Podcasts about bolts

On this episode of Chargers Weekly, Bolts radio play-by-play announcer Matt “Money” Smith & host Chris Hayre sit down with ESPN's Dan Graziano, NFL Analyst Brett Kollmann, and The Athletic's Dane Brugler at the 2026 NFL Scouting Combine in Indianapolis. The three guys give unique perspectives on their national coverage of the league, the strengths of this year's draft class, and how the Chargers can capitalize on the opportunity to improve. Presented by Splitero.See omnystudio.com/listener for privacy information.

Nick Alberga & Jay Rosehill tee up the stretch run as the Toronto Maple Leafs return from the Olympic break to visit the Tampa Bay Lightning.With 25 games left and sitting six points out of a playoff spot, the pressure is on. The boys break down what's at stake, what needs to change, and whether this group has another gear when it matters most.Meanwhile, Leafs captain Auston Matthews is expected to play after captaining Team USA to gold — but his appearance at the White House on Tuesday lit up social media. The boys dive into the reaction, the noise, and whether any of it actually matters heading into this crucial stretch.Plus, Sid Seixeiro, host of The Sid Seixeiro Show, stops by to weigh in on the Leafs' playoff push and what the final 25 games could mean for the future of this core.#LeafsForever #LeafsMorningTake

Mark “Murch” Erhardt, Gustavo Flores Echaiz, and Mike Schmidt are joined by Misha Komarov, Erik De Smedt, and arbedout to discuss ⁠Newsletter #393⁠.News● Recent OP_RETURN output statistics (45:54) ● Bitcoin PIPEs v2 (1:33) Changes to services and client software● Second releases hArk-based Ark software (20:21) ● Amboss announces RailsX (55:35) ● Nunchuk adds silent payment support (56:11) ● Electrum adds submarine swap features (58:08) ● Sigbash v2 announced (33:56) Releases and release candidates● BTCPay Server 2.3.5 (1:00:11) ● LND 0.20.1-beta (1:01:43) Notable code and documentation changes● Bitcoin Core #33965 (1:02:55) ● Eclair #3248 (1:05:55) ● Eclair #3246 (1:07:08) ● LDK #4335 (1:08:52) ● LDK #4318 (1:14:32) ● LND #10542 (1:15:55) ● BIPs #1670 (1:17:02) ● BOLTs #1236 (1:27:50) ● BOLTs #1289 (1:29:10)

USA Hockey, Bolts are Back, and Boxing Drama

Tampa Bay Lightning reporter Ben Pierce on the Olympic Hockey Tournament that saw Team USA win the gold medal over Canada. Jake Guentzel gets gold while Brandon Hagel wins silver. Plus injury updates on Brayden Point, Emil Lilleberg, Charle-Eduoard D'Astous, Max Crozier, Anthony Cirelli and Nick Paul. Now the Bolts attention turns towards the final 27 games which starts back up on Wednesday night vs. the Maple Leafs. Into the Blue is presented by Penelope Bourbon.See omnystudio.com/listener for privacy information.

Use promo code BOLTBROS on Sleeper and get 100% match up to $100! https://Sleeper.com/promo/BOLTBROS. Terms and conditions apply. #SleeperThe Bolt Bros are taking over the front office! In this 2026 offseason special, we step into the GM shoes to navigate a massive $100 million in cap space and a roster full of critical decisions. Following another Wild Card exit and the arrival of Mike McDaniel as offensive coordinator, the Chargers are ready to reload.What would YOU do with $100M? We're taking control of the Bolts' 2026 offseason to make the tough calls that Joe Hortiz is facing right now. From deciding the future of franchise legends to hunting for elite weapons for Justin Herbert, no stone is left unturned!Social Media Links:https://www.Beacons.ai/boltbros  / discord  https://www.riverslake.org/Bolt Bros Merch!https://nflshop.k77v.net/Ry9ymXhttps://www.boltbros.live/merchhttps://forms.gle/vp8sJeDkNr2XpdKW8#Chargers #BoltUp #BoltBros #NFLOffseason2026 #JustinHerbert #MikeMcDaniel #JoeHortiz #NFLFreeAgency #KeenanAllen #KhalilMack #NFLRumors #ChargersDraft #SalaryCap #LAChargers

Nick Wize has a big question: Are you taking the Bolts winning the Stanley Cup OR Team USA winning gold?

Will there be a bigger boxing story in 2026 than Conor Benn leaving Matchroom? Richie Woodhall joins Buncey to reflect on Benn's decision to sign with Dana White's Zuffa Boxing, while Eddie Hearn gives his immediate reaction. Plus, they ask what's next for Leigh Wood and Josh Warrington after their Nottingham rematch, with Wood the clear winner. We hear from both fighters.

Electric vehicle development continues to accelerate as manufacturers seek greater range, improved efficiency, and reduced production costs. Tanaka Precision Industries focuses on component innovation that supports these goals through advanced materials engineering and precision manufacturing. The company develops lightweight inverter cases and structural components designed to reduce overall vehicle mass, improve energy efficiency, and streamline assembly processes.Weight reduction remains one of the most effective ways to extend the driving range of electric vehicles. While battery chemistry receives much of the industry's attention, the structural components surrounding the powertrain also play a significant role in determining efficiency. By reducing the mass of these components, less energy is required to move the vehicle, allowing each charge to support longer distances. Tanaka Precision Industries approaches this challenge through material optimization and advanced casting techniques that maintain strength while reducing thickness.Ultra‑Thin Inverter Case TechnologyOne of the company's most notable developments is an inverter case engineered with significantly thinner walls than traditional designs. The component uses ADC12 aluminum and is manufactured at a thickness of approximately 1.5 millimeters, compared to the industry standard of around 2.5 millimeters. This reduction represents a meaningful decrease in weight while maintaining the structural integrity required for electric drivetrain systems.The thinner design also supports cost efficiency. By reducing material usage and optimizing the casting process, the component can be produced at a lower cost without compromising performance. These improvements contribute to more affordable electric vehicles and support broader adoption as manufacturers seek ways to balance performance with price.Advancing Assembly Through Friction WeldingIn addition to lightweight casting, Tanaka Precision Industries is exploring friction welding as an alternative to traditional fasteners. Bolts, nuts, and screws contribute more weight than many consumers realize, and they also add time and complexity to the assembly process. Friction welding allows two components to be joined without mechanical fasteners, creating a strong bond while reducing both weight and production time.Early evaluations suggest that friction welding may reduce cycle times and simplify manufacturing workflows. These improvements can scale significantly when applied to high‑volume production environments, supporting cost savings and operational efficiency. The combination of lightweight components and streamlined assembly reflects a holistic approach to improving electric vehicle manufacturing.A History of Precision ManufacturingTanaka Precision Industries has a long history of supplying components to major automotive manufacturers. The company began its North American operations in the mid‑1990s, supporting engine production for Honda before expanding into additional supply chains. Its experience in precision machining, casting, and component engineering provides a foundation for the development of next‑generation EV technologies.Research and development efforts are based in Japan, while North American production is positioned to support regional automotive partners. This structure allows the company to combine global engineering expertise with localized manufacturing capabilities, ensuring that components meet regional standards and supply chain requirements.ConclusionTanaka Precision Industries advances electric vehicle efficiency through lightweight inverter cases, friction‑welded components, and precision manufacturing techniques. By reducing material usage, lowering production costs, and improving assembly processes, the company supports the broader industry goal of creating more efficient, affordable, and sustainable electric vehicles. As EV adoption continues to grow, innovations in component engineering will remain essential to improving performance and expanding accessibility.Interview by Don Baine, The Gadget Professor.Sponsored by: Get $5 to protect your credit card information online with Privacy. Amazon Prime gives you more than just free shipping. Get free music, TV shows, movies, videogames and more. Secure your connection and unlock a faster, safer internet by signing up for PureVPN today.

Electric vehicle development continues to accelerate as manufacturers seek greater range, improved efficiency, and reduced production costs. Tanaka Precision Industries focuses on component innovation that supports these goals through advanced materials engineering and precision manufacturing. The company develops lightweight inverter cases and structural components designed to reduce overall vehicle mass, improve energy efficiency, and streamline assembly processes.Weight reduction remains one of the most effective ways to extend the driving range of electric vehicles. While battery chemistry receives much of the industry's attention, the structural components surrounding the powertrain also play a significant role in determining efficiency. By reducing the mass of these components, less energy is required to move the vehicle, allowing each charge to support longer distances. Tanaka Precision Industries approaches this challenge through material optimization and advanced casting techniques that maintain strength while reducing thickness.Ultra‑Thin Inverter Case TechnologyOne of the company's most notable developments is an inverter case engineered with significantly thinner walls than traditional designs. The component uses ADC12 aluminum and is manufactured at a thickness of approximately 1.5 millimeters, compared to the industry standard of around 2.5 millimeters. This reduction represents a meaningful decrease in weight while maintaining the structural integrity required for electric drivetrain systems.The thinner design also supports cost efficiency. By reducing material usage and optimizing the casting process, the component can be produced at a lower cost without compromising performance. These improvements contribute to more affordable electric vehicles and support broader adoption as manufacturers seek ways to balance performance with price.Advancing Assembly Through Friction WeldingIn addition to lightweight casting, Tanaka Precision Industries is exploring friction welding as an alternative to traditional fasteners. Bolts, nuts, and screws contribute more weight than many consumers realize, and they also add time and complexity to the assembly process. Friction welding allows two components to be joined without mechanical fasteners, creating a strong bond while reducing both weight and production time.Early evaluations suggest that friction welding may reduce cycle times and simplify manufacturing workflows. These improvements can scale significantly when applied to high‑volume production environments, supporting cost savings and operational efficiency. The combination of lightweight components and streamlined assembly reflects a holistic approach to improving electric vehicle manufacturing.A History of Precision ManufacturingTanaka Precision Industries has a long history of supplying components to major automotive manufacturers. The company began its North American operations in the mid‑1990s, supporting engine production for Honda before expanding into additional supply chains. Its experience in precision machining, casting, and component engineering provides a foundation for the development of next‑generation EV technologies.Research and development efforts are based in Japan, while North American production is positioned to support regional automotive partners. This structure allows the company to combine global engineering expertise with localized manufacturing capabilities, ensuring that components meet regional standards and supply chain requirements.ConclusionTanaka Precision Industries advances electric vehicle efficiency through lightweight inverter cases, friction‑welded components, and precision manufacturing techniques. By reducing material usage, lowering production costs, and improving assembly processes, the company supports the broader industry goal of creating more efficient, affordable, and sustainable electric vehicles. As EV adoption continues to grow, innovations in component engineering will remain essential to improving performance and expanding accessibility.Interview by Don Baine, The Gadget Professor.Sponsored by: Get $5 to protect your credit card information online with Privacy. Amazon Prime gives you more than just free shipping. Get free music, TV shows, movies, videogames and more. Secure your connection and unlock a faster, safer internet by signing up for PureVPN today.

A while back I ran a three part 17 hour workshop on how to sponsor featuring Tim M leading a workshop that took place over about 6 months. This speak is Tim on the topic of: The Nuts and Bolts of Sponsorship, is a VERY abbreviated version of that. There is some Q & A at the end, and I dont have a where and when for this one, but I would guess fairly resent. NSFW Support Sober Cast: https://sobercast.com/donate Email: sobercast@gmail.com Sober Cast has 3200+ episodes available, visit SoberCast.com to access all the episodes where you can easily find topics or specific speakers using tags or search. https://sobercast.com

On this episode of Chargers Weekly, Bolts radio play-by-play announcer Matt “Money” Smith & host Chris Hayre answer fan questions ahead of the free agency, NFL Combine, and draft. They give their takes on what to expect this offseason, their favorite free agents & prospects to look out for, and what the team could look like next season. Presented by Splitero.See omnystudio.com/listener for privacy information.

Another Tampa Bay Lightning player down? Plus, USA wins!!

Song: The Reach Words by: Wendell Berry Music by: Malcolm Dalglish   Notes: I learned this beautiful round from the amazing Moira Smiley, who connected me with Malcolm Dalglish to request his permission to share on this podcast. I continue to be amazed by the seeds we sow when we befriend and tend each other, unfolding understanding and trust. It's not always easy to reach forward or back -- but these seeds we plant with each other open, even if the winter is long and icy! At least that's where this poem is taking me today. And let me tell you, I practiced deep love and care for all of you whose voices are comfortable in higher ranges than mine! I taught it where I'm comfy -- but I did the reprise where Malcolm actually set the sheet music (nice tenor/soprano range) -- so if you want to buy the sheet music from him and sing along with it, that's where you should head...   Songwriter Info: Malcolm Dalglish is a hammer dulcimer player and composer from Bloomington Indiana, whose many vocals celebrate a love of our natural world. He has set many poems by Kentucky author, and farmer, Wendell Berry.    Sharing Info: Please buy sheet music on Malcolm's website if you plan to share this song as a songleader.   Song Learning Time Stamps: Start time of teaching: 00:02:50 Start time of reprise: 00:14:29   Links: Malcolm's website: https://oooliticmusic.com/    Nuts & Bolts: 4:4, major, round with optional descant   Join this community of people who love to use song to help navigate life? Absolutely: https://dashboard.mailerlite.com/forms/335811/81227018071442567/share   Help us keep going: reviews, comments, encouragement, plus contributions... we float on your support. https://www.abreathofsong.com/gratitude-jar.html

Dave, Josh, and Mario are breaking down Tyreek Hill being released and linked to Los Angeles. The crew talk about Odafe Oweh and why the Chargers need him back. Then the guys look at updated NFL Mock Drafts.

https://traffic.libsyn.com/thebeerreport/TW_108.mp3 Episode 108 Welcome to the automotive podcast that saw its shadow and will have more shows for 2026! On this episode of Throwin' Wrenches…   I need a special bmw wrench??? Daryl, can you help me out?  Robots… Not made... The post Episode 108 – Plows, Bots and Bolts appeared first on Throwin' Wrenches Automotive Podcast.

On this episode of Chargers Weekly, Bolts radio play-by-play announcer Matt “Money” Smith & host Chris Hayre recap Super Bowl LX and preview impending Free Agency. They look at former Chargers EDGE Uchenna Nwosu's contributions for the Seattle Seahawks, discuss Justin Herbert's MVP vote, and go in depth on which position groups are top priority this offseason for the front office. Presented by Splitero.See omnystudio.com/listener for privacy information.

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

Most of the Tampa Bay Lightning are on Olympic break OR playing at the Olympics...but WE THE THUNDER is NOT! No guests but we have a chance to talk about some of the things we missed and....

Pick one or the other!

TKras shares an interesting dilemma that he and many Lightning fans are dealing with...

Tampa Bay Lightning reporter Ben Pierce on the Lightning's 5-game win streak heading into the Olympics break, Nikita Kucherov's scoring, the 9 Lightning players participating in the Olympics and what to expect with the Bolts return to action in a couple of weeks.See omnystudio.com/listener for privacy information.

Affiliate: Use promo code BOLTBROS on Sleeper and get 100% match up to $100! https://Sleeper.com/promo/BOLTBROS. Terms and conditions apply. #SleeperThe Los Angeles Chargers are making waves this 2026 offseason! Join us as we break down the high-profile names reportedly joining Jim Harbaugh's staff to support quarterback Justin Herbert and a revamped defense.Adam Gase (Potential Passing Game Coordinator): A veteran of the NFL sidelines, the former Dolphins and Jets head coach is "strongly considering" a return to the league as the Chargers' passing game coordinator. Known for his work with Peyton Manning in Denver, Gase would bring 16 years of pro experience to the Bolts' offense.Jimmy Thompson (Defensive Backs Coach): A rising star from the college ranks, Thompson is set to join the Chargers after five seasons at Vanderbilt. A former Notre Dame linebacker, he has quickly climbed the coaching ladder through LSU and Vanderbilt, where he most recently served as the nickels coach.Max McCaffrey (Running Backs Coach): The McCaffrey legacy continues in the NFL! Max is expected to follow Mike McDaniel from Miami to LA to take over as the running backs coach. The older brother of Christian McCaffrey, Max has spent the last three seasons as an offensive assistant for the Dolphins, helping develop one of the league's most explosive run games.With Mike McDaniel leading the offense and Harbaugh at the helm, these additions signal a major shift in strategy. Can this new-look staff finally push the Bolts past the Wild Card round?#chargers #BoltUp #JimHarbaugh #MikeMcDaniel #AdamGase #JimmyThompson #MaxMcCaffrey #JustinHerbert #NFL2026 #NFLRumors #ChargersNews #NFLCoachingCarousel #LAChargers

On this episode of Chargers Weekly, Bolts radio play-by-play announcer Matt “Money” Smith and host Chris Hayre recap the latest news involving LA and the entire NFL. The hosts are joined by the Bolts' newly appointed Defensive Coordinator, Chris O'Leary. As the Chargers safeties coach in 2024 and the Defensive Coordinator for Western Michigan University in 2025, O'Leary talks about his prior experience coaching in football, what he has learned under Head Coach Jim Harbaugh and prior Defensive Coordinator Jesse Minter, and his excitement to be returning to the Bolts organization as the DC. Presented by Splitero.See omnystudio.com/listener for privacy information.

The Heat make no moves at the NBA trade deadline, even after weeks of rumors and speculation. Joe also urges coach Spo to play Kel'el Ware more minutes. The Panthers lose big to the Lightning in a game that ended with a 3rd period brawl. Now the NHL takes a break as the Winter Olympics get going. Super Bowl 60 is this Sunday and Joe looks back on the last time the Patriots and Seahawks met in the Super Bowl.

The Tampa Bay Lightning beat the Florida Panthers 6-1 to run their streak to 19-1-1. The 6 goals were all scored by Olympians that are heading to Italy today to play in the Winter Olympics. Andrei Vasilevskiy is now tied for the NHL lead in wins with 27, Nikita Kucherov has his 13th 10-game point streak of his career and the Bolts have won 9 straight games at Benchmark International Arena. The Lightning will break until February 18th when they return to practice. They won't play again until Wednesday, February 25th when the Toronto Maple Leafs come to town.See omnystudio.com/listener for privacy information.

Bolts beatdown on Florida, Big Game must haves, and new beef in Tampa

Bolts beatdown in the Battle of Florida and Rays Stadium Rendering

Rick Stroud and Steve Versnick on the Tampa Bay Lightning's comeback win over the Sabres as they have points in 19 of their last 20 games. Nikita Kucherov had another 4-point night and Darren Raddysh continues to bomb away from the point. Plus Rob Manfred and Governor Ron DeSantis support the Rays stadium at HCC in a press conference on Tuesday, Tom Moore packs his back to leave Tampa as retirement begins and we answer your mailbag question on the Bolts trade deadline needs Hosted on Acast. See acast.com/privacy for more information.

Song: Heading Home Music by: Ben & Dom   Notes: I read a commentary by Ailey Jolie, saying, "You cannot breathe your way out of patriarchy." She was observing that regulating the body's emergency responses is good to do as a way of caring for ourselves -- but not if it means simply increasing our ability to tolerate a situation that is causing our bodies to cry "emergency." We need to breathe and steady ourselves, as this song of Ben & Dom's does so beautifully -- and the reason is to bring ourselves even more fully into the present, ready to respond effectively to what actually is because we have the capacity to look directly at what is not working. So let this song bring you home to yourself -- whole, rested, and ready.   Songwriter Info: Ben & Dom are a singing duo, weaving their voices around songs old and new. Ben takes the high notes and Dom takes the low notes (most of the time). Their lyrics touch on friendship, nature and what it means for two men to sing together in this modern day.    Sharing Info: Ben & Dom say: "We would love this song to be sung and shared in any circle. It is a parting song suitable for lots of different situations and occasions. If you feel like you want to share the song we have sheet music and teaching tracks available on our website. There is a tiered pricing structure to suit groups of different sizes and setups. If the cost is any kind of challenge for you then please be in touch and we can send you the materials free of charge. "   Song Learning Time Stamps: Start time of teaching: 00:04:04 Start time of reprise: 00:18:29   Links: Ben & Dom's website: www.BenAndDom.com Ben & Dom's Bandcamp: https://benanddom.bandcamp.com Buy score for Heading Home: https://benanddom.bandcamp.com/  Essay of Ailey Jolie: https://www.facebook.com/share/p/16LcZ7iR43/    Nuts & Bolts: 4:4, major, 3-part harmony   Join this community of people who love to use song to help navigate life? Absolutely: https://dashboard.mailerlite.com/forms/335811/81227018071442567/share   Help us keep going: reviews, comments, encouragement, plus contributions... we float on your support. https://www.abreathofsong.com/gratitude-jar.html

College Golf Talk is back in 2026, as Burko and Brentley preview the spring semester, from favorites in the Power 4 conferences to the big storylines to follow. Also, Michael La Sasso's departure from Ole Miss to join LIV Golf is discussed, plus Mike McGraw's retirement prompts a look-back on one of the best coaching careers in the sport. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

A lot has happened since the Bolts 16-3 loss to the Pats in the Wildcard round. And the boys are back to talk all about it! From Jesse Minter's departure to the Mike Mcdaniel's hiring, everything down to Teair Tarts new deal and Chris O'Leary being named as new DC… Colin and Miles break it all down and share what they feel it means for a new era of Chargers football. Bolt Up!

Rick Stroud and Steve Versnick on the Tampa Bay Lightning's wild 6-5 shootout win over the Boston Bruins outside at Raymond James Stadium. The Bolts overcame a 4-goal deficit to win for the first time in franchise history, they scored a goal 11-seconds in and Andrei Vasilevskiy got in a goalie fight. Plus an update on Bucs Assistant Coaching search and Mike Evans Hosted on Acast. See acast.com/privacy for more information.

Tampa Bay Lightning reporters Ben Pierce and Julie Stewart-Binks recap the crazy outdoor Stadium Series game last night at Raymond James Stadium that saw the Bolts overcome a 4-goal deficit to win in a shootout over the Boston Bruins. They discuss the event, the crowd and the goalie fight. Plus they look ahead to two division games this week before the Olympics break.See omnystudio.com/listener for privacy information.

Windy City Thunderbolts GM Mike LaScherve is my guest this week to discuss his team's new branding that includes vibrant colors, eye-catching fonts, and a whole new style at Ozinga Field this season.

The Tampa Bay Lightning host the 2026 Stadium Series game tonight at Raymond James Stadium against the Boston Bruins. The Bolts are looking to go 2-0 in outdoor games after winning the 2022 Stadium Series game in Nashville. Ryan McDonagh is a perfect 5-0 in outdoor games while Andrei Vasilevskiy is looking to become the 6th goalie to win his first two outdoor games. Captain Victor Hedman is set to make his return tonight after missing the last 22 games with an upper-body injury. Hear from Head Coach Jon Cooper, Anthony Cirelli, JJ Moser and Brandon Hagel.See omnystudio.com/listener for privacy information.

Roy and David are in Tampa, coming to us from Raymond James Stadium, where they are situated for Sunday's Stadium Series game between the Tampa Bay Lightning and Boston Bruins. The boys set the scene for us before jumping into updates to the league's discipline process. Then, it's time to take a step into the Panthers' Den, where Ethan is feeling very dejected after two brutal losses this week for the Florida Panthers, who now find themselves eight points out of a playoff spot with 30 games to go. Rosa En Un Minuto takes us to Philly before the gang dives into their wins and fails of the week, which highlights the rotten year the New York Rangers are having. This week's guest is Diandra Loux, who covers the Lightning for The Hockey News, and Roy and David speak with her about the Stadium Series and the great season the Bolts are having so far this year. Learn more about your ad choices. Visit podcastchoices.com/adchoices

On this episode of Chargers Weekly, Bolts radio play-by-play announcer Matt “Money” Smith and host Chris Hayre recap the latest news involving LA and the entire NFL. The hosts are joined by former Miami Dolphins Head Coach Mike McDaniel, the newly hired Offensive Coordinator for the Bolts. McDaniel talks about his decision to accept the role with the Chargers, his confidence in executing his offensive scheme under the leadership of Head Coach Jim Harbaugh, and his excitement to work with talented offensive players like quarterback Justin Herbert, running back Omarion Hampton, and wide receiver Ladd McConkey. This episode was recorded on Tuesday, January 27th, 2026. Presented by Splitero.See omnystudio.com/listener for privacy information.

The hot mess express is back on Jason's Big Wooden Deck and we got caught in our first mid show storm. But not before we got a great interview with the one and only Capt Cefus MCrae of Nuts and Bolts of Fishing and Boating. Capt Cefus is bringing his Fishing Fest to Murrells Inlet this fall and we hope you will come join us for a fun filled weeknd right here on the Grand Strand. Thanks to our friends Julie and Rob at The Brookwood Inn in Historic Murrells Inlet for hosting the group this fall. We are excited to have Cefus choose our area and we look forward to participating and promoting the event. Capt. Jason and Capt. Nathan had a long run together early in MIFC days of growth and we get to hear some stories from those days and plenty of other crazy tales from the boats. Thanks to all that joined us this week and we will be back on Tuesday afternoon to record again. Also a big thank you to Surfwater and Surf Signs and designs for all the work they do crating and printing our hats, shirts, and banners that can be purchased on Tuesdaysl See yall next Tuesday!!!!!Become a supporter of this podcast: https://www.spreaker.com/podcast/trilogy-outdoors--5441492/support.

Hosted by Grant Mona, this episode brings you two distinct looks at major Southern California teams — a new offensive direction in the NFL and a tough loss in the NBA — featuring key media availability, reactions, and context straight from the locker room and press room. Segment One — Mike McDaniel's Intro Press Conference Grant opens the show reacting to the official introduction of Mike McDaniel as the Los Angeles Chargers' new offensive coordinator. McDaniel, 42 and previously head coach of the Miami Dolphins, spoke about his vision for the Chargers' offense under Jim Harbaugh, emphasizing creativity, balance, and getting the best out of franchise quarterback Justin Herbert. McDaniel noted that part of his plan involves reducing Herbert's workload at times to maximize his impact and keep him fresher in key situations — a strategy aimed at boosting efficiency after the Chargers finished 12th in total offense (333.8 yards per game) and 20th in scoring (21.6 points) this past season. Grant breaks down how McDaniel's philosophy could reshape the Bolts' attack and what fans should expect as the 2026 campaign ramps up. Segment Two — Lakers Speak After Blowout Loss to Cavaliers In the second segment, Grant digs into the Lakers' tough road loss to the Cleveland Cavaliers, a game where L.A. fell 129–99 and were outplayed for large stretches. The Lakers trailed early and were outscored significantly in the third quarter, reflecting a night when offense and defense both lagged, with Cleveland shooting better and controlling the glass. Hear from: JJ Redick, who addressed the media about the team's execution struggles and acknowledged that the Lakers were simply beaten on this night. LeBron James, reflecting on effort and intensity, and what the team must fix going forward. Additional Lakers voices discussing key moments where the game slipped away and how the group plans to respond after a disappointing performance. Produced by: Grant Mona Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Song: Pray With Our Feet Words by: Paul Vasile, based on a quotation by Rabbi Abraham Joshua Heschel Music by: Paul Vasile   Notes: This is what folk in Minneapolis are doing -- praying with their feet, getting out on the street. They're singing songs to bless each other, the people they're protecting, and the ICE agents who are so misguided. And this is not the only place in the world where people of "middle power" gather -- not super-powers, not uber-wealthy -- just people who care about living in a world where we feed and educate our children, hold jobs with purpose. Paul Vasile describes it as "belonging belovedness -- you are safe, you are seen, you are heard." In this conversation, we dive into the sometimes knotty weeds of faith and how someone who is deeply connected in a faith tradition might want to expand into questions of what world we want to create together. Big trees. "Talk less, sing more." "As a queer person, I experienced a faith that helped me be a bigger, better me."   Songwriter Info: Paul Vasile (he/him) is a church musician, teacher, coach, and composer who finds his greatest joy in collaborative and community-centered work. Committed to modeling expansive, imaginative, and hospitable experiences of music making wherever he goes, Paul's leadership builds trust, invites spaces of creativity, vulnerability, and play, and supports practices of reflection and holistic learning. For the past decade he has offered short- and long-term transitional leadership, consulting services, and creative resources to faith communities in seasons of discernment, challenge, and transformation. From 2016 to 2023, Paul served as the Executive Director of Music that Makes Community, a non-profit that shares "paperless" (oral tradition) leadership practices and songs. He traveled across North America modeling distinctive approaches to communal singing and learning at retreat centers, conferences, denominational gatherings, seminaries, and in congregations of all sizes. Paul also composes music that invites communities to express and explore their connection to sacred stories, their bodies, and the ecosystems that sustain us. His music is represented in Glory to God, All Creation Sings, and Voices Together, as well as The Hymn Society's resource, Songs for the Holy Other: Hymns Affirming The LGBTQIA2S+ Community.   Sharing Info: Please buy sheet music on Paul's website if you plan to share this song as a songleader.   Song Learning Time Stamps: Start time of teaching: 00:04:35 Start time of reprise: 01:15:00   Links:   Paul's website: https://www.paulvasile.com/  Paul's Instagram: https://www.instagram.com/lovedintobeing/  Sheet music for Pray With Our Feet: https://www.paulvasile.com/products/pray-with-our-feet Feb 7th workshop at Lutheran church in Ft. Washington, MD with Maren Marchesini: https://www.sharingthesong.org/ Inspiration for lyrics, including quote by Rabbi Abraham Joshua Heschel: https://www.tbemaine.org/praying-with-hearts-and-feet    Music that Makes Community: https://www.musicthatmakescommunity.org/  Rev. Donald Schnell, a 2012 interview as Music Makes Community was beginning: https://www.conversations.org/story.php?sid=329 Bobby McFerrin: https://bobbymcferrin.com/  Taizé chants: https://www.taize.fr/en/the-songs  Chanda Rule: https://www.chandarule.com/ Hold Me by Nina Wise: https://www.youtube.com/watch?v=yxU_egWayc4&t=16s Nina Wise: https://ninawise.com East Coast songleader trainings: Mila Redwood in Toronto: https://www.milaredwood.ca/song-leader-training Patricia in Burlington, VT: https://www.juneberrymusic.com/songleader-training.html  Alice Parker: https://en.wikipedia.org/wiki/Alice_Parker Liz Rog -- here's a place to find her book on songleading: https://www.centerforbelonging.earth/ Arvo Pärt – Passio: https://en.wikipedia.org/wiki/Passio_(P%C3%A4rt)  Spencer LaJoye: https://www.spencerlajoye.com/  Spencer's song Plowshare Prayer: https://youtu.be/MhOZv5i7CHY?si=S5bbjdBDJP2cU_4I   Nuts & Bolts: 2:2, minor, unison   Join this community of people who love to use song to help navigate life? Absolutely: https://dashboard.mailerlite.com/forms/335811/81227018071442567/share   Help us keep going: reviews, comments, encouragement, plus contributions... we float on your support. https://www.abreathofsong.com/gratitude-jar.html

Dave and Mario are live celebrating Mike McDaniel officially signing as the offensive coordinator for the Los Angeles Chargers. The crew then discuss possible new defensive coordinator options for the Bolts. 

On this episode of Chargers Weekly, Bolts radio play-by-play announcer Matt “Money” Smith, host Chris Hayre, and former Chargers safety Jahleel Addae recap the latest news involving LA and the entire NFL. The hosts discuss the breaking news of former Miami Dolphins Head Coach Mike McDaniel being named the new Offensive Coordinator of the Chargers. They break down how McDaniel’s scheme meshes with Head Coach Jim Harbaugh’s philosophy as well as analyze the impact the hire will have on the Bolts' key players including quarterback Justin Herbert, running back Omarion Hampton, tackles Rashawn Slater and Joe Alt, and wide receiver Ladd McConkey. Presented by Splitero.See omnystudio.com/listener for privacy information.

Ben Pierce and Julie Stewart-Binks preview this week's Stadium Series game between the Tampa Bay Lightning and the Boston Bruins, look at Nikita Kucherov's rise in the Art Ross Trophy standings and more injuries for the Bolts to deal with.See omnystudio.com/listener for privacy information.

The Tampa Bay Lightning host the Utah Mammoth to start a 6-game homestand. The Bolts had played 10 of their last 12 games on the road. Hear from Anthony Cirelli and Darren Raddsyh about coming home and Head Coach Jon Cooper on this team's 15-game point streak that was snapped on Saturday night. Nikita Kucherov keeps moving up the franchise record books, on this date in Lightning History, the Bolts tied a home-win streak record and Dave Mishkin will call his 2,000th NHL game tonight on radio.See omnystudio.com/listener for privacy information.

I never thought I'd be glued to my screen watching the Supreme Court in Washington, D.C., turn into the hottest drama in town, but here we are, listeners, on this chilly January day in 2026. Just yesterday, on January 21st, the justices wrapped up their January argument session with Trump, President of the United States v. Cook, a case that's got everyone buzzing about whether President Donald Trump can fire Federal Reserve Board Governor Lisa Cook at will. Picture this: the marble halls of One First Street, packed with lawyers, clerks, and even a few Capitol Hill interns. Paul Clement, arguing for the Trump administration, tried to push that the president has broad firing powers over Fed officials, but the justices weren't buying it. Justice Neil Gorsuch cut him off mid-sentence, saying, "I asked you to put that aside for the moment," according to live coverage from SCOTUSblog. NPR reported the court seemed doubtful of Trump's claim to fire Fed governors by fiat, while Fox News noted the justices signaling skepticism. Newsweek even hinted the Supreme Court may be preparing to deal Trump a disappointing blow, and Politico said they cast doubt on his power without proper review. An extraordinary friend-of-the-court brief from every living former Fed chair, six former Treasury secretaries, and top officials from both parties warned that letting Trump oust Cook would wreck the Federal Reserve's independence and tank the credibility of America's monetary policy, as highlighted by The New York Times.This isn't isolated—Trump's name is all over the docket. Earlier in the session, on January 12th, the court heard Trump v. Cook's opening arguments, listed right there in the Supreme Court's Monthly Argument Calendar for January 2026. SCOTUSblog's Nuts and Bolts series explained how January's the cutoff for cases to squeeze into this term's April arguments, starting April 20th at the Supreme Court Building, or they get bumped to October. Trump's push here echoes last term's Trump v. CASA, where the court expedited a birthright citizenship fight and ruled against nationwide injunctions on June 27th, 2025.But the action's not just at the Supreme Court. Down in the House Judiciary Committee on Thursday, January 23rd, Representative Steve Cohen from Tennessee grilled former Special Counsel Jack Smith during a hearing titled "Hearing Evidence of Donald Trump's Criminal Actions." Cohen pressed Smith on the evidence from federal grand jury indictments—Trump's alleged conspiracy to overturn the 2020 election and illegally retaining classified documents at Mar-a-Lago. Smith stood firm, detailing Trump's witness intimidation attempts, and Cohen called him a great American we can all respect, as recounted in Cohen's e-newsletter. Meanwhile, Lawfare's Trump Administration Litigation Tracker notes a dismissal on January 14th in a case over Trump dismantling the Corporation for Public Broadcasting, ruled moot.And get this—House Speaker Mike Johnson, during a Wednesday press conference covered by The Hill, backed impeaching two federal judges who've ruled against Trump: Judge James Boasberg of the U.S. District Court for the District of Columbia, who blocked deportations under the Alien Enemies Act, and Judge Deborah Boardman of the Maryland District Court, criticized for her sentencing of Sophie Roske, charged as Nicholas Roske for plotting to kill Justice Brett Kavanaugh. California Republicans even filed an emergency application Tuesday against their state's 2026 election map for racial gerrymandering.It's a whirlwind, listeners—Trump's second term, one year in as the ACLU marked on January 20th, is a battlefield of lawsuits from the Federal Reserve to election interference probes. The justices' private conference tomorrow, January 23rd—no, wait, reports say after the 22nd—could add more cases, with opinions possibly dropping February 20th.Thanks for tuning in, listeners. Come back next week for more, and this has been a Quiet Please production. For more, check out Quiet Please Dot A I.Some great Deals https://amzn.to/49SJ3QsFor more check out http://www.quietplease.aiThis content was created in partnership and with the help of Artificial Intelligence AI

We are watching the last few days of the Panarin era in NY, so enjoy it Rangers fans. But where will he end up? The Avs, the Stars, the Panthers, the Bolts, the Ducks??? It's going to be an exciting few weeks. Team Sweden takes a hit ahead of the Olympics. The Professors are back in class giving you dialed in winners for your weekend. And DP snipes a game of What's The Connection. Can you beat him? NEW EPISODES EVERY TUESDAY & THURSDAY! Watch full episodes, shorts, and clips right here on YouTube. Listen to the podcast on Spotify or anywhere you get your pods. Subscribe & follow Empty Netters everywhere: YouTube: / @emptynetters Instagram: @EmptyNetters TikTok: @EmptyNetters X: @EmptyNetters PRESENTED by BetMGM. Download the BETMGM app and use code “NETTERS” and enjoy up to $1500 in bonus bets if you lose your first wager! Thanks to our Sponsors! BetMGM: Use bonus code NETTERS when signing up to receive up to $1500 in bonus bets if your first bet loses. Bauer: Get your hands on Bauer's newest innovation — the PULSE stick — and feel the difference. Get your hands on one at https://Bauer.com Gambling problem? Call 1-800-GAMBLER (Available in the US) 877-8-HOPENY or text HOPENY (467369) (NY) 1-800-327-5050 (MA), 1-800-NEXT-STEP (AZ), 1-800-BETS-OFF (IA), 1-800-981-0023 (PR) 21+ only. Please Gamble Responsibly. See BetMGM.com for Terms. First Bet Offer for new customers only. Subject to eligibility requirements. Bonus bets are non-withdrawable. In partnership with Kansas Crossing Casino and Hotel. This promotional offer is not available in New York, Nevada, Ontario, or Puerto Rico. Learn more about your ad choices. Visit megaphone.fm/adchoices

Song: Trust the Work Words by: Pierre Teilhard de Chardin, S.J. Music by: Paul Vasile   Notes: Trusting the work of love inside us, as excruciatingly slow as it seems sometimes.... this is a mantra I can fold right into my life, singing it on the regular to remind me back into the power of inner trust. Next episode is a conversation with Paul where we talk about the choice (he ok'd) to replace the word "God" with "love" -- and what it is we all seek. I offer this as a somatic check in as well -- as you sing it in different ranges, how does your voice and body respond?   Songwriter Info: Paul Vasile (he/him) is a church musician, teacher, coach, and composer who finds his greatest joy in collaborative and community-centered work. Committed to modeling expansive, imaginative, and hospitable experiences of music making wherever he goes, Paul's leadership builds trust, invites spaces of creativity, vulnerability, and play, and supports practices of reflection and holistic learning. For the past decade he has offered short- and long-term transitional leadership, consulting services, and creative resources to faith communities in seasons of discernment, challenge, and transformation. From 2016 to 2023, Paul served as the Executive Director of Music that Makes Community, a non-profit that shares "paperless" (oral tradition) leadership practices and songs. He traveled across North America modeling distinctive approaches to communal singing and learning at retreat centers, conferences, denominational gatherings, seminaries, and in congregations of all sizes. Paul also composes music that invites communities to express and explore their connection to sacred stories, their bodies, and the ecosystems that sustain us. His music is represented in Glory to God, All Creation Sings, and Voices Together, as well as The Hymn Society's resource, Songs for the Holy Other: Hymns Affirming The LGBTQIA2S+ Community.   Sharing Info: Please buy sheet music on Paul's website if you plan to share this song as a songleader.   Song Learning Time Stamps: Start time of teaching: 00:03:43 Start time of reprise: 00:16:07   Links: Paul's website: https://www.paulvasile.com/  Paul's Instagram: https://www.instagram.com/lovedintobeing/    Nuts & Bolts: 4:4, minor, optional round   Join this community of people who love to use song to help navigate life? Absolutely: https://dashboard.mailerlite.com/forms/335811/81227018071442567/share   Help us keep going: reviews, comments, encouragement, plus contributions... we float on your support. https://www.abreathofsong.com/gratitude-jar.html

Micah Hanks joins us again tonight for a conversation that starts out about Giants, but meanders all over the place, from the Nephilim, to Lost Civilizations, to much, much more. This one also runs a bit long, clocking in around an hour and a half.Micah Hanks is a writer, researcher, lecturer, and radio personality whose work addresses a variety of scientific concepts and unexplained phenomena. Over the last decade, his research has examined a variety of approaches to studying the unexplained, cultural phenomena, human history, and the prospects of our technological future as a species as influenced by science.He is author of several books, including Magic, Mysticism and the Molecule,Reynolds Mansion: An Invitation to the Past, and his 2012 New Page Books release, The UFO Singularity. Hanks is an executive editor of Intrepid Magazine, and consulting editor/contributor for FATE Magazine and The Journal of Anomalous Sciences. He also writes for a variety of other publications including UFO Magazine, Mysterious Universe, and New Dawn. Hanks has appeared on numerous TV and radio programs, including Coast to Coast AM with George Noory, Whitley Strieber's Dreamland, National Geographic's Paranatural, the History Channel's Guts and Bolts, CNN Radio, The Jeff Rense Program, and many others. A weekly podcast that follows his research is available at his popular Website, www.gralienreport.com. Hanks lives in the heart of Appalachia near Asheville, North Carolina. For everything Micah does, go to www.MicahHanks.com. Hosted on Acast. See acast.com/privacy for more information.

On this episode of Chargers Weekly, Bolts radio play-by-play announcer Matt “Money” Smith and host Chris Hayre recap the latest news involving LA and the entire NFL. The hosts recap the Bolts 2025 season, including an analysis of the AFC Wild Card matchup against the New England Patriots. They then break down recent coaching changes throughout the league and discuss potential candidates as the Chargers search for a new Offensive Coordinator following Head Coach Jim Harbaugh & General Manager Joe Hortiz's post-season press conference on Thursday, January 15th. Presented by Splitero.See omnystudio.com/listener for privacy information.