Podcasts about boltz

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

This week we dig into the hardware shortage caused by AI, answer your questions, and dig into managing ZFS via the web! -- During The Show -- 00:45 Intro Cheap managed POE switch Switch hops 05:35 Certificates - Randy Step CA (https://smallstep.com/docs/step-ca/) XCA (https://www.hohnstaedt.de/xca/) Certificate Authority (https://en.wikipedia.org/wiki/Certificate_authority) ACME (https://en.wikipedia.org/wiki/Automatic_Certificate_Management_Environment) LDAP (https://en.wikipedia.org/wiki/Lightweight_Directory_Access_Protocol) Kerberos (https://en.wikipedia.org/wiki/Kerberos_(protocol)) Steve's use of LDAP LDAP with PKI link (https://enterprise.arcgis.com/en/portal/11.4/administer/linux/use-ldap-and-pki-to-secure-access-to-your-portal.htm) ACME and Domain registrars dot tk (http://www.dot.tk/en/index.html?lang=en) Ansible collection (https://docs.ansible.com/projects/ansible/latest/collections/community/crypto/acme_certificate_module.html) 19:19 Ebook Management - Jeremy Steve went to audio books Calibre (https://docs.ansible.com/projects/ansible/latest/collections/community/crypto/acme_certificate_module.html) PDF manuals folder Audio bookshelf (https://www.audiobookshelf.org/) Paperless NGX (https://docs.paperless-ngx.com/) 23:50 Light Sync - Peter UltraStar Deluxe (https://usdx.eu/) Animux (https://usdb.animux.de/) USBD_Syncer (https://github.com/bohning/usdb_syncer/releases) Doing events Why Noah likes Karaoke Effect of "shared experiences" Steve's Christmas tree lights DMX lighting WLED Project (https://kno.wled.ge/) 33:03 News Wire Firefox 147 - firefox.com (https://www.firefox.com/en-US/firefox/147.0/releasenotes/) Thunderbird 147 - thunderbird.net (https://www.thunderbird.net/en-US/thunderbird/147.0/releasenotes/) Grub 2.14 - phoronix.com (https://www.phoronix.com/news/GRUB-2.14-Released) Gnome 49.3 - discourse.gnome.org (https://discourse.gnome.org/t/gnome-49-3-released/33609) Wine 11 - theregister.com (https://www.theregister.com/2026/01/15/wine_11_arrives_faster_and/) Q4OS 6.5 - q4os.org (https://www.q4os.org/forum/viewtopic.php?id=5903) Endeavour OS Genymede Neo - endeavouros.com (https://endeavouros.com/news/ganymede-neo-is-out-with-core-updates-and-upstream-nvidia-changes/) Tails 7.4 - torproject.org (https://blog.torproject.org/new-release-tails-7_4/) Linux Mint 22.3 - blog.linuxmint.com (https://blog.linuxmint.com/?p=4981) BeaglePlay PowerVR - phoronix.com (https://www.phoronix.com/news/BeaglePlay-PowerVR-Success) StackChan - cnx-software.com (https://www.cnx-software.com/2026/01/13/m5stack-stackchan-is-a-cute-open-source-ai-desktop-robot/) Mentra's Smart Glasses - engadget.com (https://www.engadget.com/wearables/mentras-first-smart-glasses-are-open-source-and-come-with-their-own-app-store-150021126.html) VoidLink - checkpoint.com (https://research.checkpoint.com/2026/voidlink-the-cloud-native-malware-framework/) darkreading.com (https://www.darkreading.com/cloud-security/voidlink-malware-advanced-threat-linux-systems) csoonline.com (https://www.csoonline.com/article/4117038/sophisticated-voidlink-malware-framework-targets-linux-cloud-servers.html) Boltz-1 - labmanager.com (https://www.labmanager.com/mit-researchers-release-boltz-1-an-open-source-alternative-to-alphafold-3-33385) Photoshop on Linux - videocardz.com (https://videocardz.com/newz/adobe-photoshop-can-now-install-on-linux-after-a-redditor-discovers-a-fix#disqus_thread) No Commits to MySQL Repo - devclass.com (https://devclass.com/2026/01/13/open-source-mysql-repository-has-no-commits-in-more-than-three-months/) Senate Inquiry - jdsupra.com (https://www.jdsupra.com/legalnews/recent-inquiry-from-senate-intelligence-2158429/) EU Tech Sovereignty - cybernews.com (https://cybernews.com/tech/europe-looks-for-ways-to-cut-cord-from-big-tech/) biometricupdate.com (https://www.biometricupdate.com/202601/eu-calls-for-input-on-open-source-as-it-looks-toward-tech-sovereignty) 35:03 SysAdmins & Smartphones Lowering friction Graphical vs CLI Webzfs (https://github.com/webzfs/webzfs) Exposing ZFS via Web UI Cockpit Putting Webzfs into Cockpit Write in! 43:43 New ESP32 ESP32-E22 Tri-band WiFi What is an ESP32 Steve's use of ESP32 Bandwidth Getting started with ESP32 linuxgizmos.com (https://linuxgizmos.com/esp32-e22-debuts-with-tri-band-wi-fi-6e-and-dual-mode-bluetooth/) 48:05 AI Hardware Run RAM spikes 300%-400% SSD price spikes Fab Capacity Bitcoin effect ARS Technica (https://arstechnica.com/gadgets/2026/01/ram-shortage-chaos-expands-to-gpus-high-capacity-ssds-and-even-hard-drives/) -- The Extra Credit Section -- For links to the articles and material referenced in this week's episode check out this week's page from our podcast dashboard! This Episode's Podcast Dashboard (http://podcast.asknoahshow.com/476) Phone Systems for Ask Noah provided by Voxtelesys (http://www.voxtelesys.com/asknoah) Join us in our dedicated chatroom #GeekLab:linuxdelta.com on Matrix (https://element.linuxdelta.com/#/room/#geeklab:linuxdelta.com) -- Stay In Touch -- Find all the resources for this show on the Ask Noah Dashboard Ask Noah Dashboard (http://www.asknoahshow.com) Need more help than a radio show can offer? Altispeed provides commercial IT services and they're excited to offer you a great deal for listening to the Ask Noah Show. Call today and ask about the discount for listeners of the Ask Noah Show! Altispeed Technologies (http://www.altispeed.com/) Contact Noah live [at] asknoahshow.com -- Twitter -- Noah - Kernellinux (https://twitter.com/kernellinux) Ask Noah Show (https://twitter.com/asknoahshow) Altispeed Technologies (https://twitter.com/altispeed)

Cofounders Jeremy Wohlwend and Gabriele Corso join the a16z podcast to discuss the launch of Boltz, a public benefit company building AI infrastructure for molecular biology. The conversation explains how breakthroughs following AlphaFold moved the field beyond protein structure prediction into modeling biomolecular interactions and binding strength, why open-source Boltz models saw rapid adoption across pharma and biotech, and how that work is now being productized. They outline the launch of Boltz Lab, a platform that brings protein and small-molecule design agents into scientist workflows, Boltz's decision to operate as an infrastructure company rather than a therapeutics company, and how AI could reduce early drug discovery bottlenecks by improving molecular design and speeding iteration between computation and the lab. Resources: Follow Gabriele on X: https://twitter.com/GabriCorso Follow Jeremy on X: https://twitter.com/jeremyWohlwend Follow Jorge X: https://twitter.com/jorgecondebio Follow Zak on X: https://twitter.com/zakdoric   Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X:https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://twitter.com/eriktorenberg](https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A biztonsági garanciák a legfőbb téma Zelenszkij és Trump floridai tárgyalásán Donald Trump már látja a békemegállapodást Boltzár, 4 napos hosszú hétvége jön: íme a Lidl, Aldi, Penny és a többi üzletlánc nyitvatartása szilveszterkor, újévkor Tombol a hideg, Demcsák Zsuzsa mégis egy tűzpiros, lenge nyári ruhában mutatta meg tökéletes alakját a tetőteraszon A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A biztonsági garanciák a legfőbb téma Zelenszkij és Trump floridai tárgyalásán Donald Trump már látja a békemegállapodást Boltzár, 4 napos hosszú hétvége jön: íme a Lidl, Aldi, Penny és a többi üzletlánc nyitvatartása szilveszterkor, újévkor Tombol a hideg, Demcsák Zsuzsa mégis egy tűzpiros, lenge nyári ruhában mutatta meg tökéletes alakját a tetőteraszon A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

En los 2 últimos años han aparecido una nueva familia de billeteras "lightning" que realmente no lo son. Muun abrió camino con este tipo de soluciones pero ahora tenemos cosas como Aqua, Bull o un plugin de BTCPayServer que hace que no necesites gestionar canales. ¿Qué esta pasando? ¿Qué son todas estas nuevas capas 2 como ARK o SPARK que dicen mejorar Lightning? ¿Ha muerto la capa 2 de Bitcoin? ¿Sigue siendo útil? Todo esto y mucho mucho más, en una genial charla con Kilian de Boltz.exchangeLINKSX de Kilian: https://x.com/kilrauWeb de Boltz: https://boltz.exchange/Más información de billeteras mencionadas en mi blogÚnete a mi correo 

Recently, Super Testnet built Papa swaps: a novel & optimistic way of doing atomic swaps on Bitcoin. In this episode, he talks about his new projects, why he is in favor of filtering the mempool + BIP 444 activation, and prediction markets. Time stamps: 00:01:28 - Intro: Super Testnet's Return to the Bitcoin Takeover Podcast 00:03:03 - Lightning Privacy Wars: Recapping Super's Monero Challenge & Layer 2 Debates 00:03:43 - Papa Swaps Unleashed: Super's Lightning-Fast Innovation Explained 00:04:57 - Submarine Swaps 101: From Layer 1 to Layer 2 in a Flash 00:06:04 - Phoenix Wallet Magic: Splicing vs. Submarine Swaps – Why Capacity Matters 00:07:55 - Birth of Papa Swaps: From Mexico Chats to Single-Transaction Breakthrough 00:09:19 - Why "Papa"? The Hilarious Submarine Speed Pun Behind the Name 00:10:23 - Hedgehog Protocol Update: When Will It Launch? (Spoiler: Probably Never) 00:12:00 - Hedgehog's Fate & Super's Conference Show-and-Tells 00:12:47 - Papa Swaps Deep Dive: Relative Time Locks & Happy vs. Sad Paths 00:14:47 - How Papa Swaps Work: Secrets, HTLCs, & Atomic Swaps Simplified 00:18:33 - Risks & Tradeoffs: Double Spends, RBF, & Trust in Small Transactions 00:21:02 - Block Space Savings: Papa Swaps vs. Boltz, Moon Wallet, & Lightning Loop 00:22:29 - Papa Swaps' Edge in a Crowded Layer 2 World 00:23:29 - Sidechain Shoutouts: Citrea, Alpen, & Scaling Debates Revisited 00:24:32 - Papa Swaps Today: Proof-of-Concept, Mainnet Risks, & Wallet Adoption 00:27:02 - Will Phoenix & Breeze Integrate Papa or Hedgehog? 00:28:47 - Boltz Exchange Scoop: CEO Kilian Rausch & Co-Founder Michael 00:29:35 - Lightning History: Joule, Bottle Pay, & Nostr Wallet Connect Ideas 00:33:06 - Ad Break: Layer 2 Labs' Drivechains 00:34:57 - Sideshift.ai: Swap Stables for BTC 00:37:10 - BIP 444 Drama 00:38:18 - Spam Filters Work for Bandwidth Savings 00:39:32 - Miners' Risks: Orphan Blocks & 50% Filter Adoption Scenarios 00:42:44 - Mempool Art: Portland Hodl's Block Painting Software & Mara Pool Deals 00:44:28 - Spam Defined: Extra Data vs. Permissionless Purity Debate 00:48:21 - BitVM Dreams: Catching Pikachu on Bitcoin Without Spam 00:50:29 - Citrea & Alpen: BitVM 2/3, ZK Proofs, & Data Availability Concerns 00:52:55 - Citrea Marketing Myths: Inscriptions Over OP_RETURN in Launch 00:53:29 - OP_RETURN vs. Inscriptions: Base Space Scarcity & Pruning Debates 00:56:35 - BIP 444 Breakdown: Temporary Spam Ban & Consensus Changes 01:00:06 - Legal Slippery Slope? OFAC Lists, Sanctions, & Permissionless Fears 01:02:05 - BIP 444 Odds & Details 01:07:15 - Inscriptions as Anchors: Layer 2 Onboarding or Hidden Spam? 01:10:26 - OP_RETURN Drama: V30 Update vs Filters 01:13:31 - Community Toxicity: "Knotzis," "Coretards," & Ad Hominem Fallacies 01:16:40 - Pleb Slop & Purity Quests: Dogma vs. Base Layer Privacy Push 01:20:01 - Spam Doesn't Pay Node Runners – Miners Only 01:21:42 - Pro-Choice Nodes: Custom Policies, Wizards, & Hackathon Ideas 01:25:18 - Windows Wizards & Idea Generation: Super's Creative Process 01:26:24 - Ad Break: Bitcoin.com News – Balanced Global Crypto Coverage 01:27:28 - NoOnes: Ray Youssef's P2P Marketplace for the Global South 01:29:57 - Chat Q&A: Money Transmitters, Legal Fears, & Miner Roles 01:34:00 - Spark Wallet Exposed: Privacy Leaks & Statechain Explorer Risks 01:35:31 - Mercury Wallet Nod: Statechains' Real-World Usage Milestone 01:35:31 - Statechains' Demand: Spark's Success vs. Mercury's Shutdown 01:36:05 - Blinded Servers: Hiding Balances & History in Statechains 01:37:20 - Privacy Mitigations: IP Hiding, VPNs, & Avoiding Key Reuse 01:38:43 - Spark Improvements: CoinJoins & Future Privacy Features 01:40:27 - GDPR Compliance: Bull Bitcoin's CoinJoins & Legal Privacy Push 01:41:57 - Nostr Frustrations: Searching Old Posts Sucks 01:42:58 - ARCash DExplained 01:48:31 - Spam Subjectivity & Consensus Rules Debate 01:51:08 - Objective vs. Subjective: Mempool Policies as Good Rules 01:53:10 - No Hard Fork: BIP 444's Low Adoption & Hash Rate Doubts 01:54:41 - Cultural Conflicts: Ossified Bitcoin & Soft Fork Stalls 01:55:26 - Influencer Consensus 02:37:56 - Bitcoin Prediction Markets: Non-Interactive DLCs & Proxies 02:38:41 - Poly Market UX: Early Exits & Position Transfers 02:40:03 - PSBT Auctions: Non-Interactive Sales Explained 02:41:48 - Agias Protocol: Native Bitcoin Prediction Markets 02:43:03 - Paul Sztorc Story 02:44:19 - Oracle Problem 02:45:48 - Hivemind Insights 02:46:57 - Build Agias 02:48:39 - Predix Collaboration 02:49:16 - Favorite Thinkers: Robin Linus, Liam Eagen 02:50:24 - BitVM's BSV Origins 02:52:52 - Turing Completeness & Craig Wright 02:54:58 - BitVM Evolution 02:55:24 - 2010 Spam Debates with Satoshi & OGs 02:56:52 - Block Size Wars vs. Current Fights 02:57:28 - Nostr Threads with Aaron van Wirdum 02:58:01 - Follow Super: Supertestnet.org 02:59:03 - Infighting Fuels Ethereum & Zcash Growth 02:59:29 - Outro: Thanks to Sponsors & Farewell

uniQure's “game changing” data announced in September, which showed significant slowing of Huntington's disease (HD) progression in patients treated with its gene therapy candidate AMT-130, may not be enough to secure FDA approval. We also discuss Recursion's pivotal leadership transitions, as Najat Khan, PhD, chief R&D officer and chief commercial officer, is set to take over as the company's CEO effective January 1. The AI drug developer has made big bets filling the biology data gap and recently announced a "Google Map of the brain" to advance neurodegenerative disease targets. In open-source AI for drug discovery, the release of the latest Boltz model, BoltzGen, advances the platform from structural predictions to the design of "any" therapeutic modality, all available for commercial use. Join GEN editors Corinna Singleman, PhD, Alex Philippidis, Fay Lin, PhD, and Uduak Thomas for a discussion of the latest biotech and biopharma news.    Listed below are links to the GEN stories referenced in this episode of Touching Base:  uniQure Staggers as FDA Questions Data for Huntington's Gene Therapy Candidate By Alex Philippidis, GEN Edge, November 3, 2025   StockWatch: uniQure Shares Reach Five-Year High on “Game Changing” Huntington's Data Alex Philippidis, GEN Edge, September 28, 2025 Gene Therapy Significantly Slows Huntington Disease Progression GEN, September 24, 2025 Recursion, Roche Unveil Microglia Map of Neuro Disease Targets By Alex Philippidis, GEN Edge, October 29, 2025 BoltzGen Democratizes AI Therapeutic Design, Expands Druggable Universe By Fay Lin, PhD, GEN, October 27, 2025 The State of AI in Drug Discovery On Demand    Touching Base Podcast  Hosted by Corinna Singleman, PhD  Behind the Breakthroughs  Hosted by Jonathan D. Grinstein, PhD    Hosted on Acast. See acast.com/privacy for more information.

Max and Q cover the latest happenings in the world of Bitcoin, privacy and much more. NEWSSoft fork proposalAlby attackSolo Miner wins a blockBitKey collaborative custoday improvement BIPLugano StreamWoS Spark privacy concernsUPDATES/RELEASESTrezor releaseLedger releaseArkade betaCake v5.5.0 + v5.5.1Bull by Bull BitcoinBitcoin for SignalSatGo integrates Spark Peach BTCPay PluginStack Duo v1.3.0RoninDojo v2.4.0EducationPassport guideAnd anotherCupcake deep diveSeth Ark articleArk explainer by NeilVALUE FOR VALUEThanks for listening you Ungovernable Misfits, we appreciate your continued support and hope you enjoy the shows.You can support this episode using your time, talent or treasure.TIME:- create fountain clips for the show- create a meetup- help boost the signal on social mediaTALENT:- create ungovernable misfit inspired art, animation or music- design or implement some software that can make the podcast better- use whatever talents you have to make a contribution to the show!TREASURE:- BOOST IT OR STREAM SATS on the Podcasting 2.0 apps @ https://podcastapps.com- DONATE via Monero @ https://xmrchat.com/ugmf- BUY SOME STICKERS @ https://www.ungovernablemisfits.com/shop/FOUNDATIONhttps://foundation.xyz/ungovernableFoundation builds Bitcoin-centric tools that empower you to reclaim your digital sovereignty.As a sovereign computing company, Foundation is the antithesis of today's tech conglomerates. Returning to cypherpunk principles, they build open source technology that “can't be evil”.Thank you Foundation Devices for sponsoring the show!Use code: Ungovernable for $10 off of your purchaseCAKE WALLEThttps://cakewallet.comCake Wallet is an open-source, non-custodial wallet available on Android, iOS, macOS, and Linux.Features:- Built-in Exchange: Swap easily between Bitcoin and Monero.- User-Friendly: Simple interface for all users.Monero Users:- Batch Transactions: Send multiple payments at once.- Faster Syncing: Optimized syncing via specified restore heights- Proxy Support: Enhance privacy with proxy node options.Bitcoin Users:- Coin Control: Manage your transactions effectively.- Silent Payments: Static bitcoin addresses- Batch Transactions: Streamline your payment process.Thank you Cake Wallet for sponsoring the show!MYNYMBOXhttps://mynymbox.netYour go-to for anonymous server hosting solutions, featuring: virtual private & dedicated servers, domain registration and DNS parking. We don't require any of your personal information, and you can purchase using Bitcoin, Lightning, Monero and many other cryptos.Explore benefits such as No KYC, complete privacy & security, and human support.(00:00:41) Welcome, show format, and brief housekeeping(00:05:19) UK weather banter and setting the scene(00:05:22) Events and product updates: Bitfest, Envoy 2.10, Passport audit(00:08:06) BIT-444 proposal to restrict arbitrary data on Bitcoin(00:12:03) Critiques: miniscript breakage, Peter Todd demo, and soft vs hard fork risk(00:18:26) Mining politics, hash power, and potential chain splits(00:18:33) Security incident: Alby password reset spam and email exposure(00:20:45) Feel-good story: solo miner finds a block via Public Pool on Umbrel(00:23:05) New BIP: Chaincode Delegation for private collaborative multisig(00:28:08) Conference notes and a privacy PSA on Spark implementations(00:32:28) Boosts and community feedback: swaps, Moon wallet UX, and Boltz reliance(00:37:09) Q&A: consolidating UTXOs, PayJoin, Whirlpool, and Robosats flows(00:42:11) Q&A: Running a self-hosted AlbyHub LDK node—backup and privacy(00:46:12) Hardware wallet releases: Trezor Safe 7 and Ledger Nano Gen 5(00:52:35) Multisig device choices and inheritance practicality(00:52:38) ARC in the wild: Arcade.money public beta hands-on(00:53:55) Cake Wallet 5.5 updates and hardware support(00:54:22) Bull Bitcoin releases Bull Wallet: features and roadmap(00:58:12) eCash in Signal fork: UX gains vs custodial trade-offs(01:02:30) Spark adoption notes: SatGo and Wallet of Satoshi privacy caveats(01:03:31) Peach plugin for BTCPay and Stack Duo's Frost multisig progress(01:05:06) RoninDojo 2.4 and Fulcrum 2.0 stability improvements(01:06:03) Education picks and closing logistics(01:07:29) Stats corner addendum by John: RoboSats, Whirlpool, Bisq, and more

Elárulta a Tisza, milyen kompromisszumot kötöttek a Sziget megmentésére Sztrájkot is kilátásba helyeztek a budapesti közszolgáltatások dolgozói Karácsony kijelentése után Népszerű ásványvizet hívott vissza a fogyasztóvédelem – emiatt tilos az elfogyasztása Lázár János ismét beszólt Mészáros Lőrincnek Megvalósult egy majdnem 150 éves román álom Hatalomváltás a Fidesz-közeli médiabirodalomban – a Mediaworkstől érkezett az erdélyi Kesma új vezetője Még idén dönthet a parlament az ingyenes készpénzfelvétel bővítéséről "Csodálatos megbeszélés": Donald Trump és Hszi Csin-ping sikeresen megállapodtak Nem volt feszültségtől mentes Erdogan és Merz ankarai sajtótájékoztatója Érkezik Jimmy első unokája? Zámbó Krisztián így készül az apaságra Boltzár jön a hétvégén - így alakul az Aldi, Lidl, Penny, Spar és a többi üzletlánc nyitvatartása A földbe döngölték Massát a bíróságon a 2008-as világbajnoki címért folytatott pereskedés során Szalah máris talált magának új csapatot? Első kézből jött a válasz Kellemes idővel kezdődik a hétvége, de nem tart ki mindenhol; vajon hozzád is elér az eső? A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Elárulta a Tisza, milyen kompromisszumot kötöttek a Sziget megmentésére Sztrájkot is kilátásba helyeztek a budapesti közszolgáltatások dolgozói Karácsony kijelentése után Népszerű ásványvizet hívott vissza a fogyasztóvédelem – emiatt tilos az elfogyasztása Lázár János ismét beszólt Mészáros Lőrincnek Megvalósult egy majdnem 150 éves román álom Hatalomváltás a Fidesz-közeli médiabirodalomban – a Mediaworkstől érkezett az erdélyi Kesma új vezetője Még idén dönthet a parlament az ingyenes készpénzfelvétel bővítéséről "Csodálatos megbeszélés": Donald Trump és Hszi Csin-ping sikeresen megállapodtak Nem volt feszültségtől mentes Erdogan és Merz ankarai sajtótájékoztatója Érkezik Jimmy első unokája? Zámbó Krisztián így készül az apaságra Boltzár jön a hétvégén - így alakul az Aldi, Lidl, Penny, Spar és a többi üzletlánc nyitvatartása A földbe döngölték Massát a bíróságon a 2008-as világbajnoki címért folytatott pereskedés során Szalah máris talált magának új csapatot? Első kézből jött a válasz Kellemes idővel kezdődik a hétvége, de nem tart ki mindenhol; vajon hozzád is elér az eső? A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Francis is the Founder of Bull Bitcoin, one of the best bitcoin brokerage services. They recently launched a self custody mobile wallet on ios and android: Bull Wallet. This rip was earlier today, in person, in Lugano. We are both here for the PlanB Forum.Francis on Nostr: https://primal.net/francisFrancis on X: https://x.com/francispouliot_Bull Wallet: https://wallet.bullbitcoin.comPlanB Forum: https://planb.lugano.ch/planb-forum/EPISODE: 181BLOCK: 920238PRICE: 929 sats per dollar(00:00:00) Catching up from Lugano: old rips, 2019 memories, and lore(00:01:07) Introducing Bull Wallet: goals, BDK under the hood, and UX vision(00:02:21) Lightning lessons: LDK attempts, liquidity pain, and Phoenix tradeoffs(00:04:38) Fee shocks and “just-in-time” channels: why Liquid entered the plan(00:06:24) Liquid + Boltz atomic swaps: architecture, Rootstock notes, and standards push(00:07:37) Designing for privacy and power users: Sparrow on mobile, Payjoin journey(00:12:21) Data minimization in practice: no push, no cloud leaks, strict dependencies(00:13:56) Shipping realities: iOS approval grind and cross‑platform necessity(00:15:11) Secure versus Instant: Liquid framing, hype cycles, and collaborative custody(00:19:13) The UX–compromise matrix: placing Liquid among trust models(00:21:24) Auto‑swap safety rails: keeping Liquid balances modest by default(00:24:26) Confidential transactions and buying flows: privacy wins via Liquid(00:27:24) Swap providers and resilience: Boltz today, multi‑provider tomorrow(00:31:05) Builders who ship: moving fast, potentially Ark or Spark in the future(00:32:11) Why Ark: unilateral exits, pre‑confirmed states, and costs(00:50:00) Ark tradeoffs without soft forks: watchtowers, refresh, and liveness(00:57:15) Ark vs Liquid in Bull Wallet: timelines, reckless testing, and user defaults(01:04:12) Payjoin, heuristics, and passive consolidation(01:11:01) Consolidation strategies: Liquid swaps, CT, and fee‑aware UTXO management(01:15:48) Lightning address UX with Liquid: directories, costs, and tiny payments(01:17:10) Nostr as a wallet backend: zaps, contact book, and secure comms(01:18:01) Multisig in practice: BitPay's UX, Miniscript future, and mobile approvals(01:20:19) Operator messaging and alerts: following npubs inside the app(01:20:49) Core v30, OP_RETURN, and policy vs consensus(01:31:09) Funding open source: OpenSats, private companies, and shipping culture(01:35:15) Why open source keeps us sane: creativity, legacy, and closing notesmore info on the show: https://citadeldispatch.comlearn more about me: https://odell.xyz

Steve Boltz in conversation with David Eastaugh  https://www.steveboltz.co.uk/ https://www.facebook.com/groups/920848814634353/ In 1971 he was recruited into the band Atomic Rooster, part of a new line-up for a tour supporting the band's third album In Hearing of Atomic Rooster and their No. 4 charting single "The Devil's Answer". The band was also recording their fourth LP Made in England which was released in 1972 with a more funky sound replacing their original progressive rock leanings. Bolton also appeared on Devil's Answer: Live on the BBC released in 1998, and on the release of In Satan's Name: The Definitive Collection.  

Welcome back to the "Rock Vault" and today Joe Lynn Turner remmebers his audition with Ritchie Blackmores Rainbow , Chuck Wright talks about being part of Quiet Riot as they scored what was the first ever heavy metal number one album in the US, talking about auditions Richie Kotzen remembers his nightmare audition with Poison.Deborah Bonham talks about the advice Robert Plant gave her as she set off on a career in music, Steve "Boltz" Bolton recalls the day he nearly missed the gig as Atomic Rooster were on the bill supporting headliners The Who and Rod Stewart at "The Oval" we have a few tales of the times with Dave Brock of Hawkwind he remembered a time on the road when their equipment was imounded there were Police raids, they were accused of tax evasion and a special guest who was coming live from a state pen and Dave Ellefson looks back at what it was like to be part of Ozzy Osbournes final show with Black Sabbath just a few months ago.

Welcome back to the show and coming up on this edition we have an exclusive feature with Steve Hackett talking about his favourite Genesis album plus also releasing a new album this month Atomic Rooster and Steve "Boltz' Bolton recalls wild days on tour in the 70's as indeed does Roger Earl of Foghat who looks back at touring with the legendary Steve Marriot and Humble Pie.What would you do if you hit the big time and landed a gig with one of the most iconinc bands in heavy metal history well thats what happened to Blaze Bayley when he got a call on Xmas eve to tell him that he had got the gig with Iron Maiden , so what was his furst purchase ?? and from a big break to a first ever live gig in the UK , Doug Aldrich looks back at his first trip to the UK when he arrived as part of Dio's band he looks back at the whole culture shock of life in England

In this episode, we sit down with Liz Boltz-Ranfeld to talk about what happens when we take back the pen. For so many of us, writing was once locked inside the rigid frame of quiet times and journaling for the Lord. But Liz invites us to re-envision spiritual writing as something wildly more powerful: a practice of healing, resistance, and reclaiming our own sacred voice.This is a conversation for anyone who's ever felt their words stolen by purity culture, evangelical guilt, or spiritual performance. Liz shows us how those same words can be reclaimed—reborn as rituals of self-tending, liberation, and joy.Let's keep ghosting the past, and write our way into a future that belongs to us.Support this podcast at — https://redcircle.com/holy-ghosting/exclusive-contentAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Horror collector Steven T. Boltz joins Noel Anderson to discuss iconic creatures from outer space in film — from childhood favorites like The Blob to modern adaptations such as The Color Out of Space. They share memories, debate remakes and recommend must-see films across different life stages, and explore how extraterrestrial monsters reflect cultural fears? Space invaders, consider tipping Harlequin Ink. It keeps the mic on and monsters fed.  More Info: https://linktr.ee/noelanderson    

In the fast-paced world of dental marketing, what actually works? Hint: it starts with your time and your truth. In this episode of the Just DeW It podcast, Anne Duffy sits down with her friend and advisory board member, Kristie Boltz, a powerhouse Chief Marketing Officer, strategist, and passionate philanthropist redefining dental industry norms. Kristie opens up about her unconventional path (from teaching math to shaping powerhouse dental practices) by cultivating marketing cultures that boost results without burnout. She shares hard-won lessons on how teams can implement smart, simple strategies with clarity, revealing why time is a dental practice's greatest marketing asset. But it doesn't stop at business success; Kristie and Anne dive into the deeper impact of weaving passion and purpose into your professional journey. Kristie pulls back the curtain on her philanthropic mission with the Challenged Athletes Foundation, demonstrating that true giving starts with willingness, not wealth. Their candid conversation encourages dental professionals to blend their work and personal passions, embrace authenticity, and create genuine, lasting impact far beyond the office walls. What You'll Learn in This Episode: How to build a marketing-driven culture in your dental practice Practical ways to measure and maximize your marketing ROI Why “time” is your most valuable marketing currency The keys to consistent, authentic, team-based marketing success Strategies to avoid team overwhelm while executing big ideas Insights into integrating philanthropy with your professional life Why willingness matters more than wealth in meaningful giving Steps to connect your workplace with your greater purpose Ideas for bringing authenticity to your leadership and brand Tune in now to discover how authenticity and purpose can ignite both your marketing and your mission! Learn More About Kristie Boltz Here! Website: mydentalcmo.com Challenged Athletes Foundation: give.challengedathletes.org/participants/Kristie-Boltz Don't Forget to Sign Up for the Next DeW Retreat! 7th Annual DeW Life Retreat November 13-15, 2025 Charlotte, NC Love the podcast? Please leave us a review! It will help us help more entrepreneurs just like you ❤️ Want to get more involved? Join our membership and community below for exclusive perks! Join the DeW Life movement by becoming a member using this link.Join the Dental Entrepreneur movement by becoming a member using this link.Read the most recent edition of DeW Life Magazine here.Just DeW It Podcast is the official podcast of Dental Entrepreneur Women (DeW), founded by Anne Duffy, RDH. The mission of DeW is to inspire, highlight, empower, and connect all women in dentistry. To join the movement or to learn more, please visit dew.life. Together, we can DeW amazing things! References: Events:DeW Retreat 2025Nike Women's MarathonDress for Success Benefit Program (At the DeW Retreat) Organizations:IronmanNBCSacramento State UniversityThe Wall Street Journal People:Katherine Eitel BeltSarah ReinertsenBill GatesJeff Bezos Tools:CliftonStrengths Test

X11Libre is a fork of xorg, do the developers of xorg not want xorg to continue? This week Steve and Noah give you the details of what can only be described as a weird situation. SELF is coming up this weekend, Steve, Noah and executive producer JT will be there and we hope to see you! -- During The Show -- 00:52 Intro Two ways of traveling Noah's airline story Personal entertainment Ente (https://next.ente.io/) 10:28 Self This year is "retro year" old tech could be loaded anything requiring sign in can't be used retro swap Free admission Free HAM radio test Free HAM cram session Lan Party GPG Signing party HAM Radio Meshtastic 22:22 XLibre Project to continue the Xorg display server Xorg developed over several decades Xorg server client model Wayland comes in less developement in Xorg, more in Wayland Freedesktop.org Lots of merge requests closed People running RHEL just take what comes down the pipe Cool that they are improving not just keeping it alive theregister.com (https://www.theregister.com/2025/06/10/xlibre_new_xorg_fork/?td=rt-3a) Github (https://github.com/X11Libre/xserver) Lunduke YouTube Video (https://www.youtube.com/watch?v=ujJCyXfWpOo) Fedora Project Wiki (https://fedoraproject.org/wiki/Changes/X11Libre) 36:06 News Wire Snapd 2.69 - github.com (https://github.com/canonical/snapd/releases) Sway 1.11 - github.com (https://github.com/swaywm/sway/releases) PeerTube 7.2 - joinpeertube.org (https://joinpeertube.org/news/release-7.2) RockyLinux 9.6 - rockylinux.org (https://rockylinux.org/news/rocky-linux-9-6-ga-release) Linux Mint 20 EOL - news.itsfoss.com (https://news.itsfoss.com/linux-mint-20-eol/) Open Source Supply Chain Attacks - thehackernews.com (https://thehackernews.com/2025/06/malicious-pypi-npm-and-ruby-packages.html) Chaos RAT - scworld.com (https://www.scworld.com/news/open-source-chaos-rat-used-in-recent-attacks-targeting-linux) Kali AI - gbhackers.com (https://gbhackers.com/kali-gpt-revolutionizing/) Dots.llm1 - compterworld.com (https://www.computerworld.com/article/4004272/rednote-joins-chinas-open-source-ai-wave-with-the-launch-of-dots-llm1.html) Boltz-2 - nasdaq.com (https://www.nasdaq.com/articles/mit-and-recursion-unveil-boltz-2-revolutionary-open-source-biomolecular-co-folding-model) ChatGPT Used to Disable SecureBoot - tomshardware.com (https://www.tomshardware.com/tech-industry/artificial-intelligence/chatgpt-used-to-disable-secureboot-in-locked-down-device-modded-bios-reflash-facilitated-fresh-windows-and-linux-installs) 37:25 Warp Terminal Foss Force (https://fossforce.com/2025/06/warp-takes-your-terminal-to-light-speed-and-beyond/) It is closed source Uses AI How does Warp stack up against the new RHEL AI? RHEL AI will probly run on the local machine Where it may make sense What is the target audiance? 43:22 Ereader for Music - Steven Tablets? Convertible laptops? Noah bought the PineTab2 for this purpose Nashville Number System SongbookPro (https://songbook-pro.com/) Tabbed PDF viewer Trying proton 51:07 Run OPNsense - Kevin You can't install anything else on netgate appliances Used Sophos with sata drives -- The Extra Credit Section -- For links to the articles and material referenced in this week's episode check out this week's page from our podcast dashboard! This Episode's Podcast Dashboard (http://podcast.asknoahshow.com/445) Phone Systems for Ask Noah provided by Voxtelesys (http://www.voxtelesys.com/asknoah) Join us in our dedicated chatroom #GeekLab:linuxdelta.com on Matrix (https://element.linuxdelta.com/#/room/#geeklab:linuxdelta.com) -- Stay In Touch -- Find all the resources for this show on the Ask Noah Dashboard Ask Noah Dashboard (http://www.asknoahshow.com) Need more help than a radio show can offer? Altispeed provides commercial IT services and they're excited to offer you a great deal for listening to the Ask Noah Show. Call today and ask about the discount for listeners of the Ask Noah Show! Altispeed Technologies (http://www.altispeed.com/) Contact Noah live [at] asknoahshow.com -- Twitter -- Noah - Kernellinux (https://twitter.com/kernellinux) Ask Noah Show (https://twitter.com/asknoahshow) Altispeed Technologies (https://twitter.com/altispeed)

Send us a textIn an obtuse world Mark & Jefe are here to keep you vertical.This time Boltz N Holez talks about our recent Kalashni-Con experience and shooting competitions in general. FIND BOLTZ ON INSTAGRAM HERE: https://www.instagram.com/boltz_n_holez/THANK YOU TO OUR SPONSOR XS SightsXS Sights - https://xssights.com/20% Discount with code LARPBooks We Recommend:Herbal Medic: https://amzn.to/3ArhUGXTriphasic Tactical Training Manual: https://a.co/d/0I1iYRuThe Merck Manual of Diagnosis and Therapy : https://a.co/d/6jU0EDWTarascon Pocket Pharmacopoeia: https://a.co/d/fZm4jqpFollow us on Instagram @livelaughlarp_podcastEmail us questions/topics at live.laugh.larp.podcast@gmail.comFind the Fit'n Fire YouTube Channel at https://www.youtube.com/fitnfireIntro/Outro Music: Elysium · Karl Casey

Persino la BBC si accorge del potenziale trasformativo del mining di Bitcoin. Noi lo diciamo da tempo: è il rapporto univoco tra corrente elettrica e mining ad essere l'incentivo più potente all'adozione. Africa, Asia, Sud America e persino l'Europa iniziano ad accorgersene.Inoltre: Electrum wallet pionierizza il coordinamente dei coinjoin usando il protocollo NOSTR, Boltz censura le transazioni nella blacklist OFAC, e arriva il BIP del "great consensus cleanup".It's showtime!

“Eventually, my dream would be to simulate a virtual cell.”—Demis HassabisThe aspiration to build the virtual cell is considered to be equivalent to a moonshot for digital biology. Recently, 42 leading life scientists published a paper in Cell on why this is so vital, and how it may ultimately be accomplished. This conversation is with 2 of the authors, Charlotte Bunne, now at EPFL and Steve Quake, a Professor at Stanford University, who heads up science at the Chan-Zuckerberg Initiative The audio (above) is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube.TRANSCRIPT WITH LINKS TO AUDIO Eric Topol (00:06):Hello, it's Eric Topol with Ground Truths and we've got a really hot topic today, the virtual cell. And what I think is extraordinarily important futuristic paper that recently appeared in the journal Cell and the first author, Charlotte Bunne from EPFL, previously at Stanford's Computer Science. And Steve Quake, a young friend of mine for many years who heads up the Chan Zuckerberg Initiative (CZI) as well as a professor at Stanford. So welcome, Charlotte and Steve.Steve Quake (00:42):Thanks, Eric. It's great to be here.Charlotte Bunne:Thanks for having me.Eric Topol (00:45):Yeah. So you wrote this article that Charlotte, the first author, and Steve, one of the senior authors, appeared in Cell in December and it just grabbed me, “How to build the virtual cell with artificial intelligence: Priorities and opportunities.” It's the holy grail of biology. We're in this era of digital biology and as you point out in the paper, it's a convergence of what's happening in AI, which is just moving at a velocity that's just so extraordinary and what's happening in biology. So maybe we can start off by, you had some 42 authors that I assume they congregated for a conference or something or how did you get 42 people to agree to the words in this paper?Steve Quake (01:33):We did. We had a meeting at CZI to bring community members together from many different parts of the community, from computer science to bioinformatics, AI experts, biologists who don't trust any of this. We wanted to have some real contrarians in the mix as well and have them have a conversation together about is there an opportunity here? What's the shape of it? What's realistic to expect? And that was sort of the genesis of the article.Eric Topol (02:02):And Charlotte, how did you get to be drafting the paper?Charlotte Bunne (02:09):So I did my postdoc with Aviv Regev at Genentech and Jure Leskovec at CZI and Jure was part of the residency program of CZI. And so, this is how we got involved and you had also prior work with Steve on the universal cell embedding. So this is how everything got started.Eric Topol (02:29):And it's actually amazing because it's a who's who of people who work in life science, AI and digital biology and omics. I mean it's pretty darn impressive. So I thought I'd start off with a quote in the article because it kind of tells a story of where this could go. So the quote was in the paper, “AIVC (artificial intelligence virtual cell) has the potential to revolutionize the scientific process, leading to future breakthroughs in biomedical research, personalized medicine, drug discovery, cell engineering, and programmable biology.” That's a pretty big statement. So maybe we can just kind of toss that around a bit and maybe give it a little more thoughts and color as to what you were positing there.Steve Quake (03:19):Yeah, Charlotte, you want me to take the first shot at that? Okay. So Eric, it is a bold claim and we have a really bold ambition here. We view that over the course of a decade, AI is going to provide the ability to make a transformative computational tool for biology. Right now, cell biology is 90% experimental and 10% computational, roughly speaking. And you've got to do just all kinds of tedious, expensive, challenging lab work to get to the answer. And I don't think AI is going to replace that, but it can invert the ratio. So within 10 years I think we can get to biology being 90% computational and 10% experimental. And the goal of the virtual cell is to build a tool that'll do that.Eric Topol (04:09):And I think a lot of people may not understand why it is considered the holy grail because it is the fundamental unit of life and it's incredibly complex. It's not just all the things happening in the cell with atoms and molecules and organelles and everything inside, but then there's also the interactions the cell to other cells in the outside tissue and world. So I mean it's really quite extraordinary challenge that you've taken on here. And I guess there's some debate, do we have the right foundation? We're going to get into foundation models in a second. A good friend of mine and part of this whole I think process that you got together, Eran Segal from Israel, he said, “We're at this tipping point…All the stars are aligned, and we have all the different components: the data, the compute, the modeling.” And in the paper you describe how we have over the last couple of decades have so many different data sets that are rich that are global initiatives. But then there's also questions. Do we really have the data? I think Bo Wang especially asked about that. Maybe Charlotte, what are your thoughts about data deficiency? There's a lot of data, but do you really have what we need before we bring them all together for this kind of single model that will get us some to the virtual cell?Charlotte Bunne (05:41):So I think, I mean one core idea of building this AIVC is that we basically can leverage all experimental data that is overall collected. So this also goes back to the point Steve just made. So meaning that we basically can integrate across many different studies data because we have AI algorithms or the architectures that power such an AIVC are able to integrate basically data sets on many different scales. So we are going a bit away from this dogma. I'm designing one algorithm from one dataset to this idea of I have an architecture that can take in multiple dataset on multiple scales. So this will help us a bit in being somewhat efficient with the type of experiments that we need to make and the type of experiments we need to conduct. And again, what Steve just said, ultimately, we can very much steer which data sets we need to collect.Charlotte Bunne (06:34):Currently, of course we don't have all the data that is sufficient. I mean in particular, I think most of the tissues we have, they are healthy tissues. We don't have all the disease phenotypes that we would like to measure, having patient data is always a very tricky case. We have mostly non-interventional data, meaning we have very limited understanding of somehow the effect of different perturbations. Perturbations that happen on many different scales in many different environments. So we need to collect a lot here. I think the overall journey that we are going with is that we take the data that we have, we make clever decisions on the data that we will collect in the future, and we have this also self-improving entity that is aware of what it doesn't know. So we need to be able to understand how well can I predict something on this somewhat regime. If I cannot, then we should focus our data collection effort into this. So I think that's not a present state, but this will basically also guide the future collection.Eric Topol (07:41):Speaking of data, one of the things I think that's fascinating is we saw how AlphaFold2 really revolutionized predicting proteins. But remember that was based on this extraordinary resource that had been built, the Protein Data Bank that enabled that. And for the virtual cell there's no such thing as a protein data bank. It's so much more as you emphasize Charlotte, it's so much dynamic and these perturbations that are just all across the board as you emphasize. Now the human cell atlas, which currently some tens of millions, but going into a billion cells, we learned that it used to be 200 cell types. Now I guess it's well over 5,000 and that we have 37 trillion cells approximately in the average person adult's body is a formidable map that's being made now. And I guess the idea that you're advancing is that we used to, and this goes back to a statement you made earlier, Steve, everything we did in science was hypothesis driven. But if we could get computational model of the virtual cell, then we can have AI exploration of the whole field. Is that really the nuts of this?Steve Quake (09:06):Yes. A couple thoughts on that, maybe Theo Karaletsos, our lead AI person at CZI says machine learning is the formalism through which we understand high dimensional data and I think that's a very deep statement. And biological systems are intrinsically very high dimensional. You've got 20,000 genes in the human genome in these cell atlases. You're measuring all of them at the same time in each single cell. And there's a lot of structure in the relationships of their gene expression there that is just not evident to the human eye. And for example, CELL by GENE, our database that collects all the aggregates, all of the single cell transcriptomic data is now over a hundred million cells. And as you mentioned, we're seeing ways to increase that by an order of magnitude in the near future. The project that Jure Leskovec and I worked on together that Charlotte referenced earlier was like a first attempt to build a foundational model on that data to discover some of the correlations and structure that was there.Steve Quake (10:14):And so, with a subset, I think it was the 20 or 30 million cells, we built a large language model and began asking it, what do you understand about the structure of this data? And it kind of discovered lineage relationships without us teaching it. We trained on a matrix of numbers, no biological information there, and it learned a lot about the relationships between cell type and lineage. And that emerged from that high dimensional structure, which was super pleasing to us and really, I mean for me personally gave me the confidence to say this stuff is going to work out. There is a future for the virtual cell. It's not some made up thing. There is real substance there and this is worth investing an enormous amount of CZIs resources in going forward and trying to rally the community around as a project.Eric Topol (11:04):Well yeah, the premise here is that there is a language of life, and you just made a good case that there is if you can predict, if you can query, if you can generate like that. It is reminiscent of the famous Go game of Lee Sedol, that world champion and how the machine came up with a move (Move 37) many, many years ago that no human would've anticipated and I think that's what you're getting at. And the ability for inference and reason now to add to this. So Charlotte, one of the things of course is about, well there's two terms in here that are unfamiliar to many of the listeners or viewers of this podcast, universal representations (UR) and virtual instrument (VIs) that you make a pretty significant part of how you are going about this virtual cell model. So could you describe that and also the embeddings as part of the universal representation (UR) because I think embeddings, or these meaningful relationships are key to what Steve was just talking about.Charlotte Bunne (12:25):Yes. So in order to somewhat leverage very different modalities in order to leverage basically modalities that will take measurements across different scales, like the idea is that we have large, may it be transformer models that might be very different. If I have imaging data, I have a vision transformer, if I have a text data, I have large language models that are designed of course for DNA then they have a very wide context and so on and so forth. But the idea is somewhat that we have models that are connected through the scales of biology because those scales we know. We know which components are somewhat involved or in measurements that are happening upstream. So we have the somewhat interconnection or very large model that will be trained on many different data and we have this internal model representation that somewhat capture everything they've seen. And so, this is what we call those universal representation (UR) that will exist across the scales of biology.Charlotte Bunne (13:22):And what is great about AI, and so I think this is a bit like a history of AI in short is the ability to predict the last years, the ability to generate, we can generate new hypothesis, we can generate modalities that we are missing. We can potentially generate certain cellular state, molecular state have a certain property, but I think what's really coming is this ability to reason. So we see this in those very large language models, the ability to reason about a hypothesis, how we can test it. So this is what those instruments ultimately need to do. So we need to be able to simulate the change of a perturbation on a cellular phenotype. So on the internal representation, the universal representation of a cell state, we need to simulate the fact the mutation has downstream and how this would propagate in our representations upstream. And we need to build many different type of virtual instruments that allow us to basically design and build all those capabilities that ultimately the AI virtual cell needs to possess that will then allow us to reason, to generate hypothesis, to basically predict the next experiment to conduct to predict the outcome of a perturbation experiment to in silico design, cellular states, molecular states, things like that. And this is why we make the separation between internal representation as well as those instruments that operate on those representations.Eric Topol (14:47):Yeah, that's what I really liked is that you basically described the architecture, how you're going to do this. By putting these URs into the VIs, having a decoder and a manipulator and you basically got the idea if you can bring all these different integrations about which of course is pending. Now there are obviously many naysayers here that this is impossible. One of them is this guy, Philip Ball. I don't know if you read the language, How Life Works. Now he's a science journalist and he's a prolific writer. He says, “Comparing life to a machine, a robot, a computer, sells it short. Life is a cascade of processes, each with a distinct integrity and autonomy, the logic of which has no parallel outside the living world.” Is he right? There's no way to model this. It's silly, it's too complex.Steve Quake (15:50):We don't know, alright. And it's great that there's naysayers. If everyone agreed this was doable, would it be worth doing? I mean the whole point is to take risks and get out and do something really challenging in the frontier where you don't know the answer. If we knew that it was doable, I wouldn't be interested in doing it. So I personally am happy that there's not a consensus.Eric Topol (16:16):Well, I mean to capture people's imagination here, if you're successful and you marshal a global effort, I don't know who's going to pay for it because it's a lot of work coming here going forward. But if you can do it, the question here is right today we talk about, oh let's make an organoid so we can figure out how to treat this person's cancer or understand this person's rare disease or whatever. And instead of having to wait weeks for this culture and all the expense and whatnot, you could just do it in a computer and in silico and you have this virtual twin of a person's cells and their tissue and whatnot. So the opportunity here is, I don't know if people get, this is just extraordinary and quick and cheap if you can get there. And it's such a bold initiative idea, who will pay for this do you think?Steve Quake (17:08):Well, CZI is putting an enormous amount of resources into it and it's a major project for us. We have been laying the groundwork for it. We recently put together what I think is if not the largest, one of the largest GPU supercomputer clusters for nonprofit basic science research that came online at the end of last year. And in fact in December we put out an RFA for the scientific community to propose using it to build models. And so we're sharing that resource within the scientific community as I think you appreciate, one of the real challenges in the field has been access to compute resources and industry has it academia at a much lower level. We are able to be somewhere in between, not quite at the level of a private company but the tech company but at a level beyond what most universities are being able to do and we're trying to use that to drive the field forward. We're also planning on launching RFAs we this year to help drive this project forward and funding people globally on that. And we are building a substantial internal effort within CZI to help drive this project forward.Eric Topol (18:17):I think it has the looks of the human genome project, which at time as you know when it was originally launched that people thought, oh, this is impossible. And then look what happened. It got done. And now the sequence of genome is just a commodity, very relatively, very inexpensive compared to what it used to be.Steve Quake (18:36):I think a lot about those parallels. And I will say one thing, Philip Ball, I will concede him the point, the cells are very complicated. The genome project, I mean the sort of genius there was to turn it from a biology problem to a chemistry problem, there is a test tube with a chemical and it work out the structure of that chemical. And if you can do that, the problem is solved. I think what it means to have the virtual cell is much more complex and ambiguous in terms of defining what it's going to do and when you're done. And so, we have our work cut out for us there to try to do that. And that's why a little bit, I established our North Star and CZI for the next decade as understanding the mysteries of the cell and that word mystery is very important to me. I think the molecules, as you pointed out earlier are understood, genome sequenced, protein structure solved or predicted, we know a lot about the molecules. Those are if not solved problems, pretty close to being solved. And the real mystery is how do they work together to create life in the cell? And that's what we're trying to answer with this virtual cell project.Eric Topol (19:43):Yeah, I think another thing that of course is happening concurrently to add the likelihood that you'll be successful is we've never seen the foundation models coming out in life science as they have in recent weeks and months. Never. I mean, I have a paper in Science tomorrow coming out summarizing the progress about not just RNA, DNA, ligands. I mean the whole idea, AlphaFold3, but now Boltz and so many others. It's just amazing how fast the torrent of new foundation models. So Charlotte, what do you think accounts for this? This is unprecedented in life science to see foundation models coming out at this clip on evolution on, I mean you name it, design of every different molecule of life or of course in cells included in that. What do you think is going on here?Charlotte Bunne (20:47):So on the one hand, of course we benefit profits and inherit from all the tremendous efforts that have been made in the last decades on assembling those data sets that are very, very standardized. CELLxGENE is very somehow AI friendly, as you can say, it is somewhat a platform that is easy to feed into algorithms, but at the same time we actually also see really new building mechanisms, design principles of AI algorithms in itself. So I think we have understood that in order to really make progress, build those systems that work well, we need to build AI tools that are designed for biological data. So to give you an easy example, if I use a large language model on text, it's not going to work out of the box for DNA because we have different reading directions, different context lens and many, many, many, many more.Charlotte Bunne (21:40):And if I look at standard computer vision where we can say AI really excels and I'm applying standard computer vision, vision transformers on multiplex images, they're not going to work because normal computer vision architectures, they always expect the same three inputs, RGB, right? In multiplex images, I'm measuring up to 150 proteins potentially in a single experiment, but every study will measure different proteins. So I deal with many different scales like larger scales and I used to attention mechanisms that we have in usual computer vision. Transformers are not going to work anymore, they're not going to scale. And at the same time, I need to be completely flexible in whatever input combination of channel I'm just going to face in this experiment. So this is what we right now did for example, in our very first work, inheriting the design principle that we laid out in the paper AI virtual cell and then come up with new AI architectures that are dealing with these very special requirements that biological data have.Charlotte Bunne (22:46):So we have now a lot of computer scientists that work very, very closely have a very good understanding of biologists. Biologists that are getting much and much more into the computer science. So people who are fluent in both languages somewhat, that are able to now build models that are adopted and designed for biological data. And we don't just take basically computer vision architectures that work well on street scenes and try to apply them on biological data. So it's just a very different way of thinking about it, starting constructing basically specialized architectures, besides of course the tremendous data efforts that have happened in the past.Eric Topol (23:24):Yeah, and we're not even talking about just sequence because we've also got imaging which has gone through a revolution, be able to image subcellular without having to use any types of stains that would disrupt cells. That's another part of the deep learning era that came along. One thing I thought was fascinating in the paper in Cell you wrote, “For instance, the Short Read Archive of biological sequence data holds over 14 petabytes of information, which is 1,000 times larger than the dataset used to train ChatGPT.” I mean that's a lot of tokens, that's a lot of stuff, compute resources. It's almost like you're going to need a DeepSeek type of way to get this. I mean not that DeepSeek as its claim to be so much more economical, but there's a data challenge here in terms of working with that massive amount that is different than the human language. That is our language, wouldn't you say?Steve Quake (24:35):So Eric, that brings to mind one of my favorite quotes from Sydney Brenner who is such a wit. And in 2000 at the sort of early first flush of success in genomics, he said, biology is drowning in a sea of data and starving for knowledge. A very deep statement, right? And that's a little bit what the motivation was for putting the Short Read Archive statistic into the paper there. And again, for me, part of the value of this endeavor of creating a virtual cell is it's a tool to help us translate data into knowledge.Eric Topol (25:14):Yeah, well there's two, I think phenomenal figures in your Cell paper. The first one that kicks across the capabilities of the virtual cell and the second that compares the virtual cell to the real or the physical cell. And we'll link that with this in the transcript. And the other thing we'll link is there's a nice Atlantic article, “A Virtual Cell Is a ‘Holy Grail' of Science. It's Getting Closer.” That might not be quite close as next week or year, but it's getting close and that's good for people who are not well grounded in this because it's much more taken out of the technical realm. This is really exciting. I mean what you're onto here and what's interesting, Steve, since I've known you for so many years earlier in your career you really worked on omics that is being DNA and RNA and in recent times you've made this switch to cells. Is that just because you're trying to anticipate the field or tell us a little bit about your migration.Steve Quake (26:23):Yeah, so a big part of my career has been trying to develop new measurement technologies that'll provide insight into biology. And decades ago that was understanding molecules. Now it's understanding more complex biological things like cells and it was like a natural progression. I mean we built the sequencers, sequenced the genomes, done. And it was clear that people were just going to do that at scale then and create lots of data. Hopefully knowledge would get out of that. But for me as an academic, I never thought I'd be in the position I'm in now was put it that way. I just wanted to keep running a small research group. So I realized I would have to get out of the genome thing and find the next frontier and it became this intersection of microfluidics and genomics, which as you know, I spent a lot of time developing microfluidic tools to analyze cells and try to do single cell biology to understand their heterogeneity. And that through a winding path led me to all these cell atlases and to where we are now.Eric Topol (27:26):Well, we're fortunate for that and also with your work with CZI to help propel that forward and I think it sounds like we're going to need a lot of help to get this thing done. Now Charlotte, as a computer scientist now at EPFL, what are you going to do to keep working on this and what's your career advice for people in computer science who have an interest in digital biology?Charlotte Bunne (27:51):So I work in particular on the prospect of using this to build diagnostic tools and to make diagnostics in the clinic easier because ultimately we have somewhat limited capabilities in the hospital to run deep omics, but the idea of being able to somewhat map with a cheaper and lighter modality or somewhat diagnostic test into something much richer because a model has been seeing all those different data and can basically contextualize it. It's very interesting. We've seen all those pathology foundation models. If I can always run an H&E, but then decide when to run deeper diagnostics to have a better or more accurate prediction, that is very powerful and it's ultimately reducing the costs, but the precision that we have in hospitals. So my faculty position right now is co-located between the School of Life Sciences, School of Computer Science. So I have a dual affiliation and I'm affiliated to the hospitals to actually make this possible and as a career advice, I think don't be shy and stick to your discipline.Charlotte Bunne (28:56):I have a bachelor's in biology, but I never only did biology. I have a PhD in computer science, which you would think a bachelor in biology not necessarily qualifies you through. So I think this interdisciplinarity also requires you to be very fluent, very comfortable in reading many different styles of papers and publications because a publication in a computer science venue will be very, very different from the way we write in biology. So don't stick to your study program, but just be free in selecting whatever course gets you closer to the knowledge you need in order to do the research or whatever task you are building and working on.Eric Topol (29:39):Well, Charlotte, the way you're set up there with this coalescence of life science and computer science is so ideal and so unusual here in the US, so that's fantastic. That's what we need and that's really the underpinning of how you're going to get to the virtual cells, getting these two communities together. And Steve, likewise, you were an engineer and somehow you became one of the pioneers of digital biology way back before it had that term, this interdisciplinary, transdisciplinary. We need so much of that in order for you all to be successful, right?Steve Quake (30:20):Absolutely. I mean there's so much great discovery to be done on the boundary between fields. I trained as a physicist and kind of made my career this boundary between physics and biology and technology development and it's just sort of been a gift that keeps on giving. You've got a new way to measure something, you discover something new scientifically and it just all suggests new things to measure. It's very self-reinforcing.Eric Topol (30:50):Now, a couple of people who you know well have made some pretty big statements about this whole era of digital biology and I think the virtual cell is perhaps the biggest initiative of all the digital biology ongoing efforts, but Jensen Huang wrote, “for the first time in human history, biology has the opportunity to be engineering, not science.” And Demis Hassabis wrote or said, ‘we're seeing engineering science, you have to build the artifact of interest first, and then once you have it, you can use the scientific method to reduce it down and understand its components.' Well here there's a lot to do to understand its components and if we can do that, for example, right now as both of AI drug discoveries and high gear and there's umpteen numbers of companies working on it, but it doesn't account for the cell. I mean it basically is protein, protein ligand interactions. What if we had drug discovery that was cell based? Could you comment about that? Because that doesn't even exist right now.Steve Quake (32:02):Yeah, I mean I can say something first, Charlotte, if you've got thoughts, I'm curious to hear them. So I do think AI approaches are going to be very useful designing molecules. And so, from the perspective of designing new therapeutics, whether they're small molecules or antibodies, yeah, I mean there's a ton of investment in that area that is a near term fruit, perfect thing for venture people to invest in and there's opportunity there. There's been enough proof of principle. However, I do agree with you that if you want to really understand what happens when you drug a target, you're going to want to have some model of the cell and maybe not just the cell, but all the different cell types of the body to understand where toxicity will come from if you have on-target toxicity and whether you get efficacy on the thing you're trying to do.Steve Quake (32:55):And so, we really hope that people will use the virtual cell models we're going to build as part of the drug discovery development process, I agree with you in a little of a blind spot and we think if we make something useful, people will be using it. The other thing I'll say on that point is I'm very enthusiastic about the future of cellular therapies and one of our big bets at CZI has been starting the New York Biohub, which is aimed at really being very ambitious about establishing the engineering and scientific foundations of how to engineer completely, radically more powerful cellular therapies. And the virtual cell is going to help them do that, right? It's going to be essential for them to achieve that mission.Eric Topol (33:39):I think you're pointing out one of the most important things going on in medicine today is how we didn't anticipate that live cell therapy, engineered cells and ideally off the shelf or in vivo, not just having to take them out and work on them outside the body, is a revolution ongoing, and it's not just in cancer, it's in autoimmune diseases and many others. So it's part of the virtual cell need. We need this. One of the things that's a misnomer, I want you both to comment on, we keep talking about single cell, single cell. And there's a paper spatial multi-omics this week, five different single cell scales all integrated. It's great, but we don't get to single cell. We're basically looking at 50 cells, 100 cells. We're not doing single cell because we're not going deep enough. Is that just a matter of time when we actually are doing, and of course the more we do get down to the single or a few cells, the more insights we're going to get. Would you comment about that? Because we have all this literature on single cell comes out every day, but we're not really there yet.Steve Quake (34:53):Charlotte, do you want to take a first pass at that and then I can say something?Charlotte Bunne (34:56):Yes. So it depends. So I think if we look at certain spatial proteomics, we still have subcellular resolutions. So of course, we always measure many different cells, but we are able to somewhat get down to resolution where we can look at certain colocalization of proteins. This also goes back to the point just made before having this very good environment to study drugs. If I want to build a new drug, if I want to build a new protein, the idea of building this multiscale model allows us to actually simulate different, somehow binding changes and binding because we simulate the effect of a drug. Ultimately, the redouts we have they are subcellular. So of course, we often in the spatial biology, we often have a bit like methods that are rather coarse they have a spot that averages over certain some cells like hundreds of cells or few cells.Charlotte Bunne (35:50):But I think we also have more and more technologies that are zooming in that are subcellular where we can actually tag or have those probe-based methods that allow us to zoom in. There's microscopy of individual cells to really capture them in 3D. They are of course not very high throughput yet, but it gives us also an idea of the morphology and how ultimately morphology determine certain somehow cellular properties or cellular phenotype. So I think there's lots of progress also on the experimental and that ultimately will back feed into the AI virtual cell, those models that will be fed by those data. Similarly, looking at dynamics, right, looking at live imaging of individual cells of their morphological changes. Also, this ultimately is data that we'll need to get a better understanding of disease mechanisms, cellular phenotypes functions, perturbation responses.Eric Topol (36:47):Right. Yes, Steve, you can comment on that and the amazing progress that we have made with space and time, spatial temporal resolution, spatial omics over these years, but that we still could go deeper in terms of getting to individual cells, right?Steve Quake (37:06):So, what can we do with a single cell? I'd say we are very mature in our ability to amplify and sequence the genome of a single cell, amplify and sequence the transcriptome of a single cell. You can ask is one cell enough to make a biological conclusion? And maybe I think what you're referring to is people want to see replicates and so you can ask how many cells do you need to see to have confidence in any given biological conclusion, which is a reasonable thing. It's a statistical question in good science. I think I've been very impressed with how the mass spec people have been doing recently. I think they've finally cracked the ability to look at proteins from single cells and they can look at a couple thousand proteins. That was I think one of these Nature method of the year things at the end of last year and deep visual proteomics.Eric Topol (37:59):Deep visual proteomics, yes.Steve Quake (38:00):Yeah, they are over the hump. Yeah, they are over the hump with single cell measurements. Part of what's missing right now I think is the ability to reliably do all of that on the same cell. So this is what Charlotte was referring to be able to do sort of multi-modal measurements on single cells. That's kind of in its infancy and there's a few examples, but there's a lot more work to be done on that. And I think also the fact that these measurements are all destructive right now, and so you're losing the ability to look how the cells evolve over time. You've got to say this time point, I'm going to dissect this thing and look at a state and I don't get to see what happens further down the road. So that's another future I think measurement challenge to be addressed.Eric Topol (38:42):And I think I'm just trying to identify some of the multitude of challenges in this extraordinarily bold initiative because there are no shortage and that's good about it. It is given people lots of work to do to overcome, override some of these challenges. Now before we wrap up, besides the fact that you point out that all the work has to be done and be validated in real experiments, not just live in a virtual AI world, but you also comment about the safety and ethics of this work and assuming you're going to gradually get there and be successful. So could either or both of you comment about that because it's very thoughtful that you're thinking already about that.Steve Quake (41:10):As scientists and members of the larger community, we want to be careful and ensure that we're interacting with people who said policy in a way that ensures that these tools are being used to advance the cause of science and not do things that are detrimental to human health and are used in a way that respects patient privacy. And so, the ethics around how you use all this with respect to individuals is going to be important to be thoughtful about from the beginning. And I also think there's an ethical question around what it means to be publishing papers and you don't want people to be forging papers using data from the virtual cell without being clear about where that came from and pretending that it was a real experiment. So there's issues around those sorts of ethics as well that need to be considered.Eric Topol (42:07):And of those 40 some authors, do you around the world, do you have the sense that you all work together to achieve this goal? Is there kind of a global bonding here that's going to collaborate?Steve Quake (42:23):I think this effort is going to go way beyond those 40 authors. It's going to include a much larger set of people and I'm really excited to see that evolve with time.Eric Topol (42:31):Yeah, no, it's really quite extraordinary how you kick this thing off and the paper is the blueprint for something that we are all going to anticipate that could change a lot of science and medicine. I mean we saw, as you mentioned, Steve, how that deep visual proteomics (DVP) saved lives. It was what I wrote a spatial medicine, no longer spatial biology. And so, the way that this can change the future of medicine, I think a lot of people just have to have a little bit of imagination that once we get there with this AIVC, that there's a lot in store that's really quite exciting. Well, I think this has been an invigorating review of that paper and some of the issues surrounding it. I couldn't be more enthusiastic for your success and ultimately where this could take us. Did I miss anything during the discussion that we should touch on before we wrap up?Steve Quake (43:31):Not from my perspective. It was a pleasure as always Eric, and a fun discussion.Charlotte Bunne (43:38):Thanks so much.Eric Topol (43:39):Well thank you both and all the co-authors of this paper. We're going to be following this with the great interest, and I think for most people listening, they may not know that this is in store for the future. Someday we will get there. I think one of the things to point out right now is the models we have today that large language models based on transformer architecture, they're going to continue to evolve. We're already seeing so much in inference and ability for reasoning to be exploited and not asking for prompts with immediate answers, but waiting for days to get back. A lot more work from a lot more computing resources. But we're going to get models in the future to fold this together. I think that's one of the things that you've touched on the paper so that whatever we have today in concert with what you've laid out, AI is just going to keep getting better.Eric Topol (44:39):The biology that these foundation models are going to get broader and more compelling as to their use cases. So that's why I believe in this. I don't see this as a static situation right now. I just think that you're anticipating the future, and we will have better models to be able to integrate this massive amount of what some people would consider disparate data sources. So thank you both and all your colleagues for writing this paper. I don't know how you got the 42 authors to agree to it all, which is great, and it's just a beginning of something that's a new frontier. So thanks very much.Steve Quake (45:19):Thank you, Eric.**********************************************Thanks for listening, watching or reading Ground Truths. Your subscription is greatly appreciated.If you found this podcast interesting please share it!That makes the work involved in putting these together especially worthwhile.All content on Ground Truths—newsletters, analyses, and podcasts—is free, open-access, with no ads..Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. 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Kilian Rausch is the founder and CEO of Boltz, a non-custodial Bitcoin bridge, allowing swaps between Bitcoin and layer 2 technologies such as Lightning, Liquid, and more to come. We discuss the history of Boltz, the current state of the Lightning network, the future of layer 2 technologies on Bitcoin, and much more!  Connect with Kilian: https://x.com/kilrau https://x.com/boltzhq https://boltz.exchange/ Connect with Us: https://www.bitcoininfinityshow.com/ https://bitcoininfinitystore.com https://primal.net/freedom https://primal.net/knut https://primal.net/luke https://twitter.com/BtcInfinityShow https://twitter.com/knutsvanholm https://twitter.com/lukedewolf Thanks to our sponsors - check out their websites for info: BitBox: https://bitbox.swiss/infinity StampSeed: https://www.stampseed.com/shop/21m-titanium-seed-plate.html Bitcoin Adviser: https://content.thebitcoinadviser.com/freedom ShopInBit: https://shopinbit.com/bitcoininfinity - Use code INFINITY for a €5 discount!    The Bitcoin Infinity Show is a Bitcoin podcast hosted by Knut Svanholm and Luke de Wolf.

Lenny and Jackie join Joe on this episode of the podcast to talk about their fight for conservative principles in Langlade County and how that impacts the rest of the state and even the country!See omnystudio.com/listener for privacy information.

This week Eric Hendricks joins us to help us solve problems, and bring some insight to RHEL 10 beta! -- During The Show -- 00:52 Intro Eric Hendricks ITGuyEric Red Hat Technical Marketer Fedora Podcast Host Steve's PSA - Spook (https://spook.boo/) Entities Other problems Help Steve Out - Firefox and authenticated proxy Mac OS breaking open source Gatekeeper 20:05 Threema for Messaging - Michael Technology is a tool for relationships Paid app Designed for private communication Checks a lot of boxes Network effect threematrix (https://github.com/bitbetterde/Threematrix) not updated recently Beeper 31:30 7 Inch Touch Screen Make the touch screen the primary display USB cable emulates a mouse Crash cart tech 35:07 News Wire Gnome 46.7 - gnome.org (https://discourse.gnome.org/t/gnome-46-7-released/25560) KDE Frameworks 6.9 - kde.org (https://kde.org/announcements/frameworks/6/6.9.0/) KDE Gear 24.12 - kde.org (https://kde.org/announcements/gear/24.12.0/) XFCE 4.20 - github.io (https://alexxcons.github.io/blogpost_14.html) QEMU 9.2 - qemu.org (https://wiki.qemu.org/ChangeLog/9.2) CentOS Stream 10 - centos.org (https://blog.centos.org/2024/12/introducing-centos-stream-10/) Red Hat has announced that CentOS Stream 10 is available. Kali Linux 2024.4 - bleepingcomputer.com (https://www.bleepingcomputer.com/news/security/kali-linux-20244-released-with-14-new-tools-deprecates-some-features/) Fedora Asahi 41 - forbes.com (https://www.forbes.com/sites/jasonevangelho/2024/12/17/fedora-asahi-remix-41-released-linux-on-your-apple-silicon-mac/) Fedora Asahi Remix 41 Released Pumakit - bleepingcomputer.com (https://www.bleepingcomputer.com/news/security/new-stealthy-pumakit-linux-rootkit-malware-spotted-in-the-wild/) Open Source Malware - helpnetsecurity.com (https://www.helpnetsecurity.com/2024/12/11/open-source-malware/) Boltz-1 - mit.edu (https://news.mit.edu/2024/researchers-introduce-boltz-1-open-source-model-predicting-biomolecular-structures-1217) 36:30 Self Hosting Hiccups SwiftFin app Jellyfin (https://jellyfin.org/) Nextcloud photo sync PhotoSync app Infuse app had to update the server side infuse plugin Immich (https://immich.app/) 47:10 RHEL 10 Public Beta Do Not install in production Relation between RHEL 10 Beta and CentOS 10 Special Interest Groups (SIGs) Get it for free with a developer account -- The Extra Credit Section -- For links to the articles and material referenced in this week's episode check out this week's page from our podcast dashboard! This Episode's Podcast Dashboard (http://podcast.asknoahshow.com/420) Phone Systems for Ask Noah provided by Voxtelesys (http://www.voxtelesys.com/asknoah) Join us in our dedicated chatroom #GeekLab:linuxdelta.com on Matrix (https://element.linuxdelta.com/#/room/#geeklab:linuxdelta.com) -- Stay In Touch -- Find all the resources for this show on the Ask Noah Dashboard Ask Noah Dashboard (http://www.asknoahshow.com) Need more help than a radio show can offer? Altispeed provides commercial IT services and they're excited to offer you a great deal for listening to the Ask Noah Show. Call today and ask about the discount for listeners of the Ask Noah Show! Altispeed Technologies (http://www.altispeed.com/) Contact Noah live [at] asknoahshow.com -- Twitter -- Noah - Kernellinux (https://twitter.com/kernellinux) Ask Noah Show (https://twitter.com/asknoahshow) Altispeed Technologies (https://twitter.com/altispeed) Special Guest: Eric Hendricks.

Debbie de Boltz, Head of Fundraising at Farleigh Hospice in Essex talks about the Christmas Tree recycling programme. #gardeningtips #gardeningadvice #winter #xmas #garden == We're delighted to have Gro-rite Horticulture sponsoring World Radio Gardening, find out about automatic pot watering systems available for mail order delivery: bit.ly/3wCPyHy For 2024, World Radio Gardening is planning a series of 4 exclusive newsletters. These will be loaded with extra special content and deals for you as a gardener. Make sure you don't miss out by signing up today via sign-up page: bit.ly/3RWwhYR The second newsletter is out now here: bit.ly/3RWwhYR – don't miss the next one! Also, don't forget – if you like what we do, why not tip Ken and team with a coffee – Buy us a coffee (bit.ly/48RLP75) – as a thank you for the work done to bring this website to life.

Thema: OMV kündigt Gas-Liefervertrag mit Gazprom

I'm joined by guests Stephan Livera, Rijndael & Ben Carman to go through the list.Housekeeping (00:05:29) Bitcoin Black Friday (00:06:05) AnchorWatch gains Lloyd's of London Coverholder statusMajor/Urgent Vulnerability Disclosures(00:10:25) Krux releases security fix for AES-CBC encryption flawBitcoin • Software Releases & Project Updates (00:16:05) Bitcoin-script-hints(00:17:14) BDK(00:17:38) BTCPay Server(00:19:53) Nunchuk Desktop(00:20:20) Proton Wallet(00:23:58) Bitcoin Keeper(00:24:40) Nix Bitcoin(00:25:09) Krux(00:26:49) RoboSats(00:26:57) Bitkey App(00:31:07) RewindBitcoin(00:35:15) Umbrel(00:35:32) ESP-Miner(00:35:36) Boltz web-app• Project Spotlight(00:35:43) Wallet Dev Kit(00:35:51) ScriptLab(00:36:04) Mempal(00:36:44) Debifi(00:37:41) Dart-bip85 package(00:37:45) Descriptors Go(00:37:47) Tick-tock-tui App(00:37:56) Awesome-Bitcoin-guide(00:38:30) BitAxe HAS Dashboard(00:38:39) Radpool(00:38:43) SatadelicaVulnerability Disclosures(00:39:16) Nearest neighbor attack - Exploiting nearby Wi-Fi for covert access(00:39:27) New Ghost Tap attack exploits NFC mobile payments(00:39:38) Graykey partially unlocks iPhones with iOS 18/18.0.1, from iPhone 12 to 16 series(00:40:38) OverSecured finds seven flaws in Android and Google Pixel, affecting millions of users(00:41:03) Apple urgently patches vulnerabilities affecting macOS and iOS(00:41:32) Decades-old security flaws in Ubuntu's Needrestart Package(00:41:48) Chinese man arrested in Bangkok for operating from a van-based SMS blaster(00:42:28) Five dollar wrench attackPrivacy & Other Related Bitcoin Projects• Software Releases & Project Updates(00:42:53) SimpleX(00:43:06) Signal• Project Spotlight(00:43:28) DeFlockLightning & L2(+)• Project Spotlight(00:43:56) Phoenix Server Lightning Wallet(00:44:32) Cashu Brrr(00:45:08) SING4SATS• Software Releases & Project Updates(00:45:44) Primal(00:47:27) LND(00:48:05) CDK Cashu Development Kit(00:48:36) Phoenix Wallet(00:48:47) Zeus(00:49:56) Nutstash(00:49:59) Nutshell(00:50:07) Fountain(00:50:10) Breez SDK(00:50:14) Lnp2pBot(00:50:17) BoardWalk(00:50:19) Geyser(00:50:20) Aqua Wallet(00:50:47) Clams RemoteNostr • Project spotlight (00:50:52) NDK Mobile(00:50:55) Olas(00:51:26) Knox(00:51:32) Nostraut(00:51:35) Ezdvm(00:51:40) Futr(00:51:45) Nosey(00:51:49) Nos2x for Firefox• Software Releases & Project Updates(00:52:00) GifBuddyBoosts(00:52:30) Shoutout to top boosters Chris, podconf & btconboardNews & Noteworthy• Bitcoin(00:55:05) Chaincode Labs launches the 2025 BOSS program• Nostr(00:55:49) Kagi HQ joins Nostr• Business & Finance(00:57:06) Block, Inc. unveils Proto(00:57:11) Casa introduces Casa Enterprise(00:57:30) Swan Bitcoin sues former law firm for conflict of interest in Tether case• Tradfi(00:57:46) Revolut X expands to 30 European countries(00:57:58) Newmarket Capital, launches Battery Finance• Funding(00:58:06) OpenSats announces long term support and educational grants• Mining(00:58:12) Russia bans winter digital currency mining in Siberia• Privacy(00:58:40) U.S. Congress critiques Tornado Cash and urges for stricter regulations(00:58:44) Chinese government employees are selling citizen data for profit(00:59:20) Mullvad VPN cancels remaining PayPal subscriptions and aligns with privacy-focused policy• Cryptography(01:00:59) Two mathematicians break an 18-year record with an elliptic curve of rank 29• Events(01:01:22) SATS'N'FACTS, Bitcoin Technical Unconference(01:01:26) Hackalajara, Guadalajara's first Bitcoin-focused hackathon(01:01:39) Bengaluru BitDevs SummitReads:(01:01:46) Here's a list of our top recently published readsLinks & Contacts: Website: https://bitcoin.review/ Substack: https://substack.bitcoin.review/ Twitter: https://twitter.com/bitcoinreviewhq NVK Twitter: https://twitter.com/nvk Telegram: https://t.me/BitcoinReviewPod Email: producer@coinkite.com Nostr & LN: ⚡nvk@nvk.org (not an email!) Full show notes: https://bitcoin.review/podcast/episode-88

In this episode Brian Barney sits down with his friend Jim Boltz. Jim is an incredibly talented artist and avid outdoorsman. Jim has some great perspectives on being productive, finding happiness and living one's best life. The guys have a great conversation including discussing practices to help mitigate stress and making changes to the way we think about things. Black Ovis - www.blackovis.com Camo Fire - www.camofire.com Eberlestock - www.eberlestock.com Kryptek - www.kryptek.com Mathews - www.mathews.com Savage - www.savagearms.com Sig Sauer - www.sigsauer.com/electro-optics.html Silencer Central - www.silencercentral.com Zamberlan - www.zamberlan.com

The stars aligned just right for this episode as a very special guest @Boltz_n_Holes joined me and we had a great talk about Guns, shooting competition,practice,working out and of course favorite cereal. So give this a thumbs up while you listen and ENJOY IT!! Please follow my guest on Instagram you'll be happy you did.https://www.instagram.com/boltz_n_holez?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw== Please check out the store at https://akallday.net/ and help support all the things we do and it is greatly appreciated.

Imagina crear una empresa, 100% legal y que no toque en ningún momento el sistema fiat tradicional. Sin cuentas bancarias. Nunca. Eso es Boltz Exchange, que además es un servicio increíble - que debes conocer - para pasar de Bitcoin a Lightning, Liquid y Rootstock de la forma más cómoda. En el pod de hoy, Kilian, su CEO, me lo cuenta todo tanto a nivel empresa como a nivel proyecto bitcoin. LINKS: Boltz exchange: https://boltz.exchange/ Boltz en testnet: https://testnet.boltz.exchange/ Nostr Boltz: https://njump.me/nprofile1qyfhwumn8ghj7ur4wfcxcetsv9njuetn9uqsuamnwvaz7tmwdaejumr0dshsqgqvxu04ak2swesngslgxvwykcyz3mt8hn004gtf376ua8tmx2zllv2rhafu X: https://x.com/Boltzhq Escúchame en Fountain aquí https://fountain.fm/lunaticoin Más información en mi BLOG https://lunaticoin.blog Nostr: ⁠https://njump.me/nprofile1qy88wumn8ghj7mn0wvhxcmmv9uq3qamnwvaz7tmwdaehgu3wd4hk6tcqyqjwxlq7tvxgh2xauf65hnluvw6m9x0cqe8clwfghne3twwyje0nk0weuag⁠ X: https://twitter.com/lunaticoin Canal de Telegram: https://t.me/lunaticoinpodcast Contenido adicional en mi Patreon: https://www.patreon.com/lunaticoin Mención especial a los sponsors de este podcast: Compra bitcoin en HodlHodl: https://bit.ly/hodlhodl-luna Custodia tus bitcoin con Coldcard de Coinkite: https://bit.ly/coinkite-lunaticoin Vive con bitcoin en Bitrefill: https://bit.ly/Luna_Bitrefill Crea, edita y comparte hypertexto sin que nadie te frene en Seed Hypermedia (antes Mintter) https://seedhypermedia.com/ Aprende Bitcoin desde 0 con la Mentoría de Custodia https://bit.ly/Luna_Semilla

In my latest episode, I chat with Brooke Boltz, founder of Boltz Legal. Raised in Volusia County and a Political Science graduate, Brooke spent years in civil litigation before starting her own firm in 2017. Her unique experience working for and against insurance companies gives her a strategic edge in representing clients. I met Brooke at a rotary club where she was presenting on personal injury, and she inspired me to take action and start my own grassroots marketing. Brooke is simply a wonderful person, dedicated attorney, and loving mother of three. Don't miss this episode to hear her incredible journey and insights. Become a supporter of this podcast: https://www.spreaker.com/podcast/the-lawman-s-lounge--4267400/support.

Just like how English is the international language of business, Bitcoin's lightning network is how various layers and apps and products will ‘talk' to each other. Roy Sheinfeld, founder and CEO of Breez (@Breez_Tech) rejoins me to talk about updates in lightning and what's new with Breez SDK.  We discuss the various layers and ways of interacting with Bitcoin and lightning, using Liquid (@Liquid_BTC) with swaps, and Bitcoin as a medium of exchange.  Summary: In this conversation, Stephan and Roy discuss the current state of Bitcoin and Lightning. They explore the contrasting narratives of Lightning startups shutting down and new partnerships being formed. Roy emphasizes that Lightning is a manifestation of the payments use case of Bitcoin and that the majority of Bitcoin usage is still for trading and as an asset. He believes that the value proposition of Bitcoin lies in its potential as peer-to-peer electronic cash. They also discuss the role of custodial and non-custodial solutions, the regulatory landscape, and the control exerted by governments and private companies.  Roy argues that Lightning is the common language of the Bitcoin economy, enabling interoperability between different subnetworks and protocols. He highlights the importance of Lightning as a rail for communication and the incentives for moving Bitcoin within the Lightning network. Breez is working on integrating Lightning Network into existing applications to make Bitcoin more accessible and usable. They have developed the Breez SDK, which allows developers to easily integrate Lightning functionality into their apps.  Breez offers two implementations: Greenlight, which is a pure Lightning implementation, and Liquid, which is a nodeless implementation of Lightning using the Liquid sidechain. The goal is to provide a seamless user experience for Lightning transactions and make Bitcoin a common language for payments. Breez is also exploring hybrid solutions and partnerships to expand the adoption of Lightning. Key Takeaways: The majority of Bitcoin usage is currently for trading and as an asset, but the value proposition lies in its potential as peer-to-peer electronic cash. Lightning is the common language of the Bitcoin economy, enabling interoperability between different subnetworks and protocols. There is a need for both custodial and non-custodial solutions, with a growing focus on self-custodial options. The regulatory landscape and control exerted by governments and private companies pose challenges to the use of Bitcoin as a medium of exchange. Incentives within the Lightning network encourage the movement of Bitcoin and the use of Lightning as a rail for communication. Breez is focused on integrating Lightning Network into existing applications to make Bitcoin more accessible and usable. They have developed the Breez SDK, which allows developers to easily integrate Lightning functionality into their apps. Breez offers 2 implementations: Greenlight, which is a pure Lightning implementation, and Liquid, which is a nodeless implementation of Lightning using the Liquid sidechain. The goal is to provide a seamless user experience for Lightning transactions and make Bitcoin a common language for payments. Breez is exploring hybrid solutions and partnerships to expand the adoption of Lightning. Timestamps: (00:00) - Intro (00:56) - The current state of Bitcoin & Lightning network (03:17) - Mutiny wallet's contributions to Bitcoin (04:54) - Regulatory landscape, Dollarisation of Bitcoin & Importance of non-custodial solutions (14:43) - Sponsor (16:07) - HODLing vs Spending Bitcoin (18:55) - Lightning is the common language of the Bitcoin economy (22:50) - Lightning: Bitcoin's interoperability protocol (29:42) - Liquid network - overview (31:27) - Existing frictions with the Lightning network; Greenlight implementation, Liquid implementation  (36:55) - Sponsors (38:57) - Boltz.exchange ( @Boltzhq ) X Breez (40:33) - Differences b/w Greenlight & Liquid implementation (49:00) - Lightning on Liquid soon? (52:25) - Possible future integrations & way forward for Breez (57:25) - Outro Links:  https://medium.com/breez-technology/lightning-is-the-common-language-of-the-bitcoin-economy-eb8515341c11  https://x.com/Breez_Tech  Sponsors: CoinKite.com (code LIVERA) Mempool.space Nomadcapitalist.com/apply Stephan Livera links: Follow me on X: @stephanlivera Subscribe to the podcast Subscribe to Substack

She's a Mom...and entrepreneur, and a former professional football player! She also leads a very successful law firm in Oviedo. Brooke Boltz shares how she went from being a PK (preacher's kid) to running an insurance and personal injury law firm.

Send us a Text Message.Brooke Boltz - Managing Attorney at Boltz Legal - invites us into her faith journey including growing up a pastor's kid and a rule follower; being surrounded by friends that didn't share the same values, but respecting their choices; how her commitment to staying pure until marriage made relationships almost non-existent; the confusion around her divorce; the premature birth of her daughter; how she used a guitar and a dictaphone to worship in the hospital; playing tackle football on the boys team; how a degree in political science actually steered her to her career in law; moving from the defense to the plaintiff side and the challenges, and eventual blessing, that came with that; how her own firm gives her a freedom to express her faith and much more. https://boltzlegal.com/Support the Show.

"Submarine Swaps employ hash time-locked contracts (HTLCs), which in layperson’s terms, enable two transactions (both sides of the swap) to happen simultaneously by linking them together cryptographically inside of a smart contract. Using this technology, there is no way for one exchange to occur without the other occurring at the same moment. This is the most trustless solution that exists for moving sats between Bitcoin layers." ~ Frank Corva What role do trustless bridges play in connecting Bitcoin's various layers? Dive into the world of Boltz, a non-custodial exchange service that's quietly revolutionizing how we move Bitcoin between the base chain, Lightning Network, and Liquid. Discover how atomic swaps and submarine swaps are making cross-layer transfers more efficient and seamless. What implications do they hold for the future of Bitcoin's layered ecosystem? Check out the original article at Between Bitcoin Layers: Boltz Builds Trustless Transfers (Link: https://tinyurl.com/3rv7xuar) Links to check out: Boltz (Link: boltz.exchange) What Are Taproot Swaps? by Che Kohler (Link: https://tinyurl.com/4kwekbar) Breeze Wallet [iOS (Link: https://tinyurl.com/3yyfeh6d) / Android (Link: https://tinyurl.com/mr2xdatd)] Aqua Wallet [iOS (Link: https://tinyurl.com/3uekhxhu)/ Android (Link: https://tinyurl.com/358wjk8x)] Phoenix Wallet [iOS (Link: https://tinyurl.com/4np9k8me) / Android (Link: https://tinyurl.com/2hkwbrer)] Pears (Link: https://pears.com/) Keet (Link: https://keet.io/) Nostr.com (Link: https://nostr.com/) Nostr.how (Link: https://nostr.how/) Hedgehog: A protocol for improved layer two bitcoin payments (Link: https://github.com/supertestnet/hedgehog) Host Links ⁠Guy on Nostr ⁠(Link: http://tinyurl.com/2xc96ney) ⁠Guy on X ⁠(Link: https://twitter.com/theguyswann) Guy on Instagram (Link: https://www.instagram.com/theguyswann) Guy on TikTok (Link: https://www.tiktok.com/@theguyswann) Guy on YouTube (Link: https://www.youtube.com/@theguyswann) ⁠Bitcoin Audible on X⁠ (Link: https://twitter.com/BitcoinAudible)

Ashley is a mom of 3 and is currently nursing her 18 month old twins girls. She breastfed her son through 16 months and has started the process of slowly weaning her twins. She shares her everyday life and relatable mom content as a 2 mom family on instagram @lovedby2moms. This episode is brought to you by the free Extended Breastfeeding Quiz. Click here to learn which of the 5 stages of extended breastfeeding you are in today!

Rachel Boltz is the founder of Boltz Media. who are a full service digital marketing agency. and serve business owners who are looking for a striking difference in digital marketing and also the co-host of the Ms. Biz Podcast. Our podcast is geared toward Christian female business owners with a focus on extreme marketing.Here's where to find more:www.boltzmedia.comhttps://www.facebook.com/BoltzMedia1https://www.instagram.com/boltzmedia1https://www.tiktok.com/@boltz_mediahttps://twitter.com/boltz_mediahttps://www.linkedin.com/company/boltzmediahttps://www.youtube.com/@boltzmedia1___________________________________________________________Welcome to The Unforget Yourself Show where we use the power of woo and the proof of science to help you identify your blind spots, and get over your own bullshit so that you can do the fucking thing you ACTUALLY want to do!We're Mark and Katie, the founders of Unforget Yourself and the creators of the Unforget Yourself System and on this podcast, we're here to share REAL conversations about what goes on inside the heart and minds of those brave and crazy enough to start their own business. From the accidental entrepreneur to the laser-focused CEO, we find out how they got to where they are today, not by hearing the go-to story of their success, but talking about how we all have our own BS to deal with and it's through facing ourselves that we find a way to do the fucking thing.Along the way, we hope to show you that YOU are the most important asset in your business (and your life - duh!). Being a business owner is tough! With vulnerability and humor, we get to the real story behind their success and show you that you're not alone._____________________Find all our links to all the things like the socials, how to work with us and how to apply to be on the podcast here: https://linktr.ee/unforgetyourself

Brooke Boltz is the founder of Boltz Legal - Brooke is a 15 year attorney, practicing injury and insurance law and also has a podcast called "Ms. Biz".Here's where to find more:https://www.facebook.com/BoltzLegal?mibextid=LQQJ4dhttps://www.facebook.com/MsBizPodcast?mibextid=LQQJ4dWww.boltzlegal.comWww.msbiz.com___________________________________________________________Welcome to The Unforget Yourself Show where we use the power of woo and the proof of science to help you identify your blind spots, and get over your own bullshit so that you can do the fucking thing you ACTUALLY want to do!We're Mark and Katie, the founders of Unforget Yourself and the creators of the Unforget Yourself System and on this podcast, we're here to share REAL conversations about what goes on inside the heart and minds of those brave and crazy enough to start their own business. From the accidental entrepreneur to the laser-focused CEO, we find out how they got to where they are today, not by hearing the go-to story of their success, but talking about how we all have our own BS to deal with and it's through facing ourselves that we find a way to do the fucking thing.Along the way, we hope to show you that YOU are the most important asset in your business (and your life - duh!). Being a business owner is tough! With vulnerability and humor, we get to the real story behind their success and show you that you're not alone._____________________Find all our links to all the things like the socials, how to work with us and how to apply to be on the podcast here: https://linktr.ee/unforgetyourself

► Bitcoin Needs Unstoppable Privacy Tools! We are joined by creator of the boltz exchange Michael, this 20 something wiz kid is attempting to solve some of the biggest challenges for scaling and privacy. ✔ Special Guest/Fireside Chat: ► @michael1011at ► https://boltz.exchange/ ► https://twitter.com/boltzhq ► michael1011.at ✔ Numbers: ► https://x.com/jebus/status/1788615986607735132?s=61&t=KglbeFNESvZWy6xNoYX4YQ ► https://x.com/italiansatoshi/status/1787221142035390808?s=52&t=CKH2brGypO5fEYTgQ-EFhQ ✔ REKT: ► https://twitter.com/bitcoinnewscom/status/1788227216708411573?s=52&t=CKH2brGypO5fEYTgQ-EFhQ ► https://twitter.com/mononautical/status/1788739557418320323?s=61&t=KglbeFNESvZWy6xNoYX4YQ ✔ Bitcoin Hopium: ► https://en.bitcoinsistemi.com/is-china-the-next-step-for-bitcoin-and-ethereum-etfs-much-talked-about-btc-eth-step-from-the-giant-company/ ► https://en.wikipedia.org/wiki/Harvest_Fund_Management ► https://www.harvestglobal.us/hgi/index.php ✔ Twitter Handles: @coinicarus @AEHW1 ✔ ShoutOuts: ► @BitkoYinowsky - our PlebUnderground Logo ► @WorldofRusty - Our YT backgrounds and segment transitions ► @luckyredfish - Outro Graphic ► @plebsTaverne - Intro video ► @robbieP808x - Outro music ✔ Links Mentioned: ► https://timechaincalendar.com/en ► https://timechainstats.com/ ✔ Check out our Sponsor, support Bitcoin ONLY Businesses: ► https://cyphersafe.io/ We offer a full line of physical stainless steel and brass products to help you protect your bitcoin from various modes of failure. CypherSafe creates metal BIP39 / SLIP39 bitcoin seed word storage devices that backup your bitcoin wallet and protect them from physical disaster ► http://btcpins.com/ Pick up the latest pin about the latest thing! Btcpins has it all; rare bitcoiner themed pins, stickers, apparel and a whole lot more. Get a 5% discount on any of the iconic gear found here using code ‘plebunderground' ► https://proofofink.com/ Come check out awesome high quality clothes using screen printed tech for long lasting bright colorful prints that last. NO third parties, Bitcoin Only Designs! Get a 5.8% discount on any of the amazing gear found here using code ‘plebunderground' For Awesome pleb content daily http://plebunderground.com/ GM #Bitcoin (mon-fri 10:00 am ET) and The #Bitcoin Council of Autism Spaces on twitter Timecodes: 0:00 - Intro 00:24 - Waltons Rap 02:04 - Numbers 19:00 - FiresideChat: Michael(boltz Exchange) 41:13 - REKT 51:41 - Hopium #Bitcoin #crypto #cryptocurrency #weekly The information provided by Pleb Underground ("we," "us," or "our") on Youtube.com (the "Site") our show is for general informational purposes only. All information on the show is provided in good faith, however we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the Site. UNDER NO CIRCUMSTANCE SHALL WE HAVE ANY LIABILITY TO YOU FOR ANY LOSS OR DAMAGE OF ANY KIND INCURRED AS A RESULT OF THE USE OF THE SHOW OR RELIANCE ON ANY INFORMATION PROVIDED ON THE SHOW. YOUR USE OF THE SHOW AND YOUR RELIANCE ON ANY INFORMATION ON THE SHOW IS SOLELY AT YOUR OWN RISK.

- OpenSats Receives Additional $21M Funding from #startsmall Initiative https://opensats.org/blog/opensats-receives-additional-funding-of-dollar21m-from-startsmall - Block to Invest 10% of Bitcoin Profits into BTC Each Month for the Rest of 2024 https://www.nobsbitcoin.com/block-to-invest-10-of-bitcoin-profits-into-btc-each-month/ - Spiral Renews Grants to Bitcoin Zavior and Matt Morehouse https://www.nobsbitcoin.com/spiral-renews-grants-to-bitcoin-zavior-matt-morehouse/ - Lightning Payments Biz Mash to Shut Down https://x.com/getmash/status/1787863323825893499 - AgoraDesk / LocalMonero P2P Exchange to Shut Down on November 7 https://www.nobsbitcoin.com/agoradesk-localmonero-shutting-down/ - libsecp256k1 v0.5.0: Faster Key Generation & Signing https://www.nobsbitcoin.com/libsecp256k1-v0-5-0/ - COLDCARD Mk4 v5.3.0 and Q v1.2.0Q: Shared Codebase https://www.nobsbitcoin.com/coldcard-mk4-v5-3-0-and-v1-2-0q/ - Geyser v0.8.0: On-chain to Lightning Donation Swaps via Boltz https://www.nobsbitcoin.com/geyser-v0-8-0/ - LDK v0.0.123: 'BOLT12 Dust Sweeping' https://www.nobsbitcoin.com/ldk-v0-0-123/ - BDK v1.0.0-alpha.10: Improved Address API, Simplified Esplora API https://www.nobsbitcoin.com/bdk-v1-0-0-alpha-10/ - Zeus v0.8.4: Select Wallet/Node on Start-up https://www.nobsbitcoin.com/zeus-v0-8-4/ - Bitcoin Keeper v1.2.6: Canary Wallets to Detect Unauthorized Key Usage https://www.nobsbitcoin.com/bitcoin-keeper-v1-2-6/ - Emessbee: Construct Coinjoin Transactions Without a Coordinator https://www.nobsbitcoin.com/introducing-emessbee/ - Blockstream Green Desktop v2.0.5: Add Singlesig Watch-only Wallets & More https://www.nobsbitcoin.com/blockstream-green-desktop-v2-0-5/ - Swan Discloses Mining Partnership with Tether https://twitter.com/Swan/status/1787951439576232315 - Tether Enhances Compliance Measures with Chainalysis Ecosystem Monitoring Solution https://tether.io/news/tether-enhances-compliance-measures-with-chainalysis-ecosystem-monitoring-solution/ 3:41 - US/Japan swap line 8:22 - Dashboard 11:16 - OpenSats receives funding 18:52 - Social engineering attacks 27:41 - Block investing in bitcoin 32:19 - Spiral grants 33:31 - Mash 36:12 - AgoraDeak shutting down 37:31 - Boosts 40:09 - Open source fixes this 44:17 - Software updates 52:25 - Pubkey RHR 56:29 - Swan mining 1:01:56 - Tether Chainalysis 1:04:57 - Final thoughts Shoutout to our sponsors: Unchained Capital https://unchained.com/concierge/ Coinkite https://coinkite.com/ TFTC Merch is Available: Shop Now https://merch.tftc.io/ Join the TFTC Movement: Main YT Channel https://www.youtube.com/c/TFTC21/videos Clips YT Channel https://www.youtube.com/channel/UCUQcW3jxfQfEUS8kqR5pJtQ Website https://tftc.io/ Twitter https://twitter.com/tftc21 Instagram https://www.instagram.com/tftc.io/ Follow Marty Bent: Twitter https://twitter.com/martybent Newsletter https://tftc.io/martys-bent/ Podcast https://tftc.io/podcasts/ Follow Odell: Twitter https://twitter.com/ODELL Newsletter https://tftc.io/the-sat-standard/ Podcast https://citadeldispatch.com/

Welcome back to the regularly scheduled 2nd Opinion Podcast! We are back in full swing with the show and are excited to share our opinions! We plan on setting up a lot of content for the future, including interviews, bringing back our Head2Head segment, as well as having our co-host rejoin the show! We hope you enjoy this episode of the podcast and please make sure to subscribe! “In this audio episode, Caleb “Soleb” Gayle and Boltz discuss Star Wars: Outlaws, Xbox's New Console Coming in 2028, Helldivers II, and more!" “LIFE A BITCH, HOW ABOUT YOU?” Show introduction, this used to be called “what you've been drinking and gaming” but …who has time for that now? GAMING TOPICS 1. "Star Wars Outlaws Gets New Trailer and a Release Date" “In-depth with a discussion on Star Wars: Outlaws.What we've seen so far and what we want from this game" 2. “Players Have Defeated An Entire Enemy Faction In Helldivers 2 but now they are back!” “Discuss the ongoing development of Helldivers 2, the eradication of the Automatons, and the future of the game!” 3. Xbox Moving 'Full Speed Ahead' on Next-Gen Console 4. Ermac Coming to Mortal Kombat 1 on April 16th 5. All 8 Episodes Of Amazon's Fallout TV Series Now Dropping One Day Earlier 6. Motive Studio To Work On Battlefield As Iron Man Game Hits 'Major Internal Milestone' **END SHOW** --- Send in a voice message: https://podcasters.spotify.com/pod/show/2ndopinionpodcast/message

Welcome back to the regularly scheduled 2nd Opinion Podcast! We are back in full swing with the show and are excited to share our opinions! We plan on setting up a lot of content for the future, including interviews, bringing back our Head2Head segment, as well as having our co-host rejoin the show! We hope you enjoy this episode of the podcast and please make sure to subscribe! “In this audio episode, Caleb “Soleb” Gayle and Boltz discuss Dragons Dogma II, Video Game Shows and Movies, New Consoles Already Coming, and more!" “LIFE A BITCH, HOW ABOUT YOU?” Show introduction, this used to be called “what you've been drinking and gaming” but …who has time for that now? GAMING TOPICS 1. Dragons Dogma II, Is it a Big Deal that it has MicroTransactions? 2. Should Video Game Shows & Movies Follow Game Lore? 3. New Video Game Consoles are already on the way? 4. New Video Game Coming Soon! **END SHOW** --- Send in a voice message: https://podcasters.spotify.com/pod/show/2ndopinionpodcast/message

Welcome back to the regularly scheduled 2nd Opinion Podcast! Things have been rough these past few weeks, with the holidays upon us things have been getting crazier every day! We plan on setting up a lot of content for the future, including interviews, bringing back our Head2Head segment, as well as having our co-host rejoin the show! We hope you enjoy this episode of the podcast and please make sure to subscribe! “In this audio episode, Caleb “Soleb” Gayle and Boltz discuss Toys for Bob, Fallout 5, and more! “LIFE A BITCH, HOW ABOUT YOU?” Show introduction, this used to be called “what you've been drinking and gaming” but …who has time for that now? GAMING TOPICS 1. "Toys for Bob is Going Independent" 2. "Marvel 1943: Rise of Hydra has been Announced" 3. "Todd Howard talks Fallout 5" "END SHOW" --- Send in a voice message: https://podcasters.spotify.com/pod/show/2ndopinionpodcast/message

How do we interact with Bitcoin in a multi layer world? What cost savings and trade offs are necessary here? What even counts as a layer? Kilian from boltz.exchange joins me to talk about swapping across Bitcoin on-chain, Lightning and Liquid. We discuss: How swapping works What makes it atomic and non-custodial What counts as layer 2 Fee savings and trade offs Links: X: @kilrau Site: boltz.exchange X: @boltzhq Taproot feature blog post: Introducing Taproot Swaps: Putting the "Fun" Back into Refunds!  Sponsors: Swan.com (code LIVERA) CoinKite.com (code LIVERA) Mempool.space Nomadcapitalist.com Stephan Livera links: Follow me on X: @stephanlivera Subscribe to the podcast Subscribe to Substack Chapters/Timestamps: 00:00 - Introduction and Background 03:07 - Bolts.exchange: Moving Between Bitcoin Layers 05:35 - Use Cases for Lightning Service Providers 09:45 - Enhancing Wallet Functionality 13:20 - Non-Custodial Atomic Swaps 21:43 - Coinkite.com 23:07 - NomadCapitalist.com 24:25 - Fees and Fee Savings 29:35 - Trust Model of Liquid 32:40 - Regulatory Risks for Liquid 37:10 - Transaction Limits on Liquid 38:58 - Mempool.space 39:59 - Swan.com 41:02 - Scalability of Liquid 44:15 - Integration of Liquid with Other Services and Wallets 51:22 - Pragmatism in Bitcoin Adoption 55:45 - Operating in a High-Fee Environment 57:45 - Closing Thoughts

We were very fortunate to have Al Boltz from A Scent Like Wolves on the podcast to talk about their new album, "Distant Dystopia". Enjoy! A Scent Like Wolves Socials: Twitter: https://twitter.com/aslwofficial Instagram: https://www.instagram.com/aslwofficial/ Facebook: https://www.facebook.com/ascentlikewolves/ TikTok: https://www.tiktok.com/@aslwofficial Apple Music: https://music.apple.com/us/artist/a-scent-like-wolves/428805018 Spotify: https://open.spotify.com/artist/45MtkhYiPMUHwT4Wv7qBji Bandcamp: https://ascentlikewolves.bandcamp.com/ Website: https://www.aslwofficial.com/ Grab some GNP Merch!: https://goodnoisepodcast.creator-spring.com/ Check out the recording gear we use: https://www.amazon.com/shop/goodnoisepodcast Support the show on Patreon: https://www.patreon.com/goodnoisepodcast Good Noise Podcast Socials: Twitter: https://twitter.com/good_noise_cast Instagram: https://www.instagram.com/goodnoisepodcast/ Facebook: https://www.facebook.com/goodnoisepod Discord: https://discord.gg/nDAQKwT YouTube: https://www.youtube.com/channel/UCFHKPdUxxe1MaGNWoFtjoJA Spotify: https://open.spotify.com/show/04IMtdIrCIvbIr7g6ttZHi All other streaming platforms: https://linktr.ee/goodnoisepodcast Bandcamp: https://goodnoiserecords.bandcamp.com/

Welcome to a brand new episode of Ian's Untitled Scene Show! In this episode, we welcome back the great Al Boltz of A Scent Like Wolves! Al returns, once again, to talk everything A Scent Like Wolves, early 2000s screamo/post-hardcore scene music, and so much more! PLUS, A Scent Like Wolves' brand new full-length album, ‘Distant Dystopia,' is out RIGHT NOW, through Theoria Records! I bet we discuss that in-depth as well…Prepare for a great catch-up episode with a lot of insight into A Scent Like Wolves and the scene!Note: Al is feeling a bit sick and Ian lost his voice earlier in the week, so nothing is perfect, haha.Check out the exclusive interview below, including iTunes, Spotify, and more:Spotify

How do you transform a high-stress law firm into a culture of balance and shared values? In this refreshing episode, we engage with Brooke Boltz, an accomplished attorney who specializes in injury and insurance law. Brooke candidly shares her journey from working at a firm with a high-demand culture to founding her own firm where she prioritizes work-life balance, employee well-being, and the integration of faith into her business practices. Join us as our host Nicole Jansen and Brooke Boltz discuss how to create a business environment that values not just productivity, but also the personal happiness and fulfillment of its employees. They talk about the impact of firm culture on profitability, stress management, and employee retention, and how faith can play a role in guiding a company's ethos. Discover the actionable steps Brooke took to pivot her business approach, her strategies for time management, and her initiatives to foster community among Christian entrepreneurs through her Facebook groups and top-performing podcast, the Ms. Biz Podcast. What We Discuss in this Episode Brooke's unexpected career path to injury and insurance law. The significance of work-life balance and faith in personal and professional life. Strategies for achieving a humane and employee-centric workplace. The advantages of Christian-based businesses and faith-driven decision-making. Building unity and community among Christians in the business world. Tips on maintaining employee satisfaction and retention through workplace culture. Insights into managing a successful multi seven-figure business and personal commitments. The effects of legislative changes on law firm profitability and adaptation strategies. How blending personal values with business can offer unique challenges and rewards. Managing increased responsibilities, delegation, and effective prioritization. Podcast Highlights 0:00 - Unexpected Career Paths: From Criminal Law to Injury and Insurance 5:15 - Work-Life Balance: Making Time for What Matters 7:30 - Faith in Business: The Foundation of Success 9:55 - Employee Motivation: Recognition and Rewards 12:40 - "Jesus Tribe": Fostering Community Among Christians 17:00 - Legal Landscape: Adaptation in the Face of Change 19:25 - Delegation Mastery: Brooke's Approach to Workload Management 22:30 - Corporate Culture and Faith: Integrating Personal Values 24:50 - A Discussion on Firm Culture Transformation 29:15 - Ms. Biz Podcast and Contributions to Nonprofits Dive deep into the conversation as Brooke Boltz shares her experiences and strategies on fostering a transformative and fulfilling business culture. Episode Show Notes: https://leadersoftransformation.com/podcast/business/483-faith-law-leadership-redefining-success-with-brooke-boltz  Check out our complete library of episodes and other leadership resources here: https://leadersoftransformation.com ________

Moving from chain to chain is a pain in the ass. Layer on custodian risk and this area is ripe for better solutions. That was Boltz is working on with their non-custodial cross-chain/multi-layer swaps from different Bitcoin focued chains. Kilian is the co-founder of Boltz. We discuss: - How he got into Bitcoin? - The benefits of building things non-custodial - Using L2s to solve Lightning scaling problems and much more! Follow Kilian on Twitter: @kilrau Check out Boltz at https://boltz.exchange/

As Only7Seconds has heavily shifted our focus to youth, we wanted to bring on one of our partners to share a perspective on why youth belonging is critical to making change in the youth loneliness epidemic. Our guest today is Erich Boltz from The Center For Educational Effectiveness (CEE). CEE develops and delivers quality surveys, data tools and services that catalyze community, district, school, and individual growth. For more on this collaborative conversation, our team joined CEE on their podcast, you can listen to Eric Driessen & Luke Wall chat with the CEE team here. Learn more about CEE and the work they do at www.effectiveness.org — Learn more about ⁠⁠Only7Seconds⁠⁠ Connect with us on social media: ⁠⁠Instagram⁠⁠ | ⁠⁠Facebook⁠⁠ | ⁠⁠Twitter⁠⁠ | ⁠⁠LinkedIn⁠⁠ — Music written & produced: ⁠⁠Dash8 --- Send in a voice message: https://podcasters.spotify.com/pod/show/only7seconds/message