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Learn more about Refrigeration Mentor Customized Technical Training Programs at www.refrigerationmentor.com/courses Join the Refrigeration Mentor Hub here This conversation was from our latest Refrigeration Mentor Community Meetup, talking about refrigeration controls and electrical systems with Andrew Freeburg and Erik Holland. We cover control fundamentals such as transformers, multiplex board setup, communication basics, polarity, baud rate, cable practices, and fail-safe settings for loads. We also discuss how to build confidence through competence - studying, repetition, applying skills on real systems, asking questions, using community support, setting goals, and learning by teaching. Interested in joining the next Refrigeration Mentor Community Meetup? Click here. In this episode, we discuss: (00:30) Confidence and Competence (06:02) Learning How to Learn (09:58) Setting Goals and Support Groups (15:42) Dunning Kruger Effect (21:58) Electrical Basics and Safety (22:21) Center Tap Transformers (24:30) Multiplex Boards and Dip Switches (25:59) Binary Addressing Switches (26:37) Power and Comms Terminals (27:11) Comms Voltage and LEDs (29:40) Wiring Noise and Shielding (30:47) Fail Safe Dip Switches (33:46) Analog Inputs and Outputs (34:54) Software vs Hardware Logic (39:06) Panel Safety Basics (43:30) Meter Testing and Ratings (47:47) Electrical Safety Mindset Helpful Links & Resources: Episode 371. A 6-Step Process for Faster Electrical Troubleshooting Episode 215. Understanding Refrigeration System Controls with Larry Herman of Redline Control Design
The perfect AI storm happened, and no one has noticed yet.
Wie hat dir die Folge gefallen?Gut
Nothing speaks more to a changing world than our own mind and body. We are designed for change; we change every second of every day—naturally. If we choose to resist change, we fight nature and interfere with the innate flow of life. This is why John avoids the term “anti-aging.” When we fight aging, we age faster, grow tired, and give power to the very things we do not want. In this episode, John offers tips on how to relax into the "flow" of life to improve your quality of life—physically, mentally, emotionally, and spiritually. Now in his mid-60s, John shares how scientific measures recently placed his “internal age” at just 33 years.
Nothing speaks more to a changing world than our own mind and body. We are designed for change; we change every second of every day—naturally. If we choose to resist change, we fight nature and interfere with the innate flow of life. This is why John avoids the term “anti-aging.” When we fight aging, we age faster, grow tired, and give power to the very things we do not want. In this episode, John offers tips on how to relax into the "flow" of life to improve your quality of life—physically, mentally, emotionally, and spiritually. Now in his mid-60s, John shares how scientific measures recently placed his “internal age” at just 33 years.
Nothing speaks more to a changing world than our own mind and body. We are designed for change; we change every second of every day—naturally. If we choose to resist change, we fight nature and interfere with the innate flow of life. This is why John avoids the term “anti-aging.” When we fight aging, we age faster, grow tired, and give power to the very things we do not want. In this episode, John offers tips on how to relax into the "flow" of life to improve your quality of life—physically, mentally, emotionally, and spiritually. Now in his mid-60s, John shares how scientific measures recently placed his “internal age” at just 33 years.
How did prompt engineering die so quickly? ☠️And what the heck does context engineering even mean? One of the trickiest things about LLMs is they're changing daily, yet they're the engines that drive business results. But if the engine is constantly changing, then you also have to change how you drive and the roads you take. That's why we're tackling context engineering in this installment of our Start Here Series, the essential beginners guide to understanding AI basics and growing your skills. Context Engineering: How to Get Expert-Level Outputs From AI Chatbots -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Evolution from Prompt to Context EngineeringWhy Prompt Engineering Is Now ObsoleteDefining Context Engineering in AI ChatbotsSix-Part Framework for Context EngineeringFour Layer System for Structuring AI ContextBuilding Reusable Context Vaults and SkillsConnecting Business Data to AI ModelsTechniques to Achieve Expert-Level AI OutputsImportance of Context Windows in Large Language ModelsContext Engineering Best Practices and ScalabilityTimestamps:00:00 "Access AI Community & Tools"03:08 "Mastering Context in AI"07:23 "Smart Models Require Less Precision"12:01 "Context Engineering Beats Prompt Engineering"15:49 "AI Context: Six Key Blocks"16:47 "Building Context for Better Results"19:53 "AI: Training, Not Easy Button"25:17 "Chain of Thought Prompting Decline"29:11 "Show, Don't Tell Techniques"32:13 "Context, Reuse, and Scalable Systems"33:19 "AI Chatbots: Memory and Skills"Keywords: context engineering, AI chatbots, expert level outputs, prompt engineering, large language models, business context, AI models, custom instructions, data access, context window, prime prompt polish, reusable context vaults, context vaults, skills file, memory enabled models, ChatGPT, Claude, Google Gemini, Microsoft Copilot, connectors, apps, searchable index, business data, personalized AI, context clues, reference material, examples, procedures, evaluation rubric, chain of thought prompting, generative AI, nondeterministic behavior, show don't tell technique, few shot examples, rubric first technique, grading criteria, output quality, scalable AI systems,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner
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
Outcomes, not Outputs We wonder why Agile is dying. Should we be so surprised? You’re probably watching your wallet these days. Prices are up, the economy is slow and uncertain. Your employers are no different. In 2026, story points, velocity, lead time and throughput might matter to YOU, but they don’t matter to THEM. Not to say that those measures aren’t important. Heck, you can’t tune your development, testing and deployment operations if you’re not looking at them. But if you were called to the mat tomorrow, if you were asked to prove the ROI of what you do, those metrics don’t tell a compelling story. It’s about Outcomes, not Outputs. Coaches and SM’s are now being called to defend their worth. They’re not interested in how well-tuned the development machine is if that doesn’t translate to real business results. The measures that the business cares about have to do with their financial outcomes, not outputs that don’t face the customer. Are your efforts helping the company create shareholder value? Do they impact earnings-per-share? If your boss spends a few million on a cadre of Agile Coaches this year, is that a net-positive investment for the company? More cashflow? More customers? Fewer abandoned carts? New subscribers? I know. I sound super-corporate right now. And maybe you hate that. But if you’re looking to accelerate your career in 2026, you can’t ask the people who fund it to trust you, sight unseen. They’re taking a closer look at the books, and these questions are long overdue. How are you contributing to our results? The Dev team might be considered a necessary expense, but if your Agile Shirpa talent aren’t making the team more impactful than they would be on their own, why are you even here? Remember, its Outcomes, not Outputs. This is the future of Agile practice in large enterprise. You have to collaborate with the business, and drive results for them. If you want to learn how, you should check out my brand-new Business Outcomes Partner Playbook. Get the edge so you can get your career back where it belongs, and say good-bye to to the upheaval and uncertainty that’s ripping through our industry. Did you enjoy this episode? You might also like these: The 2026 18th State Of Agile Report Episode 224 – Circulate Value – The Agile Survival Skill Episode 235 – Agile Is A Doorway **LEARN HOW TO DELIVER UNDENIABLE ROI THAT SAVES YOUR JOB AND ACCELERATES YOUR FUTURE** Get the Business Outcomes Partner Playbook Now! https://learning.fusechamber.com/offers/AFGm3tSy/checkout **FORGE GENESIS IS HERE** All the skills you need to stop relying on job postings and start enjoying the freedom of an Agile career on YOUR terms. First cohort starts in Jan 2026 https://learning.fusechamber.com/forge-genesis **THE ALL NEW FORGE LIGHTNING** 12 Weeks to elite leadership! https://learning.fusechamber.com/forge-lightning **JOIN MY BETA COMMUNITY FOR AGILE ENTREPRENEURS AND INTRAPRENEURS** The latest wave in professional Agile careers. Get the support you need to Forge Your Freedom! Join for FREE here: https://learning.fusechamber.com/offers/Sa3udEgz **CHECK OUT ALL MY PRODUCTS AND SERVICES HERE:** https://learning.fusechamber.com **ELEVATE YOUR PROFESSIONAL STORYTELLING – Now Live!** The most coveted communications skill – now at your fingertips! https://learning.fusechamber.com/storytelling **JOIN THE FORGE*** New cohorts for Fall 2025! Email for more information: contact@badassagile.com **BREAK FREE OF CORPORATE AGILE!!*** Download my FREE Guide and learn how to shift from roles and process and use your agile skills in new and exciting ways! https://learning.fusechamber.com/future-of-agile-signup We’re also on YouTube! Follow the podcast, enjoy some panel/guest commentary, and get some quick tips and guidance from me: https://www.youtube.com/c/BadassAgile ****** Follow The LinkedIn Page: https://www.linkedin.com/showcase/badass-agile ****** Our mission is to create an elite tribe of leaders who focus on who they need to become in order to lead and inspire, and to be the best agile podcast and resource for effective mindset and leadership game. Contact us (contact@badassagile.com) for elite-level performance and agile coaching, speaking engagements, team-level and executive mindset/agile training, and licensing options for modern, high-impact, bite-sized learning and educational content.
AI was supposed to help humans think better, decide better, and operate with more agency. Instead, many of us feel slower, less confident, and strangely replaceable.In this episode of Design of AI, we interviewed Ovetta Sampson about what quietly went wrong. Not in theory—in practice. We examine how frictionless tools displaced intention, how “freedom” became confused with unlimited capability, and how responsibility dissolved behind abstraction layers, vendors, and models no one fully controls.This is not an anti-AI conversation. It's a reckoning with what happens when adoption outruns judgment.Ovetta Sampson is a tech industry leader who has spent more than a decade leading engineers, designers, and researchers across some of the most influential organizations in technology, including Google, Microsoft, IDEO, and Capital One. She has designed and delivered machine learning, artificial intelligence, and enterprise software systems across multiple industries, and in 2023 was named one of Business Insider's Top 15 People in Enterprise Artificial Intelligence.Join her mailing list | Right AI | Free Mindful AI Playbook Why 2026 Will Force Teams to Rethink How Much AI They Actually NeedThe risks are no longer abstract. The tradeoffs are no longer subtle. Teams are already feeling the consequences: bloated tool stacks, degraded judgment, unclear accountability, and productivity that looks impressive but feels empty.The next advantage will not come from adding more AI. It will come from removing it deliberately.Organizations that adapt will narrow where AI is used—essential systems, bounded experiments, and clearly protected human decision points. The payoff won't just be cost savings. It will be the return of clarity, ownership, and trust. This is going to manifest first with individuals and small startups who were early adopters of AI. My prediction is that this year they'll start cutting the number of AI models they pay for because the era of experimentation is over and we're now entering a period where deliberate choices will matter more than how fast the model is. Read the full article on LinkedIn. Do You Really Need Frontier Models for Your Product to Work?For most teams, the honest answer is no.Open-source and on-device models already cover the majority of real business needs: internal tooling, retrieval, summarization, classification, workflow automation, and privacy-sensitive systems. The capability gap is routinely overstated—often by those selling access.What open models offer instead is control: over data, cost, latency, deployment, and failure modes. They make accountability visible again. This video explains why the “frontier advantage” is mostly narrative:Independent evaluations now show that open-source AI models can handle most everyday business tasks—summarizing documents, answering questions, drafting content, and internal analysis—at levels comparable to paid systems. The LMSYS Chatbot Arena, which runs blind human comparisons between models, consistently ranks open models close to top proprietary ones.Major consultancies now document why enterprises are switching: predictable costs, data control, and fewer legal and governance risks. McKinsey notes that open models reduce vendor lock-in and compliance exposure in regulated environments.Thanks for reading Design of AI: Strategies for Product Teams & Agencies! Subscribe for free to receive new posts and support my work.What Happens When “Freedom” Becomes an Excuse Not to Set Boundaries?We've confused freedom with capability. If a system can do something, we assume it should. That logic dissolves moral boundaries and replaces responsibility with abstraction: the model did it, the system allowed it.When no one owns the boundary, harm becomes an emergent property instead of a design failure.What If AI Doesn't Have to Be Owned by Corporations?We're going to experience a rise in AI experts challenging the expectations that Silicon Valley should control AI.What if AI doesn't need to be centralized, rented, or governed exclusively by corporate interests?On-device models and open ecosystems offer a different future—less extraction, fewer opaque incentives, and more meaningful choice.Follow Antoine Valot as him and Postcapitalist Design Club explore new ways of liberating AI.Are We Using AI for Anything That Actually Matters?Much of today's AI usage is performative productivity and ego padding that signals relevance while eroding self-trust. We're outsourcing thinking we are still capable of doing ourselves.AI should amplify judgment and creativity. Use this insanely powerful technology to make you achieve greater outcomes, not deliver a higher amount of subpar work to the world.If We Know the Risks Now, Why Are We Still Acting Surprised?The paper “The AI Model Risk Catalog” removes the last excuse.Failure modes are documented. Harms are mapped. Blind spots are known.Continuing to deploy without contingency planning is no longer innovation—it's negligence. If a team can't explain how its system fails safely, who intervenes, and what happens next, it isn't ready for real-world use.If Guardrails Don't Work, What Actually Protects Us?Every AI model and product is at risk of a major attack and exploit.AI systems are structurally vulnerable. The reason we haven't seen a catastrophic failure yet isn't safety—it's limited adoption and permissions.Guardrails fail under pressure. Policies collapse at scale. The only real protection is limiting blast radius: constraining autonomy and refusing to grant authority systems can't safely hold.Why Should Teams Decide Before They Build?The Decision-Forcing AI Business Case Canvas from Unhyped is essential for planning how to leverage AI in your products.Before discussing capabilities, teams must answer:* Who is accountable when this fails?* What judgment must remain human?* What harms are unacceptable—even if the system works?This canvas offers alignment on vision, responsibility, and impact isn't bureaucracy.It's baseline design discipline.Consider the TradeoffsThe conversation with Ovetta Sampson challenges a belief that shaped the last phase of AI adoption: that faster is always better, and that dependence on OpenAI, Google, or Anthropic is inevitable.That belief works during experimentation.It breaks the moment your product starts to matter.As teams scale, speed stops being the constraint. Trust, cost predictability, and accountability take its place. The question shifts from How fast can we ship? to What are we tying our business to—and what happens when it fails?One path optimizes for immediate momentum and simplicity. The other requires more upfront effort, but fundamentally changes where risk, data, and control live.This isn't a technical choice. It's a business one.As usage grows, externalized risk stops being abstract and starts showing up in margins, contracts, and customer trust.As that pressure builds, the impact becomes visible in the product experience itself.Latency creeps in. Costs compound quietly. Outputs vary in ways teams struggle to explain. What once felt powerful starts to feel fragile. Teams spend more time managing side effects than delivering value.At that point, you realize you didn't just choose a model.You chose a UX trajectory.Frontier models feel impressive early, but often lead to expensive, inconsistent experiences over time. Smaller, tuned models trade spectacle for reliability—and reliability is what users actually trust.Eventually, the conversation moves from UX to business fundamentals.Token pricing that felt negligible becomes material. Vendor updates change behavior you didn't choose. Security and compliance questions become harder to abstract away. You realize that outsourcing intelligence also outsourced leverage.This final image makes the tradeoff explicit. Paid frontier models buy speed and simplicity. Open or self-managed approaches buy independence, cost control, and long-term defensibility. Pretending these lead to the same outcomes is the mistake.This transition, from novelty to ownership, is exactly where Right AI Now is focused. Through her consultancy, Ovetta helps teams redesign AI decisions around outcomes that actually matter at scale: customer trust, data sovereignty, operational stability, and long-term value creation.These are also the themes we hear most consistently from the Design of AI audience. Founders and product leaders aren't asking for more tools—they're asking for clearer decisions. They want to know why AI products succeed and fail. We'll be going deeper on this shift throughout 2026, including a rebrand of the podcast, name and all.Improve Your AI ProductIf your organization is at the inflection point where AI needs to deliver real value without eroding trust, this is where I can help you. I've worked with teams at Microsoft, Spotify, and Mozilla to help leaders decide what to build, how to deliver value, and prioritize roadmaps. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com
Today's guest is Hayden Mitchell, Ph.D. Hayden is a sports performance coach, educator, and researcher specializing in movement ecology and pedagogy, helping coaches design environments that support learning, resilience, self-actualization, and sustainable athletic performance through play and exploration. There is a great deal of conversation in sports performance around methods, including exercises, drills, systems, and models, but far less attention is given to coaching itself. Coaching methodology quietly shapes how athletes experience training, how they relate to challenge and failure, and ultimately how fully they are able to express themselves in performance. On the show today, Hayden speaks about exploring how coaching and physical education shape not just performance, but the whole human being. Hayden shares his path through sport, teaching, and doctoral work, including how life experiences changed his approach to leadership, control, and play. Together they discuss movement ecology, value orientations in coaching, such as mastery, learning process, self-actualization, social responsibility, and ecological integration, and why environment often matters as much as programming. The conversation highlights rhythm, joy, and exploration, along with practical ways coaches can use restraint, better questions, and playful constraints to help athletes own their development. Today's episode is brought to you by Hammer Strength. Use the code “justfly20” for 20% off any Lila Exogen wearable resistance training, including the popular Exogen Calf Sleeves. For this offer, head to Lilateam.com Use code “justfly10” for 10% off the Vert Trainer View more podcast episodes at the podcast homepage. (https://www.just-fly-sports.com/podcast-home/) Timestamps 0:00 – Hayden's coaching background 6:42 – Learning through experimentation 13:55 – Movement quality versus output 21:18 – Constraints based coaching 30:07 – Strength that transfers 39:50 – Variability and resilience 48:26 – Developing youth athletes 57:41 – Decision-making under fatigue 1:06:10 – Simplifying training programs 1:14:22 – Long term coaching philosophy Actionable Takeaways 6:42 – Learning through experimentation builds better coaches and athletes. Early coaching growth often comes from trying ideas, observing outcomes, and refining approaches. Allow room for trial and error in training rather than locking into rigid systems too early. Encourage athletes to feel and explore movement solutions instead of chasing perfect reps. Reflection after sessions helps clarify what actually transferred versus what just looked good. 13:55 – Movement quality creates the foundation for sustainable performance. Chasing outputs too early can hide inefficient movement strategies. Build positions, shapes, and rhythm before emphasizing max speed or max load. Use submaximal work to groove coordination and reduce compensation patterns. Improved movement quality often raises outputs without directly training them. 21:18 – Constraints guide learning better than constant verbal correction. Design drills that naturally guide athletes toward desired solutions. Reduce cue overload by letting the task do the teaching. Constraints promote adaptability instead of dependency on coaching feedback. This approach scales well in team settings with limited coaching bandwidth. 30:07 – Strength training should support movement, not replace it. Choose lifts that reinforce postures and force directions seen in sport. Avoid chasing strength numbers that disrupt rhythm or coordination. Use strength work to enhance confidence and robustness, not fatigue accumulation. Strong athletes still need to move well under dynamic conditions. 39:50 – Variability is a key driver of resilience. Expose athletes to multiple movement patterns and speeds. Avoid over standardizing drills to the point of robotic execution. Small variations build adaptability without sacrificing intent. Resilient athletes tolerate change better during competition. 48:26 – Youth athletes need exposure, not specialization. Prioritize broad skill development over early performance metrics. Multiple sports and movement environments improve long term ceilings. Avoid labeling young athletes too early based on temporary traits. Early diversity reduces burnout and overuse issues. 57:41 – Decision-making matters when athletes are tired. Fatigue reveals movement habits and decision quality. Train cognition alongside physical outputs when appropriate. Simple competitive games expose real world decision challenges. Performance under fatigue reflects true readiness. 1:06:10 – Simple programs executed well outperform complex plans done poorly. Clarity improves athlete buy in and consistency. Fewer exercises done with intent beat bloated sessions. Complexity should serve adaptation, not ego. Great programs are easy to repeat and sustain. 1:14:22 – Long term development requires patience and perspective. Short term gains should not compromise future potential. Progress is rarely linear, especially in young athletes. Coaching success is measured in years, not weeks. Build athletes you would want to train again in five years. Quotes from Hayden “Good movement solves a lot of problems before strength ever enters the conversation.” “When you design the environment well, you do not need to talk nearly as much.” “Outputs are easy to measure, but they are not always the most important thing.” “Variability is not chaos. It is preparation.” “Athletes who only know one solution struggle when conditions change.” “Young athletes do not need more specialization, they need more experiences.” “Strength should support expression, not restrict it.” “Simple does not mean easy. It means intentional.” “Fatigue exposes habits, not flaws.” “The goal is not just better athletes, but athletes who last.” About Hayden Mitchell Hayden Mitchell, PhD is a sports performance coach, educator, and researcher whose work sits at the intersection of movement ecology, pedagogy, and human development. He has coached and taught across a wide range of settings, from youth and collegiate sport to military, adaptive populations, and general fitness, working with ages 4 to 90. Hayden holds a doctorate in Human Performance and Sport Pedagogy and focuses on how environment, values, and teaching behaviors shape learning, resilience, and performance. His work emphasizes play, rhythm, and self-actualization, helping coaches and athletes move beyond rigid systems toward practices that develop both performance capacity and the whole human being.
In this episode of Tech Talks, Julian Dibbell is joined by partner Brian Nolan and associate Megan Fitzgerald to unpack how companies can protect AI assets and outputs using IP strategies. The conversation maps the key protectable components of AI—algorithms and code, trained models and parameters, proprietary datasets, and outputs—and evaluates the strengths and limits of trade secrets, copyrights, patents, and contracts. They highlight why trade secrets are particularly powerful for AI while probing emerging "improper means" issues like scraping, prompt injection, and ToS violations. They also survey evolving copyright law on human authorship and fair use in training, and discuss patent inventorship guidance and eligibility trends, before closing with practical contracting approaches to allocate data rights, output ownership, and IP strategy. Show Notes: 00:02 Introduction to Protecting AI Assets and Outputs 02:00 Protectable AI Assets: Algorithms, Models, Data, Outputs 06:07 IP Toolkit: Trade Secrets, Copyright, Patents, Contracts 09:55 "Improper means" in AI: Scraping, Prompts, ToS 12:44 Using Copyright to Protect AI 16:21 Copyright: Human Authorship, Code Protection, Fair Use 21:27 Patents: Inventorship Guidance, Eligibility, Open Issues 29:58 Contracts: Data Rights, Output Ownership, Strategy
Join the All In Mastermind: 100 Men, committed to their goals - https://www.muscleintelligence.com/apply/ Truth is, 98% of people set goals… and end the year in the same place. In this solo episode, Ben breaks down the 12 "Power Moves" framework he uses with elite founders, executives, and high performers to create repeatable wins in 2026. You'll learn why measuring inputs is a trap, how to build an outcomes-based scorecard for your body and life, and the real definition of success: intelligence + agency. Ben also explains the Mission–Map–Mentor model, the 4 resiliencies that determine follow-through (body, mind, stress, energy), and the 12 power moves that created exponential change in his own life. If you want a year that actually moves the needle, start here. 5 Bullet Points: Why "more information" can keep you stuck The scorecard that turns goals into outcomes Intelligence vs agency: the real success equation The 4 resiliencies that predict follow-through 12 Power Moves to build momentum fast Whenever you're ready... here are 3 ways we can help you look, feel and perform at your best: 1. Grab a free copy of 1 of our BRAND NEW Peak Performance Protocols. This is for high performers looking to 10x their training and nutrition results by becoming 10x more effective. Click here - https://go.muscleintelligence.com/high-performance-executive-report/ 2. Join the Muscle Intelligence Community and connect with other men like you who want to uplevel their health and fitness. It's our new Facebook group where I coach members live, share what's working with my private clients and announce tickets to my upcoming trainings and events. Click here - https://www.muscleintelligence.com/community 3. Read the Newsletter Join 200,000 men in their prime, reading our weekly newsletter: http://muscleintelligence.com/newsletter Time Stamps: 00:00 Introduction to Power Moves 01:25 Curating Inputs and Outputs 02:17 Scorecards for Body, Health, and Wealth 06:13 Understanding Agency and Intelligence 20:12 The Importance of Strong Humans 22:17 Taking Personal Responsibility 22:36 Power Moves for Success 24:29 Creating a Scoreboard 27:33 Mastering Your Environment 28:35 Mastering Your Morning 30:32 Nurturing Family and Marriage 31:54 Connecting to a Higher Purpose 34:15 Becoming an A Player 37:48 Final Thoughts and Mentorship Invitation
Zeit zurückzublicken heißt es auch dieses Jahr wieder im JediCast. Wir sprechen über unsere Highlights und die Enttäuschungen, die wir in diesem Jahr erlebt haben, und darüber, warum Andor nicht überall gewinnen kann, weil es sonst langweilig wäre. Außerdem geht es um unsere Convention-Auftritte, das Interview mit Claudia Gray, das Finale der Hohen Republik und die Frage, warum The Mandalorian and Grogu bei uns nur eine Reaktion auslöst: bof. Zeitmarken 00:00:00 - Begrüßung 00:01:20 - Das eine Wort 00:06:26 - Selbstlob incoming 00:12:26 - Die Comic Con 00:22:33 - Das Claudia-Gray-Interview 00:25:37 - Enttäuschung des Jahres 00:31:09 - Schaut endlich Andor!!!! 00:40:33 - Schaut nicht unbedingt Geschichten der Unterwelt... 00:43:58 - Das Finale der Hohen Republik 00:55:02 - Die Überraschung des Jahres 01:01:35 - Charakter des Jahres 01:05:25 - Highlight des Jahres 01:09:15 - Hoffnungsträger des Jahres 01:16:50 - Ausblick 2026 01:32:07 - Das ändert sich (nicht) beim JediCast 2026 In eigener Sache Am Ende der Folge gibt es genauere Infos, aber hier die Kurzfassung: Der JediCast wird ab 2026 im dreiwöchigen statt zweiwöchigen Rhythmus erscheinen. Gründe dafür sind vor allem die Zeit, die wir in unserer Freizeit zur Vorbereitung und Aufnahme haben, aber auch der Wegfall des hohen Outputs an neuem Star Wars-Content nach Die Hohe Republik. Trotzdem bleiben wir regelmäßig on air und der Plan für 2026 ist schon jetzt ziemlich voll. Ein Vorgeschmack gefällig? Wir sprechen 2026 über 15 Jahre Jedi-Bibliothek, die Bane-Trilogie im Darth Bane-HerbstSommer, The Mandalorian and Grogu und natürlich all die neuen Romane, die 2026 erscheinen. Von Outlaws: Low Red Moon bis Reign of the Empire: Edge of the Abyss. Den JediCast abonnieren Wir sind auf allen gängigen Podcast-Plattformen vertreten! Abonniert uns also gerne auf Spotify, Apple Podcasts, Google Podcasts (etc.), oder fügt bequem unsere Feeds in euren präferierten Podcast-Player ein. Alle Links dazu findet ihr oben unter dem Player verlinkt sowie auch jederzeit unter dem Audioplayer in der rechten Sidebar. Wir freuen uns auch immer über Bewertungen auf den jeweiligen Podcast-Seiten. Falls ihr umfangreichere Anmerkungen habt, schreibt auch gerne eine Mail an podcast@jedi-bibliothek.de! Eure Meinung Was sind eure Highlights und Enttäuschungen des Jahres? Welche Figur ist die beste und worauf freut ihr euch 2026 am meisten? Nutzt gerne unsere Kategorien und teilt uns eure Preisträger in den Kommentaren mit!
This time Ned, Adam and Laura talk targets - and why the third Cycling and Walking Investment Strategy (CWIS3) needs outputs, not simply outcomes. They are joined by the CEO of the Walk, Wheel, Cycle Trust (formerly Sustrans), Xavier Brice, who knows all about strategies, and delivering active transport networks.The government recently ended a consultation on CWIS3 but, frustratingly, the proposals lacked any investment or much strategy. There were no SMART targets, or any outputs, i.e. routes; simply the unachievable outcome that by 2035 walking, wheeling and cycling will be "a safe, easy and accessible option for everyone". Road Investment Strategies, by contrast, focus heavily on routes and infrastructure, so why do we treat walking, wheeling and cycling differently?Xavier Brice has been CEO of the Walk Wheel Cycle Trust since 2016. In 2007 Brice led the development of a new walking and cycling strategy for London, with Transport for London.This month Adam, Laura and Xavier Brice coordinated an open letter to the Secretary of State supporting a better CWIS3. That letter was signed by more than 50 organisations across health, active travel and beyond. It asked that central government maps a true national network of routes by 2030, and sets targets to deliver that network to a proper, accessible standard by 2050.You can read the letter here: https://bsky.app/profile/adamtranter.bsky.social/post/3m7fv3vhyks2rThe letter was covered in the Guardian by Peter Walker: https://www.theguardian.com/politics/2025/dec/12/drivers-cyclists-transport-policy-conservatives-culture-wars-road-safety Shortly after that, Walker interviewed transport minister, Lilian Greenwood, about the importance of 'creating a system that works for everyone': https://www.theguardian.com/politics/2025/dec/12/drivers-cyclists-transport-policy-conservatives-culture-wars-road-safetyLaura's Freedom of Information requests to English local authorities found just 2 per cent had used legal powers to purchase land - something that's done routinely for roads https://substack.com/home/post/p-178788505And her article on CWIS3: https://lauralaker.substack.com/p/a-cycling-and-walking-strategy-walksThe Walk, Wheel Cycle Trust has been improving the National Cycle Network (NCN). In 2023/24 1.7km of an off-road muddy track connecting the residential area of Newton, in West Doncaster, to Danum retail park, was widened (on NCN62), with seven barriers removed or redesigned, along with improved wayfinding and signage. Estimated annual usage rose by 196% according to the Walk, Wheel Cycle Trust, from 150,000 trips in 2022 to 450,000 in 2024. Pedestrian and cycling trips increased by 191% and 192% respectively, while other users increased by 270%. Another path improvement project in Redcar and Cleveland saw ten barriers removed on NCN1 and NCN68. Wheelchair user trips increased four-fold, from 200 to 800, with 100% of disabled users saying they now use the route as the most convenient option.For ad-free listening, behind-the-scenes and bonus content and to help support the podcast - head to (https://www.patreon.com/StreetsAheadPodcast). We'll even send you some stickers! We're also on Bluesky and welcome your feedback on our episode: https://bsky.app/profile/podstreetsahead.bsky.social Hosted on Acast. See acast.com/privacy for more information.
How do volunteer leaders move from being seen as “extra hands” to strategic drivers of mission success? In this episode of the Volunteer Nation Podcast, Tobi Johnson is joined by Chris Wade and Matthew Cobble, co-hosts of the Time for Impact Podcast in the UK, for a practical and thought-provoking conversation about building influence through impact. Together, they explore why volunteering needs to be reframed as community participation and talent, not just unpaid labor and how leaders of volunteers can use data, stories, and strategic thinking to elevate their role inside organizations. This episode goes beyond counting hours or outputs and dives into how volunteer engagement directly contributes to outcomes, organizational strategy, and long-term change. Full show notes: 193. Building Influence with Impact with Chris Wade and Matthew Cobble Building Influence - Episode Highlights [00:31] - Introducing Special Guests: Chris Wade and Matthew Cobble [01:12] - Building Influence with Impact [01:57] - Meet Chris Wade: A Leader in Volunteerism [03:58] - Meet Matthew Cobble: A Journey in Volunteer Engagement [07:42] - The Importance of Volunteerism in Today's World [12:42] - Volunteers as a Strategic Asset [14:10] - Measuring Impact and Building Influence [24:12] - Challenges and Solutions in Volunteer Leadership [31:15] - Hypotheses and Program Design [32:18] - Vision Week and Volunteer Planning [33:06] - Shifting Mindsets on Volunteerism [34:12] - Strategic Planning and Data Utilization [36:13] - Design Thinking in Volunteer Management [37:39] - Collaborative Data Collection [40:32] - Practical How-Tos for Volunteer Impact [42:46] - Measuring Volunteer Impact [53:44] - Collecting Evidence and Surveys Helpful Links VolunteerPro Impact Lab 2025 Volunteer Management Progress Report – The Recruitment Edition Time for Impact Podcast, Tobi Johnson on the Challenging, Brave Journey of Volunteer Leadership Volunteer Nation Episode #175: Outputs vs Outcomes: Why Counting Hours Isn't Enough Info on Lewin's Force Field Analysis Info on Balanced Scorecard for Nonprofits Info on the Double Diamond Design Process Info on the Outcomes Star Thanks for listening to this episode of the Volunteer Nation podcast. If you enjoyed it, please be sure to subscribe, rate, and review so we can reach more people like you who want to improve the impact of their good cause. For more tips and notes from the show, check us out at TobiJohnson.com. For any comments or questions, email us at WeCare@VolPro.net.
In this solo episode, Sam ties a bow on our ratios series — walking us through how to go from a rookie to an expert. Many recruiters are flying blind — not knowing what exactly is working, or why. Not knowing, definitively, what it takes to make a great placement. Sam breaks down the ratios of candidates needed for screening calls, interviews, and placements, contrasting the approaches of rookie and expert recruiters.Another takeaway? The importance of tracking recruitment metrics and creating a structured plan to achieve your goals effectively. You can't double-down on winning behaviors if you don't know what they are — so step one is to experiment, step two is to document, and step three is to create a feedback loop that ensures continued success.
Every few years, the world of product management goes through a phase shift. When I started at Microsoft in the early 2000s, we shipped Office in boxes. Product cycles were long, engineering was expensive, and user research moved at the speed of snail mail. Fast forward a decade and the cloud era reset the speed at which we build, measure, and learn. Then mobile reshaped everything we thought we knew about attention, engagement, and distribution.Now we are standing at the edge of another shift. Not a small shift, but a tectonic one. Artificial intelligence is rewriting the rules of product creation, product discovery, product expectations, and product careers.To help make sense of this moment, I hosted a panel of world class product leaders on the Fireside PM podcast:• Rami Abu-Zahra, Amazon product leader across Kindle, Books, and Prime Video• Todd Beaupre, Product Director at YouTube leading Home and Recommendations• Joe Corkery, CEO and cofounder of Jaide Health • Tom Leung (me), Partner at Palo Alto Foundry• Lauren Nagel, VP Product at Mezmo• David Nydegger, Chief Product Officer at OvivaThese are leaders running massive consumer platforms, high stakes health tech, and fast moving developer tools. The conversation was rich, honest, and filled with specific examples. This post summarizes the discussion, adds my own reflections, and offers a practical guide for early and mid career PMs who want to stay relevant in a world where AI is redefining what great product management looks like.Table of Contents* What AI Cannot Do and Why PM Judgment Still Matters* The New AI Literacy: What PMs Must Know by 2026* Why Building AI Products Speeds Up Some Cycles and Slows Down Others* Whether the PM, Eng, UX Trifecta Still Stands* The Biggest Risks AI Introduces Into Product Development* Actionable Advice for Early and Mid Career PMs* My Takeaways and What Really Matters Going Forward* Closing Thoughts and Coaching Practice1. What AI Cannot Do and Why PM Judgment Still MattersWe opened the panel with a foundational question. As AI becomes more capable every quarter, what is left for humans to do. Where do PMs still add irreplaceable value. It is the question every PM secretly wonders.Todd put it simply: “At the end of the day, you have to make some judgment calls. We are not going to turn that over anytime soon.”This theme came up again and again. AI is phenomenal at synthesizing, drafting, exploring, and narrowing. But it does not have conviction. It does not have lived experience. It does not feel user pain. It does not carry responsibility.Joe from Jaide Health captured it perfectly when he said: “AI cannot feel the pain your users have. It can help meet their goals, but it will not get you that deep understanding.”There is still no replacement for sitting with a frustrated healthcare customer who cannot get their clinical data into your system, or a creator on YouTube who feels the algorithm is punishing their art, or a devops engineer staring at an RCA output that feels 20 percent off.Every PM knows this feeling: the moment when all signals point one way, but your gut tells you the data is incomplete or misleading. This is the craft that AI does not have.Why judgment becomes even more important in an AI worldDavid, who runs product at a regulated health company, said something incredibly important: “Knowing what great looks like becomes more essential, not less. The PM's that thrive in AI are the ones with great product sense.”This is counterintuitive for many. But when the operational work becomes automated, the differentiation shifts toward taste, intuition, sequencing, and prioritization.Lauren asked the million dollar question. “How are we going to train junior PMs if AI is doing the legwork. Who teaches them how to think.”This is a profound point. If AI closes the gap between junior and senior PMs in execution tasks, the difference will emerge almost entirely in judgment. Knowing how to probe user problems. Knowing when a feature is good enough. Knowing which tradeoffs matter. Knowing which flaw is fatal and which is cosmetic.AI is incredible at writing a PRD. AI is terrible at knowing whether the PRD is any good.Which means the future PM becomes more strategic, more intuitive, more customer obsessed, and more willing to make thoughtful bets under uncertainty.2. The New AI Literacy: What PMs Must Know by 2026I asked the panel what AI literacy actually means for PMs. Not the hype. Not the buzzwords. The real work.Instead of giving gimmicky answers, the discussion converged on a clear set of skills that PMs must master.Skill 1: Understanding context engineeringDavid laid this out clearly: “Knowing what LMS are good at and what they are not good at, and knowing how to give them the right context, has become a foundational PM skill.”Most PMs think prompt engineering is about clever phrasing. In reality, the future is about context engineering. Feeding models the right data. Choosing the right constraints. Deciding what to ignore. Curating inputs that shape outputs in reliable ways.Context engineering is to AI product development what Figma was to collaborative design. If you cannot do it, you are not going to be effective.Skill 2: Evals, evals, evalsRami said something that resonated with the entire panel: “Last year was all about prompts. This year is all about evals.”He is right.• How do you build a golden dataset.• How do you evaluate accuracy.• How do you detect drift.• How do you measure hallucination rates.• How do you combine UX evals with model evals.• How do you decide what good looks like.• How do you define safe versus unsafe boundaries.AI evaluation is now a core PM responsibility. Not exclusively. But PMs must understand what engineers are testing for, what failure modes exist, and how to design test sets that reflect the real world.Lauren said her PMs write evals side by side with engineering. That is where the world is going.Skill 3: Knowing when to trust AI output and when to override itTodd noted: “It is one thing to get an answer that sounds good. It is another thing to know if it is actually good.”This is the heart of the role. AI can produce strategic recommendations that look polished, structured, and wise. But the real question is whether they are grounded in reality, aligned with your constraints, and consistent with your product vision.A PM without the ability to tell real insight from confident nonsense will be replaced by someone who can.Skill 4: Understanding the physics of model changesThis one surprised many people, but it was a recurring point.Rami noted: “When you upgrade a model, the outputs can be totally different. The evals start failing. The experience shifts.”PMs must understand:• Models get deprecated• Models drift• Model updates can break well tuned prompts• API pricing has real COGS implications• Latency varies• Context windows vary• Some tasks need agents, some need RAG, some need a small finetuned modelThis is product work now. The PM of 2026 must know these constraints as well as a PM of the cloud era understood database limits or API rate limits.Skill 5: How to construct AI powered prototypes in hours, not weeksIt now takes one afternoon to build something meaningful. Zero code required. Prompt, test, refine. Whether you use Replit, Cursor, Vercel, or sandboxed agents, the speed is shocking.But this makes taste and problem selection even more important. The future PM must be able to quickly validate whether a concept is worth building beyond the demo stage.3. Why Building AI Products Speeds Up Some Cycles and Slows Down OthersThis part of the conversation was fascinating because people expected AI to accelerate everything. The panel had a very different view.Fast: Prototyping and concept validationLauren described how her teams can build working versions of an AI powered Root Cause Analysis feature in days, test it with customers, and get directional feedback immediately.“You can think bigger because the cost of trying things is much lower,” she said.For founders, early PMs, and anyone validating hypotheses, this is liberating. You can test ten ideas in a week. That used to take a quarter.Slow: Productionizing AI featuresThe surprising part is that shipping the V1 of an AI feature is slower than most expect.Joe noted: “You can get prototypes instantly. But turning that into a real product that works reliably is still hard.”Why. Because:• You need evals.• You need monitoring.• You need guardrails.• You need safety reviews.• You need deterministic parts of the workflow.• You need to manage COGS.• You need to design fallbacks.• You need to handle unpredictable inputs.• You need to think about hallucination risk.• You need new UI surfaces for non deterministic outputs.Lauren said bluntly: “Vibe coding is fast. Moving that vibe code to production is still a four month process.”This should be printed on a poster in every AI startup office.Very Slow: Iterating on AI powered featuresAnother counterintuitive point. Many teams ship a great V1 but struggle to improve it significantly afterward.David said their nutrition AI feature launched well but: “We struggled really hard to make it better. Each iteration was easy to try but difficult to improve in a meaningful way.”Why is iteration so difficult.Because model improvements may not translate directly into UX improvements. Users need consistency. Drift creates churn. Small changes in context or prompts can cause large changes in behavior.Teams are learning a hard truth: AI powered features do not behave like typical deterministic product flows. They require new iteration muscles that most orgs do not yet have.4. The PM, Eng, UX Trifecta in the AI EraI asked whether the classic PM, Eng, UX triad is still the right model. The audience was expecting disagreement. The panel was surprisingly aligned.The trifecta is not going anywhereRami put it simply: “We still need experts in all three domains to raise the bar.”Joe added: “AI makes it possible for PMs to do more technical work. But it does not replace engineering. Same for design.”AI blurs the edges of the roles, but it does not collapse them. In fact, each role becomes more valuable because the work becomes more abstract.• PMs focus on judgment, sequencing, evaluation, and customer centric problem framing• Engineers focus on agents, systems, architecture, guardrails, latency, and reliability• Designers focus on dynamic UX, non deterministic UX patterns, and new affordances for AI outputsWhat does changeAI makes the PM-Eng relationship more intense. The backbone of AI features is a combination of model orchestration, evaluation, prompting, and context curation. PMs must be tighter than ever with engineering to design these systems.David noted that his teams focus more on individual talents. Some PMs are great at context engineering. Some designers excel at polishing AI generated layouts. Some engineers are brilliant at prompt chaining. AI reveals strengths quickly.The trifecta remains. The skill distribution within it evolves.5. The Biggest Risks AI Introduces Into Product DevelopmentWhen we asked what scares PMs most about AI, the conversation became blunt and honest. Risk 1: Loss of user trustLauren warned: “If people keep shipping low quality AI features, user trust in AI erodes. And then your good AI product suffers from the skepticism.”This is very real. Many early AI features across industries are low quality, gimmicky, or unreliable. Users quickly learn to distrust these experiences.Which means PMs must resist the pressure to ship before the feature is ready.Risk 2: Skill atrophyTodd shared a story that hit home for many PMs. “Junior folks just want to plug in the prompt and take whatever the AI gives them. That is a recipe for having no job later.”PMs who outsource their thinking to AI will lose their judgment. Judgment cannot be regained easily.This is the silent career killer.Risk 3: Safety hazards in sensitive domainsDavid was direct: “If we have one unsafe output, we have to shut the feature off. We cannot afford even small mistakes.”In healthcare, finance, education, and legal industries, the tolerance for error is near zero. AI must be monitored relentlessly. Human in the loop systems are mandatory. The cycles are slower but the stakes are higher.Risk 4: The high bar for AI compared to humansJoe said something I have thought about for years: “AI is held to a much higher standard than human decision making. Humans make mistakes constantly, but we forgive them. AI makes one mistake and it is unacceptable.”This slows adoption in certain industries and creates unrealistic expectations.Risk 5: Model deprecation and instabilityRami described a real problem AI PMs face: “Models get deprecated faster than they get replaced. The next model is not always GA. Outputs change. Prompts break.”This creates product instability that PMs must anticipate and design around.Risk 6: Differentiation becomes hardI shared this perspective because I see so many early stage startups struggle with it.If your whole product is a wrapper around an LLM, competitors will copy you in a week. The real differentiation will not come from using AI. It will come from how deeply you understand the customer, how you integrate AI with proprietary data, and how you create durable workflows.6. Actionable Advice for Early and Mid Career PMsThis was one of my favorite parts of the panel because the advice was humble, practical, and immediately useful.A. Develop deep user empathy. This will become your biggest differentiator.Lauren said it clearly: “Maintain your empathy. Understand the pain your user really has.”AI makes execution cheap. It makes insight valuable.If you can articulate user pain precisely.If you can differentiate surface friction from underlying need.If you can see around corners.If you can prototype solutions and test them in hours.If you can connect dots between what AI can do and what users need.You will thrive.Tactical steps:• Sit in on customer support calls every week.• Watch 10 user sessions for every feature you own.• Talk to customers until patterns emerge.• Ask “why” five times in every conversation.• Maintain a user pain log and update it constantly.B. Become great at context engineeringThis will matter as much as SQL mattered ten years ago.Action steps:• Practice writing prompts with structured context blocks.• Build a library of prompts that work for your product.• Study how adding, removing, or reordering context changes output.• Learn RAG patterns.• Learn when structured data beats embeddings.• Learn when smaller local models outperform big ones.C. Learn eval frameworksThis is non negotiable.You need to know:• Precision vs recall tradeoffs• How to build golden datasets• How to design scenario based evals for UX• How to test for hallucination• How to monitor drift• How to set quality thresholds• How to build dashboards that reflect real world input distributionsYou do not need to write the code.You do need to define the eval strategy.D. Strengthen your product senseYou cannot outsource product taste.Todd said it best: “Imagine asking AI to generate 20 percent growth for you. It will not tell you what great looks like.”To strengthen your product sense:• Review the best products weekly.• Take screenshots of great UX patterns.• Map user flows from apps you admire.• Break products down into primitives.• Ask yourself why a product decision works.• Predict what great would look like before you design it.The PMs who thrive will be the ones who can recognize magic when they see it.E. Stay curiousRami's closing advice was simple and perfect: “Stay curious. Keep learning. It never gets old.”AI changes monthly. The PM who is excited by new ideas will outperform the PM who clings to old patterns.Practical habits:• Read one AI research paper summary each week.• Follow evaluation and model updates from major vendors.• Build at least one small AI prototype a month.• Join AI PM communities.• Teach juniors what you learn. Nothing accelerates mastery faster.F. Embrace velocity and side projectsTodd said that some of his biggest career breakthroughs came from solving problems on the side.This is more true now than ever.If you have an idea, you can build an MVP over a weekend. If it solves a real problem, someone will notice.G. Stay close to engineeringNot because you need to code, but because AI features require tighter PM engineering collaboration.Learn enough to be dangerous:• How embeddings work• How vector stores behave• What latency tradeoffs exist• How agents chain tasks• How model versioning works• How context limits shape UX• Why some prompts blow up API costsIf you can speak this language, you will earn trust and accelerate cycles.H. Understand the business deeplyJoe's advice was timeless: “Know who pays you and how much they pay. Solve real problems and know the business model.”PMs who understand unit economics, COGS, pricing, and funnel dynamics will stand out.7. Tom's Takeaways and What Really Matters Going ForwardI ended the recording by sharing what I personally believe after moderating this discussion and working closely with a variety of AI teams over the past 2 years.Judgment becomes the most valuable PM skillAs AI gets better at analysis, synthesis, and execution, your value shifts to:• Choosing the right problem• Sequencing decisions• Making 55 45 calls• Understanding user pain• Making tradeoffs• Deciding when good is good enough• Defining success• Communicating vision• Influencing the orgAgents can write specs.LLMs can produce strategies.But only humans can choose the right one and commit.Learning speed becomes a competitive advantageI said this on the panel and I believe it more every month.Because of AI, you now have:• Infinite coaches• Infinite mentors• Infinite experts• Infinite documentation• Infinite learning loopsA PM who learns slowly will not survive the next decade. Curiosity, empathy, and velocity will separate great from goodMany panelists said versions of this. The common pattern was:• Understand users deeply• Combine multiple tools creatively• Move quickly• Learn constantlyThe future rewards generalists with taste, speed, and emotional intelligence.Differentiation requires going beyond wrapper appsThis is one of my biggest concerns for early stage founders. If your entire product is a wrapper around a model, you are vulnerable.Durable value will come from:• Proprietary data• Proprietary workflows• Deep domain insight• Organizational trust• Distribution advantage• Safety and reliability• Integration with existing systemsAI is a component, not a moat.8. Closing ThoughtsHosting this panel made me more optimistic about the future of product management. Not because AI will not change the job. It already has. But because the fundamental craft remains alive.Product management has always been about understanding people, making decisions with incomplete information, telling compelling stories, and guiding teams through ambiguity and being right often.AI accelerates the craft. It amplifies the best PMs and exposes the weak ones. It rewards curiosity, empathy, velocity, and judgment.If you want tailored support on your PM career, leadership journey, or executive path, I offer 1 on 1 career, executive, and product coaching at tomleungcoaching.com.OK team. Let's ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com
In dieser Folge von Unboxing New Work spreche ich mit Ragnhild Struss – Karriereberaterin, Gründerin von Struss & Claussen und eine leidenschaftliche Begleiterin für alle, die herausfinden wollen, was sie beruflich wirklich wollen. Seit über 20 Jahren unterstützt sie Schüler:innen, Berufseinsteiger:innen und vor allem Führungskräfte dabei, Klarheit über ihre Stärken, Motive und ihre Zukunft zu gewinnen. Ragnhild bringt eine außergewöhnliche Mischung mit: psychologische Tiefe, jahrzehntelange Erfahrung und einen Blick für gesellschaftliche Trends, der weit über den Arbeitsmarkt hinausreicht. Gemeinsam werfen wir einen sehr persönlichen und gleichzeitig zukunftsgerichteten Blick darauf, wie KI Karrieren verändert – und was das für uns alle bedeutet. Wir sprechen darüber, warum Purpose heute wichtiger ist denn je, welche Fähigkeiten in den kommenden Jahren entscheidend sein werden und weshalb die Qualität unseres Inputs die Qualität unseres Outputs bestimmt – sowohl bei uns selbst als auch bei künstlicher Intelligenz. Es ist ein intensives und inspirierendes Gespräch über Wandel, Selbstführung, Sinn und die Frage, wie wir eine Arbeitswelt gestalten können, in der Menschen wirklich gerne arbeiten.
Most sellers dabble with AI prompts, but the results often come back generic, off-brand, or flat-out wrong. That's because effective prompts aren't about luck , they follow a recipe.In Part 2 of our 3-part AI for Sales series, we took you beyond definitions and into practical frameworks.Learn how to structure prompts step by step, spot the difference between weak and strong instructions, and apply proven prompt patterns that generate reliable outputs every time.This is a hands-on and tactical session, with good vs. bad examples, a starter prompt library, and troubleshooting tips you can apply instantly. By the end, you'll know how to turn everyday sales tasks into plug-and-play workflows anyone on your team can run.You'll Learn:The proven recipe for writing prompts that generate high-quality outputsGood vs. bad examples of prompts for emails, recaps, and researchA starter library of plug-and-play prompts sellers can use immediatelyThe Speaker:Jed MahrleIf you want to catch The Daily Sales Show live, join hereFollow Sell Better to get the latest actionable tactics from sales pros at the top of their gameExplore our YouTube ChannelThank you to our sponsors: Aligned and Winn.AI
Work with a DDS coach: https://datadrivenstrength.com/coaching/0:02:14 - Zac's Work on Individual Response Variation (and his conclusion)0:08:35 - Key takeaway: programming around constraints, not different principles0:15:18 - New podcast approach: Building on their own work (Research & Coaching)0:19:03 - Experience vs weak Scientific Evidence0:29:17 - Coaching Systems Review: The 5 Core Values in the training process0:37:48 - The value of Training Skill for long-term success0:44:36 - Listener Question: How to interpret years of Training Data0:52:43 - Coaching as the Application of Principles within the athlete's Constraints0:59:11 - Key Inputs and Outputs to evaluate Strength & Hypertrophy programming1:17:50 - The 3 Big Unanswered Questions in Training Science (Fatigue, Creatine, Growth Limit)
Rob Wilson is a performance educator with over twenty years of experience helping people build durable, high-functioning bodies and minds. He joins us on The Ready State Podcast to unpack the uncomfortable truth about performance: it's not free. In this powerful conversation with Kelly and Juliet Starrett, Rob dives into the real price of pushing your limits, why sleep is non-negotiable, and how to reframe “selfish” self-care as the foundation for showing up better in every area of life. Together, they tackle burnout, aging, and what it takes to sustain health and high output in a world that rewards constant hustle.What You'll Learn in This EpisodeThe three waves of fitness, and why Rob Wilson's book represents the vanguard of the third wave.The problem with the democratization of health metrics like Heart Rate Variability (HRV) if you don't know how to interpret the data or take action.The story behind the "Check Engine Light" metaphor, which helps high performers prioritize what to address and what to ignore.Why the phrase "self-care" often fails with service-oriented and high-performing individuals and the analogy used instead.The "Cobra Effect" or Goodhart's Law, and how chasing a metric like a high HRV can lead to misleading and useless outcomes.How to stop the "medical cascade" and apply an experimental framework (test/retest) to chronic, nagging pain and everyday health issues.The true cost of high performance and the crucial need for a "cost mitigation strategy" to avoid burnout.Why context matters more than perfect protocols, and how to create a personal longevity dashboard for continuous adaptation.For more info, follow Rob on Instagram and definitely pick up a copy of his new book, Check Engine Light: Tuning Your Body and Mind to Achieve Performance Longevity.Key Highlights: (00:00) - Intro(00:48) - Check Engine Light Book Overview(06:49) - Check Engine Light Metaphor Explained(14:06) - Importance of Check Engine Light for Everyone(17:49) - Inputs and Outputs in Life(20:37) - One Size Fits All Approach: Myth or Reality?(25:25) - Identifying What Matters Most to You(27:28) - Performance Costs: Understanding Trade-offs(29:27) - Recommended Supplements for Health(32:55) - LMNT: Importance of Hydration Explained(37:10) - Resistance: Creativity's Universal Challenge(39:48) - Becoming Reasonable: A Personal Journey(45:18) - Heart Rate Variability (HRV): Benefits Explained(47:15) - Using Tracking Devices Mindfully(49:57) - The Cobra Effect: Understanding Consequences(57:55) - Setting Up Environments for Success(1:00:20) - Changes Since Writing the Book(1:03:14) - What's Next for Rob: Future Plans(1:04:30) - Finding Rob: Where to ConnectSponsorsThis episode of The Ready State Podcast is brought to you by LMNT and Momentous.
What does it mean to focus on outcomes over outputs? In this podcast hosted by iDonate VP of Product Nacho Andrade, LucidLink Chief Product Officer Richard Yu will be speaking on driving product strategy through outcome-focused innovation. Richard shares his unique approach to product management, blending technical insight with commercial strategy to create transformative solutions that solve real-world problems.
Most founders think they have a “people problem” or a “process problem.” The truth? It's almost always both. Without clear systems, people get frustrated. Without empowered people, processes get ignored. And now with AI and automation accelerating, the stakes are higher than ever.In this episode of Founder Talk, Ryan Weiss, founder of EPS Optics, shares how he helps companies streamline workstreams, align teams, and prepare for a future where AI is rapidly reshaping jobs. From diagnosing broken processes to balancing structure with creativity, Ryan explains why the companies that win are the ones who combine people and process to create real impact.We dive into how poor order entry created billing chaos at one client, why “healthy conflict” is essential for accountability, and what happens when you let blind spots hold your business back. Ryan also shares his journey from building a lawn care company at 15, to living in the Philippines and building outsourcing teams, to writing Optics, his Amazon bestselling book on process and perception.You'll learn:✅ Why most business frustrations come from missing processes or ignored systems✅ How to balance creativity with structure in your team✅ Why AI will replace many jobs and how to adapt before it happens✅ The SIPOC framework (Suppliers, Inputs, Process, Outputs, Customers) that transforms workflows✅ Why the future belongs to leaders who impact people, not just profitsIf you've been searching “how to fix broken processes,” “people vs. process in business,” or “how AI will impact jobs,” this episode gives you the no-fluff truth.Connect with RyanGuest LinkedIn: https://www.linkedin.com/in/ryancweiss/Guest Website: https://learnmore.epsoptics.com/If you are a B2B company that wants to build your own in-house content team instead of outsourcing your content to a marketing agency, we may be a fit for you! Everything you see in our podcast and content is a result of a scrappy, nimble, internal content team along with an AI-powered content systems and process. Check out pricing and services here: https://impaxs.comTimecodes00:00 Introduction and Name Pronunciation00:12 German Heritage and Pronunciation Variations00:54 The Importance of Process in Business03:49 Balancing Process and Creativity06:59 Diagnosing and Solving Process Issues19:03 The Role of External Experts31:32 Living and Working in the Philippines34:16 The Future of Customer Service and AI34:38 AI Replacing Jobs: The Future of Work35:22 Streamlining Business Operations36:01 Preparing for Automation and AI37:40 Impact of AI on Computer Science Careers38:39 Adapting to Technological Changes43:36 The Importance of Mindset in Career Evolution49:14 Writing a Book: Process and Benefits56:09 Building a Business and Making an Impact59:55 Connecting and Growing Through Relationships
Generative AI has captured global attention, powering everything from chatbots to intelligent assistants. Yet in the enterprise, its promise often hits a dead end. According to Gartner, 80 per cent of enterprise data remains unused or “dark,” because conventional AI struggles to interpret complex, domain-specific information.In this episode of the Don't Panic It's Just Data podcast, EM360Tech host Trisha Pillay speaks with Andreas Blumauer, Senior Vice President at Graphwise, about how retrieval-augmented generation (RAG) and its advanced application, Graph RAG, are levelling up enterprise AI. Together, they explore the limitations of traditional AI, the critical role of knowledge graphs in improving data accuracy, and what it takes for organisations to successfully adopt these technologies.Why Graph RAG MattersWhile RAG enhances Generative AI by enabling it to retrieve relevant data from large knowledge bases, Graph RAG takes it further. By integrating knowledge graphs, Graph RAG preserves the relationships, sequences, and meaning inherent in enterprise data. This ensures AI outputs are not just collections of facts, but structured insights that reflect the logic of an organisation's knowledge.These advantages include:Higher accuracy: Retrieval precision can increase from 80% to 95%, reducing errors in AI outputs.Trustworthy results: Outputs are explainable and traceable, providing transparency that enterprises require.Scalable integration: Connects data across silos and departments, making AI adoption enterprise-ready.“Graph RAG respects the structure of enterprise data instead of flattening it. That's what makes it trustworthy,” explains Blumauer.Generative AI opened the door to possibilities. RAG made it actionable. Graph RAG takes it to the next level. By transforming dark, siloed data into structured, actionable knowledge, Graph RAG helps organisations achieve the accuracy, trust, and scalability essential for navigating the next frontier of enterprise intelligence.Takeaways80 per cent of enterprise data remains unused or dark.Traditional AI struggles to interpret complex enterprise data.RAG retrieves information from within the enterprise data landscape.Graph RAG improves the accuracy of AI outputs.Knowledge graphs link data points across different silos.Building a knowledge graph is a strategic investment.Incremental growth is possible with knowledge graphs.Graph RAG can increase accuracy from 80 per cent to 95 per cent.Data quality and governance are essential for AI success.The future of enterprise AI relies on effective knowledge management.Chapters00:00 Introduction to RAG and Graph RAG03:04 Understanding the Importance of Knowledge Graphs05:46 Adopting RAG: Organisational Readiness and Strategic Investment08:51 Real-World Applications and Benefits of Graph RAG11:56 The Evolution of Knowledge Graphs in AI14:46 Future of GraphRAG and Enterprise AI17:36 Rapid Fire Questions and Closing ThoughtsAbout GraphwiseGraphwise is a leading enterprise AI company specialising in knowledge graph technologies. By combining retrieval-augmented generation (RAG) with advanced graph-based approaches, Graphwise helps organisations turn siloed, complex data into accurate, actionable insights, enabling smarter decisions, scalable...
Udhay Durai, Executive Director of Data Platform and Engineering at Evolus, joins the show to unpack his journey from consulting to leading enterprise data teams. He shares how the high-pressure, quick-delivery mindset from consulting can be a secret weapon in a corporate setting, and what changes when you shift from delivering outputs to owning long-term outcomes. From navigating different types of pressure to building sustainable systems that scale, Udhay offers candid insights for anyone considering a similar transition.Key Takeaways• The consulting mindset of speed and adaptability can be a major advantage in enterprise roles when paired with long-term thinking• Pressure exists in both consulting and full-time roles, but the nature of that pressure—and how you manage it—differs greatly• Consultants focus on outputs, while enterprise leaders are measured on outcomes that stand the test of time• Generalist experience across domains can complement deep subject matter experts in a corporate team• Bringing incremental change and a “flywheel” approach from consulting can accelerate enterprise delivery without sacrificing reliabilityTimestamped Highlights01:34 — Why quick wins and stakeholder empathy are essential in consulting03:28 — How the pressure changes when you own the platform instead of just delivering a project05:32 — Outputs vs outcomes and why the shift matters in enterprise leadership09:48 — Turning generalist consulting experience into an asset in a full-time role11:43 — The biggest mindset and skill gaps to address when making the switch13:42 — Adapting consulting habits for long-term success in product companiesQuote of the Episode“Pressure is there in both consulting and enterprise. The difference is in consulting you deliver outputs—enterprise leaders deliver outcomes.”Resources MentionedUdhay Durai on LinkedIn — https://www.linkedin.com/in/udhay-duraiCall to ActionIf this episode gave you new perspective on career transitions, share it with a colleague or friend who's considering a similar move. Follow the show for more real-world tech leadership conversations.
In this episode of the Volunteer Nation Podcast, Tobi Johnson dives into why it's time to move beyond traditional volunteer metrics like hours logged and retention rates. She unpacks the difference between outputs and outcomes, sharing practical examples and actionable strategies for measuring and showcasing the true impact volunteers have on communities and organizations. Tobi explains how tracking outcomes can inspire volunteers, improve program quality, strengthen recruitment messaging, guide strategic decisions, and demonstrate real value to stakeholders. You'll also learn how to create visual graphics to communicate these outcomes! Full show notes: 175. Outputs vs Outcomes: Why Counting Hours Isn't Enough Outputs vs Outcomes - Episode Highlights [02:47] - Proving Volunteer Impact [03:37] - Key Metrics to Track [06:14] - The Importance of Outcome Metrics [09:41] - Communicating Impact Effectively [13:58] - Why Volunteer Outcomes Matter [13:58] - Why Volunteer Outcomes Are Essential [18:36] - Communicating Volunteer Impact [22:24] - Improving Program Quality with Outcomes [25:40] - Strengthening Recruitment Messaging [28:42] - Supporting Strategic Decision Making [31:53] - Creating Visual Impact Graphics Helpful Links Volunteer Management Progress Report VolunteerPro Impact Lab Volunteer Nation Episode #135: How to Use Video Storytelling to Connect with Aaron Walton and Emmanuel LeGrair Thanks for listening to this episode of the Volunteer Nation podcast. If you enjoyed it, please be sure to subscribe, rate, and review so we can reach more people like you who want to improve the impact of their good cause. For more tips and notes from the show, check us out at TobiJohnson.com. For any comments or questions, email us at WeCare@VolPro.net.
“Agile isn't about following the rules. It's about delivering real value together.” In this episode, host Rebecca Kalogeris speaks with Jenny Martin, seasoned facilitator, coach, and creator of the OOPSI framework. While Agile has transformed software development over the past two decades, many organizations struggle to scale it effectively or to see the benefits they were promised. Jenny explains why so many teams fall into the trap of focusing on ceremonies and tools instead of the principles that actually drive results, like collaboration, value delivery, and rapid feedback. She introduces OOPSI—short for Outcomes, Outputs, Process, Scenarios, Inputs—a lightweight, non-prescriptive framework that helps teams break down complex problems, align on value, and accelerate delivery. Jenny shares how OOPSI can resolve common Agile pitfalls like “water-scrum-fall,” where work still flows through waterfall-style handoffs despite sprint-based development. If your teams don't understand user stories, are unclear on priorities, or struggle to collaborate across functions, this conversation offers a practical path to restoring focus, alignment, and energy in Agile. For show notes and more resources, visit: pragmaticinstitute.com/resources/podcasts Pragmatic Institute is the global leader in Product, Data, and Design training and certification programs for working professionals. Learn more at pragmaticinstitute.com.
Thank you for tuning in. Love and peace always, Amber xoxo
Tessa Burg and Aaron Grando break down one of the most talked-about topics in AI right now: context engineering. If you've been using AI tools and wondering why your outputs sometimes sound generic or miss the mark, this episode is for you. Aaron explains how context engineering helps AI better understand what you want by feeding it the right information—from your brand voice to real-time data—before you even type your prompt. Aaron and Tessa also explore how context engineering plays a key role in reducing hallucinations, maintaining brand integrity, and building automated agents that can actually take meaningful action. Listeners will take away clear, practical tips on how to make generative AI more accurate, creative, and tailored to specific goals so you can get more value out of your tools—without needing to be a tech expert Leader Generation is hosted by Tessa Burg and brought to you by Mod Op. About Aaron Grando: Aaron Grando, VP, Creative Innovation on Mod Op's Innovation team, is a seasoned technologist with over 15 years of experience at creative agencies. With a background in strategy, design, engineering, and marketing, Aaron has worked extensively in industries like media, entertainment, gaming, food & beverage, fashion, and technology. At Mod Op, Aaron leads efforts to integrate AI into creative processes, creating tools that connect creatives and clients with insights, spark ideas, and enable new brand experiences. Projects include collaborations with companies like NBCUniversal, Bethesda Softworks, Under Armour, Planet Fitness, Dietz & Watson, and more, focusing on infusing creative strategies with innovative technology to create cutting-edge brand experiences. Aaron can be reached on LinkedIn or at Aaron.Grando@modop.com. About Tessa Burg: Tessa is the Chief Technology Officer at Mod Op and Host of the Leader Generation podcast. She has led both technology and marketing teams for 15+ years. Tessa initiated and now leads Mod Op's AI/ML Pilot Team, AI Council and Innovation Pipeline. She started her career in IT and development before following her love for data and strategy into digital marketing. Tessa has held roles on both the consulting and client sides of the business for domestic and international brands, including American Greetings, Amazon, Nestlé, Anlene, Moen and many more. Tessa can be reached on LinkedIn or at Tessa.Burg@ModOp.com.
In this episode of The Future of Teamwork, Dane Groeneveld welcomes back author and collaboration expert Leo Bottary to dive into the second edition of his book Peernovation. With experience working with over 800 groups and teams, Leo shares what he's learned about the dynamics that make high-performing teams thrive – focusing on continuous improvement and celebration, learning (both intentional and collateral), and the Servant Leadership Triad.Whether you lead a team or are part of one, this episode is packed with actionable insight on how to build a culture where people feel seen, supported, and motivated to bring their best selves to the table.Key Takeaways:00:00 Introduction to the Future of Teamwork Podcast01:41 Meet Leo Bottary: Founder of Peernovation02:03 Diving into Peernovation's Second Edition03:16 The Learning Achieving Cycle05:06 The Role of Psychological Safety09:00 The Power of Asking for Help15:29 Celebrating Achievements and Reflecting on Gains23:56 Quarterly Review Cycle Insights24:15 Embracing Intelligent Failure25:33 Intentional and Collaborative Learning28:31 Coaching and Collateral Learning30:34 Intentionality in Leadership33:52 Commitment and Team Dynamics40:40 Asynchronous Meetings and Participation43:33 Balancing Outcomes and Outputs
On this episode of the Self-Publishing News Podcast, Dan Holloway explains how the European Accessibility Act will impact authors selling into the EU, with practical tips on accessible formats and why EPUB matters more than ever. He also covers a major U.S. court ruling against Anthropic for scraping copyrighted books—while still declaring its AI-generated content fair use. Sponsors Self-Publishing News is proudly sponsored by Bookvault. Sell high-quality, print-on-demand books directly to readers worldwide and earn maximum royalties selling directly. Automate fulfillment and create stunning special editions with BookvaultBespoke. Visit Bookvault.app today for an instant quote. Self-Publishing News is also sponsored by book cover design company Miblart. They offer unlimited revisions, take no deposit to start work and you pay only when you love the final result. Get a book cover that will become your number-one marketing tool. Find more author advice, tips, and tools at our Self-publishing Author Advice Center, with a huge archive of nearly 2,000 blog posts and a handy search box to find key info on the topic you need. And, if you haven't already, we invite you to join our organization and become a self-publishing ally. About the Host Dan Holloway is a novelist, poet, and spoken word artist. He is the MC of the performance arts show The New Libertines, He competed at the National Poetry Slam final at the Royal Albert Hall. His latest collection, The Transparency of Sutures, is available on Kindle.
Episode Summary What if the most important system in your organization… is made of people?In this episode, Matt traces his path from creative ops leader to go-to-market operator—and reveals what never changed: his job has always been creating the conditions for others to succeed.We explore what it's like to build trust while scaling, why confusion and surprise are deadly for team performance, and how creative ops skills translate far beyond the creative department.Whether you're navigating hypergrowth or eyeing a new career path, this episode offers a powerful reminder: the most valuable systems aren't the ones that run the work—they're the ones that help people run well together. Key TakeawaysCreative ops isn't about managing creativity—it's about enabling performance.Confusion and surprise spread fast—and kill clarity if left unchecked.Prioritization and trust-building are what make scale sustainable.Outputs matter—but outcomes earn you a seat at the table.Creative ops skills are highly transferable across sales, growth, and GTM strategy.Passive Listening to Active Thinking Use these prompts to reflect solo—or spark meaningful conversations with your team:What conditions actually help your team do their best work?Where are you relying on process—when relationships would solve more?How might your creative ops skills translate into a different business function?Are you measuring outputs—or impact?What early signs of confusion or surprise are you overlooking right now?Guest: Matt Eonta, Head of Growth & Operations at Scleraworx | Former Head of Creative Project Management at HubSpot From agencies to in-house, from creative ops to go-to-market—Matt Eonta has built systems that help people do their best work. At HubSpot, he helped scale creative operations during a period of explosive growth. Today, he brings that same operational clarity to growth strategy and sales enablement at a fast-moving tech services company.
What topic would you like us to cover next?Let's be honest, the PR world still loves a good-looking coverage book. A glossy spread in the national press, a namecheck in a podcast, a flurry of social mentions. It feels like success. But what's it actually doing for the business? Not much, in most cases. For too long, we've let vanity metrics run the show – chasing likes, impressions and clippings instead of asking the tough questions: Did it shift perception? Did it drive action? Did it move the bloody needle?That's why Stuart Bruce is worth listening to. He's not peddling the latest buzzword or flogging a new AI subscription. He's a PR Futurist who actually gets it. Someone who helps senior comms leaders cut through the noise, sidestep the hype, and use technology to do better work, not just faster work. He's advised more than 400 organisations across the globe, and he's still banging the drum for strategy, substance and measurement that matters. His latest take? The new Barcelona Principles 4.0, and why the way we measure communications is finally getting smarter.Here's what stood out from our chat:• Set clear objectives upfront. If you don't know what success looks like, how can you measure it? • The new Barcelona Principles 4.0 focus on learning and iteration, not chasing perfection. • Shift your attention from outputs like media hits to outcomes that drive real business impact. • AI tools like Copilot and Gemini can save you hours each week. But only if your team knows how to use them. • Buying AI without training? That's the "AI adoption illusion". We hate it. • “Generative AI optimisation” is the next battleground. It's how you shape what AI says about your brand. • Trade publications might now outrank national press in AI-driven search. Yes, seriously. • Misinformation and AI-generated video are your new crisis comms nightmares. • Authentic content and human interaction are back in fashion. Thank goodness.If you want to measure what matters, ditch the ego metrics and start with the AMEC framework: amecorg.com Is your marketing strategy ready for 2025? Book a free 15-min discovery call with Chris to get tailored insights to boost your brand's growth.
Or on the types of prioritization, their strengths, pitfalls, and how EA should balance them The cause prioritization landscape in EA is changing. Prominent groups have shut down, others have been founded, and everyone is trying to figure out how to prepare for AI. This is the first in a series of posts examining the state of cause prioritization and proposing strategies for moving forward. Executive Summary Performing prioritization work has been one of the main tasks, and arguably achievements, of EA. We highlight three types of prioritization: Cause Prioritization, Within-Cause (Intervention) Prioritization, and Cross-Cause (Intervention) Prioritization. We ask how much of EA prioritization work falls in each of these categories: Our estimates suggest that, for the organizations we investigated, the current split is 89% within-cause work, 2% cross-cause, and 9% cause prioritization. We then explore strengths and potential pitfalls of each level: Cause [...] ---Outline:(00:37) Executive Summary(03:09) Introduction: Why prioritize? Have we got it right?(05:18) The types of prioritization(06:54) A snapshot of EA(16:45) The Types of Prioritization Evaluated(16:57) Cause Prioritization(20:56) Within-Cause Prioritization(25:12) Cross-Cause Prioritization(30:07) Summary Table(30:53) What factors should push us towards one or another?(37:27) Possible Next Steps(39:44) Conclusion(40:58) Acknowledgements(41:01) en-US-AvaMultilingualNeural__ Modern geometric logo design with text RETHINK PRIORITIES(41:55) Appendix: Strengths and Pitfalls of Each Type(42:07) Within-Cause Prioritization Strengths(42:12) Decision-Making Support(42:37) Comparability of Outputs(44:18) Disciplinarity Advantages(45:45) Responsiveness to Evidence(46:48) Movement Building(48:06) Within-Cause Prioritization Weaknesses and Potential Pitfalls(48:12) Responsiveness to Evidence(50:54) Decision-Making Support(52:45) Cross-Cause Prioritization Strengths:(53:06) Decision-Making Support(54:49) Responsiveness to Evidence(56:08) Movement Building(56:22) Comparability of Outputs(56:45) Decision-Making Support(57:14) Cross-Cause Prioritization Weaknesses and Potential Pitfalls(57:20) Comparability of Outputs(58:01) Disciplinarity Advantages(58:41) Movement Building(59:09) Decision-Making Support(01:00:27) Cause Prioritization Strengths(01:00:32) Decision-Making Support(01:02:01) Responsiveness to Evidence(01:02:52) Movement Building(01:03:28) Cause Prioritization Weaknesses and Potential Pitfalls(01:04:28) Decision-Making Support(01:06:08) Responsiveness to EvidenceThe original text contained 23 footnotes which were omitted from this narration. --- First published: April 16th, 2025 Source: https://forum.effectivealtruism.org/posts/ZPdZv8sHuYndD8xhJ/doing-prioritization-better-2 --- Narrated by TYPE III AUDIO. ---Images from the article:
What's the difference between Quality by Design (QbD) and Design Controls—and why should you care if you're developing drug-device combination products?In this episode of Let's Combinate, Subhi Saadeh breaks down the key distinctions between QbD, used in pharmaceutical development, and design controls, the regulatory framework guiding medical device design. Learn how these two approaches tackle product realization, why they're not interchangeable, and how both are essential when building safe, effective, and compliant combination products.Whether you work in drug development, medical devices, or the space in between, this episode will help you:-Understand the regulatory foundations of QbD (ICH Q8) and design controls (FDA 21 CFR 820.30)-Learn the core tools and deliverables (like CQAs, QTPP, design verification & validation, and risk assessments)-See how each system addresses user needs, therapeutic effects, and process control-Apply both systems effectively in combination product developmentTimestamps:00:00 – Intro: Why Compare QbD and Design Controls?01:31 – Philosophical Differences: Process vs. Product Control03:10 – Practical Examples: Drugs vs. Devices05:13 – Origins and Frameworks: ICH Q8 and Design Controls Regulation06:46 – Deep Dive: What Are Design Controls? (Inputs, Outputs, DHF, V&V, Transfer)11:51 – What Is Quality by Design (QbD)? (QTPP, CQAs, Design Space, DOE)15:39 – Final Takeaways: How to Use Both in Combination ProductsSubhi Saadeh is a Quality Professional and host of Let's Combinate. With a background in Quality, Manufacturing Operations and R&D he's worked in Large Medical Device/Pharma organizations to support the development and launch of Hardware Devices, Disposable Devices, and Combination Products for Vaccines, Generics, and Biologics. Subhi serves currently as the International Committee Chair for the Combination Products Coalition(CPC) and as a member of ASTM Committee E55 and also served as a committee member on AAMI's Combination Products Committee.For questions, inquiries or suggestions please reach out at letscombinate.com or on the show's LinkedIn Page.------------------------ICH Q8, Q9, Q10, and Q12ISO 14971 Risk ManagementDifferences between usability engineering and clinical trialsThe role of control strategies and process monitoring in pharmaRelevant for:Regulatory affairs professionalsQuality engineers in pharma and medtechDrug/device development teamsAnyone preparing for combination product submissions or audits
After the huge success of the previous Rewire Your Brain podcast episode, I'm going to take you through a new level up called Outputs vs Inputs in this podcast to take you to the next level. If you're getting frustrated or disappointed with the results you're achieving in your life, business or portfolio, this is the one Blueprint you need to use. To move up to Director and even Chairman level, you must start to focus on the inputs rather than outputs in your business and in your life. This is the mindset, this is leadership and this is advanced wealth management. The Outputs vs Inputs Blueprint is the next one you need to use to Rewire Your Brain and move to the next level. Success and failure are both very predictable. I hope you enjoy.
How do you approach reclaiming or disavowing your creative outputs that you don't love? This week, Aaron, Joey, and Jess talk about Smithee-ing, TV edits, résumé writing, Spike Jonze, sportsball, and the DC Universe. They don't talk about Cordwainer Bird. references "God of Wine" Alan Smithee on IMDB Spike Jonze Charles Barkley The Room Spread your wings
In this week's episode, I am celebrating by sharing my favorite lessons from the Top 25 Most-listened episodes of The 20% Podcast. Over the past few weeks, I have shared the Top 20 episodes (links to those episodes below), but wanted to compile all of these lessons into 1 episode. But first, let's start by counting down Episodes 25-21:25. Episode 106: “Figure 8s” with Landon Meyers24. Episode 112: “Embrace Nervousness” with Mike Wander 23. Episode 87: “Your Vibe Attracts Your Tribe” with Ariel Lee22. Episode 98: “Daniel's School of Business” with Daniel Ryan21. Episode 50: “Sales: An Underrated Profession” with Scott LeeseSee below for the rest of the Top 25 List:20. Episode 93: “Only Expose Yourself To Things You Have Space For” with Lindsay Boccardo19. Episode 80: “Proud of The Struggle in His Life” with Collin Mitchell18. Episode 5: “Great Ideas Unexecuted Are Bad Ideas” with Brian Bobeck17. Episode 72: “Managing The Course” with John Morris16. Episode 20: “Tough Times Are An Opportunity” with Larry Long Jr15. Episode 3: “Investing 101” with Tim Chubb14. Episode 75: “The Start of Gratitude” with Kevin Carpenter13. Episode 76: “Finding A Job That Fits Your Personality” with Joel Lalgee12. Episode 92: “Sellers Need To Be Mini-Marketers” with Jason Bay11. Episode 84: “The Law of Reciprocity” with Belal Batrawy10. Episode 97: “The 3.99 GPA” with Morgan Buchanan9. Episode 108: “Knowing Your Hourly Rate” with Ian Koniak8. Episode 66: “Showing Up Authentically” with Darren McKee7. Episode 1: “Finding The Angle That Motivates You” with Drew Cohen6. Episode 100: “Day in the Life of The Meckes Chief Residence Officer” Dana Cohen5. Episode 58: “Get To The Truth” with Nick Cegelski4. Episode 96: “Building SaaSBros In Public” with Erik McKee3. Episode 79: “Creating The Evangelist Role” with Jen Allen-Knuth2. Episode 63: “Focus on Outputs, Not Outcomes” with Ian Koniak1. Episode 78: “How SaaS Saved His Life” with Anthony Natoli Check out the best of from the top 1-5 episodes:https://podcasts.apple.com/us/podcast/148-lessons-from-the-top-5-episodes-of-the/id1528398541?i=1000617537318 Check out the best of from the top 6-10 episodes:https://podcasts.apple.com/us/podcast/156-the-best-of-the-20-podcast-round-2/id1528398541?i=1000624379182Check out the best of the top 11-15 episodes:https://podcasts.apple.com/us/podcast/158-the-best-of-the-20-podcast-round-3-the-top/id1528398541?i=1000625921806 Check out the best of from the top 16-20 episodes:https://podcasts.apple.com/us/podcast/163-the-best-of-the-20-podcast-round-4-the-top/id1528398541?i=1000629890702 Thank you so much for your support. If there are any guests you'd like to hear me talk with on The 20% Podcast, send me a message on LinkedIn. Please enjoy this week's episode of The 20% Podcast.
I'm joined by guests Rob Hamilton & Rijndael to go through the list.Housekeeping (00:01:09) OP_Next recapBitcoin • Software Releases & Project Updates (00:15:18) Coldcard (00:42:53) Bitcoin Core (00:47:21) BDK (00:48:12) Coinswap (00:48:56) Electrum Wallet (00:52:45) BTCPay Server (00:53:33) Nunchuk Android (00:54:04) Liana (00:54:51) The Mempool Open Source Project (00:57:01) BoltzExchange boltz-web-app (00:57:16) RoboSats (00:57:21) Bitcoin Safe (00:57:58) Blockstream Green (00:58:08) Rust Payjoin (01:01:15) Zaprite (01:01:48) Krux (01:02:29) Iris Wallet Desktop (01:02:46) Bitcoin Core Config Generator (01:02:52) UTXOracle• Project Spotlight (01:04:14) SwiftSync (01:04:43) PrivatePond (01:05:00) JoinMarket Fidelity Bond Simulator (01:05:52) DahLIAS (01:06:00) Satoshi Escrow (01:06:12) Taplocks (01:15:48) bitcoin.softforks.org (01:15:52) CTV and CSFS Enabled Bitcoin Node (01:16:03) UTXOscope (01:16:13) Block Bitcoin Treasury (01:16:47) Waye (01:17:08) Sovereign Craft(Not) a Vulnerability Disclosure (01:17:17) Pay-to-Anchor outputs now exploited for blockchain spamAudience Questions (01:23:46) How do we use open time stamps for transfer of assets using two party integrity between holders? (01:24:50) Does Cove have testnet4? (01:25:15) Can you explain like I'm 5 what opcodes are, how they are used on the network, and the level of optionality that applies to them? (01:26:49) Please discuss this idea: Block-based TOTP for bitcoin wallet passphrase validation.Privacy & Other Related Bitcoin Projects • Software Releases & Project Updates (01:28:48) Tor Browser (01:28:51) TailsOS (01:28:53) NymVPN (01:28:55) MapleAILightning + L2+ • Project Spotlight (01:29:17) Misty Breez (01:29:25) Sovereign Tools (01:29:28) Silk Road on Lightning (01:29:37) Cashu Token Decoder• Software Releases & Project Updates (01:29:48) Zeus (01:29:49) LDK (01:31:40) Minibits Wallet (01:31:42) HydrusNostr • Project Spotlight (01:31:44) Atomic Signature Swaps over Nostr (01:31:51) Lantern (01:31:59) Promenade (01:32:09) Noauth-enclaved (01:32:27) GM SwapBoosts (01:33:04) Shoutout to top boosters Rod Palmer Bugle News, pink monkey, btconboard, jespada, AVERAGE_GARY & larryoshi finkamotoLinks & Contacts:Website: https://bitcoin.review/Substack: https://substack.bitcoin.review/Twitter: https://twitter.com/bitcoinreviewhqNVK Twitter: https://twitter.com/nvkTelegram: https://t.me/BitcoinReviewPodEmail: producer@coinkite.comNostr & LN: ⚡nvk@nvk.org (not an email!)Full show notes: https://bitcoin.review/podcast/episode-95
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Today, Nathan Stuck is joined by Lexie Newhouse, Program Director at Boomtown Innovation. Lexie is a powerhouse in the entrepreneurship space, working to bridge the gap between startup concepts and small business success. With experience in policy advocacy, ecosystem building, and direct support for founders, she’s passionate about helping businesses thrive—not just by tracking outputs, but by measuring real-world outcomes. In this conversation, Lexie and Nathan talk about the importance of innovation in small business, the barriers entrepreneurs face, and the policies shaping the future of entrepreneurship. Whether you’re an entrepreneur, an executive, or just a fan of small business and small business owners, you won’t want to miss this episode. RESOURCES RELATED TO THIS EPISODE Visit https://btinnovation.com/ Check out https://www.startupatlanta.com/ Follow Lexie on LinkedIn at https://www.linkedin.com/in/lexienewhouse/ CREDITS Theme Music
Part two of two episodes! Adam and Dong get together for the second time to discuss all of the input and output sources that encompass our reef aquariums. This episode covers mostly outputs: from skimming, to GFO, to water changes and many other intentional or unintentional husbandry methods.Dong has a PhD in chemistry and worked for several pharmaceutical companies in various therapeutic areas including anti-inflammatory, cancer, pain management and anti-infectious diseases. He developed his first interest in marine invertebrates when he was working as a post doc at the University of Virginia. He has been in aquarium hobby since he was in college and he got into the saltwater hobby in 2004 after setting up his first marine fish tank for a Nemo and after he discovered the Boston Reefers Society.About 10 years ago, he cofounded his first company on drug discovery. Soon after that, he was able to combine his passion for coral and his experience in the pharmaceutical industry and cofounded a new company, eCove BioMarine. This company focused on aquaculture coral for drug discovery and bone grafting. His current company, AcroGarden Inc, was then founded to hold the intellectual properties and to study coral farming. AcroGarden is now his primary focus. The company produces aqua-cultured coral, mainly SPS, for the hobby.Acrogarden Links:https://www.acrogarden.com/https://www.bostonreefers.org/forums/index.php?forums%2Facro-garden.148%2FFrag Garage Links:https://www.patreon.com/BeyondTheReefPodcasthttps://fraggarage.ca/https://www.instagram.com/fraggarage/https://www.youtube.com/channel/UCLkiAJNqvoIRDRTFs34e6Twhttps://www.facebook.com/fraggarageBeyond the Reef Merch!https://fraggarage.ca/product-category/swag/Products Discussed:https://www.coralvue.com/abyzz-afc150-flow-pumphttps://aquaticlog.com Hosted on Acast. See acast.com/privacy for more information.
Today, Nathan Stuck is joined by Lexie Newhouse, Program Director at Boomtown Innovation. Lexie is a powerhouse in the entrepreneurship space, working to bridge the gap between startup concepts and small business success. With experience in policy advocacy, ecosystem building, and direct support for founders, she’s passionate about helping businesses thrive—not just by tracking outputs, but by measuring real-world outcomes. In this conversation, Lexie and Nathan talk about the importance of innovation in small business, the barriers entrepreneurs face, and the policies shaping the future of entrepreneurship. Whether you’re an entrepreneur, an executive, or just a fan of small business and small business owners, you won’t want to miss this episode. RESOURCES RELATED TO THIS EPISODE Visit https://btinnovation.com/ Check out https://www.startupatlanta.com/ Follow Lexie on LinkedIn at https://www.linkedin.com/in/lexienewhouse/ CREDITS Theme Music
Part one of two episodes! Adam and Dong get together to discuss all of the input and output sources that encompass our reef aquariums. This episode covers inputs: from feeding, trace element dosing and other external sources, whether intentional or unintentional.Dong has a PhD in chemistry and worked for several pharmaceutical companies in various therapeutic areas including anti-inflammatory, cancer, pain management and anti-infectious diseases. He developed his first interest in marine invertebrates when he was working as a post doc at the University of Virginia. He has been in aquarium hobby since he was in college and he got into the saltwater hobby in 2004 after setting up his first marine fish tank for a Nemo and after he discovered the Boston Reefers Society.About 10 years ago, he cofounded his first company on drug discovery. Soon after that, he was able to combine his passion for coral and his experience in the pharmaceutical industry and cofounded a new company, eCove BioMarine. This company focused on aquaculture coral for drug discovery and bone grafting. His current company, AcroGarden Inc, was then founded to hold the intellectual properties and to study coral farming. AcroGarden is now his primary focus. The company produces aqua-cultured coral, mainly SPS, for the hobby.Acrogarden Links:https://www.acrogarden.com/https://www.bostonreefers.org/forums/index.php?forums%2Facro-garden.148%2FFrag Garage Links:https://www.patreon.com/BeyondTheReefPodcasthttps://fraggarage.ca/https://www.instagram.com/fraggarage/https://www.youtube.com/channel/UCLkiAJNqvoIRDRTFs34e6Twhttps://www.facebook.com/fraggarageBeyond the Reef Merch!https://fraggarage.ca/product-category/swag/Products Discussed:https://www.coralvue.com/abyzz-afc150-flow-pumphttps://aquaticlog.com Hosted on Acast. See acast.com/privacy for more information.
Welcome to Boundaries, Balance, and Breakthroughs! We're diving into some of the most meaningful conversations we've had on emotional health—the sweet spot where mental and spiritual well-being meet. You'll hear incredible insights about balance, boundaries, and living with intention from Jon Acuff, Lysa TerKeurst, Shauna Niequist, Terra Mattson, and Emily P. Freeman. Timestamps: (1:09) - Balancing Inputs and Outputs with Jon Acuff (08:23) - Lysa TerKeurt's definition of Boundaries (13:56) - Taking Care of Ourselves and Being Easily Delighted with Shauna Niequist (19:32) - Discovering a Work-Rest Balance with Terra Mattson (23:01) - Being a Soul Minimalist with Emily P. Freeman Check Alli out on YouTube I hope you loved this episode!
In this engaging episode of Partnering Leadership, Mahan Tavakoli is joined by Jeff Gothelf, a globally recognized author, speaker, and coach known for his expertise in organizational agility, product design, and customer-centricity. Jeff is the co-author of the insightful book Who Does What By How Much? A Practical Guide to Customer-Centric OKRs. Drawing on decades of experience and collaboration with leading organizations, Jeff shares practical strategies to help leaders implement Objectives and Key Results (OKRs) in ways that drive meaningful outcomes.The conversation explores why OKRs, often misunderstood as just another goal-setting framework, are in fact a transformative tool for aligning teams, fostering collaboration, and focusing on outcomes that truly matter. Jeff delves into the critical difference between outputs and outcomes, highlighting how focusing on behavior changes in customers creates measurable impact. His practical advice is grounded in real-world examples, making this episode a must-listen for leaders seeking to elevate organizational performance.Jeff also shares actionable steps for starting small with OKRs, experimenting with pilot teams, and scaling successful initiatives across the organization. He provides a nuanced perspective on the common pitfalls that derail OKR adoption, including the temptation to prioritize outputs over outcomes and the misalignment of accountability. This candid discussion offers a roadmap for leaders to overcome these challenges and create a culture of agility and continuous learning.From balancing transparency with accountability to maintaining alignment with an organization's brand promise, this episode equips CEOs and senior executives with the tools and frameworks needed to navigate strategic change effectively. Whether you're familiar with OKRs or exploring them for the first time, Jeff's insights will challenge conventional thinking and inspire leaders to rethink their approach to goal-setting and customer success.Actionable TakeawaysYou'll learn why OKRs are more than just another framework—they're a mirror for your organization's ability to deliver on its brand promise.Hear how to differentiate between outputs (what you make) and outcomes (the changes you create) and why this shift is crucial for organizational success.Discover why starting small with OKR implementation—through pilot teams—leads to greater long-term success.Explore Jeff's perspective on the most common mistake leaders make with OKRs and how avoiding it can transform your team's performance.Learn the secret to transparency in goal-setting without sacrificing accountability or collaboration within teams.Find out how OKRs enable agility, empowering organizations to adapt quickly to change while staying customer-focused.Uncover the importance of clarity, starting with the 'why' behind transformations, and how it strengthens buy-in from teams.Hear Jeff's take on aligning OKRs with strategic priorities, even during moments of crisis or market disruption.Understand how OKRs differ from KPIs and how they serve as a bridge between strategy and execution.Connect with Jeff GothelfJeff Gothelf WebsiteJeff Gothelf LinkedIn Who Does What By How Much? A Practical Guide to Customer-Centric OKRs Connect with Mahan Tavakoli: Mahan Tavakoli Website Mahan Tavakoli on LinkedIn Partnering Leadership Website
Jiunta and Shallow examine how facing challenges and adapting to stress play a critical role in building resilience. The discussion covers how lifestyle choices, mental health, and societal pressures influence our approach to health, with an emphasis on proactive strategies for longevity. From finding purpose to solving problems, this episode offers a grounded perspective on navigating the complexities of health and well-being through everyday decisions and experiences. "Pre-Script Level 1 is a course the industry needs. If you're a coach and you want to do better by your clients, this is the course. It helped join a lot of the dots for me and provided me with clarity. Make better decisions, quicker" Learn more at https://www.pre-script.com/psl1 FREE Coach's Field Guide: https://www.pre-script.com/coachs-field-guide We've got a new sponsor! Marek Health is a health optimization company that offers advanced blood testing, health coaching, and expert medical oversight. Our services can help you enhance your lifestyle, nutrition, and supplementation to medical treatment and care. https://marekhealth.com/rxd Code RXD Don't miss the release of our newest educational community - The Pre-Script ® Collective! Join the community today at pre-script.com. For other strength training, health, and injury prevention resources, check out our website, YouTube channel, and Instagram. For more episodes, subscribe and tune in to our podcast. Also, make sure to sign up to our mailing list at pre-script.com to get the first updates on new programming releases. You can also follow Dr. Jordan Shallow and Dr. Jordan Jiunta on Instagram! Dr. Jordan Shallow: https://www.instagram.com/the_muscle_doc/ Dr. Jordan Jiunta: https://www.instagram.com/redwiteandjordan/ Health and Longevity: A Changing Conversation (00:12:50) Finding Purpose in Health and Life (00:26:13) The Cycle of Inputs and Outputs (00:31:04) Building Resilience Through Stress (00:39:09) The Value of Seeking Challenges (00:44:51) Finding Fulfillment in Sacrifice (00:50:15) Health as Problem-Solving (00:57:36)
In today's episode, Andy & DJ are joined in the studio by James "The Iron Cowboy" Lawrence, known for doing 50 Ironmans in 50 States and 100 consecutive Ironmans. They discuss the importance of setting the right example for future generations, how inputs determine the outputs in your success, and the best way to fix the leadership crisis in America.
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