDFA) and Minnesota-qualified mediator. She founded Divorce Smart and helps couples understand assets, budgeting, and post-divorce financial planning.Resource Links:https://collaborativedivorcecalifornia.com/dwrw/https://collaborativelaw.org/https://www.mydivorcesmart.com/
This episode continues our three-part "Collaborative Delivery 101" series that details the perspectives, benefits, and experiences of water industry professionals that have taken the plunge into this alternative pathway to project delivery.In this episode, our series guest host Jim Good (Parkview Advisors) sits down with Brian Balchunas (HDR) to share the engineer's perspective on collaborative delivery.
Pennsylvania state turkey biologist Reina Tyl rejoins us to share updates on the long-term, multi-state Mid-Atlantic Turkey Research Collaborative to better understand differences in hen survival across states. We also discuss the status of turkeys in PA and potential changes to the spring season structure. Resources: Wild turkey populations in PA (Part 1/2) | Ep 25 Wild turkey populations in PA (Part 2/2) | Ep 26 Vangilder, Larry D., and Eric W. Kurzejeski. "Population ecology of the eastern wild turkey in northern Missouri." Wildlife Monographs (1995): 3-50. Our lab is primarily funded by donations. If you would like to help support our work, please donate here: http://UFgive.to/UFGameLab Don't miss out on a chance to win a custom Benelli Super Black Eagle 3! This 28-gauge shotgun features a 28' barrel, 3" chamber, and is exclusively dipped in Mossy Oak Full Foliage not available to the public. Enter the online raffle below for a shot at owning this one-of-a-kind gun! This is literally a one-of-one collectable item. https://e.givesmart.com/events/Nqy/ We've launched our second online wild turkey course ! Enroll in Wild Turkey Manager: Biology, History & Habitat to learn about the principal biology, mating, behavior, food selection, human dimensions, hunter interactions, and historical context of wild turkeys. This course is accredited by the Society of American Foresters as a Category 2 course worth 7 Continuing Forestry Education credits. Participants can also earn up to 5 CEUs in Category I of The Wildlife Society's Certified Wildlife Biologist Program. Enroll now: https://tinyurl.com/WildTurkeyManagerBio Be sure to check out our first comprehensive online wild turkey course featuring experts across multiple institutions that specialize in habitat management and population management for wild turkeys. Earn up to 20.5 CFE hours! Enroll Now! Dr. Marcus Lashley @DrDisturbance, Publications Dr. Will Gulsby @dr_will_gulsby, Publications Turkeys for Tomorrow @turkeysfortomorrow UF Game Lab @ufgamelab, YouTube Want to help wild turkey conservation? Please take our quick survey to take part in our research! Do you have a topic you'd like us to cover? Leave us a review or send us an email at wildturkeyscience@gmail.com! Watch these podcasts on YouTube Please help us by taking our (quick) listener survey - Thank you! Check out the DrDisturbance YouTube channel! DrDisturbance YouTube Want to help support the podcast? Our friends at Grounded Brand have an option to donate directly to Wild Turkey Science at checkout. Thank you in advance for your support! Leave a podcast rating for a chance to win free gear! This podcast is made possible by Turkeys for Tomorrow, a grassroots organization dedicated to the wild turkey. To learn more about TFT, go to turkeysfortomorrow.org. Music by Artlist.io Produced & edited by Charlotte Nowak
Pennsylvania state turkey biologist Reina Tyl rejoins us to share updates on the long-term, multi-state Mid-Atlantic Turkey Research Collaborative to better understand differences in hen survival across states. We also discuss the status of turkeys in PA and potential changes to the spring season structure. Resources: Wild turkey populations in PA (Part 1/2) | Ep 25 Wild turkey populations in PA (Part 2/2) | Ep 26 Vangilder, Larry D., and Eric W. Kurzejeski. "Population ecology of the eastern wild turkey in northern Missouri." Wildlife Monographs (1995): 3-50. Our lab is primarily funded by donations. If you would like to help support our work, please donate here: http://UFgive.to/UFGameLab Don't miss out on a chance to win a custom Benelli Super Black Eagle 3! This 28-gauge shotgun features a 28' barrel, 3" chamber, and is exclusively dipped in Mossy Oak Full Foliage not available to the public. Enter the online raffle below for a shot at owning this one-of-a-kind gun! This is literally a one-of-one collectable item. https://e.givesmart.com/events/Nqy/ We've launched our second online wild turkey course ! Enroll in Wild Turkey Manager: Biology, History & Habitat to learn about the principal biology, mating, behavior, food selection, human dimensions, hunter interactions, and historical context of wild turkeys. This course is accredited by the Society of American Foresters as a Category 2 course worth 7 Continuing Forestry Education credits. Participants can also earn up to 5 CEUs in Category I of The Wildlife Society's Certified Wildlife Biologist Program. Enroll now: https://tinyurl.com/WildTurkeyManagerBio Be sure to check out our first comprehensive online wild turkey course featuring experts across multiple institutions that specialize in habitat management and population management for wild turkeys. Earn up to 20.5 CFE hours! Enroll Now! Dr. Marcus Lashley @DrDisturbance, Publications Dr. Will Gulsby @dr_will_gulsby, Publications Turkeys for Tomorrow @turkeysfortomorrow UF Game Lab @ufgamelab, YouTube Want to help wild turkey conservation? Please take our quick survey to take part in our research! Do you have a topic you'd like us to cover? Leave us a review or send us an email at wildturkeyscience@gmail.com! Watch these podcasts on YouTube Please help us by taking our (quick) listener survey - Thank you! Check out the DrDisturbance YouTube channel! DrDisturbance YouTube Want to help support the podcast? Our friends at Grounded Brand have an option to donate directly to Wild Turkey Science at checkout. Thank you in advance for your support! Leave a podcast rating for a chance to win free gear! This podcast is made possible by Turkeys for Tomorrow, a grassroots organization dedicated to the wild turkey. To learn more about TFT, go to turkeysfortomorrow.org. Music by Artlist.io Produced & edited by Charlotte Nowak
Jeff Latture serves as Executive Vice President at Barnhart Crane and Rigging and as chairman of Strategic Resource Group, but his journey into collaborative generosity began long before boardrooms and grant strategies. Growing up in Arkansas, he watched his parents come to genuine faith, yet it was later in Dallas through a vibrant church community and a pivotal sermon from Bill Bright, that his faith became personal. A simple conversation with his future wife about missions planted an early vision: if they could not go, perhaps they could send. God reshaped that vision when He closed the door to starting their own business and instead led Jeff to Barnhart, a company committed to investing its profits in the Great Commission. Over three decades, Jeff has helped cultivate GROVE, Barnhart's internal collaborative giving team, and advance a mutual fund model of ministry through the Strategic Resource Group. He has learned that wise generosity requires shared mission, deep relationships, patient trust, and a willingness to surrender control. Collaborative structures, clear strategy, and long-term partnerships have multiplied Kingdom impact across frontier regions. This conversation offers a thoughtful vision for pursuing obedience, discernment, and deeper partnership in your own stewardship journey. Major Topics Include: Give collaboratively, not independently Build trust through long-term relationships Shifting from transactions to partnerships Organize giving around focused teams Leveraging business wisdom for Kingdom impact Investing in ministry capacity and health How to balance metrics with Spirit-led discernment Surrendering control in stewardship QUOTES TO REMEMBER “What I really wanted in starting a business was to be in control. And God wasn't going to let me have that.” “He said, I'll let you serve the ones in control, but I'm not going to let you be the ultimate steward.” “We would never have thought ever that we would have the viewpoint that we have for what God's doing around the world. But it just started one relationship and one gift at a time.” “Giving together is way better than trying to do it on your own.” “You just learn faster in a group than you can by yourself.” “It's our secret sauce is doing things together and getting the thinking of many people pointing the same direction.” “A good collaborative will cost you something. You have to give up some rights for a collaborative to work.” “Knowing the heart of the leader is the most important thing.” “It's very relational. We like to measure things. We're a bunch of engineers. But it's really the relationships that drive us.” “If we'll go to God first and really lean in to listening to what Scripture says and what the Holy Spirit is saying before we make those funding decisions, somehow it comes out with a better result.” “My personal experience over 30 years, He will do far more than you can ever imagine with the things that He's given you.” LINKS FROM THE SHOW Alan Barnhart (see our past interview here) When Helping Hurts (see our interview with coauthor Brian Fikkert) Mission Increase Strategic Resource Group The India Alliance IllumiNations Bible Translation Alliance (see our interview with Todd Peterson or John Chesnut) MacLellan Foundation (see our interview with Director of Generosity, John Cortines) Mergon Doug Cobb, Founder of The Finishing Fund (see our past interview here) Achieve Alliance BIBLE REFERENCES FROM THE SHOW Matthew 28:19–20 | The Great Commission “Go therefore and make disciples of all nations, baptizing them in the name of the Father and of the Son and of the Holy Spirit, teaching them to observe all that I have commanded you. And behold, I am with you always, to the end of the age.” Esther 4:14 | For Such a Time as This “For if you keep silent at this time, relief and deliverance will rise for the Jews from another place, but you and your father's house will perish. And who knows whether you have not come to the kingdom for such a time as this?” TAKE A STEP DEEPER On the Finish Line podcast, we are all about stories, seeing how God draws us into generosity over a lifetime. But sometimes these stories can leave us thinking, “What's that next step look like for me?” That's exactly why we've launched a whole new podcast called Applied Generosity which explores the full landscape of the generous life across 7 different dimensions of generosity. Applied Generosity helps make sense of the hundreds of stories we've shared on the Finish Line Podcast to help you find that best next step. If you've been inspired by these stories and want to take things to the next level, check out Applied Generosity anywhere you listen to podcasts or at appliedgenerosity.com.
Dr. Nancy Foster, President of the National Rehabilitation Association, shares the compelling journey that propelled her into transformative roles—highlighting how self-assessment, strategic focus, and authentic engagement can elevate your career and community involvement. In this episode, we discover how pivotal moments and personal insights shaped Dr. Foster's dedication to service, from her inspiring beginnings in St. Louis to pioneering initiatives in the NRA. She reveals the vital importance of professional nurturing, the power of mentorship, and why active participation in associations isn't just optional, but an ethical obligation for growth and societal betterment. We break down practical advice for emerging leaders: how to recognize your strengths and weaknesses, when to step into leadership roles, and the mindset that drives successful advocacy and organizational success. Plus, get an exclusive look at NRA's upcoming events—like the Disability Summit—and innovative programs fostering community, policy change, and professional development. This episode is perfect for anyone eager to turn passion into impact, whether you're just starting out, aiming for leadership, or seeking to deepen your engagement in your field. Dr. Foster's insights will motivate you to embrace your innate leadership qualities and show you how strategic involvement can shape not only your career but the wider world. With her extensive background in rehabilitation, psychology, and community service, Dr. Foster exemplifies a transformational leader who believes in nurturing others and staying ethically grounded. Tune in right here!Timestamps00:00 - Welcome and introduction to Dr. Nancy Foster02:30 - Dr. Foster's background and early influences06:15 - How her education shaped her rehabilitation career09:45 - The significance of serving underserved communities12:20 - Leadership and her role as NRA President16:00 - The NRA's upcoming disability summit and professional development events19:15 - Strategies to re-engage members and students in professional organizations22:45 - The importance of ethics and active participation in shaping the field26:55 - Collaborative efforts with academia and community initiatives30:50 - Personal mentorship stories and influential figures in her life35:16 - Advice for emerging leaders and professionals interested in NRA involvement37:31 - Final thoughts and Dr. Foster's call to action for professional growthResources & Links:National Rehabilitation AssociationDisability Summit 2024Rehab Counselors and Educators JournalMSU Rehabilitation Counseling ProgramLinkedIn - Dr. Nancy FosterInternational Psychology - APAEmail: nancy.foster@illinois.govFollow Mentor Moments on Instagramhttps://www.instagram.com/mentor.moments/Follow Mentor Moments on TikTokhttps://www.tiktok.com/@mentor.moments?_t=ZT-8z1T1S1i8Xf&_r=1Subscribe to Mentor Moments on Spotifyhttps://open.spotify.com/show/3mu3ZVqpsBaGxDfncaNmSn?si=851fd465a0f94399Subscribe to Mentor Moments on Apple Podcastshttps://podcasts.apple.com/us/podcast/mentor-moments/id1590063983Subscribe to Wooters' Wisdoms Newsletterhttps://substack.com/@wooterscounseling
Ammiel Bushakevitz is a celebrated pianist and chamber musician whose journey through classical music has taken him across six continents, performing in prestigious venues such as Carnegie Hall, the Concertgebouw, Wigmore Hall, among many more. Born in Jerusalem and raised in South Africa, he discovered his passion for music early on, later studying in Leipzig and Paris under renowned teachers. Ammiel has a particular affinity for the art song, collaborating with leading vocalists and releasing multiple award-winning recordings, including the ambitious project to record all of Schubert's piano works by 2028.In this engaging conversation with Tony, Ammiel reflects on his deep connection to Schubert's music, exploring themes of joy and melancholy that resonate in the composer's works. He shares personal anecdotes about his musical journey, the evolution of his interpretations, and the importance of musical spontaneity in live performances. With insights into the nuances of piano playing and his collaborative experiences with singers, Ammiel's reflections reveal the profound impact of music on both his life and artistry.
In this episode, host Kristina chats with Jaimie Stoltzfus, owner of Cowgirl Meat Co. in Big Timber, Montana. Jaimie discusses nuances of her business including how she makes shipping meat worthwhile, the partnerships that support her work, and her marketing strategies. She also talks about her experiences working her way up from ranch hand to ranch manager and other roles that helped her as a business owner now.
Are you beating yourself up for dropping your New Year's resolutions? Are you losing your patience over tiny things and feeling guilty about it? It's not because you're failing—it's because your brain is at capacity.In this episode, we are talking about self-compassion, cognitive capacity, and how to give yourself (and your kids) grace when the demands of your environment exceed what you feel like you can handle. I'm introducing the "Brain Capacity Jar" and sharing how you can apply these practical SEL tools to your own life and classroom.
During our continued news hour coverage on NightSide, discussed the latest news coming out of Red Sox Spring training in Florida!8:05PM: Voices of Hope Upcoming event - Bruins Alumni vs Chelmsford/Westford Police Hockey Game to Fight Cancer on March 7th from 6pm to 10pm at the Warrior Ice Arena Guest: Greg Chastain - founder and President of Voices of Hope - a registered, not-for-profit, 501(c)(3) organization with more than 150 active volunteer members who share a passion for the performing arts and a desire to join the fight against cancer by raising awareness and funds for cancer research. 8:15PM: The latest news on Red Sox Spring Training down in Florida. Guest: Tim Healey – Boston Globe Sports Reporter 8:30PM: Youth anxiety, depression, suicidality, chronic absenteeism, and concerning school behaviors are all higher than ever. Why so many kids are struggling, why traditional discipline makes things worse, and how schools can transform their approach to become proactive, collaborative, and helpful. Guest: Dr. Ross Greene - Clinical child psychologist, former Harvard Medical School Department of Psychiatry faculty member (for over 20 years) and New York Times bestselling author. Also, the originator of the innovative, evidence-based approach called Collaborative & Proactive Solutions (CPS) - the evidence-based, trauma-informed, neurodiversity affirming model of care that helps caregivers focus on identifying the problems that are causing concerning behaviors in kids and solving those problems collaboratively and proactively. 8:45PM: The cleanup from the storm continues, and WBZ reporter Jared Brosnan was in the thick of it.Guest: Jared Brosnan – WBZ NewsRadio reporterSee omnystudio.com/listener for privacy information.
Send a textThis Daily Drop covers multiple days of movement across the force—and there's a lot to unpack.The Army is integrating AI into doctrine writing, launching drone competitions, and standing up a rapid soldier innovation office. The Navy is chasing new anti-radar missile capability while looking at sailor burnout and at-sea tour changes. The Marine Corps is digitizing the battlefield and pushing hard on mental health messaging.The Air Force? It's a mix of progress and pain. The A-10 depot mission at Hill is officially ending. The B-21 Raider just got a $4.5B acceleration deal targeting 2027. Collaborative combat aircraft are entering armed testing. AI is moving into air operations centers.Space Force is arguing for faster expansion after real-world operational demand in Iran and Venezuela highlighted capability gaps.Plus: VA disability rule backlash, Medal of Honor news, fraud indictments, pet PCS warnings, and why abandoning your dog makes you a terrible human.No hype. Just what's moving.⏱️ Timestamps: 00:00 Intro and sponsor 02:00 Army using AI in doctrine development 04:00 Drone warfighter competition 06:00 Delayed Purple Heart recognition 08:00 Rapid soldier innovation office 10:30 Pet PCS warning to Korea 12:30 Navy anti-radar missile requirement 14:30 Sailor burnout and at-sea tour review 16:30 Marine digital battlefield push 18:00 Mental health leadership appeal 20:30 A-10 depot mission ends 22:00 B-21 acceleration contract 24:00 Collaborative combat aircraft testing 26:00 Space Force expansion push 28:00 VA disability rule halted 30:00 Medal of Honor recognition
Dr. Ross Greene's work has profoundly shaped how so many of us think about kids' behavior and what they actually need from the adults in their lives, so I'm thrilled to welcome him back to the show to talk about his brand new book, The Kids Who Aren't Okay: The Urgent Case for Reimagining Support, Belonging, and Hope in Schools. Together, we explore the urgent need to reimagine how we support children in schools, especially as mental health concerns continue to rise. We dig into the importance of recognizing developmental variability, why meeting kids where they are is non-negotiable, and how current behavior-focused systems miss the real problems underneath. Ross also highlights the role parents and caregivers can play in advocating for meaningful change. About Dr. Ross Greene Ross W. Greene, Ph.D., is a clinical psychologist and the originator of the innovative, evidence-based approach called Collaborative & Proactive Solutions (CPS), as described in his influential books The Explosive Child, Lost at School, Lost & Found, and Raising Human Beings. He also developed and executive produced the award-winning documentary film The Kids We Lose, released in 2018. Dr. Greene was on the faculty at Harvard Medical School for over 20 years, and is now founding director of the non-profit Lives in the Balance. He is also currently adjunct Professor in the Department of Psychology at Virginia Tech and adjunct Professor in the Faculty of Science at the University of Technology in Sydney, Australia. Dr. Greene has worked with several thousand kids with concerning behaviors and their caregivers, and he and his colleagues have overseen implementation and evaluation of the CPS model in countless schools, inpatient psychiatric units, and residential and juvenile detention facilities, with dramatic effect: significant reductions in recidivism, discipline referrals, detentions, suspensions, and use of restraint and seclusion. Dr.Greene lectures throughout the world and lives in Freeport, Maine. Things you'll learn from this episode How kids today are facing unprecedented challenges that require new ways of thinking and responding Why developmental variability matters and why every child needs support tailored to their unique profile How schools can create more supportive ecosystems by using proactive rather than reactive approaches Why behavior is often a late signal of unmet expectations, not the problem itself How managing expectations and understanding root causes can reduce concerning behaviors Why parents' advocacy and the Collaborative & Proactive Solutions model can transform how children are supported in education Resources mentioned The Kids Who Aren't Okay: The Urgent Case for Reimagining Support, Belonging, and Hope in Schools by Dr. Ross Greene Never Too Early: CPS with Young Kids (documentary) The Explosive Child: A New Approach for Understanding and Parenting Easily Frustrated, Chronically Inflexible Children by Dr. Ross Greene Lives in the Balance (Dr. Greene's website) The B Team (Facebook group) Lost at School: Why Our Kids With Behavioral Challenges are Falling Through the Cracks and How We Can Help Them by Dr. Ross Greene Raising Human Beings: Creating a Collaborative Partnership with Your Child by Dr. Ross Greene Lost and Found: Helping Behaviorally Challenge Students (and While You're At It, All the Others by Dr. Ross Greene The Kids We Lose (documentary) How to Parent Angry and Explosive Children, with Dr. Ross Greene (Tilt Parenting podcast) Ken Wilbur Learn more about your ad choices. Visit podcastchoices.com/adchoices
Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 2915: Dr. Lisa Firestone explores how collaborative communication can transform romantic relationships by fostering empathy, shared understanding, and emotional safety. She breaks down practical tools, like attuned listening, separating past triggers from present conflicts, and repairing after ruptures, that help couples feel truly known and supported. Learning to communicate with vulnerability and calm creates a stronger, more resilient bond built on trust and teamwork. Read along with the original article(s) here: https://www.psychalive.org/want-a-better-relationship-work-on-collaborative-communication/ Quotes to ponder: "Collaborative communication does not just refer to the words that come out of our mouths. Rather, it encompasses all the intricate ways we communicate through tone, expression, body signals, etc." "Communicating collaboratively means taking actions that draw our partner out and trying to understand an interaction from their perspective." "Our communication will always (and pretty much only) go more smoothly when we learn tools to calm down within ourselves."
Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 2915: Dr. Lisa Firestone explores how collaborative communication can transform romantic relationships by fostering empathy, shared understanding, and emotional safety. She breaks down practical tools, like attuned listening, separating past triggers from present conflicts, and repairing after ruptures, that help couples feel truly known and supported. Learning to communicate with vulnerability and calm creates a stronger, more resilient bond built on trust and teamwork. Read along with the original article(s) here: https://www.psychalive.org/want-a-better-relationship-work-on-collaborative-communication/ Quotes to ponder: "Collaborative communication does not just refer to the words that come out of our mouths. Rather, it encompasses all the intricate ways we communicate through tone, expression, body signals, etc." "Communicating collaboratively means taking actions that draw our partner out and trying to understand an interaction from their perspective." "Our communication will always (and pretty much only) go more smoothly when we learn tools to calm down within ourselves."
"We didn't have to grow up with that." — Ross Greene, on school shootingsOne of the most persistent worries these days is that our kids aren't okay. With most of the blame, of course, now being placed on the ubiquity of social media. But psychologist Ross W. Greene, author of the bestselling Lost at School, has a new book out today called The Kids Who Aren't Okay which doesn't place all the blame on social media. Indeed he argues that if we focus only on the internet, we'll fail to understand the broader psychological struggle that many of our kids face today.It's not that Greene is in total denial about the destructive nature of social media. But none of his leading reasons for today's crisis in schools are associated with technology. His top three:● School shootings● High-stakes testing● Zero-tolerance policies with a focus on punishment rather than empathyThe new book, Greene impishly promises, has things in it that will offend just about anybody on both the left and right. He calls out teacher unions for failing to support legislation against restraints and seclusions—pinning kids to the ground, dragging them to locked rooms. And he criticizes both parties for bipartisan policies that have made it harder for educators to educate.The definition of good teaching, Greene insists, is meeting every kid where they're at. Standard testing is exactly the opposite. If you try to treat everybody exactly the same, he warns, you will meet nobody where they're at. We need to get busy teaching kids how to collaborate on solving problems, he says—otherwise they'll turn out like us—only worse. Five Takeaways● Social Media Isn't in the Top Three: Greene's top factors making it harder to be a kid: school shootings, high-stakes testing, and zero-tolerance policies. If we focus only on social media, he says, we'll miss the rest of the picture.● We're Still Pinning Kids to the Ground: Schools still use restraints and seclusions—pinning kids down, dragging them to locked rooms. Legislation has been available since 2011. The two largest teacher unions have yet to support it.● High-Stakes Testing Is the Opposite of Good Teaching: Good teaching means meeting every kid where they're at. Telling every kid they have to get over the same bar by the end of the school year is exactly not what the doctor ordered.● Fairness Means Treating Every Kid Differently: If you try to treat everybody exactly the same, you will meet nobody where they're at. Meeting each kid where they are isn't unfair to the rest—it's fair to everyone.● This Book Will Offend Just About Anybody: Greene calls out both political parties, teacher unions, and policies on both sides of the aisle. Somebody's got to wade in, he says. Somebody's got to call it. About the GuestRoss W. Greene, PhD is the author of Lost at School and The Explosive Child. He is the founder of the nonprofit Lives in the Balance and the inventor of the Collaborative and Proactive Solutions approach. He has worked with nearly 3,000 kids and their caregivers.ReferencesBooks mentioned:● The Kids Who Aren't Okay by Ross W. Greene — his new book on reimagining support, belonging, and hope in schools.● Lost at School by Ross W. Greene — his bestselling earlier work on kids with behavioral challenges.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States—hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:00) - Introduction: The kids who aren't okay (01:17) - Are most kids struggling? (02:51) - Top three factors: Not social media (04:11) - Is this an American problem? (05:15) - Distrust of authorities—even PhDs (06:47) - Which kids are struggling most? (08:04) - Where's the cultural rebellion? (09:55) - Helicopter parenting (11:34) - Wading into the culture wars (13:00) - Restraints and seclusions: We're still pinning kids down (15:10) - Were schools always this punitive? (17:23) - Why teachers are underpaid and leaving (18:57) - Public vs. private schools (19:59) - Is this about money? (21:07) - Every kid is different (24:06) - The problem with 'fairness' (26:27) - Medication: Not black and white (28:34) - Social media: Correlational, not causal (31:54) - What happens to kids who aren't okay?
This episode kicks off a three-part "Collaborative Delivery 101" series that details the perspectives, benefits, and experiences of water industry professionals that have taken the plunge into this alternative pathway to project delivery.In this episode, our series host Jim Good (Parkview Advisors) sits down with Bruce Husselbee (HRSD) to share the owner's perspective on collaborative delivery.
Join Dr. Libby Crockett as she talks with Joanna Murray and Cleo Zagurski from the Reproductive Health Collaborative, Nebraska to learn about Title X in the State of Nebraska and how HR1 affects the delivery of reproductive healthcare in our state.Reproductive Health Collaborative Nebraska Advocacy Ask: Contact your U.S. Senators and Representative and urge them to reach out to HHS and demand the immediate release of the Title X - Year 5 non-compete continuation application and guidance. Nebraska stands to lose over $2 million dollars for the coming year if Title X funding is not released, which will lead to possible clinic closures and Nebraskans not receiving life-saving preventative care. Senator Fischer's Office: 202-224-6551 Senator Ricketts' Office: 202-224-4224Representative Flood's Office: 202-225-4806 Representative Bacon's Office: 402-938-0300 Representative Smith's Office: 308-384-3900 Reproductive Health Collaborative Nebraska Links: https://rhcnebraska.org/https://incontrolnebraska.com/Link to HR1 - One Big Beautiful Bill Acthttps://www.congress.gov/bill/119th-congress/house-bill/1If you have questions or answers, please email us at contact@nebraskaallianceforphysicianadvocacy.org Please check out our website at: https://nebraskaallianceforphysicianadvocacy.org/Nebraska Alliance for Physician Advocacy Follow on social media: @NEAllianceforPhysicianAdvocacy on Instagram https://www.facebook.com/neallianceforphysicianadvocacy on Facebook The views and opinions expressed on this podcast are solely those of the speakers and guests and do not necessarily reflect the official policy or position of their employers or any other organization or entity. This podcast is for informational and entertainment purposes only and does not constitute professional medical, legal, or financial advice. If you have a specific concern, please consult with a qualified professional.
The Dad Edge Podcast (formerly The Good Dad Project Podcast)
In this powerful conversation, I sit down with Dr. Ross Greene, clinical psychologist and creator of the Collaborative and Proactive Solutions (CPS) model, to unpack why traditional rewards and punishments often make behavior worse — not better. We dive deep into why "because I said so" stops working, what your child's frustration is actually communicating, and how to shift from authoritarian control to collaborative leadership that builds trust, accountability, and critical thinking. If you've ever thought, "Why is this not working anymore?" this episode will give you a radically different lens — and practical tools you can use immediately. Timeline Summary [0:00] Why power struggles are so common in parenting [2:00] Introducing Dr. Ross Greene and the CPS model [6:17] Why rewards and punishments don't solve the real problem [8:33] Concerning behavior as a frustration response [12:04] The 3-step collaborative problem-solving process explained [16:19] Real-life example: solving teeth brushing battles with a 3-year-old [30:56] Curfew conflict and how to navigate teenage resistance [37:16] How collaborative parenting builds critical thinking [41:56] Why authoritarian parenting may cause long-term harm [47:06] Developmental variability — why every child is different [49:23] Why noncompliance is informative, not defiance [56:31] Accountability through collaboration — not punishment Five Key Takeaways Concerning behavior is a signal, not a character flaw. It communicates an unsolved problem. Rewards and punishments modify behavior — they don't solve the underlying issue. The 3-step CPS process (Empathy, Define Adult Concern, Invitation) reduces conflict and builds trust. Noncompliance is information. It tells you an expectation may exceed your child's current skill set. Collaborative leadership builds accountability, emotional regulation, and critical thinking. Links & Resources Dad Edge Alliance Preview Call: http://thedadedge.com/preview Dad Edge Business Boardroom (Mastermind): https://thedadedge.com/mastermind Dr. Ross Greene — Lives in the Balance (Free Resources): https://livesinthebalance.org Episode Show Notes & Resources: https://thedadedge.com/1442 Closing Remark If this episode challenged how you think about discipline, accountability, and leadership at home, don't just sit on it — put it into practice. Try the empathy step tonight. Lead with curiosity. Solve one unsolved problem. If this conversation impacted you, please rate, review, follow, and share the podcast. The way we parent today shapes the leaders of tomorrow. From my heart to yours — go out and live legendary.
Healing Is a Collaborative Endeavour In this "Mission Accomplished" Saturday morning movie session, David Hoffmeister uses a mini-movie to illuminate a radical spiritual breakthrough: the realization that our innocence is already complete. The narrative, condensed from multiple action films, serves as a powerful metaphor for the ego's complex plots and the Holy Spirit's swift undoing of them. David focuses on two pivotal metaphysical themes: The Illusion of Time and Control: Drawing on A Course in Miracles Lesson 158, David explains that "time is a trick, a sleight of hand." He emphasizes that the ego uses the past and future to distract us from the Holy Instant, where our safety lies. By letting go of the need to control the "script" of our lives, we discover that the mission of salvation is already accomplished through the Atonement. Transcending the Ego's Distractions: The session explores how the mind is often fooled into believing it is a separate, physical entity at the mercy of external circumstances. David invites us to "peer behind the curtain" and see that all conflict—whether a nuclear threat on screen or a personal grievance—is merely a projection of a split mind. Through his commentary, David guides listeners to hear the gentle whisper of Jesus saying, "Mission Accomplished," allowing us to surrender our worries and judgments in favor of the happiness that is our natural inheritance. These gatherings take place every weekend in February and March 2026 and include teachings, films, music, panel discussions, and live Q&A sessions with David Hoffmeister. Ready to join the adventure? Register for the upcoming retreats for free here: https://programs.the-christ.net/courses/the-way-of-the-mystic To participate online in a Movie Gathering, join our online community: https://programs.the-christ.net/courses/membership-weekly-online-movie-gatherings Join our online community: https://programs.the-christ.net/products/communities/tribe-of-christ If you are interested to know more about David Hoffmeister and Living Miracles events, here is more information: https://circle.livingmiraclescenter.org/events. Read A Course in Miracles online here: https://acourseinmiraclesnow.com/ Learn more about David Hoffmeister here: https://davidhoffmeister.com
In Part 2 of this conversation, Marlon and Noēsis Collaborative Founder & CEO Ron Ivey shift their focus to what comes next. They explore what it means to design AI in service of human flourishing, unpack the difference between knowledge and wisdom, and wrestle with questions about suffering, belonging, and what it actually means to be human. As we close out the season, this episode turns toward agency, hope, and the choices still in front of us. Learn about the imapct of AI on Youth Well-Being: aiandyouthimpact.org Keep up with the work of Ron Ivey and Noēsis Collaborative: noesiscollaborative.org
This podcast episode features Julie O'Hara, director of the Preaching Collaborative. She details how the Collaborative fosters "effective evangelistic preachers" through peer cohorts and feedback, emphasizing that preaching should invite people to their "next yes" to Jesus. Lifelong Learning Code: 28473 Click here to learn about Lifelong Learning.
What if the biggest barrier to your fundraising success isn't your strategy or your donor list — but your workplace culture?In this insightful episode, I sit down with Marcia Beckner, a nationally recognized nonprofit founder, CEO mentor, culture strategist, and host of the Nonprofit CEO SPARK podcast. Marcia is the creator of the Culture CARES® Framework, a proven model for helping nonprofit leaders build healthy, inclusive, and empowering organizational cultures where everyone can thrive — and where fundraising can flourish.Marcia shares her powerful personal journey, beginning with a life-altering ovarian cancer diagnosis in her 20s that led her to found MyLifeLine Cancer Foundation. Under her leadership, the organization grew nationally and eventually merged with a global nonprofit, reaching over 300,000 cancer patients and families.Today, Marcia works with mission-driven leaders across the country to tackle a commonly overlooked truth: your culture is your growth engine. Without alignment, even the best fundraising strategies fall flat. But when leadership, values, and strategy are united? Teams thrive. Revenue grows. Burnout fades.
(Fremont County, WY) – The Bootstrap Collaborative’s Startup Challenge returns to Fremont County and applications are open now! Jett Odle joined the County 10 Podcast this week to discuss what the challenge is, how you can pitch your business ideas and a new “moonshot” competition for folks with especially big business dreams! For more information on the Bootstrap Collaborative, click here. For more information on the Startup Challenge, click here. To hear our conversation with Jett simply hit play below or find the County 10 Podcast anywhere you listen to podcasts!
In this episode of the Successful Stylist Academy podcast, Ambrosia Carey dives into the real challenges beauty professionals face with social media marketing and the pressure to constantly create content. She explores the psychological burden of social media, how marketing psychology affects decision-making, and why relying solely on Instagram or TikTok can create instability in your beauty business. Ambrosia shares practical marketing strategies for sustainable business growth, emphasizing client retention, referral systems, and Google reviews as foundational drivers of salon success. She challenges listeners to rethink their approach to marketing and consider whether their business could thrive without social media dominating their strategy. If you're a stylist or salon owner looking to build a more grounded and profitable beauty business, this conversation will shift your perspective. Get 2 months of GlossGenius Gold or Platnium for FREE with code SUCCESSFUL: http://glossgenius.com/successfulstylist Enjoy our Marketing Guide Freebie: https://small-kiwi-98108.myflodesk.com/ke6k90nlq2 Join the SSA LAB Membership Waitlist here: https://small-kiwi-98108.myflodesk.com/gnfbcgfrjq Key Takeaways: 1. If social media disappeared tomorrow, would your beauty business still grow? 2. Many beauty professionals feel overwhelmed by the constant pressure of social media marketing. 3. Client referrals account for 62% of new clients, making relationship building essential. 4. Google reviews significantly impact client bookings and salon visibility. 5. Focusing on client retention creates more sustainable business growth than chasing new followers. 6. Social media should complement your marketing strategy, not control it. 7. Authentic content builds stronger engagement than perfection-driven posting. 8. Collaborative efforts and community support expand your reach without algorithm pressure. 9. Tracking key business metrics helps beauty professionals make informed decisions. 10. Long-term salon success comes from systems, referrals, and meaningful client relationships. Take 15% off our favorite skincare line, Pharmagel with code SSA15: https://pharmagel.net/?ref=SSA15 Find us on Instagram for more buisness tips: https://www.instagram.com/successfulstylistacademy/ If you prefer video: https://youtube.com/@successfulstylist?si=-GLeHybcbHMmkq0t
Minneapolis poet, musician and activist Joe Davis and his band, The Poetic Diaspora, is preparing a long lineup of musicians to celebrate Black music and poetry. Their show, “Diaspora: On the Rise” is set for Feb. 21 at 7 p.m. at Northrop Auditorium in Minneapolis. Davis joined MPR News host Nina Moini to talk about showing up for his community as an artist in hard times, expression through poetry and the musicians he's working with to celebrate the work he's done with his band.
What if the real key to success isn't hustle — but collaboration?In this compelling episode of Women Road Warriors, Shelley Johnson and Kathy Tuccaro explore The Power of Collaborative Relationships with leadership expert and author Leslie Grossman. A visionary in women's leadership development, Leslie is the Faculty Director of the Women's Leadership Program at The George Washington University Center for Excellence in Public Leadership, a Senior Fellow, executive coach, and founder of five businesses.For years, women have been told to push harder, compete smarter, and climb alone. Leslie offers a different path — one rooted in courage, executive presence, trusted relationships, and intentional collaboration.Drawing from her groundbreaking book Circles of Collaboration, she reveals how women can access mentorship and sponsorship, create inclusive professional networks, and build authentic relationships that accelerate career growth. She also shares insights from her transformative framework, The 7 Habits of Highly Effective Women, showing how collaboration fuels confidence, influence, and momentum.If you're ready to shift from solo striving to shared success, this conversation will change how you think about leadership, connection, and power.Because when women collaborate, they don't just advance — they transform industries.
Health care outcomes in the U.S. differ substantially depending on race. How much are health care discrepancies based on structural and historical racism? What needs to change to promote health justice in the U.S. and what kinds of policies are needed to promote this change? How important is diversity of health care providers in building a more just healthcare system? [ dur: 58mins. ] Keisha Ray teaches Bioethics and Humanities as a tenured associate professor in Texas. She is the author of Black Health: The Social, Political, and Cultural Determinants of Black People’s Health. Ruqaiijah Yearby is the Judge Clifford Scott Green Chair in Law at Temple University Beasley School of Law. She is also Co-Founder of the Institute for Healing Justice & Equity and one of the Co-Founders of the Collaborative for Anti-Racism & Equity. She is the book chapter author of “Gender Bias, Mental Health Inequities, and Health Justice” in the book Mental Health Equity. Dylan H. Roby is the Chair and Professor of Health, Society, and Behavior at UC Irvine. He is the co-author of Ending Structural Racism in the US Health Care System to Eliminate Health Care Inequities. This program is produced by Doug Becker, Ankine Aghassian, Maria Armoudian, Anna Lapin and Sudd Dongre. Health, Politics and Activism, Medicine , Racism
Today, we're putting The Tonearm's needle on Stephen Vitiello.Stephen is an electronic musician and media artist. His sound installations are in the permanent collections of MoMA, the Whitney, and the Museum of Contemporary Art in Lyon. He's worked with Pauline Oliveros, Ryuichi Sakamoto, and Joan Jonas. By day, he teaches Kinetic Imaging at Virginia Commonwealth University.Stephen's latest project is Trinity, a collaborative album with Lawrence English, who you heard on last week's show. Each of Trinity's five tracks brings in a different third musician: Brendan Canty from Fugazi, Chris Abrahams from The Necks, Marina Rosenfeld, Aki Onda, and the late Steve Roden. The album came out last November.Stephen shares how this project came together, what it's like to work with each of these artists, and how he's built a career turning everyday sounds into sonic experiences.(The musical excerpts heard in the interview are from Lawrence English & Stephen Vitiello's album Trinity)–Dig DeeperArtist and AlbumVisit Stephen Vitiello at stephenvitiello.com and follow him on Soundcloud, Instagram, and BandcampPurchase Lawrence English & Stephen Vitiello's Trinity from American Dreams, Bandcamp, or Qobuz and listen on your streaming platform of choicePrevious collaborations: Acute Inbetweens (2011) and Fable (2014) with Lawrence EnglishStephen Vitiello & Brendan Canty: Second (with Hahn Rowe)Trinity CollaboratorsLawrence English and Room40 RecordsBrendan Canty - drummer (Fugazi, The Messthetics)Chris Abrahams - pianist (The Necks)Marina Rosenfeld - turntablist and composerAki Onda - electronic musician and sound artistSteve Roden - late sound artist and visual artistWorld Trade Center ProjectWorld Trade Center Artist Residency - Lower Manhattan Cultural CouncilWorld Trade Center Recordings: Winds After Hurricane Floyd (1999)Bright and Dusty Things - album featuring WTC recordingsStephen Vitiello: Listening With Intent - documentary by ABC-TV AustraliaEducational InstitutionVCU Kinetic Imaging - Virginia Commonwealth UniversityKinetic Imaging Graduate Program at VCUInfluences and Collaborators MentionedNam June Paik - video art pioneerPauline Oliveros - composer and accordionistRyuichi Sakamoto - composer and musicianFred Frith - guitarist and composerIkue Mori - drummer and electronic musician (DNA)Maryanne Amacher - sound artist and composerR. Murray Schafer - composer and writer on acoustic ecologyRobin Rimbaud (Scanner) - electronic musicianColin Newman - Wire guitarist and vocalistTaylor Deupree - 12k Records founderKey Venues and InstitutionsThe Kitchen - New York performance spaceElectronic Arts Intermix - video art distributorAnthology Film Archives - New York cinemaMASS MoCA - Massachusetts Museum of Contemporary ArtThe High Line - New York elevated parkWhitney Museum of American Art - 2002 BiennialMuseum of Modern Art - Soundings exhibition (2013)Punk and No Wave ReferencesFugazi - influential post-hardcore bandDNA - no wave bandThe ClashNo Wave movement - late 1970s NYCMusic Theory and PracticeFluxus movement - experimental art movementJohn Cage and prepared pianoAmbisonic audio - spatial sound formatDolby Atmos - immersive audio formatArticles and InterviewsSteve Roden and Stephen Vitiello conversation in Bomb magazineThe Collaborative Recent History of Stephen Vitiello - Fluid Radio interview-Dig into this episode's complete show notes at podcast.thetonearm.com–• Did you enjoy this episode? Please share it with a friend! You can also rate The Tonearm ⭐️⭐️⭐️⭐️⭐️ and leave a review on Apple Podcasts. • Subscribe! Be the first to check out each new episode of The Tonearm in your podcast app of choice. • Looking for more? Visit podcast.thetonearm.com for bonus content, web-only interviews + features, and the Talk Of The Tonearm email newsletter. You can also follow us on Bluesky, Mastodon, YouTube, and LinkedIn. • Be sure to bookmark our online magazine, The Tonearm! → thetonearm.com Hosted on Acast. See acast.com/privacy for more information.
What if patient access isn't just an operational problem—but a shared organizational belief system?In this episode of All Access Pass, host Chris Profeta, Senior Director of Research and Analytics at the Patient Access Collaborative, sits down with Austin Loomis, AVP of Ambulatory Access and Analytics, Mandy Newman, MAAL, Vice President of Ambulatory Operations, and Catherin Mims, MD, Vice President and Associate Chief Physician Executive for the Ambulatory Practice at OU Health. Together, they unpack a new evolution of the Patient Access Framework: a group-based assessment that captures how access is perceived across an entire organization.With more than 20 leaders—from finance and HR to physician chairs and executive leadership—participating in the assessment, OU Health gained a rare, enterprise-wide view of access. The conversation explores what happens when those perspectives align, where they diverge, and why those gaps often represent the greatest opportunities for system improvement. Along the way, the group reflects on why access blind spots persist, how shared language changes strategy, and what it means to truly measure access as an enterprise responsibility.Tune in to hear how group assessments can surface hidden friction, strengthen cross-departmental alignment, and turn access from a siloed function into a shared organizational priority—covering the evolution of the Patient Access Framework, the rationale behind group scoring, leadership alignment, organizational blind spots, and practical insights for system-level access improvement.Woodcock, E., Profeta, C. A framework for patient access management: consensus from a Delphi panel of US health system leaders. BMC Health Serv Res 25, 524 (2025). https://doi.org/10.1186/s12913-025-12561-8https://link.springer.com/article/10.1186/s12913-025-12561-8
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
This week on the New Music Business podcast, Ari sits down with Tema Siegel and Zach Blankstein of the band Couch. Tema is the singer and Zach is the guitarist/manager of this seven-piece soul-pop band from Boston. Their explosive live shows and fully DIY approach have helped them sell out major venues across the country. Formed from lifelong friendships and built during the pandemic, Couch has grown into a globally touring act while self-producing their acclaimed debut album 'Big Talk'.In this episode, Tema and Zach break down how they built an engaged fanbase without label support, the logistics of touring as a seven-member group, and the creative and vulnerable writing process behind 'Big Talk'. Ari dives into their ad strategy, their partnership with the Salt Lick Incubator, how they secured major support tours with Lake Street Dive and Cory Wong, and what it takes to balance musicianship, management, and sustainable growth as an independent band in 2025. http://instagram.com/couch.theband07:16 – Adding the seventh member & early chemistry09:15 – Touring as an introvert and finding group balance12:19 – Managing a 12-person touring party16:34 – How fans are reacting to the new songs live18:22 – How the band uses VIP sections to build superfans19:40 – Collaborative songwriting process & Temma's Notes app22:19 – “Middleman” and the band's unexpected EDM influence26:22 – Self-producing the entire album & working with mixers27:15 – Living in Boston and breaking out beyond the local scene32:56 – Growing during the pandemic while in separate cities36:32 – Early viral video & their online strategy40:07 – How Couch finances the band with day jobs41:24 – Why Zach became their in-house manager55:52 – Opening for Cory Wong & Lake Street Dive58:44 – Deep dive into their ad strategyEdited and mixed by Peter SchruppMusic by Brassroots DistrictProduced by the team at Ari's TakeOrder the THIRD EDITION of How to Make It in the New Music Business: https://book.aristake.com Hosted on Acast. See acast.com/privacy for more information.
This episode of Destination on the Left is a special roadshow edition recorded live at the American Bus Association's Marketplace 2026 in Reno, Nevada. I'm joined by a fantastic group of industry leaders to hear first-hand how collaboration is playing a critical, mission-driven role in the future of group travel, moving beyond a "nice-to-have" and becoming essential for success. My guests discuss the economic impact of group travel and the importance of restoring cross-border connections between the U.S. and Canada, to creative regional itineraries, and share ideas for navigating the political and economic headwinds facing the industry. What You Will Learn in This Episode: How ABA's leadership is addressing current challenges in group travel, including restoring the crucial relationship between the U.S. and Canada Why collaboration between industry partners, including operators, suppliers, associations, and destinations, is mission-critical for the future of group travel Trends are shaping group travel in 2026 and beyond, such as the rise of experiential itineraries, smaller group sizes, and the impacts of major events like the FIFA World Cup and MA250 How regional and cross-border partnerships are creating unique travel experiences and tour products that benefit entire ecosystems, not just individual destinations Collaborative initiatives, such as Tour in 64 are demonstrating the value of teamwork How industry organizations like ABA can accelerate personal and professional growth The Future of Group Travel is Collaboration The ABA 2026 Marketplace is filled with optimism, but nobody shies away from today's realities. Fred Ferguson sets the stage by underscoring the economic importance of group travel—$158 billion in economic output and 800,000 jobs in North America. The entire travel flywheel, operators, destinations, restaurants, and attractions, only spins when everyone works in sync. Fred Ferguson and Terry Fischer both stress that now, more than ever, strategic alliances and industry unity are essential for lasting impact. It's no longer enough to operate in silos. As Terry puts it, "It takes a village," and internal and external collaboration are the most important ingredients in weathering disruptions, from politics to pandemics. Collaboration in Action My guests also share what deep collaboration looks like in practice. Across the group travel ecosystem, leaders are getting creative. We hear how DMOs are embracing regionalism, and Todd Read from SoIN Tourism and Whitney Lubbers of Dubois County described how Tour in 64, a partnership among destinations along Interstate 64, helps smaller markets punch above their weight. By pooling marketing budgets, sharing top experiences, and even jointly running an ABA booth, collective impact outweighs any competition for hotel nights. With mega-events like the FIFA World Cup and MA250 (America's 250th anniversary) on the horizon, Stacey David detailed how municipalities, nonprofits, small businesses, and big attractions can co-create fan zone festivals that benefit everyone. Every stakeholder has a seat at the table for shared successes. The Power of Relationships The ABA conference is a great way to build relations, get involved, and engage with others in the travel and tourism industry. Jim Warren of Anderson Vacations credits strong partnerships as the industry's lifeblood, helping everyone survive the inevitable economic and political storms. Those connections are formed and deepened at conferences like ABA Marketplace. Investments in community, from Women in Buses initiatives to association days, make all the difference for newcomers and veterans alike. Leaders are putting collaboration-first strategies in place—whether it's regional joint marketing, creating new products, or advocating for the sector at the policy level. Resources: Fred Ferguson: https://www.buses.org/staff/fred-ferguson-mba-ms/ Terry Fischer: https://www.linkedin.com/in/terry-fischer-31895b122/ Patricia Cowley: https://www.linkedin.com/in/pcowley/ Jim Warren: https://www.linkedin.com/in/jim-warren-1b2b59187/ Stacey David: https://www.linkedin.com/in/stacey-david-940a214/ Todd Read: https://www.linkedin.com/in/todd-read-182ab73a/ Whitney Lubbers: https://www.linkedin.com/in/whitney-lubbers-07374360/ We value your thoughts and feedback and would love to hear from you. Leave us a review on your favorite streaming platform to let us know what you want to hear more of. Here is a quick tutorial on how to leave us a rating and review on iTunes!
FREE RESOURCES & TOOLS:Join my newsletter for weekly job search and career tips you won't find anywhere else: https://www.resumeassassin.com/newsletter/ (Get instant access to my free ATS-friendly resume template)Resume Assassin: www.resumeassassin.com - Professional resume writing services and career coaching to land your dream roleResume Sidekick: www.resumesidekick.io - AI-powered resume optimization tools that help you beat Applicant Tracking SystemsLand Your Dream Job Course: https://academy.resumeassassin.com - My complete step-by-step system including the 2-Hour Job Search method, resume templates, LinkedIn optimization, and interview strategiesCONNECT WITH ME:LinkedIn: www.linkedin.com/in/mary-southernInstagram: @resumeassassinTikTok: @resume_assassin_maryEPISODE TEASER I used to read 50-100 resumes a day at Amazon. Most of them had good metrics, clean formatting, all the "right" stuff. But they still didn't get interviews.Turns out, there are seven specific things recruiters scan for that nobody talks about. Not the obvious stuff!! The hidden signals that separate the "maybe" pile from the "interview immediately" pile.That's what this video is about. Let's get into it.TIMESTAMPS0:00 - Intro: Why your "good" resume isn't getting interviews2:15 - Signal #1: Evidence of decision-making under ambiguity5:30 - Signal #2: The "scope creep" signal8:20 - Signal #3: Recovery stories (not just success stories)11:10 - Signal #4: The velocity indicator13:45 - Signal #5: Collaborative tension navigation16:20 - Signal #6: Scale language18:40 - Signal #7: Self-directed learning evidence21:15 - Recap & next steps
RISE is a nonprofit dedicated to bridging educational and opportunity gaps in East Palo Alto through culturally responsive, family-centered programs for children from birth through high school. Its initiatives include early childhood and literacy support, afterschool academic enrichment, and tutoring and personal growth programs for middle and high school students. RISE also leads the Little Blue House Birth-5 Collaborative, a community hub where nonprofits across San Mateo County work together to better serve families with young children.Tune in each week on KNBR as we spotlight incredible individuals and organizations making a difference in the Bay Area with the Forever Young Foundation Hero of the Week.See omnystudio.com/listener for privacy information.
Simple Pin Podcast: Simple ways to boost your business using Pinterest
When I first learned about Work Play Branding, I instantly loved their business model, but what made me fall in love with them even more is how they are creatively using Pinterest as a collaborative tool with their clients. To surface the clients' ideals in photos and pictures, to be able to support their photographers and designers to give the clients exactly what they are looking for. We're going to dive into an interview with their founder and CEO Lyrik Fryer. WorkPlay Branding —-------Here are some helpful links from the podcast:
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