Podcasts about Supervision

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

Virtual contrast supervision is reshaping diagnostic imaging. With CMS's 2026 permanent rule, remote radiologists now supervise contrast procedures via real-time audio-video, cutting costs and expanding access—especially in rural areas. But how do imaging centers implement this compliantly? Learn more at https://www.contrast-connect.com/blog-post/cms-immediate-availability-definition-explained-meaning-distance-coverage-requirements-2026-update ContrastConnect City: Las Vegas Address: Las vegas Website: https://www.contrast-connect.com/

CMS permanently authorized virtual direct supervision for contrast-enhanced imaging starting January 2026. But state laws, ACR standards, and technologist training create a complex compliance environment. What does your facility need to know right now? ContrastConnect City: Las Vegas Address: Las vegas Website: https://www.contrast-connect.com/

Herzlich willkommen beim Podcast „Kalifornische Terrassen“.Hier geht es um psychologischen und systemischen Austausch – und darum, guten Ideen Gehör zu verschaffen.ZusammenfassungIn dieser Folge spricht Daniela Ries, systemische Beraterin, Therapeutin, Supervisorin und Dozentin, mit Kevin Baron über das Verständnis und die Praxis systemischer Supervision. Im Zentrum steht die Frage, was Supervision eigentlich ist, wo ihre besondere Qualität liegt und wie sie sich von Formaten wie Teamentwicklung, Coaching oder Organisationsentwicklung unterscheidet. Daniela beschreibt Supervision als Reflexionsanregung, die darauf abzielt, professionelle Handlungsfähigkeit in unterschiedlichen Arbeitsfeldern zu erhalten. Ein besonderer Fokus liegt auf der Auftragsklärung zwischen Geschäftsführung, Leitung, Team und weiteren auftraggebenden Instanzen. Deutlich wird, wie wichtig es ist, Erwartungen transparent zu machen und den Auftrag immer wieder gemeinsam zu überprüfen. Ein zentrales Thema der Folge ist die Unterschiedsbildung: Schon kleine Perspektivwechsel können große Wirkungen entfalten – auf der Beziehungs-, Körper- und Handlungsebene. Anhand zahlreicher Praxisbeispiele zeigt Daniela, wie Supervision Räume schafft, in denen psychologische Sicherheit, Begegnung und Entwicklung möglich werden. Dabei geht es nicht um das Abarbeiten von Methoden, sondern um das Erleben von Unterschieden, die für das jeweilige System relevant sind. Das Gespräch beleuchtet außerdem die Rolle der Leitung, den Umgang mit Erwartungsdruck, die Bedeutung von Transparenz sowie die Frage, woran Erfolg in Supervision eigentlich erkennbar wird.KeywordsSupervision, systemische Beratung, Teamentwicklung, Auftragsklärung, psychologische Sicherheit, Reflexion, professionelle Handlungsfähigkeit, Methoden der Supervision, Unterschiedsbildung, Leitung in der SupervisionHast du Fragen, Gedanken oder willst deiner Idee Gehör verschaffen?Schreib uns gerne an: support@systemischer-club.deKostenfreie Online-Events: https://kalifornische-terrassen.de/events

$300 billion wiped from software stocks in a single week. Is SaaS officially dead or is this the buying opportunity of a decade?In this episode of Bricks, Bucks & Bytes, Owen, Patric, Dustin, and Martin break down the so-called "SaaSpocalypse" rocking global markets, welcome back Bedrock CTO Kevin Peterson fresh off a massive $270M Series B that puts his autonomous excavator company into unicorn territory, and dissect the hilarious beef between Anthropic and OpenAI playing out on the Super Bowl stage.Topics discussed:The $300B+ SaaS valuation crash and whether it's a death sentence or a market correction back to normalWhy software engineers are now "juicing" with AI coding tools and what that means for the industryBedrock's journey from Series A to unicorn status, their autonomous excavators on real job sites, and why Google's Capital G is backing themWhere smart money is moving as investors flee software stocks (hint: Europe is winning)Anthropic's brilliant Super Bowl ad mocking OpenAI's plan to put ads in ChatGPT and Sam Altman's salty response38% of Stanford students claiming disability for perks and what it says about elite institutions

Coaching wird komplexer, die Verantwortung für Menschengrößer. In dieser Folge des ICF Germany Podcasts schauen wir auf Supervision als professionellen Reflexionsraum für Coaches: Was sie ist, was sie nicht ist – und warum sie kein „Nice-to-have“ oder Luxus, sondern ein zentraler Bestandteil professioneller Coachingpraxis sein sollte.Zu Gast ist Katja von Glinowiecki – sie ist Co-Gründerin von supervision.partners und Dozentin für angewandtes Coaching, Coach Mentoring und Supervision an derInternational Coach Academy. Ab Januar 2026 wird sie die Arbeitsgruppe Supervision beim ICF Germany Chapter mitgestalten. Ihr Anliegen ist es, Supervision sichtbarer, zugänglicher und selbstverständlicher im Coaching zu verankern.In dieser Folge sprechen wir über:· Was Supervision von Coaching, Mentoring und kollegialer Beratung unterscheidet· Typische Themen, die Coaches in Supervision reflektieren· Warum Supervision in komplexen organisationalen Kontexten besonders wichtig ist· Wie regelmäßige Supervision Qualität und Verantwortung im Coaching stärkt· Welche Hürden Coaches von Supervision abhalten – und welche niederschwelligen Angebote den Einstieg erleichtern könnenWeiterführende LinksWebsite & Angebote zu Supervision:· Katjas Webseite: https://www.my-circus.de/· Supervision Partners: https://supervision.partners/· Katja kennenlernen: https://calendly.com/coachkatja/grow-as-a-coach-chemistry-callICF Supervisions Angebote https://coachingfederation.de/icf-supervision/Wenn dir diese Folge gefallen hat,abonniere den ICF Germany Podcastteile die Folge mit anderen Coaching-InteressiertenFolge uns auf LinkedIn: https://www.linkedin.com/company/coachfederation-de/hinterlasse uns eine Bewertung oder schreib uns Feedback – wir freuen uns auf den Austausch!  team.podcast@coachingfederation.de⁠Bis zur nächsten Folge – bleibt neugierig, bleibt mutig, bleibt wirksam.Eure PodcastHosts – Nesli & Tine

In celebration ofCanadas International Development Week, Toronto Centre hosted a virtual executive panel on building financial resilience.The discussion explored insights from the World Banks Globa

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).

In dieser Episode greifen wir ein Thema auf, das auf den ersten Blick ungewöhnlich klingt – auf den zweiten aber erstaunlich viel erklärt. Im Zentrum stehen die Metaphern Hubschrauber und U-Boot, entwickelt von Rosemary Napper.

Après près d’un an de fermeture, le point de passage de Rafah Crossing rouvre. Un événement qui pourrait sembler humanitaire… mais qui est en réalité hautement stratégique.

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.

WXPR News for 1-30-26

In this final episode of the summer listening series Nicole Hatch, Speak Up podcast reference group member reflects on the conversation about supporting new graduates in transitioning into the workforce originally from 2023. Practice owners Ishwari Samarakoon and Nicola Dann discuss the supports supervisors can offer, and then early career professional Steven Pitt and then new graduate speech pathologist Jen Wong reflect on their experiences, hopes, and expectations. SPA resources: Work Ready resources https://www.speechpathologyaustralia.org.au/Members/Members/Career-and-work/Work-ready/Work-Ready-resources.aspx?hkey=1752026b-1b65-46e3-a5ff-0a5d311db32b&WebsiteKey=12e7819f-4437-4942-9ef0-d1d740ca04aa Supervision: https://www.speechpathologyaustralia.org.au/Members/Members/Professional-practice/Supervision/About-Supervision.aspx Early Career Support: https://www.speechpathologyaustralia.org.au/Members/Public/join/Students/Early-career.aspx?hkey=cc643232-7029-4494-8329-8a50ed1ba225 Resources from presenters: Hawkins, P. and Shohet, R. (2012) Supervision in the Helping Professions (4th Edition) Bouverie Centre – Clinical Supervision Training www.latrobe.edu.au St Luke's Innovative Resources – Stepping into Supervision: A Strengths Approach Online Training – St Luke's Innovative Resources The Grad Life Roadmap - https://thespeechielife.com.au/product/the-grad-life-roadmap/ Burnout E-book - https://thespeechielife.com.au/free-burnout-ebook/ Free resources - https://thespeechielife.com.au/avoid-burnout-blog/ Speech Pathology Australia acknowledge the Traditional Custodians of lands, seas and waters throughout Australia, and pay respect to Elders past and present. We recognise that the health and social and emotional wellbeing of Aboriginal and Torres Strait Islander peoples are grounded in continued connection to culture, country, language and community and acknowledge that sovereignty was never ceded. Free access to transcripts for podcast episodes are available via the SPA Learning Hub (https://learninghub.speechpathologyaustralia.org.au/),, you will need to sign in or create an account. For more information, please see our Bio or for further enquiries, email speakuppodcast@speechpathologyaustralia.org.au Disclaimer: © (2026) The Speech Pathology Association of Australia Limited. All rights reserved. Important Notice, Please read: The views expressed in this presentation and reproduced in these materials are not necessarily the views of, or endorsed by, The Speech Pathology Association of Australia Limited (“the Association”). The Association makes no warranty or representation in relation to the content, currency or accuracy of any of the materials comprised in this recording. The Association expressly disclaims any and all liability (including liability for negligence) in respect of use of these materials and the information contained within them. The Association recommends you seek independent professional advice prior to making any decision involving matters outlined in this recording including in any of the materials referred to or otherwise incorporated into this recording. Except as otherwise stated, copyright and all other intellectual property rights comprised in the presentation and these materials, remain the exclusive property of the Association. Except with the Association's prior written approval you must not, in whole or part, reproduce, modify, adapt, distribute, publish or electronically communicate (including by online means) this recording or any of these materials.

In this episode of The Aspiring Psychologist Podcast, we explore what it really means to be care experienced and how early experiences of foster care and the care system can shape identity, relationships, and career journeys into clinical psychology.I'm joined by Jacob Maspero Bottaio, one of the youngest trainee clinical psychologists in the UK, who shares his experience of entering the care system at a young age, moving between foster placements, experiencing a failed adoption, and navigating the emotional and systemic barriers on the path to clinical psychology training.Together, we discuss care experience, hyper-independence, attachment, supervision dynamics, readiness for training, and what compassionate inclusion in psychology genuinely looks like. This is a thoughtful, emotionally rich conversation about resilience, systems, safety, and what it takes to feel able to lean on others after a lifetime of survival.This episode is especially relevant for aspiring psychologists, trainee clinical psychologists, supervisors, and anyone interested in widening access to clinical psychology with compassion rather than tokenism.Highlights(Timestamps)00:00 – Jacob's early life in the care system and why his story matters01:50 – Defining “care experienced” and why it's broader than people think03:26 – Why care experience can make psychology careers harder to access05:29 – Disclosure, stigma, and fear of how lived experience will be received07:26 – Financial and systemic barriers before training even begins10:53 – Hyper-independence, driving, and needing control over your future15:06 – Growing up fast and being “older than your years”18:27 – Forever people, attachment, and learning to exhale22:59 – Safety, the amygdala, and relearning trust27:15 – Adoption, systems, and not being asked what you want32:35 – Supervision styles, boundaries, and relational triggers39:46 – Readiness for training and compassion toward yourself47:35 – Repair, relationships, and what Jacob hopes for othersLinks:

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.

Greg Brady is joined by Anwar Knight, Education Advocate and Founder of Hold Schools Accountable Parent Network, to talk about Ontario's Education Minister placing the Peel District School Board under provincial control amid financial concerns and potential teacher layoffs, what this means for parents, students, and the future of school governance in the province Learn more about your ad choices. Visit megaphone.fm/adchoices

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

Send us a textWe explore why culture beats cash, how leader–member exchange strengthens trust, and why sergeants are the keystone of retention. Dr. Marshall Jones makes the case for borrowing proven business models, fixing communication beyond email, and putting guardrails on AI so cases stand up in court.• education versus training and why the difference matters• applying business and I‑O psychology models to policing• toxic behavior definitions and positive accountability• building a leadership pipeline from FTO to sergeant• leader–member exchange as the daily discipline• communication beyond email with face‑to‑face touchpoints• recruiting less by retaining more through culture• research bias, better data, and practitioner context• AI report risks, court scrutiny, and policy safeguardsDr. Jones is the co-author of the best-selling book, "Law Enforcement Leadership, Management, and Supervision." It's a must-read, and you can purchase it here. Join other courageous leaders at TravisYates.orgJoin Our Tribe of Courageous Leaders: Get The BookGet Weekly Articles by Travis YatesJoin Us At Our WebsiteGet Our 'Courageous Leadership' TrainingJoin The Courageous Police Leadership Alliance

What happens when a client walks into a session with an issue you did not prepare for, and you have to trust your presence rather than your plan? In this episode of the Podcast, we to explore one of the most real and sometimes unsettling parts of being a coach: not knowing what a client is going to bring, yet being fully responsible for creating a space that can hold it. We reflected on how often coaches ask questions like, how do I coach confidence, fear, conflict, burnout, overwhelm, or decision making. Beneath those questions is usually something deeper. A desire to feel competent. A wish to feel resourced. A fear of being caught out when a client arrives with something emotionally charged, complex, or unfamiliar. What struck us during the conversation is how much of coaching is about unlearning the need for control. In most areas of life, we walk into conversations with a sense of the agenda. Coaching is different. The agenda emerges. The topic may be named, but the real work often sits underneath in emotion, belief, identity, or uncertainty. We talked about how coach training gives us core skills that apply to any topic, yet many coaches still crave practical anchors. Questions, frames, observations, and ways of working that help them feel steady when a client says, I feel overwhelmed, I am stuck in fear, I cannot decide, or I have lost confidence. That is where topic based learning and community become powerful, not as scripts to follow, but as ways to deepen awareness and broaden choice. We shared how, as coaches, we can sometimes narrow in too quickly on the words a client uses, or unconsciously overlay our own relationship with that topic. When a client brings fear, uncertainty, or burnout, it can trigger our own stories and associations. Building familiarity with common coaching themes helps us stay grounded, curious, and spacious rather than reactive or overly cognitive. We also explored the fine balance between holding space and offering structure. There are moments when a client genuinely wants to hear what might be possible. A menu of approaches. A sense of what others have found useful. Knowing when to lean in with suggestions and when to stay with emergence is part of the art of coaching, and it develops with experience, supervision, and reflective practice. One of the deepest reflections for us is that clients rarely bring what they actually need to work on. They bring what they can currently see. The coaching happens in the gap between the stated goal and the hidden pattern, emotion, or belief that is getting in the way. When we deepen our understanding of themes like uncertainty, self trust, overwhelm, decision making, and emotional regulation, we become better at noticing what is present but unspoken. This episode is also an invitation to coaches who want to accelerate their confidence and capability. Through our how to series and accredited CPD, we are creating spaces to explore topics such as beliefs, burnout, confidence, conflict, fear, overwhelm, procrastination, certainty, metaphors, and constellations. Not to provide formulas, but to build presence, perception, and practical range so that whatever walks into the room, you can meet it with calm, clarity, and skill. Coaching is not about mastering topics. It is about mastering yourself in the presence of whatever topic arrives.   Timestamps: 00:00 Welcome and why coaches ask how do I coach specific topics 02:20 The unpredictability of coaching and letting go of control 04:30 Building confidence through topic familiarity and CPD 06:40 Balancing suggestion with client led focus 08:10 Fear, uncertainty, and staying resourced as a coach 10:05 Deep dive into coaching uncertainty and emotional states 12:00 Clients bring goals, but the work is often underneath 14:00 The art of observation and naming what is emerging 15:00 CPD programme and community invitation   Key Lessons Learned: Coaching competence grows when we trust the core skills rather than seeking topic specific formulas Clients rarely name the real issue at the start of a session Emotional states such as fear, overwhelm, and uncertainty often drive the presenting topic Supervision, community, and shared learning accelerate a coach's confidence and pattern recognition The balance between presence and practical structure is a developmental edge for every coach Observations offered with care can reveal what clients cannot yet see for themselves   Keywords: How to coach confidence, coaching uncertainty, emotional coaching, coaching overwhelm, coaching fear, coaching decision making, coach development, coaching presence, coaching supervision, coaching CPD, leadership coaching, self trust in coaching   Links and Resources: https://igcompany.co.uk/howto

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

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.

Natalie Westerbarkey and Thorsten Guthke analyse how the EU's Market Integration and Supervision Package could reshape trading, clearing, settlement, and digital bond markets.

In this episode, we sat down with Mellanie Page, BCBA, MBA, and founder of The ABA Collective, to dive into the world of Organizational Behavior Management (OBM) and how it can transform the way we experience our work. We talked about why so many BCBAs feel burnt out or stuck in leadership and supervisory roles and how OBM offers practical, behavior-analytic solutions that go far beyond surface-level self-care.Mellanie shared how OBM can help us pinpoint high-impact problems, improve workplace culture, and increase job satisfaction—whether we're clinicians, supervisors, or business owners. We explored why burnout isn't just an individual issue, how small behavior changes can create meaningful ripple effects across teams, and why reinforcing the process matters just as much as reinforcing outcomes.This conversation is full of practical insights, real-world examples, and a refreshing reminder that we can apply the science we already know to organizations, leadership, and even ourselves. If you've ever wondered how to use ABA to improve your work life, this episode is for you.What's Inside:What OBM is and why it matters for BCBAsUsing behavior analysis to address burnout and job satisfactionApplying OBM to leadership, supervision, and workplace culturePractical examples of small changes with big organizational impactMentioned in This Episode:The ABA CollectiveThe OBM Practitioner ProgramHowToABA.com/joinHow to ABA on YouTubeFind us on FacebookFollow us on Instagram

Send us a text  One year down , how many to go ? This week Snow is back to talk about Martin Luther King , vacationing in the Cayman Islands, no escaping MAGA, the NFL playoffs, checking your eyesight and then we are joined by our good friend Jim Foley Navy Veteran and foreign policy expert to talk about Minneapolis, The Military , legal orders , NATO, Greenland, Russia, Ukraine, the US dollar, who is the puppet master ? Conspiracies and what the 2026 election might be like. A lot to fit into an hour but it's a good conversation  Follow us at Reality Redemption on Facebook, Instagram, Threads, BlueSky and Tik Tok

The final episode - the one where we mark the ending and celebrate successClare and Steve bring the podcast to a close, celebrating the journey, the friendship, the learning, the relationship - and with true parallel process, notice how these are all elements of the coach/client and coach/supervision relationship.The experience had been deeply reflective for both Clare and Steve and somewhat unexpectedly, they have gained so much personally from the explorations they have shared. They exchange what they are taking with them from the five years and what opportunity an ending offers.Go well listeners and a great big 'thank you!' from us both.

One Year On: Alex Gulland's Journey into Practice – Confidentiality in Counselling Case Studies In Episode 362 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', we explore working with clients who have a psychiatric diagnosis, considering how to approach this work ethically, including navigating risk, understanding medication, and maintaining person-centred care. Then in ‘Practice Matters', Rory catches up with Alex Gulland, a year after she qualified, to hear what the transition from student to practitioner has really been like – from building a client base to discovering a passion for equine-assisted therapy. And finally in ‘Student Services', Ken and Rory explore how to protect client confidentiality when writing case studies – including anonymisation techniques and data protection guidance. Sarah Henry joins to share her frontline insights into balancing academic and ethical responsibilities. Working with Clients Who Have a Psychiatric Diagnosis [starts at 03:17 mins] In this section, Rory and Ken explore working with clients who have a psychiatric diagnosis, unpacking the complexities of staying within professional competence while offering relational, therapeutic support. Key points discussed include: Understanding diagnoses like bipolar disorder or schizophrenia helps reduce fear and supports ethical, informed practice. Therapists must see the person first – not the label or diagnosis – and listen to what the client needs from therapy. Medication, risk, and involvement with community mental health teams should be explored during initial assessments. Supervision is essential when working with clients who have complex mental health needs, especially during episodes of active distress or psychosis. Counsellors should seek CPD to increase confidence and competence in this area, and avoid making assumptions about diagnosis severity. One Year On: Alex Gulland's Journey into Practice [starts at 26:53 mins] In this week's ‘Practice Matters', Rory reconnects with Alex Gulland to hear how her first year as a qualified counsellor has unfolded – from business decisions to developing her niche. Key points from this conversation include: Building a client base takes time and persistence; marketing and directory presence matter. Accreditation and professional registration offer reassurance but are not always decisive factors for clients. Combining freelance roles in training with private practice has provided income and valuable experience. Alex shares how equine-assisted therapy has become a core part of her practice, offering creative, non-verbal connection. Continued learning, especially in areas like attachment theory and shadow work, has been central to her growth. Confidentiality in Counselling Case Studies [starts at 55:18 mins] In this section, Rory and Ken provide a detailed guide on how to write case studies while protecting client identity – a key consideration in counselling education. Key points include: Use anonymisation techniques such as pseudonyms, vague job titles, and generalised locations to remove identifying details. Only include information directly relevant to the assignment question – avoid unnecessary specifics or rare events. Gain informed consent where possible, and understand awarding body and agency policies on client data use. Refer to BACP guidance, ICO anonymisation principles, and supervisor support to ensure ethical compliance. Sarah Henry emphasises how students can reflect on their motivations and ensure they write responsibly while still demonstrating learning. 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

CMS has made virtual contrast supervision permanent in 2026, changing how imaging centers operate. Discover what compliance, patient safety, and weekend coverage look like under the new rules—and why preparation starts now. More at https://www.contrast-connect.com/blog-post/2026-the-year-of-virtual-supervision ContrastConnect City: Las Vegas Address: 309 Queens Gate Ct Website: https://www.contrast-connect.com/

The 2026 CMS rule change permanently authorizes virtual contrast supervision—but are imaging centers ready? We tackle how CT and MRI facilities can scale, stay compliant, and convert staffing challenges into operational advantages under the new guidelines. Learn more at https://www.contrast-connect.com/ ContrastConnect City: Las Vegas Address: 309 Queens Gate Ct Website: https://www.contrast-connect.com/

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. 

Virtual contrast supervision is now permanent under CMS rules starting January 2026. Discover how medical centers are training imaging technicians safely, integrating compliant audiovisual platforms, and using this shift to expand access and reduce costs. Read more at https://www.contrast-connect.com/blog/contrast-media-and-reactions ContrastConnect City: Las Vegas Address: 309 Queens Gate Ct Website: https://www.contrast-connect.com/

✅ Download the Infographic and 2026 State of Social Work Report:https://agentsofchange.kit.com/2026-state-of-social-work✅ Learn more about the course here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.agentsofchangeprep.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Dr. Meagan Mitchell, the founder of Agents of Change, is a Licensed Clinical Social Worker who has been providing individualized and group test prep for the ASWB for over 11 years. From all of this experience helping others pass their exams, she created a course to help you prepare for and pass the ASWB exam!Find more from Agents of Change here:► Agents of Change Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://agentsofchangeprep.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠► Facebook Group: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/groups/aswbtestprep⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠► Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/agentsofchangeprep/

Feeling Safe vs Being Safe in Therapy - How to Reference Non-Academic Resources In Episode 361 of the Counselling Tutor Podcast, your hosts Rory Lees-Oakes and Ken Kelly explore three vital topics for students and practitioners of counselling and psychotherapy: Firstly, in ‘Ethical, Sustainable Practice', Rory and Ken examine managing isolation in counselling practice, highlighting why counsellors may experience isolation, the impact of emotional labour, and the importance of maintaining professional connections. Then in ‘Practice Matters', Rory speaks with psychotherapist Claire Ratcliffe about the subtle but essential distinction between feeling safe and being safe in therapy, and what this means for clients and therapeutic practice. And finally, in ‘Student Services', Rory and Ken offer practical guidance on how to reference non-academic resources in academic assignments. Managing Isolation in Counselling Practice [starts at 03:29 mins] Rory and Ken reflect on managing isolation in counselling practice, exploring the isolating nature of counselling work and how the emotional labour involved can take a toll if not proactively addressed. Counsellors often work alone - especially in private or online practice - reducing professional interaction. Confidentiality prevents therapists from sharing day-to-day experiences, contributing to feelings of disconnection. Emotional labour builds up over time and, without an outlet, can lead to burnout or compassion fatigue. Proactive self-care, social hobbies, and peer engagement are essential for maintaining emotional balance. Supervision and CPD groups offer vital opportunities for connection and support. Personal development through reflective practice helps therapists process isolation and stay grounded. Feeling Safe vs Being Safe in Therapy [starts at 19:00 mins] Claire Ratcliffe joins Rory to unpack the nuanced difference between being in a safe environment and actually feeling safe enough to engage in therapy. A therapist may offer a structurally safe space, but clients may not feel safe due to past trauma or relational history. Feeling safe is a nervous system response developed through consistent, attuned, and non-defensive relational experiences. Transference may cause clients to project past harm onto the therapist - naming and exploring this supports healing. Emotional safety allows clients to express discomfort or anger - a sign of growth and nervous system regulation. Therapists must do their own reflective and supervision work to hold client projections non-defensively. The therapeutic process itself may not always feel safe. Even with a safe therapist, clients need time to build tolerance to emotional vulnerability. How to Reference Non-Academic Resources [starts at 44:52 mins] Ken and Rory explain how to correctly use and cite non-academic resources in counselling training assignments. Academic work should be referenced using primary academic sources (e.g. Rogers, Freud), not interpretations by tutors or websites. Resources like Counselling Tutor are useful for understanding theory but should not be cited as the original source. Counselling Tutor provides full reference lists for its materials, supporting students in finding and citing academic texts. There are exceptions: students can cite guest experts in CPD lectures, or books authored by Counselling Tutor. A dedicated reference guide is available at https://counsellingtutor.com/reference/ Understanding referencing standards supports academic integrity and successful assignment writing. 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

Most therapists think supervision is something they'll do later—after they feel more confident, have more time, or somehow feel “ready.” In this episode, I'm breaking down why that mindset keeps so many supervisors stuck, underpaid, or holding onto their credentials without ever using it.I walk you through how to add supervision as a side hustle in a way that actually makes sense for your schedule, your income goals, and your license. We talk about common myths—like believing more supervisees automatically means more money—and what really matters if you want supervision to support your practice instead of draining it.If you've ever wondered how to price supervision, structure your schedule, market to the right people, or avoid difficult supervisee situations before they start, this episode gives you a clear, practical framework to get moving with confidence.In this episode, I cover:How to structure individual, triadic, and group supervision so you don't give away your highest-value client hoursWhy “if you build it, they will come” doesn't work for supervision—and what marketing actually does workThe biggest myths about difficult supervisees, volume-based income, and being “ready,” and how to protect your license while you growIf you're holding onto your supervisor credential “just in case,” or you want supervision to become a sustainable income stream instead of a stressor, this episode will help you build it intentionally—from the very start.For supervisors who want clearer expectations, better boundaries, and fewer headaches down the line, the Supervisor Checklist gives you a practical starting point for contracts, structure, and decision-making.And if you want ongoing CE credit, leadership support, and real-world training you can actually apply, the Step It Up Membership is where this work deepens over time. 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.

Pool Pros text questions hereIn this episode of the Talking Pools podcast, Natalie Hood and Casey McGovern discuss the critical topic of water safety and drowning prevention. They address common myths surrounding swim lessons, the importance of early education, and the need for constant supervision around water. Casey shares her personal journey as a mother of a drowning victim and her advocacy work with Every Child a Swimmer. The conversation emphasizes the importance of year-round swim lessons, the dangers of floaties, and the need for funding and resources to support water safety programs. They also highlight the necessity of normalizing discussions about water safety to prevent future tragedies.takeawaysWater safety is layered and often misunderstood.Supervision alone is not enough to prevent drowning.Swim lessons can start as early as six months.Drowning can happen quickly and silently.Floaties provide a false sense of security.Drowning risks exist beyond just pools.Year-round swim lessons are essential for skill retention.Funding for swim programs is often limited.Education is key to preventing drowning incidents.Every child deserves access to swim lessons.Sound Bites"Drowning is fast and silent.""Invest in swim lessons early.""Every child can learn to swim."Chapters00:00Introduction to Water Safety and Myths01:25Casey's Personal Journey and Advocacy04:35The Importance of Early Swim Lessons08:56Understanding Drowning: Myths and Realities12:41Supervision and Safety Measures19:32Drowning Risks Beyond the Pool24:48The Dangers of Floaties31:33Year-Round Swim Lessons37:19Funding for Water Safety Programs43:32Normalizing Water Safety Conversations Support the showThank you so much for listening! You can find us on social media: Facebook Instagram Tik Tok Email us: talkingpools@gmail.com

In this episode of Supervision Simplified, Dr. Amy Parks is joined by counseling supervision researcher and educator Gulsah Kemer for a thoughtful conversation about what supervision truly is and what it is not.Together, they challenge the idea that supervision is simply a form of management or administrative oversight. Instead, they explore supervision as a relational, reflective, and sustainability-focused practice that supports both supervisors and supervisees over time. Gulsah shares insights from more than a decade of research and teaching in counselor education, including the development of her Cohesive Model of Supervision, a research-informed framework grounded in how experienced supervisors think and make decisions in practice.The conversation addresses why supervisors are often left to develop their identities and styles in isolation, particularly outside of academic settings, and how this lack of support can contribute to burnout, ethical strain, and uncertainty. Dr. Parks and Gulsah discuss the importance of supervisor self-awareness, intentional reflection, and lifelong development, as well as the role supervision plays in gatekeeping, professional identity formation, and client care.This episode is especially relevant for supervisors working with students, licensure-seeking clinicians, and early-career professionals, as well as anyone interested in the future of supervision, counselor education, and sustainable clinical practice.

Text a Message to the ShowFollowing the law should be straightforward and black and white.  Following department policy should be the same way.  But navigating the murky waters of how to lead and inspire people to do their best work in a competitive and demoralizing environment… well, that's going to take some courage.  Our guest today is Travis Yates, who rose up through the police ranks, saw a lot of good and bad leadership, had to practice it himself, and now is ringing the alarm that bad leadership can destroy an agency.  Today we're specifically talking about courageous leadership and what it's opposite, cowardly leadership, looks like in a police department.Resources:Travis' website: travisyates.orgMusic is by Chris Haugen Hey Chaplain Podcast Episode 131Tags:Police, Career, Cowardice, Courage, Family, Interviews, Leadership; Police Culture, Politics, Promotions, Ride Alongs, Supervision, Fort Smith, Little Rock, Tulsa, Arkansas, OklahomaSupport the showThanks for Listening! And, as always, pray for peace in our city.Subscribe/Follow here: Apple Podcasts: https://podcasts.apple.com/us/podcast/hey-chaplain/id1570155168 Spotify: https://open.spotify.com/show/2CGK9A3BmbFEUEnx3fYZOY Email us at: heychaplain44@gmail.comYou can help keep the show ad-free by buying me a virtual coffee!https://www.buymeacoffee.com/heychaplain

The Story of the Blob Tree - Ian Long Interview – Rethinking Transference in Person-Centred Therapy In Episode 360 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 rupture and repair in counselling, looking at the inevitable ruptures that can occur in the therapeutic relationship and how recognising and repairing these moments builds resilience and strengthens the therapeutic alliance. Then in ‘Practice Matters', Rory speaks with Ian Long, illustrator and co-creator of the iconic Blob Tree, about the origins, evolution, and global impact of these powerful visual tools in therapy and education. And finally in ‘Student Services', Rory and Ken rethink transference in person-centred therapy, offering insight into how these dynamics show up during training and why they matter - even outside of client work. Rupture and Repair in Counselling [starts at 03:30 mins] Rory and Ken explore rupture and repair in counselling, emphasising that relational ruptures in therapy are not failures but essential opportunities for growth, learning, and repair. Key points discussed include: Ruptures are normal in therapy and can result from misattunement, transference, or triggering of past trauma. Common signs include client withdrawal, silence, defensiveness, or abrupt subject changes. Effective repair relies on naming the shift gently and being curious about the client's inner experience. Understanding your own attachment and trauma patterns is crucial for managing ruptures. Repairing a rupture models emotional resilience and can deepen the therapeutic relationship. Supervision is essential for processing ruptures and supporting ethical, attuned practice. The Story of the Blob Tree – Ian Long Interview [starts at 23:20 mins] Rory speaks with Ian Long, the illustrator and co-creator of the Blob Tree, about how this simple yet profound tool has supported emotional expression worldwide for four decades. Key points from the interview include: The Blob Tree features a group of non-verbal, genderless characters that reflect different emotional states and interactions. Originally created for youth work, the tool has become widely used in counselling, education, and social work. It facilitates expression for those who struggle with words, including children, non-verbal individuals, and trauma survivors. The success of the tool lies not just in the illustrations but in the skill of the practitioner asking open-ended, exploratory questions. The Blob Tree and related tools are now used in over 150 countries and across various disciplines, including business and humanitarian work. Ian reflects on his creative partnership with the late Pip Wilson and the lasting legacy of their work in fostering emotional literacy. Rethinking Transference in Person-Centred Therapy [starts at 44:58 mins] Rory and Ken challenge the notion that transference has no place in person-centred practice, encouraging students to explore how it shows up in training and personal development. Key points include: Carl Rogers acknowledged transference, though he chose to respond to it rather than interpret it. Transference can occur even outside of client work - in group projects, PD groups, and skills practice. Feelings like the urge to rescue, impress, or withdraw may signal countertransference and warrant reflection. Journaling, peer reflection, and personal therapy can support deeper understanding of relational dynamics. Bringing transference into PD groups (not as blame, but as exploration) helps develop emotional awareness. Recognising these patterns early prepares students for ethical, attuned work with future clients. 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

Paul Collette spent years working inside the criminal justice system as a New York probation officer before leaving to become a federal probation officer. In this episode, Paul pulls back the curtain on what probation really looks like, sharing real stories from the job while breaking down the biggest myths and truths the public gets wrong. He talks about supervising violent criminals and sex offenders, handling high-risk situations, and the daily pressure of balancing public safety with rehabilitation. From the differences between state and federal probation to the realities of working with dangerous individuals, this conversation offers an unfiltered, insider look at life behind the badge and what it truly means to work inside the probation system. _____________________________________________ #ProbationOfficer #CriminalJustice #TrueCrimePodcast #LawEnforcementStories #PrisonSystem #JusticeSystem #BehindTheBadge #realcrimestory _____________________________________________ Connect with Paul Collette: https://sapservicesct.com/about Hosted, Executive Produced & Edited By Ian Bick: https://www.instagram.com/ian_bick/?hl=en https://ianbick.com/ Shop Locked In Merch: http://www.ianbick.com/shop _____________________________________________ Timestamps: 00:00 Intro: Supervising High-Risk Offenders on Probation 02:00 Meet Paul Collette: Life After 20+ Years in Probation 05:00 Therapy, Family & Rebuilding After Law Enforcement 07:00 Growing Up in a Military Family & Constant Relocation 11:00 Early Influences That Shaped His Career Path 14:00 Social Work vs Law Enforcement: Choosing Probation 17:00 First Jobs & Breaking Into the Probation System 20:00 Becoming a New York Probation Officer 24:00 The Reality of Probation Work: Stress, Risk & Responsibility 29:00 Probation Officers vs Police: Power, Authority & Limits 33:00 Supervising Dangerous Offenders: Daily Risks on the Job 38:00 Empathy vs Enforcement: How the Job Changes You 40:00 Transitioning From State to Federal Probation 44:00 Inside Federal Probation: Reports, Caseloads & Pressure 48:00 Judges, Sentencing & the Flaws in the Justice System 53:00 How Much Power Does a Probation Officer Really Have? 58:00 Transfers, Burnout & Career Turning Points 01:02:00 Becoming a Federal Sex Offender Specialist 01:09:00 Supervising Sex Offenders: Monitoring, Limits & Reality 01:17:00 Recidivism: Why the System Struggles to Prevent Reoffending 01:23:00 Restitution, Supervision & Overlooked System Details 01:28:00 Cooperators, PSI Reports & Prison Outcomes 01:34:00 Mental Health, Trauma & the Hidden Cost of the Job 01:41:00 Why He Finally Left Probation After 20+ Years 01:48:00 Advice for Returning Citizens & People on Supervision 01:53:00 Life After Probation: Therapy, Teaching & Helping Others 01:57:00 Final Thoughts on the Justice System & Closing Learn more about your ad choices. Visit megaphone.fm/adchoices

Join host Alan Kaplinsky, founder and former longtime leader of Ballard Spahr's Consumer Financial Services Group and one of the foremost thought leaders in the industry, as he welcomes two special guests for a timely and insightful conversation about the most significant deregulatory developments in banking law during 2025. Alan is joined by his Ballard Spahr colleague Scott Coleman, a partner with more than 30 years of experience guiding banks and bank holding companies through mergers, acquisitions, and all facets of regulatory compliance, especially in the community banking sector. They're also joined by Dr. Sean Campbell, Chief Economist and Head of Policy Research at the Financial Services Forum, where he represents the eight U.S. global systemically important banks. Dr. Campbell is a distinguished economist, former senior Federal Reserve official, and published academic. In this episode, Alan, Scott, and Sean break down the latest developments and ongoing trends in bank regulation and supervision, and digital innovation. You'll get expert analysis and practical takeaways on: ·                 The Deregulatory Wave: How the Trump administration's aggressive deregulatory agenda is streamlining exams, reducing supervisory burdens, and shifting the focus toward core financial risk-while eliminating reputational risk as a part of President Trump's Debanking Executive Order. ·                 Supervision and Stress Testing Reform: Why new Federal Reserve proposals to increase transparency in stress testing mark a turning point for large banks, moving away from a "check-the-box" approach to a laser focus on tangible risks like capital, liquidity, and asset quality. ·                 Deposit Insurance Debate: The pros, cons, and historical lessons of raising FDIC insurance limits-especially in the wake of recent bank failures and how the right balance can preserve market discipline. ·                 Community Reinvestment Act in the Digital Age: Why the CRA's geography-based model is due for an overhaul as banking goes mobile and regulatory priorities shift. ·                 Crypto, Stablecoins, and Regulatory Parity: What the Bipartisan Enactment of the GENIUS Act (regulating stablecoins) means for banks and fintechs, and why applying anti-money laundering rules across the board could level the playing field. ·                 Eliminating Reputational Risk: How regulators are eliminating the use of "reputational risk" as a catch-all supervisory and enforcement tool and what this means for fair access and bank governance. ·                 Looking to the Future: The group reflects on what's next for the bank regulatory landscape, Wall Street's view on the industry, and the practical impacts on banks and consumers. Whether you're a banker, regulator, or just want to understand how Washington and Wall Street are shaping the future of finance, this episode delivers the highlights of 2025 and insights you need going into 2026. Tune in for expert opinions, real-world examples, and a roadmap to what's ahead! Consumer Finance Monitor is hosted by Alan Kaplinsky, Senior Counsel at Ballard Spahr, and the founder and former chair of the firm's Consumer Financial Services Group. We encourage listeners to subscribe to the podcast on their preferred platform for weekly insights into developments in the banking and the consumer finance industry.

Terry closes out the year with a deep dive into Medicare's newly permanent “Virtual Supervision” rule taking effect in 2026, along with a refresher on the current requirements for reporting services under a physician's NPI versus an NPP's NPI. She urges listeners to approach this shift carefully, outlining the risks tied to non‑compliance, malpractice exposure, and whether patient‑safety concerns outweigh any potential financial upside. Tune in to get the context you need before jumping into this virtual model. Subscribe and Listen You can subscribe to our podcasts via: Apple Podcasts – https://podcasts.apple.com/us/podcast/codecast-medical-billing-coding-insights/id1305926627 Spotify – https://open.spotify.com/show/1lA69Q7EnjSMuVr3sXVWlX TuneIn – https://tunein.com/radio/CodeCast–Medical-Billing-p1056702/ YouTube – https://www.youtube.com/channel/UCoNm5vs6PFMIEDa5Undidlg YouTube Music – https://www.youtube.com/playlist?list=PLQ8tk23yZroZslhtTVe-PEIjQsAoJZJIQ Pandora – https://www.pandora.com/podcast/codecast-medical-billing-and-coding-insights/PC:1000156874 Amazon Podcasts – https://music.amazon.com/podcasts/c9d8dc99-fced-45a2-82b4-0efdf144c897/CodeCast-Medical-Billing-and-Coding-Insights iHeart Radio – https://www.iheart.com/podcast/256-codecast-medical-billing-a-31135434/ The post Is Virtual Supervision a Good Idea for 2026? appeared first on Terry Fletcher Consulting, Inc..

Watch The X22 Report On Video No videos found (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:17532056201798502,size:[0, 0],id:"ld-9437-3289"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="https://cdn2.decide.dev/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs");pt> Click On Picture To See Larger Picture The [CB] is losing control of the economy, they wanted a crash instead Trump has turned it around and the economy is growing very quickly. The D’s are trying to convince the people that the economy is worse than what Trump is letting on, this will fail.Watch gold, silver and Bitcoin. The [DS] tried to gain control the military by having the seditious 6 tell the military not to obey, Trump gives them a dividend check to show he cares about them. The Epstein files were released, it all points to the Clinton’s and the D’s. The entire plan backfired on the [DS], boomerang. Every step of the way they are feeling the pain. The [DS] wants war and Trump is fighting against those countries who are suppose to be our allies. He will get peace in the end. Economy (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:18510697282300316,size:[0, 0],id:"ld-8599-9832"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="https://cdn2.decide.dev/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs"); Treasury Secretary Scott Bessent BODIES Elizabeth “Pocahontas” Warren with a Devastating Reminder After She Claims Trump is Setting the Stage for the Next Economic Crash  Senator Elizabeth “Pocahontas” Warren (D-MA) made a poor decision trying to school Treasury Secretary Scott Bessent earlier this week, and it spectacularly backfired. https://twitter.com/atrupar/status/2000915011154112623?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2000915011154112623%7Ctwgr%5E4c8d9bec902c32b0cd01ee05619255f6315a3493%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2025%2F12%2Ftreasury-secretary-scott-bessent-bodies-elizabeth-pocahontas-warren%2F  substantial increase in private credit which is outside of the regulated banking system — that tells me that the regulated system is too constrained.” https://twitter.com/SenWarren/status/2001375798947885283?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2001375798947885283%7Ctwgr%5E4c8d9bec902c32b0cd01ee05619255f6315a3493%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2025%2F12%2Ftreasury-secretary-scott-bessent-bodies-elizabeth-pocahontas-warren%2F https://twitter.com/SecScottBessent/status/2002138930410324028?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2002138930410324028%7Ctwgr%5E4c8d9bec902c32b0cd01ee05619255f6315a3493%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2025%2F12%2Ftreasury-secretary-scott-bessent-bodies-elizabeth-pocahontas-warren%2F  Administration. Over-regulation is not the solution to what ails the American banking system. Rigorous, responsible supervision is. The initial report on the 2023 debacle by former Vice Chairman for Supervision, Michael Barr, was an exercise in obfuscation and sophistry. The American people deserve supervisors who are not asleep at the wheel, and the incoming Chairman of the Federal Reserve should undertake a thorough investigation of the systemic and oversight failures that led to that disaster. Source: thegaetwaypundit.com Trump announces that they've sold $1.3 BILLION worth of Gold Cards within Days Political/Rights https://twitter.com/RepJamesComer/status/2002011743254380602?s=20 More than a dozen politically exposed people and government officials’ names appear in the hundreds of thousands of pages of Jeffrey Epstein files made public Friday, sources said. And Deputy Attorney General Todd Blanche said the DOJ discovered more than 1,200 victims and their families during the exhaustive review, explaining the process behind determining which files could be released in a letter to Congress exclusively obtained by Fox News Digital. https://twitter.com/Badhombre/status/2002388917618610413?s=20   home in New York to solicit money for her campaign and the DCCC. FBI was warned that Jeffrey Epstein was into child porn — but ignored it for 10 years, docs show   A former employee of late sex predator Jeffrey Epstein alerted the FBI that he was interested in “child pornography” and that he threatened to “burn her house down” decades before Epstein became an international fixation — but feds apparently did nothing. Source: nypost.com   If there was every anything about Trump, it would have been released before he reached the bottom of the escalator in 2015, the Comey FBI would have leaked it, and the Dems would have brought it up at some point while Biden was in office. But none of that happened. Why? Because Epstein leads to the Dems, and people like myself have been trying to warn the world about it for 10+ years.  https://twitter.com/WarClandestine/status/2002408563193368834?s=20  and it worked brilliantly. Could you imagine if in Trump's first term he released all this stuff about Epstein? The public would not have believed it, and the Dems/MSM would have claimed it was all politically motivated and fabricated by Trump. The only way this Epstein disclosure was going to work, was to get the public to beg for it. So that's what Trump did. https://twitter.com/MikeBenzCyber/status/2002450017647301084?s=20 https://twitter.com/WarClandestine/status/2002530633394934144?s=20   partner with Wolfe via the TerraMar project, which is also connected to the Clintons and the Clinton Foundation. What is Nathan Wolfe known for? Searching for bat coronaviruses in Ukraine via USAID Project PREDICT, via his biolab company, Metabiota, which was funded via Rosemont Seneca, which is partially owned by Hunter Biden. Russia accused Wolfe and his biolab company of creating genome-specific biological weapons in Ukraine. This situation has been addressed by RFK Jr. and Tulsi multiple times, and has been a major topic at the UN for over 3 years now. So Epstein had an interest in eugenics and he had financial/social connections to virologists who were making genome-specific biological weapons via USAID grants in Ukraine. Nathan Wolfe even directly thanked Epstein in his 2011 book “The Viral Storm: The Dawn of the New Pandemic Age” where Wolfe predicted the COVID pandemic 8 years before it happened… So what am I getting at? I think Epstein had plans to engage in ethnic cleansing/population control/genocide via biological weapon, and I think he had something to do with Covid. Epstein is at the epicenter of the Deep State empire. He was essentially a real life James Bond villain. The timing could not be worse. He and Hillary are in the middle of trying to fight subpoenas to testify in person to the House Oversight Committee on the Epstein matter and what they might know. They want to submit sworn statements. Republican Committee Chair James Comer (KY-1) wants to be able to question and cross-examine them in person.  DOGE Geopolitical U.S. Snatches Venezuela Oil Tanker in Dark‑Hour Strike on Narco‑Terror Funding In a stealth operation carried out before dawn on Dec. 20, the U.S. Coast Guard—working alongside the Department of War—seized an oil tanker last seen in the terrorist state of Venezuela. The United States accused the ship's operators of moving sanctioned crude to fuel narco‑terror activity. Officials issued a stark warning to traffickers: “We will find you, and we will stop you. https://twitter.com/Sec_Noem/status/2002481990755627050?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2002481990755627050%7Ctwgr%5E0acb5b51ea0ddfb03f7a0e25a375c9245159ce68%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.breitbart.com%2Ft%2Fassets%2Fhtml%2Ftweet-5.html2002481990755627050 https://twitter.com/PeteHegseth/status/2002504193924342003?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2002504193924342003%7Ctwgr%5E1410e2476c70f24b31810862ee2f8e034c77bc3e%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.breitbart.com%2Ft%2Fassets%2Fhtml%2Ftweet-5.html2002504193924342003  conduct maritime interdiction operations — through OPERATION SOUTHERN SPEAR — to dismantle illicit criminal networks. Violence, drugs, and chaos will not control the Western Hemisphere. Source: breitbart.com U.S. imposes sanctions on family and associates of Venezuela’s Maduro and his wife The United States on Friday imposed sanctions on family members and associates of Nicolás Maduro and his wife, as Washington ratchets up pressure on the Venezuelan president. The U.S. Treasury Department said in a statement that it had imposed sanctions on seven people it said were tied to Maduro and his wife. U.S. Treasury Secretary Scott Bessent accused them of “propping up Nicolás Maduro’s rogue narcostate.” “ Source: cbc.ca War/Peace Zelenskyy Announces Eastern Ukraine Citizens Will Not Be Allowed to Vote in Elections Ukraine President Volodymyr Zelenskyy has agreed to hold elections if there is a ceasefire.  However, eastern Ukraine citizens, those currently living in the Donbas region, who are supportive of Russia, will not be permitted to vote. This creates a rather bizarre official hypocrisy within the Zelenskyy regime.  The official position of Zelenskyy is that Eastern Ukraine will never be accepted as a part of the Russian federation. Zelenskyy has recently noted, with EU leadership support, that his government will never recognize Eastern Ukraine as part of the Russian federation.  However, this same region, approximately 20% of Ukraine, will not be permitted to participate in his controlled election. Essentially, any Ukraine resident who does not support Zelenskyy will not be permitted to vote in any election, if any election is ever permitted.  Additionally, Zelenskyy notes that “there is the practice of voting abroad,” however, any region not controlled by Zelenskyy cannot submit votes. Source: zerohedge.com A Lie And Propaganda’: Gabbard Fact-Checks Reuters’ Russia Scaremongering In Real Time    Reuters posted an anonymously-sourced story pushing the idea that Russia is bent on reconstituting the Soviet Union. Before the metaphorical ink had dried, Director of National Intelligence Tulsi Gabbard pounced, condemning the story as “a lie and propaganda” on behalf of “warmongers” seeking to derail President Trump’s drive to end the long and bloody Ukraine war.   Reuters vaguely attributed the purported US intelligence conclusions about Russia to “six sources familiar with US intelligence.”    https://twitter.com/DNIGabbard/status/2002484806978834862?s=20  narrative to block President Trump's peace effort, and fomenting hysteria and fear among the people to get them to support the escalation of war, which is what NATO and the EU really want in order to pull the United States military directly into war with Russia. The truth is the US intelligence community has briefed policymakers, including the Democrat HPSCI member quoted by Reuters, that US Intelligence assesses that Russia seeks to avoid a larger war with NATO. It also assesses that, as the last few years have shown, Russia's battlefield performance indicates it does not currently have the capability to conquer and occupy all of Ukraine, let alone Europe. https://twitter.com/TulsiGabbard/status/2002503405156151648?s=20   invade/conquer Europe (in order to gin up support for their pro-war policies). The truth is that ‘US intelligence' assesses that Russia does not even have the capability to conquer and occupy Ukraine, what to speak of ‘invading and occupying' Europe.   Source: zerohedge.com WATCH: US CENTCOM Releases Footage from Operation Hawkeye Strikes Against 70+ ISIS Targets  US Central Command released footage from Operation Hawkeye strikes against ISIS militants and facilities on Friday night. “Tonight, U.S. and Jordanian forces struck 70+ ISIS targets in Syria with 100+ precision munitions. Peace through strength,” CENTCOM said on X. This is one of 10 operations conducted in Syria and Iraq since the December 13 ambush in Syria, which left multiple American service members injured and two soldiers and a civilian interpreter killed. Twenty-three terrorist operatives have been killed or detained, according to CENTCOM. “We will continue to relentlessly pursue terrorists who seek to harm Americans and our partners across the region,” CENTCOM Commander Admiral Brad Cooper said. TAMPA, Fla.- Following the attack on U.S. and partner forces last Saturday, U.S. Central Command (CENTCOM) commenced Operation Hawkeye Strike at 4 pm ET against ISIS in Syria, Dec. 19, at the Commander in Chief's direction. Source: thegatewaypundit.com   of Syria, led by a man who is working very hard to bring Greatness back to Syria, and is fully in support. All terrorists who are evil enough to attack Americans are hereby warned — YOU WILL BE HIT HARDER THAN YOU HAVE EVER BEEN HIT BEFORE IF YOU, IN ANY WAY, ATTACK OR THREATEN THE U.S.A. DONALD J. TRUMP PRESIDENT OF THE UNITED STATES OF AMERICA Medical/False Flags [DS] Agenda https://twitter.com/ElectionWiz/status/2002717078722052256?s=20  reclassify serious crimes as less severe “intermediate offenses” that are not publicly reported. https://twitter.com/EndWokeness/status/2002421989886075083?s=20 BREAKING: HUD Sec. Scott Turner CONFIRMS major investigation into Boston for anti-white public housing discrimination“They were using discriminatory housing policies in their city! We found a quote on their website that said they will integrate ‘racial equity at every level of city government.'”“They put race above reality. They put race above merit and need. Our job at HUD is to enforce and uphold the fair housing – and they were evading and encouraging landlords and property owners to evade the Fair Housing Act!”“They have been put on NOTICE. We uphold and enforce this law.” https://twitter.com/EricLDaugh/status/2002091915819253766?s=20  weaponized against Minnesota!” GOOD. IT’S CALLED ACCOUNTABILITY, TIM. “They’re threatening us with this. And this is what happens when you have a floundering presidency, and it is about those ballrooms and everything else. Now we’re back on transgender folks. And these are healthcare providers providing the best guidance to parents and children to get their care.” “It’s on every front! It’s CDLs, it’s transportation money, it’s money across the board that they have weaponized!” He should be worried. https://twitter.com/AAGDhillon/status/2002596210620969230?s=20 https://twitter.com/ScottAdamsSays/status/2002531244131991931?s=20 https://twitter.com/cb_doge/status/2001646253655097726?s=20 https://twitter.com/RapidResponse47/status/2002203857955549464?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2002203857955549464%7Ctwgr%5E7d1378774cdcbdfe43552d1c5b5ef213bd4f721f%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.breitbart.com%2Ft%2Fassets%2Fhtml%2Ftweet-5.html2002203857955549464 President Trump's Plan Democrats Have Devised a Plan to Compete With Turning Point USA for Young Voters and it's Going to be a Disaster Democrats have decided that they need to have their own version of Turning Point USA in order to appeal to young voters and what they have come up with is the most Democrat thing ever. It's going to be a total disaster. It's called the ‘DNC National Youth Coordinated Table'. It's not a grassroots group, it's completely fabricated. And you can just imagine how meetings of this group are going to go, with mini-groups within the group fighting for dominance and power. Newsweek reported on this: Source: thegatewaypundit.com https://twitter.com/CynicalPublius/status/2002577300802711720?s=20 DOJ Appeals Controversial Ruling That Disqualified Trump-Appointed U.S. Attorney Lindsey Halligan, Resulting in the Dismissal of Charges Against Letitia James and James Comey The Department of Justice has formally appealed a controversial ruling that disqualified Interim U.S. Attorney Lindsey Halligan, a decision that directly led to the dismissal of federal charges against James Comey and Letitia James. According to a Notice of Appeal filed on December 19, the Trump-led DOJ is asking the U.S. Court of Appeals for the Fourth Circuit to overturn a lower-court ruling that declared Halligan's appointment unconstitutional and voided every prosecutorial action she took while in office. Source: thegatewaypundit.com JUST IN: DOJ Wins Motion to Unseal Documents on Investigation into Trump Shooter Thomas Crooks The Department of Justice announced that it successfully moved to unseal documents related to the investigation into would-be Trump assassin Thomas Crooks.  “The Department of Justice received court approval to disclose to Congress documents gathered as part of the FBI's investigation of Thomas Crooks and his attempt to assassinate President Trump,” the Western District of Pennsylvania announced on X. A copy of the motion and order can be found here. Source: thegatewaypundit.com https://twitter.com/AAGDhillon/status/2002596363138445539?s=20 Justice Department Sues Four States Including Georgia After Secretary of State Brad Raffensperger Sides With Democrats in Failure to Produce Voter Rolls https://twitter.com/AAGDhillon/status/2001775020566286614?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2001775020566286614%7Ctwgr%5Ee92dad24c2453e3b35c6a465ec1523cafbc35499%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2025%2F12%2Fjustice-department-sues-four-states-including-georgia-after%2F Source: thegatewaypundit.com https://twitter.com/MAGAVoice/status/2001992915850260516?s=20 https://twitter.com/MarkPaoletta/status/2002483634251461079?s=20   memorial to President John F. Kennedy and now additionally honors President Donald J. Trump, who has brought America back and saved the Trump-Kennedy Center. The Board's action is permissible under the statute and no legislation is necessary. The Board’s action does nothing to change the statutory title. Instead, the Board has–in line with longstanding Executive Branch practice–designated a new name. For example, The Office of the Federal Chief Information Officer, within the Office of Management & Budget, is designated by statute as the “Office of Electronic Government.” But it's long gone by the name “Office of the Federal Chief Information Officer” in official, public, and internal communications. Similarly, the Consumer Financial Protection Bureau is designated by statute as the “Bureau of Consumer Financial Protection.” But since the beginning, the agency has long gone by the name Consumer Financial Protection Bureau or CFPB in all official communications, correspondence with the Hill, titles and signage on its buildings. The “United States Institute of Peace” was established by statute but was renamed by the Department of State as the “Donald J. Trump United States Institute of Peace.” The Department of War was established as the “Department of Defense” by statute in 1947. Earlier this year, President Trump authorized the use of the name “Department of War” and the name is now etched on the Pentagon's building and in official correspondence and public communications. It is entirely fitting for the Board of Trustees to vote to add President Trump to the title so that this Center is now named The Donald J. Trump And The John F. Kennedy Memorial Center for the Performing Arts. President Trump has provided superb leadership at every level to save the Kennedy Center from financial ruin and wokeness, and to bring our national treasure to new heights! Thank you, @kencen Board of Trustees for honoring President Trump. I have been going to the Kennedy Center for decades and have never seen such energy and excitement as I did at the Christmas tree lighting and Noel performance. The Golden Age is here!   AND ORDER. As your next Governor, Bruce will continue to fight hard to Grow the Economy, Cut Taxes, and Regulations, Promote MADE IN THE U.S.A., Champion American Energy DOMINANCE, Strengthen our Military/Veterans, Advance Election Integrity, and Protect our always under siege Second Amendment!   Bruce Blakeman is a FANTASTIC guy, will win the big November Election and, without hesitation, has my Complete and Total Endorsement for Governor of the ONCE GREAT STATE OF NEW YORK (IT CAN BE GREAT AGAIN!). BRUCE BLAKEMAN WILL NEVER LET YOU DOWN!  (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:13499335648425062,size:[0, 0],id:"ld-7164-1323"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="//cdn2.customads.co/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs");

Special guest, Denisha Gingles, returns to the show to update our perceptions of good leadership with a decidedly non-Western traditional mindset of culturally responsive practices. For example, did you know that some cultures appreciate a greater sense of unity from their leaders? Apparently one giant know-it-all doesn't necessarily make for the best boss—looking at you, American CEOs! Learn how embracing a more diverse workforce can also benefit goals of meaningful mentorship and growth in our field with some examples from the medical world and how much we really should learn from rugby. This episode is available for 1.0 CULTURAL (ETHICS) CEU. Articles discussed this episode: Sriram, V., Atwal, A., & McKay, E.A. (2024). Exploring aspects of mentoring for black and minoritised healthcare professionals in the UK: A nominal group technique study. BMJ Open, 14. doi: 10.1136/bmjopen-2024-089121 Kemzang, J., Bekolo, G., Jaunky, S., Mathieu, J., Contant, H., Oguntala, J., Rahmani, M., Louisme, M.C., Medina, N., Kendall, C.E., Ewurabena, S., Hubert, D., Omecq, M.C., & Fotsing, S. (2024). Mentoring for admission and retention of black socio-ethnic minorities in medicine: A scoping review. Journal of Medical Education and Curricular Development, 11, 1-9. doi: 10.1177/23821205241283805 Shaikh, A.N., Gummaluri, S., Dhar, J., Carter, H., Kwag, D. (2024). Application of the principles of anti-oppression to address marginalized students and faculty's experiences in counselor education. Teaching and Supervision in Counseling, 6, 94-105. doi: 10.7290/tsc06laio Laloo, E. (2022). Ubuntu leadership - an explication of an Afrocentric leadership style. The Journal of Values-Based Leadership, 15, 1-9. doi: 10.22543/1948-0733.1383 Mathur, S.K. & Rodriguez, K.A. (2022). Cultural responsiveness curriculum for behavior analysts: A meaningful step toward social justice. Behavior Analysis in Practice, 15, 1023-1031. doi: 10.1007/s40617-021-00579-3 If you're interested in ordering CEs for listening to this episode, click here to go to the store page. You'll need to enter your name, BCBA #, and the two episode secret code words to complete the purchase. Email us at abainsidetrack@gmail.com for further assistance.