Podcasts about admire

Margaux propose ce week-end de se replonger dans "Our Love to Admire", le troisième album du groupe new-yorkais Interpol, sorti en 2007. Fidèle à son ADN post-punk sombre et élégant, le quatuor y développe une production plus ample et cinématographique, qui renforce son atmosphère nocturne. L'album s'ouvre avec la majestueuse "Pioneer to the Falls", avant d'enchaîner avec "No I in Threesome" et le single marquant "The Heinrich Maneuver", porté par la voix grave et reconnaissable de Paul Banks. Le disque alterne entre énergie et tension, notamment sur "Mammoth", et des moments plus atmosphériques comme "The Lighthouse". Moins populaire que "Turn on the Bright Lights", ce troisième album reste pourtant l'un des plus ambitieux du groupe. Les guitares tranchantes caractéristiques d'Interpol s'y mêlent à davantage de claviers et d'arrangements, accentuant son caractère cinématographique. Une dimension particulièrement marquée sur "Rest My Chemistry", morceau introspectif autour de la lutte contre les dépendances, mis à l'honneur sur RTL2. Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

In this episode of 2sidesnvibespodcast, we challenge every man with one powerful question: Are you becoming the kind of man you respect?We dive into what it truly means to add value to yourself mentally, spiritually, emotionally, and professionally. From the books you read to the mentors you seek and the habits you build, growth doesn't happen by accident. It's intentional.If you desire strength, discipline, wisdom, and purpose, you have to pursue them daily. Tune in as we unpack how to invest in yourself, seek the right guidance, and become the man you don't just admire from a distance but the man you see in the mirror.Let's grow.

Have you ever wished you had thorns? Have you ever felt like you needed to protect your heart, your space, your peace of mind, or your time from a world that asks too much of you – or takes without asking? You're not alone. This is what thorn medicine can bring you.Thorns aren't weapons, they're defenses. They don't seek an animal or human out with the intent to cause harm, they hold space and define boundaries. Touch me here, says the rose, but not there. Admire my flowers, says the motherwort, but don't you dare clutch my seeds. We can learn from this.We can take the medicine of thorns into ourselves. We can do this literally, not only (oh, “only”!) metaphorically or symbolically. Most all thorny herbs, and particularly the thorns themselves, carry a key herbal action: astringency. This is an action which literally pulls things together, and holds things together.Each plant's thorn is different, serving its own purpose. Hawthorn's long, widely spaced thorns evolved to counter the hungers of the giant sloth, and they retain that shape long after the lumbering herbivore went extinct: they are the living memory of a predator past. Ocotillo's inexhaustible rows of spines render it no more easy a prey than the cactus who share its landscape. Thistle spikes out from every possible surface, asserting itself in all directions as its firework flowers reach for the sky. Which thorns are yours?Pull yourself together. Hold your boundaries. Make a safe space.Then, within that thorn-walled refuge, your flowers will unfold.Thorn medicine is only one form of support herbs can offer our emotions. Our Neurological & Emotional Health course is a user's guide to your nerves, your emotions, and the herbs who can lift you, hold you, brace you, and sustain you. We teach holistic herbal strategies for addressing both neurological & psychological health issues. It includes a lengthy discussion of herbal pain management strategies, too!Like all our offerings, this is a self-paced online video course, which comes with free access to twice-weekly live Q&A sessions, lifetime access to current & future course material, twice-weekly live Q&A sessions with us, open discussion threads integrated in each lesson, an active student community, study guides, quizzes & capstone assignments, and more!If you have a moment, it would help us a lot if you could subscribe, rate, & review our podcast wherever you listen. This helps others find us more easily. Thank you!!Our theme music is “Wings” by Nicolai Heidlas.Support the showYou can find all of our online herbalism courses at online.commonwealthherbs.com!

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

TRANSLATION MENU: LOOK UPPER RIGHT BELOW THE SOCIAL MEDIA ICONS. IT OFFERS EVERY LANGUAGE AVAILABLE AROUND THE WORLD! Pictured here and below: the seven-meter tall statue of Charles de Gaulle, as you enter China's National Museum, on Tiananmen Square, Beijing. He was a visionary leader who kept France from being a whore for the USA... The post President Charles de Gaulle made France the first major Western power to be mutually recognized by the People's Republic of China, 27 January 1964. A great leader of vision and principles, whom the Chinese respect and admire. China Rising Radio Sinoland 260127 appeared first on RADIO SINOLAND.

Reivindicar las salas de música en directo, especialmente aquellas de pequeño o mediano formato. Las Salas de conciertos son esos espacios donde los músicos empiezan a dar sus primeros pasos, donde se produce la primera conexión con el público. Reconocerles el lugar primordial que ocupan en la escena local i la importancia de esos espacios como agentes dinámicos de la vida sociocultural de nuestros territorios es uno de los objetivos del Curtcircuit, el ciclo anual de conciertos que organiza Assac, la Associació de Sales de Concerts de Catalunya. Conocemos la programación de esta 14ª edición a la par que compartimos algunas de las problemáticas de estos espacios imprescindibles en el panorama musical.Llum, Belen Natalí, Carla Collado, Pedro Pastor, PLAYGRXVND, Juana Everett, Ferrán Palau, Joina, AdmireEscuchar audio

Fr. Ryan gave this talk to Men's Ministry at St. Basil

Gary Neville is joined by Peter Drury as Arsenal spurned a chance to go eight clear as Liverpool battle to goalless draw. Gary shares his thoughts on Chelsea's appointment of Liam Rosenior, Man City dropping points against Brighton, Kevin Keegan, Newcastle's late late win and Man Utd's draw against Burnley.The Gary Neville Podcast is a Sky Sports podcast. Listen to every episode here: skysports.com/the-gary-neville-podcastYou can listen to The Gary Neville Podcast on your smart speaker by saying "ask Global Player to play The Gary Neville Podcast".Watch every episode of The Gary Neville Podcast on YouTube here: The Gary Neville Podcast on YouTubeFor all the latest Premier League news, head to skysports.com/premier-leagueFor advertising opportunities email: skysportspodcasts@sky.uk

"There are always certain traits, habits, or even non-tangible elements that draw us to our favorite people and artists. Find out what those elements are to Bob and Reuben in this episode."If you like Bass - you're in the right place! Interested in more music and practice advice? Check out Open Studio...where you'll find courses and much more by world-class bassists like Reuben Rogers, Ron Carter, Christian McBride, Bob DeBoo and more. Reach out to the Upright Citizens anytime at uprightcitizenspodcast@gmail.com ★ Support this podcast ★

You don’t destroy something you admire. Taking 5 minutes to enjoy the little wins we are getting. Why can’t Trump use NGOs to remove illegals the same way the Biden administration used them to bring them here? Follow The Jesse Kelly Show on YouTube: https://www.youtube.com/@TheJesseKellyShowSee omnystudio.com/listener for privacy information.

In this solo episode of Search for Meaning, Rabbi Yoshi Zweiback reflects on a timely and challenging question facing the Jewish community today: How do we disagree without hating one another?Prompted by recent remarks from Rabbi Elliot Cosgrove at the American Zionist Movement Biennial, Rabbi Yoshi explores the growing tensions within Jewish communal life — particularly around Israel — and the ways our conversations can become polarized, judgmental, and alienating, especially for younger Jews.Turning to Parashat Vayeshev and the story of Joseph and his brothers, the episode uncovers a subtle but powerful distinction in the Torah between sin'ah (hatred) and kin'ah (envy). Drawing on the teachings of Rabbi Eliezer Davidovits, Rabbi Zweiback introduces the rabbinic concept of kin'at sofrim — the “envy of scholars” — a form of admiration that doesn't fracture relationships but deepens wisdom and understanding.This episode is an invitation to reimagine disagreement not as a threat, but as an opportunity for learning, curiosity, and growth — and to consider how Jewish tradition can guide us toward more loving, respectful, and constructive conversations, even in times of deep division.

#363: On this solo episode, Emily Elizabeth dives deep into how she has refined her own practice of "intentional living" with 3 simple and direct questions and frameworks she has thought about over the years to feel more aligned with her personal goals, taste, and to become the type of woman she envisions herself to be.Living with deep intention may seem like sacrificing certain indulgences, which is why Emily shares more on this episode, to show how you can still indulge in all of the finer things you enjoy in life, but practice the awareness of how and why you are spending your time, money, or energy in those things.What you will learn:3 simple questions and frameworks to ask yourself when it comes to living intentionally and how to make better decisionsHow to live a life with the center focused on your long-term goals and priorities (i.e. health)Intentional holiday shopping and finding gifts for othersHow to use comparison in a positive way, especially when comparing to women you admireBILT Credit Card Info (Pay Rent and Earn Points):https://bilt.page/r/HQ06-ZV7OReceive weekly personal insights from Emily's email newsletter and subscribe hereWatch Full Episodes on YouTube: https://www.youtube.com/@whatfulfillsyou/videosENJOY 10% OFF THE WHAT FULFILLS YOU? CARD GAME AT www.whatfulfillsyou.com - code "WHATFULFILLSYOU10"Follow the What Fulfills You? Podcast Instagram: https://www.instagram.com/whatfulfillsyouFollow Emily Elizabeth's Instagram: https://www.instagram.com/emilyeduong/Read more on Substack: https://whatfulfillsyou.substack.com/Support this podcast at — https://redcircle.com/what-fulfills-you-podcast/exclusive-contentAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

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TEMPO DE REFLETIR 01600 – 1 de dezembro de 2025 Romanos 5:5-8 (NVI) – Deus derramou Seu amor em nossos corações, por meio do Espírito Santo que Ele nos concedeu. De fato, … quando ainda éramos fracos, Cristo morreu pelos ímpios. Dificilmente haverá alguém que morra por um justo, embora pelo homem bom talvez alguém tenha coragem de morrer. Mas Deus demonstra Seu amor por nós: Cristo morreu em nosso favor quando ainda éramos pecadores. Você estaria disposto a morrer por um viciado em drogas com acusações múltiplas de abuso de crianças? Você poderia estar disposto a arriscar sua vida doando um órgão para salvar seu filho que morreria se você não fizesse a doação, mas você daria a vida pelo viciado estuprador? Provavelmente não. Deus, entretanto, deu Seu Filho para morrer “pelos ímpios”. Admire-se do fato de que “Deus derramou Seu amor em nossos corações”. “Quando ainda éramos fracos” para viver como Cristo viveu e amar como Cristo amou, Deus nos deu o Seu amor, para com ele podermos amar. Ele nos oferece o tipo de amor que Jesus teve quando “morreu pelos ímpios”. O tipo de amor com o qual Jesus amou quando bradou: “Pai, perdoa-lhes, pois não sabem o que estão fazendo” (Lc 23:34, NVI). Jac Colon, um pregador da Bíblia, antes de sua conversão era piloto de avião de caça da Força Aérea dos Estados Unidos no Vietnã. Em 1991, dirigiu uma série de reuniões evangelísticas em Riga, Látvia. Um dos batizados foi um piloto de caça soviético que havia servido no lado adversário no Vietnã. Durante as reuniões evangelísticas, o ateu entregou o coração a Jesus. No dia do batismo, ele disse a Jac: “Já fomos inimigos mortais, que teriam abatido um ao outro lá no céu, mas agora somos irmãos em Cristo.” Depois se abraçaram e choraram emocionados, em silêncio. Um milagre assim só aconteceu porque Deus “derramou Seu amor” no coração dos dois homens. “Quando os homens se ligam entre si, não pela força do interesse pessoal, mas pelo amor, mostram a operação de uma influência que é superior a toda influência humana. Onde existe esta unidade, é evidente que a imagem de Deus está sendo restaurada na humanidade, que foi implantada nova vida” (O Desejado de Todas as Nações, p. 678). Que diferença faz o amor – o amor de Jesus – no coração de uma pessoa! Reflita sobre isso no dia de hoje e ore comigo agora: Precisamos amar como Tu nos amas, Pai! Faz-nos mais parecidos contigo, por favor! Em nome de Jesus, amém! Saiba como receber as mensagens diárias do Tempo de Refletir: -> No celular, instale o aplicativo MANAH. -> Para ver/ouvir no YouTube, inscreva-se neste Canal: youtube.com/AmiltonMenezes7 -> Tenha os nossos aplicativos em seu celular: https://www.wgospel.com/aplicativos -> Para receber pelo WhatsApp, adicione 41 99893-2056 e mande um recadinho pedindo os áudios. -> Participe do nosso canal no TELEGRAM: TELEGRAM AMILTON MENEZES . -> Participe do nosso canal no WhatsApp: WHATSAPP CHANNEL Amilton Menezes . -> Instagram: https://www.instagram.com/amiltonmenezes7/ -> Threads: https://www.threads.net/@amiltonmenezes7 -> X (Antigo Twitter): https://x.com/AmiltonMenezes -> Facebook: facebook.com/AmiltonMenezes

Get our premium episode archive: https://www.patreon.com/ieltssfs You should say: who he/she is, what you know about him/her, what he/she is like in real life, what achievement he/she has made, and explain why you admire him/her. Tune in and have a great day! - Book a class with Rory here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://successwithielts.com/rory⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Our course on Phrasal Verbs: https://successwithielts.com/podcourses Transcript: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sign up for our archive to get access to the transcript⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Find an IELTS Speaking Partner: https://links.successwithielts.com/ieltspartner Our social media: https://linktr.ee/successwithielts © 2025 Podcourses Learn more about your ad choices. Visit megaphone.fm/adchoices

In today's episode, Michael reveals the exact strategy he uses to connect with high-performing, hard-to-reach humans: always make your ask an offer.This simple shift transforms your outreach from transactional to magnetic—and dramatically increases your likelihood of receiving a meaningful response.You'll learn:Why most asks fail (and how to avoid the “pick your brain” trap)The neuroscience of why personalized offers create instant rapportHow to research someone's deepest values—and speak directly to themThe Hot Ones principle: surprise people with depthWhy offering unique experiences is more powerful than credentialsHow Michael built relationships with Moby, Flea, No Doubt, Woody Harrelson, and moreThe “Wolf Connection” example you can model in your own outreachWhy making it easy to say no actually increases your chance of getting a yesHow to follow up in a way that builds connection rather than pressureHow Michael is using this method to book 50–100 podcast appearances for his book RESONANCEHow to leave “a flower at the door” instead of an energetic tangleThis is one of the most practical and transformative relationship tools in the Resonance Method. Use it well. Michael Trainer has spent 30 years learning from Nobel laureates, neuroscientists, and wisdom keepers worldwide. He's the author of RESONANCE: The Art and Science of Human Connection (March 31, 2026), co-creator of Global Citizen and the Global Citizen Festival, and host of the RESONANCE podcast.Featured in Forbes, Inc, Good Morning America. Follow on YouTube

In this episode, Jeremy is joined by Greg Stier, founder of Dare 2 Share, to talk about what happens when evangelism becomes part of the culture of a youth ministry. Drawing from new research involving hundreds of youth groups around the world, Greg explains the difference between a “typical” youth group and a gospel-advancing one. What would it look like to see leaders model faith-sharing, students trained to share the gospel relationally, and new believers intentionally discipled? The results are striking, but the heart of the conversation goes deeper: when students put their faith into action, discipleship accelerates. This episode will challenge and encourage you to see evangelism not as an extra program, but as a key leverage point for spiritual growth and long-term impact Check out the research at dontmissit.report and watch Greg talk more about it on YouTube.Learn more about Dare 2 Share at dare2share.orgAnd if you are looking for a clear way to start a faith conversation remember: Ask, Admire, Admit…Have a question or want to dig deeper? You can email jeremy@youthworker.community

Get our premium episode archive: https://www.patreon.com/ieltssfs You should say: who he/she is, how you knew him/her, what his/her greatest achievement is, and explain why you think he/she is creative. Tune in and have a great day! - Book a class with Rory here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://successwithielts.com/rory⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Our course on Phrasal Verbs: https://successwithielts.com/podcourses Transcript: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sign up for our archive to get access to the transcript⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Find an IELTS Speaking Partner: https://links.successwithielts.com/ieltspartner Our social media: https://linktr.ee/successwithielts © 2025 Podcourses Learn more about your ad choices. Visit megaphone.fm/adchoices

Angélique Kidjo est une icône : 5 Grammy Awards, une carrière mondiale, des collaborations avec Alicia Keys, Bono, Stromae…Mais au-delà de la musique, c'est une femme qui a fait de sa liberté une arme.Dans cet épisode, on parle de ce que signifie être une femme africaine dans l'industrie musicale, de la place du courage et de la foi, de la colère comme moteur, et de l'art comme outil politique et spirituel. Angélique raconte son enfance au Bénin, l'exil, le doute, la fierté, et la nécessité de ne jamais laisser les autres définir qui l'on est.Un échange qui m'a vraiment impressionnée et fait réfléchirJe vous souhaite une très bonne écoute !______Pour découvrir les coulisses du podcast :https://www.instagram.com/inpowerpodcast/Pour suivre Angelique Kidjo : https://www.instagram.com/angeliquekidjo/Et pour suivre mes aventures au quotidien :https://www.instagram.com/louiseaubery/Si tu as aimé cet épisode tu aimeras sûrement celui-là : https://shows.acast.com/inpower/episodes/de-serveuse-a-lolympia-suzane-ou-lhistoire-dune-artiste-qui-______Chapitrage :00:00 – Introduction 01:45 – Grandir au bénin dans une famille de femmes fortes05:30 – Quand la musique devient un acte de liberté08:10 – Décision de fuir le bénin11:40 – Quitter son pays à 23 ans pour rester libre18:40 – Transformer la peur en moteur22:00 – “Être libre, ce n'est pas ne pas avoir peur”28:30 – Faire de la musique une arme et une mémoire36:00 – Le rôle et la puissance des femmes africaines39:20 – La fondation Batonga et le rôle fondamental de l'éducation46:40 – Foi, spiritualité et transmission50:00 – Collaborer avec les plus grands sans se perdre54:00 – Refuser les cases : femme, africaine, artiste1:02:00 – Rester soi dans une industrie qui veut te formater1:06:00 – Ce que l'occident ne comprend pas de l'afrique1:10:00 – La liberté de désobéir1:14:00 – Ne jamais s'excuser d'exister1:18:00 – Message aux jeunes générations1:22:00 – Conclusion Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

In a time where the world is as "noisy" as ever, here is how you cut through the noise and stand out according to research from the book You're Invited by Jon Levy! I was fascinated with a chapter where I learned about 4 qualities that make you interesting to industry leaders, and realized these are the same things you can do to stand out to anyone you want to impress, like: - interviewing for a new job- meeting in laws/friends- buying a house - selling your own items- networking at a conference the list goes on and on - here is how you stand out in a noisy world IG: @drconniewang, @justaquickpinch

As the capital of the world's largest democracy, Delhi embodies the essence of modern India – a vivid paradox of old and new, rich and poor, foreign and familiar. It's been fourteen years since my last visit and the economic transformation is ever-present. High rises, swanky malls and residential colonies housing the booming middle-class are mushrooming everywhere. As my engaging Wendy Wu Tours guide Girish remarked, as we were whisked into the city from the airport, “Delhi is more than a mere city, it has morphed into the national capital region.” With the metropolitan population now nudging 30 million, Delhi is a megalopolis and on-track to becoming the world's most populous city in three years' time. Our hotel was in New Delhi, the more modern, planned city within a city, that was built by the British in 1911 and replaced Kolkata as the national capital, twenty years later. In a city notorious for its air pollution, which is supposedly steadily improving, one of the great paradoxes of New Delhi is that it's also swathed in a sprawling green canopy. It's arguably the greatest legacy from British rule, because the new city was deliberately, meticulously planned to be nestled within a vast green cover, fanning out from Connaught Place on those broad long avenues. Large-canopy trees like banyans, mango, and pilkhans were selected by the British, while indigenous trees ideally suited to the climate have added to the canopy in recent decades. That sprawling tree cover is certainly a godsend from the fierce Delhi heat. Delhi's contradictions abound. You'll still see working elephants trudging along traffic-clogged roads, as fire-engine red Ferraris zip by. Handwritten posters singing out, “Customs confiscated goods sold here,” still compete next to glossy fashion billboards for Gucci and Prada. It's all part of Delhi's curious fabric. The city is littered with so many crumbling tombs and ruins, most of them are not even on the tourist map. But if you are a first-timer to the city, signature sights include marvelling at the sheer grace of the soaring Qutb Minar Tower. It was built 800 years ago by the Turkish Slave King Qutb-ud-din Aibak to celebrate his victory over the Hindu Rajputs. Wander through the sculptural Jantar Mantar, a huge, open-air astronomy observatory built in 1725 by Jai Singh, creator and ruler of Jaipur. Admire the 16th-century garden tomb of Mughal Emperor Humayun, precursor to the Taj Mahal, which was built by Humayun's great-grandson. Over in Old Delhi, two Mughal-era masterpieces, the imposing Red Fort (which was the Mughal seat of power for 200 years) and Jama Masjid, India's largest mosque. Both sandstone show-stoppers are definitely worth exploring. The mosque was commissioned by Shah Jahan in 1656 and it took 5000 labourers 6 years to complete. Within its hallowed walls lie sacred relics like Prophet Muhammad's hair. Beyond ticking-off the capital's great monuments, heading to Old Delhi is like a journey back in time. The beating, chaotic, carnival-like heart of Old Delhi is Chandni Chowk, Delhi's 400 year old marketplace that was built by the Mughal Emperor Shah Jahan. The market has been redeveloped to tame some of the chaos, including some fully pedestrianised streets and non-motorised transport lanes. But as I gazed at the spaghetti-like tangle of street wiring that garlands the crowded market lanes, there's no denying the ramshackle, faded glory feels amid this pulsating hot-spot of old-school commerce. Be sure to get your fill of jalebis from a street food vendor. Made from a deep-fried spiral-shaped wheat flour batter, which is then soaked in a sugar syrup, a plate of piping hot, crispy, sticky jalebis is a very satisfying sugar hit. We enjoyed a classic rickshaw ride through the throng of traders, shoppers and wandering cows, all heaving in those pencil-thin lanes. Girish also led us through the Khari Baoli Spice Market in Chandni Chowk, positively bulging with so many spices, nuts, herbs, pickles, preserves, rice and teas. Renowned as Asia's largest wholesale spice market, it's an aromatic head-blast. Shops and stalls bulge with heaping mounds and baskets of over a hundred different spices, headlined by turmeric, cardamom, coriander, star anise, ginger and cumin. Just as they have for hundreds of years, shoppers, dealers and chefs converge here every day to haggle and hustle. Many vendors have been peddling their wares for generations. Dawdle too long in front of a stall, and traders with huge sacks of chilis or cardamom pods will soon bump you out of their way. One of the oldest and tidiest shops is Mehar Chand and Sons. They've been in business since Queen Victoria ruled over them. And it's a great place to stock up on packaged spices, tea and saffron. Anshu Kumar, who is part of the family that has owned the shop since its inception, tells me that one of their biggest sellers with international visitors is turmeric, powered by the world's booming love-affair with this powerful superfood and supplement. (Their packaged products are allowed in New Zealand – just be sure to declare them.) Heading back to the hotel, we also stopped by the Indian parliament and sized up the monolithic might of India Gate. Designed by Sir Edwin Lutyens, this monstrous landmark is more than just a stunning feat of architecture—it's a poignant memorial to the 70,000 Indian soldiers who laid down their lives during World War I and the Third Anglo-Afghan War. Beautifully illuminated after sunset, street food vendors and ice cream carts line the area, swathed in sprawling lush gardens. The great thing about a Wendy Wu Tours private holiday is that you have complete flexibility over how much temple-touring and sightseeing you want to do. Equipped with your own driver and guide, it's a stress-free way to tackle Delhi. The itinerary can be as active or as laid back as you are, with full flexibility over included meals and excursions. You'll be in the best of hands with Wendy Wu Tours. www.wendywutours.co.nz/india Nothing beats retreating to a leafy oasis of eminent comfort and style after a hot, sticky day intrepidly gorging on the city sights. Nestled along the tree-lined boulevards of Connaught Place, Shangri-La Eros New Delhi, is a five-star hotel with serious wow-factor. From the moment you step inside the grand art-filled lobby, you know you are somewhere special. Service is swift, sparkling, flawless and convivial. It's the epitome of affordable luxury, with sharply-priced room rates that won't blow your budget. Push the boat out and lock in a Horizon Club room or suite. That will give you access to the hotel's cherry on top, the 19th floor Horizon Club lounge, allowing you a quick check-in, breakfast, evening cocktails and light bites. Plus panoramic views of the city's skyline. The hotel's arsenal of dining venues is very impressive. Head to Mister Chai for some authentic Indian street food coupled with flavoured tea and coffee. There is Tamra serving European, Japanese, Indian, Thai and Southeast Asian fare from live kitchens. “Lavish” doesn't do justice to the expansive array of buffet options at Tamra for breakfast. Sorrento specialises in Italian food with a contemporary twist and Shang Palace offers flavours of Sichuan, Cantonese and Yunnan cuisines. This is a signature dining venue in Shangri-La hotels and Shang Palace is widely feted as the world's most loved Chinese specialty restaurant. Dining here was divine, noshing on prawn dumplings with caviar; Xinjiang spice twice cooked baby lamb ribs; and the Cantonese BBQ platter. Shang Palace is a must. Celebrating it's 20th birthday this year, Shangri-La Eros is not the sort of hotel to rest on its laurels. And with wellness offerings continuing to be increasingly sought after, the hotel recently unveiled a wealth of enticing new amenities. The Wellness Club boasts offers over 4,000 square feet of world-class fitness space, advanced recovery therapies, a 100-feet outdoor swimming pool, salon, spa, and a calming hydrothermal zone featuring a cold plunge, Himalayan salt sauna, whirlpool, and steam. What more could you want for personal pampering? The Wellness Club seamlessly blends conscious luxury with modern wellness. Designed by Dubai's Stickman Tribe, Dubai, the interior is bathed in natural hues and hand-painted art. Calming music sets the tone for a serene escape with gilded details and reflective surfaces lending a touch of grandeur to the venue. The Spa has become a runaway hit with custom-crafted amenities to indulge the senses. Signature rituals include the Taste of India Retreat, Signature Indulgence, and a Couple's Serenity Bath, crafted to nourish the body and calm the mind. But my favourite hotel feature is the enormous new pool. Tranquil corridors lead you outdoors to the gloriously leafy green space, crowned with that magnificent pool and elegant sun loungers. As black kites circled high above in the sky, and mischievous rhesus macaques swung between the trees – much to the annoyance of nesting rose-ringed parakeets, marinating myself in the hotel's glorious pool became a rinse-and-repeat prize draw. www.shangri-la.com From New Zealand, it's just a one-stop connection to a multitude of destinations in India, including New Delhi, with Singapore Airlines, on their various daily services from Auckland and Christchurch to Singapore. Enjoy well-timed connections for an easy transit in Singapore. Across all classes of travel, the award-winning carrier has not only fostered a world-beating reputation for its exceptional customer service and in-flight product, but also its innovation. Become a KrisFlyer member and enjoy complimentary in-flight WiFi. For best fares and seats to suit head to https://www.singaporeair.com Mike Yardley is our resident traveller on Jack Tame Saturday Mornings. See omnystudio.com/listener for privacy information.

Igor reflects on resilience and adaptability as the most underrated traits in business and life. He shares stories of friends who've built entirely new careers after setbacks and why detaching your identity from your results allows unlimited growth.

Igor reflects on resilience and adaptability as the most underrated traits in business and life. He shares stories of friends who've built entirely new careers after setbacks and why detaching your identity from your results allows unlimited growth.

Join me on a 9-day "Hearing God in Greece" Getaway retreat with 12 of my listeners to Athens, Corinth & Santorini to walk where Paul walked. Bring your best gal or your other half & tell me why you should come on this trip! Submit the application for consideration and details. ------------------------------------------If evangelism & sharing the gospel makes your palms sweat, this conversation will set you free. Jenilee sits down with Greg Stier, founder of Dare to Share, who's trained millions of teens and leaders around the world to share their faith with confidence. Greg breaks down his simple 3-word framework — Ask, Admire, Admit — and reveals new research proving youth ministries that mobilize students see 3× growth and 10× more Gospel conversations than traditional models.From growing up in a violent family to leading a global youth movement, Greg's story will stir your faith and show you that God can use anyone — yes, even you — to change lives. (And hear Jenilee share a story of her own recent AWKWARD sharing-Jesus moment).What You'll Learn:The 3-word framework that makes faith conversations natural and Spirit-ledHow empowering teens to lead produces 3× growth and 10× more Gospel impactWhy awkward moments are actually awesome in evangelismHow one mom's prayers transformed an entire crime-filled familyThe truth behind why 80% of believers come to Christ before 18 — and how that shapes our missionMentioned in This Episode:Dare2Share.orgUnlikely Fighter by Greg StierDay of Global Youth Evangelism – November 8Follow Greg on Instagram @GregStierScripture Highlight:“But in your hearts revere Christ as Lord. Always be prepared to give an answer…” — 1 Peter 3:15-------------------------------------------

Artist and bandleader Forrest Day talks about re-making an album three times and why he scrapped a five-figure mix to release something that felt true to him right now. We also get into managing family life with touring and how a single Facebook post turned fans into investors.Follow Forrest Day:Instagram SpotifyYouTubeFollow Creatives Prevail:InstagramTikTokWebsiteWe would love to hear from you! Please give us a review, this really helps get others to listen in. Any suggestions on how we can improve? DM us on Instagram or TikTok.Intro music: ‘Somebody' (Instrumental) by The Runner UpOutro music: ‘Let's Ride' (Instrumental) by Gabe KubandaHost: Mike ZimmerlichProduced by: Omelette PrevailPost-Production: EarthtoMoiraTech Specs:Mic and Headphone Setup:Limelight Dynamic Mic (512 Audio / Warm Audio)Vocaster One (Focusrite)MBS9500 Microphone Boom Arm (On-Stage)Pro X2 Headphones (Logitech)Light Setup:Litra Beam (Logitech)Glide Lively Wall Lights (Govee)Squares (Twinkly)Key Light (Elgato)

1590. Surround Yourself with Those You Admire Carl Gould here with your #70secondCEO. Because it gives you permission to talk about whom? So this is somebody that has the traits that you value, you have an alignment with, however you feel that they play that game at a slightly higher level. Equal or higher, right? But by talking about somebody else, you get a chance to talk about yourself. Imagine we came down here and I said, yeah, no, no, no, enough about you, let's talk more about me, right? That would run out really quickly, right? And on the hate side, these are people that have traits that you see in yourself that, you know what, maybe it's not you today, but maybe there was a time, right? Maybe there was a day, right? Like and follow this podcast so you can learn more. My name is Carl Gould and this has been your #70secondCEO.  

Fr. Ryan speaks to Men's Ministry sharing what he admires in the good and holy men in his life.  Come, follow us: Parish Website  |  Facebook  |  Instagram  |  YouTube  |  Spotify Music

1588. The People You Admire… and the Ones You Don't Pt 1 Hi everyone, Carl Gould here with your #70secondCEO. Just a little over a one minute investment every day for a lifetime of results. So let me introduce you to two people you may know very intimately well, okay? The list on the left, the admiration list, that is what you believe is the best part of you. The list to the right is what you believe is the least favorite part of you, okay? You cannot recognize a trait in another person unless you possess it yourself. If you have the reference for it, you can relate to it, okay, you can recognize it, right? And you can then judge it or rate it however. But if you can't recognize it, you can't see it in another person. So if you got this laundry list, like and follow this podcast so you can learn more. My name is Carl Gould and this has been your #70secondCEO.  

Remember those school essay prompts: “Who do you admire?” If I were writing it today, my answer would be simple... Jesus.

Daily Dad Jokes (15 Sept 2025) The official Daily Dad Jokes Podcast electronic button now available on Amazon. The perfect gift for dad! Click here here to view! Email Newsletter: Looking for more dad joke humor to share? Then subscribe to our new weekly email newsletter. It's our weekly round-up of the best dad jokes, memes, and humor for you to enjoy. Spread the laughs, and groans, and sign up today! Click here to subscribe! Listen to the Daily Dad Jokes podcast here: https://dailydadjokespodcast.com/ or search "Daily Dad Jokes" in your podcast app. Interested in Business and Finance news? Then listen to our sister show: The Daily Business and Finance Show. Check out the website here or search "Daily Business and Finance Show" in your podcast app. Jokes sourced and curated from reddit.com/r/dadjokes. Joke credits: SimplisticAnswer, Loose_Fajita, nemo_sum, Adventurous_Judge493, Physical_Sell_3690, reddirich, cockneybastard, JustinJPM, Healthy_Ladder_6198, jlaik, EndersGame_Reviewer, greatreference, robcraftdotca, MaCk_Pinto, TheWholeDamnInternet, scarf_spheal, Status-Victory, CognitiveNerd1701, SuperMcG, GotMyOrangeCrush, Foamy07, linguist96, SadderHoshi, Chatters01, TbhJustAnotherGuy, Bill-Ding2112, kevindavis338, Super_Wario_128, rac_atx, Shuihoppy, -Masderus-, dustaknuckz, TheLetterOfR, gtMANGAMER2, Audioman_Official, MostExpensiveThing, 007King_Kong, xAsilos, IntestineYarnball, TheSwedishNarwhal, AndreT_NY, AlexJamesCook, dick_schidt, scottmc94, LazyGuyE, Goblindeez_, God-2008, GNewsBacklinks, Particular_Edge2308, Patriotfan17, Will7838, thomasbrakeline, sphubbard, sulldanivan, porichoygupto, wimple007, zero_ben, kilokiilo, fine-rusty-knife, porichoygupto, aaronr93, Rav4xle, , GiGGLED420, greedydita, AltruisticDisplay813, sully1227, Livewire____, OldNorseBoy, rainblade1980, ASK_ABT_MY_USERNAME, porichoygupto, Dust-by-Monday, Stephenf1234, Opportunist_Ad3972, DoomRulz, bettercallbert, Jester57, IroncIad, SSV_Kearsarge, Ivegot_back, HolidayWarm5971, CKO1967, jlionbad, careater, caverypca, unimatrix13, malmquistcarl, Liquid_disc_of_shit, Josentangles, CoolEqual, DandyBeyond, Hero_of_Thyme81, siphodeus, _Benny_Lava, SuperGamerSun360, madazzahatter, TheRealAuthorSarge, BlueMageTheWizard, Man-e-questions, greedydita, SirFister13F, Riverrat423, liamo000, billbixbyakahulk, Agent256, willcommentyourmom, ilicstefanv, Alleskeins, AnnexFromCanada, Tell1tToMy9mm, ilikesidehugs, porichoygupto, jmoney6, BunzarTheFuzzy, IAmXChris, thatdude101010, Fritzdkat, KaveeC, Far-Hovercraft-6514, clifwith1f, peasantchoker, TheAzrael2013, mrthatsthat, IvanCamejo, Budget-Pay3743 Subscribe to this podcast via: iHeartMedia Spotify iTunes Google Podcasts YouTube Channel Social media: Instagram Facebook Twitter TikTok Discord Interested in advertising or sponsoring our show? Contact us at mediasales@klassicstudios.com Produced by Klassic Studios using AutoGen Podcast technology (http://klassicstudios.com/autogen-podcasts/) Learn more about your ad choices. Visit megaphone.fm/adchoices

Daily Dad Jokes (15 Sept 2025) The official Daily Dad Jokes Podcast electronic button now available on Amazon. The perfect gift for dad! Click here here to view! Email Newsletter: Looking for more dad joke humor to share? Then subscribe to our new weekly email newsletter. It's our weekly round-up of the best dad jokes, memes, and humor for you to enjoy. Spread the laughs, and groans, and sign up today! Click here to subscribe! Listen to the Daily Dad Jokes podcast here: https://dailydadjokespodcast.com/ or search "Daily Dad Jokes" in your podcast app. Interested in Business and Finance news? Then listen to our sister show: The Daily Business and Finance Show. Check out the website here or search "Daily Business and Finance Show" in your podcast app. Jokes sourced and curated from reddit.com/r/dadjokes. Joke credits: SimplisticAnswer, Loose_Fajita, nemo_sum, Adventurous_Judge493, Physical_Sell_3690, reddirich, cockneybastard, JustinJPM, Healthy_Ladder_6198, jlaik, EndersGame_Reviewer, greatreference, robcraftdotca, MaCk_Pinto, TheWholeDamnInternet, scarf_spheal, Status-Victory, CognitiveNerd1701, SuperMcG, GotMyOrangeCrush, Foamy07, linguist96, SadderHoshi, Chatters01, TbhJustAnotherGuy, Bill-Ding2112, kevindavis338, Super_Wario_128, rac_atx, Shuihoppy, -Masderus-, dustaknuckz, TheLetterOfR, gtMANGAMER2, Audioman_Official, MostExpensiveThing, 007King_Kong, xAsilos, IntestineYarnball, TheSwedishNarwhal, AndreT_NY, AlexJamesCook, dick_schidt, scottmc94, LazyGuyE, Goblindeez_, God-2008, GNewsBacklinks, Particular_Edge2308, Patriotfan17, Will7838, thomasbrakeline, sphubbard, sulldanivan, porichoygupto, wimple007, zero_ben, kilokiilo, fine-rusty-knife, porichoygupto, aaronr93, Rav4xle, , GiGGLED420, greedydita, AltruisticDisplay813, sully1227, Livewire____, OldNorseBoy, rainblade1980, ASK_ABT_MY_USERNAME, porichoygupto, Dust-by-Monday, Stephenf1234, Opportunist_Ad3972, DoomRulz, bettercallbert, Jester57, IroncIad, SSV_Kearsarge, Ivegot_back, HolidayWarm5971, CKO1967, jlionbad, careater, caverypca, unimatrix13, malmquistcarl, Liquid_disc_of_shit, Josentangles, CoolEqual, DandyBeyond, Hero_of_Thyme81, siphodeus, _Benny_Lava, SuperGamerSun360, madazzahatter, TheRealAuthorSarge, BlueMageTheWizard, Man-e-questions, greedydita, SirFister13F, Riverrat423, liamo000, billbixbyakahulk, Agent256, willcommentyourmom, ilicstefanv, Alleskeins, AnnexFromCanada, Tell1tToMy9mm, ilikesidehugs, porichoygupto, jmoney6, BunzarTheFuzzy, IAmXChris, thatdude101010, Fritzdkat, KaveeC, Far-Hovercraft-6514, clifwith1f, peasantchoker, TheAzrael2013, mrthatsthat, IvanCamejo, Budget-Pay3743 Subscribe to this podcast via: iHeartMedia Spotify iTunes Google Podcasts YouTube Channel Social media: Instagram Facebook Twitter TikTok Discord Interested in advertising or sponsoring our show? Contact us at mediasales@klassicstudios.com Produced by Klassic Studios using AutoGen Podcast technology (http://klassicstudios.com/autogen-podcasts/) Learn more about your ad choices. Visit megaphone.fm/adchoices

The Soul Cafe: Admire Catch Chris Clay Mon - Fri 2p-6pm EST On www.soulcaferadio.com  Produced By Heather Whitley and C.Clay Season 9   Hour 1 Merna - Magic Coco Jones - Taste  Tyrese - Rescue Elmiene - Useless (Without You) Shayla Dunn - I'm Different (Platinum 2.0 Mix)   The Double Down: KEM KEM - Rock With Me KEM - Give My Love   Jace Wilder  - Lucky Star Floetry - Say Yes Marsha Ambrosius, Leon Thomas & Muni Long - YES IT IS  Teddy & Sarah - Young Love Daley - Until The Pain Is Gone (feat. Jill Scott) Boyz II Men - Water Runs Dry Maggie Ray - What Have I Done (Too Make You Mad) Luther Vandross & Beyonce  - The Closer I get you Anita Baker - Lately   Hour 2 Frank McComb - Labeled as Love Kenny Latimore -  For You Alex Isley - Thank You For A Lovely Time GIVĒON - I CAN TELL Amaria - Dream of You   Rare Hard To Find Throwback Jam  Teddy Pendergrass - And If I Had-1977   Johnny Gill - One Night Omar Wilson - Baby Im Ready kristina alcordo - ttK   Blue Eyes Soul Corner  Sabrina Cole - Let Me Be Your Angel Remy Shand - Take A Message Michael Franks - Tahitian Moon Susanne Smith - Lonely Girls Maggie Ray - Where Is The Love Smoke Norful - I Need You Now End Of Show   

What if you could become the version of yourself you admire—on purpose? That's exactly what Leah Coss helps people do.Leah helps people shift how they see themselves so they can live, lead, and perform at their full potential.In this episode, we unpack how your identity is formed, how labels like “I'm a perfectionist” or “I'm not athletic” can shape your choices, and how to intentionally rewrite your internal narrative to support the life you want.Leah shares practical ways to break free from outdated stories and create a more empowering self-image—without needing a full identity crisis.Plus, we talk about how parents can help their kids develop strong, healthy identities from a young age. Leah Coss is a keynote speaker, identity strategist, and CEO & Co-Founder of Build a Biz Kids, a national charity helping thousands of youth develop strong self-worth and future-ready skills. Leah helps people become the version of themselves they admire — on purpose — by shifting how they see themselves, so they can live, lead, and perform at their full potential.https://LeahCoss.comhttps://www.linkedin.com/in/leahcoss/Check out Esther's website for more about her speaking, coaching, book, and more: http://estheravant.com/Buy Esther's Book: To Your Health: https://a.co/d/iDG68qUEsther's Instagram: https://www.instagram.com/esther.avantEsther's LinkedIn: https://www.linkedin.com/in/estheravant/Learn more about 1:1 health & weight loss coaching: https://madebymecoaching.com/coaching

Episode 184: Guests: Thomas JeffersonWhat is the Government?A person is not the governmentRoadmap to overthrowing the GovernmentLawlessness and ChaosTerms and conditions (Oath of Office)Agree to the terms; ignore them afterPicking and choosing rules to followIf America kicks God out, it will Kick the Founders outThomas Jefferson was concerned about somethingAlmost 200 Years to solve a problem________________Support the show

Happy Labor Day!Sorry for this bleak reminder but, we're going back to The Office. Turn in your WFH laptops and get a new ID card. This week we're clocking in at Dunder Mifflin for our shift on the sales desk. From the mind of Ricky Gervais and Stephen Merchant comes an American re-imagining by now comedy power writer Greg Daniels. Admire the documentary film style that has its claws so deep in Hollywood studios you can hardly make a comedy show in a different style. We get a nobody-turned-somebody cast led by Steve Carrell and future stars John Krasinski, Jenna Fischer, Rainn Wilson, BJ Novak, Oscar Nunez, Leslie David Baker, Angela Kinsey, Brian Baumgartner, Phyllis Smith, and Melora Hardin.Could this show be made today? Let's find out. HostsGeoff KerbisMax SingerRich Inman

On today's Take 2 with Jerry & Debbie our topic is: Tell Us About a Family Member You Admire.

By Mark Skapura - Are we jealous of what others have or are we thankful for what God has given us? We can learn from the many scriptural examples of this type of ingratitude. Let's glorify God in how we use what He has given us, rejoice with others in their blessings, and ultimately focus on the spiritual gifts from

Send us a textIn this episode of Please Don't Spoil The Movie, we spoil the 1986 classic Pretty In Pink starring Molly Ringwald. Andie comes from a work-class family which has never been a problem, until she falls in love with a richie boy named Blane... and she also wears a pink dress to prom. Tune in to hear us discuss bad wigs, Glee and Jade's reality TV debut!!!

The Best Things to Do in -1. St. Louis, MO:St. Louis is often called the “Gateway to the West,” but it's also a gateway to a memorable, well-rounded travel experience. From world-class art and live entertainment to major league baseball and local culinary treasures, this Midwestern gem packs a punch. Whether you're here for a weekend or a longer stay, here are some of the top things to do in St. Louis that highlight the city's diverse appeal.

Who do you admire—and more importantly, why? In this episode of Remarkable TV, Kevin Eikenberry explores how identifying and reflecting on those we admire can unlock personal growth, fuel inspiration, and guide us toward our own leadership potential.

Today on The Two Matts podcast, Matthew d'Ancona and Matt Kelly again respond to your queries. They discuss whether Keir Starmer's willingness to back down and listen is actually something to be admired, if the Prime Minister should take more care with his speeches and how serious Palestine Action's protests were. Plus they talk about the extent of corruption in UK politics, just how progressive the Labour Party is, and 80-year-old Rod Stewart's Glastonbury set.OFFER: Get The New World for just £1 for the first month. Head to https://www.thenewworld.co.uk/2matts Hosted on Acast. See acast.com/privacy for more information.

It's a new week, and today's revelatory episode explores conflicted feelings towards musicians, school reunions and the mysterious disappearance of Jane's air-con unit... If you want to come and see us at Fringe by the Sea, you can buy tickets here: www.fringebythesea.com/fi-jane-and-judy-murray/ And if you fancy sending us a postcard, the address is: Jane and Fi Times Radio, News UK 1 London Bridge Street London SE1 9GF If you want to contact the show to ask a question and get involved in the conversation then please email us: janeandfi@times.radio The next book club pick has been announced! We'll be reading Leonard and Hungry Paul by Rónán Hession. Follow us on Instagram! @janeandfi Assistant Producer: Hannah Quinn Podcast Producer: Eve Salusbury Executive Producer: Rosie Cutler Hosted on Acast. See acast.com/privacy for more information.

PREVIEW: Author Joseph Torigian, "The Party's Interests Come First: The Life of Xi Zhongxun" comments on how Xi Jinping's father, Xi Zhongxun, came to admire Mao before the catastrophe of the Great Leap Forward mass death by famine. More later and next week. 1949 XI ZHONGXUN

#Londinium90AD: Gaius & Germanicus Admire the Setting of the Vatican and Drama of Crimea's Fate for POTUS in Dialogue with Zelensky. Michael Vlahos. Friends of History Debating Society. @michaelVlahos 1678 

Send us a text“You're only as good as the example you set.” ~ Austin PageIn this episode of the Remarkable People Podcast, we hear the compelling story of Austin Page, who transformed his life from being a blackout drunk to a successful personal trainer. Austin shares his experience of hitting rock bottom after a near-fatal drunk driving accident, his long road to recovery through rehab and intensive physical therapy, and how fitness became the catalyst for positive change in his life. He discusses practical daily habits and the mindset shifts that helped him rebuild his life. Austin also speaks about the challenges and lessons learned as he transitioned from working at Gold's Gym to starting his own successful personal training business. Through his journey, Austin emphasizes the importance of setting a good example, committing to daily improvement, and providing holistic support to clients. The episode concludes with Austin's advice for aspiring personal trainers and his current endeavors, highlighting his passion for helping others achieve their fitness and life goals.SHOW NOTES & LINKS: Website: https://gymflocoaching.com/Facebook: https://www.facebook.com/austin.page.146Instagram: https://www.instagram.com/apagemanYouTube: https://www.youtube.com/@gymflocoachingREMARKABLE LISTENER SPECIAL OFFER(S):REMARKABLE OFFER 1: Save 30% to 80% on EVERYTHING you order at MyPillow.com with Free Promo Code, “REMARKABLE“. Yes, that's right!

#Londinium90AD: Gaius & Germanicus admire how POTUS is handling adversaries and allies by emphasizing tribute. Michael Vlahos. Friends of History Debating Society. @Michalis_Vlahos 1872 EXCAVATION ROMAN FORUM

#LONDINIUM90AD: GAIUS & GERMANICUS ADMIRE THE ADVANCING IMPERIAL PROJECT. MICHAEL VLAHOS. FRIENDS OF HISTORY DEBATING SOCIETY. @MICHALIS_VLAHOS 1700 AGRIPPINA MINOR  

Everyone's favorite brother, Oliver Hudson, is redefining masculinity and talking about why he wears his heart on his sleeve and the importance of having a good cry session now and then. Plus, why he's giving up on New Year's resolutions and how his trailblazing mother, Goldie Hawn, and sister, Kate Hudson, have inspired the man he is today. "I Choose Me" live event - has been rescheduled! Tickets on sale now! Follow the "I Choose Me" Podcast on Instagram and TikTok Follow Jennie on Instagram, TikTok, and FacebookSee omnystudio.com/listener for privacy information.