Podcasts about Budget

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Welcome to Channel Nine. This week Mike Mo Capaldi joins us to discuss the Awaysted "Into Otherness" video, the West LA Courthouse added some new obstacles, Budget Or Buttery, the Retail Report featuring Plus Skateshop in Michigan, Matt Tomasello's "Repent" part, PSL recap and much more! Become a Channel Member & Receive Perks: https://www.youtube.com/TheNineClub/joinNew Merch: https://thenineclub.com Sponsored By: AG1: Get a FREE Welcome Kit worth $76 when you subscribe, including 5 AG1Travel Packs, a shaker, canister, scoop & bottle of AG Vitamin D3+K2. https://drinkag1.com/nineclub LMNT: Grab a free Sample Pack with 8 flavors when you buy any drink mix or Sparkling. https://drinklmnt.com/nineclub Woodward: Purchase camp with code NINECLUB and receive a $150 discount off of summer camp. https://www.woodwardpa.com Monster Energy: Monster Energy's got the punch you need to stay focused and fired up. https://www.monsterenergy.com Yeti: Built for the wild, Yeti keeps you ready for any adventure. https://www.yeti.com Richardson: Custom headwear for teams, brands, and businesses crafted with quality in every stitch. https://richardsonsports.com Etnies: Get 20% off your purchase using our code NINECLUB or use our custom link. https://etnies.com/NINECLUB éS Footwear: Get 20% off your purchase using our code NINECLUB or use our custom link. https://esskateboarding.com/NINECLUB Emerica: Get 20% off your purchase using our code NINECLUB or use our custom link. https://emerica.com/NINECLUB Find The Nine Club: Website: https://thenineclub.com Instagram: https://www.instagram.com/thenineclub X: https://www.twitter.com/thenineclub Facebook: https://www.facebook.com/thenineclub Discord: https://discord.gg/thenineclub Twitch: https://www.twitch.tv/nineclub Nine Club Clips: https://www.youtube.com/nineclubclips More Nine Club: https://www.youtube.com/morenineclub I'm Glad I'm Not Me: https://www.youtube.com/chrisroberts Chris Roberts: https://linktr.ee/Chrisroberts Links We Talked About: Awaysted "Into Otherness": https://www.youtube.com/watch?v=IuySBDnrN7Q Matt Tomasello's "Repent" Part: https://www.youtube.com/watch?v=RE1tOwcRBh8 PSL Skateboarding: https://www.pslskateboarding.com Plus Skateboarding Website: https://pluskateboarding.com Plus Skateboarding Instagram: https://www.instagram.com/pluskateboarding Timestamps (00:00:00) Channel Nine (00:00:30) Mike Mo Capaldi is in the building (00:02:30) PSL Week 1 recap (00:23:00) Retail Report Plus Skateshop in Michigan (00:43:00) Industry news (00:48:00) West LA Courthouse Renovation Event (00:51:00) Awaysted "Into Otherness" video (00:58:00) Budget or Buttery (01:08:00) Matt Tomasello's "Repent" Part (01:18:00) Thank you's, sign offs and apricots Learn more about your ad choices. Visit megaphone.fm/adchoices

Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 3456: Marjolein Dilven shares nine practical strategies to stay motivated and stick to your budget without feeling deprived or overwhelmed. From paying yourself first to using vision boards and rewarding progress, these tips are designed to help you build momentum toward your financial goals while enjoying the journey. Read along with the original article(s) here: https://radicalfire.com/stick-to-your-budget/ Quotes to ponder: "Pay yourself first. If you have savings or debt payoff goals, pay those things first." "If you're feeling deprived by your budget, you will be more likely to break with it." "Don't be the person that buys the cookies and tries to train their willpower attempting not to eat them." Episode references: You Need a Budget (YNAB): https://www.ynab.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Curious? Watch Our Money Makeover Bootcamp!Ready? Buy Our Simplified Budget System Now!Hey budget besties — today we're hanging out with Jamie (Twin Cities, MN!) to talk about what it actually looked like to go from “shooting from the hip and hoping for the best” to having a real plan, real peace, and real progress.Jamie and her husband Kevin have been together since they were teenagers (married 23+ years), and like a lot of us, they weren't raised with money skills modeled clearly. They'd do “a program” for a while, fall out of rhythm, and then drift back into chaos. Add in big-life curveballs (home repairs, medical stuff, kids getting older, college around the corner), and it hit a breaking point: the stress and uncertainty started feeling too expensive to ignore.So they brought in coaching — and everything changed.Let's Take Our Relationship To The Next Level:1️⃣ Facebook Group ➡︎ budgetbesties.com/facebook2️⃣ Be on the Podcast ➡︎ budgetbesties.com/livecall3️⃣ Private 1-on-1 Coaching. ➡︎ budgetbesties.com/coachingThis podcast is for educational and informational purposes only and is not personal financial, legal, or tax advice.This description may contain affiliate links, meaning we may get a commission at no cost to you if you click & purchase.Click here to view our privacy policy.

David Waldman delivers a lovely Friday the 13th KITM Valentine. May everyone you know receive exactly what they deserve. Speaking of romance, Kristi Noem fired a Coast Guard pilot for coming between her (and probably Corey Lewandowski's) favorite blanket. Corey's security blanket would be a badge and a gun, if he had them. The heated rivalry between Kristi and her non-botoxed twin, Tom Homan continues to rage behind the scenes. Who would think that Jared Kushner would be implicated in a national security scandal with Tulsi Gabbard and foreign nationals? Yeah, well, everybody. When John A. Sarcone III was caught unlawfully impersonating a US Attorney, the Northern District of New York appointed the most qualified attorney for the position, Donald Kinsella. That is just about the opposite of what the Trump administration wanted, and they fired Kinsella in under 4 hours, preferring to have no one at any wheel. ICE says that they are leaving Minneapolis, to places where they are wanted, who will soon learn to not want them. Meanwhile, most U.S. Attorneys and their staffs are bugging out of Minnesota, shutting down the vindictive prosecutions on their way out. The healing will take years, however, as the sickness continues to spread. The SAVE tool creates confusion and chaos, as it was designed. Pam Bondi, She-Wolf of the DOJ, will soon present Donald K. Trump with 10 billion dollars, but for now spends her time freeing accused drug dealers. Budget director Russell Vought found some money nobody needed anymore and put it into an entourage. Jeanine Pirro will be suing someone for $250,000 after a large wooden block leapt into the path of her staggering.

Todd Archer of ESPN joins the show to discuss if Jerry Jones will really bust the budget this offseason full 976 Fri, 13 Feb 2026 14:46:01 +0000 xnunPiKHpxXZhluZTe1ptVPPnt3J78eF nfl,dallas cowboys,sports Shan and RJ nfl,dallas cowboys,sports Todd Archer of ESPN joins the show to discuss if Jerry Jones will really bust the budget this offseason DFW sports fans, this one's for you. The Shan & RJ show brings the heat with honest takes, sharp insight, and plenty of laughs covering the Cowboys, Mavericks, Rangers, Stars, and everything Texas sports. Hosted by longtime local favorites Shan Shariff and RJ Choppy, along with insider Bobby Belt, the show blends deep knowledge with real fan vibes — plus regular guests like Cowboys owner Jerry Jones, Head Coach Brian Schottenheimer and former players who keep the conversation fresh and real. New episodes drop Monday-Friday, or you can listen to Shan & RJ live on 105.3 The Fan, weekdays from 6–10 a.m. CT. © 2025 Audacy, Inc. Sports False

The uncertainty of federal funding has increased over the past year, making this fiscal plan JB Pritzker's most challenging so far.

This week on CockTales: Dirty Discussions, we're asking the real question: Who has shut-up money?!We kick things off with travel nostalgia and vacation dreams before running straight into the hilarious reality of adulthood. We dive into the chaos of planning family vacations, organizing group trips, setting budgets, and why everyone wants a luxury trip until it's time to pay the deposit.In this episode, we talk about:• The stress of planning trips with friends and family• Budget conversations nobody wants to have• The difference between work travel vs real vacations• Why group chats can't plan trips to save their lives• Dream destinations vs real-life responsibilitiesThen it's time for Weird Sex, and this story is truly unforgettable. A shocking medical discovery sparks a conversation about embarrassment, health, and the wild things people do in relationships.We wrap things up with Valentine's Day vibes, listener advice, and plenty of laughs along the way.Cocktail of the Week

What does it feel like to be the first in your family to graduate, earn a salary, and suddenly make more money than the people who raised you? In this episode, I'm talking with Maria Melchor, creator of FirstGenLiving, about the emotional and financial weight of being first-gen.Maria shares how she navigated guilt, money confusion, and cultural expectations while trying to build a secure financial life for herself. She talks about finding her footing, setting boundaries, and creating money habits that feel supportive instead of stressful.If you are the first in your family to “make it,” or you're working to rewrite old money stories, you'll feel right at home in this conversation.Connect with Maria:Instagram: @firstgenlivingTikTok: @firstgenlivingYou Might Like: Get the FREE Goodbye Debt Tracker! Grab my FREE Budgeting Cheat Sheet. Get the Budget My Paycheck Spreadsheet. Follow Allison on Instagram! @inspiredbudget Check out Inspired Budget's blog. Take my FREE class on How to Budget to Build Wealth!

Subscribe to Simplify My Money: https://www.debtfreedad.com/newsletters/simplify-my-money If you've ever made a budget and felt great about it, only to watch it fall apart, this episode is for you.Budgets don't fail because you don't care. They fail because of sneaky habits that quietly drain your money month after month.In this episode, Brad breaks down the top 10 budget busters he's seen over the last decade — and how to fix them without making your life miserable.Support the showThe Totally Awesome Debt Freedom Planner https://www.debtfreedad.com/planner Connect With Brad Website- https://www.debtfreedad.com Facebook - https://www.facebook.com/thedebtfreedad Private Facebook Group - https://www.facebook.com/groups/debtfreedad Instagram - https://www.instagram.com/debtfreedad/ TikTok - https://www.tiktok.com/@debt_free_dad YouTube - https://www.youtube.com/@bradnelson-debtfreedad2751/featured Thanks For Listening Like what you hear? Please, subscribe on the platform you listen to most: Apple Podcasts, iHeartRadio, Spotify, Tune-In, Stitcher, YouTube Music, YouTube We LOVE feedback, and also helps us grow our podcast! Please leave us an honest review in Apple Podcasts, we read every single one. Is there someone that you think would benefit from the Debt Free Dad podcast? Please, share this episode with them on your favorite social network!

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

Sponsors - Claim your exclusive savings from our partners with the links below:Sourcewhale - Check Out Sourcewhale & Claim Your Exclusive Offer Here.Atlas - Check Out Atlas & Claim Your Exclusive Offer HereRaise - Check Out Raise & Claim Your Exclusive Offer Here.-------------------------Extra Stuff:Learn more about our online skills development platform Hector here: https://bit.ly/47hsaxeJoin 6,000+ other recruiters levelling up their skills with our Limitless Learning Newsletter here: https://limitless-learning.thisishector.com/subscribe-------------------------Get in touch:Linkedin: https://www.linkedin.com/in/hishemazzouz/-------------------------

SummaryIn this episode of What in the Wedding, hosts Hannah and Ashley discuss the evolving landscape of wedding planning, focusing on trends in venues, the impact of AI on photography, and the importance of adapting to client priorities. They explore how budget constraints are shaping vendor choices and the significance of networking within the industry. The conversation highlights the shift towards more personalized and experience-driven weddings, emphasizing the need for vendors to stay updated and flexible in their offerings.Chapters00:00 Welcome to Wetting the Wedding Podcast01:45 Trends in Wedding Venues and AI Impact03:52 Shifts in Wedding Photography Priorities07:44 Adapting to Changing Wedding Trends11:42 The Role of AI in Wedding Planning17:37 Navigating Budget Constraints in Weddings23:30 The Evolution of Wedding Vendor Relationships29:23 The Future of Wedding Planning and Trends35:32 Networking and Learning in the Wedding Industry41:27 Closing Thoughts and Listener EngagementTakeawaysExpect the unexpected in wedding planning.AI is changing the landscape of wedding photography.Budget constraints are affecting vendor choices.Couples are prioritizing experiences over traditional elements.The wedding industry is seeing a shift towards backyard and tent weddings.Networking is crucial for staying updated in the wedding industry.Vendors need to adapt to changing client priorities.Communication between vendors is key to a successful wedding day.The importance of customization in wedding packages.Understanding generational differences in wedding planning.Keywordswedding planning, wedding trends, photography, AI in weddings, budget constraints, vendor relationships, wedding venues, wedding industry, networking, wedding photography Hosted on Acast. See acast.com/privacy for more information.

Episode Overview In this final installment of the Conquer the Operational Chaos series, John Kitchens and Joel Perso break down one of the most overlooked — yet most powerful — CEO disciplines: knowing your numbers. Growth without visibility creates chaos. More agents, more leads, more deals — without proper tracking — only amplifies inefficiencies. In this session, John and Joel unpack how to measure what actually matters, how to assign the right metrics to each role, and how to move from emotional decision-making to data-driven leadership. If you've ever wondered why your P&L says you're profitable but your bank account feels tight… or why your team feels busy but results are inconsistent… this episode will reset how you think about performance. Because if you don't know your numbers, you don't know your business. Key Topics Covered The Final Piece of Operational Clarity Recap of the Conquer the Operational Chaos framework: Week 1: The Operational Hire Week 2: Building Processes Week 3: Core Buyer & Listing Systems Week 4: Measuring What Matters Why growth without tracking leads to internal breakdown How knowing your numbers protects profitability and performance What "Know Your Numbers" Really Means The difference between tracking data and making decisions Why metrics exist to improve leadership — not to create busywork The CEO mindset shift from guessing to measuring The Financial Foundations Every CEO Must Understand Profit & Loss (P&L): Revenue, expenses, and true profitability Balance Sheet: Assets, liabilities, and owner equity Cash Flow: Why profit and cash are not the same Budget vs. Actual: Where silent leaks in your business happen Assigning Metrics to Every Role Every role in your business must have at least one key metric. Why? People want to know what winning looks like Clear agreements eliminate emotional performance conversations Numbers create accountability without friction Metrics vs. Targets (The Critical Distinction) Tracking a number isn't enough. You must define: What is success? What is the agreed target? What happens when we miss? Agreements replace expectations. Expectations create frustration. Agreements create alignment. Leading Indicators vs. Lagging Indicators Lagging indicators: Closings, GCI, volume Leading indicators: Conversations, appointments set, follow-up activity You can't control closings. You can control conversations. John's breakdown: Conversations → Appointments Set → Appointments Met → Agreements Signed → Closings Reverse engineer your goals down to conversations per hour. The Conversations Per Hour Framework This was one of the most tactical moments of the episode. Instead of asking: "How many conversations per day?" Ask: "How many conversations per hour?" Then reverse engineer: How many conversations does it take to set one appointment? How many appointments does it take to sign a client? How many signed clients does it take to close one deal? How many hours per week must be dedicated to outbound activity? When you know this math, success becomes predictable — not accidental. The "Protein, Carbs, and Fats" Principle Borrowed from Blake Sloan: Protein = Conversations Carbs = Appointment Asks Fats = Face-to-Face Meetings You can hit your main metric and still fail if supporting metrics are ignored. One metric matters. But supporting behaviors matter too. Where to Start Don't try to fix everything. Focus on one priority per quarter. If you're spending significant money in one area (Zillow, PPC, mailers, client events), optimize that first. Clarity compounds. Chaos compounds faster. Resources Mentioned Simple Numbers, Straight Talk, Big Profits – Greg Crabtree Financial Intelligence – Karen Berman & Joe Knight Measure What Matters – John Doerr CSU Dashboard / CTE Business Tracking The Growth Centric – Systems Audit with Joel Perso John Kitchens Executive Coaching → JohnKitchens.coach Final Takeaway There are two major breakdowns in most small businesses: They don't know their financial numbers. They don't know how they're allocating their time. If you know your money and you know your time, you control your growth. If you don't — you're guessing. As Joel put it: "If you don't know your numbers, you don't know your business." And as John reinforced: "It's not conversations per day. It's conversations per hour." Measure what matters. Build agreements. Track leading indicators. Execute with clarity. That's how CEOs eliminate chaos. Connect with Us: Instagram: @johnkitchenscoach LinkedIn: @johnkitchenscoach Facebook: @johnkitchenscoach If you enjoyed this episode, be sure to subscribe and leave a review. Stay tuned for more insights and strategies from the top minds. See you next time!

What separates organizations that successfully fend off ransomware from those that don't? What were the top threats facing organizations? Can we (pretty please) get a sneak peek into the 2025 Year in Review?Amy is joined by Dave Liebenberg, Strategic Analysis Team Lead, to break down key findings from Q4 2025's Cisco Talos Incident Response Quarterly Trends Report. From the top threats facing organizations — like the persistent exploitation of public-facing applications and the rise of new vulnerabilities such as Oracle EBS and React2Shell — to the unexpected drop in ransomware cases, this episode is packed with useful info. Episode resources:Q4 2025 Quarterly Trends Report: https://blog.talosintelligence.com/ir-trends-q4-2025/Qilin blog: https://blog.talosintelligence.com/uncovering-qilin-attack-methods-exposed-through-multiple-cases/Cybersecurity on a Budget blog: https://blog.talosintelligence.com/cybersecurity-on-a-budget-strategies-for-an-economic-downturn/

Southwest Michigan's Morning News podcast is prepared and delivered by the WSJM Newsroom. For these stories and more, visit https://www.wsjm.com and follow us for updates on Facebook. See omnystudio.com/listener for privacy information.

In this episode of Smarter Podcasting, Niall interviews Brian Biedenbach from Summit City Studios about how to kickstart a podcast without spending a fortune. They challenge the idea that podcasting requires costly equipment by revealing how they both began their successful podcasts with just a laptop, free software, and an affordable microphone.Key Talking Points:Essential budget-friendly gear for podcastingUsing free software for recording and editingStrategies for effective guest interviewsOvercoming common production challengesFocusing on content over equipmentThis episode is a must-listen for aspiring podcasters who want to get started with minimal resources. Save Frustration. And time!Let my team and I save you the time and frustration it takes to edit a podcast. From start to finish, we can help you share your story with the world with minimum fuss and cost. – Niall Mackay, The Podcast GuyFor my Audience Only: Audio Episodes Edited for ONLY $27! Save $127!!Book a FREE consultation now!Need a stunning new logo for your brand? Or maybe a short animation?Whatever you need, you can find it on Fiverr.I've been using Fiverr for years for everything from ordering YouTube thumbnails, translation services, keyword research, writing SEO articles to Canva designs and more!These are the programs the Seven Million Bikes Podcasts uses. These are affiliate links so they will give us a small commission, only if you sign up , and at no extra cost to you! You'll be directly supporting Seven Million Bikes PodcSend us a textEmail me (niall@sevenmillionbikes.com) or contact me on Seven Million Bikes Podcasts Facebook or Instagram to book your free Podcast Audit!Thanks to James Mastroianni from The Wrong Side Of Hollywood for the endorsement!Sign up for Descript now!Need a stunning new logo for your brand? Or maybe a short animation?Whatever you need, you can find it on Fiverr.I've been using Fiverr for years for everything from ordering YouTube thumbnails, translation services, keyword research, writing SEO articles to Canva designs and more!Send a textEmail me (niall@sevenmillionbikes.com) or contact me on Seven Million Bikes Podcasts Facebook or Instagram to book your free Podcast Audit!Thanks to James Mastroianni from The Wrong Side Of Hollywood for the endorsement! Sign up for Descript now! Need a stunning new logo for your brand? Or maybe a short animation?Whatever you need, you can find it on Fiverr.I've been using Fiverr for years for everything from ordering YouTube thumbnails, translation services, keyword research, writing SEO articles to Canva designs and more!

The First Minister answers questions from Party Leaders and other MSPs in this weekly question time. Topics covered this week include:   Michelle Thomson MSP To ask the First Minister how often he or the Scottish Ministers engage with the Scottish Government's Washington DC International Office regarding the Scotch whisky industry and other economic interests in the United States.   Rachael Hamilton MSP To ask the First Minister whether the Scottish Government will provide an update on the Service Delivery Review of the Scottish Fire and Rescue Service.   Mark Griffin MSP To ask the First Minister what the Scottish Government's response is to reports that police stations across Lanarkshire, including in Bellshill, will be closed permanently to the public or have their hours reduced from 1 April.   Jamie Greene MSP To ask the First Minister whether the Scottish Government will make further changes to the draft Budget 2026-27 published in January in relation to business rates, hospices and the care sector.    A full transcript of this week's First Minister's Questions will be available on the Scottish Parliament website: https://www.parliament.scot/chamber-and-committees/official-report

The government says businesses are benefiting from its Investment Boost policy - introduced in last year's Budget, and is calling on Labour to retain it. Chief Executive of United Machinists, Sarah Ramsay spoke to Ingrid Hipkiss.

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In today's episode, we are stripping back the luxury labels to find the breakthrough products that outperform their pricey counterparts. From the viral Korean “pudding” blushes that have officially infiltrated the drugstore to a $13 watery moisturizer that saturates skin, we're showcasing “recession-proof” musts that don't sacrifice your self-care routine.Shop everything in this episode hereYou'll hear about:The “micro-emulsion” breakthrough: Why everyone is buzzing about a $13 milky moisturizer – and the specific way it outshines heavy-duty creams Red carpet secrets for less: The exact $12 lip treat used on Lainey Wilson for the Grammys (and why it might be better than the “status” balms in your bag)The return of the “mousse” moment? We road-test the new 16-hour cheek and lip mousse that's giving us major 2000s nostalgia, now with a sophisticated, K-beauty twist Fresh and flushed: Is red blush low-key the most underrated runway beauty hack? Get in on the viral trend for $12. Foundation innovation: Soft matte is everything in 2026 – and a new small-but-mighty foundation formula is not to be overlookedFragrance that flatters: The $16 glass-bottle “sunshine” scent for hair and body that you'd swear is designed (just in time for Valentine's Day!)

AGHHH *SHE'S FUNDING A FELON* --- this is beyond crazy, she can't take care of herself, but she is so desperate for an evil man that *she is sending him all of her MONEYYYY* Watch post show here: ➡️ https://bit.ly/chpostshow

The Nation discusses if Jerry Jones will be a big spender this offseason.

Zach Wolchuk has a Daily Football Fix, The latest news on Jerry Jones going 'all in' this offseason, and Wolchuk talks all sports with a Mixed Bag.

Buzz City is alive. The Charlotte Hornets have ripped off their first nine-game winning streak since 1999 and now sit just one win away from tying the franchise record set in 1997-98. In just three weeks, they've gone from the bottom of the Eastern Conference to the 10th seed at 25-28 — completely flipping their season. So what changed? We break down the Hornets' stunning surge, powered by a true “committee” approach:Brandon Miller emerging as the offensive engine, leading the team in scoring in 6 of 9 gamesMiles Bridges stepping into the closer roleKon Knueppel catching fire from deep, already top-3 in the NBA in made three-pointersLaMelo Ball orchestrating as the floor general, averaging nearly 19 PPG and 9 APGBut this run isn't just about offense. Charlotte owns the #3 Net Rating in the NBA over this stretch, with a top-5 Defensive Rating and a dominant +13 point differential. Moussa Diabaté has turned into a double-double force, controlling the glass with monster rebounding performances. Is this sustainable? Or is this a Play-In mirage?Plus, we shift to the NBA buyout market as teams position themselves post-All-Star break. We analyze potential fits and destinations for:Chris PaulLonzo BallKhris MiddletonCam ThomasMike ConleyWhich contenders should make a move? And who could swing the playoff race? Tap in for elite NBA analysis, advanced metrics, roster breakdowns, and playoff implications — only on The Baseline NBA Podcast.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-baseline-nba-podcast--3677698/support.Visit: https://prizepicks.onelink.me/LME0/CLNS and use code CLNS and get $50 in lineups.This show was sponsored in part by Raycon: Visit https://buyraycon.com/baseline for 20% off new buds!Get NBA League Pass: https://link.nba.com/LP19MG Looking to book a vacation? Our travel partner Exquiste Travel & Tours has you covered: Call 954-228-5479 or visit https://exquisitetravelandtours.com/Discover our favorite podcast gear & shop our studio must-haves on our Amazon Affiliate page! https://www.amazon.com/shop/19mediagroupWant to join the conversation or invite us to your platform? Budget-friendly collaborations welcome! https://bit.ly/19GuestFollow The Baseline on X: https://twitter.com/nbabaselineFollow The Baseline on IG: https://instagram.com/nba_baseline

Josh Simons has been building businesses since most kids were just building sandcastles. Lemonade stands. Go-karts he leased out. Hustles that taught him early how money moves and how people work.At 17, Josh dropped out of school and made a feature film, not just for fun, but as a real business. Budgets. Hiring. Deadlines. Pressure. It was his first crash course in entrepreneurship, and it burned him out just as fast as it lit him up. In his early twenties, he hit reset mowing lawns, cleaning toilets, and actually living life for a minute.Then music pulled him back in. Josh started a band and naturally ran the business behind it too. That's when he saw a massive gap in the industry: musicians had platforms to stream, monetize, and build audiences… but nowhere to actually connect. LinkedIn wasn't built for creatives.So he built what didn't exist.VAMPR - the “Tinder for musicians.” A hyper-granular networking platform that grew to over 1.7 million users and changed how artists collaborate globally. That success eventually led to a strategic exit into Australia's public music tech ecosystem with Vinyl Group, turning VAMPR into part of a broader music technology portfolio.But Josh's real superpower isn't just ideation, it's evolution. He knows teams change as companies scale. Skillsets shift. Ego gets shelved. Transparency wins.From bootstrap hustle to tech exits, Josh Simons proves one thing: the path isn't clean but resilience compounds. And the entrepreneurs who survive the dark chapters are the ones who end up rewriting industries.Subscribe to Young Boss with Isabelle Guarino wherever you get your podcasts, and be sure to like, share and follow on Instagram and TikTok.And remember, youth is your power.

The Mavs lost to the Suns last night. What's the latest on the Mavs ownership selling the team back to Mark Cuban? Nick Harris of The Fort Worth Star telegram had an article on Jerry Jones "busting the budget" this offseason. The Polymarkets have controversy now.

The guys discuss if Jerry Jones will actually spend this offseason.

Join the conversation! Send Magic a text here!Today, Magic shares a personal story about her gardening mishaps, leading into a detailed discussion on creating a natural first aid kit. Explore the healing properties of plants like calendula, lavender, yarrow, comfrey, aloe vera, plantain, and chamomile, as well as kitchen staples like honey, ginger, and garlic. Additionally, learn about the benefits of colloidal silver, essential oils, and other natural remedies. Magic also emphasizes the importance of a positive mindset in healing and touches on the role of binders like activated charcoal and fulvic and humic minerals. Tune in to discover how to make a natural first aid kit that reflects a holistic lifestyle.Support the showConnect with Magic:A Magical Life Podcast on Facebook: https://www.facebook.com/amagicallifepodcast/On Instagram: https://www.instagram.com/wholisticnaturalhealth/Online: https://wholisticnaturalhealth.com.auA Subito Media production

AI isn't just coming from Silicon Valley anymore. A growing number of startups — and companies like Airbnb — are turning to Chinese open-source AI models instead of US-based APIs. Not because it's trendy. Because it's cheaper, more flexible, and often good enough. In this episode, Sophia Matveeva speaks with Alex Hern, AI correspondent at The Economist, about what's driving this shift. They break down how DeepSeek disrupted the market, why constraints fueled smarter engineering, and what founders can realistically try today if they want more AI options without more spend. Alex Hern is The Economist's AI Writer, focusing on the science and technology of artificial intelligence. Before joining the paper, he covered technology for 11 years at The Guardian, where he was the UK technology editor. In this episode, you will hear: Why relying on US AI APIs may be quietly limiting your product and your margins How Chinese open-source models let founders experiment, customize, and ship faster without runaway costs The real reason DeepSeek shocked Silicon Valley — and what it reveals about building under constraints What you can realistically try today if you want AI leverage without an AI-sized budget Free AI Mini-Workshop for Non-Technical Founders Learn how to go from idea to a tested product using AI — in under 30 minutes. Get free access here: techfornontechies.co/aiclass Follow and Review: We'd love for you to follow us if you haven't yet. Click that purple '+' in the top right corner of your Apple Podcasts app. We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts. Simply select "Ratings and Reviews" and "Write a Review" then a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast. Episode Credits If you like this podcast and are thinking of creating your own, consider talking to my producer, Emerald City Productions. They helped me grow and produce the podcast you are listening to right now. Find out more at https://emeraldcitypro.com Let them know we sent you. For the full transcript, go to https://www.techfornontechies.co/blog/290-why-airbnb-switched-from-openai-to-chinese-ai-and-what-it-means-for-your-budget  

In this episode of PPC Live, Anu Adegbola speaks with Andrea Cruz, an award-winning B2B digital marketer, about the importance of navigating mistakes in client communication, the dynamics of team collaboration, and the role of AI in marketing. They discuss common pitfalls in agency practices, the significance of a solutions-oriented mindset, and how to leverage AI effectively in marketing strategies. Andrea shares her insights on the importance of learning from mistakes and fostering an environment where team members feel comfortable discussing challenges. The conversation concludes with a light-hearted exchange about Andrea's passion for her work and her unique experiences in the industry.TakeawaysMistakes are opportunities for growth and learning.Communication with clients is crucial, especially when mistakes happen.A solutions-oriented mindset fosters better team dynamics.Recognizing your own mistakes as a leader builds trust with your team.Budget constraints should dictate campaign strategies in B2B marketing.AI can enhance marketing efforts beyond basic summarization.Understanding client needs is essential for effective communication.Regular check-ins with clients can help identify roadblocks early.Fostering an open environment encourages team members to share challenges.Passion for the work can lead to greater job satisfaction and success.Chapters00:00 Introduction and Connection09:38 Navigating Mistakes in Client Communication18:28 The Importance of Team Dynamics and Solutions25:21 Learning from Mistakes: A Manager's Perspective29:03 Common Agency Mistakes in B2B Marketing31:08 Leveraging AI in Marketing37:01 Conclusion and Final Thoughts41:53 Outro.mp3Find Andrea on on ⁠⁠LinkedIn⁠ ⁠PPC Live The Podcast features weekly conversations with paid search experts sharing their experiences, challenges, and triumphs in the ever-changing digital marketing landscape.Join us for PPC Live Online on Feb 18thJoin our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Whatsapp group⁠Subscribe to our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Newsletter⁠

Feb. 11, 2026- New York Farm Bureau Director of Public Policy Renée St. Jacques discusses the governor's budget proposal and makes the case for additional investments in a housing tax credit and agricultural research.

"You either need to call it fascism or you need to invent a new word with more or less the same meaning." — Jonathan RauchJonathan Rauch's viral Atlantic essay has reignited the debate over what to call the Trump administration. Having previously settled on "semi-fascist," Rauch now argues that Trump ticks all 18 boxes on his checklist of fascist characteristics — from the glorification of violence and territorial ambitions to Carl Schmitt's philosophy of "enemies, not adversaries." We spar over whether the term obscures more than it reveals: Is this really fascism, or just authoritarianism with American characteristics? The conversation sharpens around Minneapolis, where citizens were shot face down, and the government initially denied it happened. You don't do that to win votes, Rauch argues — you do it because you believe that's how the social contract should work. He predicts Trump will fail to turn America into a fascist country but warns that institutions like the newly expanded ICE will outlast this administration. About the GuestJonathan Rauch is a senior fellow at the Brookings Institution and a contributing writer for The Atlantic. He is the author of nine books, including The Constitution of Knowledge: A Defense of Truth (2021), Cross Purposes: Christianity's Broken Bargain with Democracy (2025), and Kindly Inquisitors: The New Attacks on Free Thought (1993). He received the 2005 National Magazine Award.ReferencesThinkers discussed:·      Carl Schmitt was a Nazi political theorist whose "friend-enemy distinction" argued that politics is fundamentally about identifying and crushing enemies, not managing disagreements with adversaries.·      George Orwell wrote in his 1946 essay "Politics and the English Language" that "the word 'fascism' has now no meaning except insofar as it signifies something not desirable."·      Hannah Arendt was a German-American political theorist and refugee from Nazi Germany whose book The Origins of Totalitarianism examined both Nazism and Stalinism, preferring "totalitarianism" to "fascism" as the more encompassing term.Historical figures:·      Benito Mussolini invented the term "fascism" (from the Latin fasces, a bundle of rods symbolizing collective strength) and ruled Italy as dictator from 1922 to 1943.·      Francisco Franco ruled Spain from 1939 to 1975. Whether he was truly a fascist or merely an authoritarian remains debated; he never got along well with Hitler and outlasted the fascist era by three decades.·      Viktor Orbán is the prime minister of Hungary whose systematic capture of media, courts, and civil society has become known as the "Orbán playbook" — a template Rauch argues the Trump administration is following.Contemporary figures mentioned:·      Stephen Miller is a senior advisor to Trump who declared that "force is the iron law of the world" and told progressives "you are nothing" at a memorial service where the widow of the deceased had just offered Christian forgiveness to an assassin.·      Russell Vought is the director of the Office of Management and Budget, identified by Rauch as one of the younger ideologues building Trumpism into something more like a coherent ideology.·      Chris Rufo is a conservative activist and culture war strategist who has employed what Rauch calls "revolutionary language" in his campaigns against universities and public institutions.Essays and books mentioned:·      "Politics and the English Language" (1946) is Orwell's essay arguing that the corruption of language enables the corruption of politics, and that vague or meaningless words like "fascism" make clear thinking impossible.·      The Origins of Totalitarianism (1951) is Hannah Arendt's study of Nazism and Stalinism as parallel forms of total domination, examining how mass movements, propaganda, and terror enable regimes to control entire societies.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States—hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:00) - (00:13) - The viral essay (02:10) - Why Rauch changed his mind (03:41) - Fascism vs. authoritarianism (05:54) - Carl Schmitt and "enemies not adversaries" (06:14) - Orwell on the word "fascism" (09:12) - Can old people be fascists? (11:51) - Blood and soil nationalism (14:14) - Minneapolis (17:51) - Kristallnacht comparisons (20:07) - The postmodern right (26:34) - Following the money (32:05) - ICE as paramilitary force

2-11-2026: Wake Up Missouri with Randy Tobler, Stephanie Bell, John Marsh, and Producer Drake

Have you ever dated someone, or are currently married to a person whose relationship with money looks very different from yours? Maybe they like to spend and you're more of a saver? Or, you're self-employed and want to know the best strategy for saving for retirement? Then give this week's special episode a listen. In celebration of five years of Uncorked with Funny Wine Girl, I'm hosting several live, on-line opportunities for you to ask questions of "experts." This week's episode was recorded live on Jan. 28 with Emily Hickox of Budget Through Life, who shared her expertise in financial literacy with question askers Oliver Ord and Tina Gallagher who will be answering questions on my next Ask the Expert on Feb. 18 at 7PM, ET. Tina is a self-published romance author who is ready to answer your questions about writing, self-publishing, marketing, book signings and much more.If you would like to learn about Emily's financial coaching services, email Emily at budgetthroughlife.com.Thank you to my podcast sponsors Reinvented Threads with Gabby Lynn and Healthy Lifestyle Management with Lisa Rigau. Gabby is busy at work making beautiful, spring-inspired ecofriendly handmade hats, bags and monster dolls. Follow Reinvented Threads on Facebook and Instagram to stay up to date on Gabby's latest creations and you can shop online on ReinventedThreads.com. Lisa of Healthy Lifestyle Management offers a variety of health coaching services including her upcoming 8-week mindfulness-based stress reduction course beginning in March. Enroll before Feb. 18 to save $50. Register here. If you would like to support this podcast through sponsorship, email Jeannine.Luby@gmail.com for a list of affordable sponsorship packages. Remember that you can also show support by sharing an episode of this podcast with a friend, foe, or anyone you know, or by rating the podcast or writing a review. Follow Funny Wine Girl Jeannine on Facebook and Instagram to stay in the know and get some laughs. Click here.

Learn more about Your Digital Mentor Podcast: https://coursesandconferences.wellcomeconnectingscience.org/our-events/your-digital-mentor-podcast/Explore free resources & templates: https://wcscourses.github.io/YDMP/Learn more about our guest interviewees : https://github.com/WCSCourses/YDMP/blob/main/Our%20Guest%20Speakers.pdf

In this episode of The New Money Habits Podcast, Coach Nino Villa answers listener questions about budgeting for gift giving, paying off debt, and planning for spontaneous spending without creating financial stress. Many people struggle to balance generosity, debt elimination, and everyday enjoyment. Nino walks through how intentional financial planning makes room for all three — without guilt or chaos. In this conversation, you'll learn: • How to budget for gifts without disrupting your financial plan• Why planning for “unexpected” spending reduces stress• Practical steps for managing and eliminating debt• How to build a money plan that supports both responsibility and enjoyment Money management isn't about restriction. It's about intention. When you plan ahead, you create space for generosity, progress, and peace of mind. If you're working to build better financial habits while staying grounded and realistic, this episode will help you think differently about budgeting, debt, and everyday spending. For more tools and resources, visit NewMoneyHabits.com Join the New Money Habits Community Join our free community and connect with others building healthier money habits Become a member starting at $5/month Start your 7-day free trial today Helpful Resources Mentioned in This Episode Watch on YouTube: Full video version of this episode Payday Power Planner (FREE): Streamline your budgeting processhttps://www.newmoneyhabits.com/budgeteers/helpful-tools Food Number Calculator (FREE): Simplify food budgeting and planninghttps://www.newmoneyhabits.com/budgeteers/helpful-tools Submit Your Questions: Email us at podcast@newmoneyhabits.com Join Our Free Facebook Group:https://www.facebook.com/groups/newmoneyhabits Schedule a Free Call with Coach Nino:https://www.newmoneyhabits.com/budgeteers/contact Online Course: How to Create a Better Budget: Your Foundation to Financial Freedomhttps://www.newmoneyhabits.com/bootcamp Music CreditsThis episode features music by Summer School. Connect With UsFollow @newmoneyhabits on social media for more insights, tools, and updates.

Food keeps getting more expensive, so how do shoppers respond? They change what they buy, right? It's not just that cheaper foods get more popular. Shoppers are more nuanced than that. So, today on the show, we choose one classic meal that is tailor-made for this anxious economic moment. Why Hamburger Helper is poised to win 2026.Related episodes: How niche brands got into your local supermarketCan you trust you're getting the same grocery prices as someone else?Hits of the Dips: Songs of recessions pastFor sponsor-free episodes of The Indicator from Planet Money, subscribe to Planet Money+ via Apple Podcasts or at plus.npr.org. Fact-checking by Sierra Juarez. Music by Drop Electric. Find us: TikTok, Instagram, Facebook, Newsletter.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

Are you making great money… but still not building real wealth? You're not alone.In this episode, I chat with Kaitlyn Carlson, former Wall Street wealth manager and founder of Theory Planning Partners, who helps 7-figure female entrepreneurs build financial legacies—not just businesses.This isn't your typical finance convo. We're breaking down:✨ Why high income doesn't equal financial freedom✨ Mindset blocks that keep you stuck, even at 7 figures✨ How childhood money dynamics impact your business✨ Why you need a wealth team, not just a bookkeeperKaitlyn shares practical strategies and powerful shifts to help you move from hustle to legacy—and finally keep more of what you earn.Whether you're making $500K or $5M, this conversation will help you rethink how you lead, earn, and build a future on your terms.Click play to hear all of this and:[03:44] Why most 7-figure entrepreneurs focus on income but ignore long-term wealth[08:19] The financial wake-up call that led Kaitlyn to serve female founders[11:11] How childhood experiences shape your financial mindset[16:05] The key differences between an income strategy and a wealth strategy[23:45] Why building wealth requires a team (and not just a bookkeeper!)[29:32] How to evaluate if your business is scalable, sellable, or sustainable[37:51] The mental shift that changes everything: from “I have enough” to “I'm enough”Listen to Related Episodes:Tax Secrets Every Entrepreneur Should Know with Karlton Dennis8 Mindset Shifts to Transform Your Relationship with MoneyWealth Building Strategies: How to Budget, Invest, and Raise Your PricesConnect With Kaitlyn Carlson:Instagram: https://www.instagram.com/theoryplanningpartnersFacebook: https://www.facebook.com/profile.php?id=100090129347132Website: https://theoryplanning.com/

James Solomon, mayor of Jersey City, talks about the major budget deficit of about $250 million dollars he is facing, which he blames the former mayor, Steve Fulop for, and other Jersey City news.new)

What's the worst and best relationship advice you've ever heard? My podcast guest, Monica Tanner, shares her relationship advice as a Relationship Coach and host of the Secrets of Happily Ever After podcast. She transforms marriages with simple communication, connection, conflict resolution and commitment strategies. Her mission is to lower the divorce rate and improve marital satisfaction. Through her engaging podcast, new Amazon Best-Selling book, Bad Marriage Advice, vibrant social media community, and couple's coaching practice, Monica's expert guidance has impacted thousands of couples, by helping them ditch resentment and roommate syndrome and get back to living their happily ever after love story.In this episode:The worst relationship adviceThe top 3 secrets to happily ever afterThe cure for “Roommate Syndrome” Connect With MonicaWebsites https://monicatanner.com , https://badmarriageadvice.com IG https://instagram.com/monitalksmarriage YouTube https://youtube.com/@secretsofhappilyeverafter Free Gift 300+ Date Night Ideas for Every Season and Budget https://monicatanner.com/300dates ►Please subscribe/rate and review the podcast on Apple Podcasts http://bit.ly/lastfirstdateradio ►If you're feeling stuck in dating and relationships and would like to find your last first date, sign up for a complimentary 45-minute breakthrough session with Sandy https://lastfirstdate.com/application ►Join Your Last First Date on Facebook https://facebook.com/groups/yourlastfirstdate ►Get Sandy's books, Becoming a Woman of Value; How to Thrive in Life and Love https://bit.ly/womanofvaluebook , Choice Points in Dating https://amzn.to/3jTFQe9 and Love at Last https://amzn.to/4erpj7C ►Get FREE coaching on the podcast! https://bit.ly/LFDradiocoaching ►FREE download: “Top 10 Reasons Why Men Suddenly Pull Away” http://bit.ly/whymendisappear ►FREE download: “The Green Light Guide to Dating After 50” https://lastfirstdate.com/green-light-guide/ ►Group Coaching: https://lastfirstdate.com/the-woman-of-value-club/ ►Website → https://lastfirstdate.com/ ► Instagram → https://www.instagram.com/lastfirstdate1/ ►Get Amazon Music Unlimited FREE for 30 days at https://getamazonmusic.com/lastfirstdate  

Oh baby, are you ever in for a treat! Commander Cookout Podcast returns to form with a funny new Orzhov deck brewed by none other that the President of CCONation himself! Join us!Huge thank you to our sponsors, Fusion Gaming Online. They're your source for all of your gaming needs. You can find them here: www.FusionGamingOnline.com. You want a 5% discount off all of your MTG order? Head over to Fusion Gaming Online and use exclusive promo code: CCONATION at checkout.This week's community decklist: https://archidekt.com/decks/19497671/abigales_29_big_black_brown_budget_beatersWant your deck or topic featured on Commander Cookout Podcast? Check out the reward tiers at Patreon.com/CCOPodcast. There are a lot of fun and unique benefits to pledging. Like the CCO Discord or getting your deck featured on the show.Ryan's solo podcast, Commander ad Populum:https://www.spreaker.com/show/commander-ad-populumInterested in MTG/Commander History? Check out Commander History Podcast: https://www.spreaker.com/podcast/mtg-commander-history--6128728You can listen to CCO Podcast anywhere better podcasts are found as well as on CommanderCookout.com.Now, Hit our Theme Song!Social media:https://www.CommanderCookout.comhttps://www.Instagram.com/CommanderCookouthttps://www.Facebook.com/CCOPodcast@CCOPodcast and @CCOBrando on Twitterhttps://www.Patreon.com/CCOPodcasthttps://ko-fi.com/commandercookout

Learn how to apply Super Bowl ad ideas to your Facebook and Instagram ads, even without a large budget. I share some great tactics as well as my favorite Super Bowl ads this year and why. Website: https://philgrahamdigital.com

Does Google PPC still work for home service businesses—or is it just an expensive mistake?In this Clicks to Calls episode of Owned and Operated, John Wilson sits down with Service Scalers CEO Sam Preston to break down the truth about Google Ads (PPC) for HVAC, plumbing, and electrical companies. Some operators swear PPC is dead. Others are spending six figures a month and winning. The difference isn't the platform—it's execution.They walk through why PPC fails for most owners, how it's fundamentally different from Local Service Ads, and what has to be in place before PPC becomes a scalable, predictable lead channel. From budget minimums and landing pages to tracking revenue (not just calls), this episode lays out a clear framework for deciding if PPC belongs in your business—and how to avoid burning cash if you try it.If you've ever said “Google Ads don't work for us,” this episode will challenge that assumption.What you'll learn in this episode:Why PPC still works—and why most operators think it doesn'tThe real difference between LSA and PPC (and why PPC breaks first)Budget thresholds you actually need to make PPC viableWhy landing pages matter more than ad copyShout Out to FieldPulse

The massive ice storm brought the area to a standstill and Heath and Candace were caught in one of the worst hit areas. But things are starting to return to normal and they are back to discuss everything from the biggest tip to cooking with venison, Heath's brand new chili seasoning, and how to have a delicious Valentine's Day dinner without blowing your budget, it's all happening on today's episode of the Shootin' The Que podcast!0:00 - Start0:35 - After Effects Of Crippling Ice Storm10:33 - Brand New Chili Seasoning13:45 - How To Cook With Deer Meat22:30 - Different Types Of Game In Chili26:00 - Valentine's Day Is Coming35:55 - Budget Friendly Valentine's Meal39:40 - Best BBQ Restaurants For Valentine's44:48 - Unique Dishes Made Out Of BarbecueJoin our online BBQ community "Shootin' the Que" on Facebook. Talking all things BBQ! https://www.facebook.com/groups/shootinthequeheathriles/Follow Heath Riles BBQ:https://www.heathrilesbbq.comFacebook: https://www.facebook.com/HeathRilesBBQInstagram: https://www.instagram.com/heathrilesbbq/Twitter: https://twitter.com/heathrilesbbqTikTok: https://www.tiktok.com/@heathrilesbbqPinterest: https://www.pinterest.com/heathrilesbbq6901/Heath Riles BBQ Products: https://www.heathrilesbbq.com/collections/allMerch: https://www.heathrilesbbq.com/collections/merchandiseMore Heath Riles BBQ Recipe Videos: https://www.youtube.com/@HeathRilesBBQ/videosPrintable recipes at 'Shootin' The Que' recipe blog: https://www.heathrilesbbq.com/blogs/favorite-recipesAffiliate Disclaimer: Some of the links in this description are affiliate links where we may earn a small commission if you use them. This is no additional cost to youHeath Riles, pitmaster• 81x BBQ Grand Champion,• 2022, 2024 & 2025 Memphis in May World Rib Champion • 2025 Memphis in May Grand Champion • Award-Winning Rubs, Seasonings, Sauces, Glazes and Marinades/Injections#icestorm #podcast #mississippi #hunting #deer #recipe #best #valentines #onabudget

Gov. Patrick Morrisey has made his case for a 10% state income tax cut – but not everyone is convinced that's the way to go. Assistant News Director Maria Young spoke with Kelly Allen, executive director of the West Virginia Center on Budget and Policy, to learn more. The post State Center On Budget, Policy Weighs In On Morrisey's Tax Cut Proposal, This West Virginia Morning appeared first on West Virginia Public Broadcasting.

Aujourd'hui, Abel Boyi, éducateur, Barbara Lefebvre, professeur d'histoire-géographie, et Didier Giraud, agriculteur de Saône-et-Loire, débattent de l'actualité autour d'Alain Marschall et Olivier Truchot.

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Ever feel like you need a second mortgage to afford the organic chicken and grass-fed beef your hormones are literally begging for?You are doing your absolute best to keep everyone fed, healthy, and happy, but it feels like your bank account is constantly at war with your hormones. I've been right where you are, until I realized I was missing a huge piece of the puzzle: safety around money. That's why I brought on the Budget Besties, Vanessa and Shana, today. We're having a girlfriend-to-girlfriend chat designed to help you stop putting yourself last and start creating a budget that feels like a nervous system reset.You'll learn:The 90-day audit that replaces financial shame with pure, helpful dataA simple account trick that ensures you always have money for the good chickenHow to put your finances on autopilot so you can stop making stressful micro-decisions while hormonalYou need to listen to this now because financial stress is a silent hormone disruptor that you don't have to live with anymore. Pop it on while you pick up your groceries this week!Nourish Tracker - Discount code: HAPPILYHORMONALDownload the new 20-min private podcast training - Simply Nourished CyclesBook a FREE Hormone Strategy Call with meCONNECT WITH THE BUDGET BESTIES:PodcastNEED HELP FIXING YOUR HORMONES? CHECK OUT MY RESOURCES:Hormone Imbalance Quiz - Find out which of the top 3 hormone imbalances affects you most!Join Nourish Your Hormones Coaching for the step-by-step and my eyes on YOUR hormones for the next 4 months.Send us a text with episode feedback or ideas! (We can't respond to texts unless you include contact info but always read them)FREE Podcast Training - Simply Nourished CyclesDon't forget to subscribe, share this episode, and leave a review. Your support helps us reach more women looking for answers.Disclaimer: Nothing in this podcast is to be taken as medical advice, please take informed accountability and speak to your provider before making changes to your health routine.This podcast is for women and moms to learn how to balance hormones naturally in motherhood, to have pain-free periods, increased fertility, to decrease PMS mood swings, and to increase energy without restrictive diet plans. You'll learn how to balance blood sugar, increase progesterone naturally, understand the root cause of estrogen dominance, irregular periods, PCOS, insulin resistance, hormonal acne, post birth-control syndrome, and conceive naturally. We use a pro-metabolic, whole food, root cause approach to functional women's health and focus on truly holistic health and mind-body connection.If you listen to any of the following shows, we're sure you'll like ours too! Pursuit of Wellness with Mari Llewellyn, Culture Apothecary with Alex Clark, Found My Fitness with Rhonda Patrick, Just Ingredients Podcast, Wellness Mama, The Dr Josh Axe Show, Are You Menstrual Podcast, The Model Health Show, Grounded Wellness By Primally Pure, Be Well By Kelly Leveque, The Freely Rooted Podcast with Kori Meloy, Simple Farmhouse Life with Lisa Bass