Podcasts about nda

•⁠ ⁠அடுத்த வாரம் வெளியாகிறது இறுதி வாக்காளர் பட்டியல்.•⁠ ⁠ஆட்சியில் பங்கு கேட்பது எங்கள் உரிமை - மாணிக்கம் தாக்கூர். •⁠ ⁠இனி தேர்தலில் நிற்கமாட்டேன் - மாணிக்கம் தாகூர்.•⁠ ⁠அனைத்துலக வள்ளலார் மாநாட்டில் முதலமைச்சரின் 8 அறிவிப்புகள்! -•⁠ ⁠மத்திய கைலாஷ் மேம்பாலத்தை திறந்து வைத்தார் மு.க.ஸ்டாலின்? •⁠ ⁠``தோப்புக்கரணம் போடு என்றால், நான்-ஸ்டாப்பாக பல்டி அடிப்பார் பழனிசாமி'' - ஸ்டாலின் கடும் தாக்கு •⁠ ⁠NDA கூட்டணியில் புதிய கட்சிகள் இணையும் என ஜி.கே.வாசன் நம்பிக்கை •⁠ ⁠NDA-வில் 10 தொகுதிகளை கேட்டுள்ளோம் - ராம்தாஸ் அத்வாலே •⁠ ⁠சசிகலா பங்கேற்கும் பொதுக்கூட்டத்திற்கான ஏற்பாடுகள் தீவிரம் •⁠ ⁠விஜய் குறித்த நயினார் நாகேந்திரன் பேச்சுக்கு கனிமொழி எம்.பி கண்டனம். •⁠ ⁠'த்ரிஷா குறித்து நயினார் நாகேந்திரன் பேசியது வருத்தம்' - வானதி சீனிவாசன்.•⁠ ⁠"பெண்களை வெறும் உடலாக மட்டும் பார்ப்பதே RSS, BJP-இன் சித்தாந்தம்" - நயினார் மீது ஜோதிமணி காட்டம்.•⁠ ⁠இன்று சிவராத்திரி ராஜ்நாத் சிங்: ஈஷாவுக்கு வருகை? •⁠ ⁠புத்தாக்க திட்டத்துக்கு 1000 கோடி ஒதுக்கீடு? - அமைச்சரவை ஒப்புதல்? •⁠ ⁠ஈரானுக்கு எதிராக அமெரிக்க போர் கப்பல் தயார் நிலை?

It's Hot to go with stories about Bad Bunny relationship, Donna Kelce's NDA and Halle Berry rumor.Plus we play a Rom Com edition of 4 and No More and come see use at the Golf Show! See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Sorry Ladies... Bad Bunny is taken! Also Donna Kelce does not need an NDA for Trav and Taylor's wedding and another reason to love Margot Robbie. Halle Berry relationship rumors and give Tori Spelling a break. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Dave and Chuck the Freak talk about checking in with Jason, Italy's Ice Queen's Olympics bouncy ass compilation, farmer who got his butt kicked by kangaroo gives first interview, amazing foods that people think are overrated, update on the guy who was detained in Nancy Guthrie investigation, elderly woman kidnapped for ransom, man charged with attacking several women on dates, parking spot fights, investigation into explosive devices at an old guy's house in The Villages, coach collapsed during basketball game, update on kid who swam for 4 hours to save family lost at sea, online retailer makes it hard for Dave to make return, update on the Olympian who admitted to cheating on GF during interview, Olympics update, Lindsey Vonn latest surgery, NBA suspends players for Pistons vs Hornets fight, new sprot where competitors run straight into each other, RIP James Van Der Beek, court date for Blake Lively and Justin Baldoni, Michael Douglas to publish memoir, Gene Simmons complaining about rappers inducted into Rock Hall, pantsless mayor at city hall, guy arrested for stealing beehive, woman swallowed a spoon, massive rats take over family's home, unexpected dating scene at Medieval Times, sextinction, drunk guy fell off e-bike in front of cops, woman is repeat property squatter, woman threatened to assault airline employee on flight, guy had to sign NDA to return item to Lowe's, average person falls passionately in love twice in life, Space X sifting focus to moon base, Ace Hardware pies, and more!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

No episódio do Hipsters.Talks, PAULO SILVEIRA, CVO do Grupo Alura, conversa com KUNTUALA ZELI, diretora de vendas na Oracle, sobre como funciona toda a jornada de vendas em empresas de software, cloud e SaaS. Vamos explorar o papel do pré-vendas/arquitetos, que fazem a ponte entre as demandas do cliente e a tecnologia, e dos engenheiros, que mergulham na profundidade técnica e conduzem as POCs. Uma conversa que mostra como vendas em tecnologia deixaram de ser apenas comerciais e passaram a exigir profundo entendimento técnico, visão de negócio e colaboração entre times. além de revelar como carreiras híbridas entre tech e vendas estão se tornando cada vez mais comuns.

The FBI is seeking doorbell footage from two January dates near Nancy Guthrie's Tucson-area home as investigators test a black glove found nearby and search for a masked suspect believed to be local. Donna Kelce says she hasn't signed an NDA for Travis Kelce and Taylor Swift's expected June wedding in Rhode Island, insisting she can keep details private and is excited for her mother-son dance. Nicole Curtis apologized after leaked footage showed her using a racial slur on “Rehab Addict,” leading HGTV to pull the show and end her 15-year run with the network. Hosts: Charlie Cotton, Deven Rall Learn more about your ad choices. Visit podcastchoices.com/adchoices

Recently, Prime Minister Narendra Modi visited Kerala and Tamil Nadu, scheduled to have Assembly elections soon, and stressed the need for a double-engine government. The term double-engine government refers to having the same political party (in this case, the Bharatiya Janata Party or its allies in the National Democratic Alliance) in power at both the Centre and in a State. Critics of this model include Tamil Nadu Chief Minister M.K. Stalin, who has asked how Opposition-ruled States are doing well without a double-engine government. Does a double-engine government ensure growth? Louise Tillin and Yamini Aiyar discuss the question in a conversation moderated by Sai Charan. Edited excerpts:Does the discourse around a double-engine government imply an inherent bias against non-BJP/non-NDA governments in the States? Guests: Louise Tillin is Professor of Politics at King's India Institute, LondonYamini Aiyar is former President and Chief Executive of the Centre for Policy Research and senior visiting fellow at Brown University Host: Sai Charan Edited by Sharmada Venkatasubramanian Learn more about your ad choices. Visit megaphone.fm/adchoices

Abu Lahya's mentorship: https://application.imaccelerator.com/fg1

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

Combo treatment aims to be latest to enter the weight loss space; Amgen refuses to remove drug from market despite FDA request; Novo Nordisk threatens legal action against Hims & Hers; an oral film formulation of sildenafil is approved; and the FDA accepts the NDA for zanzalintinib plus atezolizumab for metastatic colorectal cancer.

Tamil Nadu's 234-seat assembly election hinges on an unprecedented question: can superstar Vijay's TVK disrupt the established DMK-AIADMK duopoly? Host Anirban Chowdhury talks to political analyst Sumanth Raman, who dissects the math Vijay polls around 15% vote share but may win zero seats, potentially acting as a spoiler splitting anti-DMK votes. The AIADMK-led NDA gains ground after securing PMK and TTV Dhinakaran's crucial Thevar community votes, while DMK battles anti-incumbency yet holds firm thanks to Stalin's personal appeal. The magic number: 40% vote share. With caste equations and celebrity politics injecting chaos, Tamil Nadu's outcome remains defiantly unpredictable.You can follow Anirban Chowdhury on his social media: X and Linkedin Check out other interesting episodes like: How Will a Volatile ₹ Impact You in 2026?,Capital Pains: Budget 2026's Loud Silences, India’s Labour Law Reboot, Viral to Valuation: Building Women’s Cricket as a Brand, Why are Music Labels Buying Into Film Companies? and much more. Catch the latest episode of ‘The Morning Brief’ on The Economic Times Online, Spotify, Apple Podcasts, JioSaavn, Amazon Music and Youtube. Credits: TheHinduOfficialSee omnystudio.com/listener for privacy information.

 Groundhog day in comics.  What does that mean? Apparently several different things as the boys mix up the topic proposed and come up with two different topics! lol Topic 1- Time loop stories Topic 2- recurring tropes and storylines used over and over again. We got ’em both!! Hear about Moira Mactaggert’s quest to get to a Red Lobster. Or Spider-Man’s poor hygiene. Secret identities revealed over and over and over again! But we start with a SNEAK PEAK at AVENGERS : DOOMSDAY! We toss aside our NDA and tell you EVERYTHING we saw! We swear it’s true! Pinky swear! Dr Doom! Richard Kind! Josh Gad! White Vision! AC/DC! and SO MUCH MORE! Direct Download: MP3

People are sharing secrets they’ve been keeping for years and can finally spill now that their NDA's expired...We're going over the craziest ones!See omnystudio.com/listener for privacy information.

Broadcasting live from the O'Leary Ventures boardroom at The Hive in Grand Forks, Scott Hennen hosts a wide-ranging Town Hall. U.S. Senator Kevin Cramer joins the program from Washington D.C. to pull back the curtain on the "negotiation fest" surrounding the government shutdown and why Democrats may have trapped themselves by targeting DHS funding. The dialogue shifts to a fiery but civil debate with North Dakota State Senator Tim Mathern regarding federal overreach and the "Minnesota Contagion." Later, we explore the cutting edge of history and technology with an update on the Theodore Roosevelt Presidential Library's realistic AI hologram and a sit-down with military vet turned tech entrepreneur JC Van Leer on the future of "one-way" drone defense. Standout Moments & Timestamps [00:02:03] The DHS "Horrible Box" Senator Kevin Cramer explains why Democrats' decision to target ICE and DHS funding in the appropriations battle is a "special kind of stupid," as a shutdown would primarily impact Democratic priorities like FEMA and TSA. [00:15:34] The 10th Amendment Debate State Senator Tim Mathern argues that federal ICE actions in Minnesota constitute a violation of states' rights, comparing the federal overreach to the central governments of China and Russia. [00:19:33] "Macro vs. Micro" Ethics A tense exchange occurs as Senator Mathern suggests individuals have a "right to resist arrest," while Scott and Kevin challenge the morality of interfering with law enforcement. [00:23:40] The Don Lemon Arrest The crew reacts to the breaking news of Don Lemon's arrest at a Minneapolis church for allegedly violating the FACE Act, debating the media's role in local unrest. [00:26:34] Talking Back to TR Matt Briney reveals that the new Theodore Roosevelt Presidential Library will feature a realistic, AI-driven hologram of TR that can hold personal conversations with visitors at his cabinet table. [00:28:44] Radio Graffiti: "The Wet Sponge" A caller roasts political dodging, stating that expecting a straight answer from a politician is "like squeezing a wet sponge and wondering why you're not getting orange juice." [00:30:44] HCMC's Financial Crisis A live look at the 100 job cuts at Hennepin County Medical Center, leading to a debate on whether state-level fraud has drained resources for essential public healthcare. [00:39:00] Drone Warfare in Grand Forks Entrepreneur JC Van Leer shares his transition from flying Blackhawks to developing NDA-compliant, "one-way" drones designed to keep the U.S. ahead of China in Group 1 and Group 2 technology.

En '¡Buenos días, Javi y Mar!' en CADENA 100, se informa del funeral en Huelva por 45 víctimas y la petición de conocer la verdad. La sanidad vasca revacuna a 98 bebés y 5 adultos por vacunas caducadas, investigándose más dosis defectuosas, coincidiendo con la salida de España de la lista de países libres de sarampión. En Villamanín (León), los agraciados del Gordo de Navidad deben inscribirse y renunciar al 10% del premio por sobreventa. Se repasan las nominaciones a los Grammy para Lady Gaga, Bruno Mars y Justin Bieber. La boda de Taylor Swift genera expectación, con invitaciones que exigen NDA y una ubicación secreta. Oyentes comparten leyendas como la del 'Tío Baldomerín' o la dama blanca del Salto Lucero. Entre otras historias, una cirujana recibe una propuesta inusual de agradecimiento, se destaca la sinceridad en las relaciones y se comenta el caso de un hombre que viajó a Croacia por error buscando un médico. Finalizan con consejos sobre la etiqueta de ...

A woman goes viral for playing Hannah Montana songs too her kids because her husband used to date Miley Cyrus back in the day. Anna and Raven discuss growing up with famous people.  Major fails. What's something you attempted, and it just never worked. Couldn't reach the finish line? Ravens was just about a lazy Sunday project. Annas was a bit more intense, with far more feedback. Todays trending is Taylor and Travis WEDDING INVITES! (Maybe). Her invitations were sent out with an NDA. Typical. Tesla's making robots. At least the Grammys are this weekend! January birthdays and all the feels that come with them. It is proven that most January birthdays dislike their DOB. Everyone's tired, burnt out, broke, and cold.  3 songs nominated for song of the year this year at the Grammys are EAR WORMS. Songs that just do not leave your head the second you hear it. Anna and Raven discuss the songs you can't stop once you start.  Every week Anna and Raven invite a middle school student to participate in Middle Schooler News! They report the headline news and Anna and Raven comment on it! This week Muhammed joins the show and Anna and Raven find out what's going on in the world!     It's Mommy's Margarita Friday!  For all you do, and all you put up with this week, you earned yourself a reward!  What did you do for your Mommy Margarita? NFL Sideline reporter Morgan Bedard Joins Anna & Raven to discuss NFL Championship weekend! Dawn and Leo's friend own a restaurant, and they usually go every Thursday night. What they've noticed over the last couple of months is that the quality of the food has gone down, and that the service has been rough. They've waited a long time for their meals and most times it's cold and bland. She thinks that they need to tell him that it's going downhill and to do something about it. He thinks that it's none of their business and if he isn't asking for feedback, they shouldn't be giving it. What do you think? Colin has a chance to win $800! All he has to do is answer more pop culture questions than Raven in Can't Beat Raven!    

•⁠ ⁠அதிமுகவில் சேர கோரிக்கை வைத்த ஓபிஎஸ்.. நிராகரித்த இபிஎஸ்!•⁠ ⁠ஓபிஎஸ்ஸை அதிமுகவில் சேர்க்க மறுத்த எடப்பாடி பழனிசாமி… ஓபிஎஸ் கொடுத்த பதில் •⁠ ⁠கரூர் துயர சம்பவம் குறித்து 125 நாட்களுக்குப் பிறகு விஜயை தாக்கி விமர்சித்துள்ள எடப்பாடி பழனிசாமி•⁠ ⁠எடப்பாடி பழனிசாமிக்கு தவெகவின் நாஞ்சில் சம்பத் பதிலடி•⁠ ⁠தமிழகத்தில் நாள்தோறும் அதிகரிக்கும் குற்றச்சம்பவம் - விஜய் கண்டனம்? •⁠ ⁠விஜய் எங்களை விமர்சித்ததால் நாங்கள் விஜயை விமர்சித்தோம் - செல்லூர் ராஜு•⁠ ⁠“கேப்டன் நல்லவர்.. தேமுதிக நல்ல கட்சி.. அப்புறம் ஏன் ஜெய்க்க விட மாட்டேங்குறீங்க?” - விஜய பிரபாகரன்•⁠ ⁠Suzuki Hayabusa பைக்கில் முதல்வர்!•⁠ ⁠"வழக்குகளால் மிரட்டப்பட்டு சுயநலத்தால் உருவாக்கப்பட்ட கூட்டணி NDA" - மு.க.ஸ்டாலின்•⁠ ⁠கோவையில் வானதி சீனிவாசன், அண்ணாமலை டிக் செய்த `தொகுதிகள்' - பின்னணி தகவல்!•⁠ ⁠தங்கம் விலை உயர்வை குறிப்பிட்டு இந்திய பொருளாதார நிலையை விளக்கிய முன்னாள் நிதியமைச்சர் ப.சிதம்பரம் •⁠ ⁠துணிச்சலாக சிங்கப் பெண்ணாக! - செல்வப்பெருந்தகை*  ̀முரண்பாட்டு பட்டியல்' வாக்காளர் விவரம் வெளியிட வேண்டும் - உச்சநீதிமன்றம்?•⁠ ⁠தமிழ்நாடு அரசின் திரைப்பட & சின்னத்திரை விருதுகள்; அசுரன், ஜெய் பீம் `டு' மாநகரம்! - முழு விவரம்! •⁠ ⁠சபரிமலைக் கோயில் தங்கம் திருட்டு: `செல்வம் பெருகும் என்றார்கள்' - விசாரணையில் நடிகர் ஜெயராம்•⁠ ⁠பொருளாதார ஆய்வு அறிக்கையில் சொல்லப்பட்டது என்ன? •⁠ ⁠அவையில் தேவையில்லாமல் பேசக் கூடாது என MP-களை கண்டித்த மக்களவை சபாநாயகர் ஓம் பிர்லா •⁠ ⁠மஹாராஷ்டிரா மேம்பாலம்:  மேல்பாலத்துல பாதி எங்க... திறப்பு விழாவிற்கு முன்னரே பொங்க வைக்கும் நெட்டிசன்கள் •⁠ ⁠மகாத்மா காந்தி நினைவு தினம்!•⁠ ⁠அருட்பிரகாச வள்ளலார் நினைவு தினம்!

Have you had to sign an NDA? Are you driving around with no heat? We spin the wheel of old topics and do not so great date. When did you audition for something? We hear WILD true stories from the listeners. When was the last time you went to the "movies" in the car? See omnystudio.com/listener for privacy information.

Have you had to sign an NDA? Are you driving around with no heat? We spin the wheel of old topics and do not so great date. When did you audition for something? We hear WILD true stories from the listeners. When was the last time you went to the "movies" in the car? See omnystudio.com/listener for privacy information.

2026 is shaping up to be a hectic political year in India. The ruling Bharatiya Janata Party (BJP) of Prime Minister Narendra Modi has appointed the relatively unknown Nitin Nabin to take over as party president. The BJP and its opposition challengers are gearing up for high-stakes assembly elections in five states later this spring. And the Election Commission of India (ECI) is in the midst of a controversial revision of India's gargantuan electoral rolls.To discuss these and the country's other key political stories, Sunetra Choudhury—the national political editor of the Hindustan Times—joins Milan to kick off the fifteenth season of Grand Tamasha. The two sat down for a special episode recorded live in HT's New Delhi studio. Listeners will know Sunetra from her past appearances on the podcast, as well as from her reporting for the Hindustan Times—and, of course, from her book Black Warrant, which has since been adapted into a hit Netflix crime drama of the same name. Sunetra has over two decades of reporting experience and was the recipient of the Red Ink award in journalism in 2016 and the Mary Morgan Hewett award in 2018.Milan and Sunetra discuss the prevailing political winds in Delhi, the BJP's surprising new president, and the long shadow of the 2025 Bihar assembly elections. Plus, the two discuss the upcoming state elections, the inner turmoil within the Congress Party, and the ECI's controversial “special intensive review.”Episode notes:“Interpreting the 2025 Bihar Verdict (with Roshan Kishore),” Grand Tamasha, November 19, 2025.“How India's Women Are Redefining Politics (with Ruhi Tewari),” Grand Tamasha, November 5, 2025.Sunetra Choudhury, “NDA's landslide win will cause ripples far beyond Bihar,” Hindustan Times, November 15, 2025.

•⁠ ⁠அஜித் பவார் விமான விபத்தில் மரணம்! •⁠ ⁠Ajit Pawar: 'நிறைவேறாத முதல்வர் கனவு; ஆட்சியைப் பிடித்த வாக்குறுதி' - யார் இந்த அஜித் பவார்? •⁠ ⁠இன்று முதல் பட்ஜெட் கூட்டத்தொடர்... என்ன ஸ்பெஷல்?•⁠ ⁠அல்வா தயாரிப்பு நிகழ்வில் நிர்மலா சீதாராமன்?•⁠ ⁠ஆளுநர் விவகாரத்தை நாடாளுமன்றத்தில் எழுப்புவோம் - திமுக & காங்கிரஸ்.•⁠ ⁠"அம்பேத்கர் பெயரை எப்படி தவிர்க்க முடியும்?" - கொந்தளித்த அதிகாரி மன்னிப்பு கேட்ட பாஜக அமைச்சர்! #ViralVideo •⁠ ⁠ஜம்மு காஷ்மீரில் கட்டடங்களை மூடிய பனிப் பொழிவு! #ViralVideo•⁠ ⁠பனியில் சிக்கி உயிரிழந்த உரிமையாளர்.. 4 நாட்களாக நகராமல் நின்ற வளர்ப்பு நாய் #ViralVideo•⁠ ⁠Feb 1 முதல் திமுக பிரசாரம்?•⁠ ⁠திமுக தேர்தல் அறிக்கை கதாநாயகியாகவும் இருக்கும்? - கனிமொழி.•⁠ ⁠கல்வி கடன்: திமுக அரசு மீது ராமதாஸ் விமர்சனம்•⁠ ⁠“யாருடன் கூட்டணி என ராமதாஸ் 2 நாட்களில் அறிவிப்பார்..” - ஸ்ரீகாந்தி•⁠ ⁠யாருடன் கூட்டணி பிப்ரவரி 14-ல் கிருஷ்ணசாமி அறிவிப்பு.•⁠ ⁠கூட்டணி பற்றி பேசினாலே டெல்லியிருந்து வந்துவிடுகிறார்கள் - செங்கோட்டையன்.•⁠ ⁠`த.வெ.க கூட்டணிக்கு வாங்க..!' - காங்கிரஸுக்கு அழைப்பு விடுத்த எஸ்.ஏ.சி•⁠ ⁠"நானோ என் மனைவியோ வரும் தேர்தலில் போட்டியிட மாட்டோம்" - தூத்துக்குடி செய்தியாளர் சந்திப்பில் சரத்குமார் பேட்டி.•⁠ ⁠NDA கூட்டணி சீட் ஷேரிங்.. யாருக்கு எவ்வளவு தொகுதி?•⁠ ⁠நீதிபதியாக இருக்கும் வரை சனாதனத்தை கடைபிடிப்பேன் - ஜி.ஆர்.சுவாமிநாதன்.•⁠ ⁠"அவர் நீதிபதியாக நீடிக்கும் தகுதியை இழந்துவிட்டார்" -பெ.சண்முகம்

Representative Tom Gann - the highest ranked conservative in the Oklahoma House of Representatives - talks about his upcoming bills including banning NDA, restricting use of lisence plate readers, and mandating lobbyist impact statements to track the money and influence of lobbyists over legislators. If you like freedom and government transparency, you don't want to miss this one!  Here's the link to the People's Audit discussed in the podcast.  https://www.oklahomastatecapital.com/sl/pa2025   And check out my amazing sponsors!    Motus Health - (405) 494-0165  https://motushealth.com   This is where my wife and go for a reason!  They offer the best in chiropractic care and true functional medicine.  Are you ready to start your journey to true health and stop just managing chronic pain, neuropathy, or gut issues? At Motus Health in Yukon, OK, they get to the root cause with advanced, non-invasive care. From spinal and knee decompression, functional neurology, weight loss support, neuropathy treatment, and personalized nutraceutical supplementation— they help you restore brain function, reduce inflammation, and reclaim your vitality. Visit MotusHealth.com today to schedule your consultation and start feeling your best! Motushealth.com or call  (405) 494-0165  ."     Michael Mcguire with McGuire Capitol  https://mcguirecap.com "Planning for a secure retirement? Then you need to Meet my friend, Michael McGuire, CEO of McGuire Capital Advisors with over 30 years of experience helping Oklahomans protect and grow their wealth. From asset allocation and risk management to income planning, life insurance, Medicare strategies, retirement planning, enhanced estate planning, and smart tax strategies—Michael delivers personalized solutions tailored to you. Get your complimentary consultation today! Call Michael directly at 405-760-5863  or visit:  https://mcguirecap.com   Stevens Trucking Stevens Trucking maintains over 350 power units in our fleet so they ensure their customers and drivers always have top of the line equipment With over 1,600 trailers, they are able to offer a drop-and-hook solution to keep your freight moving quickly and secure. While also helping their drivers get extra miles so they can keep on pullin' more loads. https://stevenstrucking.com

Your host, Sebastian Hassinger, talks with Alumni Ventures managing partner Chris Sklarin about how one of the most active US venture firms is building a quantum portfolio while “democratizing” access to VC as an asset class for individual investors. They dig into Alumni Ventures' co‑investor model, how the firm thinks about quantum hardware, software, and sensing, and why quantum should be viewed as a long‑term platform with near‑term pockets of commercial value. Chris also explains how accredited investors can start seeing quantum deal flow through Alumni Ventures' syndicate.Chris' background and Alumni Ventures in a nutshellChris is an MIT‑trained engineer who spent years in software startups before moving into venture more than 20 years ago.Alumni Ventures is a roughly decade‑old firm focused on “democratizing venture capital” for individual investors, with over 11,000 LPs, more than 1.5 billion dollars raised, and about 1,300 active portfolio companies.The firm has been repeatedly recognized as a highly active VC by CB Insights, PitchBook, Stanford GSB, and Time magazine.How Alumni Ventures structures access for individualsMost investors come in as individuals into LLC‑structured funds rather than traditional GP/LP funds.Alumni Ventures always co‑invests alongside a lead VC, using the lead's conviction, sector expertise, and diligence as a key signal.The platform also offers a syndicate where accredited investors can opt in to see and back individual deals, including those tagged for quantum.Quantum in the Alumni Ventures portfolioAlumni Ventures has 5–6 quantum‑related investments spanning hardware, software, and applications, including Rigetti, Atom Computing, Q‑CTRL, Classiq, and quantum‑error‑mitigation startup Qedma/Cadmus.Rigetti was one of the firm's earliest quantum investments; the team followed on across multiple rounds and was able to return capital to investors after Rigetti's SPAC and a strong period in the public markets.Chris also highlights interest in Cycle Dre (a new company from Rigetti's former CTO) and application‑layer companies like InQ and quantum sensing players.Barbell funding and the “3–5 year” viewChris responds to the now‑familiar “barbell” funding picture in quantum— a few heavily funded players and a long tail of small companies—by emphasizing near‑term revenue over pure science experiments.He sees quantum entering an era where companies must show real products, customers, and revenue, not just qubit counts.Over the next 3–5 years, he expects meaningful commercial traction first in areas like quantum sensing, navigation, and point solutions in chemistry and materials, with full‑blown fault‑tolerant systems further out.Hybrid compute and NVIDIA's signal to the marketChris points to Jensen Huang's GTC 2025 keynote slide on NVIDIA's hybrid quantum–GPU ecosystem, where Alumni Ventures portfolio companies such as Atom Computing, Classiq, and Rigetti appeared.He notes that NVIDIA will not put “science projects” on that slide—those partnerships reflect a view that quantum processors will sit tightly coupled next to GPUs to handle specific workloads.He also mentions a large commercial deal between NVIDIA and Groq (a classical AI chip company in his portfolio) as another sign of a more heterogeneous compute future that quantum will plug into.Where near‑term quantum revenue shows upChris expects early commercial wins in sensing, GPS‑denied navigation, and other narrow but valuable applications before broad “quantum advantage” in general‑purpose computing.Software and middleware players can generate revenue sooner by making today's hardware more stable, more efficient, or easier to program, and by integrating into classical and AI workflows.He stresses that investors love clear revenue paths that fit into the 10‑year life of a typical venture fund.University spin‑outs, clustering, and deal flowAlumni Ventures certainly sees clustering around strong quantum schools like MIT, Harvard, and Yale, but Chris emphasizes that the “alumni angle” is secondary to the quality of the venture deal.Mature tech‑transfer offices and standard Delaware C‑corps mean spinning out quantum IP from universities is now a well‑trodden path.Chris leans heavily on network effects—Alumni Ventures' 800,000‑person network and 1,300‑company CEO base—as a key channel for discovering the most interesting quantum startups.Managing risk in a 100‑hardware‑company worldWith dozens of hardware approaches now in play, Chris uses Alumni Ventures' co‑investor model and lead‑investor diligence as a filter rather than picking purely on physics bets.He looks for teams with credible near‑term commercial pathways and for mechanisms like sensing or middleware that can create value even if fault‑tolerant systems arrive later than hoped.He compares quantum to past enabling waves like nanotech, where the biggest impact often shows up as incremental improvements rather than a single “big bang” moment.Democratizing access to quantum ventureAlumni Ventures allows accredited investors to join its free syndicate, self‑attest accreditation, and then see deal materials—watermarked and under NDA—for individual investments, including quantum.Chris encourages people to think in terms of diversified funds (20–30 deals per fund year) rather than only picking single names in what is a power‑law asset class.He frames quantum as a long‑duration infrastructure play with near‑term pockets of usefulness, where venture can help investors participate in the upside without getting ahead of reality.

פרק מספר 511 של רברס עם פלטפורמה, שהוקלט ב-18 בינואר 2026. אורי ורן מקליטים בכרכור (הגשומה והקרה) ומארחים את נמרוד וקס - CPO ו-Co-Founder של BigID - שחצה את כביש 6 בגשם זלעפות כדי לדבר על אתגרים טכנולוגיים בעולם המופלא של Data Production ו-Security.

•⁠ ⁠தமிழ்நாடு NDA-வுடன் இருக்கிறது - பிரதமர் மோடி•⁠ ⁠கீழடி அறிக்கை; ஆளுநரின் அராஜகம் எப்போ முடிவுக்கு வரும்?'- தமிழகம் வரும் மோடிக்கு ஸ்டாலின் கேள்வி•⁠ ⁠அதிமுகவினர் பேனர்களில் டிடிவி தினகரன் படம் புறக்கணிப்பு? •⁠ ⁠தேஜகூ மாநாட்டில் நடந்த சுவாரஸ்யங்கள்?* அமமுக ஆரம்பிக்கப்பட்டதற்கு அர்த்தமே இல்லாமல் போய்விட்டது! - மாணிக்கராஜா•⁠ ⁠தை முடிவதற்குள் உரிய பதில் - ஓ.பன்னீர்செல்வம்•⁠ ⁠“முரசு சின்னத்துல ஜெயிச்சு...” - விஜய பிரபாகரன் கோரிக்கை•⁠ ⁠தமிழ்நாடு சட்டமன்றத்தில் நடந்தது என்ன?•⁠ ⁠ஒரத்தநாடு தொகுதி காலியானதாக சட்டப்பேரவை அறிவிப்பு.•⁠ ⁠"விஜய் தனித்தே நிற்பார்; அவருக்கு கூட்டணி அமையும்." - செங்கோட்டையன் •⁠ ⁠"தமிழ்நாட்டின் 2026 தேர்தலுக்கான ‘விசில்' ஊதப்பட்டுவிட்டது!"- காங்கிரஸ் பிரவீன் சக்ரவர்த்தி பதிவு•⁠ ⁠ஜனநாயகன் படத்தின் சென்சார் தொடர்பான வழக்கில் ஜன.27ம் தேதி தீர்ப்பு?•⁠ ⁠குஜராத்: திறப்பு விழாவிற்கு முன்பே இடிந்த விழுந்த நீர் தொட்டி!•⁠ ⁠காஷ்மீரில் 20 அடி பள்ளத்தில் வாகனம் கவிழ்ந்து 10 ராணுவ வீரர்கள் பலி?•⁠ ⁠மணிப்பூரில் மீண்டும் பதற்றம் அதிகரிப்பு?•⁠ ⁠நிதின் நபினை பின்னுக்கு தள்ளிய மோடி #ViralVideo•⁠ ⁠சிறுமிகளை விரட்டிவிட்டு கங்கைக்குப் பால் அபிஷேகம்; வைரலான வீடியோவும்... எழுந்த விவாதங்களும்!•⁠ ⁠முதற்கட்ட மக்கள்தொகை கணக்கெடுப்பில் 33 கேள்விகள்! - ஒன்றிய அரசு அரசாணை•⁠ ⁠பிரான்ஸ், கனடா தலைவர்களை தொடர்ந்து ஜெர்மனி அதிபர் மெர்ஸ், உலக பொருளாதார மன்றத்தில் அதிரடி முழக்கம்!•⁠ ⁠அமெரிக்கா முன்மொழிந்த அமைதி வாரியத்தில் இணைய 19 நாடுகள் கையெழுத்து!•⁠ ⁠உக்ரைன் போரை முடிவுக்கு கொண்டுவர அமீரகத்தில் பேச்சுவார்த்தை தொடங்குகிறது!

What does it really feel like when you decide to sell and the process officially begins?In this Sell-Side Master Class episode, we walk through month zero and the first 30 days of a sell-side process: the pre-market foundation, the time commitment, and the “transfer” that has to happen so an advisor can speak like they're part of your team. We cover the core information you'll be asked to assemble (financials, customer data, employee data, forecasting, go-to-market materials), plus the practical reality that founders often need to keep the circle tight to avoid data leakage internally.We also explain the role of the three key documents that drive early-stage buyer movement:Teaser (anonymous, broad interest)Confidential Information Memorandum (CIM) (post-NDA, full story)Financial packet / data room (deeper dive, typically after qualification)Finally, we talk through a critical leadership question that often evolves during the process: are you selling in or selling out? And we close with a simple reminder: preparation equals leverage because speed and clarity protect value. Other Episodes in this SeriesPart 1. Knowing When It's Time to Sell: Listen now >>Part 2. Get Your House in Order: Listen now >>Part 3. Valuation Drivers: Listen now >>Part 4. What is my Take Home? Listen now >>Part 5. It Takes a Village. Listen now >> Listen to Shoot the Moon on Apple Podcasts or Spotify.Buy, sell, or grow your tech-enabled services firm with Revenue Rocket.

What's good everyone in this episode I'm talking about the secrets i learned from companies and clients where I didn't sign an NDA. This is a different type of episode Let me know what you think

Send us a textCheck us out at:  https://www.cisspcybertraining.com/Get access to 360 FREE CISSP Questions:  https://www.cisspcybertraining.com/offers/dzHKVcDB/checkoutGet access to my FREE CISSP Self-Study Essentials Videos:  https://www.cisspcybertraining.com/offers/KzBKKouvPodcast Link(s):  https://www.cisa.gov/news-events/news/dhs-launches-over-100-million-funding-strengthen-communities-cyber-defensesCyber attacks don't skip small towns, and today we dig into how local governments can turn policy into protection. We start with the new funding landscape for state, local, tribal, and territorial agencies—what's approved, where the dollars flow, and why alignment with CISA and the NIST Cybersecurity Framework is the difference between good intentions and measurable risk reduction. From staffing gaps to critical infrastructure dependencies, we break down a practical way to prioritize controls, track progress, and build lightweight governance that keeps projects moving and leaders informed.Then we pivot into CISSP Domain 1.8 with real scenarios that security teams face every week. What do you do when phishing simulations stall at a 40% click rate? We outline how to redesign awareness with role-based content, immediate coaching, and the right technical controls to lower human-driven risk. What's the right response when a new admin refuses to sign an NDA? Bring legal in, set the standard, and be ready to stand firm on conditions for sensitive access. We also unpack training repayment disputes during offboarding and why access revocation, asset return, and exfiltration monitoring must come before chasing dollars.We don't stop there. An employee's personal cybersecurity blog can be a liability or an asset—depending on how you set guidelines and review content. And when insider risk hits hard—a soon-to-be-terminated analyst copying files to a USB drive—the immediate play is decisive: disable access, secure devices, preserve evidence, and coordinate with HR and legal. Throughout, we keep the focus on clear policy, consistent enforcement, and actionable steps that work for resource-constrained teams as well as larger enterprises.If you're a security leader, an aspiring CISSP, or the de facto defender for a small community, you'll leave with concrete actions to raise your defenses, educate your people, and respond fast when signals turn red. Subscribe, share this with a teammate who needs a sharper playbook, and leave a review to help more practitioners find the show.Gain exclusive access to 360 FREE CISSP Practice Questions at FreeCISSPQuestions.com and have them delivered directly to your inbox! Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!

Listen as Bleu discusses how Kody and Robyn play good cop, bad cop with Meri and Janelle. Bleu also makes reference to how Kody and Sobyn are Game of Thrones Fans, how they play the Sister Wives to conquer and divide by creating the narrative of a back door deal Janelle helped write. Bleu will also examine Janelle's shady past behaviors against Meri. Bleu then explains how Meri snitching on Janelle to Kody brought on this current rapture in their relationship. Bleu then exposes how Sobyn and Kody expose her as the catfisher by using the same tactic to get Janelle and Meri to sign an NDA.

Elizabeth is the founder of No More Victims, an advocacy organization that passes child protection laws, and has served as the executive director of the Phillips Foundation since 2013. After her younger brother Trey died by suicide in 2019, following childhood sexual abuse at Kanakuk Kamps and a restrictive NDA. She is a SMU who has become a national voice for survivor justice. Elizabeth works on cases related to child sexual abuse, trafficking and negligence as a certified crime victim advocate. In 2025 she passed Trey's Law unanimously in Texas and Missouri, banning NDAs that silence child victims of sexual abuse and trafficking It was named in honor of her late brother who was abused and whose perpetrator is in prison for three life terms. She also led the Campaign for Camp Safety with families who lost daughters at Camp Mystic, passing the Heaven's 27 Camp Safety and Youth CAMPER Acts in Texas (2025) to establish baseline regulations for summer camps. A certified crime victim advocate, Elizabeth exposed decades of alleged abuse at Kanakuk (FactsAboutKanakuk.com), works globally on prevention, and is scaling innovative treatments for both survivors and offenders. Elizabeth is a wife and mother of three. Elizabeth's dedication to these reforms is now expanding nationally, and this interview is the first time Elizabeth has spoken publicly about this collective work and what's ahead. Shawn Ryan Show Sponsors: Join thousands of parents who trust Fabric to help protect their family—apply today in just minutes at https://meetfabric.com/SHAWN. Go to https://helixsleep.com/srs for 27% Off Sitewide Ready to upgrade your eyewear? Check them out at https://roka.com and use code SRS for 20% off sitewide. Elizabeth Phillips Links: IG - https://www.instagram.com/elizcphillips X - https://x.com/ElizCPhillips Phillips Foundation - https://phillipsfdtn.org No More Victims - https://www.nmvalliance.org Linktree - https://linktr.ee/elizcphillips Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode:The Rundown (00:38): Dane Zumbahlen returns with NDA updates and key reminders — Demolition Phoenix (Feb. 4-7) and the Summer 2026 Quarterly Member Meeting (June 5-6). He then breaks down what's driving early-2026 scrap conditions: nonferrous strength led by record-setting copper, quietly firm aluminum, steady ferrous pricing — and the volatility to watch as tariffs, demand and logistics shape the next quarter.From the Field (7:28): Dan Bolster of Homrich (and the NDA Safety Committee) shares two simple routines that pay off big: morning huddles and stretch-and-flex. He explains how a quick, intentional reset — covering today's plan, hazards, controls and what changed since yesterday — helps crewscommunicate better, stay focused and reduce strains, sprains and preventable incidents.Member Conversation (14:37): Jake Lindamood of LindamoodDemolition walks through the high-stakes Parkland Hospital project in Dallas: a 1.6-million-square-foot teardown in a tight medical district, with multiple active hospitals connected to the work. He details how engineering-led planning, disciplined communication with stakeholders and the right high-reach equipment helped the team separate structures safely, protect the public and finish ahead of schedule — with safety and execution driving the result.

Welcome to HCPLive's 5 Stories in Under 5—your quick, must-know recap of the top 5 healthcare stories from the past week, all in under 5 minutes. Stay informed, stay ahead, and let's dive into the latest updates impacting clinicians and healthcare providers like you!Interested in a more traditional, text rundown? Check out the HCPFive!Top 5 Healthcare Headlines for January 11-17, 2025:1. FDA Clears MiniMed Go Smart MDI App for Adults and Children with T1D, T2DThe FDA cleared Medtronic's MiniMed Go Smart MDI app for pediatric and adult patients with type 1 or type 2 diabetes, enabling integrated smart pen and sensor–based decision support to improve insulin dosing with multiple daily injections.2. FDA Approves ProlivRx, First At-Home Neuromodulation Device for MDDThe FDA approved ProlivRx as the first at-home, physician-directed neuromodulation therapy for adults with major depressive disorder who have not responded adequately to antidepressant treatment.3. FDA Requests Removal of Suicidal Ideation and Behavior Warning From GLP-1 RA TherapiesThe FDA requested removal of suicidal ideation and behavior warnings from GLP-1 receptor agonist labels after determining there is no increased risk associated with these therapies.4. FDA Extends Sparsentan (Filspari) sNDA Review for Focal Segmental GlomerulosclerosisThe FDA extended the review timeline for sparsentan's supplemental NDA in focal segmental glomerulosclerosis, delaying the PDUFA decision without requesting additional safety or manufacturing data.5. FDA Approves New Narcan Packaging, Aiming to Boost Carry RatesThe FDA approved new, more portable packaging for over-the-counter Narcan nasal spray to improve usability, reduce stigma, and increase the likelihood that naloxone is carried and available during overdose emergencies.

F2 is the AI platform for private markets investors, automating due diligence and portfolio monitoring workflows with agentic AI. After building ARK into a digital banking platform that scaled from tens of millions to tens of billions in loan volume, Donald Muir developed AI technology to automate debt placement on ARK's marketplace. When upmarket institutional lenders requested access to the AI for their entire deal flow—not just ARK's marketplace deals—Donald recognized the technology's standalone value. In this episode of BUILDERS, Donald shares how he's commercializing enterprise-grade AI for an industry where he personally spent years in the private equity bullpen, and how F2 is addressing the reliability and trust barriers that prevent AI adoption in high-stakes financial decision-making. Topics Discussed How F2 emerged from ARK's internal need to automate debt marketplace screening memos The technical approach to eliminating hallucination in Excel-based financial analysis Replicating private equity's "super day" interview format to prove AI capability with live deal data Sales team composition: hiring ex-finance professionals instead of traditional sales reps AI's role in evolving private equity analysts from menial tasks to system operators Product roadmap from due diligence to portfolio monitoring to deal syndication platform Maintaining operational independence while preserving strategic alignment with ARK GTM Lessons For B2B Founders Solve your own hardest problem first, then productize: Donald built F2's core technology to scale ARK's debt marketplace, focusing on the most difficult engineering challenge—reliable financial analysis of unstructured Excel data—because the marketplace required it. This resulted in technology that foundation models still haven't replicated over a year later. The aha moment came when institutional lenders wanted the AI for all their deal flow, not just marketplace transactions. Organic internal development created category-leading capabilities and validated product-market fit before commercialization. B2B founders should identify which internal operational challenges, if solved, could become standalone products serving the broader market. Design sales processes that mirror how your ICP evaluates talent: Donald replicated private equity's "super day" format where analyst candidates receive a data room, laptop without internet access, and three hours to produce an LBO model and investment thesis. F2 runs identical timed tests—customers send live deal data rooms under NDA, F2 generates investment committee memos using their templates, and presents same-day results. This proves the AI can perform at the standard funds use to evaluate human analysts they hire 18 months before start dates. B2B founders selling into industries with rigorous talent evaluation processes should reverse-engineer those frameworks into product demonstrations that speak to buyer expectations. Prioritize credibility over sales experience in technical markets: Donald's entire sales team consists of ex-finance professionals who lived in the seat—no traditional salespeople. These reps can screen-share investment memos created that morning and discuss them authentically with MDs and principals using industry-specific language. After 4.5 years running go-to-market at ARK, Donald teaches sales methodology to domain experts rather than teaching domain expertise to salespeople. For deals averaging half a billion dollars flowing through the platform, buyer credibility outweighs sales polish. B2B founders in specialized verticals should evaluate whether domain fluency or sales pedigree matters more for their specific buyer personas and deal complexity. Engineer for auditability before optimizing for speed: F2 focused on eliminating hallucination and achieving mathematical accuracy—solving what Donald calls the "reliability and trust" gap—before addressing workflow efficiency. The company name references the F2 keystroke used to audit Excel calculations at 3 AM in the PE bullpen. This positioning directly addresses the barrier preventing AI adoption for investment decisions: LLMs hallucinate, can't do math, and lack auditability. Only after proving the AI produces auditable, trustworthy output did F2 layer on speed benefits. B2B founders building for high-stakes decision environments should identify the fundamental trust barrier and make it the core technical focus before feature expansion. Leverage institutional knowledge as competitive differentiation: Beyond automating existing workflows, F2 enables firms to pipe in decades of institutional knowledge via API—instantly benchmarking new deals against thousands of historical transactions by vertical, revenue size, leverage levels, and management quality. This transforms screening memos from isolated analyses into context-rich evaluations informed by complete firm history. The AI doesn't just work faster; it has comprehensive context that individual analysts manually searching SharePoint folders could never access. B2B founders should identify where accumulated institutional data creates compounding value beyond point-in-time automation. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Comment on the Show by Sending Mark a Text Message.The loudest truths in the workplace rarely make it into court filings. We're changing that by opening our mic to employees who've lived through discrimination, retaliation, whistleblowing pressure, or predatory noncompetes—and want their experiences to protect others. After decades in employment law, we're pivoting from parsing opinions to documenting reality, on the record yet safely off the radar.Here's how it works. We invite fact-backed stories from any industry and any level. Each interview is focused and brisk—30 minutes designed to move from “What happened?” to “How did they do it?” to “What can someone else learn today?” We shield your identity with voice masking, scrub names and specifics, and cut anything that could point back to you or your company. No lawsuit required. In fact, many of the most valuable lessons come from situations that never reached court—because people didn't know their rights or because the matter settled before a judge ever looked at it.We're especially looking at four common flashpoints: discriminatory treatment tied to protected classes, retaliation after speaking up, whistleblowing on fraud or safety issues, and noncompetes that box workers out of their field. By surfacing patterns—sudden PIPs after complaints, code words that signal bias, unenforceable contract clauses—we turn isolated experiences into a public playbook. The goal is simple: when a listener faces a surprise HR meeting or a chilling NDA reminder, they'll have the language, steps, and confidence to respond.If you have a story and evidence to back it, we want to hear from you. Head to employeesurvival.com and hit the contact link. Share the episode with a coworker who needs it, subscribe for future interviews, and leave a review to help more employees find this resource. Your experience could be the blueprint that saves someone's job tomorrow. If you enjoyed this episode of the Employee Survival Guide please like us on Facebook, Twitter and LinkedIn. We would really appreciate if you could leave a review of this podcast on your favorite podcast player such as Apple Podcasts and Spotify. Leaving a review will inform other listeners you found the content on this podcast is important in the area of employment law in the United States. For more information, please contact our employment attorneys at Carey & Associates, P.C. at 203-255-4150, www.capclaw.com.Disclaimer: For educational use only, not intended to be legal advice.

Geschäftsgeheimnisse schützen selten klare Blackbox, sondern tägliche Gratwanderung zwischen Vertrauen, Vorsorge und rechtlichem Ernstfall. Was Unternehmen teuer zu stehen kommt, entsteht oft aus Nachlässigkeit: falsche Verträge, fehlende IT-Sicherheit, zu wenig Differenzierung, was wirklich geschützt werden müsste. Technologierechtler Benedikt Flöter und Strafrechtsexperte David Rieks liefern erhellende Praxisbeispiele, warnen vor Illusionen beim NDA und zeigen, warum Prävention mehr als gute Absichten verlangt. Du erfährst... ...wie Unternehmen ihre Geschäftsgeheimnisse effektiv schützen können. ...welche rechtlichen Schritte bei Geheimnisverletzungen möglich sind. ...wie ein starkes Schutzkonzept für geistiges Eigentum aussieht. __________________________ ||||| PERSONEN |||||

In this week's tell-nothing, the Browns discuss the sale of Coyote Pass, whether or not Janelle proposed a back door deal with Kody, why Meri doesn't trust anyone, and Robyn trying to force an NDA... Love the girls? Get more of their cringey, awesome content at Patreon.com/realitytvcringe!Follow us on IG https://instagram.com/realitytvcringeSubscribe to see our raccoon faces on YouTube! https://www.youtube.com/channel/UC_2CgqXLWjIEKV9PCtH3Kjw?sub_confirmation=1Leave a message for us on SpeakPipe: https://speakpipe.com/realitytvcringeSupport the pod by leaving a 5-star review on your favorite podcast platform! Thank you so much

As the country hurtles toward the 2026 midterms, Chuck Todd breaks down why winning in this moment may be more about margins than movements—and why messaging is about to get brutally hard. With a fragmented, exhausted electorate and a president acting emboldened despite weak political standing, Democrats face a defining choice: run as resistance to Donald Trump or position themselves as problem-solvers ready to turn the page. History suggests forward-looking messages work best, especially with independents who vote in self-interest and are tired of constant political warfare, even as Trump continues to dominate GOP primaries and sideline Congress. The conversation also looks at why Republicans’ razor-thin majority leaves them unable to govern, how Trump’s grip on the party could backfire in a general election, and why Democrats are increasingly optimistic about putting the Senate in play—with strong recruiting wins and favorable midterm math. New polling on the Renee Good shooting and ICE shows Trump-aligned tactics playing poorly outside the MAGA base, particularly with independents and women, underscoring just how volatile—and opportunity-rich—the political landscape could be heading into 2026. Then, Jon Slavet, a tech entrepreneur and Republican candidate for California governor, joins the Chuck ToddCast to discuss why he's entering one of the nation's most challenging political races. Slavet argues that while national politics dominates headlines, state and local governance has far more impact on people's daily lives—yet California's local politics receives inadequate attention despite intense interest from outsiders. He advocates for radical transparency in government, criticizing the use of NDAs in taxpayer-funded projects and calling out the influence of union spending. Addressing his controversial pursuit of Trump's endorsement despite never voting for him, Slavet positions himself as someone who sees Trump as an effective disrupter but not a builder, and he aligns with the administration on housing policy, particularly prioritizing temporary shelter over California's $1 million-per-unit permanent housing approach. Running as a Republican in deep-blue California, Slavet believes the state's pleasant weather has masked serious problems affecting middle and lower-income residents while the wealthy thrive. He proposes declaring an "affordability emergency" on day one, scrapping the high-speed rail project, and using AI to root out the billions lost to waste and fraud in California's healthcare system. Slavet defends his decision to run as a Republican rather than an independent, arguing that party identification remains essential for effectiveness despite his moderate positions on social issues like same-sex marriage and abortion. He contends that polls show half of Californians would support the "right kind" of Republican, and he's betting his business success and willingness to challenge both parties' orthodoxies will resonate with voters frustrated by the state's affordability crisis and regulatory burdens on small businesses. Finally, Chuck gives his ToddCast Top 5 championship winning Miami Hurricanes football teams & answers listeners’ questions in the “Ask Chuck” segment. Get your wardrobe sorted and your gift list handled with Quince. Don't wait! Go to https://Quince.com/CHUCK for free shipping on your order and 365-day returns. Now available in Canada, too! Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at https://ethos.com/chuck. Application times may vary. Rates may vary. Thank you Wildgrain for sponsoring. Visit http://wildgrain.com/TODDCAST and use the code "TODDCAST" at checkout to receive $30 off your first box PLUS free Croissants for life! Timeline: (Timestamps may vary based on advertisements) 00:00 Chuck Todd’s introduction 03:45 Messaging will be incredibly difficult in this political environment 04:15 If Dems only win house, it will be considered a partial rebuke of Trump 05:15 Trump is weak politically but acting like he has a ton of capital 06:00 Trump feels emboldened to play “Risk” with the world 06:45 Administration will likely back off investigation into Jay Powell 08:00 Talking to a fragmented and exhausted electorate will be tough 09:15 Elections are always won on the margins, not just by the base 10:30 Analysts project their preferences onto the electorate & swing voters 11:30 What is the most effective way to win elections in 2026? 12:30 Democrats will have to balance accountability vs. forward facing 14:15 “Would you work with Donald Trump?” a defining question for Dems 16:15 Dems have to decide whether they are resistance or problem solvers 18:00 History suggests forward looking messages are more effective 19:00 Most voters vote in self-interest, not for the greater good 19:45 After 10 years of Trump, the public is exhausted by politics 20:45 "Turn the page” messaging may appeal to independents 22:30 GOP primaries will be all about Trump, but cause problem in general 24:00 Trump can break ideological rules other Republicans can’t 25:00 GOP majority is so narrow they can’t really govern 25:45 Trump is increasingly uninterested in working with congress 27:15 “Vote GOP to stop impeachment” isn’t enough to woo swing voters 28:00 Growing number of R’s uncomfortable with Trump’s actions 28:45 Dems got a huge recruiting victory in Alaska with Mary Peltola 30:30 Peltola puts Alaska in play, making senate control more possible 31:45 Sherrod Brown has won in midterm years in Ohio 33:15 Democrats much further along in putting senate in play 34:30 Historically the party out of power picks up 4 seats, Dems need 4 36:30 Majority of Americans say Renee Good shooting not justified 37:15 Majority of independents & women say it was unjustified 38:15 82% of respondents were aware of the Renee Good shooting 39:15 57% disapprove of ICE 40:30 ICE + Minneapolis not playing well outside MAGA base 41:30 Kristi Noem is an easy scapegoat, could get dumped 49:00 Jon Slavet joins the Chuck ToddCast 50:45 Why enter politics and run for governor of California? 52:30 It’s fraught & dangerous to run for office in this political climate 53:15 National politics get attention, state & local more impactful 54:45 Serious lack of attention to local politics in California 56:00 There’s a ton of interest in California from people outside it 57:15 NDA’s should not be allowed in taxpayer funded projects 58:45 Major government discussions need more transparency in CA 59:15 Unions raise $1B in CA and spend $300 on political donations 1:00:00 Why seek Trump’s endorsement after not voting for him 3x? 1:01:15 Trump is good as a disrupter but not as a builder 1:02:00 Where do you agree with Trump on policy? 1:02:30 HUD’s new policy prioritizes temporary, not permanent housing 1:03:30 Permanent housing for homeless costs $1M per unit in CA 1:05:00 The priority should be getting homeless into temp housing ASAP 1:06:00 Could you have been as successful in a state other than CA? 1:07:15 California’s great weather masks many of its problems 1:08:30 Tech doesn’t suffer from regulatory issues like other industries 1:09:15 Middle & lower income Californians are suffering, rich doing great 1:11:30 Half of California’s work for small businesses that are overtaxed 1:14:00 Where will you find the money to fund your proposals? 1:14:30 California should scrap the high speed rail to nowhere 1:15:30 Jon would declare an affordability emergency on day 1 1:17:00 California’s healthcare costs $160B, Medicare fraud is 10–15% 1:18:30 AI can be used to root out waste, fraud and abuse in state budget 1:20:00 Californians pay huge taxes on fuel & energy 1:21:30 Why run as a Republican and not an independent? 1:23:00 We should be way beyond the same-sex marriage debate 1:24:00 Abortion is settled law in California 1:25:30 Why Jon was against Prop 50 in California 1:26:30 Gerrymandering is terrible for democracy 1:28:15 Running as independent isn’t effective, people identify w/ parties 1:29:45 The race will be a Trump referendum, how does a Republican win? 1:31:30 Polls show half of CA will vote for the “right kind” of Republican 1:32:45 CA has had plenty of billionaires run & fail to win elections 1:35:00 Has Silicon Valley been regulated enough? 1:36:15 AI boom has been critical for San Francisco’s comeback 1:37:45 Would you ban social media for kids under 16? 1:39:30 Would you ban phones in schools? 1:40:15 How do you convince CA voters the wealth tax is a bad idea? 1:42:15 How would you work in a bipartisan manner with Dem state house? 1:45:00 Chuck’s thoughts on interview with Jon Slavet 1:45:30 There’s a lack of star power in CA governor race 1:46:15 ToddCast Top 5 Miami Hurricanes football teams if ’25 team wins title 1:49:00 #1 2001 1:51:00 #2 1987 1:52:30 #3 1993 1:54:00 #4 1983 1:55:00 #5 2025 1:58:30 Ask Chuck 1:58:45 In today’s climate would it benefit a candidate to avoid negative ads? 2:02:15 Are traditional Republicans missing a chance to push back on Trump? 2:09:15 Did Biden cater too much to the far left & hurt appeal with swing voters? 2:11:15 How is public media likely to continue on? 2:13:45 Can Democrats make Mississippi competitive? 2:19:30 Are we still capable of recognizing normalization of democratic collapse?See omnystudio.com/listener for privacy information.

Jon Slavet, a tech entrepreneur and Republican candidate for California governor, joins the Chuck ToddCast to discuss why he's entering one of the nation's most challenging political races. Slavet argues that while national politics dominates headlines, state and local governance has far more impact on people's daily lives—yet California's local politics receives inadequate attention despite intense interest from outsiders. He advocates for radical transparency in government, criticizing the use of NDAs in taxpayer-funded projects and calling out the influence of union spending. Addressing his controversial pursuit of Trump's endorsement despite never voting for him, Slavet positions himself as someone who sees Trump as an effective disrupter but not a builder, and he aligns with the administration on housing policy, particularly prioritizing temporary shelter over California's $1 million-per-unit permanent housing approach. Running as a Republican in deep-blue California, Slavet believes the state's pleasant weather has masked serious problems affecting middle and lower-income residents while the wealthy thrive. He proposes declaring an "affordability emergency" on day one, scrapping the high-speed rail project, and using AI to root out the billions lost to waste and fraud in California's healthcare system. Slavet defends his decision to run as a Republican rather than an independent, arguing that party identification remains essential for effectiveness despite his moderate positions on social issues like same-sex marriage and abortion. He contends that polls show half of Californians would support the "right kind" of Republican, and he's betting his business success and willingness to challenge both parties' orthodoxies will resonate with voters frustrated by the state's affordability crisis and regulatory burdens on small businesses. Get your wardrobe sorted and your gift list handled with Quince. Don't wait! Go to https://Quince.com/CHUCK for free shipping on your order and 365-day returns. Now available in Canada, too! Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at https://ethos.com/chuck. Application times may vary. Rates may vary. Thank you Wildgrain for sponsoring. Visit http://wildgrain.com/TODDCAST and use the code "TODDCAST" at checkout to receive $30 off your first box PLUS free Croissants for life! Timeline: (Timestamps may vary based on advertisements) 00:00 Jon Slavet joins the Chuck ToddCast 01:45 Why enter politics and run for governor of California? 03:30 It’s fraught & dangerous to run for office in this political climate 04:15 National politics get attention, state & local more impactful 05:45 Serious lack of attention to local politics in California 07:00 There’s a ton of interest in California from people outside it 08:15 NDA’s should not be allowed in taxpayer funded projects 09:45 Major government discussions need more transparency in CA 10:15 Unions raise $1B in CA and spend $300 on political donations 11:00 Why seek Trump’s endorsement after not voting for him 3x? 12:15 Trump is good as a disrupter but not as a builder 13:00 Where do you agree with Trump on policy? 13:30 HUD’s new policy prioritizes temporary, not permanent housing 14:30 Permanent housing for homeless costs $1M per unit in CA 16:00 The priority should be getting homeless into temp housing ASAP 17:00 Could you have been as successful in a state other than CA? 18:15 California’s great weather masks many of its problems 19:30 Tech doesn’t suffer from regulatory issues like other industries 20:15 Middle & lower income California’s are suffering, rich doing great 22:30 Half of California’s work for small businesses that are overtaxed 25:00 Where will you find the money to fund your proposals? 25:30 California should scrap the high speed rail to nowhere 26:30 Jon would declare an affordability emergency on day 1 28:00 California’s healthcare costs $160B, Medicare fraud is 10-15% 29:30 AI can be used to root out waste, fraud and abuse in state budget 31:00 Californians pay huge taxes on fuel & energy 32:30 Why run as a Republican and not an independent? 34:00 We should be way beyond the same-sex marriage debate 35:00 Abortion is settled law in California 36:30 Why Jon was against Prop 50 in California 37:30 Gerrymandering is terrible for democracy 39:15 Running as independent isn’t effective, people identify w/parties 40:45 The race will be a Trump referendum, how does a Republican win? 42:30 Polls show half of CA will vote for the “right kind” of Republican 43:45 CA has had plenty of billionaires run & fail to win elections 46:00 Has Silicon Valley been regulated enough? 47:15 AI boom has been critical for San Francisco’s comeback 48:45 Would you ban social media for kids under 16? 50:30 Would you ban phones in schools? 51:15 How do you convince CA voters the wealth tax is a bad idea? 53:15 How would you work in a bipartisan manner with Dem state house?See omnystudio.com/listener for privacy information.

In this episode, host Travis Chappell and producer Eric use a ridiculous on‑air nicotine experiment and some Kroger pickle‑jar banter to launch into a serious conversation about the power of saying no with your money. From friends asking to “spot me, bro” to sketchy investments, unpaid collabs, lifestyle upgrades, and sponsors that don't feel right, they walk through real scenarios where saying yes can quietly wreck your finances—or your brand—if you're not intentional.​ On this episode we talk about: Eric nearly puking on mic after trying a 6mg mojito ZYN, why “no” would have been the better choice, and how that sets up the theme of the episode.​ How Travis handles friends and family asking for money—why he almost always says no to “investment” pitches now, and how he decides when helping actually becomes enabling.​ When to say yes (and when to stop) with unpaid collaborations, speaking gigs, and local partnerships—plus the story of how saying yes to a low‑ROI volleyball promo still led to a profitable tournament relationship for AuraVela.​ Lifestyle spending boundaries: cars, first‑class flights, subscriptions, Klarna‑financed Chipotle, and how Travis finally justified buying a genuinely nice car after years of driving beaters.​ The importance of asking “Does this matter to me—or just to other people?” before dropping money on status symbols, upgrades, or brand‑driven purchases.​ Eric's recent decision to drop a meaningful podcast sponsor after loyal, long‑time listeners said it felt off, and why he chose long‑term trust over short‑term cash.​ The hidden risks of programmatic ads (like political spots or government agencies slipping in) and how both hosts have had to tighten ad category filters to protect their brands.​ Saying no to shady money: Travis turning down a $3,000 crypto‑related interview offer that required an NDA and looked like reputation rehab for a founder with bad press.​ Top 3 Takeaways Not every “opportunity” is for you. Saying no to friends' investments, high‑risk plays, or repeated bailouts protects your own financial runway and keeps you from funding other people's bad patterns.​ Your brand is worth more than a short‑term check. Dropping a sponsor or declining a stage when it feels misaligned can cost money now but preserves audience trust that's worth far more over a decade.​ Buy for your life, not their approval. Big purchases and lifestyle upgrades should be driven by your values, convenience, and experiences—not by keeping up with people you don't even like.​ Notable Quotes “For investments right now it's basically a no—if I don't have true ‘play money,' I'd rather put it in something more certain than somebody else's ‘sure thing.'”​ “If you're asking me for help the fifth time, at some point I'm not helping—you're just making bad decisions and I'm funding them.”​ “You can have the life you want now and later, but only if you stop buying stuff just to impress people and start asking if it actually matters to you.”​​ ✖️✖️✖️✖️

•⁠ ⁠பொங்கலுக்கு 5 நாட்கள் விடுமுறை - பள்ளி மாணவர்களுக்கு குட் நியூஸ்!•⁠ ⁠பொங்கல் பரிசு நாளையும் வழங்கப்படும்: அரசு அறிவிப்பு!•⁠ ⁠சென்னையில் காணும் பொங்கல் அன்று சிறப்பு பாதுகாப்பு ஏற்பாடுகள்!•⁠ ⁠அரசுப் பேருந்துகளில் 4.88 லட்சம் பேர் பயணம்?•⁠ ⁠TVK Vijay: 7 மணி நேர விசாரணை: சிபிஐ-யிடம் விஜய் கொடுத்த வாக்குமூலம் என்ன?•⁠ ⁠ஜன.19ம் தேதி விஜயை சிபிஐ மீண்டும் விசாரணைக்கு அழைக்க வாய்ப்பு.•⁠ ⁠`சிங்கத்தின் வாயில் மாட்டிய விஜய்; சிபிஐ மூலம் பாஜக ஸ்கெட்ச்' - செல்வப்பெருந்தகை விமர்சன•⁠ ⁠ஜனநாயகன் மேல்முறையீட்டு வழக்கு - ஜன. 19 விசாரணை?•⁠ ⁠ஜனநாயகனுக்கு ஆதரவாக ராகுல் பதிவு!•⁠ ⁠அதிமுக கூட்டணியில் அமமுக, தேமுதிக, ராமதாஸ் தரப்பு பாமக?•⁠ ⁠விருப்ப மனுக்களைப் பெற மேலும் 2 நாட்கள் நீட்டிப்பு - ராமதாஸ் அறிவிப்பு!•⁠ ⁠NDA கூட்டணியில் தொடர்வதாக ஜான் பாண்டியன் தகவல்.•⁠ ⁠NDA கூட்டணி கட்சிகளின் பொதுக்கூட்டத்தை சென்னையில் நடத்த திட்டம்•⁠ ⁠ஜன.23ல் சென்னை வருகிறார் பிரதமர் மோடி•⁠ ⁠நயினாருடன் அண்ணாமலையும் டெல்லி பயணம்! •⁠ ⁠“அமைச்சர் பதவி என் வீட்டின் கதவை தட்டியபோது நிராகரித்தேன்” - வைகோ •⁠ ⁠வானில் நட்சத்திரங்களைகூட எண்ணிவிடலாம்... - துரை வைகோ•⁠ ⁠அமைச்சர் துரைமுருகனுக்கு அண்ணா விருது?•⁠ ⁠புதுச்சேரியில் பொங்கலுக்கு பின் பொங்கல் பரிசு வழங்கப்படும் -ரங்கசாமி அறிவிப்பு.•⁠ ⁠ஈரானுடன் வர்த்தகம் செய்யும்நாடுகளுக்கு, 25% வரி?•⁠ ⁠அதிபர் ட்ரம்புக்கு ஈரான் எச்சரிக்கை!

Angus Reid update on Crime in Canada Guest: Shachi Kurl, President of the Angus Reid Institute Update from the Streets of Minneapolis Guest: Ellason Montgomery (Female), protestor Poor Mental health and alcohol use persists after the Pandemic Guest: Dr. Hayley Hamilton, Senior Scientist and Co-Director of the Institute for Mental Health Policy Research at CAMH What is Light Therapy? Guest: Norman Farb, Associate Professor Department of Psychology, University of Toronto Mississauga Why is BC so slow to get a change to NDA legislation? Guest: Jennifer Kohr, lawyer New rental report shows BC is doing well on rents Guest: Christine Boyle, BC Minister of Housing Learn more about your ad choices. Visit megaphone.fm/adchoices

Mark Hamill really wanted Han, Luke and Leia to reunite onscreen one last time -- like nearly every Star Wars fan. And Disney dropped the ball. It seems like Hamill is speaking out more against Disney Star Wars these days. Is somebody's NDA up?Watch this podcast episode on YouTube and all major podcast hosts including Spotify.CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles.D/REZZED News covers Pixels, Pop Culture, and the Paranormal! We're an independent, opinionated entertainment news blog covering Video Games, Tech, Comics, Movies, Anime, High Strangeness, and more. As part of Clownfish TV, we strive to be balanced, based, and apolitical. Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/On YouTube - https://www.youtube.com/c/ClownfishTVOn Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvgOn Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629

SPONSORS: 1) MANDO: Control Body Odor ANYWHERE with @shop.mando and get $5 off off your Starter Pack (that's over 40% off) with promo code JULIAN at https://Mandopodcast.com/JULIAN!#mandopod 2) AMENTARA: Go to https://www.amentara.com/go/JULIAN and use code JD22 for 22% off your first order. 3) HOLLOW SOCKS: For a limited time, Hollow Socks is having a Buy 3, Get 3 Free Sale—visit https://hollowsocks.com to get up to 50% off your order. (***TIMESTAMPS in description below) ~ Kendis Gibson is is a 2x Emmy Winning Belizean-born American journalist. His book, "Five Trips" recounts his 5 psychedelic trips he took in an effort to heal his severe trauma. KENDIS' LINKS - IG: https://www.instagram.com/kendisgibson/?hl=en - BOOK: https://www.amazon.com/Five-Trips-Investigative-Journey-Psychedelic/dp/B0DB2PCVY9 FOLLOW JULIAN DOREY INSTAGRAM (Podcast): https://www.instagram.com/juliandoreypodcast/ INSTAGRAM (Personal): https://www.instagram.com/julianddorey/ X: https://twitter.com/julianddorey JULIAN YT CHANNELS - SUBSCRIBE to Julian Dorey Clips YT: https://www.youtube.com/@juliandoreyclips - SUBSCRIBE to Julian Dorey Daily YT: https://www.youtube.com/@JulianDoreyDaily - SUBSCRIBE to Best of JDP: https://www.youtube.com/@bestofJDP ****TIMESTAMPS**** 0:00 - Intro 1:31 - Living in Bedstuy as a Kid, Working in New York Media early 4:27 - Covering 9/11 live, Building 7 Theories 15:53 - Epstein Files, Epstein Death & News Cycle Weirdness, Kendis knows Epstein's Chefs 32:25 - Epstein's NDA was INSANE, Kash Patel & Dan Bongino 41:21 - Kendis' 5 Trips Book, Heroic Dose in Belize Ruins (STORY) 51:53 - Kendis witnesses tragic death, SSRIs 56:32 - Kendis' Childhood Abuse (STORY), Finding friend's body 1:02:15 - Processing Abuse, MDMA Trip, Forgiveness 1:21:18 - Getting rid of accent, Growing up w/ abusive brother, ABC made Kendis want to end it 1:26:03 - 2018 Struggles, Kendis decides to end it (STORY), Plant Medicine 1:33:53 - Most Physically Abusive Trip He Had, Paul Rosolie, Ayahuasca 1:48:32 - Ayahuasca Trip in Peru (STORY), Mario the Shaman 2:01:05 - The Shamanism, Julian recalls his Ayahuasca Trip, Ayahuasca made Kendis realize 2:20:15 - Julian reflects on his Ayahuasca trip in Peruvian Amazon 2:30:23 - Kendis' Work CREDITS: - Host, Editor & Producer: Julian Dorey - COO, Producer & Editor: Alessi Allaman - https://www.youtube.com/@UCyLKzv5fKxGmVQg3cMJJzyQ - In-Studio Producer: Joey Deef - https://www.instagram.com/joeydeef/ Julian Dorey Podcast Episode 371 - Kendis Gibson Music by Artlist.io Learn more about your ad choices. Visit podcastchoices.com/adchoices

What does it take to redefine psychiatric care with psychedelic therapies? In this episode, Dr. Dan Karlin of MindMed joins host Dr. Talar Hopyan to discuss the clinical, regulatory and cultural shifts underway in mental health. They explore the momentum behind MindMed's LSD program for anxiety and depression, how the team is preparing for NDA filing and commercialization and why purposeful growth—not hype—is key to long-term success in psychedelics. What you'll learn: Why MindMed's MM120 program could reshape the treatment landscape for anxiety How collaboration with regulators is enabling more responsible innovation What intentional scaling means for the future of psychedelic biotech The views expressed in this podcast belong solely to the speakers and do not represent those of their organization. If you want access to more future-focused, actionable insights to help biopharmaceutical companies better execute and succeed in a constantly evolving environment, visit the Syneos Health Insights Hub. The perspectives you'll find there are driven by dynamic research and crafted by subject matter experts focused on real answers to help guide decision-making and investment. You can find it all at https://www.syneoshealth.com/insights-hub. Like what you're hearing? Be sure to rate and review us! We want to hear from you! If there's a topic you'd like us to cover on a future episode, contact us at podcast@syneoshealth.com.

On this episode of Bulture Podcast:Is going to church on new year eve more performative than actually getting the word?Is the person you say you looking for actually in the club on New Years Eve?Pastor Jamal Bryant went off on people for criticizing his wife's 'transparent' dress at a church fundraiser: "I bought the dress.Cam Newton questions why a man would take a baddie seriously, Baddies star Natalie Nunn struggled to explain what qualities a “baddie” brings to the table in a relationshipBig Boogie Gangsters Grillz project fireCardi B responds to backlash over Stefon Diggs spending Christmas with his other two newborns instead of hersCourt documents have now revealed Stefon Diggs' personal chef alleges she was sl**ped, chok*d and thrown onto her bed over a pay dispute. The woman claims Diggs demanded she sign an NDA after the assault as a condition of further payment.Celina Powell exposed Offset for linking with her last nightKevin Durant committed $10M to establish a free Maryland-based after-school facility supporting underserved scholars looking to obtain a bachelor's degree.Netflix wants to keep movies in theaters for only 17 days after buying Warner Bros.Woman accuses James Harden of being the father of her newborn child “It's time for you to step up and take care of your son”. After James Harden announced he was having a baby with Mario Chalmers BM Paije Speights & was in his dad era with his 6-yr-old son, Kelsea Moyer goes to IG to say he's hiding their newborn son from the world & is a deadbeat dad.Ex-Lakers Christian Wood accuses Trevon Diggs' BM Yasmine Lopez of putting a hit out of him after she posted King Von's lyrics shortly before 3 gunmen tried to kill him in his home. Drake has been accused in a federal class-action RICO lawsuit of using the online sweepstakes casino to boost the play counts of his music across the major streaming platforms.Tennessee news anchor Cornelia Nicholson unknowingly introduced her own marriage proposal from boyfriend Riley Nagel during a local newscast.First win of the season for Coco united cupFederal prosecutors in San Diego are seeking a two-year prison sentence for Boosie over a felon-in-possession case, as he hopes for a Trump pardon.Caribbean flights canceled after U.S. Military Action in VenezuelaKarlous Miller explains that people treat a man different if his girlfriend is unattractive because they don't respect him as much.G Herbo just proposed to his longtime girlfriend and the mother of his children, Taina Williams, on New Years.Glasses Malone reacts to Adin Ross trying to call wack100 to call him even though he continues to disrespect DoechiiRussell Westbrook is now the NBA's all-time leading scorer for a point guardTikTok star Pinkydoll shares heartbreaking health update about her kidneys - urges youth to prioritize health & wellnessWill Smith sued by former tour violinist over sexual harassment and retaliation; accuses him of grooming and making “unusually intimate” comments.Dealership scams 21-year-old man out of $30k for 2017 Jeep SRTJaylen Brown speaks on his recent play — “no diss to Shai and Brunson, but neither one of them had a better month than I did.”

Show Notes: Remco Visser talks about Saga, an AI product used by 150 law firms. Remco explains that Saga is a legal AI innovation company helping law firms and legal departments implement AI into their practice and daily workflows.  AI Training and Integration The platform includes AI training and adoption sessions to help firms integrate AI into their daily practices. However, Remco highlights the importance of understanding the viability space where AI can be effectively used if the firm is not yet ready for full AI implementation. Saga helps firms understand  when AI integration is the best option and offers training on using AI in the workflow.  He talks about LLMs and more standard software options Demonstrating Saga Remco demonstrates the AI platform's ability to draft legal documents, using an NDA as an example. The platform automatically recognizes when a user wants to draft a document and opens a window for AI-generated documents, and he shows the platform's ability to ask follow-up questions for customization, such as what country state law should govern it? What's the main purpose? How long do you want it to last? Remco explains the platform's new updates require more context to draft accurate documents.    The Saga Prompt Improver Remco introduces the concept of a prompt improver that automatically improves prompts for users. The platform can suggest variables for users to fill in, making it easier to draft accurate documents. To highlight the program's efficiency, he gives an example of a prompt improved by the platform. Remco emphasizes the importance of providing context, such as jurisdiction and language, to improve the accuracy of AI-generated documents. He explains how the prompts can be stored in the Prompt Library  improver and how the Saga lab tests and shares prompts for customers to access. Saga Use Cases Remco discusses various use cases, including drafting letters of intent and reviewing agreements, and mentions that people with no legal experience can use it. The platform can use templates and attach documents to draft comprehensive legal documents. Remco demonstrates the ability to redline documents and suggest changes based on templates. The platform can also assist with litigation by drafting arguments, memos, and letters, and providing detailed timelines. Saga's Assistance Roles Remco explains the concept of assistance roles, such as devil's advocate, contract assistant, and legal research assistant. These roles help users challenge their arguments and improve their legal strategies.  Data Accuracy and AI Hallucinations Remco addresses the issue of hallucinations in AI-generated documents, explaining how Saga mitigates this risk. The platform uses citations and reasoning models to ensure the accuracy of generated documents. He demonstrates the various assistant models available from proofreader and tax authority to Judge and goes into detail on how to check citations and ensure data is accurate.  Saga's Implementation Process Remco outlines the implementation process for firms, recommending starting with a smaller group for training. The training program includes sessions on AI basics, prompting, workflows, and legal databases. Firms can also bring practice groups together to brainstorm AI use cases and share information. Remco emphasizes the importance of hands-on exercises during training to ensure users understand how to use the platform effectively.   Time-saving Features and Pricing Remco explains that Saga charges per seat per month, with a price of 125 euros per user. Firms save an average of four to five hours a week and see an improvement in work quality, especially for juniors. The platform is designed to be a no-brainer for firms looking to improve efficiency and quality in legal work. He acknowledges the challenges of implementing new software but believes the value proposition is clear. Grid Review Feature Remco demonstrates the grid review feature, which extracts information from documents and provides detailed insights. The platform can handle various types of documents, such as lease agreements, Chamber of Commerce extractions, and shareholders agreements. The grid review feature highlights the context around extracted information, providing a comprehensive understanding. In conclusion, Remco emphasizes the platform's ability to save time and improve the accuracy of legal reviews.   Timestamps:  01:34: Demonstration of AI Platform Features  03:05: Improving Prompts and Context  05:25: Use Cases and Advanced Features  10:34: Assistance Roles and Safeguards  14:07: Implementation and Training  17:02: Pricing and Value Proposition  20:47: Advanced Features and Customization  Links: Website: https://www.sagalegal.io/ LinkedIn: https://www.linkedin.com/in/remco-visser-1645a39b/ This episode on Umbrex: https://umbrex.com/?post_type=unleashed&p=224463&preview=true Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated show notes and transcript  

Throwbacks are where I re-release old episodes from the archives. So don't worry if you have heard it already, as 'New episodes' will continue to come out on Sundays. To get some of the old episodes heard.~~~In this episode, we meet Steven from Idaho. Steven has encountered UFOs on several occasions and had a very interesting near-death experience at an early age, and he will also be sharing several of his paranormal experiences as well.More information on this episode on the podcast website:https://ufochroniclespodcast.com/ep-194-joshua-tree/Want to share your encounter on the show? Email: UFOChronicles@gmail.comOr Fill out Guest Form: https://forms.gle/WMX8JMxccpCG2TGc9Podcast Merchandise:https://www.teepublic.com/user/ufo-chronicles-podcastHelp Support UFO CHRONICLES by becoming a Patron:https://patreon.com/UFOChroniclespodcastTwitter: https://twitter.com/UFOchronpodcastThank you for listening!Like share and subscribe it really helps me when people share the show on social media, it means we can reach more people and more witnesses and without your amazing support, it wouldn't be possible.Become a supporter of this podcast: https://www.spreaker.com/podcast/ufo-chronicles-podcast--3395068/support.

Grandma Lucille is a viral sensation! In this episode of the 100% Wild Podcast, Matt Drury breaks down the hilarious "What the hell was I supposed to do?" moment that racked up over 4 million views after she took a shot while the deer was still moving . We also celebrate a major milestone as Matt's daughter, Lola, harvests her very first buck during the Missouri youth season using a suppressed Winchester 350 Legend over standing beans . From the "unintended comedy" of 92-year-old Lucille in deer camp to the pure excitement of a child's first successful hunt, this episode captures the heart of what makes hunting season special for every generation. Beyond the highlights, we dive into the heavy-hitting topics affecting hunters today, specifically the controversial $250,000 deer cull in Wildwood, MO. We discuss why the city is hiring sharpshooters and how local bowhunters are fighting to prove they are a more efficient, cost-effective solution for suburban management . We also break down late-season tactics for hunting the "vast wasteland" of winter, the reality of "buck fever," and a candid debate on the MDC and NDA's stance on moon phase science versus what we actually see in the woods . This episode features gear designed for success, including DeerCast for movement prediction, Muddy Blinds, and Bog Pods for the perfect shot. 01:41 – Hunting the "vast wasteland" of the late season 03:13 – Caught on Time-Lapse: Mature bucks sparring 05:22 – Milestone Hunt: Lola harvests her very first buck  08:45 – 92-Year-Old Lucille: The hilarious reality of deer hunting 11:11 – The Ultimate Youth Setup: Winchester 350 Legend & Suppressors 13:33 – Clutch Shot: Lola's success under pressure 15:45 – Managing "Buck Fever" and the importance of youth results 21:16 – NASP Archery: Getting kids involved in school shooting sports 24:45 – The $250,000 Debate: Suburban deer culling in Wildwood, MO 34:10 – Management Debates: Moon phase science vs. real-world history Join the Rack Pack Facebook Group : / n73gskjt7bfb2ngc Get ahead of your Game with DeerCast available on iOS and Android devices App Store: https://itunes.apple.com/us/app/deerc... Play Store: https://play.google.com/store/apps/de... Don't forget to stock up for your next hunt! 1st Phorm has you covered! Protein Sticks: https://1stphorm.com/products/protein... Level-1 Bars: https://1stphorm.com/products/level-1... Energy Drinks: https://1stphorm.com/products/1st-pho... Hydration Sticks: https://1stphorm.com/products/hydrati... Send us a voice message on Speakpipe! https://www.speakpipe.com/100PercentW... For exciting updates on what's happening on the field and off, follow us on social Facebook: / officialdruryoutdoors Twitter: @DruryOutdoors Be sure to check out http://www.druryoutdoors.com for more information, hunts, and more! Music provided by Epidemic Sound http://player.epidemicsound.com/ #dodtv    

Grandma Lucille is a viral sensation! In this episode of the 100% Wild Podcast, Matt Drury breaks down the hilarious "What the hell was I supposed to do?" moment that racked up over 4 million views after she took a shot while the deer was still moving . We also celebrate a major milestone as Matt's daughter, Lola, harvests her very first buck during the Missouri youth season using a suppressed Winchester 350 Legend over standing beans . From the "unintended comedy" of 92-year-old Lucille in deer camp to the pure excitement of a child's first successful hunt, this episode captures the heart of what makes hunting season special for every generation. Beyond the highlights, we dive into the heavy-hitting topics affecting hunters today, specifically the controversial $250,000 deer cull in Wildwood, MO. We discuss why the city is hiring sharpshooters and how local bowhunters are fighting to prove they are a more efficient, cost-effective solution for suburban management . We also break down late-season tactics for hunting the "vast wasteland" of winter, the reality of "buck fever," and a candid debate on the MDC and NDA's stance on moon phase science versus what we actually see in the woods . This episode features gear designed for success, including DeerCast for movement prediction, Muddy Blinds, and Bog Pods for the perfect shot. 01:41 – Hunting the "vast wasteland" of the late season 03:13 – Caught on Time-Lapse: Mature bucks sparring 05:22 – Milestone Hunt: Lola harvests her very first buck 08:45 – 92-Year-Old Lucille: The hilarious reality of deer hunting 11:11 – The Ultimate Youth Setup: Winchester 350 Legend & Suppressors 13:33 – Clutch Shot: Lola's success under pressure 15:45 – Managing "Buck Fever" and the importance of youth results 21:16 – NASP Archery: Getting kids involved in school shooting sports 24:45 – The $250,000 Debate: Suburban deer culling in Wildwood, MO 34:10 – Management Debates: Moon phase science vs. real-world history Join the Rack Pack Facebook Group : / n73gskjt7bfb2ngc Get ahead of your Game with DeerCast available on iOS and Android devices App Store: https://itunes.apple.com/us/app/deerc... Play Store: https://play.google.com/store/apps/de...  Don't forget to stock up for your next hunt! 1st Phorm has you covered! Protein Sticks: https://1stphorm.com/products/protein... Level-1 Bars: https://1stphorm.com/products/level-1... Energy Drinks: https://1stphorm.com/products/1st-pho... Hydration Sticks: https://1stphorm.com/products/hydrati... Send us a voice message on Speakpipe! https://www.speakpipe.com/100PercentW... For exciting updates on what's happening on the field and off, follow us on social Facebook: / officialdruryoutdoors Instagram: @DruryOutdoors Twitter: @DruryOutdoors Be sure to check out http://www.druryoutdoors.com for more information, hunts, and more! Music provided by Epidemic Sound http://player.epidemicsound.com/ #dodtv  

Ann from the We Signed an NDA podcast is here and we talk about way more than pop culture. We briefly touch the Vanderpump world, then we get real about Asian American identity, family trauma, bipolar disorder, learning boundaries as an adult, dating in LA post pandemic, sexuality, and how humor can be a survival tool. This episode is equal parts hilarious and healing and it felt like meeting a soul friend for the first time!! Send us your questions and stories to be featured on da pod https://www.brokegirltherapy.com/contact-page Support our sponsors and BGT by using the codes below: BetterHelp: As a listener, you'll get 10% off your first month by visiting our sponsor at BetterHelp.com/brokegirl Mood: Mood.com PROMO CODE: BROKEGIRL for 20% off your first order Dipsea: DIPSEAstories.com/brokegirl Stefanie Maegan https://www.instagram.com/brokegirltherapy/ https://www.instagram.com/stefaniemaegan/ Ann Maddox https://www.instagram.com/mikiannmaddox/ https://www.instagram.com/wesignedannda/ Tom Sandoval Video: https://www.youtube.com/watch?v=_ZVbG0aY2Es&t=184s Learn more about your ad choices. Visit megaphone.fm/adchoices