Podcasts about Supervision

Legislation impacts your daily practice more than most clinicians realize. In this episode, we sit down with Derik J. Sven to break down what truly drives change in dental hygiene policy — and what doesn't.   Derik shares insight into the realities behind dental care standards, the ongoing fight for hygienist autonomy, and the complex supervision structures that shape scope of practice across the country. He also explains why legal expertise often carries significant influence in regulatory conversations and how business, public health, and law intersect in advancing the profession.   In This Episode We Cover:   The political and structural forces behind dental care standards   The ongoing battle for dental hygienist autonomy   Supervision requirements and why they matter   Why attorneys often influence dental policy decisions   Lesser-known factors that can directly affect your career   About the Guest Derik J. Sven began his career as a certified dental technician before transitioning into clinical dental hygiene. He earned degrees in Dental Hygiene and Health Care Administration, followed by a Master of Public Health and a Master of Business Administration. He is currently pursuing doctoral research at George Washington University, focusing on dental therapy advancement and hygienist autonomy, while also completing a Master's in Health Care Law. Derik is actively involved in the American Dental Hygienists' Association, where he was inducted into the inaugural class of ADHA Fellows in 2023 and serves as President-Elect of Virginia's Dental Hygienists' Association. This episode offers a practical foundation for understanding how legislation truly moves — and what it means for the future of dental hygiene.   Resources: derik@dentistrywithderik.com https://www.linkedin.com/in/derikjsven/

Supervision isn't just about cases—it's about people. And people don't grow in isolation.In Episode 56 of Supervision Simplified, Dr. Amy Parks is joined by grief counselor, educator, and supervisor Debi Jenkins Frankle to explore why supervising alone may be limiting your clinicians more than helping them.Debi shares her approach to group supervision, including how connection, support, and real-time feedback shape confident, capable clinicians. From starting supervision with “how's your heart and soul” to creating environments where clinicians can learn from each other, this conversation reframes supervision as a developmental process—not just a requirement.If you're a clinical supervisor, practice owner, or stepping into leadership, this episode will challenge your current model and give you a more effective path forward.Connect with Debi Jenkins Frankle:https://www.calabasascounseling.comhttps://www.facebook.com/groups/privatepracticegriefhttps://www.instagram.com/debijenkinsfrankleSponsor:Clinical Supervision Directorywww.clinicalsupervisiondirectory.comNote: This is a previously released episode we're bringing back because the conversation is just as relevant today—especially for supervisors looking to build stronger, more supported clinicians.

Working with Limerent – Feeling Out of Your Depth as a Student Counsellor In Episode 369 of the Counselling Tutor Podcast, your hosts Rory Lees-Oakes and Ken Kelly take us through this week's three topics: Firstly, in ‘Ethical, Sustainable Practice', they explore working with shame in the therapy room – how shame presents, how it differs from guilt, and how to work with it gently and ethically. Then in ‘Practice Matters', Rory speaks with Nadine Pittam about limerence – a powerful and often overwhelming state of obsessive romantic attachment – and how therapists can work safely and effectively with clients experiencing it. And finally, in ‘Student Services', Rory and Ken discuss what to do when you feel out of your depth as a counselling student, offering reassurance, practical guidance, and encouragement. Working with Shame in the Therapy Room [starts at 03:24 mins] In this section, Rory and Ken explore working with shame in the therapy room, unpacking the complex and often hidden nature of shame, how it presents in clients, and how therapists can respond sensitively and ethically. Key points discussed include: Shame is identity-based (“there is something wrong with me”), whereas guilt relates to behaviour (“I did something wrong”). Shame often hides itself and may present subtly through withdrawal, minimising, avoidance, anger, or difficulty maintaining eye contact. Triggers can include criticism, rejection, humiliation, invalidation, bullying, coercion, or conditional approval. The cycle of shame involves activation, negative self-beliefs, coping strategies (withdrawal, control, emotional numbing), temporary relief, and reinforcement. Working with shame requires gentleness – noticing body language, naming shame carefully, and pacing the work to avoid overwhelming the client. Reflective questions such as “When do you first remember feeling this way?”, “Who taught you that you were not good enough?”, and “What did you need at that time that you didn't receive?” can open healing dialogue. Separating identity from experience is central – helping clients understand that what happened to them does not define who they are. Supervision and reflective practice are essential when working with shame, both for client safety and therapist self-awareness. Working with Limerent [starts at 33:53 mins] In this week's ‘Practice Matters', Rory speaks with Nadine Pittam about limerence – a term coined by Dorothy Tennov to describe an intense, involuntary state of romantic obsession. Key points from this conversation include: Limerence is not simply infatuation or love; it is an addictive, dysregulated state marked by intrusive thoughts and emotional dependency. It can feel life-or-death in intensity and may result in relationship breakdowns, loss of identity, and significant emotional distress. The limerent object is often someone partially known (e.g. a colleague, acquaintance, former partner), allowing projection of unmet attachment needs. Therapy focuses on the client's unmet needs and attachment history, rather than on analysing the limerent object. The therapist validates the emotional pain while gently challenging the belief that the other person will “solve” the distress. Limerence may involve “eroticised abandonment”, where rejection or unavailability intensifies obsession. Clear professional boundaries are vital, as therapists themselves may become the limerent object through transference. This is often longer-term work, requiring emotional honesty, self-compassion, and sustained therapeutic engagement. Feeling Out of Your Depth as a Student Counsellor [starts at 57:30 mins] In this section, Rory and Ken explore the common experience of feeling overwhelmed or inadequate during counselling training and placement. Key points include: Feeling out of your depth is common and often reflects care, responsibility, and commitment rather than incompetence. Imposter syndrome affects both students and qualified practitioners – it does not disappear after training. Clients may bring complex or distressing material that feels very different from classroom skills practice. Your role is not to fix clients or have all the answers, but to offer warmth, empathy, and a safe, non-judgemental space. Being deeply heard is rare and powerful – the therapeutic relationship itself is often the primary healing factor. If the work feels overwhelming, take it to supervision, personal therapy, and peer discussion rather than carrying it alone. You were accepted onto your course because your tutors believe in your readiness and potential. Developing robustness is part of training – feeling stretched can be a sign of growth. Reflective practice and open dialogue prevent self-doubt from becoming hidden shame. Links and Resources Counselling Skills Academy Advanced Certificate in Counselling Supervision Basic Counselling Skills: A Student Guide Counsellor CPD Counselling Study Resource Counselling Theory in Practice: A Student Guide Counselling Tutor Training and CPD Facebook group Website Online and Telephone Counselling: A Practitioner's Guide Online and Telephone Counselling Course

Our co-heads of Securitized Products Research Jay Bacow and James Egan discuss the impact of upcoming regulatory changes on U.S. mortgage rates and home sales.Read more insights from Morgan Stanley.----- Transcript -----Jay Bacow: It is March and there's some madness going on. I'm Jay Bacow, here with Jim Egan, noted Wahoo Wa fan. James Egan: Hey, it looks like Virginia's going to be back in the tournament this year, hoping for a three seed, looking like a four seed. It's the first year that my son is really excited about it. So, hoping we can win a few games. Jay Bacow: Let's hope they don't lose the first game and make him cry like you did a few years ago. But … Welcome to Thoughts on the Market. I'm Jay Bacow, co-head of Securitized Products Research at Morgan Stanley. James Egan: And I'm Jim Egan, the other co-head of Securitized Products Research at Morgan Stanley. Jay Bacow: Today, with everything going on in the world, we thought it'd be prudent to discuss the U.S. mortgage and housing market. It's Thursday, March 12th at 10:30am in New York. James Egan: Jay, as you mentioned, there is a lot going on in markets right now, but hey, people need to live somewhere. And those somewheres remain pretty unaffordable. But this administration has been very focused on affordability, and we also have some updates on what is clearly the most exciting part of the housing and mortgage markets – regulation. What's going on there? Jay Bacow: Look, nothing gets me more excited than thinking about the regulatory outlook for the mortgage market. We've been focusing a lot on what's happening in D.C. with possible changes that could be helping out affordability, changes to the investor program, changes to the policy rate. But Michelle Bowman, who is the Vice Chair of Supervision, has been recently on the tape saying that we could get an update and a proposal for the Basel Endgame by the end of this month; and that proposal for the Basel Endgame is likely to make it easier for banks to hold loans on their balance sheet. It's going to give banks excess capital and the combination of these, along with some other changes that are going to be coming from the Fed, the FDIC and the OCC around: For instance, the GSIB surcharge that our banking analysts led by Manan Gosalia have spoken about – it's really going to help out the mortgage market in our view. James Egan: Alright, so freeing up capital, helping the mortgage market. When we think about the implications to affordability specifically, what do you think it means for mortgage rates? Jay Bacow: Right. So, it's important that [when] we think about the mortgage rate, we realize where it's coming from. The mortgage rate starts off with the level of Treasury rates, and then you add upon that a spread. And the spread is dependent among a number of different factors. But one of the biggest ones is just the demand. And one of the reasons why mortgage rates have been so high over the previous four years was (a) Treasury rates were high, but also the spread was wide. And we think one of the biggest reasons why the spread was wide is that the domestic banks, who are the largest asset type investor in mortgages – they own $3 trillion of mortgages – basically weren't buying them over the past four years. And one of the reasons they weren't buying was they didn't have the regulatory clarity. And so, if the banks come back, that will cause that spread to tighten, which will likely cause the mortgage rate to come down. That is presumably, Jim, good about affordability, right? James Egan: Yes. And I want to clarify, or at least emphasize, that affordability itself has been improving. Over the course of the past four to five months at this point, we've been close to, if not at the lowest mortgage rate we've seen in three years. And when we think about what that has practically done to the monthly principal and interest payment on homes purchased today. Like that monthly payment on the median priced home is down $150 over the past year. That's about a 7 percent decrease. When we lay in incomes – or when we layer in incomes to get into that actual affordability equation, we're at our most affordable place since the second quarter of 2022. So yes, big picture, this is still a challenge to affordability environment. But it's not as challenged as it's been over the past three years. Jay Bacow: All right, so affordability improving. It's still challenged though. What does that mean for home prices then? James Egan: So, when we think about the home price implication of mortgage rates coming down; of mortgage rates coming down in an environment where incomes are going up – we're thinking about demand for shelter, purchase volumes and supply of that shelter. And demand really has not reacted to the improved affordability environment. That's not unusual. Normally takes about 12 months for affordability improvement to pull through in terms of increased transaction volumes. But we do think that the lock-in effect that we've talked about in detail on this podcast in the past, that is going to play a role here. Mortgage rates end of February finally hit a five handle, really, for the first time in three years. They're back above that now with the volatility in the interest rate markets. But from 4 percent to 6 percent, mortgage rates is effectively an air pocket. We don't think you're going to get a lot of unlocking at these levels. So we think that transaction volumes will pick up. We're calling for 3 to 4 percent growth in purchase volumes this year. But they've been largely flat for two to three years at this point. And more importantly, any improvement in affordability that comes from a decrease in mortgage rates is going to lead to commensurately more supply alongside that growth in demand – which is going to keep home prices, specifically, very range bound here. The pace of growth is slowed to about 1.3 to 1.5 percent right now. We've been here for four or five months. We think we're pretty much going to stay here. We we're calling for 2 percent growth, so a little bit acceleration. But we think you're in a very range bound home price market. Jay Bacow: All right, so home prices range bound, affordability improved. But still has a little bit of room to go. Some possible tailwinds from the deregulatory path that will make homes being a little bit more affordable. Fair amount going on. Jim, always a pleasure speaking to you James Egan: And always great speaking to you too, Jay. And to all of our regular listeners, thank you for adding us to your playlist. Let us know what you think wherever you get this podcast. And share Thoughts on the Market with a friend or colleague today.Jay Bacow: Go smash that subscribe button!

In June 2025, when stepping into the Federal Reserve Board's role of vice chair for supervision, Michelle W. Bowman announced a comprehensive review of the bank capital framework. Since that time, she has introduced changes to two of the framework's four pillars, the supplementary leverage ratio and the stress-testing regime. As a next step in the comprehensive review, the Federal Reserve, together with the other federal bank regulatory agencies, will introduce proposed changes to the risk-based bank capital requirements.Join Vice Chair for Supervision Bowman at the Cato Institute as she details her comprehensive review and what is next for bank capital requirements and Basel III. Hosted on Acast. See acast.com/privacy for more information.

The UK's anti-money laundering system involves 88 organizations across policy, supervision, and enforcement, but does this complex network actually work? In this episode, host Robert Barrington speaks with Diana Bociga about her research using social network analysis to map how these organizations collaborate. Diana's findings reveal a system operating across two disconnected dimensions, strategic policy-making and tactical intelligence-sharing, where engagement in one often doesn't translate to the other. While public sector bodies serve as crucial brokers connecting different parts of the network, some brokerage roles are duplicated while others are missing entirely. The conversation explores whether the solution to improving effectiveness lies in adding more connections or fundamentally rethinking how the network is organized. Diana Bociga, Elisa Bellotti, Nicholas Lord, The Network Architecture of Anti-money Laundering: Strategic and Tactical (Dis)Connections in the UK's Policy, Supervision, and Enforcement Landscape, The British Journal of Criminology, 2025. https://academic.oup.com/bjc/advance-article/doi/10.1093/bjc/azaf101/8368980

Send a textHeather and I are talking about how we handle endings.  Endings are less dramatic than we thinkMedicine can manage a lot of the physical distressEmotional support can help you find peaceGrief and guilt can remainHeather and I want to hear about times you have felt a loss as a counselor or supervisor and what you did to take care of yourself. https://www.psychologytoday.com/us/blog/the-regret-free-life/202602/what-we-get-wrong-about-death

The Effective Workplace Feedback episode of Brain Chatter explores the role of effective feedback in building strong workplace cultures and improving leadership. Organizational psychologist Dr. Ken Chapman, founder of Ken Chapman & Associates, Inc. and author of The Leader's Code discusses why traditional annual performance reviews often fail and what leaders should do instead. Drawing on decades of experience advising organizations around the world, Dr. Chapman explains why timely, specific, and goodwill-driven feedback is far more valuable than infrequent evaluations. The conversation begins by examining what meaningful feedback looks like in practice and why leaders should actively encourage feedback from employees, customers, and colleagues alike.Throughout the episode, Dr. Chapman highlights the elements that make feedback constructive—clear specifics, appropriate timing, mutual respect, and a shared interest in improvement. He contrasts this with feedback that becomes destructive or unhelpful and explains why the absence of constructive feedback is one of the most common causes of employee dissatisfaction with leadership. The discussion also explores the importance of trust, listening, willingness to cooperate, and empathy when giving or receiving feedback, as well as strategies for normalizing regular feedback in workplace environments where it may initially feel uncomfortable.The conversation concludes by addressing common challenges leaders and employees face around feedback, including defensiveness, resistance, or the misuse of feedback. Dr. Chapman shares practical strategies for soliciting useful feedback, responding to unfair or petty criticism disguised as feedback, and holding people accountable for growth and improvement. Ultimately, the episode emphasizes that feedback is a cultural practice—one that, when handled thoughtfully and consistently, strengthens relationships, improves performance, and helps organizations thrive. This forty minute episode answers many other questions related to this topic, as well. EPISODE RESOURCES: >Bio of Ken Chapman, Ph.D.>Follow Ken Chapman & Associates, Inc. on LINKEDIN>Follow Ken Chapman & Associates, Inc. on FACEBOOK>Books Authored or Co-Authored by Dr. Ken ChapmanThanks to Michael Gordon for editing this episode. Brain Chatter, a podcast where we listen past the daily noise and explore topics at the intersection of leadership, workplace culture, profit, and sustainability.

The most important decision that grad students have to make is who to work with as their supervisor. A common joke in grad school is that graduate student-supervisor relationships outlast many marriages. Your choice of supervisor helps determine the trajectory of your graduate and postgraduate careers with supervisors.So on this episode we talk about what its like to be a supervisor. What to expect, how to be ethical, and what its like to be supervisors as racialized faculty. Joining us is Dr. Nhung Tran, Associate Professor of History at the University of Toronto.Related LinksNature article on "shadow supervision"Thanks for listening! Get more information, support the show, and read all the transcripts at academicaunties.com. Get in touch with Academic Aunties on BlueSky, Instagram, or by e-mail at podcast@academicaunties.com.

You can listen wherever you get your podcasts or check out the fully edited transcript of our interview at the bottom of this post.I am so excited I was able to interview a parenting thought leader I greatly admire. Lenore did not disappoint! So much wisdom, and so much fun! I think you'll love this podcast episode.In this episode of The Peaceful Parenting Podcast, I interview Lenore Skenazy, author of “Free-Range Kids,” which grew into the Free-Range Kids movement. Now she is president of Let Grow, the national nonprofit that is making it easy, normal, and legal to give kids back independence. We talk about screens, anxiety, free play, and why childhood independence matters more than ever.

"This came from an experience with a patient. It was early in my analytic training, and I was working with a supervisor who I really admired, and worked with her for a number of years. She was post-Kleinian, and was great at interpretation, formulation, and she was really helpful with just starting to guide me towards a lot of this work. I remember describing to her a patient session, and I was going through my process notes, and I said, 'I feel like the patient is inside of me. I feel like they want something that's in me, and I don't know what it is, and I can't quite access my own self, I don't know what to do'. It was through this initial experience where I really felt why analytic training versus other less intense training, we were also right at the time doing infant development, offered so much. It was early in my training and she suggested I think about an infant or even a toddler when they want something from their parents - they want something from their mother. The mother kind of feels this kind of gripping or this yearning from them, the baby wanting something. I started to think of my patients, not as infants or babies, but that what I was feeling was that there was something that the person I was working with needed, and they didn't have words yet to tell me what that was."    Episode Description: We begin by recognizing the unique journeys that lead clinicians to become psychoanalysts. Pam shares with us her initial exposure to dynamic thinking but felt that she was missing some awareness of what was happening in herself and in the patients she was working with - "I was curious...I wanted to go deeper, to know more." This led her to enroll in full-time analytic training. She shares with us her understanding of the 'difficult to reach patients' that she was treating and presents a fictionized case that represents the many countertransference struggles she faced. She noted that "instead of the patient realizing that she wanted something from me, she instead felt attacked by me." Supervision was essential in helping her make sense of her experiences and of learning to 'listen to the music'. We close by noting her open-ended curiosity and interest in learning more - lifelong attributes of analysts who continue to take pleasure in our work.   Our Guest: Pamela Polizzi, LCSW maintains a full-time private practice in New York City. She specializes in working with patients struggling with eating disorders, complex personality struggles, anxiety, depression, relational trauma, and life transitions. She earned her Master of Social Work (MSW) in Advanced Standing Clinical Practice from Fordham University at Lincoln Center in 2011. Currently, she is an Advanced Candidate at the Psychoanalytic Training Institute of the Contemporary Freudian Society (CFS) in Manhattan, working toward becoming a psychoanalyst. She completed a 2015 Two-Year Advanced Psychodynamic Psychotherapy Certificate in the Integrated Treatment of Eating Disorders from the Institute of Contemporary Psychotherapy (ICP), Center for the Study of Anorexia and Bulimia (CSAB). She also completed the Contemporary Freudian Society's (CFS) Two-Year Psychoanalytic Psychotherapy Program in 2019.  Recommended Readings: Readings for Psychoanalytic Candidates:  Bach, S. (2011). The How-To Book For Students of Psychoanalysis and Psychotherapy. Karnac.   Busch, F. (2021). Dear Candidates: Analysts From Around The World Offer Personal Reflections on Psychoanalytic Training, Education, and The Profession. Routledge.    Readings on Clinical Practice with the Patient who is Difficult to Reach:   Bollas, C. (1996). Borderline Desire. Int. Forum Psychoanal., (5)(1):5-9.   Joseph. B., Feldman, M., & Spillius, M. (1989). Psychic Equilibrium and Psychic Change: Selected Papers of Betty Joseph. New Lib. of Psycho-Anal., (9):1-222. (on Pep-web).  Joseph, B. (1975) The patient who is difficult to reach.  Joseph, B. (1982) Addiction to near-death.  Joseph, B. (1983) On understanding and not understanding: some technical issues.  Riesenberg-Malcolm, R. (1999). On Bearing Unbearable States of Mind. Routledge.    Steiner, J. (1993). Psychic Retreats: Pathological Organizations in Psychotic, Neurotic and Psychotic Patients. Routledge.    Winnicott, D.W. (1974). Fear of Breakdown. Int. R. of Psycho-Analysis. 1: 103-107.

This podcast discusses the GPSA's education and supervision resources.Click here to view a list of the resources mentioned in the webinar.

Attachment: What Counsellors Need to Know – Why Check-Ins and Check-Outs Matter In Episode 368 of the Counselling Tutor Podcast, your hosts Rory Lees-Oakes and Ken Kelly guide you through three key areas of counselling practice, learning, and development. In Ethical, Sustainable Practice, Rory and Ken explore when media coverage enters the counselling room, examining how major reporting on trauma and abuse can increase client contact and shape presentations. In Practice Matters, Rory is interviewed by Sarah Henry about his latest CPD lecture on attachment, exploring why attachment theory is central to therapeutic work. And in Student Services, Rory and Ken discuss the role of check-ins and check-outs in counselling training, and why these processes matter far beyond the classroom. When Media Coverage Enters the Counselling Room [starts at 03:18 mins] In this section, Rory and Ken explore when media coverage enters the counselling room, examining how high-profile reporting of abuse and trauma can trigger an increase in client enquiries and influence therapeutic presentations. Key points discussed include: Major news stories can act as a trigger, prompting clients to seek therapy for historic trauma. The “Savile Effect” explains why disclosures often surge following widespread media attention. Therapists may notice increases in presentations such as flashbacks, shame, hyperarousal, and crisis responses. Working in a trauma-informed way prioritises safety, pacing, choice, and avoiding re-traumatisation. Having a surge plan in place helps therapists manage capacity, referrals, and ethical boundaries. Supervision is essential for managing risk, vicarious trauma, and professional decision-making during these periods. Attachment: What Counsellors Need to Know [starts at 26:54 mins] In this week's Practice Matters, Sarah Henry interviews Rory Lees-Oakes about his recent lecture on attachment theory and its relevance to counselling practice. Key points from this discussion include: Therapy itself is an attachment process, with the therapist offering stability, presence, and emotional availability. Attachment styles are patterns, not pathology, and shape how clients relate to themselves and others. The therapist can become a secure base, supporting repair and earned security within the therapeutic relationship. Boundaries, consistency, and predictability are central to creating safety in attachment work. Ruptures and repairs are inevitable and can become powerful corrective relational experiences. Attachment dynamics show up in first contact, transference, countertransference, and endings in therapy. Why Check-Ins and Check-Outs Matter [starts at 51:42 mins] In this section, Rory and Ken explore the purpose of check-ins and check-outs in counselling training and how these practices translate into professional work. Key points include: Check-ins help students transition from the outside world into a reflective learning space. They allow tutors to assess group safety, emotional readiness, and potential risk. Sharing emotional states builds empathy, cohesion, and self-awareness within the group. Check-outs support reflection, integration of learning, and emotional containment at the end of sessions. These processes mirror therapeutic practice, modelling how sessions begin and end with clients. Developing this discipline in training supports ethical, present, and grounded practice post-qualification. Links and Resources Counselling Skills Academy Advanced Certificate in Counselling Supervision Basic Counselling Skills: A Student Guide Counsellor CPD Counselling Study Resource Counselling Theory in Practice: A Student Guide Counselling Tutor Training and CPD Facebook group Website Online and Telephone Counselling: A Practitioner's Guide Online and Telephone Counselling Course

This discussion convened senior global experts to examine how supervisory and regulatory authorities can prepare for rapid technological change. It focused on artificial intelligence and the evolution

In dieser Ausgabe von WUNDERWERKE SKYPT unterhält sich Martin Scott mit dem Host des Podcasts Schöner Glauben und Öffentlichen Theologen Jason Liesendahl aus Offenbach. Wie können wir Gott denken und verstehen und an ihn glauben angesichts von Umständen und Zuständen und Dynamiken in der Welt, in der Gott offensichtlich zu fehlen scheint? Kann Gott möglicherweise nicht alles, obwohl es eigentlich ein christliches Dogma ist, dass Gott allmächtig ist? War das Kreuz Jesu ein Scheitern Gottes oder Ausdruck eines souveränen Handelns? Und wo war Gott im Holocaust? Jason Liesendahl überrascht mit für viele Ohren neuen Antworten aus der Prozesstheologie und findet angesichts der herausfordernden Fragen neue Zugänge für sich und andere zu einem Glauben an Gott, hierzu an seinen Antworten leicht zweifelnd befragt von Moderator Martin Scott. Entstanden und aufgenommen wurde ein lebendiges, intensives, tiefgehendes und humorvolles Gespräch, das dieses Mal eine starke theologische Schlagseite mit sich bringt.Für die Unterstützung dieser Sendung bedanken wir uns herzlich bei unserem Werbepartner proCEO, dem Ausbildungsinstitut für Coaching und Supervision.Nächste Ausgabe: WUNDERWERKE SKYPT mit Steve Volke (Compassion Deutschland).WUNDERWERKE SKYPT ist ein kostenfreies Angebot, das uns bei Wunderwerke dennoch etwas kostet. Hierbei kannst du uns mit deiner Spende helfen. Jeder Euro hilft. Weitere Informationen zum Spenden an Wunderwerke, unsere Bankverbindung und auch ein bequemes Online-Spendenformular findest du unter wunder-werke.de/spendenPayPal-Direkt-SpendePayPal-Spendenadresse: spenden@wunder-werke.deWir bedanken uns zutiefst für die 2025 erhaltenen Spenden!Mehr Infos zum Podcast-Format unter wunder-werke.de/wunderwerke-skypt.Sendungs-Archiv: wunder-werke.de/podcastVeröffentlicht u. a. auch auf: Spotify, Deezer, YouTube Music und bei Apple Podcasts.Feedback? Gäste-Wünsche? -> info@wunder-werke.delinktr.ee/wunderwerke#wunderwerke_skypt #jasonliesendahl #schoenerglauben #martinscottVerwendete Musik: DC Talk - "Between You And Me" - Welcome to the Freakshow (Live - 1997)

Mein Buch "Kongruente Kommunikation in der Kita" ist in der Reihe „Pädagogik : Wissen“ im Herder Verlag erschienen.Kongruenz in derKommunikation bedeutet, dass innere Zustände und äußere Ausdrucksformen miteinander übereinstimmen. Was eine Person denkt und fühlt, passt zu dem, was sie sagt und wie sie es sagt. Kongruente Kommunikation ist ehrlich – und zugleich nicht ungefiltert –, sie schafft Vertrauen und trägt so maßgeblich zum Beziehungsaufbau bei.  Iris van den Hoeven ist Fachbuchautorin, Gründerin von Blickpunkt Erziehung, war viele Jahren im Kinderschutz und in der Elternbildung tätig. Als Master der Erziehungs- undBildungswissenschaften, psychosoziale Beraterin, im Expert:innenpool der WKO gelistete Supervisorin arbeitet sie unter anderem im Bereich elementarpädagogischer Fortbildungen als Lehrbeauftragte an verschiedenenPädagogischen Hochschulen. Zudem bietet sie in Verbindungmit zahlreichen Kooperationen Vorträge, Keynotes, Fortbildungen, Webinare, Inhouse-Seminare undAusbildungslehrgänge zum Thema der gewaltfreien und liebevollen Begleitung kindlicher Entwicklung an, sowie psychosoziale Beratung für Eltern und Elementarpädagog:innen, Einzel- und Teamsupervision u. a. im Bereich der Elementar- und Hortpädagogik sowie der Frühförderung , und Lehrsupervision nachLSB-Gewerbeordnung 2006, §4 Abs.(4) 1a und 1b an. Auf Social Media steht sie im täglichen Austausch mit knapp 60.000 Abonnent:innen.⁠⁠⁠Kongruente Kommunikation in der Kita | ⁠⁠⁠Fachbuch Herder Verlag⁠Ein guter Start in die Schule⁠ | Webinar am 08. April 2026 von 19:00 bis 20:30 Uhr⁠Ein guter Start in die Schule⁠ | Webinar am 28. Mai 2026 von 19:00 bis 20:30 Uhr⁠Achtsame Kommunikation⁠ | Webinar am 16. April 2026 von 19:30 bis 21:00 Uhr⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Quereinstieg ins Kita-Team⁠ | MedienpaketDon Bosco Verlag⁠⁠⁠Selbst*wirksam* – Relevanz & Ressource⁠⁠ | Webinar aus der Reihe „ALLEINerziehend“ am 13. April 2026 von 19:30bis 21:00 Uhr⁠⁠Bei DIR sein - Kindliche Entwicklung, Grundbedürfnisse⁠⁠ | Webinar aus der Reihe „ALLEINerziehend“ am 09. Februar 2026 von 19:30 bis 21:00 Uhr ⁠⁠⁠Fortbildung Salzburg 2026⁠⁠⁠ | Eintägige Fortbildung zum Thema der Kongruenten Kommunikation am 02. Oktober 2026⁠⁠⁠⁠⁠Veranstaltungen⁠⁠⁠⁠⁠⁠⁠⁠Download: Wut im kleinen Bauch⁠⁠⁠ ⁠⁠⁠YouTube: TROTZ der PHASE⁠⁠⁠⁠⁠⁠Anmeldung zum Blickpunkt Erziehung Newsletter⁠⁠⁠⁠⁠⁠⁠Kontakt / Anfragen⁠⁠⁠⁠⁠⁠⁠Über mich⁠⁠⁠⁠⁠⁠⁠www.blickpunkt-erziehung.at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Frage zur Rubrik „Hingehört & Nachgefragt“ einreichen⁠⁠⁠⁠⁠⁠⁠⁠⁠Beratung & Supervision⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BPE Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BPE Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠BPE Threads⁠⁠⁠⁠⁠⁠⁠⁠BPE LinkedIn⁠⁠⁠⁠ 

As Kevin McCallister once taught us: just because the house is still standing doesn't mean everything's under control. Everyone's racing to adopt AI, but has anyone actually read the fine print? For this year's International Women's Day episode, we are joined by Aubrey Blanche to unpack the hype, the hidden tradeoffs, and the quiet ways teams are giving up agency in the name of "productivity." We explore how data and tech teams are uniquely prepared and positioned to ask better questions, measure what really matters, and avoid letting the AI teenager run the house. Learn more about "phantom value" and why faster isn't always better… or even cheaper! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Everyone is talking about the future of ABA, but when we really sit down and think about it, many of the “new” ideas have actually been building for years . So what does future-ready ABA actually look like in real, everyday practice?In this episode, we explore how our field is growing up. We talk about expanding ABA beyond clinic walls and into schools, systems, organizations, and communities. We reflect on what compassion really means at 4:45 p.m. when staff are exhausted and safety is a concern. And we dive into the role of AI and technology, and how it can reduce burnout and administrative burden without replacing clinical thinking or humanity.We also discuss what supervision must look like moving forward. It is not just about competency checklists. It is about building clinical reasoning, ethical decision-making, and sustainable practices that support both clients and clinicians.The future of ABA is not something that just happens. It starts with how we practice, supervise, and prioritize today.What's Inside:What “future-ready” ABA actually means in day-to-day practiceExpanding ABA beyond traditional clinical settingsUsing AI and technology without losing humanityBuilding clinical reasoning and sustainable supervision modelsMentioned in This Episode:Ethics CEU: The Future of ABA: Building Clinical Judgement and CompassionEpisode 203: Balancing Safety and Compassion in InterventionsHowToABA.com/joinHow to ABA on YouTubeFind us on FacebookFollow us on Instagram

www.marktreichel.comhttps://www.linkedin.com/in/mark-treichel/Episode Title: My Takeaways from Monday at GAC: Structure, Supervision, and StablecoinsIn this episode, I share my takeaways from Monday at GAC in Washington, D.C.This was my first GAC in 2000 as Deputy Executive Director at NCUA. I've attended more than 20 since. It was good to be back in D.C., reconnect with colleagues, clients, and former NCUA staff — and to see how the tone of the conference felt this year.Three sessions stood out:1️⃣ Scott Simpson – Stewardship & AdvocacyScott Simpson's first GAC as head of America's Credit Unions set a different tone.He emphasized:Credit unions as a social movementThe importance of advocacyThe reality that tax status and field of membership are not automaticUnity between large and small institutionsIn a chaotic political and regulatory environment, the reminder that credit unions exist because Congress allows them to exist matters.2️⃣ Brené Brown – Strengthening the FoundationBrené Brown's keynote focused on “strong ground.”Her theme: leaders often compensate around weaknesses instead of strengthening the foundation.Key ideas:Vulnerability = uncertainty, risk, and exposureNo risk, no courageArmor (resistance, avoidance, overconfidence) blocks real leadershipIn times of uncertainty, strengthen the coreIn an environment shaped by technology shifts, mergers, geopolitical tension, and regulatory changes, that message resonated.3️⃣ Chairman Hauptman – Supervision & StablecoinsChairman Hauptman's fireside chat focused on rethinking supervision and discussing stablecoins.SupervisionWith NCUA staffing down significantly (I reference roughly 27%), he raised the question:Is the juice worth the squeeze?Topics discussed:Consistency and transparency in examsFewer document requestsRethinking supervisory touchpointsReorganization within NCUAExtending exam cycles for well-run institutionsI also discuss how regulatory inconsistency — when priorities swing dramatically — can create real operational risk for credit unions.Sometimes NCUA can be a credit union's biggest risk — not due to bad intent, but because uncertainty affects strategic decisions.ConsolidationConsolidation is happening. That's math.But it's not inevitable individually.Every mature industry consolidates over time. The key is leadership, strategy, and execution.StablecoinsChairman Hauptman framed stablecoins as infrastructure and global dollar dominance.The key question I raise (credit to Kiah Haslett's framing):What problem does stablecoin actually solve that existing rails don't?We already have:FedwireACHRTPFedNowIs the value international? Domestic? Structural? Or hype?Time will tell.Final ThoughtAcross all three speakers, one theme connected the day:Are we strengthening the foundation — or compensating around it?It was a fun and informative day at GAC, and I'll continue sharing observations as the week unfolds.If you were there and saw something differently, let me know.

There comes a point in many therapy careers where working harder is no longer the solution. You can raise your fees. You can tighten your cancellation policy. You can fill every slot on your calendar. And still feel financially vulnerable.In this episode, Ashley Stephens and I explore why supervision often becomes the smarter revenue stream at that stage. Not because it is easy. Not because it is trendy. But because it is structurally different from therapy income.Supervision is tied to licensure. Associates are required to have it in order to practice and accrue hours. That built-in demand creates a level of predictability that weekly therapy referrals simply do not. When designed intentionally, supervision can become a steady arm of your income instead of a reactive scramble.We also slow down and talk about ethics. Required does not mean exploitative. Supervisees deserve clarity, transparency, and the ability to reassess the relationship. Supervisors have obligations too. Contracts matter. Review points matter. Documentation matters. When those systems are in place, supervision supports both parties instead of draining them.And we address the legal realities. Supervising across state lines is not something you assume your way into. The compact does not automatically grant supervision privileges. Most states require full licensure and specific supervisor training. Getting this wrong can cost a supervisee their hours. That is not a risk worth taking.In this episode, we discuss:How supervision creates more predictable income than session-based therapy aloneThe difference between stable revenue and predatory practicesWhy long-term supervisory relationships can reduce burnoutWhat to confirm before offering supervision in another stateIf you have been thinking about adding supervision to your practice or shifting more fully into it, this conversation will help you evaluate that decision through an ethical and business lens. Not as a side hustle. Not as a last resort. But as a deliberate professional move.Download our free resource, Stop Working for Free: The Therapist Fee Reset, to identify where your practice may be leaking money.And if you are ready to build supervision into your model with strong systems and clean boundaries, that is exactly what we teach inside our Step It Up Membership.Get your step by step guide to private practice. Because you are too important to lose to not knowing the rules, going broke, burning out, and giving up. #counselorsdontquit.

Erie County Legislator Frank Todaro on efforts to allow younger hunters with adult supervision full 219 Fri, 27 Feb 2026 08:35:00 +0000 FlmE8DSrX7wDPIKUK9VBOc70K6X412fM news & politics,news WBEN Extras news & politics,news Erie County Legislator Frank Todaro on efforts to allow younger hunters with adult supervision Archive of various reports and news events 2024 © 2021 Audacy, Inc. News & Politics News False

WBEN's Tom Puckett on a bill to allow Erie County's 12 and 13 year olds to hunt with adult supervision full 61 Fri, 27 Feb 2026 08:50:00 +0000 OouJU6OJ9J8UvhZIk6rDp6do3JUqRZdI news & politics,news WBEN Extras news & politics,news WBEN's Tom Puckett on a bill to allow Erie County's 12 and 13 year olds to hunt with adult supervision Archive of various reports and news events 2024 © 2021 Audacy, Inc. News & Politics News False

La mort du militant néofasciste Quentin Deranque le 14 février 2026 a brutalement remis en lumière une galaxie souvent méconnue : celle de l'extrême droite radicale.Identitaires, nationalistes-révolutionnaires, royalistes : ces mouvances, composées quasi exclusivement d'hommes, ne se définissent pas seulement par leurs idées, mais par une certaine culture de la virilité et du corps, par un certain rapport au territoire, à la hiérarchie et à la confrontation.Qui sont ces jeunes hommes qui s'engagent dans l'extrême droite radicale ? Pourquoi la violence y tient lieu de langage politique ? Et comment ces réseaux minoritaires parviennent-ils à irriguer des espaces bien plus larges ?Tal Madesta reçoit Sébastien Bourdon, journaliste indépendant et auteur de Drapeau noir, jeunesses blanches. Enquête sur le renouveau de l'extrême droite radicale (éd. du Seuil, 2025).Retrouvez toutes les références citées dans l'épisode à la page : https://www.binge.audio/podcast/les-couilles-sur-la-table/virilites-radicalesCRÉDITS : Les Couilles sur la table est un podcast créé par Victoire Tuaillon produit par Binge Audio. Cet entretien a été préparé, mené et monté par Tal Madesta et enregistré le mardi 17 février au studio Virginie Despentes de Binge Audio (Paris, 19e). Prise de son, réalisation et mixage : Jude Rigaud. Supervision éditoriale et de production : Naomi Titti. Direction de production : Albane Fily. Communication : Lise Niederkorn. Rédacteur en chef : Thomas Rozec. Responsable des productions éditoriales : Charlotte Baix. Responsable administrative et financière : Adrienne Marino. Musique originale : Théo Boulenger. Composition identité sonore : Jean-Benoît Dunckel. Voix identité sonore : Bonnie El Bokeili. Direction des programmes : Joël Ronez.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

2/26/2026 PODCAST Episodes #2310 GUESTS: Rep. Harshbarger, Dr. Paul Alexander, Doug Burris+ YOUR CALLS! at 1-888-480-JOHN (5646) and GETTR Live! @jfradioshow #GodzillaOfTruth #TruckingTheTrut

What separates organizations that pass audits from those that survive real incidents? In this episode of The Segment, host Raghu Nandakumara sits down with Phil Park, global cybersecurity and risk leader at IBM. With more than 25 years advising financial institutions across the U.S., Europe, and Asia-Pacific, Phil brings a practical perspective on how supervision is rapidly evolving from compliance checklists to real-world operational readiness. Together, Raghu and Phil unpack the industry's biggest mindset shift: regulators no longer ask “Are you protected?” — they ask “Can you operate through disruption?” They explore why prevention alone is no longer enough, why containment and recovery now define security maturity, and how CISOs are moving from siloed operators to enterprise-wide risk leaders accountable to boards and regulators alike. The conversation also dives into: Why regulators evaluate response quality rather than technical perfection   How organizations are turning tabletop exercises into realistic resilience testing   The growing pressure created by third-party and supply-chain dependencies   Why evidence and outcomes matter more than policies and frameworks   How overlapping reporting requirements are reshaping incident response playbooks   The double-edged role of AI in both defense and attack, including deepfake risks   Why security fundamentals matter even more in the AI era   This episode is a must-listen for security leaders and executives navigating a world where passing the audit is no longer the goal — proving you can withstand disruption is. Also, if you're attending FSISAC, join Illumio, IBM, and Palo Alto Networks for an exclusive dinner at Capital Grille! Save your seat here: https://lp.illumio.com/20260302-Steak-And-Security-Dinner.html?utm_medium=email&utm_source=marketo

Discover how virtual contrast supervision is reshaping imaging centers in 2026. With permanent regulatory backing from ACR and CMS, remote radiologist coverage offers scalability, addresses staffing shortages, and improves patient access—all while maintaining compliance and safety. Learn more at https://www.contrast-connect.com/ ContrastConnect City: Las Vegas Address: Las vegas Website: https://www.contrast-connect.com/

Hvad gør den dygtige psykolog anderledes?Hvad er det, “superterapeuten” forstår om relationen, som andre overser – selv når de bruger de samme teknikker?I dette afsnit går vi helt ind i maskinrummet i Acceptance and Commitment Therapy (ACT) og undersøger den faktor, der igen og igen viser sig at være afgørende for effekt:Relationen mellem klient og terapeut. Sammen med psykolog Morten Hedegaard og psykolog Philip Harrill Skovgaard taler vi om:Hvad den kompetente ACT-terapeut konkret gør i rummetHvordan relationen bliver en aktiv interventionDe fælles faktorer bag succesfuld psykoterapiHvad man kan gøre forkert – og hvorfor selv små relationelle brud betyder nogetHvordan man reparerer alliancen og bruger brud som drivkraft for udviklingACT er kendt for sine metaforer og øvelser.Men uden en stærk terapeutisk alliance mister selv de bedste interventioner deres kraft. Dette afsnit er til dig, der vil:✔️ styrke din relationelle bevidsthed som terapeut✔️ forstå, hvorfor dine interventioner nogle gange ikke “lander”✔️ få indblik i, hvad der faktisk skaber forandring i terapirummet✔️ kigge med under motorhjelmen på psykologens arbejdeSamtalen tager afsæt i deres bog Den terapeutiske relation i ACT, som samler teori, praksis og konkrete greb til at arbejde mere relationelt og fleksibelt i ACT.Hvis du vil udvikle din praksis og bruge relationen som et aktivt terapeutisk redskab, kan du finde bogen her:Frydenlund: https://www.frydenlund.dk/den-terapeutiske-relation-i-act-21574Saxo:https://www.saxo.com/dk/den-terapeutiske-relation-i-act_bog_9788776230357

Comme en témoigne l'affaire “des viols de Mazan”, 42% des agressions et viols par soumission chimique se déroulent dans un cadre privé. Depuis qu'il s'est ouvert le 2 septembre 2024, ce procès très médiatisé nous pousse à démonter les mythes sur la soumission chimique : elle n'est pas circonscrite aux contextes festifs ou perpétrée seulement par des inconnus avec du GHB - appelé la “drogue du violeur”. Qui sont ces hommes qui utilisent de la drogue pour agresser des femmes ? Quels sont leurs modes opératoires et leurs motivations ? En quoi les agresseurs par soumission chimique sont un miroir grossissant d'une culture masculine de la sexualité ? Pour répondre à ces questions, Naomi Titti reçoit Félix Lemaître, journaliste, écrivain, scénariste et auteur de l'essai « La Nuit des hommes. Une enquête sur la soumission chimique » (éd. Les nouveaux jours, JC Lattès, 2024). Alors qu'il croyait partir à la chasse aux monstres dans les bars, les clubs et les festivals, Félix Lemaître a découvert qu'enquêter sur la soumission chimique revenait plutôt à interroger l'apprentissage masculin de la séduction et la construction de leurs fantasmes.Un épisode initialement diffusé le 21/11/2024.RÉFÉRENCES CITÉES DANS L'ÉMISSION Et la joie de vivre de Gisèle Pelicot avec Judith Perrignon (Editions Flammarion, 2026)Retrouvez toutes les références citées dans l'épisode et sa transcription écrite à la page : https://www.binge.audio/podcast/les-couilles-sur-la-table/soumission-chimique-il-ny-a-pas-de-drogue-du-violeur CRÉDITSLes Couilles sur la table est un podcast de Victoire Tuaillon produit par Binge Audio. Cet entretien a été préparé, mené et monté par Naomi Titti, et enregistré le jeudi 31 octobre 2024 au studio Virginie Despentes de Binge Audio (Paris, 19e). Prise de son, réalisation et mixage : Paul Bertiaux. Supervision éditoriale et de production : Naomi Titti. Production, édition et communication : Marie Foulon avec Lise Niederkorn. Rédaction en chef : Thomas Rozec. Direction de production : Albane Fily. Générique : Théo Boulenger. Identité graphique : Pierre Hatier (Upian). Composition identité sonore : Jean-Benoît Dunckel. Voix identité sonore : Bonnie El Bokeili. Direction des programmes : Joël Ronez.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Send a Text to the Moms - please include your contact info if you want a response. thanks!Guest:Deb Bushong, MS, LPC-S  -  a  licensed therapist for over 20 years."Conversations Therapy is focused on the therapeutic work that helps develop youth and young adults who are in the early parts of their journey of living with Psychosis. I offer supports of various kinds for the clients but also support and psychoeducation for the parents, family members, and support systems. The goal is to move these young people towards living a recovery-oriented, fulfilling life!"We ask questions (and push back a bit on “psychosis can be a gift”) - and share listener questions as well.Conversations Therapy & Supervision(469) 727-TALK {call or text}https://www.conversationstherapy.orghttps://linktr.ee/deb_bushongNAMI Ask the Expert Webinar: https://www.nami.org/namis-ask-the-expert/nami-ask-the-expert-roles-in-recovery-part-2-parents/Randye's substack:https://randyekaye.substack.com/My daughter thinks we are not her parents, just friends. Is this considered psychosis?Our LO was diagnosed with schizophrenia 2 years ago. He has been so afraid to begin running again for fear the voices will get loud and tell him to stop. Do you have any advice to give him the courage to try the one thing he absolutely loved doing?Advice for a recreation therapist in state hospital. What are the best groups and topics to offer to patients going through the forensic/civil system to prevent readmission/relapse?What to say and not to say to a loved one when they are in psychosis, especially when they cannot recognize that they are having hallucinations.How to help a LO from miles away, while they are in psychosis.-My son laughs a lot and very hearty laughs. But whenever we ask him if he would like to share what the joke is or what's funny. He says no it's nothing.Please ask her if she knows if they ever share that information with anyone ? It's been over 20 years and he still won't reveal anything.What is the difference between psychosis and delusions? And how long can they last? Untreated… this seems to be a major obstacle to getting treatment.This Ability PodcastReal stories, advocacy, and inclusion from the disability community.Listen on: Apple Podcasts SpotifyWant to know more?Join our facebook page Our websites:Randye KayeMindy Greiling Miriam (Mimi) Feldman

An internal research study at Meta found that parental supervision may not help teens regulate their social media, and teens with trauma are more inclined to overuse social media. Also, as the AI hardware space heats up, the iPhone maker has multiple smart products in development. Learn more about your ad choices. Visit podcastchoices.com/adchoices

An internal research study at Meta dubbed “Project MYST” created in partnership with the University of Chicago, found that parental supervision and controls — such as time limits and restricted access — had little impact on kids' compulsive use of social media. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Chris Paterson emailed Claire Pedrick:   I wonder if there is a podcast around the title “What can't I bring to supervision?” which could be interesting to explore. My sense is that lots of coaches will have things that would be valuable for them to discuss but they either don't trust their supervisor enough or don't feel they would be allowed to discuss things that might not be directly related to their coaching practice. Of course, this comes down to the contracting between them and their supervisor and I wonder if a conversation which included examples of topics such as grief and loss, forgiveness, caring responsibilities, life events and choices might give coaches more permission to discuss what really matters to them in supervision rather than having to perform in some way.   Listen to find out more!   Resources & Links: Talk to Claire or Chris about coaching supervision The Road Less Travelled by M. Scott Peck The Inner Game of Tennis by Timothy Gallwey   Contact: Contact Chris through Linked In https://www.linkedin.com/in/smilebecurious/  Contact Claire by emailing info@3dcoaching.com  or check out our Substack where you can talk with other listeners. Further Information: Subscribe or follow The Coaching Inn on your podcast platform or our YouTube Channel to hear or see new episodes as they drop. Find out more about 3D Coaching and get new ideas and offers in our weekly email. Keywords: coaching supervision, personal growth, professional excellence, human connection, emotional processing, vulnerability, authenticity, trust and safety, group supervision, self-awareness, psychological safety, coaching practice, resilience, emotional intelligence, leadership development, reflective space, coaching community, presence and compassion, supervision accessibility, coaching support   We love having a variety of guests join us! Please remember that inviting someone to participate does not mean we necessarily endorse their views or opinions. We believe in open conversation and sharing different perspectives.

When we think about improving client outcomes, it's easy to focus on goals, programs, and data collection. In this episode, we zoom out and talk about what's happening behind the scenes. We dive into Organizational Behavior Management (OBM) and how the systems we work within, including training, communication, leadership, and culture, have a powerful ripple effect on everyone involved.We explore how OBM applies the same ABA principles we use with learners to organizations, teams, and leadership. From analyzing systems using an ABC framework to pinpointing key metrics like staff performance, burnout, and treatment fidelity, we discuss how small, strategic changes can lead to meaningful, sustainable impact. We also talk about leadership, feedback loops, and reinforcement systems, and how clear expectations and compassionate data use can build trust and alignment.Ultimately, we reflect on the ripple effect of strong systems. Better supervision leads to stronger future BCBAs and improved outcomes for clients and families. When we strengthen the system, we strengthen the forest, not just one tree.What's Inside:What Organizational Behavior Management (OBM) really is and why it matters in ABAHow systems and leadership directly impact client outcomesUsing behavioral systems analysis and data to drive meaningful changeThe ripple effect of strong supervision and organizational practicesMentioned in This Episode:Supervision Resource BundleCEU Event: Organizational Behavior Management (OBM) for BCBAs: Driving Change and Improving Workplace Performance with BCBA Mellanie PageHowToABA.com/joinHow to ABA on YouTubeFind us on FacebookFollow us on Instagram

When Jeffrey Epstein was released from jail, he was forced to register as a sex offender. This should mean that he would have to check in before going overseas. As usual, the rules didn't apply to Jeffrey Epstein. One of the more concerning things we have heard about Epstein's time under the 'watchful' eye of the authorities, was that he was still traveling around, even abroad with young girls. He never reported these movements as he status as a predator demanded, according to the same reports. Why was he not arrested for violating the conditions of his release?To contact me:bobbycapucci@protonmail.comsource:https://www.theguardian.com/us-news/2019/sep/11/jeffrey-epstein-underage-girls-2018-investigation

When Jeffrey Epstein was released from jail, he was forced to register as a sex offender. This should mean that he would have to check in before going overseas. As usual, the rules didn't apply to Jeffrey Epstein. One of the more concerning things we have heard about Epstein's time under the 'watchful' eye of the authorities, was that he was still traveling around, even abroad with young girls. He never reported these movements as he status as a predator demanded, according to the same reports. Why was he not arrested for violating the conditions of his release?To contact me:bobbycapucci@protonmail.comsource:https://www.theguardian.com/us-news/2019/sep/11/jeffrey-epstein-underage-girls-2018-investigationBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-moscow-murders-and-more--5852883/support.

When Jeffrey Epstein was released from jail, he was forced to register as a sex offender. This should mean that he would have to check in before going overseas. As usual, the rules didn't apply to Jeffrey Epstein. One of the more concerning things we have heard about Epstein's time under the 'watchful' eye of the authorities, was that he was still traveling around, even abroad with young girls. He never reported these movements as he status as a predator demanded, according to the same reports. Why was he not arrested for violating the conditions of his release?To contact me:bobbycapucci@protonmail.comsource:https://www.theguardian.com/us-news/2019/sep/11/jeffrey-epstein-underage-girls-2018-investigationBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-epstein-chronicles--5003294/support.

Deux joueurs de hockey, rivaux sur la glace, amants dans l'ombre. Série canadienne devenue phénomène mondial, Heated Rivalry fait de la romance gay un terrain d'observation des masculinités contemporaines : virilité comme performance, domination comme langage et intimité sous contrainte dans un univers où l'hétérosexualité reste la norme implicite.Naomi Titti et Tal Madesta dissèquent les raisons de ce succès et ce qu'il dit de l'époque. L'historienne Fleur Hopkins-Loféron prolonge l'analyse en replaçant la série dans la longue histoire des romances gays, entre fantasmes codifiés, censures sociales et réinventions politiques du désir.RECOMMANDATIONS :Le film Challengers, de Luca Guadagnino (2024, 2h11min) Dark romance, guide amoureux, de Fleur Hopkins-Loféron, à paraître aux éditions Goater.RÉFÉRENCES CITÉES DANS L'ÉMISSIONRetrouvez toutes les références citées dans l'épisode à la page : https://www.binge.audio/podcast/les-couilles-sur-la-table/heated-rivalry-sortir-du-vestiaireCRÉDITS Les Couilles sur la table est un podcast créé par Victoire Tuaillon produit par Binge Audio. Cet entretien a été préparé, mené et monté par Naomi Titti et Tal Madesta. Prise de son, réalisation et mixage : Paul Bertiaux. Supervision éditoriale et de production : Naomi Titti. Direction de production : Albane Fily. Communication : Lise Niederkorn. Rédacteur en chef : Thomas Rozec. Responsable des productions éditoriales : Charlotte Baix. Responsable administrative et financière : Adrienne Marino. Musique originale : Théo Boulenger. Composition identité sonore : Jean-Benoît Dunckel. Voix identité sonore : Bonnie El Bokeili. Direction des programmes : Joël Ronez.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

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

Supervision isn't just about signing off on hours. It's about shaping skills, building confidence, and developing thoughtful future behavior analysts. In this episode, we dive into how we can apply the very same behavior-analytic principles we use with clients to our supervision practices. From assessment and goal setting to shaping, reinforcement, and feedback, we break down what it really means to lead with ABA as a BCBA supervisor.We talk about why relationship-building and trust are foundational, how to move away from compliance-based supervision toward a coaching and mentorship model, and why feedback needs to be frequent, specific, and actionable. We also explore the importance of modeling professionalism, values-based decision-making, and ethical reasoning, especially for skills that don't always show up neatly on a task list.Whether you're new to supervising or looking to refine your leadership approach, this conversation will help you reframe supervision through a behavior-analytic lens and feel more confident supporting the next generation of BCBAs.What's Inside:Using ABA principles like shaping, reinforcement, and BST in supervisionBuilding trust, rapport, and a strong supervisory relationshipGiving effective, meaningful, and two-way feedbackShifting from compliance-based supervision to a coaching modelMentioned in This Episode:Supervision Resource BundleHowToABA.com/joinHow to ABA on YouTubeFind us on FacebookFollow us on Instagram

Comment élever des garçons bien dans leurs bottes dans le monde d'aujourd'hui ? Que ce soit pour leur apprendre à participer aux tâches du quotidien, à accéder à leurs émotions, créer un dialogue autour de la sexualité, ou encore leur permettre d'être vigilant face aux contenus sexistes sur Internet… Le rôle des parents et de toutes celles et ceux qui éduquent les enfants et notamment les petits garçons est déterminant et exigeant.À quoi pourrait ressembler une éducation égalitaire ? Quels sont les grands défis que rencontrent les parents d'aujourd'hui pour élever des garçons hors des injonctions virilistes qui pèsent sur eux ? Comment contrer l'influence des masculinistes, qui prennent les jeunes hommes comme cible privilégiée sur les réseaux sociaux ? Dans cet épisode enregistré en public au festival Longueur d'ondes, Naomi Titti reçoit deux invitées : Camille Froidevaux-Metterie, philosophe féministe, professeure en sciences politiques, autrice et conseillère scientifique du film documentaire Les petits mâles (Laurent Metterie, 2023), et Julie Gavras réalisatrice de films et autrice de podcasts documentaires, notamment de la série Pas mes fils (du podcast Injustices, produit par Louie Media).RECOMMANDATIONS DES INVITÉES :La recommandation de Camille Froidevaux-Metterie : lire le livre Photosynthèses de Camille Cornu (Éd. Cambourakis, 2024), pour réfléchir à comment s'extirper de la binarité de genre.La recommandation de Julie Gavras : regarder Sex Education (Laurie Nunn, sur Netflix, 2019-2023) et suivre le compte TikTok de prévention @amadou.782 ou @ml_782 sur Instagram.RÉFÉRENCES CITÉES DANS L'ÉMISSIONRetrouvez toutes les références citées dans l'épisode à la page : https://www.binge.audio/podcast/les-couilles-sur-la-table/comment-eduquer-nos-filsCRÉDITS Les Couilles sur la table est un podcast créé par Victoire Tuaillon produit par Binge Audio. Cet entretien a été préparé, mené et monté par Naomi Titti et enregistré le 31 janvier 2026 au festival Longueur d'Ondes à Brest. Prise de son, réalisation et mixage : Jude Rigaud. Supervision éditoriale et de production : Naomi Titti. Direction de production : Albane Fily. Communication : Lise Niederkorn. Rédacteur en chef : Thomas Rozec. Responsable administrative et financière : Adrienne Marino. Musique originale : Théo Boulenger. Composition identité sonore : Jean-Benoît Dunckel. Voix identité sonore : Bonnie El Bokeili. Direction des programmes : Joël Ronez.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

What happens when over 1,000 supervisors show up—hungry for better leadership, clearer ethics, and supervision that actually works?Something shifted at this supervision summit—and it wasn't just the content.From the questions being asked to the conversations happening behind the scenes, it was clear that supervisors are craving something deeper than techniques and checklists. In this episode, Dr. Amy Parks pulls back the curtain and shares what she witnessed firsthand: the themes, tensions, and moments that quietly raised the bar for supervision across the field.You'll hear Amy's candid reflections on standout sessions covering:Presence and mindfulness in supervisionNeurodiversity-affirming supervisionRemediation, gatekeeping, and ethical leadershipProfessional identity developmentCulturally responsive supervisionTrauma-informed supervision and burnoutAdvanced clinical thinking and questioningEFT-informed supervision in actionMore importantly, Amy explains why this content landed so strongly, what supervisors are clearly craving right now, and how this summit raised the bar for what supervision education should look like.If you supervise clinicians—or plan to—this episode will help you decide whether the PESI self-study recording is worth your time (spoiler: Amy doesn't mince words).

Sean Vanatta, senior lecturer in financial history and policy at the University of Glasgow, joins the Business Scholarship Podcast to discuss his book Private Finance, Public Power: A History of Bank Supervision in America, which he co-authored with Peter Conti-Brown. This episode is hosted by Andrew Jennings, associate professor of law at Emory University, and was edited by Alec Johnson, a law student at Emory University.

AP correspondent Donna Warder reports on how life is for two polygamous towns after their leader was sent to prison for life.

Observée chez plus de 1500 espèces, l'homosexualité est partout dans la nature. Pourtant, elle continue de faire l'objet de débats et de controverses scientifiques, comme si son origine devait encore être expliquée.L'orientation sexuelle est-elle inscrite dans nos gènes, façonnée par nos hormones, ou est-elle le fruit de notre histoire sociale et culturelle ? Et surtout : pourquoi la recherche s'obstine-t-elle à disséquer l'homosexualité alors que l'hétérosexualité n'est jamais interrogée ? Entre quête de légitimité et besoin de définir la norme, que nous raconte cette obsession scientifique ?Dans cet épisode, Tal Madesta reçoit Mathias Chaillot, journaliste, photographe et auteur de 4% en théorie (éd. Goutte d'Or, 2023). Retrouvez toutes les références citées dans l'épisode à la page : https://www.binge.audio/podcast/les-couilles-sur-la-table/dou-vient-lhomosexualiteCRÉDITS Les Couilles sur la table est un podcast créé par Victoire Tuaillon produit par Binge Audio. Cet entretien a été préparé, mené et monté par Tal Madesta et enregistré le 6 janvier 2026 au studio Virginie Despentes de Binge Audio (Paris, 19e). Prise de son, réalisation et mixage : Paul Bertiaux et Jude Rigaud. Supervision éditoriale et de production : Naomi Titti. Direction de production : Albane Fily. Édition : Charlotte Baix & Camille Khodor. Communication : Lise Niederkorn. Rédacteur en chef : Thomas Rozec. Responsable administrative et financière : Adrienne Marino. Musique originale : Théo Boulenger. Composition identité sonore : Jean-Benoît Dunckel. Voix identité sonore : Bonnie El Bokeili. Direction des programmes : Joël Ronez.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Connect with Francis: https://francismyles.com/

Welcome to We Heart Therapy – The EFT Talk Series, where we explore the heart of Emotionally Focused Therapy (EFT) through meaningful conversations with leaders in the field. In this episode, I'm joined by Dr. Senem Zeytinoğlu, ICEEFT Certified EFT Trainer and Founder of the Turkey Center for EFT in Istanbul, for a rich and thoughtful discussion on Self of the Therapist (SOT) issues in EFT Supervision—and why this work is essential for ethical, effective, and emotionally present EFT practice. Self of the Therapist work helps EFT therapists understand how their own attachment histories, emotional triggers, and nervous system responses show up in clinical work and supervision. Dr. Zeytinoğlu shares how attending to SOT deepens therapist resilience, strengthens the therapeutic alliance, and ultimately leads to better outcomes for couples and individuals.

In this episode, we're diving into one of our favorite and most meaningful topics in ABA: relationship-based motivation. We talk about why reinforcement doesn't have to look like tokens, toys, or snacks and how you can become the most powerful reinforcer in the room. When learners enjoy being with us, motivation shifts from doing work for rewards to genuinely wanting to engage, connect, and participate.We share real-life examples from our own clinical experiences, including moments when we realized we weren't yet reinforcing enough and what changed when we leaned into play, connection, and authenticity. We also unpack common misconceptions around work versus play, breaks, and pairing, and explain why separating social interaction from reinforcement can unintentionally send the wrong message.This conversation applies not only to young learners but also to older students, parents, teachers, supervisees, and even supervisors. Strong relationships increase the value of everything else we do in ABA. When connection comes first, behavior change is more sustainable, more meaningful, and honestly, more enjoyable for everyone involved.What's Inside:Why relationship-based reinforcement is more powerful than external rewardsHow to become a preferred person, not just the person delivering demandsRethinking breaks, play, and motivation in everyday sessionsWhy authentic connection matters across learners, families, and superviseesMentioned in This Episode:Episode 221: ESDM in Action: Embedding Goals in Daily Routines and PlayThe Science Behind ESDM: Why Relationship Matters as Much as ReinforcementHowToABA.com/joinHow to ABA on YouTubeFind us on FacebookFollow us on Instagram

In this episode, Scott Becker highlights the value of colleagues who require minimal supervision and consistently deliver results, creating true leverage for leaders.

La boxe, sport de “bonhomme” par excellence, est aussi le reflet d'une violence sociale qu'on ne veut pas voir. Sport d'abord bourgeois, rapidement devenu prolétaire, dont les femmes ont été exclues des compétitions jusqu'en 1999, la boxe est à la fois un outil d'émancipation sociale par le corps pour les hommes des milieux populaires mais aussi une forme de confrontation aux codes des élites. Qui sont les hommes qui boxent ? Pourquoi est-ce qu'on associe tellement ce sport à la virilité et à la violence ? Quel rôle joue la boxe dans la fabrique des masculinités racisées, et dans les luttes sociales d'hier et d'aujourd'hui ?Dans cet épisode, Naomi Titti reçoit Selim Derkaoui, journaliste et auteur de Rendre les coups (éd. Le passager clandestin) pour nous parler de ce sport lourd d'une symbolique masculine très forte, à l'histoire trop peu connue.Retrouvez toutes les références citées dans l'épisode à la page : https://www.binge.audio/podcast/les-couilles-sur-la-table/la-boxe-ceux-qui-rendent-les-coups CRÉDITS Les Couilles sur la table est un podcast créé par Victoire Tuaillon produit par Binge Audio. Cet entretien a été préparé, mené et monté par Naomi Titti et enregistré le 9 janvier 2026 au studio Virginie Despentes de Binge Audio (Paris, 19e). Prise de son, réalisation et mixage : Paul Bertiaux et Jude Rigaud. Supervision éditoriale et de production : Naomi Titti. Direction de production : Albane Fily. Communication : Lise Niederkorn. Rédacteur en chef : Thomas Rozec. Responsable administrative et financière : Adrienne Marino. Musique originale : Théo Boulenger. Composition identité sonore : Jean-Benoît Dunckel. Voix identité sonore : Bonnie El Bokeili. Direction des programmes : Joël Ronez.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

In this episode, I'm joined by John Guercio for a wide-ranging and practical conversation about leadership through a behavioral lens. John and I dig into what it actually means to lead in applied behavior analysis, especially when so much of the existing leadership literature is vague, mentalistic, or disconnected from observable behavior. We start by talking about the need to operationalize leadership in behavioral terms and explore the four leadership hats developed by Dr. Paulie Gavoni: leading, training, coaching, and managing. We break down what each of these roles looks like behaviorally, how they function across time, and why effective leaders need to move flexibly between them rather than relying on a single style. A major theme of the episode is the role of positive reinforcement in leadership. John shares real-world examples from his OBM coursework and his work at Cornerstone Behavioral Services, highlighting how difficult—but necessary—it can be to shift away from punitive and avoidance-based management strategies. We discuss why punishment often "works" in the short term, why leaders continue to rely on it, and how reinforcement-based leadership creates better outcomes for both staff and organizations. We also spend time unpacking the distinction between leadership and management. John reflects on his own strengths and limitations, describing how he focuses on vision and direction while intentionally surrounding himself with strong managers who excel at systems, logistics, and follow-through. This leads to a powerful discussion about positional authority, seniority, and the myth that leadership status entitles people to treat others poorly. Throughout the episode, we return to the importance of psychological safety, consistent feedback, and emotional regulation in leadership roles. John shares practical strategies for navigating tough conversations, including how to balance empathy with accountability, how to manage staff expectations, and how to avoid letting emotion drive professional communication (including when not to send that email). We also talk through concrete tools and exercises for improving leadership practice, such as symbolic problem-solving activities to surface unspoken team issues, written acknowledgment systems, and using assessment tools like the Performance Diagnostic Checklist to guide supervision and coaching. John closes by sharing future directions for developing empirically grounded management assessment tools, along with a preview of his upcoming work and conference presentations. This is a practical, honest conversation for anyone supervising staff, leading teams, or trying to build reinforcing, values-consistent organizations in human services. Resources & Links Mentioned in This Episode RBT Course for Adult Services (the 'bridge' course too!) Sims and Szilagyi (1975). Leader reward behavior and subordinate satisfaction and performance Stone Soup Conference Registration (use code PODCAST26 at checkout) Carr and Wilder (2015). The Performance Diagnostic Checklist—Human Services John's previous BOP appearances Session 274: Psychological Safety in the Workplace (Supervision CEU!) Additional Books, Articles, and Ideas Discussed John's books on Amazon Komaki (1998). Leadership from an Operant Perspective McGregor (1960). The Human Side of Enterprise Daniels and Daniels (2023). The Measure of a Leader Elliot (2012). Leading Apple With Steve Jobs: Management Lessons From a Controversial Genius Covey (2020). The 7 Habits of Highly Effective People, 30th Anniversary Edition Harley (2013). How to Say Anything to Anyone Grenny et al. (2021). Crucial Conversations (Third Edition): Tools for Talking When Stakes Are High Sponsor shoutouts! Office Puzzle: A thriving ABA practice depends on systems that actually support your team, not slow them down. If you've struggled with software that's buggy, hard to navigate, or offers little support when you need it most, you're not alone. That's why so many practices are switching to Office Puzzle. Go to officepuzzle.com/bop to learn more! HRIC Recruting. Cut out the middleman and speak directly with Barbara Voss, who's been placing BCBAs in great jobs all across the US for 15 years. The 2026 Stone Soup Conference! This is one of the best values in the online conference space. I'm actually going to be one of the speakers at this year's event, along with a great cast of other characters you're probably familiar with. Save on your registration by using promo code PODCAST26 Behavior University. Their mission is to provide university quality professional development for the busy Behavior Analyst. Learn about their CEU offerings, including their 8-hour Supervision Course, as well as their RBT offerings over at behavioruniversity.com/observations. Don't forget to use the coupon code, PODCAST to save at checkout! The 2026 Verbal Behavior Conference! Taking place March 26–27, 2026, in Austin, Texas, or livestream and on-demand on BehaviorLive. Presenters will include Drs. Mark Sundberg, Patrick McGreevy, Caio Miguel, Alice Shillingsburg, Sarah Frampton, Andresa De Souza, and Danielle LaFrance will share how Skinner's analysis of verbal behavior can guide the assessment and treatment of generative learning challenges in children with autism and other developmental disabilities. And don't miss the special pre-conference workshop on Wednesday, March 25. CEUs from Behavioral Observations. Learn from your favorite podcast guests while you're commuting, walking the dog, or whatever else you do while listening to podcasts. New events are being added all the time, so check them out here.