Podcasts about interacting

Mark interviews author Julie Hiner about creating an unforgettable book event for a book launch. Prior to the interview Mark shares a brief personal update a word from this episode's sponsor. This episode is sponsored by an affiliate link to Manuscript Report. Use code MARK10 at checkout and save 10% off your own personalized report. In the interview Mark and Julie talk about: Julie's branding as being strongly tied to the 80s and music even though she fought it at first The first book Julie published about road biking (JUST A GIRL ON A BIKE) which was the story of how she overcame a lot of fears and anxiety and the donations she makes with sales of that book The 21 switch-backs on the road biking in France that became the 21 steps in Julie's book The value of learning so much about self-publishing when Julie attender her first When Words Collide conference Julie's ongoing obsession with true crime, serial killers, and 80s metal Interacting with and interviewing Homicide Detective Dave Sweet Julie's books in the Detective Mahoney series that started with the book FINAL TRACK The original venue Julie used for her first book and the newer one she has partnered with The special VIP pre-order packages Julie makes available The way the collaboration with the musicians work, including Julie creating lyrics for the bands to adapt into songs How horror is a really safe place to explore extremely dark things Studies that show that aggressive music can help calm the brain The creepiest serial killer that came up in the research Julie did Some of the things authors should consider when looking at their branding   After the interview Mark shares about an event Julie has coming up in April 2026, and then talks about the importance of pulling out atmospheric elements from the book/story/characters/setting as well as the idea of writing with authenticity and passion.   Links of Interest: Julie Hiner's Website Stella's Scream Book Launch Just a Girl and a Bike Manuscript Report (Mark's affiliate link - use MARK10 to save 10%) Buy Mark a Coffee Patreon for Stark Reflections Mark's YouTube channel ElevenLabs (AI Voice Generation - Affiliate link) Mark's Stark Reflections on Writing & Publishing Newsletter (Signup) An Author's Guide to Working With Bookstores and Libraries The Relaxed Author Buy eBook Direct Buy Audiobook Direct Publishing Pitfalls for Authors An Author's Guide to Working with Libraries & Bookstores Wide for the Win Mark's Canadian Werewolf Books This Time Around (Short Story) A Canadian Werewolf in New York Stowe Away (Novella) Fear and Longing in Los Angeles Fright Nights, Big City Lover's Moon Hex and the City Only Monsters in the Building Once Bitten (Novella) The Canadian Mounted: A Trivia Guide to Planes, Trains and Automobiles Yippee Ki-Yay Motherf*cker: A Trivia Guide to Die Hard Merry Christmas! Shitter Was Full!: A Trivia Guide to National Lampoon's Christmas Vacation I Think It's A Sign That The Pun Also Rises   Julie Hiner spent her childhood lost in the pages of books. The only thing that took precedence was her Walkman. She is still an 80s rocker. On a break from her career as a computer scientist, she published an inspirational book about facing fears by cycling up mountains. Julie now writes horror/suspense infused with heavy metal. She has published a serial killer series, three horror novellas, co-curated an anthology, and had short stories published. She recently had a deep-sea horror novella published by Torrid Waters of Crystal Lake Publishing. You can find her at KillersAndDemons.com serving up metal and murder.     The introductory, end, and bumper music for this podcast ("Laser Groove") was composed and produced by Kevin MacLeod of www.incompetech.com and is Licensed under Creative Commons: By Attribution 3.0    

Just days after their loss against Ireland, Dan Cole and Ben Youngs are at Pennyhill Park for a special episode from inside England Rugby's Six Nations camp. We share an unrivalled insight into the mentality of a team struggling for form, recall the challenges of navigating the media as a player and hear from Ollie Chessum and Jack van Poortvliet.

Rita Cosby examines a series of high-stakes political and criminal developments, primarily focusing on President Trump's resilience following a Supreme Court defeat regarding trade tariffs. Cosby frames the President as a "Rocky" figure who, despite legal setbacks and partisan friction, remains committed to his economic agenda through alternative executive actions. The program shifts into a critical analysis of a high-profile kidnapping investigation in Arizona, where local law enforcement is accused of being inept and territorial by allegedly sidelining the FBI. Interacting with callers, Cosby further explores the necessity of military strength against Iran and decries the lack of patriotism and civility among modern politicians. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of Thinking Out Loud, Nathan Rittenhouse and Cameron McAllister engage in deep theological reflection on the Epstein files, cultural corruption, and the crisis of meaning in the modern West. Referencing figures like Harvey Weinstein and drawing cultural parallels to excesses reminiscent of Nero, they explore why revelations of elite abuse, power, and moral collapse no longer shock us—and what that says about our spiritual condition. Are Christians becoming cynical, or are we awakening to the emptiness of fame, wealth, and influence as ultimate goals? Interacting with themes echoed in the “He Gets Us” Super Bowl campaign and thinkers like Aristotle, Nathan and Cameron examine the biblical concept of telos—our God-given purpose—and contrast radical individualism with the shared story of Scripture. Through reflections on the Emmaus road, the Sermon on the Mount, and the Church's role in restoring shared meaning, this conversation equips believers to pursue true human flourishing in Christ amid cultural decay. If you're a Christian seeking serious theological analysis of current events, cultural commentary grounded in biblical truth, and practical wisdom for faithful living in a confused age, this episode will challenge and encourage you.DONATE LINK: https://toltogether.com/donate BOOK A SPEAKER: https://toltogether.com/book-a-speakerJOIN TOL CONNECT: https://toltogether.com/tol-connect TOL Connect is an online forum where TOL listeners can continue the conversation begun on the podcast.

Cardinal manager Oli Marmol joins the show and starts of talking parking spots. Interacting with media and fans. Not worried about the outside noise or proving anyone wrong. His relationship with Chaim Bloom. Who does he see stepping up with a lot of experienced players gone? What he likes about JJ Wetherholt. His playing career. The art of the ejection. What are the indicators of success for this season?See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

(00:00-14:27) The sun starting to peek out this morning. Did Doug have a dungeon put in? Doug being held accountable for his Springfield, MO and MO State takes. Claibs stops by to talk in code. Jackson is denying there's a GoFundMe for him to go play golf. Claibs believes in Marmol.(14:35-29:30) Nothing wrong with a little jazz flute. Jordan Walker and Chaim Bloom will join us tomorrow. I don't negotiate with Hoosiers. Martin sent an aggressive text to Oli Marmol.(29:40-1:03:07) Cardinal manager Oli Marmol joins the show and starts of talking parking spots. Interacting with media and fans. Not worried about the outside noise or proving anyone wrong. His relationship with Chaim Bloom. Who does he see stepping up with a lot of experienced players gone? What he likes about JJ Wetherholt. Oli digs into the YouTube chat. His playing career. The art of the ejection. He won't be checking out Movie Boi. He will be holding McGreevy accountable for not playing a $500 round of golf. What are the indicators of success for this season? Having former players at camp. What makes him the right man to lead the team through this rebuild? Misconceptions about him.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Scott and Wes unpack WebMCP, a new standard that lets AI interact with websites through structured tools instead of slow, bot-style clicking. They demo it, debate imperative vs declarative APIs, and share their hottest take: this might be the web's real AI moment. Show Notes 00:00 Welcome to Syntax! 00:16 Introduction to WebMCP 01:07 Understanding WebMCP Functionality. 03:06 Interacting with AI through WebMCP. 06:49 WebMCP browser integration. 08:25 Brought to you by Sentry.io. 08:49 Benefits of WebMCP. 11:51 Token efficiency. 13:02 My biggest questions. 14:13 My take on this tech. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

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

And though a man might prevail against one who is alone, two will withstand him—a threefold cord is not quickly broken. Ecclesiastes 4:12Support the show, a product of Hope Media: https://hope1032.com.au/donate/2211A-pod/See omnystudio.com/listener for privacy information.

A Gluten Free Podcast Episode 222Today's episode is a live keynote presentation from Celiac Disease Foundation Education & Community Engagement Coordinator Emma Kowzun live onboard the Australian sailing of the Celiac Cruise. In the talk Emma discusses how we can help chart the course and make a difference in the coeliac disease and gluten free community. Emma shares the latest research and developments at the Celiac Disease Foundation, a brief history of coeliac disease and how the Celiac Disease Foundation works with the Celiac Cruise. Topics covered: * Emma introducing herself and her role at the Celiac Disease Foundation * The difficulties of living with coeliac disease * Interacting with the audience * How coeliac disease and gluten free awareness has changed over time * Brief history of coeliac disease * Policies and law changes in the US for coeliac disease and gluten free labelling * Food labels for gluten in Australia and New Zealand* Current coeliac disease clinical trials and possible treatments for coeliac disease throughout the world * iCureCeliac - Patient Registry Makes Finding A Cure Possible* How people can support and advocate for coeliac disease * Food insecurity in the US and how the Celiac Disease Foundation and Celiac Cruise are helping LinksThe Celiac CruiseCeliac Disease FoundationiCureCeliac - Patient Registry Makes Finding A Cure PossibleJoin A Gluten Free Podcast Facebook GroupPhoto taken by @theglutenfreeadvocate

Jeremy Bradley cuts to the chase this week: Interacting with people has become such a pain. It's not necessarily the people themselves, it's the channels and platforms that facilitate the contact. JB discusses the frustration of having a simple question or concern but the never-ending runaround as a customer to come to a conclusion. It leads to another rant about customer service -- or "customer experience" as it's commonly known. Why isn't the customer number 1 anymore? Do you find that companies tell you, the customer, "no" more and more often nowadays? Do you find the service employee puts an effort into finding answers if they don't know? 

Hey, Alex from W&B here

Why do the people you thought knew you best stay silent—or worse, side with the person who hurt you? This secondary betrayal often cuts deeper than the narcissistic behavior itself. Switzerland friends insist on neutrality while your pain makes them uncomfortable. Flying monkeys carry your vulnerability straight back to your abuser. When you finally name what's happening and the people closest to you rush to minimize or report back, your nervous system doesn't just register disappointment—it registers danger. Tony walks through why "I don't want to take sides" isn't actually neutral, how flying monkeys weaponize your words, and the exhausting ping-pong match of trying to be understood by people who need not to understand you in order to feel safe themselves. In this episode, you'll learn: The critical difference between Switzerland friends (who neutralize) and flying monkeys (who expose)—and why both leave you questioning reality How narcissistic systems hijack co-regulation, making everyone responsible for stabilizing the most emotionally immature person in the room Why your body's response after sharing something vulnerable is better data than the words exchanged The five ways narcissists regulate their nervous systems through you: superiority, victimhood, being right, being admired, and being defended How to stop "auditioning for belief" and start choosing relationships that can actually hold emotional weight Drawing from over 20 years of couples therapy and thousands of real conversations, Tony offers a framework for recognizing when explanation has replaced connection—and why the most regulated thing you can say is simply, "I know what I experienced." Ready to stop offering your nervous system as a resource to people who won't protect it? Subscribe and share this episode with someone who needs to hear they're not crazy—they're waking up. 00:00 Introduction and Gratitude 00:37 Sales Pitch: Magnetic Marriage Course 05:37 Understanding Narcissistic Relationships 06:46 The Pain of Secondary Betrayal 07:44 Navigating Anger and Injustice 15:04 Switzerland Friends and Emotional Avoidance 22:03 Story Time: Ned, Steve, and Fran 30:01 Avoiding Accountability and Ownership 30:17 The Role of Flying Monkeys 30:32 Switzerland Friends vs. Flying Monkeys 30:57 Emotional Honesty in Unsafe Systems 31:17 The Futility of Over-Explaining 34:02 Adjusting Expectations and Setting Boundaries 34:42 Understanding and Managing Anger 35:28 Withdrawing the Need for Permission 36:23 Grieving What Won't Change 37:14 Recognizing Emotionally Safe Relationships 39:13 The Concept of Co-Regulation 39:55 Narcissistic Systems and Emotional Regulation 45:43 Interacting with Switzerland Friends and Flying Monkeys 54:46 Choosing Relationships That Hold Emotional Weight 55:41 Final Thoughts and Encouragement Get on the waitlist today for Tony's upcoming Magnetic Marriage live course! Head to https://tonyoverbay.com/magnetic If you are interested in joining Tony's private Facebook group for women in narcissistic or emotionally immature relationships of any type, please reach out to him at contact@tonyoverbay.com or through the form on the website, HTTP://www.tonyoverbay.com If you are a man interested in joining Tony's "Emotional Architects" group to learn how to better navigate your relationship with a narcissistic or emotionally immature partner or learn how to become more emotionally mature yourself, please reach out to Tony at contact@tonyoverbay.com or through the form on the website, HTTP:www.tonyoverbay.com

US gas projects tied to data centers surge, videogame stocks dive after Google unveils “Project Genie”, Belkin shuts down cloud services for most Wemo smart home devices. MP3 Please SUBSCRIBE HERE for free or get DTNS Live ad-free. A special thanks to all our supporters–without you, none of this would be possible. If you enjoyContinue reading "Reddit-Style Moltbook Emerges With OpenClaw AI Agents Interacting – DTH"

Watch the full video version on YouTube: https://youtube.com/@readysetblowpodcast?sub_confirmation=1   Podcast-favorite, Dean Gonzalez is back on the show! The boys have a hilarious, uncensored and sometimes heated conversation about the US military's dominance, acquiring Greenland, New Years resolutions, mental health and dealing with tough times, immigration and liberal hypocrisy, Minneapolis and the ICE crackdown, the Renee Good and Alex Pretti shootings, dealing with cops, good vs. bad policing, and the role of religion in government . After a long and somewhat confrontational conversation, the fellas close with much love and respect for each other and lighten things up with the weekly news   Every Thursday, the Ready Set Blow Podcast brings you real talk with comedians, actors, musicians, entertainers, entrepreneurs, and fascinating guests from all walks of life. No scripted BS. No playing it safe…Just raw, funny, and authentic conversations you won't hear on your average podcast.   If you enjoy comedy podcasts like Your Mom's House, Flagrant, The Joe Rogan Experience, or Theo Von, you'll love this show.   What We Talk About in This Episode: 00:00  Podcast Intro 01:00  The US Military and Capture of Nicolas Maduro 10:00  Acquiring Greenland 20:00  New Year's Resolutions 30:00  Mental Health & Dealing With Tough Times 45:00  Immigration and Liberal Hypocrisy 1:00:00  Minnesota and The ICE Crackdown 1:10:00  Renee Good & Alex Pretti Shootings 1:25:00  Interacting with Law Enforcement Officers 1:35:00  Good vs. Bad Policing 1:40:00  Religion in Government 1:43:00  The Weekly News   New Episodes Every Thursday:

Topics:  This Is The Day, Welcome To The Show AI, Interacting w/Good, If You Could Talk To God, National Days, Praying For Enemies, Double Stuffed Oreos, Brant's Father BONUS CONTENT: Double Stuffed Oreos Revisited, Radio Ink Award   Quotes "We pretty classy here." "It takes mental discipline to be this ignorant." "Am I a champion or not?" "We can choose to rejoice."

Interacting with listeners Answering trivia questions The Top 4 at 4:00

Welcome to the Parenting Well podcast with Parent Engagement Network!  I am Dr. Shelly Mahon, your host and today's well source is Dan Fox. Dan has spent over 25 years working with adolescents and their families as they navigate the ups and downs of growing up. He's been a high school teacher, summer camp director, school counselor, and the director of September High School—so he really understands teens from the inside out. As a Licensed Professional Counselor, Dan brings that experience into his work with families, grounded in the belief that there is hope for teens and real relief for parents. He works with adolescents, young adults, couples, and families, and supports schools and organizations through workshops and parent coaching as well. Dan also has a podcast called Therapy Dudes with Andre Karkamaz. They put the fun back in dysfunctional as they talk about how to navigate your inner and outer world to move forward in life. In this podcast, we talk about: The cumulative effect of anxiety on our nervous systems. Being attuned to our kids. Being intentional about our relationship with our children, including the tone we set with them. Interacting with your children differently as they move from childhood to adolescence. Fueling more than steering our teens. Strategies to regulate yourself - stay centered or recenter. Training ourselves to react to negative energy differently - not taking it personally. Handling situations that you feel have crossed the line. Repairing the relationship when things haven't gone as well as you would have liked. Owning your own stuff without making it transactional - i.e., expecting something from the other person. Resources: Website: Boulder Psychological Services Podcast: Therapy Dudes with Dan Fox and Andre Karkamaz 10 Annual Reducing Stress & Anxiety Conference: Fostering Resilience & Wellbeing at Every Stage of Parenting  

"Frequency and authenticity are the two biggest drivers of engagement in today's channels."In this episode of Politely Pushy, Eric Chemi connects with Nathan C Bowser, a product strategist, podcast host of "The Tech Glow Up," and founder of Awesome Future. Interacting with Fortune 1000 companies and interviewing 300+ technology leaders has fine-tuned Nathan's data-meets-creative approach to helping businesses and the innovative go-to-market leaders behind them succeed. Tune into this episode as Nathan shares their insights into an AI-everywhere marketplace, coaching teams on nailing singular metrics, and the power of customer storytelling.

Navin Chaddha of Mayfield joins Nick to discuss A 17 Time Midas Lister on Greatness, the $6T AI Teammate Market, Why AI Sovereignty Is Critical, and Who Wins the Battle Between Incumbents and Startups. In this episode we cover: Challenges in Early Internet Video Lessons from Interacting with Tech Luminaries Investment Philosophy and Evaluation Process The Role of Psychology in Venture Capital The AI Collaboration Era Geopolitical Implications of AI Investment Strategy in a Competitive Market Guest Links: Navin's LinkedIn Navin's X Mayfield's LinkedIn Mayfield's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

Lords: * Linker * Alexa Topics: * Variations in vampire stories: types of powers, origins of vampires, lack of consent lol * Anthropomorphism/Connection in media * Get a free burger when you join my rewards * Meditation at Lagunitas by Robert Hass * https://www.poetryfoundation.org/poems/47553/meditation-at-lagunitas * What letter of the alphabet would win in a Royal Rumble * Anti-Lesbian Vampire Propaganda of the 1970s as shown in The Vampire Lovers (1970) * https://crossingsjournal.ca/index.php/crossings/article/view/283 Microtopics: * Being American and then being Canadian-American. * Elephantasy and Elephantasy Flipside. * Crazy Aaron's Thinking Putty. * 3D-printed worms. * A plastic casing attached to your keychain that used to have a clicky button on it. * What we did to fidget after the industrialization of textiles. * How office workers goofed off before the Internet. * Bella's lead skull that prevents Edward from mind-controlling her. * Making up a power to be your favorite vampire power. * What happens when you inhale a vampire in mist form. * Using a picture of Cat's Cradle to illustrate the concept of telekinesis. * Putting the vampire coffin in steerage and the familiar has to sit in coach and then pull the coffin off of the luggage conveyor belt. * Would it be fun to turn into mist and be collected in a cup and then drunk? * Miss Frizzle, vampire expert. * Getting frustrated at a stealth video game and giving up being a pacifist as a metaphor for being an old vampire. * New Money vs. Old Money vampires. * Vampires going on Fetlife to find ethically sourced food. * One mysterious vampire at Goth night at the nightclub quietly asking to drink your blood, vs. twenty vampires going around begging and everyone's like jeez, this again? * Nobody expecting you to embezzle the blood. * Talking to a computer like it's a person you have absolutely no respect for, in a way you'd never talk to a real person because you assign a baseline respect for just being a real person, and people overhearing you are like "holy shit they're really mad" but you're not mad, you're just talking to a computer. * Apologizing to the table you just ran into. * It Takes Two. * Following the instructions of the book of love. * Creation of personality in moments of friction. * Interacting with video games in similar ways as you would a person, expect without the social anxiety. * Why would you ruin this perfectly good complex system with social anxiety? * We've got teamwork at home. * Sentient burgers enslaving other sentient burgers. * Being rewarded with a free burger but then turning into a burger and being given away as a reward. * Chicken stars! They're like chicken nuggets but they're shaped like stars! * Where were you when you were drafted into the Rewards Wars? * Naming your rewards program "My Rewards" so when the mascot refers to it it sounds like the rewards belong to the mascot. * Demanding to see the terms and conditions before you eat this hamburger. * A word that is elegy to what it signifies. * Moments when the body is as numinous as words. * Taking some LSD and learning about non-symbolic states. * Ignoring poetry in the same way that you ignore ads. * Vampire jokes! * Transylvanian Hounds. * Serif H being much more combat-ready than sans serif H. * The most bouba letters. * Capital O rolling around crushing the other letters of the alphabet like in Raiders of the Lost Ark. * Distraction the ref so you can stab. * Which letter of the alphabet could do the best backflip. * Whether the ampersand counts as a letter. * Letters that are good at stabbing vs. letters with broad sturdy bases. * The nuclear family emerging in response to the financial boom following World War II. * Anti-lesbian propaganda films that are far too sexy to be effective. * Heteronormative fiances. * Carmella the lesbian vampire stealing your wife. * The vampire lesbians receiving their comeuppance and the heteronormative couple living happily every after. * Be gay do crimes. (Murder.) * Buying Linker's games so he doesn't die.

Come to a Dehoarding Accountability Zoom Session: http://www.overcomecompulsivehoarding.co.uk/ticket Subscribe to the podcast: https://www.overcomecompulsivehoarding.co.uk/subscribe Podcast show notes, links and transcript: http://www.overcomecompulsivehoarding.co.uk/  This episode, I've pulled together the best mental health advice from every guest I spoke to over the past year - academics, therapists, organisers, and people with lived experience. Each of them shared a personal habit or practice that genuinely helps them cope or keep on top of their wellbeing, and I add a couple of my own strategies too. Whether you're navigating hoarding, supporting someone who is, or just looking for affordable ways to protect your own mental health, stick around for a mix of practical, honest tips to try for yourself. Special Episode Format: Compilation of Guest Advice Throughout the year, every guest was asked about habits or practices supporting their mental health. Guest Contributions: Mental Health Habits and Practices Harriet Impey (Episode 172) Mindfulness and meditation, especially mindful self-compassion (inspired by Kristin Neff). Practical examples: Being present, guided meditation, practicing non-attachment, and self-reflection on letting go of unhelpful arguments. Dr Jan Eppingstall (Episodes 174 & 204) Practicing gratitude to counterbalance negativity bias. Unsubscribing from unwanted emails to reduce anxiety and overwhelm. Interacting with pets for grounding and emotional well-being - petting animals as a stress reliever. Visiting places where animals are accessible (e.g., city farms, pet shops, animal cafes). Jasmine Sleigh (Episode 175) Importance of good sleep for mental health. Value of pleasurable activities like reading, and the paradox of sometimes resisting enjoyable activities (self-sabotage). Reflection on how engaging in enjoyable pastimes is essential even when it's difficult to get started. Sam (Episode 178) Writing things down: Keeping lists of achievements and things to be grateful for, even small joys. Acknowledging how gratitude doesn't have to be grand - simple moments count. Exercise, particularly running, or any activity that gets you outside of your current headspace (could be walking, volunteering, etc.). The role of support from others to prompt new perspectives or activities. Dr. Victoria Ruby-Granger (Episode 179) Self-awareness and accepting what works for you, rather than trying to fit yourself to methods that don't suit. Emphasis on letting go of approaches that don't align with your own needs, and being open to alternative strategies. Carrie Lagerstedt (Episode 183) Moral neutrality: Separating self-worth from issues like executive dysfunction, lateness, and messiness. Reframing these traits as value-neutral rather than personal failings, helping to build self-esteem. That Hoarder Creative self-expression: Resentful journaling, collage, and visual arts—done primarily for personal expression, not for others' approval. Permission for creative works to be imperfect and focused on process over outcome. Nature connection: Getting outside, paying attention to natural details (flowers, leaves, colours, wildlife), and practicing mindful observation to foster grounding and perspective. Importance of self-compassion, giving oneself credit for small achievements (especially with meditation or walks). Allowing yourself pleasurable, nurturing, or healing activities without guilt. Noticing the bigger world and natural cycles as a counter to internal struggles. Encouragement for listeners to reflect on which practices resonate and to share their own tips. Links Podcast ep 172: Harriet Impey on clearing out her parents' very full home, through family belongings and personal growth, in the film Where Dragons Live Podcast episode 174: How to feel grounded when we're overwhelmed or dysregulated using ventral vagal spaces and touchstones, with Dr Jan Eppingstall Podcast ep 175: Taking the scary first steps: the courage to call a professional organiser, with Jasmine Sleigh Podcast ep 178: Growing up in a hoarded home: Sam's story as the child of a Mum who hoards Podcast ep 179: How hoarding behaviours develop and early intervention for hoarding disorder, with Dr Victoria Ruby-Granger Podcast ep 183: ADHD, executive dysfunction and creating hacks and systems to reduce clutter chaos, with Carrie Lagerstedt Podcast ep 204: Am I my things? When possessions define us: the psychological connection between identity, self-concept and hoarding with Dr Jan Eppingstall Come to a Dehoarding Accountability Zoom session: Accountability Booking Form Website: Overcome Compulsive Hoarding Become a Dehoarding Darling Submit a topic for the podcast to cover Questions to ask when dehoarding: https://www.overcomecompulsivehoarding.co.uk/podquestions Instagram: @thathoarderpodcast Twitter: @ThatHoarder Mastodon: @ThatHoarder@mastodon.online TikTok: @thathoarderpodcast Facebook: Overcome Compulsive Hoarding with That Hoarder Pinterest: That Hoarder YouTube: Overcome Compulsive Hoarding with That Hoarder Reddit: Overcome Compulsive Hoarding with That Hoarder subreddit Help out: Support this project Sponsor the podcast Subscribe to the podcast Subscribe to the podcast here

In the initial hours after Bryan Kohberger's arrest, there was a frantic dash to try to find out as much information about him as possible. During that dash, those of us who were following along were able to get a glimpse of an instagram account that allegedly belonged to Bryan Kohberger. That same account was also following and interacting with Madison's account. A few hours later and that account was purged.In this episode, we hear from the Goncalves family who also saw that account and not only saw it, but they took screenshots as well. With many questions surrounding the motive in this case and the connection between the victims and the alleged murderer still in the air, could this be the glue that binds Bryan Kohberger? Let's dive in and give it a look. (commercial at 7:18)to contact me:bobycapucci@protonmail.comsource:Idaho student murders suspect Bryan Kohberger followed victims on Instagram, says family - CBS NewsBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-moscow-murders-and-more--5852883/support.

As we continue our series in Ephesians, today we look as Ephesians 4:25-29 as Paul challenges the Ephesians and by extension us in how we interact with others and conduct ourselves. Paul is likely challenging cultural norms.

Guest: Jason Wright, Communications Director for the Georgia Tech Library. First broadcast December 5 2025. Playlist "I may have made a mistake here."

Are the internal structures of our organizations fit for the mission that God has called us to? In this episode, Brandon Stiver welcomes Kelly Strong of Safe International for a conversation on organizational identity and healthy global partnerships. They explore why mission must remain central and the role of values in shaping behavior, culture, and daily decision-making, especially within diverse, international teams. They examine common challenges like founder's syndrome and how it can limit growth if left unaddressed. If you are needing help with your nonprofit, reach out at brandon@canopy.international Support the Show Through Venmo – @canopyintl Podcast Sponsors Take the free Core Elements Self-Assessment from the CAFO Research Center and tap into online courses with discount code 'TGDJ25' Take the Free Core Elements Self-Assessment Resources and Links from the show Mission Based Management by Peter C. Brinkerhoff Safe International Online Email brandon@canopy.international if you're interested in a community of practice or one-on-one or team support. Conversation Notes Understanding the centrality of an organizations mission The collaboration that develops around Vision statements Values : Behaviors, culture and decision making Interacting and collaborating with partners across global teams The pitfalls of founder's syndrome The differences between covenant and contract in international partnerships   Theme music Kirk Osamayo. Free Music Archive, CC BY License

Character.AI is banning minors from using its chatbots amid growing concerns about the effects of artificial intelligence conversations on children. The company is facing several lawsuits over child safety. Character Technologies, the Menlo Park, California-based company behind Character.AI, said it will be removing the ability of users under 18 to participate in open-ended chats with AI characters. The changes will go into effect by November 25, and a two-hour daily limit will start immediately. Character.AI added that it is working on new features for kids, such as the ability to create videos, stories, and streams with AI characters. The company is also setting up an AI safety lab. Character.AI said it will be rolling out age-verification functions to help determine which users are under 18. A growing number of tech platforms are turning to age checks to keep children from accessing tools that aren't safe for them. But these are imperfect, and many kids find ways to get around them. Face scans, for instance, can't always tell if someone is 17 or 18. And there are privacy concerns around asking people to upload government IDs. Character.AI, an app that allows users to create customizable characters or interact with those generated by others, spans experiences from imaginative play to mock job interviews. The company says the artificial personas are designed to “feel alive” and “humanlike.” “Imagine speaking to super intelligent and lifelike chatbot characters that hear you, understand you, and remember you,” reads a description for the app on Google Play. “We encourage you to push the frontier of what's possible with this innovative technology.” Critics welcomed the move but said it is not enough, and should have been done earlier. Meetali Jain, executive director of the Tech Justice Law Project, said, “There are still a lot of details left open.” This article was provided by The Associated Press.

What's it really like to study in South Korea? In this episode, I sit down with four exchange students from around the world to talk honestly and openly about their experiences at Korean universities. We cover everything from first impressions and making Korean friends to campus culture, visa challenges, professors, trends, expectations, and the toughest parts of living here as a foreigner. Dillon Lia (https://www.instagram.com/lia_nana9/) Zhaniya Joana Topics & Timestamps 0:00 Expectations Before Coming to Korea 10:10 First Impressions 20:00 Interacting with Korean People 30:00 Making Friends in Korea 40:00 Studying in Korea 48:20 Korean Professors 1:07:00 Korean University Campus 1:13:20 Working on a Student Visa 1:21:20 Korean Trends 1:41:15 Hardest Thing About Being a Foreigner 1:56:05 Advice for Studying in Korea Thanks to Patreon members: Bhavya, Roxanne Murrell, Sara B Cooper, Anne Brennels Join Patreon: https://www.patreon.com/c/user?u=62047873 David A. Tizzard has a PhD in Korean Studies and lectures at Seoul Women's University and Hanyang University. He writes a weekly column in the Korea Times, is a social-cultural commentator, and a musician who has lived in Korea for nearly two decades. He can be reached at datizzard@swu.ac.kr. Watch this video next: https://youtu.be/vIbpLfWJoZM?si=srRVQ1vRkLvCV076 Subscribe to the channel: @DavidTizzard/videos Music by Jocelyn Clark Thank you to 한종철 for helping me record this. ▶ Get in touch: datizzard@swu.ac.kr ▶ David's Insta: @datizzard ▶ KD Insta: @koreadeconstructed Listen to Korea Deconstructed ▶ Listen on iTunes: https://podcasts.apple.com/kr/podcast/korea-deconstructed/id1587269128 ▶Listen on Spotify: https://open.spotify.com/show/5zdXkG0aAAHnDwOvd0jXEE ▶ Listen on podcasts: https://koreadeconstructed.libsyn.com

Learn how breaking out of your comfort zone and embracing new perspectives can transform your leadership and help dismantle bias in your church.Tod Bolsinger and Markus Watson discuss this quote from Erin Devers in Ep. 282, The Cost of Bias in the Church:“One of the strategies for reducing bias is to go bigger, to widen your perspective.”THIS EPISODE'S HIGHLIGHTS INCLUDE:People tend to hold onto their biases because they find comfort and safety in the familiar.Leaders confuse discomfort with genuine danger, which prevents them from widening their perspectives.Leaders can intentionally broaden their outlook by seeking out diverse voices and saying yes to experiences that feel scary.Leaders maintain vibrancy and effectiveness by practicing humility and curiosity rather than clinging to expertise.Interacting with people from different backgrounds energizes leaders and equips them to lead change more confidently.Send me a text! I'd love to know what you're thinking!Click HERE to get my FREE online course, BECOMING LEADERS OF SHALOM.

The show-ending “Smoking Gun” segment on the Tuesday Bob Rose Show 11-18-25

The Phoenix Police Department is enacting a new policy for how they interact with people experiencing homelessness. President of Phoenix Law Enforcement Association, Darrell Kriplean, joins Arizona's Morning News to discuss the department's new policy and what sort of impact it will have. 

From [ Zen and Inner Peace Vol. One ]By Chan Master Sheng Yen / Narrated by Yingshyan KuAs buddha-nature is within all beings, this book expounds the meaning of “Life is Chan, and Chan is Life.” Everyone can benefit by applying Chan wisdom and compassion in daily life, regardless of the complexity of their lives, their environments and interpersonal relationships, along with the associated stresses and conflicts.

Send us a textIn this episode of Quilting on the Side, Tori interviews her co-host Andi about her experiences participating in four virtual quilting summits this year (2025). They discuss the rise of virtual summits in the quilting community, the benefits of collaboration, and the importance of audience engagement. Andi shares insights on preparing for a summit, the importance of follow-up strategies, and the role of lead magnets in building an email list. The conversation emphasizes the value of data collection and marketing strategies to maximize the impact of summits on business growth.Don't miss an episode! Like, comment, and subscribe for more quilting stories, tips, and industry insights.Chapters00:00 Introduction to Virtual Summits in Quilting04:15 The Benefits of Collaboration and Community07:03 Choosing to Participate in Summits10:01 Testing Ideas Through Summits12:48 Engagement and Audience Building15:38 Impact on Business and Sales18:20 Follow-Up Strategies After Summits21:24 Preparing for a Summit24:10 Marketing and Promotion Strategies27:02 Interacting with Summit Organizers30:16 Final Thoughts on Summit ParticipationSee previous Episodes on summits:"My Summit Story" Listen to Tabatha's Interview Here! (Mentioned in this podcast)Subscribe to Andi's YouTube! CLICK HEREWant More Quilting Business Content?

Send us a textWelcome to episode 55 of 'The Dawah Clinic' where we will be addressing your dawah dilemma's. If you have difficulty in answering certain questions or need help in responding to polemics towards Islam and Muslims, fear no more the dawah clinic is here to help empower you. So keep a note of your dawah dilemma's and call into the show or post your questions in the live chat.  Please note : waiting lists are very high and clinic places are limited to a maximum of 10 placements at any given time so keep your questions concise, to the point and please be patient. Link to Join The Dawah Clinic: ​https://www.buymeacoffee.com/ijazthetrini Please help Br Ijaz with his monthly medical fees, if you are able to. Jzk khairDownload your free PDF copy of Abraham Fulfilled here:https://sapienceinstitute.org/abraham-fulfilled/Purchase a paperback copy from Amazon here:https://tinyurl.com/2xkv4ynu© 2025 EFDawah All Rights ReservedVoice only nasheed licence provided by vocaltunez.com Title : It's closeWebsite : https://efdawah.com/EFDawah بالعربية (Arabic)https://www.youtube.com/channel/UCWDR...EFDawah Bosniahttps://www.youtube.com/channel/UCgcz...EFDawah Indonesiahttps://www.youtube.com/channel/UCSGJ...Podcast Website (New)https://efdawah.buzzsprout.com/RSS FeedTimestamps:00:00 - Intro 01:07 - Br. Abbas joins: Format of the Stream02:36 - Dawah via Prohibition of Alcohol in Islam09:47 - Br. Jordan joins: Audio issues11:22 - Choosing & Inviting Guests12:26 - Ashraf joins & shares his background 14:07 - Dawah Advice for Non-Muslim Colleagues17:50 - Various ways of giving dawah at work19:06 - Challenges in giving dawah to colleagues21:26 - Reality of dawah to highly educated people25:58 - Discourse on approach for dawah at work29:10 - Practicing traditional vs foreign culture 33:51 - Presenting Similarities to others as Muslims36:09 - Need of being Relatable as Muslims42:34 - Discussion on being approachable to others46:03 - Hadith about Scholars' Ijma being binding47:09 - Br. Dowie joins: Being familiar in a society49:46 - Discourse on Imitating Non-Muslims 54:52 - Adapting to foreign cultures & customs 56:26 - Insights about calling to Islam at work1:04:38 - Dawah through being a good muslim1:10:33 - Exploring the muslim community in the UK1:14:43 - Our Responsibilities & Duties as Muslims1:18:28 - Spotlight on muslim crimes in the west1:19:51 - Tommy Robinson embarrassing himself1:22:28 - Uplift joins & shares his background 1:24:06 - Turning away from Islam due to Family 1:27:57 - Advice to Uplift: Islam vs Muslims1:31:41 - Starting the Journey back to Islam1:35:16 - Inviting Uplift back to Islam1:39:16 - Tips for Overcoming Trauma1:41:27 - Dawah to Uplift: The Veracity of Islam1:51:38 - Advice for learning & practicing Islam1:56:48 - Importance of Dua1:58:34 - Interacting with the Chat1:59:02 - Radio show of Br. Dowie & Br. Jordan2:03:04 - Focus on Quality & not Quantity in Islam2:04:39 - Omor joins & shares his background 2:05:52 - Father following a Spiritual Guru (Peer)2:10:31 - Discourse on following Yasir Qadhi2:14:31 - Wisdom of Prohibition of Intoxicants2:21:49 - Religious Ruling on Alcohol prior to Islam2:25:30 - The Benefits of Islamic Teachings2:29:22 - No Secret Marriages in Islam2:30:44 - Message to Non-Muslims about Islam2:34:50 - The Qur'an being Unique & Timeless2:39:46 - Future Prophecies coming true in Islam2:44:25 - Closing RSupport the show

In the initial hours after Bryan Kohberger's arrest, there was a frantic dash to try to find out as much information about him as possible. During that dash, those of us who were following along were able to get a glimpse of an instagram account that allegedly belonged to Bryan Kohberger. That same account was also following and interacting with Madison's account. A few hours later and that account was purged.In this episode, we hear from the Goncalves family who also saw that account and not only saw it, but they took screenshots as well. With many questions surrounding the motive in this case and the connection between the victims and the alleged murderer still in the air, could this be the glue that binds Bryan Kohberger? Let's dive in and give it a look. (commercial at 7:18)to contact me:bobycapucci@protonmail.comsource:Idaho student murders suspect Bryan Kohberger followed victims on Instagram, says family - CBS NewsBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-moscow-murders-and-more--5852883/support.

Q&R Night | Interacting With People of Other Religions as Christians | Johnny Fielding, Okumu Yudah Tadeo, Josh Bosheff, Crystal McCann

In this episode, Valerie and Armin share their recent trip to France, highlighting their art experiences and memorable moments. They discuss the Creativity Cruise, their adventures in medieval villages, and the inspiration they found in the French landscape. The episode also touches on their interactions with fellow artists and the cultural insights gained during their journey.The Creativity Cruise was a unique experience for artists.Topics from this adventure! Exploring medieval villages offered a glimpse into history.Artistic inspiration was found in the French landscape.Interacting with fellow artists enriched the journey.Cultural insights were gained through local experiences.The trip included teaching art on a cruise.Memorable moments were captured in photographs.The French language added to the cultural experience.The trip was both relaxing and inspiring.The journey fostered a deeper appreciation for art.Show notes: Creative 360 in Midland, MI (organizers of the Creativity Cruise 2025)Tracey Kempsell, Travel Agent at Go The Distance Travel AgencyAMA Waterways River CruiseADC Fine Art

In which I discuss a recent mistake I made in following neurotypical advice, and the lesson I learned about how important it is for neurodivergent folks to know how to assess what is safe for their nervous systems before taking action.Also in this episode: my podcast's mini rebrand and a diagnosis I got this summer.My name is Kendra and I'm an AuDHD writer, podcaster, and erstwhile social scientist.

I'm diving deep into the pros and cons of doing a live podcast, inspired by a question from Ralph over at askralph.com. If you've ever considered going live with your show—whether with video or audio—you'll want to hear my honest take after years of experience.I break down what it's really like to start live streaming, especially if you're new to podcasting or just thinking about expanding into video. From gear recommendations, background setups, and green screens to my latest waste-of-money purchase (ouch!), I share what works, what doesn't, and what you should really consider before investing.You'll hear why consistency in scheduling is key and how I built a loyal live audience for Ask the Podcast Coach, which now feels like the new Saturday morning cartoons for my regulars. I talk candidly about realistic audience expectations—don't be shocked when only a handful show up at first!—and why you shouldn't start live if you're overwhelmed by tech.I run through my favorite live streaming platforms, like Restream, eCamm Live, EVMux, and Streamyard, and why I personally steer clear of Riverside despite its flashy features. I talk camera options, from affordable 4K webcams to fancy DSLRs, and stress why you should practice, practice, practice before going live in front of folks.You'll also get my advice on handling live chat, co-host dynamics, the need for two monitors, and the occasional awkwardness of booting guests off gracefully. I share war stories about live mishaps, from surprise guests to technical gremlins, and why your audience is nearly always rooting for you.If you're weighing whether the extra expense, learning curve, and time spent prepping for live shows is worth it, I'll walk you through how it's brought me closer to my audience and generated great content for my main show. Spoiler: For me, the connection, fun, and instant feedback have made it worthwhile—even if those first few live shows felt like talking to a brick wall!Takeaways: Going live can totally change your podcast game, but it comes with a whole load of tech headaches. There are so many tools out there for live streaming, but honestly, they all have their quirks and bugs. You really need to be consistent with your live show schedule, or folks will forget about you faster than a sneeze in a windstorm. Interacting with your audience live can spark some killer ideas, so don't shy away from it! Don't overthink your video background; if they care more about your messy room than your words, that's a problem. Prepare like you're about to run a marathon, because going live isn't just 'turn it on and chat' - it's a whole production! Equipment MentionedNeewer Keylight KitObsbot Meet 4k CameraYolo 3 4K CameraLogitech MX Brio Ultra HD 4KSony ZV-E10 II DSLR...

What happens when you've built multiple companies, lost control, got burned by partners, and then finally get it right? Patrick Dillon has done it all. He's built several digital agencies, and even a janitorial company. He's taken on investors, lost equity, made painful pivots, and come out the other side with a lean, wildly profitable agency. Today, his company, WISE Digital, is scaling steadily without the risk of it all collapsing from one bad client, partner, or decision. In this episode, we break down how Pat used AI to scale without bloat, what really went wrong with his first three ventures, and why he'll never niche down again. We also get into the recruiting system that saved him thousands of hours, the legal dispute ChatGPT helped him settle, and how to avoid toxic positivity while building something that actually lasts. If you're in agency life, you'll feel this one. Listen in and find out what it really takes to build a growth engine you actually want to run.  — This episode is part of the 8FE (8-figure entrepreneur) series, where we talk to entrepreneurs who have already passed the million-dollar mark.  — Key Takeaways: 00:00:00 Intro 00:01:53 Undervalued marketing tactics  00:04:24 Unusual AI use cases  00:08:03 Patrick's entrepreneurial journey  00:18:38 Interacting with investors  00:21:29 When to pull the plug  00:27:57 Transition to WISE Digital  00:37:43 Focusing on SMB  00:40:02 Growing the business through AI  00:51:57 SEO strategies and the impact of AI  00:58:58 Advice for aspiring entrepreneurs  01:01:21 Outro — Additional Resources:

In this engaging conversation, Melissa Millett shares her journey into the world of animal training, highlighting the passion that drives her work. She discusses the importance of mentorship in navigating the complexities of the film industry, the role of animal coordinators, and the preparation required for animals on set. Melissa offers valuable advice for actors working with animals, emphasizing the need to understand animal behavior and the unique challenges presented by different species. The discussion also touches on the impact of AI in film, memorable moments from her career, and her excitement for future projects, all while advocating for the respectful treatment of animals in the industry.TakeawaysThe passion for animal training is key to success in the industry.Navigating the film industry can be confusing without proper mentorship.Mentorship is crucial for understanding the complexities of working on set.Animal coordinators play a vital role in ensuring the well-being of animals on set.Preparation and training are essential for animals to perform well in film.Actors should follow the guidance of animal trainers to ensure safety and effectiveness.Interacting with animals on set requires understanding their behavior and needs.https://www.ultimutts.ca/ https://indogswetrust.ca/ Hosted on Acast. See acast.com/privacy for more information.

In this episode of Not Alone, Valeria sits down with Amanda Hirsch, the voice behind the hit podcast and Instagram account Not Skinny But Not Fat. Known for her witty, unfiltered takes on pop culture and reality TV, Amanda opens up about her journey from acting and odd jobs in Israel to becoming one of the internet's go-to voices on celebrity culture. They dive into everything from boy-mom life and her unapologetic obsession with pop culture (yes, the Britney and Justin breakup was a turning point) to the behind-the-scenes of launching Not Skinny But Not Fat. Amanda shares how she went from a meme page to interviewing A-listers, how becoming a mom didn't change things, and how she balances sharing opinions while navigating the sensitive side of gossip. From her first big celebrity interview to the moments that made her spiral, her reality TV recs, and what it's really like meeting your icons, Amanda proves that loving the “silly stuff” can build a serious career. It's a funny, honest, and refreshing conversation about building something on your own terms, and how you can feel truly content when you embrace what you love. Follow Amanda on Social Media Instagram: https://www.instagram.com/notskinnybutnotfat/?hl=en TikTok: https://www.tiktok.com/@notskinnybutnotfat Podcast: https://www.youtube.com/@notskinnybutnotfat  Get Tickets to Amanda's Live Show: https://www.livenation.com/event/k7vGFbiQbfwrP/one-night-only-with-amanda-hirsch Shop my look from this episode: http://shopmy.us/shop/collections/2358015 Follow me: https://www.instagram.com/valerialipovetsky/  What we talked about:  0:26 - Episode Intro 0:30 - Valeria on work trips and being away from home 1:24 - Intro to Amanda Hirsch 2:58 - Start of Interview 3:01 - Being a boy mom 5:02 - Amanda's current show obsession 7:40 - The start of the pop culture obsession  8:02 - The Britney and Justin breakup 9:52 - Media-trained celeb culture 11:06 - Loving ‘Love Thy Nader' 12:06 - Amanda Hirsch's acting career 15:10 - Working odd jobs in Israel 17:41 - Being content with life 20:15 - The hard move back to New York  22:32 - Starting ‘Not Skinny but Not Fat' 23:26 - Interacting with the OG Meme Accounts 24:20 - Bringing Amanda and her opinion to the account 26:51 - Knowing something would come of it all 27:45 - Not being aesthetic, being approachable 30:02 - Kids don't stop your career 31:07 - Loving the same dumb things after kids 32:35 - Getting nitty gritty DMs & not posting 33:45 - Connecting on pop culture for water cooler talk 35:35 - Knowing all the pop culture gossip in Israel 36:50 - Sensitivity with sharing information 40:50 - Interview moments that made her spiral after 42:59 - Watching cringey celebrity interviews 44:13 - Amanda's first big celebrity interview with Erin Foster 46:30 - Still excited to do her job 48:27 - Getting cancelled on 3 times by her idol 50:00 - Meeting her pop culture heroes  51:05 - Meghan Markle 51:51 - Amanda's first live show 54:35 - Wanting to quit ‘Love Island'  55:50 - Amanda's reality TV recommendations 56:54 - Valeria's pop culture obsession Learn more about your ad choices. Visit megaphone.fm/adchoices

Join host Jesse Jackson as he celebrates the 10th anniversary of the Set Lusting Bruce podcast. This special episode features an engaging conversation with Jake Thistle, a talented singer-songwriter from New Jersey. Jake shares his journey in music, his love for Tom Petty and Bruce Springsteen, and his involvement with light of day foundation. He also talks about his YouTube journey, his new music releases, and his experiences performing both solo and with a band. Tune in for a heartfelt celebration of Bruce Springsteen's music and its impact on fans worldwide. https://jakethistle.com/ 00:00 Celebrating a Decade of Set Lusting Bruce 01:22 Introducing Jake Thistle: A Musical Journey 02:04 Jake's Early Musical Influences 03:38 Learning Guitar and Early Performances 17:55 Discovering Bruce Springsteen 27:12 Accountability in Music Practice 27:39 Discussing Recent Music Releases 28:14 Balancing Education and Music Career 28:46 Solo vs. Band Performances 30:13 Collaborations and Community Building 31:11 Involvement in David Berry's Film 32:52 Chasing Musical Mastery 34:58 Reflecting on Influences and Inspirations 37:11 Memorable Encounters with Music Legends 39:56 Backstage at the Springsteen Archives 44:38 Interacting with Monmouth University 46:24 Final Thoughts and Future Plans Learn more about your ad choices. Visit megaphone.fm/adchoices

Matt Slick Live (Live Broadcast of 09/16/2025) is a production of the Christian Apologetics Research Ministry (CARM). Matt answers questions on topics such as: The Bible, Apologetics, Theology, World Religions, Atheism, and other issues! You can also email questions to Matt using: info@carm.org, Put "Radio Show Question" in the Subject line! Answers will be discussed in a future show. Topics Include: Matt Discusses the Rise of EO/Interaction with an EO Prospect/ Those Who "Come" to Jesus/An example of Ignoring Context/ Is Jesus The "LORD" of Psalm 23?/Is Jesus the "Judge?"/ Does Biblical Text Accompany Prayer?/How Does this relate to What People Believe about a Word from The Holy Spirit?/ A Former Roman Catholic wants Advice on Interacting with Family Members who are still RC/ What About "Mega-Church" Rich Christian Pastors?/ What About Christians Carrying Personal Protection Devices?/ September 16, 2025

In this episode, Father Anthony, Sister Catherine, and their guest, Bishop Giles, OFM, engage in a thoughtful discussion addressing listener inquiries regarding interactions with non-Catholic friends, with a focus on those from Protestant backgrounds. To listen to Father Anthony reading from the book, Confessions of a Roman Catholic, along with his commentary: https://rumble.com/v5e2ll1-confession-of-a-roman-catholic-part-1-w-fr.-anthony-lentz-ofm-catholic-fait.html https://rumble.com/v5e2mgt-confession-of-a-roman-catholic-part-2-w-fr.-anthony-lentz-ofm-catholic-fait.html

Today, I am pleased to welcome Craig Armstrong, Managing Partner of Veridian, a Miami-based tax, audit, accounting, and advisory firm. Craig has over 25 years of public accounting experience, including serving clients ranging from Fortune 500 companies to local or middle market companies, as well as high net worth family offices and individuals. He began his career with the firm of Williams, Cox, Weidner, and Cox in Tallahassee, Florida, and held prior roles as Senior Manager of Corporate Accounting Special Projects with Ryder System, Inc. and South Florida Site Leader for audits of employee benefit plans at PwC in Miami. Craig co-founded CAPA, a certified public accounting firm in 2004 and merged with Hancock Askew in 2020 prior to forming Veridian in 2025. He serves on the audit and finance committee of the Board of Directors for Catholic Charities of the Archdiocese of Miami and is a member of the University of Miami's School of Business Accounting Advisory Board. Craig and his firm, Veridian, are advisor members of FOX and we are thrilled to have their expertise available within our membership community. In recent years, technological advancements have transformed the role of the advisor serving UHNW families. Craig talks about how the role of the advisor has evolved alongside technology, and particularly how the role of the CPA has changed. With the rise of AI, we are beginning to witness the next-stage transformation of the family advisor role. Craig shares his thoughts on what is likely to happen to the role of the fiduciary advisor in the era of AI – how will CPAs, estate planners, and wealth planners will coexist with the AI tools. One practical consideration advisors and families need to navigate is the increasingly divergent preferences between older-gen and rising-gen clients. Craig offers his tips on how professionals and clients can best manage the “generational tug of war” caused by their different values and preferences. Finally, Craig provides his and suggestions for UHNW clients on how best to interact with their CPA – especially given all the self-serve tools and AI solutions that are increasingly available in all professional services fields. Please enjoy this highly informative conversation with a leading expert and experienced UHNW advisor serving enterprise families.

Interacting with people from different generations has been shown to accelerate students' social skills, improve literacy, and provide valuable lessons about history and culture. However, many students have limited opportunities to engage with older generations. And when these interactions do happen, they often remain one-sided or surface-level. In this episode, MindShift explores intergenerational programs at two schools, highlighting their benefits and uncovering research-backed strategies for creating impactful and enriching experiences for all involved.

Upwork: Visit https://upwork.com right now to post your job for free Pipedrive: Get started with a 30 day free trial https://pipedrive.com/iced ZocDoc: Go to https://www.zocdoc.com/ICED and download the Zocdoc App for FREE Shopify: Sign up for a $1 per month trial period at https://shopify.com/ich $1 will provide 1 person with clean water for a year: Go to https://teamwater.org to donate today! Follow Ian Bick: On Youtube - https://youtube.com/@ianbickCT?si=NbheCwTwOkesCXzP On Instagram - https://www.instagram.com/ian_bick Website - https://www.ianbick.com/ Apply for The Index Membership: https://entertheindex.com/ Add us on Instagram: https://www.instagram.com/jlsselby https://www.instagram.com/gpstephan Official Clips Channel: https://www.youtube.com/channel/UCeBQ24VfikOriqSdKtomh0w For sponsorships or business inquiries reach out to: tmatsradio@gmail.com For Podcast Inquiries, please DM @icedcoffeehour on Instagram! Timestamps: 00:00:00 - Intro 00:01:42 - Hearing about Beast vid 00:02:35 - Are prison influencers real? 00:04:42 - Most viral prison story 00:08:07 - How MrBeast reached out 00:10:27 - Vetting for Beast vid 00:12:40 - Was Beast prison realistic? 00:13:34 - Was drama in video real? 00:18:10 - Sponsor - Upwork 00:23:36 - Beast prison library 00:25:45 - Interacting with others 00:28:49 - What Beast cut from video 00:31:37 - Living without a phone 00:33:41 - Sponsor - Pipedrive 00:34:59 - Living with a stranger 100 days 00:38:37 - Strategy for the challenge 00:42:59 - Winning $500,000 00:43:42 - Restitution payments explained 00:46:01 - Live events business and why it failed 00:57:57 - Do you regret what you did 01:04:05 - Sponsor - Zocdoc 01:05:15 - Sponsor - Shopify 01:10:28 - First day in jail 01:13:36 - Food in prison 01:14:39 - Smuggling in prison 01:16:15 - Hardest part of prison 01:16:55 - First day fight myth 01:18:28 - Most fun thing in prison 01:18:49 - Getting used to no privacy 01:21:31 - Violence in prison 01:25:07 - Smuggling continued 01:27:42 - How to improve prison system 01:29:21 - Dating in prison 01:33:23 - Making friends inside 01:33:52 - Most difficult experience 01:35:29 - Choice to not split money 01:38:24 - Watching the video back 01:39:10 - Opportunities since Beast vid 01:42:42 - Advice for people going to jail 01:43:16 - Prison habits you still have 01:44:54 - Constant distrust in prison? 01:46:43 - Smart prisoners 01:48:23 - Any innocent prisoners? *Some of the links and other products that appear on this video are from companies which Graham Stephan will earn an affiliate commission or referral bonus. Graham Stephan is part of an affiliate network and receives compensation for sending traffic to partner sites. The content in this video is accurate as of the posting date. Some of the offers mentioned may no longer be available. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Summerlin South Little League is back in Las Vegas! We talk to Coach TJ Fechser how it was interacting with the Chinese Taipei off the field and the Downtown Summerlin Parade happening on Wednesday, August 27th! Photo Credit: © Kyle Ross-Imagn Images

“Chrissie Cartwright wants more than anything, she wants specificity. So she doesn't necessarily need to know what all of the relationships are that you're playing, as long as it's clear.” In this newest episode, we chat with Michael Robert-Lowe, the actor currently playing Old Deuteronomy in the International tour of CATS. They dive into Michael's journey with the show, his thoughts on the character dynamics, and how he interprets various elements of the musical. From discussing the musical's cultural impact across various countries to entertaining theories about Old Deuteronomy's role, this episode offers fans an insightful and engaging look behind the scenes of this beloved production. 01:07 Michael's Journey with CATS 07:07 Experiences on the International Tour 15:27 Interacting with Fans 20:36 Old Deuteronomy's Decision-Making Process 30:28 The Sacrificial Gesture of Old Deuteronomy 31:32 Interpreting Relationships and Hierarchies 33:06 Character Evolution and Cast Changes 36:38 Audience Interaction and Immersion 43:39 Rapid Fire Check out Michael on Social Media: @michaelrobertlowe Check out the International Tour of CATS: ⁠tour.catsthemusical.com⁠ Produced by: ⁠⁠⁠⁠⁠⁠⁠Alan Seales⁠⁠⁠⁠⁠⁠⁠ & ⁠⁠⁠⁠⁠⁠⁠Broadway Podcast Network⁠⁠⁠⁠⁠⁠⁠ Social Media: @⁠⁠⁠⁠⁠⁠⁠TheWrongCatDied Learn more about your ad choices. Visit megaphone.fm/adchoices