Podcasts about backgrounds

Not that long ago... in this exact galaxy... Aqua Teen art director Bob Pettitt and I talked about the best Star Wars parody of all time! Bob is back on the show to talk about his kidney-hurting Pawn Shop background, side-stepping legal loopholes with mustaches, and Bob's least favorite thing he had to draw for Aqua Teen Hunger Force.R E F E R E N C E S• Aqua Teen Xmas Fan Animation: https://www.instagram.com/reel/DSrFYdsjKcz/• Rabbot Rebuilt: https://www.youtube.com/watch?v=gx4sQBySJNE• Aqua Teen artist Todd Redner on Meat Kingdom: https://www.youtube.com/watch?v=yplpqIYP_Gc• Black Dahlia Murder drummer Alan Cassidy on Meat Kingdom: https://www.youtube.com/watch?v=wEi-1D5kP8QG U E S T

Christian, Reiss and Lee discuss Nottingham Forest's 2-1 defeat away at Brighton, with the Reds missing a big opportunity to move themselves five points clear of the dropzone.Thanks to our sponsorswww.trentsidethreads.co.ukUse code RSOTT for 10% offVesta Blindswww.vestablinds.comBUY JUST CAN'T GET ENOUGH NOW!!https://tinyurl.com/bdz39zrnFollow us:Twitter/X: @redsidetrentFacebook: RedsideoftheTrentInstagram: @redsideofthetrentTikTok: @redsidetrentIntro animation@Jimmynffc 'Slept on it thoughts'Animation: @Jimmynffc Audio: @ianfinchtv Backgrounds: instagram: @jscomicsGraphics: @Ellismo17This Podcast has been created and uploaded by Red Side of the Trent. The views in this Podcast are not necessarily the views of talkSPORT. Hosted on Acast. See acast.com/privacy for more information.

Christian, Reiss and Danny discuss Nottingham Forest's 2-1 defeat at home to Fenerbahce in the Europa League R32 second leg and the prospect of facing FC Midtjylland in the L16. Thanks to our sponsorswww.trentsidethreads.co.ukUse code RSOTT for 10% offVesta Blindswww.vestablinds.comBUY JUST CAN'T GET ENOUGH NOW!!https://tinyurl.com/bdz39zrnFollow us:Twitter/X: @redsidetrentFacebook: RedsideoftheTrentInstagram: @redsideofthetrentTikTok: @redsidetrentIntro animation@Jimmynffc 'Slept on it thoughts'Animation: @Jimmynffc Audio: @ianfinchtv Backgrounds: instagram: @jscomicsGraphics: @Ellismo17This Podcast has been created and uploaded by Red Side of the Trent. The views in this Podcast are not necessarily the views of talkSPORT. Hosted on Acast. See acast.com/privacy for more information.

Neil Lanctot introduces Jane Addams, Theodore Roosevelt, and Woodrow Wilson in 1912, examining their distinct intellectual backgrounds and competing visions for America's reformist future during the Progressive era. 1

Christian, Danny and others review Nottingham Forest's emphatic 3-0 victory away at Fenerbahce in the Europa League R32 knockout round first leg. Thanks to our sponsorswww.trentsidethreads.co.ukUse code RSOTT for 10% offVesta Blindswww.vestablinds.comBUY JUST CAN'T GET ENOUGH NOW!!https://tinyurl.com/bdz39zrnFollow us:Twitter/X: @redsidetrentFacebook: RedsideoftheTrentInstagram: @redsideofthetrentTikTok: @redsidetrentIntro animation@Jimmynffc 'Slept on it thoughts'Animation: @Jimmynffc Audio: @ianfinchtv Backgrounds: instagram: @jscomicsGraphics: @Ellismo17This Podcast has been created and uploaded by Red Side of the Trent. The views in this Podcast are not necessarily the views of talkSPORT. Hosted on Acast. See acast.com/privacy for more information.

In this episode of The Gate 15 Interview, Andy Jabbour speaks with four Gate 15 analysts as Sadie-Anne Jones, Chase Snow, Mackenzie Gryder and Preston Wright share about their experiences, their work at Gate 15 and across critical infrastructure and faith-based organizations and more, including a rapid-fire round of Three Questions!Sadie-Anne on LinkedIn.Chase on LinkedIn.Mackenzie on LinkedIn.Preston on LinkedIn.In the podcast the team and Andy discuss:Backgrounds and paths to Gate 15.Surprising things the team has learned so far, and their ideas on threats, resilience, and what leaders may want to be thinking about today.The next hurdle they want to jump.We play 3 Questions! and talk late night snacks, secret skills, and where we love to chill and play.And more!

Christian, Reiss and pod regular Alex Oakes discuss the news of Vitor Pereira being made Nottingham Forest's new manager. Thanks to our sponsorswww.trentsidethreads.co.ukUse code RSOTT for 10% offVesta Blindswww.vestablinds.comBUY JUST CAN'T GET ENOUGH NOW!!https://tinyurl.com/bdz39zrnFollow us:Twitter/X: @redsidetrentFacebook: RedsideoftheTrentInstagram: @redsideofthetrentTikTok: @redsidetrentIntro animation@Jimmynffc 'Slept on it thoughts'Animation: @Jimmynffc Audio: @ianfinchtv Backgrounds: instagram: @jscomicsGraphics: @Ellismo17This Podcast has been created and uploaded by Red Side of the Trent. The views in this Podcast are not necessarily the views of talkSPORT. Hosted on Acast. See acast.com/privacy for more information.

Go behind the scenes with the team building Atlas, a game-changing DeFi protocol on Cardano and Midnight that unlocks "trapped" staking rewards through yield tokenization. Learn how they are partnering with Midnight and Iagon to bring institutional-grade privacy and liquid yield strategies to everyone in 2026.Chapters00:00 Introduction to Atlas and the Guests03:04 Backgrounds in Crypto and Cardano06:06 Understanding Atlas and Its Purpose09:10 Yield Tokenization Explained11:45 Future of Atlas and Real World Assets15:02 Partnerships and Collaborations18:06 Development Timeline and Community EngagementDISCLAIMER: This content is for informational and educational purposes only and is not financial, investment, or legal advice. I am not affiliated with, nor compensated by, the project discussed—no tokens, payments, or incentives received. I do not hold a stake in the project, including private or future allocations. All views are my own, based on public information. Always do your own research and consult a licensed advisor before investing. Crypto investments carry high risk, and past performance is no guarantee of future results. I am not responsible for any decisions you make based on this content.

Ben and Carlos answer listener questions about how bat tracking technology and athletic testing metrics have changed high school scouting, how multi-sport athletes are valued and where some of the top 2027 draft prospects would slot into the 2026 class. —Time Stamps:(0:30) How has technology like bat tracking and force plates changed high school scouting?(14:20) Do multi-sport athletes project better than baseball-only players?(23:05) Where would high-end 2027 draft prospects fit in the 2026 draft class?Do you have feedback for the show or want to ask us a  question? Email us: futureprojection@baseballamerica.com.Future Projection Twitter: @FutureProPodBen's Twitter: @BenBadlerCarlos's Newsletter: Fringe AverageBaseball America WebsiteSupport this podcast at — https://redcircle.com/future-projection-a-baseball-america-podcast/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

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

Join Dr. Arun Seraphin and Dr. Jae Yu for a conversation that explores new data on Pentagon senior civilian leadership, illuminating the backgrounds of individuals serving in STEM leadership roles focused on Emerging Technologies. This discussion draws on the NDIA ETI report published by Dr. Yu, “Mapping Government Officials in Emerging Technologies Roles,” which examines how STEM education and prior STEM experience shape career pathways within the Pentagon.The report and conversation analyze leadership backgrounds across the 14 critical technology areas identified by the Under Secretary of Defense for Research and Engineering (USD(R&E)), highlighting where STEM expertise is concentrated and where gaps remain in the Pentagon's Emerging Technologies workforce. The discussion concludes with data-driven recommendations to strengthen the Pentagon's senior civilian STEM workforce.Be sure to follow us on social media for updates, inside scoops, & more:LinkedIn: https://bit.ly/4htROo0Twitter: https://bit.ly/48LHAx3Facebook: https://bit.ly/47vlht8 And for more podcasts, articles, & publications all things emerging tech, check out our website at: https://bit.ly/47oA5K1#EmergingTech #EmergingTechETI #USDR&E #Pentagon #STEM

Karl and Erum sit down with Amy Trejo and Jose Carlos Garcia Garcia from Procter & Gamble to uncover how one of the world's largest consumer goods companies is leveraging biotechnology to innovate at unprecedented scale. Founded 189 years ago as a bio-waste upcycling partnership between a candle maker and a soap maker, P&G has always been rooted in biomaterials innovation—from pioneering laundry enzymes in the 1960s to developing cold water enzyme technologies that have saved billions in energy costs. Amy and JC reveal what makes biotech innovations stick in the marketplace (hint: it's all about performance), share candid advice for startups hoping to partner with P&G, and explain why the company views biotech as a critical enabler of both sustainability and superior consumer experiences. They discuss common misconceptions about working with large CPG companies, the importance of reducing ideas to practice, and how P&G's connect-and-develop model creates win-win partnerships that can impact billions of consumers worldwide. Whether you're a biotech founder, investor, or enthusiast curious about how innovative materials make it from lab to everyday products, this conversation offers rare insights into the intersection of consumer goods, biotechnology, and global scale manufacturing.Grow Everything brings the bioeconomy to life. Hosts Karl Schmieder and Erum Azeez Khan share stories and interview the leaders and influencers changing the world by growing everything. Biology is the oldest technology. And it can be engineered. What are we growing?Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.messaginglab.com/groweverything⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Chapters:(00:00:00) - Introduction and Opening Remarks(00:01:00) - Erum's Article on Industrial Biomanufacturing for Lichen Ventures(00:04:00) - The Vision of Boom Towns and Interplanetary Innovation(00:07:00) - Introduction to Amy Trejo and JC Garcia Garcia from P&G(00:11:00) - Amy and JC's Backgrounds and Roles at P&G(00:13:00) - Biotech Innovations Throughout P&G's 189-Year History(00:19:00) - What Makes Biotech Innovations Stick: Performance Over Everything(00:22:00) - Biggest Misconceptions About Partnering with Large CPG Companies(00:29:00) - How to Approach P&G: Show Product, Generate Data, Demonstrate Performance(00:31:00) - The Power of Reapplication Across Product Categories(00:35:00) - Successful Biotech Partnerships: SK-II, Align, New Chapter, Base Camp Research(00:39:00) - What Catches P&G's Attention at Conferences and Trade Shows(00:42:00) - The Role of Storytelling in Biotech Innovation and Consumer Engagement(00:47:00) - Five-Year Vision: The Future of CPG and Biotech Partnerships(00:49:00) - One Piece of Advice for Biotech Innovators: Reduce Ideas to Practice(00:52:00) - Quickfire Questions with Amy and JC(00:53:00) - Closing Thoughts: Impacting Billions of Lives Through Partnership(00:54:00) - Karl and Erum's Recap and Key TakeawaysLinks and Resources:Procter & Gamble (P&G)P&G Connect + DevelopP&G PartnershipsStellar: A World Beyond Limits and How To Get ThereIndustrial Biomanufacturing Needs Its Manhattan Project Moment by Erum Azeez Khan107. Glow Big or Go Home: Andy Bass's Journey with Glowing Oceans17. Beauty and the Biome with Jasmina Aganovic of ArcaeaTopics Covered: biotech, industry, biomanufacturing, bioprocessing, agriculture, agritech, strain engineering, biotech R&D, feedstocks, chemical engineering, bioengineeringHave a question or comment? Message us here:Text or Call (804) 505-5553 Music by: Nihilore Production by:  Amplafy Media

In this episode of the ATN Podcast, Oliver Thomas is joined by Ian Keough, founder of Hypar, alongside Andrew Heumann, software engineer and creator of the Human plugin, to unpack what BIM 2.0 could look like — and how automation and AI are changing architectural design.Ian brings a rare dual background in architecture and software development, while Andrew's career spans design, computation, and engineering. Together, they explore how Hypar was born out of real-world frustration with traditional tools, particularly during Ian's time at WeWork, where scale, speed, and repetition exposed the limits of conventional BIM workflows.The conversation traces Hypar's evolution from early concept to Hypar 2.0, focusing on a core philosophy: automate the boring and repetitive parts of design while keeping architects firmly in control. Rather than positioning AI as a replacement for designers, Ian and Andrew discuss how it can act as supportive infrastructure — enhancing flexibility, speed, and creative decision-making.We also dive into the realities of building architectural software:Why user experience is critical (and often overlooked)How feedback from real projects shapes the platformThe shift from consulting to product-led developmentAnd whether architecture is approaching its own “Canva moment”This episode is a must-watch for architects, computational designers, and anyone interested in the future of architectural technology, design automation, and AI-powered workflows.Andrew Heumann |   / andrew-heumann-13751414  Ian Keough |   / ian-keough-4b1ba91  Try Hypar | https://hypar.io/ATN HOST | Oliver Thomas |   / olly____t  ATN MASTERCLASS | https://archi-tech.network00:00 Introduction and Personal Updates01:30 Introduction to the ATN Podcast and Guests04:36 Backgrounds of Ian Keough & Andrew Heumann07:31 The WeWork Experience10:25 Founding Hypar13:36 The Evolution of Hypar16:31 Hypar 2.0 & Designer Control19:28 Technical & Business Evolution22:27 UX and Software Insights25:30 Automation vs Creativity28:32 Hypar Today: The Elevator Pitch37:09 Long-Term Vision40:01 The “Canva Moment” for Architecture43:41 Integrating AI into Design56:34 AI & Building Design Challenges01:00:17 From Consulting to Product01:07:20 What's Next for HyparEnjoyed the video? Be sure to like, subscribe, and share for more insights into the intersection of the Metaverse and Architecture. Don't forget to hit the bell icon to stay updated on our latest content.Like our new wall art? check out our collaborator Zaglono here:Instagram:   / zaglono  Website: https://www.pared.art/artistas/zaglono/Join the conversation on our social media platforms:INSTAGRAM |   / architech.network  LINKEDIN |   / archi-tech-network  TIKTOK |   / architech.network  EVENTS | https://linktr.ee/architech_network#hypar #BIM2.0 #Architecture #AEC #ATN #AIinDesign #BIM #architechnetwork #architecture #podcast

Christopher & Jobst im Gespräch mit Richie. Wir reden über erschreckend feststellen keine aktuellen Bands zu kennen, Musik als Dienstleister, Roland Kaiser, Background-Sänger bei Santiano, die englische Version von "Tränen lügen nicht", gute Witze, das Rote Rosen-Album, freier Mitarbeiter der Münsterschen Zeitung, die New Bomb Turks, Desmond Dekker 1987 im Odeon, Co-DJ im Jovel, das Pascal-Festival, hinterm Autoscooter die Unschuld verlieren, humanistische Werte, der Unterschied zwischen Streit & Kampf, Pferdesteaks, französische Musikquote, Gilbert Becaud bei Der Große Preis, Ted Herolds "Ich bin ein Mann", Live-Musik im Leetze am Jovel, "Tanz den Ska", Postkarten von Band zu Band, Bücher übers jamaikanische Patois, Posaune bei Skatalites spielen, der deutsche Reggae & Dancehall Boom der frühen 2000er, der passende Name Dr. Ring Ding, warum so viele Leute Ska hassen, das irre Intro von Santiano, Gedanken über die Zukunft, aufs Meer gucken, sich zur "Weinprobe" treffen, Oma-Rezepte bei TikTok, "The Beast in Me" & das neue Buch von Stefan Sandrock, "Die Nachtigall" von Kristin Hannah, "La Bonne Étoile", "Zum Paradies" von Hanya Yanagihara, die Band Jersey Moon, die HiHat von "Racist Friend", uvm.Drei Songs für die Playlist:1) Ein Lieblingslied vom 14-jährigen Richie: GLENN MILLER - Pennsylvania 6-5000 2) Der ultimative Ska-Song für Ska-Skeptiker: MADNESS - Night Boat to Cairo3) Der liebste Song an dem Richie beteiligt war: THE BUSTERS - Fall

Andrey Sabelnikov and Valeriy Pisarkov are the core devs of Zano: a privacy network which enables users to create tokens with privacy. The project's mix of Cryptonote and Zarcanum protocols can also enable private transfers of bridged BTC. In this episode, we talk about how this system works and we inquire about the tradeoffs involved. Time stamps: 00:01:51 – Introduction & Setting the Record Straight 00:02:51 – Val & Andre's Backgrounds, CryptoNote Origins 00:03:53 – Problems with Bytecoin & Monero's Launch 00:05:28 – Zano's Strategic Directions & AI Security Challenges 00:08:51 – Val's Role in Zano & Technical Evolution 00:13:45 – Network-Level Privacy Incident 00:19:15 – Proof-of-Work vs. Proof-of-Stake Privacy 00:24:11 – Zano vs. Monero: Not a Fork 00:29:22 – Monero Community Criticism & Scam Allegations 00:40:44 – Boolberry Project & Coin Swap 00:43:36 – Premine & Staking Controversy 00:48:29 – Tribalism & Ideology in Privacy Coins 00:53:21 – Differences Between Boolberry and Zano 00:57:32 – Wallet Support vs. Exchange Listings 01:01:03 – Exchange Listing Challenges & Gateway Addresses 01:04:20 – Gateway Addresses & Hard Fork 6 01:11:52 – Upcoming Roadmap: Proof-of-Stake & Full Chain Membership Proofs 01:17:40 – Bridging Bitcoin & Confidential Assets 01:27:52 – Asset Whitelisting & Stablecoin Risks 01:41:01 – Confidential Layer Bridge & Multi-Party Computation 01:51:21 – Zano's Scalability, Throughput, and Future Vision 01:59:08 – Full Chain Membership Proofs & Quantum Resistance 02:09:17 – Tech Stack Choices & Adaptability 02:27:07 – Why Be Bullish on Zano? 02:54:48 – DeFi Listings, Gateway Addresses, and Privacy Trade-offs 03:19:59 – Network-Level Privacy, Dandelion, and Mixnets 03:26:46 – VPNs, Network Privacy Tools, and Community Integration 03:44:03 – Personal Stories, Early Computing, and Closing Remarks

"Every single workshop I give is all about excellence, singing well, singing properly, and achieving the very best we can achieve. Why do we it? We don't do it because we'll be paid millions and millions of pounds. It's not cash-motivated. We do it because there is a higher purpose, a higher calling that makes us do it. I got offered a graduate scheme at university, so I could have been a corporate sellout. I'd probably arguably work less hours than I do in music, but I didn't do that. I don't have any regrets, because on an evening, I go home, I look myself in the mirror, and I can say to myself, 'we did some good stuff today.'"Caius Lee began as a chorister at Bradford Cathedral at 11 and became the Cathedral Organ Scholar at 15. At age 17, he joined Leeds Cathedral, concurrently holding the Idlewild Conducting Scholarship and Cathedral Organ Scholarship, and was appointed Assistant Organist a year later, where he was the Diocesean Organist & Director of Music for the Lourdes annual pilgrimage and he studied Music as the Neville Burston Organ Scholar at St Catharine's College, University of Cambridge. While at the university, he founded the Florence International Singing Programme, which holds several courses yearly and has sung at prestigious churches, including Westminster Abbey, St Paul's Cathedral, Saint Sulpice, Florence Cathedral, and the Vatican. For the Addamus Choral Programme Caius Lee conducts the internationally acclaimed College Choir and Boys' Choir as well as The Jericho Youth Choir and the All Sorts Community Choir. He is also responsible for a wide range of collegiate, community, and outreach initiatives, curating Music at Worcester College at the University of Oxford, including The Oxford Choral Experience, a groundbreaking instrument learning scheme, and guest lecturer as part of Institute of Sacred Music run by the University, St Stephen's House, and The Royal School of Church Music. He has worked with choirs, festivals and played solo recitals in Europe, Asia and South America. Caius's musical journey is marked by numerous collaborations that have enriched his work and excited audiences. He has conducted, played, and sang on BBC Radio (1, 2, 3 & 4), and made numerous TV appearances on BBC's Songs of Praise, and live Christmas and Easter TV broadcasts on BBC1. His commitment to community engagement and choral excellence has been recognised with a Royal Society of Arts Fellowship and a keynote speech at the 2022 National Music Teachers Association Conference.To get in touch with Caius, you can find the Addamus Choral Programme on Facebook (@addamuschoralprogramme) or Instagram (@addamus_official) or email him at caius.lee@worc.ox.ac.uk. Email choirfampodcast@gmail.com to contact our hosts.Podcast music from Podcast.coPhoto in episode artwork by Trace Hudson

 Change feels different every time—but it never is. From John Henry to today, this episode explores the recurring moment when the world moves on… and where people still fit.Every generation feels it—the sense that this time, change is different.Faster. Bigger. Final.But history tells another story.From the legend of John Henry to the modern moment, this episode explores the recurring human experience that appears whenever progress accelerates: the quiet question of where people fit when the world moves on.This isn't a story about winning, resisting, or keeping up.It's about the moment that keeps returning—and the small space where choice still exists.If this perspective resonated, consider liking, subscribing, or sharing.And thanks for spending the time here.________________________________________

Part II of Adrian Thacker and Proficiency Backgrounds is on the schedule today, a little late in the drop zone because of the schedule for GRP Studios, but it is finally up and running. Adrian talked a little bit deeper on what they do, moving from just a screening/background checks to a whole back end support for businesses to make sure they are only paying for what they need and being totally compliant in whatever industry they operate in. We talked about how some companies only care about the money they are making and not the impact they are making on the community and how Proficiency Backgrounds can perhaps change that narrative. Also the impact of AI was discussed and how cost "savings" with AI are not always being seen by the consumer as well as the loss of customer service and a disconnect with the community that the business serves!! You can find out more about what they do at  https://proficiencybackground.com/about/Email us at tim@grpstudios.com

This episode sees three banditos sit down and talk about criminals in Dungeons and Dragons. Cold Open 0:00 Opening Theme & Intro 2:02 Themes & Lore 2:38 The Numbers 5:28 Feat 11:15 Gear 12:55 Missing Features 15:22 Inspirations 20:31 Outro & Closing Theme 50:41 Post Credits (incl. Inescapable Prison) 53:18 DON'T FORGET TO LIKE & SUBSCRIBE! Patreon at https://www.patreon.com/user?u=84724626 Website: https://www.itsamimic.com Email at info@itsamimic.com Social: Instagram at https://www.instagram.com/itsamimic/?hl=en Threads at https://www.threads.net/@itsamimicpodcast Facebook at https://www.facebook.com/itsamimic/ Reddit at https://www.reddit.com/r/ItsaMimic/ Find Us On: Spotify at https://open.spotify.com/show/3Y19VxSxLKyfg0gY0yUeU1 Apple Podcasts https://podcasts.apple.com/us/podcast/its-a-mimic/id1450770037 Podbean at https://itsamimic.podbean.com/ YouTube at https://www.youtube.com/channel/UCzQmvEufzxPHWrFSZbB8uuw Dungeon Master 1:  Megan Lengle Dungeon Master 2:  Adam Nason Dungeon Master 3:  Sean O'Coin Narrator:  Pepperina Sparklegem Script By:  Adam Nason, Megan Lengle, Sean O'Coin Produced By:  Adam Nason Director:  Megan Lengle Editor:  Adam Nason Executive Producer:  Adam Nason Main Theme:  Cory Wiebe Musical Scores:  Tyler Gibson Logo by:  Megan Lengle Other Artwork is owned by Wizards of the Coast. This episode is meant to be used as an inspirational supplement for Dungeons & Dragons 5th Edition and tabletop roleplaying games in general.  It's A Mimic! does not own the rights to any Wizards of the Coasts products.

"I got employee of the year in 2016 for introducing Excel as a project management tool for the construction department in my company."That's the state of technology in the $300 billion store development industry.In today's episode of Bricks & Bytes, we sat down with Genevieve Davis and Alim Uderbekov from Surfaice. These two met on a beach in California (yes, really) and decided to tackle one of the most analog industries out there with AI agents.Here's what we covered:✅ Why companies building hundreds of stores aren't using Procore (hint: it's expensive and they're retailers, not developers)✅ The "McDonald's is a race car" analogy - why 40,000 cookie-cutter restaurants are actually the perfect use case for AI in construction✅ How code is becoming a commodity and why that changes everything about vertical software✅ Why AI won't take your job, but people who know how to use it willLink in comments - give it a listen if you're in retail, store development, or just want to hear how two people turned a beach conversation into a company.Our Sponsors:Aphex is the multiplayer planning platform where construction teams plan together, stay aligned, and deliver projects faster – check out aphex.coArchdesk -  “The #1 Construction Management Software for Growing Companies - Manage your projects from Tender to Handover” check archdesk.comBuildVision -   streamlining the construction supply chain with a unified platform - www.buildvision.ioChapters00:00 Intro03:46 Introduction to Surface and Founders' Backgrounds 06:48 The Vision Behind Surface and AI in Construction 09:41 The Meeting of Minds: Genevieve and Alim's Collaboration 12:45 Current Challenges in Store Development 15:30 AI's Role in Automating Construction Processes 18:32 Efficiency Gains and Time Savings with AI 21:44 The Future of Store Development and Human Touchpoints 24:30 Defensibility and Business Model of Surface 27:25 Funding and Future Plans for Surface 39:13 Investment Insights and Strategic Partnerships 41:31 Customer Engagement and Feedback 44:33 AI in Construction: Transforming Workflows 47:14 Navigating the Go-To-Market Strategy 54:20 Understanding Competition in the AI Space 59:25 Customization and Onboarding Process 01:03:07 Future of AI in Construction

S5E13 Future of AI-Powered Consumer Insights with Trevor Sumner & Stan SthanunathanIn Season 5, Episode 13 of The Retail Razor Show, hosts Ricardo Belmar and Casey Golden tackle one of retail's biggest blind spots in consumer insights: the consumer sentiment gap. For decades, brands relied on surveys to understand shoppers. But what people say doesn't always match what they do.Enter AI-powered shopper insights!Joining the conversation are Trevor Sumner (CEO of i-Genie AI) and Stan Sthanunathan (Executive Chairman of i-Genie AI, former EVP at Unilever and VP at Coca-Cola). Together, they reveal how billions of unfiltered signals — from searches, reviews, and social posts — can be transformed into real-time, actionable consumer insights that reshape retail decision-making.What You'll Learn in This Episode:Why traditional consumer surveys are breaking downHow AI and natural language processing uncover real customer behaviorThe role of empathy vs sympathy in understanding consumersHow disruptor brands are reshaping competitive landscapesWhy augmented intelligence (AI + human insight) is the future of retail strategy and consumer insightsSubscribe to the Retail Razor Podcast Network: https://retailrazor.com/Subscribe to our Newsletter: https://retailrazor.substack.comSubscribe to our YouTube channel: https://bit.ly/RRShowYouTubeAbout our GuestsTrevor Sumner, CEO, i-Genie.AI - https://www.linkedin.com/in/trevorsumner/Trevor is a NYC-based entrepreneur, product and marketing executive and recognized startup advisor and angel. Trevor is the CEO at i-Genie.ai, the leading AI platform for consumer insights, revolutionizing an industry that had been dominated with antiquated survey methodologies by synthesizing tens of billions of searches, social and video posts, ratings and reviews and market data for industry leaders like Kenvue, Unilever, Coca-Cola, Bayer, Clorox and more.Previously, Mr. Sumner was Head of AI and Data Platform products for Raydiant, a leading VC-backed digital experience platform that is transforming over 250,000 digital touch points across 4,500 clients with AI, computer vision and data.Mr. Sumner came to Raydiant when it acquired Perch, a recognized leader in in-store Product Engagement Marketing, interactive retail displays and augmented reality, where Mr. SUmner served as CEO. Perch was named a Top Tech Company to Watch by Forbes, a Top 10 Retail Technology company by CIO Review and has won numerous Clio, Fast Company, Edison, Bloomberg and Digi awards.Stan Sthanunathan, Executive Chairman, i-Genie.AI - https://www.linkedin.com/in/stan-sthanunathan-1ab4035/Stan Sthanunathan joined Unilever in July 2013 as Executive Vice President of Consumer & Market Insights. As chief provocateur, he headed up the insights function globally based in London. He retired from Unilever on June 1, 2021.Post retirement he has started an AI/ML enabled company called i-Genie.AI focused on delivering near real time insights and ideas to help business identify Next Big Thing. Prior to joining Unilever, he was Vice President of Marketing Strategy & Insights for The Coca-Cola Company in Atlanta, heading up the function on a global basis.Stan co-authored a paper on Building an Insights Engine that was published in Harvard Business Review, Sept 2016. He has also co-authored a book titled AI for Marketing and Product Innovation.He was awarded Lifetime Achievement award at TMRE 2022 event.Chapters:00:00 Previews 01:23 Show Intro 04:43 The Consumer Sentiment Gap 05:37 Welcome Trevor Sumner & Stan Sthanunathan 06:30 Backgrounds of Trevor and Stan 09:11 Challenges in Understanding Consumers 16:58 The Evolution of Influencers 18:32 Limitations of Surveys and the Need for AI 25:46 Augmented Intelligence: AI + Human Insight 31:46 Challenges in CPG Innovation 33:02 Innovate: A Data-Driven Product 34:42 AI and Predictive Analytics 36:21 Democratizing Data Access 38:07 Mindset Shift for Rapid Actions 40:46 Adopting AI in CPG 48:16 Retailers and Data Utilization 52:59 Future of Brand Understanding 57:23 Conclusion and Contact Information 58:15 Show CloseMeet your hosts, helping you cut through the clutter in retail & retail tech:Ricardo Belmar is an NRF Top Retail Voices for 2025 & a RETHINK Retail Top Retail Expert from 2021 – 2025. Thinkers 360 has named him a Top 10 Retail, & AGI Thought Leader, a Top 50 Management, Transformation, & Careers Thought Leader, a Top 100 Digital Transformation & Agentic AI Thought Leader, plus a Top Digital Voice for 2024 and 2025. He is an advisory council member at George Mason University's Center for Retail Transformation, and the Retail Cloud Alliance. He was most recently the director partner marketing for retail & consumer goods in the Americas at Microsoft.Casey Golden, is CEO of Luxlock, a RETHINK Retail Top Retail Expert from 2023 - 2025, and a Retail Cloud Alliance advisory council member. Obsessed with the customer relationship between the brand and the consumer. After a career on the fashion and supply chain technology side of the business, now slaying franken-stacks and building retail tech! Currently, Casey is the North America Leader for Retail & Consumer Goods at CI&T.Includes music provided by imunobeats.com, featuring Overclocked, and E-Motive from the album Beat Hype, written by Heston Mimms, published by Imuno.

Episode Title: Enhance Focus with Peaceful Rain and Thunder BackgroundsDescription:In this episode of Thunderstorm: Sleep and Relax in the Rain, immerse yourself in calming rain and gentle thunder sounds designed to help you improve focus and concentration. Whether you're working, studying, or simply need a peaceful background to clear your mind, the soothing rainstorm will create the perfect atmosphere to boost your productivity.Picture yourself in a quiet forest as a steady rain falls on leaves and distant thunder rolls softly in the background. The natural rhythm helps block distractions and centers your thoughts, allowing you to dive deep into your tasks or find mental clarity.Let the relaxing sounds of rain and thunder guide you into a state of calm alertness, making it easier to stay on track and feel refreshed throughout your day.DISCLAIMER

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Is Generative AI moving too fast? From viral deepfake videos to powerful coding assistants, AI is reshaping our world at a breathtaking pace. But with this power comes immense risk: to our privacy, to intellectual property, and even to our ability to tell what's real. How do we navigate this complex new landscape responsibly?In this episode, Allen sits down with Maggie Engler and Numa Dhamani, authors of "Intro to Gen AI, Second Edition" and veterans in the fields of cybersecurity and trust & safety. They pull back the curtain on how these powerful models are built, the societal impact they're having, and the urgent conversations we need to have about data governance, AI agents, and the looming digital trust crisis.IN THIS EPISODE00:00 - Prompt Engineering, AI Agents & More03:27 - The Guests' Backgrounds in Cybersecurity and Trust & Safety09:31 - The Hidden Risks of Sharing Your Data with AI13:02 - Copyright vs. AI18:52 - The Digital Trust Crisis24:36 - Watermarking and Digital Verification30:25 - Using Proprietary Code with AI Assistants36:56 - AI Agents42:37 - Who This Book Is For (and Who It's Not For)

Industrial Talk is onsite at SMRP 2026 and talking to Greg Raglin and Bill Broderick with AssetWatch about "Bringing context to your asset management data". Scott MacKenzie hosts an industrial podcast featuring Greg RaglIn and Bill Broderick from AssetWatch, a company specializing in predictive maintenance and reliability solutions. Greg, a solutions architect, and Bill, a vibration analyst, discuss their experiences and the benefits of AssetWatch's technology, which integrates AI and human intelligence to provide actionable insights from condition-based monitoring of assets. They emphasize the importance of accurate data analysis to avoid false alarms and the need for continuous engagement with clients to ensure the success of predictive maintenance programs. The conversation highlights the evolving role of AI in industrial settings and the potential for future technological advancements. Action Items [ ] Reach out to Greg Raglin to discuss AssetWatch's solutions further.[ ] Connect with Bill Broderick on LinkedIn to stay updated on the company's developments. Outline Introduction and Welcome to Industrial Talk Podcast Scott MacKenzie introduces the Industrial Talk Podcast, emphasizing its focus on industry professionals and their innovations.Scott welcomes listeners and highlights the importance of celebrating industry heroes who solve daily problems.The podcast is broadcasting live from the SMRP conference in Fort Worth, Texas, where Scott has been discussing asset management, reliability, and maintenance.Scott introduces Greg and Bill from AssetWatch, who will share their experiences and insights from the conference. Backgrounds of Greg and Bill Greg Raglin shares his career journey, starting in psychology, moving to logistics, and eventually to fluid motion control and automation.Greg has been with AssetWatch for three years as a solutions architect, helping customers solve problems with practical solutions.Bill Broderick has been with AssetWatch for over a year, with a background in manufacturing automation and predictive maintenance.Bill emphasizes his passion for finding inefficiencies and optimizing processes to help companies save costs and improve efficiency. Overview of AssetWatch Greg explains that AssetWatch is a reliability partner, focusing on condition-based monitoring and using data, AI, and machine learning to provide actionable insights.The company has a team of 30+ dedicated engineers who analyze data and provide recommendations to fix issues.Bill adds that AssetWatch uses AI to monitor data and filter out false alarms, ensuring that plant-level teams receive accurate and timely information.The human element behind the technology is crucial for AssetWatch, as experienced engineers can communicate effectively with plant operators. Data Analysis and Integration Scott asks about the types of data AssetWatch can analyze, and Greg mentions that they focus on vibration and temperature data, with plans to expand to other modalities.Bill explains that AssetWatch integrates with other systems like CMS to provide a comprehensive solution for predictive maintenance.The company aims to be a one-stop shop for reliability, using data from various sources to reduce downtime and improve efficiency.AssetWatch manufactures their own devices, ensuring that all components are state-side and of high quality. Deployment and Training Greg discusses the deployment process, where AssetWatch's reliability...

In this episode of Shifting Schools, Jeff Utecht reconnects with Marcus DiPaola, a successful content creator and documentary filmmaker. They discuss Marcus's journey from working in news to becoming a prominent influencer on platforms like TikTok, where he has amassed over 4 million followers. The conversation delves into the challenges of content creation, the importance of writing skills, and the role of AI in enhancing creative processes. Marcus shares insights on his documentary work, including a recent project on protests and his upcoming film about food insecurity. He emphasizes the value of internships for aspiring creators and the dedication required to succeed in the industry. Connect with him: https://www.tiktok.com/@marcus.dipaola?lang=en https://www.youtube.com/c/marcusdipaola Takeaways Content creation requires strong writing skills. Consistency is key in content creation. The journey to becoming an influencer is not instant. Internships provide invaluable experience for aspiring creators. AI can assist but cannot replace creativity. Documentary filmmaking involves significant research and planning. Understanding audience engagement is crucial for content success. Passion is essential to avoid burnout in content creation. Networking from internships can lead to lasting professional relationships. The landscape of content creation is constantly evolving.  Sound bites "You have to be super consistent." "I make one 60 second video a day." "You have to be better than that." Chapters 00:00 Reconnecting and Backgrounds 02:39 The Journey of Content Creation 05:29 Documentary Filmmaking and Its Challenges 08:23 The Role of AI in Content Creation 10:58 Advice for Aspiring Creators and Future Projects Thank you to our Sponsor Poll Everywhere Learn more about them: https://www.polleverywhere.com/plans/education?utm_source=referral&utm_medium=shiftingschools&utm_campaign=shiftingschools  

In this episode, Donovan and Ken discuss what happens when a background goes bad and how BI's can make or break an investigation.--------------------For those who aren't subscribers: Have we helped you with our podcast content, or with a phone call or email advice? You can now show your love at buymeacoffee.com! Here are the links in the event you'd like to express your appreciation if we've made a difference:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠buymeacoffee.com/kenroybal⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠buymeacoffee.com/donovanheavener⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bonus: Our books are discounted 50% for podcast subscribers!! (Email us for your discount code.)You're going to love these great new podcast offerings!!Purchase your copies today:Ken's Book: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://payhip.com/b/BFYjg⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Donovan's Book: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://payhip.com/b/AVlRT⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Contact us:ken[atsign]policebackground.netdonovan[atsign]policebackground.netPolice candidate consultations: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.policebackground.net⁠⁠⁠⁠

Altersforscherin Heike A. Bischoff-Ferrari und Musiker Lukas Hobi teilen sich bei Michèle Schönbächler die Bühne. Heike A. Bischoff-Ferrari, Ärztin und Forscherin für gesunde Langlebigkeit Seit Juli 2025 leitet Prof. Dr. Dr. Heike A. Bischoff-Ferrari den Schweizer Campus für gesunde Langlebigkeit an der Universität Basel und forscht mit ihren Kolleginnen und Kollegen über die Ausweitung der gesunden Lebensjahre. Sie wuchs in Ehingen im deutschen Bundesland Baden-Württemberg mit zwei Geschwistern auf. In ihren Jugendjahren wollte sie Künstlerin werden. In der Schüler-Lehrerband versuchte sie sich als Backgroundsängerin. «Nicht ganz so erfolgre.ich», wie sie mit einem Schmunzeln erwähnt. Umso erfolgreicher zeigt sie sich in ihre berufliche Karriere. Nach der Promotion und Assistenzarztzeit an der Universität Basel in den Fächern Geriatrie, Rheumatologie und Orthopädie arbeitete sie mehrere Jahre in Boston und war Fakultätsmitglied der Harvard Medical School. In dieser Zeit lernte sie ihren späteren Mann kennen. Nach ihrer Rückkehr in die Schweiz gründete sie in Zürich das Forschungszentrum «Alter und Mobilität» und baute den ersten Lehrstuhl für Altersmedizin und Altersforschung an der Universität Zürich auf, bevor sie im Sommer 2025 an die Universität nach Basel wechselte. Heike A. Bischoff-Ferrari ist fasziniert von der Weihnachtszeit und der Forschung zur gesunden Langlebigkeit, weil sie eine enorme Innovationskraft birgt. Sie leitet das Globale Konsortium zur Verlängerung der gesunden Lebenserwartung mit 12 Universitäten und ist Vernetzerin. ________________________________________ Lukas Hobi, Musiker «Ich hatte nie Ziele, aber ganz viele Träume», sagt Lukas Hobi. Und viele seiner Träume sind wahr geworden – nicht zuletzt, weil er überzeugt ist, Schmied seines eigenen Glücks zu sein. So stand er bereits auf zahlreichen Bühnen als Musical- und Theaterschauspieler. Mit der A-Capella-Comedy-Band Bliss tourt er durch die Schweiz und prägt die Gruppe als künstlerischer Leiter, Arrangeur und Mitproduzent weit über seine Stimme hinaus. Lukas Hobi ist zudem als Regisseur, Produzent und Komponist tätig. Sein Lied «Made in Switzerland» wurde am Eurovision Song Contest vor einem Millionenpublikum präsentiert. Beim Musikspektakel im Mai 2025 stand Lukas Hobi sogar als Backgroundsänger im St. Jakobpark in Basel auf der Bühne – und erzählt in der Sendung, warum die Technik dabei fast zur Hauptrolle wurde. Dabei war sein Weg alles andere als geradlinig: Nach seiner Ausbildung zum Lehrer unterrichtete er einige Wochen an einer Schule, bevor er sich für eine künstlerische Ausbildung zum Musicaldarsteller entschied. 2018 nutzte er eine mehrwöchige Auszeit, um sich in New York in Schauspiel, Tanz und Gesang weiterzubilden. Mit Bliss gewann Lukas Hobi dreimal den Swiss Comedy Award sowie den Prix Walo als Publikumslieblinge. Für seine Rolle als «Fritz Äberli» in der Produktion «Dällebach Kari» (Thunerseespiele, 2023) wurde er zudem als bester Nebendarsteller für den Deutschen Musicalpreis nominiert. Privat liebt Lukas Spieleabende mit seiner Familie – und hat gelernt, wie bereichernd es sein kann, auch mal allein zu reisen. ____________________ Moderation: Michèle Schönbächler ____________________ Das ist «Persönlich»: Jede Woche reden Menschen über ihr Leben, sprechen über ihre Wünsche, Interesse, Ansichten und Meinungen. «Persönlich» ist kein heisser Stuhl und auch keine Informationssendung, sondern ein Gespräch zur Person und über ihr Leben. Die Gäste werden eingeladen, da sie aufgrund ihrer Lebenserfahrungen etwas zu sagen haben, das über den Tag hinaus Gültigkeit hat.

Follow Proof of Coverage Media: https://x.com/Proof_CoverageIn this episode Connor & Mahesh sit down with Akshay Poshatwar & Nishikant Bahalkar, founders of Qiro, to discuss their ground-breaking platform aimed at tackling adverse selection through a unique credit risk underwriting protocol. Akshay shares his journey from the FinTech industry in India, highlighting the potential for global capital access through decentralized finance. Nishikant adds his insights on the evolution of blockchain and DeFi, emphasizing the benefits and challenges in Real World Assets (RWA) lending. The discussion includes how Qiro aims to bridge TradFi and DeFi, offering solutions for different asset classes and the promising future of decentralized infrastructure networks (DePIN). The episode wraps up with a look towards the company's rapid growth and their search for strategic hires, particularly in business development within the U.S. and Europe.Timestamps: 00:00 - Introduction 00:46 - Meet the Qiro Team01:53 - Founders' Backgrounds and Journey09:35 - Understanding Qiro's Unique Platform13:30 - Challenges and Innovations in RWA Lending24:36 - DePIN Networks and Future Prospects31:13 - Hiring and Growth at Qiro33:52 - Conclusion and Contact InformationDisclaimer: The hosts and the firms they represent may hold stakes in the companies mentioned in this podcast. None of this is financial advice.

Megan and Adam sit down to discuss Charlatans, including the missteps and the inspirations that can be found in the design. Cold Open 0:00 Opening Theme & Intro 2:23 Themes & Lore 4:18 The Numbers 8:18 Feat 13:23 Gear 16:47 Missing Features 18:26 Discussion 21:48 Outro & Closing Theme 45:20 Post Credits (incl. Backroom Gambling) 52:52  DON'T FORGET TO LIKE & SUBSCRIBE! Patreon at https://www.patreon.com/user?u=84724626 Website: https://www.itsamimic.com Email at info@itsamimic.com Social: Instagram at https://www.instagram.com/itsamimic/?hl=en Threads at https://www.threads.net/@itsamimicpodcast Facebook at https://www.facebook.com/itsamimic/ Reddit at https://www.reddit.com/r/ItsaMimic/ Find Us On: Spotify at https://open.spotify.com/show/3Y19VxSxLKyfg0gY0yUeU1 Apple Podcasts https://podcasts.apple.com/us/podcast/its-a-mimic/id1450770037 Podbean at https://itsamimic.podbean.com/ YouTube at https://www.youtube.com/channel/UCzQmvEufzxPHWrFSZbB8uuw Dungeon Master 1: Megan Lengle Dungeon Master 2: Adam Nason Dungeon Master 3: Ehlwyn (Ellie for Short) Script By:  Adam Nason, Megan Lengle, Ehlwyn (Ellie for Short) Producer: Adam Nason Director:  Megan Lengle Editor:  Adam Nason Executive Producer:  Adam Nason Main Theme:  Cory Wiebe Musical Scores:  Tyler Gibson Logo by:  Megan Lengle Other Artwork is owned by Wizards of the Coast. This episode is meant to be used as an inspirational supplement for Dungeons & Dragons 5th Edition and tabletop roleplaying games in general.  It's A Mimic! does not own the rights to any Wizards of the Coasts products.

In this debut episode of "Talk Fantasy to Me," hosts Chase and Kyle dive into the enchanting world of fantasy entertainment. From live-action epics and anime to animated classics and fantasy horror, they explore the genre's impact on culture and personal experiences. Join them as they discuss the latest fantasy news, upcoming releases, and their own journeys into the fantastical realms that have shaped their lives. 00:00:00 Introduction to Talk Fantasy to Me 00:03:00 Hosts' Backgrounds and Fantasy Journeys 00:09:00 Fantasy News and Upcoming Releases 00:18:00 Discussion on Fantasy's Cultural Impact 00:24:00 Marvel's Upcoming Timeline 00:30:00 Casting Choices in Fantasy Shows 00:36:00 Henry Cavill's Influence in Fantasy 00:42:00 Amazon's God of War Series 00:48:00 Highlander Reboot with Henry Cavill 00:54:00 Closing Thoughts and Future Episodes

Manche schreiben Bücher oder drehen Filme, Daniel Stoyanovs Medium der Wahl für das Erzählerische ist die Musik. Mit dem neuen Album "Greetings from Soulgaria" (VÖ: 10.10.25) nimmt uns Bulgarian Cartrader mit auf einen Roadtrip – natürlich nach Bulgarien. Dort verbringt Stoyanov die ersten Lebensjahre und wächst anschließend in Deutschland auf. Mit Humor, Nostalgie und einer unbändigen Tanzfreude lässt uns Bulgarian Cartrader an seinen Geschichten teilhaben. Auf dem neuen Album, übrigens eine Anspielung an Bruce Springsteens Album "Greetings from Asbury Park, N.J.", blickt Stoyanov in seine Vergangenheit und kulturelle Herkunft zurück. Durch die herzerwärmenden Geschichten fordert er die Zuhörer*innen auf, ihre Stereotype gegenüber osteuropäischer Kultur zu hinterfragen. Musikalische Unterstützung erhält er dabei von billigen Synthesizern und einer hundertjährigen Gitarre. Daniel Stoyanov ist seit 2021 als Bulgarian Cartrader unterwegs, hat aber auch schon auf anderen Wegen musikalische Spuren hinterlassen. Zum Beispiel in den Songwriting-Credits von Peter Fox, SEEED oder Casper. Zudem war er schon als Background-Sänger, Salsa-Tänzer und ja, wenn auch nur kurz als Autohändler aktiv. Im radioeins-Kosmos ist Bulgarian Cartrader längst keine Unbekannte mehr. Schon zwei Mal spielte er mit seiner Band auf dem radioeins-Parkfest und war zu Gast im studioeins im Bikini Berlin. Am Donnerstag stattet er uns als Lokalmatador einen Besuch in Potsdam ab.

Send us a textIn this episode of The Wealth Vibe Show, host Vinki Loomba sits down with Bill Kanatas and Ben Salzberg, two industry powerhouses in self-storage development. Bill, co-founder and CEO of Storage Developers, has partnered with major players like Public Storage and Extra Space, while Ben brings over 25 years of operational excellence and a Six Sigma Black Belt to the table. Together, they share how self-storage has become a hidden gem for long-term wealth-building, even in tough economic times.How It Works:Self-Storage Resilience: Bill explains why self-storage is one of the most resilient asset classes in real estate, thriving during both good and bad economic times, thanks to its ever-present demand driven by the "4 D's" (death, divorce, downsizing, and relocation).Operational Efficiency: Ben emphasizes the importance of efficiency and process management in the self-storage business, leveraging his Six Sigma training to reduce waste, optimize time, and increase profitability.Automation and Technology: The guys dive into how technology is transforming the self-storage industry, from virtual tours to automated access, making the customer experience smoother and the operation more hands-off for owners.Partnerships & Joint Ventures: They also highlight the value of partnerships and joint ventures in the self-storage space, and how they work with landowners to co-develop successful storage facilities.Episode Timestamps:00:00 - 01:05: Introduction01:05 - 03:00: The Resilience of Self-Storage in Any Market03:00 - 06:00: Bill and Ben's Backgrounds and the Six Sigma Approach06:00 - 09:00: The Benefits of Lean Operations and Staying Efficient09:00 - 13:00: The Role of Technology in Self-Storage13:00 - 16:00: How Self-Storage Fares Against Multifamily and Other Asset Classes16:00 - 20:00: Key Factors to Look for in a Self-Storage Investment20:00 - 23:00: Market Trends and Future of Self-Storage23:00 - 26:00: Exploring Exit Strategies for Investors in Self-Storage26:00 - 30:00: Rapid Fire Round: Insights on Self-Storage Investing30:00 - 35:00: Closing Thoughts on the Future of Self-Storage

SPACE ROBOTS! We are are talking about this exciting topic again in this episode. Icarus Robotics is a NY-based space robotics start up that just raised its $6M seed financing round. Co-founders Ethan Barajas and Jamie Palmer are our guests. 

In this conversation, Drs. Gaurav Suri and Jay Mcclelland delves into the intricate relationship between artificial intelligence and human cognition, exploring similarities and differences, the evolution of AI from rule-based systems to learning models, and the concept of emergence in both fields. The discussion also touches on the efficiency of human learning compared to AI, the role of consciousness, and the ethical implications of AI technology.Takeaways AI and human intelligence share similarities in neural network frameworks. Artificial systems lack the goal-directed nature inherent in humans. Humans learn more efficiently than current AI systems. Neural networks can adapt to language nuances better than rule-based systems. Emergence explains how collective intelligence arises from individual components. Memory in neural networks is represented through connections, not individual units. Mathematics is both invented and discovered, shaped by human needs. Understanding consciousness is crucial for AI development. Human misuse of AI poses significant risks. Recognizing ourselves as processes can foster empathy and morality.Chapters 00:00 Introduction and Backgrounds 01:00 AI vs Human Mind: Similarities and Differences 03:32 The Shift from Rule-Based AI to Learning Systems 09:07 Emergence in Cognition: Ant Colonies and Intelligence 15:25 Distributed Representations and Memory Storage 23:53 The Nature of Memory and Its Malleability 25:40 Emergence of Mathematical Concepts 29:50 The Invention vs. Discovery Debate in Mathematics 32:19 Learning Mechanisms: Brain vs. AI 36:48 Consciousness: Function and Implications 41:13 AI Risks: Human Misuse vs. AI Autonomy 43:45 Living with Emergence: Understanding Ourselves and Others 48:22 Exploring the Emergent MindFollow Gaurav Suri on LinkedIn. Follow Jay McClelland on Twitter and find their new book here.Subscribe to Breaking Math wherever you get your podcasts.Follow Breaking Math on Twitter, Instagram, LinkedIn, Website, YouTube, TikTokFollow Autumn on Twitter, BlueSky, and InstagramBecome a guest hereemail: breakingmathpodcast@gmail.com

As a part of disability Shabbat, IKAR member Matan Koch explores the ideas of openness, welcoming and access in the context of embracing each person's unique reflection of the divine. He takes us on a journey from Talmudic thought to present-day actions, exhorting us to be the kind of community where all of the reflections of the divine are known, and seen.

SummaryIn this episode of Murder in the Black, host Steph delves into the chilling true crime case of Brittany Rojas and Nikki Silas, two young women whose dreams were violently cut short in their Harlem apartment. The episode explores the investigation, the suspects, and the emotional impact on the victims' families, while also reflecting on the importance of human connections and the dangers of isolation, as highlighted by the haunting story of Joyce Carol Vincent.true crime, Brittany Rojas, Nikki Silas, Harlem murder, human connection, isolation, Joyce Carol VincentTakeaways:Hold your loved ones close and never miss a chance to tell them you love them.The holiday season can be a time of joy or heartbreak.A landlord's investigation into a leak led to a gruesome discovery.Two young women, both murdered, in a brutal and intimate crime.Nikki Silas was a promising dancer, described as the Black Marilyn Monroe.Brittany Rojas and Nikki Silas were chasing dreams in New York City.Detectives promised the families they would find the killer.Isolation can kill us; connections are essential.Joyce Carol Vincent's story is a tragedy of modern isolation.Speak out against injustice; silence can be complicit.Chapters00:00:00 Introduction and Holiday Reflections00:00:00 The Discovery in Harlem00:00:00 Victims' Backgrounds and Investigation00:00:00 Suspects and Theories00:00:01 The Haunting Story of Joyce Carol Vincent00:00:01 Reflections on Connection and Justice

This episode contains everything you need to know about the Guide Background from the 2024 Player's Handbook. Cold Open 0:00 Opening Theme and Intro 1:21 Themes & Lore 2:35 The Numbers 5:33 Feat 9:21 Gear 13:01 Discussion 16:21 Outro and Closing Theme 30:57 Post Credits (incl. The Looping Path) 33:05 DON'T FORGET TO LIKE & SUBSCRIBE! Patreon at https://www.patreon.com/user?u=84724626 Website: https://www.itsamimic.com Email at info@itsamimic.com Social: Instagram at https://www.instagram.com/itsamimic/?hl=en Threads at https://www.threads.net/@itsamimicpodcast Facebook at https://www.facebook.com/itsamimic/ Reddit at https://www.reddit.com/r/ItsaMimic/ Find Us On: Spotify at https://open.spotify.com/show/3Y19VxSxLKyfg0gY0yUeU1 Apple Podcasts https://podcasts.apple.com/us/podcast/its-a-mimic/id1450770037 Podbean at https://itsamimic.podbean.com/ YouTube at https://www.youtube.com/channel/UCzQmvEufzxPHWrFSZbB8uuw Dungeon Master 1:  Megan Lengle Dungeon Master 2:  Kyle McQuaid Dungeon Master 3:  CB Dave Narrator:  Pepperina Sparklegem Script By:  CB Dave, Kyle McQuaid, Megan Lengle Produced By:  Kyle McQuaid Director:  Megan Lengle Editor:  Adam Nason Executive Producer:  Adam Nason Main Theme:  Cory Wiebe Musical Scores:  Tyler Gibson Logo by:  Megan Lengle Other Artwork is owned by Wizards of the Coast. This episode is meant to be used as an inspirational supplement for Dungeons & Dragons 5th Edition and tabletop roleplaying games in general.  It's A Mimic! does not own the rights to any Wizards of the Coasts products.

In this episode of Longevity by Design, our host, Dr. Gil Blander, sits down with Dylan Livingston, CEO at the Alliance for Longevity Initiatives, and Dr. Brenda Eap to explore how public policy shapes the future of aging research. Dylan and Brendan share how their team pushes for legislation that supports healthy lifespan extension, aiming to bring longevity science into the center of national health priorities.Dylan and Brendan explain why policy advocacy is crucial for securing funding for research, reducing regulatory hurdles, and establishing a clear path for new therapies. They outline recent successes, including building bipartisan support in Congress and expanding access to experimental treatments in states such as Montana. Throughout their discussion, Dylan shows how effective communication,  using stories and simple analogies, helps move longevity from the lab to lawmakers' agendas.The episode closes with practical advice for listeners. Dylan highlights the power of community, clear communication, and grassroots action as tools that help turn advanced science into real-world health gains.Episode highlights:[00:00:00]: Introduction[00:01:00]: Overview of Longevity Policy and Research Funding[00:02:00]: Backgrounds and Personal Journeys into Longevity Advocacy[00:05:00]: Inspiration and Founding of Longevity Policy Organization[00:07:00]: Scientific Training and Motivation for Policy Work[00:09:00]: Mission and Approach of Longevity Advocacy Organization[00:10:00]: Policy Advocacy's Role in Advancing Longevity Research[00:12:00]: Gaps in Funding and the Importance of Government Engagement[00:13:00]: Experiences with Policy Events and Realizations about Advocacy[00:14:00]: Early Accomplishments and Congressional Engagement[00:15:00]: Building Bipartisan Support and the Longevity Science Caucus[00:16:00]: Legislative Engagement and Policy Paper Development[00:17:00]: State-Level Policy Wins and Expansion of Right to Try Laws[00:19:00]: Rationale and Strategy for State-Level Focus[00:21:00]: Plans for Geographic Expansion of Longevity Policy Initiatives[00:23:00]: Political Climate and Opportunities with the Current Administration[00:25:00]: Shifts in Federal Attitudes Toward Longevity and Healthspan[00:27:00]: Demographics, Policy Momentum, and National Health Priorities[00:29:00]: Prevention Versus Treatment: Shifting Policy Mindsets[00:32:00]: Communicating Policy Opportunities to a Wider Audience[00:34:00]: Ensuring Longevity Policy Remains Bipartisan[00:35:00]: Intersection of Policy, Science, and Public Engagement[00:37:00]: Funding and Regulatory Challenges in Longevity Research[00:39:00]: Barriers in Clinical Trials and Policy Modernization[00:40:00]: Strategies for Communicating Longevity Science to Policymakers[00:44:00]: Framing Longevity for Policy Impact and Public Understanding[00:48:00]: Future Vision and Milestones for Longevity Policy[00:51:00]: How Individuals and Organizations Can Support Longevity Advocacy[00:55:00]: Practical Longevity Advice and Episode ConclusionWe Appreciate You!As a token of our gratitude, we're excited to offer you 15% off your next purchase. Simply click the link below to redeem your discount: https://info.insidetracker.com/podcastFor science-backed ways to live a healthier, longer life, download InsideTracker's Top 5 biomarkers for longevity eBook at insidetracker.com/podcast

In this episode of

In this episode of the AdTechGod Pod, host AdTechGod speaks with Dan Callahan and Alexander Groysman from Spectrum Reach about the evolving landscape of television advertising. They discuss their backgrounds, the collaboration between product and revenue teams, the impact of technology and AI on the industry, and the importance of networking. The conversation highlights the balance between traditional and streaming TV, the significance of quality content in advertising, and the role of data in targeting audiences effectively. Takeaways Spectrum Reach is innovating in the ad tech space. Collaboration between product and revenue teams is essential. AI and machine learning are transforming advertising solutions. Streaming complements traditional TV advertising. Quality content is more important than quantity in advertising. Networking is crucial for success in the industry. First-party data is a key differentiator for Spectrum Reach. Educating advertisers about streaming is necessary. The industry is evolving rapidly with new technologies. Building relationships can lead to successful partnerships. Chapters 00:00 Introduction to Spectrum Reach and Guests 01:06 Backgrounds of Dan Callahan and Alexander Groysman 03:11 Collaboration Between Product and Revenue Teams 04:31 Evolution of Technology in the Industry 06:30 Impact of Streaming on Advertising 08:10 Targeting Audiences: Connected vs Traditional TV 10:08 Hype vs Reality in Ad Tech 13:23 AI and Innovations in Advertising Solutions 17:25 Leveraging Data for Effective Advertising 19:34 The Importance of Networking in the Industry Mastercard, Roku, Spotify, and Amazon Push Forward in Ads, and this week's Refresh dives into a wave of product updates and partnerships shaping advertising and media. Mastercard is making a bold move into commerce media, Roku and AppsFlyer are enhancing CTV measurement, Spotify expands its ad exchange reach with new DSP integrations, and Amazon rolls out new devices designed for its upgraded Alexa+ experience. Each update underscores how scale, data access, and measurement are driving the next phase of advertising innovation. 5 Key Highlights: Mastercard Commerce Media Launch: Mastercard formally enters the ads business with onsite and offsite media offerings, leveraging transaction insights from 500M cardholders and 159B annual transactions to create scale-driven opportunities for advertisers. Roku & AppsFlyer Partnership: The integration expands into a two-way API connection for stronger cross-channel measurement, giving advertisers tools to validate CTV's performance impact on mobile and social behaviors. Spotify's Ad Tech Expansion: Spotify Ad Exchange now integrates with Amazon DSP and Yahoo DSP, boosting inventory accessibility and enabling richer data-driven targeting, while programmatic adoption is already up 142% year-to-date. Amazon Alexa+ Devices: Four new Echo devices debut with chips and sensors tailored for conversational AI, positioning Alexa+ as a more personalized assistant woven into daily routines, from smart home control to wellness nudges. Commerce Media Growth Trend: Mastercard's move highlights how vertical-specific media networks (financial, travel, retail) are proliferating, with offsite inventory and data governance emerging as key factors for advertiser adoption. Learn more about your ad choices. Visit megaphone.fm/adchoices

This week on The FratChat Podcast, we're diving into the wildest celebrity backgrounds! And it doesn't get crazier than celebrity parents. These aren't just regular parents. They're the type who make your family drama look like a Norman Rockwell painting. But that's just the start. We've also have another hilarious edition of Emails From the Listeners! Like the guy stuck living with a roommate who treats his birthday like the Met Gala, except somehow more unbearable. Plus, CMo finally spills on what his actual type is. Then, in the news, Tyson Fury's daughter “Venezuela” is sixteen and already engaged. Which leads us to question if we're old, washed up, or just the last sane people left. And don't miss this week's “Not the Drag Queens,” where we break down how the biggest consumers of trans porn are the same conservatives yelling the loudest about “family values.” Shocking? Not really. Hypocritical? Absolutely. Got a question, comment or topic for us to cover? Let us know! Send us an email at fratchatpodcast@gmail.com or follow us on all social media: Instagram: http://Instagram.com/FratChatPodcast Facebook: http://Facebook.com/FratChatPodcast Twitter: http://Twitter.com/FratChatPodcast YouTube: http://YouTube.com/@fratchatpodcast Follow Carlos and CMO on social media! Carlos:  IG: http://Instagram.com/CarlosDoesTheWorld YouTube: http://YouTube.com/@carlosdoestheworld TikTok: http://TikTok.com/@carlosdoestheworld Twitter: http://Twitter.com/CarlosDoesWorld Threads: http://threads.net/carlosdoestheworld Website: http://carlosgarciacomedy.com Chris ‘CMO' Moore:  IG: http://Instagram.com/Chris.Moore.Comedy TikTok: http://TikTok.com/@chris.moore.comedy Twitter: http://Twitter.com/cmoorecomedy Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Vivarium Backgrounds are an integral part of dart frog vivarium builds. They can be simple or complex, but in the right hands they can become true works of art. This week Im joined by @mikegravia and we discuss his methods for building show room quality vivarium backgrounds. We cover materials, methods, and the learning curve,  as well as how to scale a build properly to recreate a slice of nature.Be sure to give Mike a follow @mikegraviaBroaden your knowledge of herpetology online or in person at the Amphibian Foundation. Register now at www.amphibianfoundation.org and use code AMPHIBICAST at checkout for 10% off Exo Terra is our sponsor this week. For all your amphibian needs visit: Exo-terra.com or visit your local dealer and follow @exoterrausa on social media. NEHERP is our sponsor this week For your bioactive vivarium needs visit: https://www.neherpetoculture.com/

Megan and Adam are joined by an actual sailor (Robert) to discuss this background, including the missteps and the inspirations that can be found in the design. Cold Open 0:00 Opening Theme and Intro 2:28 Themes & Lore 3:38 The Numbers 9:56 Feats 17:16 Gear 23:04 Missing Features 27:08 Open Discussion 30:13 Outro & Closing Theme 48:03 Post-Credit (incl Haunted Lighthouse) 48:53 DON'T FORGET TO LIKE & SUBSCRIBE! Patreon at https://www.patreon.com/user?u=84724626 Website: https://www.itsamimic.com Email at info@itsamimic.com Social: Instagram at https://www.instagram.com/itsamimic/?hl=en Threads at https://www.threads.net/@itsamimicpodcast Facebook at https://www.facebook.com/itsamimic/ Reddit at https://www.reddit.com/r/ItsaMimic/ Find Us On: Spotify at https://open.spotify.com/show/3Y19VxSxLKyfg0gY0yUeU1 Apple Podcasts https://podcasts.apple.com/us/podcast/its-a-mimic/id1450770037 Podbean at https://itsamimic.podbean.com/ YouTube at https://www.youtube.com/channel/UCzQmvEufzxPHWrFSZbB8uuw Dungeon Master 1:  Megan Lengle Dungeon Master 2:  Adam Nason Dungeon Master 3:  Robert Narrators:  Pepperina Sparklegem Script By:  Adam Nason, Megan Lengle, Robert Director:  Megan Lengle Editor:  Adam Nason Producer:  Adam Nason Executive Producer:  Adam Nason Main Theme:  Adam Nason and Tyler Gibson Musical Scores:  Tyler Gibson Logo by:  Megan Lengle Other Artwork is owned by Wizards of the Coast. This episode is meant to be used as an inspirational supplement for Dungeons & Dragons 5th Edition and tabletop roleplaying games in general.  It's A Mimic! does not own the rights to any Wizards of the Coasts products.  

Greg, JP, and Kent chat with restorative-justice pro Nicholas Bradford about why relationships—not punishments—actually fix classrooms. From hallway tardies to high-stakes cheating, they show how slowing down, listening, and handing students real responsibility changes behavior—plus a hilarious “Plug and Jug” confession story and some friendly jabs at Greg's vocab and Kent's Wi-Fi. Humor, heart, and practical tools—packed into one lively episode.___Nicholas' Website - https://www.nationalcenterforrestorativejustice.comNicholas Bradford LinkedInhttps://www.linkedin.com/in/bradfordnicholas/Nicholas' Bookhttps://www.amazon.com/Real-World-Guide-Restorative-Justice-Schools-ebook/dp/B08Z28LBBX/ref=tmm_kin_swatch_0?_encoding=UTF8&dib_tag=se&dib=eyJ2IjoiMSJ9.hy-8ZvqSYXuQVoNDT0y8SLnwkmI46W4M5cmvKWYG-CBst8suSn-UE2cPg2beNcALx26zq4kd6YmfLNhKh4Y7eg.IbIAeI4uAMCtFSVNkHb-Ic3a4Nf4MhQJzPi_XdU7Uxw&qid=1757759630&sr=8-1Chapters(00:00) Introduction and Backgrounds(06:08) Understanding Restorative Justice(08:59) Building Relationships in Education(12:08) Power Dynamics in Schools(14:57) The Importance of Slow Relationships(17:59) Workshops and Training in Restorative Justice(20:57) Challenges for Teachers(24:12) The Journey of New Teachers(27:38) Creating a Safe Learning Environment(32:12) Understanding Discipline and Self-Discipline(36:50) Behavioral Trends in Education(40:19) Effective Communication in Classrooms(46:35) Restorative Justice Practices(49:59) Engaging with Restorative Justice ResourcesThe International Teacher Podcast is a bi-weekly discussion with experts in international education. New Teachers, burned out local teachers, local School Leaders, International school Leadership, current Overseas Teachers, and everyone interested in international schools can benefit from hearing stories and advice about living and teaching overseas.Additional Gems Related to Our Show:Greg's Favorite Video From Living Overseas - ⁠https://www.youtube.com/watch?v=UQWKBwzF-hw⁠Signup to be our guest  ⁠https://calendly.com/itpexpat/itp-interview?month=2025-01⁠Our Website⁠ -  ⁠https://www.itpexpat.com/⁠Our FaceBook Group - ⁠https://www.facebook.com/groups/itpexpat⁠⁠JPMint Consulting Website  - ⁠https://www.jpmintconsulting.com/⁠Greg's Personal YouTube Channel: ⁠https://www.youtube.com/playlist?list=PLs1B3Wc0wm6DR_99OS5SyzvuzENc-bBdO⁠Books By Gregory Lemoine:⁠International Teacher Guide: Finding the "Right Fit" 2nd Edition (2025)⁠ | by Gregory Lemoine M.Ed.⁠⁠"International Teaching: The Best-kept Secret in Education"⁠⁠ | by Gregory Lemoine M.Ed.Partner Podcasts:Just to Know You:  https://podcasts.apple.com/au/podcast/just-to-know-you/id1655096513Educators Going Global: ⁠https://podcasts.apple.com/us/podcast/educators-going-global/id1657501409⁠Relative Hashes:#internationalteachersday #internationaleducation #overseaseducation #internationalschools #education #teacherburnout #teachersalarynews #teachersalary #teacherrecruitments #overseaseducatorfairs

HT2385 - Gathering Backgrounds Surprisingly enough, digital processing has opened the door to texture-like backgrounds for our projects. Applying a textured background is easy and can contribute significantly to the aesthetic of a project. I'm referring to the background that might be in the image as a texture or alternatively a background for the white border of paper behind the image. Gathering backgrounds is another step in the creative process. Show your appreciation for our free weekly Podcast and our free daily Here's a Thought… with a donation Thanks!

In this candid conversation, Poppy Bourg and Shannon McGough of Poppy McGough Design House unpack the evolving Dallas design scene, the challenges of modern publishing, and the importance of integrating architecture and interior design. They discuss how their unique backgrounds inform their approach, client expectations in a shifting market, and why authenticity and craftsmanship matter more than fleeting trends. Designer Resources Pacific Sales Kitchen and Home. Where excellence meets expertise. Design Hardware - A stunning and vast collection of jewelry for the home! TimberTech - Real wood beauty without the upkeep LOME-AI.com, simple, inexpensive, text to video harnessing the power of AI to grow your firm, beautifully. From the pitfalls of celebrity-driven design magazines to the nuanced demands of Dallas homeowners, Poppy and Shannon reveal how they balance creativity, technical knowledge, and client relationships to create spaces that are not only beautiful but built to last. They explore the impact of regional influences, climate challenges, and the expanding role of interior designers in shaping cohesive, livable homes. 1. The State of Design Publications & Celebrity Influence Shift in design media focus: from architecture to celebrity homes Challenges of magazines cutting back editorial staff and local flavor Dallas's design culture: diverse, not pigeonholed into one “look” 2. Client Trends and Diversity in Dallas Architecture Clients influenced by wide range of styles via online exposure Resurgence of traditional styles alongside modern, Mediterranean, Santa Barbara influences Growing trend of lake homes post-2020 pandemic and its impact on local design culture 3. Modernism and Design Inspirations Experience visiting Modernism Week in Palm Springs Dallas's limited mid-century modern presence compared to other cities Appreciation for maximalism and richly detailed interiors beyond minimalist trends 4. Backgrounds & Partnership Story Shannon's hospitality design and hotel experience, focus on durability and build process Poppy's real estate and builder project management background, deep builder and trade knowledge How their combined skills create a holistic approach to residential design and construction collaboration 5. Building Challenges in Dallas Impact of active soil, climate extremes, and shifting weather on construction and interiors Importance of realistic client expectations around timelines, soil testing, and permitting Regional differences in design challenges and neighborhood personalities across Dallas metroplex 6. Expanded Role of Interior Designers Growing client education on lighting, air quality, water filtration, and acoustics Increasing involvement in exterior design for cohesive indoor-outdoor flow Navigating intellectual property issues, brand extensions, and designer-led product lines 7. Photography, Styling, and Portfolio Strategy Not every project gets photographed due to client preference or cost In-house styling process balancing client personality with editorial needs Preference for showcasing recent projects and maintaining strong referral-based business Thank you, Poppy, Shannon. Loved our chat and appreciate the time. Thank you for listening. If you liked this episode, share it with a friend or colleague who loves design and architecture like you do, subscribe to Convo By Design wherever you get your podcasts. And continue the conversation on Instagram @convo x design with an “x”. Keep those emails coming with guest suggestions, show ideas and locations where you'd like to see the show. Convo by design at outlook.com. Thank you, Poppy, Shannon. Loved our chat and appreciate the time. Thank you for listening. If you liked this episode, share it with a friend or colleague who loves design and architecture like you do, subscribe to Convo By Design wherever you get your ...

We dive deep into the backgrounds of Damien, Jason, and Jessie. Is there anything in their history that would make you believe it's possible they could commit this kind of crime? And what about John Douglas's profile of the killer or killers? Does it rule the three out? Or in?Check out our new True Crime Substack the True Crime TimesCheck out our other show The Prosecutors: Legal Briefs for discussion on cases, controversial topics, or conversations with content creators.Get Prosecutors Podcast MerchJoin the Gallery on FacebookFollow us on TwitterFollow us on InstagramCheck out our website for case resources:Hang out with us on TikTokSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.