Podcasts about career path

This episode of the Aviation Pros Podcast features Elisabeth Dickinson and Lindsay Parrott, two franchise owners with RealClean Aircraft Detailing. Elisabeth and Lindsay share how their unique backgrounds brought them to their current leadership roles at an aviation services company, from developing key skills in different fields to falling in love with the aviation community. As women in high-level leadership roles at an aviation services company, Elisabeth and Lindsay also give insight into how women can succeed and thrive in aviation and how companies in the industry can work toward closing the gender gap in the workforce.

In this episode of FP&A Unlocked, Paul Barnhurst sits down with Robert Blanding, an experienced finance and operations leader in the ag-tech sector. Robert shares insights on building high-performing FP&A teams, partnering effectively with accounting, developing financial acumen, and applying strategic finance to drive operational impact in global businesses.Robert Blanding is the Chief Financial Officer at Fall Creek, a global leader in the Ag-Tech sector. He brings extensive experience in finance and operations from 18 years at Intel and leadership roles across industrial manufacturing, technology, and ag-tech companies. Robert combines strategic financial leadership with a focus on innovation, long-term business growth, and developing high-performing teams.Expect to Learn:What great FP&A looks like and the importance of business acumenHow to develop strong partnerships between FP&A and accountingStrategies for building high-performing, empowered finance teamsNavigating systems, processes, and technology challenges in FP&AHere are a few relevant quotes from the episode:"It starts with being connected to the business, having strong acumen, and being consulted by stakeholders." – Robert Blanding"Investing in relationships outside of crisis is critical to getting support from accounting and operations." – Robert BlandingRobert shares practical insights for FP&A professionals and aspiring CFOs, emphasizing the importance of business acumen, strong partnerships with accounting, creative problem-solving, and developing high-performing teams.Follow Robert:LinkedIn: https://www.linkedin.com/in/rfb19/Company Website: https://www.fallcreeknursery.com/Earn Your CPE Credit For CPE credit, please go to earmarkcpe.com, listen to the episode, download the app, answer a few questions, and earn your CPE certification. To earn education credits for the FPAC Certificate, take the quiz on earmark and contact Paul Barnhurst for further details.In Today's Episode[00:00] – Trailer[03:55] – Defining Great FP&A[07:39] – Career Path & First CFO Role[17:49] – Fall Creek & Ag-Tech Overview[21:18] – Importance of FP&A & Accounting Partnership[32:23] – Creative Problem Solving & Process Improvement[38:59] – Challenges in Building High-Performing Teams[45:32] – Advice for Aspiring CFOs[49:11] – Top Technical Skill for FP&A Professionals[52:50] – Personal Interests & Basketball[56:12] – How to Connect with Robert

At some point, we have to stop and ask ourselves: What are we truly building for our future? Where is our career in healthcare going? I have all of these credentials in coding, billing, compliance, practice management, and prior authorization, but what does that really say about me? I want to be clear that I believe credentials matter. I know the work, time, money, and dedication it takes to earn them. However, we also need to be honest with ourselves about something that is becoming increasingly obvious in healthcare: collecting credentials alone is not a career plan. AI is changing healthcare, and we need to start looking at the bigger picture. Terry discusses what that means in today's episode of the CodeCast podcast and asks the questions that may be uncomfortable to consider. Subscribe and Listen Find all of Terry’s official links in one place: https://www.terryfletcher.net/links The post Credentials Alone Are Not a Career Path in Healthcare appeared first on Terry Fletcher Consulting, Inc..

Annie sold a multi-million-dollar therapy center at 43. On paper, that kind of success should make a person feel safe. But money does not automatically rewrite what the body learned first. Host Syama Bunten sits down with Annie Wright, a licensed psychotherapist and executive coach specializing in trauma recovery for high achievers, for a conversation about what financial success can and cannot fix. For Annie, psychological healing and financial healing have never been separate work. Her childhood financial trauma shaped more than her beliefs about money. It shaped what safety, success, and self-worth felt like. Annie grew up between old-money privilege and real financial instability, watching money appear, disappear, and come with secrecy, shame, and survival. That early relational trauma and money mindset followed her into adulthood, even as she became the first in her family to build the kind of security she once imagined from a distance. This is a conversation about breaking the poverty cycle, first generation wealth building, and the emotional cost of becoming the person no one in your family knew how to model. Annie is honest about ambition as a survival strategy, the nervous system that still braces for everything to disappear, and why the numbers on paper do not always match the feeling of safety inside. Now, her work sits at the intersection of women and financial healing, with books, courses, and education designed to help more women move from survival into lives that feel secure, self-directed, and fully lived. Her 2026 book Decade of Decisions is part of that next chapter. If Annie's story speaks to you, keep going. The Wealth Catalyst Freedom Tour is bringing intimate money conversations to women in 32 cities this year. The Wealth Catalyst Summit lands in San Francisco this October for a full day built around what comes next. Find your city at wealthcatalyst.com.   Episode Breakdown: 00:00 Meet Annie Wright: Psychotherapist, Executive Coach, and Exited Entrepreneur 02:28 Growing Up Between Poverty and Old Money on the Coast of Maine 06:28 How Childhood Financial Trauma Shapes the Way Kids Survive 07:54 Getting a Full Ride to Brown and the Drive Behind It 11:25 The Peace Corps, a Breaking Point, and the Start of Healing 15:24 Burning Through Savings and Finding a Career Path at Esalen 17:43 Graduate School Debt, Minimum Wage Internships, and Financial Fear 23:45 Budgeting From Zero and the Financial Sobriety Journey 28:23 Launching a Therapy Center on Mat Leave and Betting on Herself 30:49 Being the Primary Earner and Making the Stay-at-Home Partner Decision 34:52 Knowing When to Sell and the Exit That Changed Everything 38:22 Trauma Recovery for High Achievers and the Mission Behind the Work 41:46 What Comes Next: Books, Courses, and Scaling the Impact 47:27 How to Find Annie Wright and What She Needs From You   Connect with Annie Wright: Visit Annie's website Subscribe to Annie's Substack   Find more from Syama Bunten: Attend a Salon near you: wealthcatalyst.com/salons Instagram: https://www.instagram.com/syama.co/ Join Syama's Substack: https://thewealthcatalystwithsyama.substack.com/ Website: https://wealthcatalyst.com Download Syama's Free Resources: https://wealthcatalyst.com/resources Wealth Catalyst Summit: https://wealthcatalyst.com/summits Speaking: https://syamabunten.com Big Delta Capital: www.bigdeltacapital.com Podcast production and show notes provided by HiveCast.fm

In this episode of the RSNA RadioGraphics team podcast mini-series, Dr. Jason Cai interviews our guest, Dr. Laura Oleaga, a neuroradiologist at the Hospital Clinic Barcelona, about her insights on the rewards and challenges of an academic radiology path. She discusses the key responsibilities, necessary skills, and mindsets, as well as tips for finding the right mentors. Whether you're interested in education, research, or leadership roles, Dr. Oleaga provides valuable guidance to help trainees make an informed decision about their future career direction.

In this episode, Pierre Michiels interviews Nicole Juhl. Nicole is an Associate Professor in Physical Education who oversees the Certified Personal Trainer program at College of DuPage. They discuss career paths in personal training, essential skills like communication and professionalism, and ways students can gain experience and build networks. After listening, we hope you better understand the personal training field and its opportunities.Full episode transcript can be found on the episode page. Below is a general timestamp summary.00:00–02:20 | Introduction & Guest Background Pierre introduces the episode and welcomes Nicole Juhl, who shares her experience in the fitness and wellness industry and outlines the focus of the Certified Personal Trainer program. 02:20–04:20 | Career Paths in Personal Training Nicole explains the wide range of opportunities in personal training, from one-on-one coaching to careers in gyms, wellness centers, and fields like kinesiology and physical therapy. 04:20–10:20 | Key Skills for Success The conversation highlights essential skills such as communication, professionalism, passion, and authenticity, along with the importance of understanding your “why.” 10:20–13:20 | Building Experience Nicole shares strategies for gaining experience, including shadowing trainers, practicing with peers, and exploring different fitness modalities to build confidence and expertise. 13:20–17:00 | Program Opportunities & Resources Discussion focuses on hands-on learning, campus facilities, networking opportunities, and new resources like the upcoming kinesiology lab. 17:00–21:20 | Networking & Personal Branding Nicole emphasizes the importance of networking, building professional relationships, and developing an authentic personal brand in the fitness industry. 21:20–26:00 | Advice for Students Entering the Field Key takeaways include taking things one step at a time, building confidence, practicing self-care, and embracing continuous growth without needing to know everything immediately. 26:00–30:00 | Program Details & How to Get Started The episode wraps with details on program structure, alternative options, and how to connect with advisors and resources to explore the personal training field further Nicole Juhl (program info & questions): juhln@cod.edu Bess Fuertes (department advising): fuertese245@cod.edu COD Personal Trainer Certificate website: https://catalog.cod.edu/programs-study/physical-education/personal-trainer-certificate/Listeners in the College of DuPage community can visit our website. All other listeners are encouraged to view the resources of their local community college, WIOA training programs, or other local support centers.Send us YOUR Listener Questions at careerpodcast@cod.edu Follow us on Instagram, Facebook, Twitter, LinkedIn @codcareercenter

This year, China's national college entrance examination is underway. It marks a major milestone, as students begin to decide on different career paths and areas of specialization. In this episode of Takeaway Chinese, we explore the professions people pursue today — and take a look back at the imperial examination system in ancient China. On the show: Niuniu & Steve. (08:43) What were the imperial exams like in ancient China? (16:58) Education systems in China today.

Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 2072: Laura Gariepy explores the complex decision between choosing a career driven by passion or one focused on financial success, revealing that the smartest path is often more nuanced than a simple either-or choice. By examining multiple career strategies, from pursuing profit first to blending fulfillment with financial stability, she offers practical insights to help you align your work with both your personal values and long-term goals. Read along with the original article(s) here: https://womenwhomoney.com/passion-profit-best-career-path/ Quotes to ponder: “Determining whether to prioritize the pursuit of wealth or personal fulfillment is perhaps the most critical aspect of choosing a career path, making a professional pivot, or pursuing an entrepreneurial idea.” “You obviously need to earn enough money to cover your desired lifestyle. But, selecting a career path exclusively for financial gain may not be a good solution for your overall wellbeing.” “Truly knowing yourself will help you make professional career choices in line with your personality and life goals.” Learn more about your ad choices. Visit megaphone.fm/adchoices

If you've been looking for a way to use your clinical skills, stay patient-facing, and make an impact—without the full weight of traditional practice—this episode may open a door you didn't know existed. Today, I'm joined by Dr. Purvi Mehra, who shares her unexpected path from fellowship into clinical research and ultimately building and selling a thriving research company. We explore what the principal investigator role really looks like, why it's far more clinical than most physicians assume, and how you can get started even without prior research experience. If you're looking for a flexible path that allows you to stay patient-facing while shifting out of traditional practice, this conversation opens a fascinating and often overlooked opportunity. In this episode we're talking about: What a principal investigator actually does day to day in clinical research Why private clinical research is more patient-facing than you might think How Dr. Mehra transitioned from fellowship to building and selling a research company The different types of clinical trial settings and what to expect in each Who this role is a good fit for and the skills you already have that apply How to find opportunities even if you have no research background Compensation insights and what physicians can expect in these roles Links for this episode: Dr. Purvi Mehra's Website Dr. Purvi Mehra's LinkedIn Exit With Intention - A book for healthcare business owners considering an exit by Dr. Purvi Mehra If you would like some confidential help with your career situation, I offer an hour-long paid consultation via Zoom. This session may be all that you need to gain clarity and have some steps for moving forward. If after this consultation you prefer additional support, there is the option of doing one of my coaching programs (subject to availability). For more information including pricing please reach out to Kati at team@doctorscrossing.com. Thank you for listening!

In this episode of the Shift AI Podcast, LaSean Smith, Product and Growth Lead at Google Cloud, joins host Boaz Ashkenazy for a wide-ranging conversation on how systems thinking and agentic AI are reshaping the way individuals, small businesses, and enterprises operate.LaSean shares a career journey that spans Microsoft HoloLens, Amazon, a successful startup exit, and now Google — plus a portfolio of small businesses that have served as his real-world AI lab. From a salad shop in Renton to a pre-construction development business in Seattle, he's applied workflow design and agent automation to solve practical problems long before it was fashionable.The conversation digs deep into how to actually build effective AI agents — not by prompting a chatbot, but by thinking in workflows first, identifying where reasoning actually needs to happen, and writing skills that make agents fast, reliable, and token-efficient. LaSean explains the "parcel grader" agent he built for his construction business, why he starts every agent build in a chat interface before moving to CLI, and how the McDonald's SOP model is the right mental framework for getting great output from AI.Boaz and LaSean also discuss the barbell economy that AI is creating — where small players and large enterprises both gain leverage while the middle gets squeezed — why Microsoft's Copilot strategy missed the point, how to think about agent security and identity, and why healthy organizational culture is the actual prerequisite for successful AI adoption.The episode closes with a reflection on what "always changing" really means as a mindset, and why building resilience and systems thinking skills now is the most important career investment anyone can make.This episode is essential listening for entrepreneurs, operators, and anyone using or thinking about deploying AI agents in their work.---Chapters[00:00] Episode 100 and LaSean's First Jobs[03:30] From Microsoft HoloLens to Amazon to Google: LaSean's Career Path[08:00] What LaSean Does at Google Cloud Today[11:00] The Entrepreneurial Side: Small Businesses as an AI Lab[16:00] The Barbell Economy: Why the Middle Is Being Squeezed[20:00] Building the Parcel Grader Agent for Pre-Construction[25:00] How to Write Better Skills: Start in Chat, Not CLI[30:00] Workflow Thinking vs. Department Thinking[35:00] Why Google Is Generating 75% of Its Code with AI[38:00] The McDonald's SOP Model for Agent Design[42:00] Agent Security for Individuals and Small Businesses[47:00] Enterprise AI: Governance, Trust, and Organizational Design[52:00] The Two-Word Future of Work: Always Changing---Connect with LaSean SmithLinkedIn: https://www.linkedin.com/in/laseansmith/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

What happens when a successful oncologist leaves clinical practice to help develop cancer treatments on a global scale? In this episode of The Lebanese Physicians Podcast, Dr. Safi Shahda shares his journey from academic oncology to leadership roles at Eli Lilly, Intellia Therapeutics, and AstraZeneca. We discuss career transitions, pharma misconceptions, innovation, AI in drug development, mentorship, and how physicians can expand their impact beyond the bedside. #LebanesePhysiciansPodcast #PharmaCareers #Oncology #ClinicalResearch #DrugDevelopment #MedicalLeadership #PhysicianCareer #AstraZeneca #Biotech #HealthcareInnovation #ArtificialIntelligence #CancerResearch #MedicalEducation #CareerGrowth #PhysicianLife #Medicine #Subscribe #Podcast #Healthcare #clinicaltrials ⁨@thelebanesephysicianspodcast⁩   ⁨@astrazeneca⁩  On all podcast apps Website: https://thelebanesephysicianspodcast.podbean.com

In this episode of the The Grad School Femtoring Podcast, I talk with Dr. Leslie Wang about writing authentically, values misalignment in academia, and choosing a career path that feels aligned with your long-term wellbeing. This episode is for anyone who feels exhausted by the pressure to constantly perform, produce, and prove themselves while questioning whether their current path still reflects who they are and how they want to live. We explore how academia, like many professional spaces, can shape people into prioritizing external validation over internal alignment, and how signs like dread, resentment, perfectionism, burnout, and comparison often point toward deeper values misalignment. Dr. Wang shares how she transitioned from a tenured professor to a coach supporting scholars with writing, publishing, and career decisions rooted in values-alignment. We also discuss how graduate students can approach career exploration more intentionally, how to identify your internal compass, and how to write for real readers instead of only writing for gatekeepers. In this episode, you will learn: How to identify early signs of values misalignment in academia Why external achievement alone often does not create long-term fulfillment How core values can guide career decisions and sustainable work practices Ways to approach writing more authentically while maintaining scholarly rigor How to identify an ideal reader beyond your dissertation committee or reviewers Why graduate students benefit from considering multiple career paths instead of defaulting to the tenure track Work with me If your institution, organization, or team is looking for workshops on sustainable productivity, executive functioning, leadership development, or culturally responsive student support, learn more here: https://gradschoolfemtoring.com/speaking/ Learn more about my coaching services for graduate students and professionals: https://gradschoolfemtoring.com/coaching/ Connect with Dr. Leslie Wang Your Words Unleashed: https://yourwordsunleashed.com Dr. Wang on LinkedIn: https://www.linkedin.com/in/leslie-k-wang-phd-a813227/ Free resource Download your Grad School Femtoring Resource Kit: https://gradschoolfemtoring.com/kit/ Explore more Listen to more episodes on Personal Development and Mindset: https://gradschoolfemtoring.com/podcast_catergory/personal-development-and-mindset/ Support the podcast with a one-time or monthly donation: https://donate.stripe.com/bJedR8dGRcs6ewGdwq38401 Access transcripts and additional resources: https://gradschoolfemtoring.com/podcast/ Audio and transcript edited by Yessi Sanchez: https://www.linkedin.com/in/yessisanchez/ This podcast is a proud member of the Genuina Media network. The Grad School Femtoring Podcast is for educational purposes only and is not a substitute for therapy or other professional services. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Careers rarely move in a straight line, and for many people, the old roadmap no longer works. In this special feed drop from LinkedIn's Hello Monday, host Jessi Hempel sits down with Caroline Wanga for a conversation about building a career with intention, authenticity, and room to evolve.As president and CEO of Essence Ventures and co-founder of Wanga Woman, Caroline has spent years helping people rethink success on their own terms. Before leading Essence, she spent 15 years at Target, rising from intern to the C-suite — a journey that taught her the value of continuously revisiting, reshaping, and even reinventing your career map.Jessi and Caroline discuss:Why your “next right move” may not look like anyone else'sWhy playing with your career map, not perfecting it, leads to clarityHow, and when, Caroline has created her own career mapsThe role of authenticity in leadership and lifeWhat it really takes to live and work with personal purposeTools and mindsets for building a meaningful career in today's worldCaroline also shares insights from her memoir, I'm Highly Percent Sure, and offers a refreshing perspective for anyone questioning what comes next.Follow Jessi Hempel and Caroline Wanga on LinkedIn, and listen to more Hello Monday wherever you get your podcastsFor the full text transcript, visit https://www.ted.com/podcasts/worklife-transcripts Hosted on Acast. See acast.com/privacy for more information.

What do you do when you want to drive human-centered change inside your organization, but you don't have the formal authority, you don't hold the budget, and you don't even have the official job title?To dive deeper into this strategy, I sat down with Max Seabrooke and Jennifer Kitchen.According to them, you stop asking for corporate permission and start getting a little bit sneaky. We step away from perfectly polished frameworks to look at the raw, political reality of practicing "undercover influence" from the inside out.In this episode, you will learn:How to quietly embed user-centric design into your organization without triggering corporate resistance or using confusing industry jargon.Why slowly building a toolkit of data on top of your company's existing expertise wins over skeptical executives.How to figure out exactly how other siloed teams measure their own success so you can align your project to their metrics.Why avoiding friction inside your team can quietly destroy project alignment and sabotage quality.So, if you want to hear raw, practical insights from professionals who are in the trenches every single day, you'll really like enjoy this one!I'm curious, if you don't have the official "Service Designer" title, what does your email signature currently say? Let me know.Enjoy the episode and keep making a positive impact.Be well, ~ Marc--- [ 1. GUIDE ] --- 00:00 Welcome to the April Round Up 202603:45 Career Paths to Service Design 05:30 Titles vs Doing the Role 06:15 Modern Human-Centered Design 07:15 UX Design for Kids 09:15 Supply Chain Overhauls 12:15 The Human Side of Blueprints 13:15 Product Repair Operations 15:30 Strategic Sneakiness 18:15 Fixing Complex Mergers 21:00 Politics as Design Material 21:45 Corporate Political Survival 24:15 Active Listening & Handoffs 28:30 Eliminating Corporate Jargon 33:45 Operational Alignment 39:15 Dangers of Toxic Politeness 44:15 Confronting Hard Truths 50:00 Customer Belief Toolkit 54:15 Leadership Evidence Layers --- [ 2. LINKS ] --- https://www.linkedin.com/in/maxseabrookehttps://www.linkedin.com/in/jennifer-kitchen-studio --- [ 3. CIRCLE ] --- Join our private community for in-house service design professionals. ⁠https://servicedesignshow.com/circle--- [ 4. FIND THE SHOW ON ] ---Youtube ~ https://go.servicedesignshow.com/inside-service-design-13-youtubeSpotify ~ https://go.servicedesignshow.com/inside-service-design-13-spotifyApple ~ https://go.servicedesignshow.com/inside-service-design-13-appleSnipd ~ https://go.servicedesignshow.com/inside-service-design-13-snipd

Most career advice is a lie. In this raw solo episode of the BRAVE Southeast Asia Tech Podcast, Jeremy Au breaks down why self-help advice from successful business leaders is often self-serving, and what young founders, operators, and professionals across Singapore, Indonesia, Vietnam, Philippines, Thailand, and Malaysia should actually pay attention to. Jeremy shares the hard-won lessons that schools and LinkedIn influencers will not teach you: the brutal tradeoffs hidden behind every "I wake up at 5am" story, why you must own your power without shame, and the dangerous trap of choosing your career outside-in instead of inside-out. He opens up about why he walked away from Bain & Company after two years and felt like a loser, only to learn a decade later that even the partners who "made it" wished they had taken the exit. You will also hear why being a small fish in a big pond is not a failure (and being a big fish in a small pond is not the goal), why sacrificing your health for your career is the dumbest tradeoff a young professional can make, and how choosing your own pain is the secret to a sustainable career. Watch, listen or read the full insight at https://www.bravesea.com/blog/choosing-your-career-path Get transcripts, startup resources & community discussions at https://www.bravesea.com WhatsApp: https://whatsapp.com/channel/0029VakR55X6BIElUEvkN02e TikTok: https://www.tiktok.com/@jeremyau Instagram: https://www.instagram.com/jeremyauz Twitter X : https://x.com/jeremyau LinkedIn: https://www.linkedin.com/company/bravesea English: Spotify | YouTube | Apple Podcasts Bahasa Indonesia: Spotify | YouTube | Apple Podcasts Chinese: Spotify | YouTube | Apple Podcasts  #SoutheastAsia #VentureCapital #Podcast #CareerAdvice #StartupFounders #SingaporeStartups #SEATech 00:00 The Illusion of Self-Help and Success 00:52 Own Your Power (And Your Quirks) 02:13 Big Fish vs Small Fish in the Pond 03:56 Outside-In vs Inside-Out Motivations 05:05 Why I Left Bain & Company 06:44 Never Sacrifice Health for Wealth 07:39 Protect Your Relationships 08:01 Choose Your Pain 08:34 Outro

SummaryWhat happens when passion for storytelling meets the entrepreneurial spirit? In this episode of the Startup Junkies podcast, Danielle Keller, media entrepreneur, award-winning podcaster, and editor-in-chief of Northwest Arkansas's beloved Peekaboo magazine, joins Daniel Koonce, Caleb Talley, and Ty Steele for a conversation packed with inspiration, nostalgia, and the realities of building community through storytelling.Danielle shares her fascinating career journey, from her beginnings in California writing for school papers, through a detour in higher education, to diving fearlessly into documentary film, video production, and ultimately acquiring and revitalizing Peekaboo magazine. She details how Peekaboo, once a crucial parental resource before the rise of social media, became a passion project resurrected through grit, research, and community demand. The print magazine's unique sensory experience illustrates the hunger for tangible connections in a digital age.Listeners will delight in anecdotes about local mascot Ozzy the Ozark Fox, created by Danielle's daughter, and how family, authenticity, and real community voices shape every issue. The episode highlights the importance of collaboration, adaptability, and embracing both print and digital platforms as Peekaboo grows and evolves.With future visions of podcasts, dynamic web offerings, and newsletters, Danielle reminds us that it's never too late to pursue fresh dreams, amplify others' voices, and savor the present. This episode is a must-listen for entrepreneurs, storytellers, and anyone who believes in the lasting power of local stories!Show Notes(00:00) Danielle's Career Path in Media(04:06) Starting Peekaboo for Parents(09:48) Evaluating Print Magazine Revival(18:10) Creating a Themed Editorial Calendar(20:37) Seasonal Advertising Opportunities(23:07) Expanding Digital Content(33:21) Closing ThoughtsLinksDaniel KoonceCaleb TalleyTy SteeleStartup JunkieStartup Junkie YouTubeDanielle KellerPeekaboo Magazine

Are you looking for a way to ignite your child's creativity and build their confidence? In this episode of the Homeschool Your Kids podcast, Jae sits down with Gaby Fadhel, the director of Royal Studios Miami, to explore the transformative power of the performing arts. Discover how theater and dance can provide a vital outlet for expression and prepare students for success in any field.In this deep dive, Gaby shares his journey from professional child actor to schoolteacher and finally to the founder of a thriving arts studio. We discuss the unique challenges within the school system and why independent programs are becoming a haven for homeschoolers and creative youth alike. From the massive success of their production of ''Six the Musical" to providing paid professional gigs for teenagers, this conversation highlights how the arts build better humans, not just better performers.Key topics include:

What does it take to build success when your last name already carries expectations? Abe Abich sits down with Donald Trump Jr. for a conversation on work ethic, unconventional career paths, and lessons learned from growing up in a high-pressure environment. They discuss how early hands-on experience shaped business instincts, how perspectives on work have evolved, and why passion plays a key role in long-term success. The conversation also touches on market sentiment and how broader economic changes may take time to unfold. Schedule your complimentary appointment today: TheRetirementKey.com Get a free copy of Abe’s book: The Retirement Mountain: The 7 Steps To A Long-Lasting Retirement Follow us on social media: YouTube | Instagram | Facebook | LinkedInSee omnystudio.com/listener for privacy information.

Collin Werner might only be 27, but don't let his age fool you. He's already worked in some of the most well-regarded kitchens in Omaha, now advancing to become the Chef de Cuisine at Au Courant. We run through Collin's career, assess the lessons he learned at different stops, discuss the value of culinary school, and more! This episode is a deep dive into creativity, discipline, and what it really takes to succeed in a modern kitchen.

what does it actually take to build a career you're excited about? in this episode, the hot pursuit girls discuss navigating the throes of corporate america and pursuing passions that don't always fit the traditional path. they share their experiences forging major career shifts throughout the years and how learning to bet on yourself opens the door to a career that is one step closer to fulfilling your dreams and passions. 0:00 overview of hp girls' corporate backgrounds 12:14 what lies were you sold at the start of your corporate career? 20:55 when did you realize corporate was a game? 25:55 planning your escape out of the corporate hole 33:00 is there a time to stay the course? 40:53 after you decide you want it, how do you get there? 50:56 common mistakes in making career jumps 54:25 last words of advice for forging your path CONNECT WITH US Connect with us @thehotpursuitpod on Instagram/TikTok/Youtube.  Email us at ⁠hello@thehotpursuitpod.com⁠.  Learn more at ⁠thehotpursuitpod.com⁠. THE HOT PURSUIT PODCAST: Hosted and written by: Jennifer Han, Emily Lin, and Madelyn Ong Produced by: Hot Pursuit Media and AsianBossGirl Edited by: Sutton Dreher of Adler Grey Videography Theme song composed by: Shawn Halim Art by: Kelsey Cordutsky Motion Graphics by: Matt Ebling Learn more about your ad choices. Visit megaphone.fm/adchoices

In this second part of the discussion on career paths for students of knowledge, we explore two important pathways outside of the traditional masjid structure: chaplaincy and education.What does a chaplain actually do?How can students of knowledge benefit Muslims in hospitals, prisons, universities, and other institutions?And why is education one of the most overlooked yet important areas for Muslims today?This video is a realistic and balanced discussion about responsibility, serving the Ummah, planning for the future, and maintaining sincerity while navigating life after studies.We also close with a reminder regarding the blessed first 10 days of Dhul-Hijjah and the importance of righteous deeds during these sacred days.BarakAllahu feekum for watching.••══ ༻✿༺══ ••❤️ Support These Efforts ⬇️: CashApp: $AtTibyanhttps://cash.app/$AtTibyanPayPal: https://www.paypal.me/MrEdge30Patreon: https://www.patreon.com/AtTibyanJoin this channel to get access to perks:https://www.youtube.com/channel/UC9aAWIAaJW0gbZsJnPAY-PA/join

What happens when you realise the people ahead of you in your career still don't have the life you want?In this episode, Matt Raad sits down with Lucy Wood, a former lawyer who walked away from an 8-year legal career and $60K in student debt to build a completely different future through business ownership.Lucy shares the moment she realised the traditional path no longer made sense, why leaving corporate felt emotionally harder than financially risky, and how finding the right entrepreneurial community completely changed her trajectory.Matt also reflects on his own experience leaving a traditional career path 30 years ago to buy a failing manufacturing business, and why surrounding yourself with people who've already done what you want to do can accelerate your growth faster than almost anything else.If you've ever questioned the path you're on, or looked at the people ahead of you and thought “I don't want that life,” this conversation will resonate deeply.In this episode:Why high achievers often feel trapped in “successful” careersThe hidden emotional cost of leaving the traditional pathWhy isolation slows down entrepreneurial growthHow community and mentorship can accelerate successThe mindset shift from career security to skill-set securityWhat Lucy learned transitioning from law into business ownershipWant To Learn How To Digital Skills That Can Replace Your Income and Buy Back Your Lifestyle?You don't need tech skills or prior experience, just the right strategy and a proven plan. Learn how 6-figure earners are buying profitable online businesses (the smart and safe way in 2026): https://www.ebusinessinstitute.com.au/dip

Welcome back to the Law School Toolbox podcast! Today we're talking about the legal jobs for which grades matter a lot, where they matter somewhat, and where they really don't matter much at all. We also share some tips for what you should do if your grades so far aren't what you had hoped for. Thanks to Juno for sponsoring this episode! If you're thinking about student loans for law school, head to JoinJuno.com to explore your options and see how Juno can help you find a better rate.    In this episode we discuss: Where grades matter a lot Where grades matter somewhat Where grades matter less than you think What if your grades are disappointing so far? Resources: Career Help with CareerDicta (https://lawschooltoolbox.com/careerdicta/career-help/) JoinJuno.com (https://joinjuno.com/) Podcast Episode 9: How To Raise Your Grades as a 2L or 3L (https://lawschooltoolbox.com/podcast-episode-9-raise-grades-2l-3l/) Podcast Episode 28: Dealing With Bad Law School Grades (https://lawschooltoolbox.com/podcast-episode-28-dealing-bad-law-school-grades/) Podcast Episode 44: How to Get a Judicial Clerkship (https://lawschooltoolbox.com/podcast-episode-44-how-to-get-a-judicial-clerkship/) Podcast Episode 98: Top 1L Questions: Non-Traditional Law Students (https://lawschooltoolbox.com/podcast-episode-98-top-1l-questions-non-traditional-law-students/) Podcast Episode 101: Preparing for a Career in Public Interest Law (With Ashley Matthews of Equal Justice Works) (https://lawschooltoolbox.com/podcast-episode-101-preparing-career-public-interest-law-ashley-matthews-equal-justice-works/) Podcast Episode 116: Life as a Small Firm Associate (With Jeremy Richter) (https://lawschooltoolbox.com/podcast-episode-116-life-as-a-small-law-firm-associate-with-jeremy-richter/) Podcast Episode 176: Talking About Judicial Clerkships with Kelsey Russell (https://lawschooltoolbox.com/podcast-episode-176-talking-about-judicial-clerkships-with-kelsey-russell/) Podcast Episode 521: Smarter Borrowing: How Juno Helps Lower Student Loans (https://lawschooltoolbox.com/podcast-episode-521-smarter-borrowing-how-juno-helps-lower-student-loans/) Download the Transcript  (https://lawschooltoolbox.com/episode-556-how-much-do-grades-matter-for-different-career-paths/) If you enjoy the podcast, we'd love a nice review and/or rating on Apple Podcasts (https://itunes.apple.com/us/podcast/law-school-toolbox-podcast/id1027603976) or your favorite listening app. And feel free to reach out to us directly. You can always reach us via the contact form on the Law School Toolbox website (http://lawschooltoolbox.com/contact). If you're concerned about the bar exam, check out our sister site, the Bar Exam Toolbox (http://barexamtoolbox.com/). You can also sign up for our weekly podcast newsletter (https://lawschooltoolbox.com/get-law-school-podcast-updates/) to make sure you never miss an episode! Thanks for listening! Alison & Lee

Not every teaching career is linear, and often the twists and turns lead us to a deeper understanding of, and appreciation for, this profession. Today's guest shares her journey over the last decade+ and how's she's navigated the ups and downs of having an unconventional career.Dr. Sarah Cegelski holds a Ph.D. in Hispanic Literature from UNC–Chapel Hill and has spent the past 18 years teaching Spanish at every level, from elementary school through college. Due to her husband's career, Sarah and her family moved six times in twelve years, a journey that led her to a wide range of teaching roles and even a brief step away from the classroom to care for her three children. While her career path has taken more twists and turns than she ever expected, she has cherished every opportunity to teach Spanish language, literature, and culture to students of all ages.Some resources that Sarah suggests:https://profemagnan.com/https://www.academicindependence.com/Joshua Cabral's World Languages Classroom (and if you haven't, check out our episode with Joshua here)Florencia Henshaw and Maris Hawkins's Common GroundAnd check out our two-part podcast with Florencia: Part 1 & Part 2

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper sit down with Scott Leshinski, President of OneStream Software, to discuss OneStream's recent acquisition by Hg Capital and the exciting changes it brings to the company's approach to finance technology. Scott shares how OneStream's focus on a unified platform is reshaping the finance space, and how integrating smart technology into financial workflows is making a real difference for businesses today.Scott Leshinski is the President of OneStream Software, where he leads OneStream's growth and innovation. With a background in corporate finance at GE Capital, co-founding Bluestone International, and working at Huron Consulting Group, Scott has a wealth of experience in driving major tech transformations. He joined OneStream in 2021 and was promoted to President in 2023 after helping the company achieve impressive growth. In this episode, you will discover:What OneStream's acquisition by Hg Capital means for its futureThe benefits of using a unified platform in financeHow automation is improving financial processesThe evolving role of automation in financial planning and decision-makingHow OneStream's tools are helping businesses become more agile and efficientScott Leshinski highlights the exciting future of finance, where unified platforms and automation are transforming how businesses manage financial workflows. He emphasizes the growing role of technology in making finance processes more efficient and discusses how OneStream is helping companies become more agile. Follow Scott:Website: https://www.onestream.com/LinkedIn: https://www.linkedin.com/in/scott-leshinski-bb5736b/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[00:00] – Intro[03:00] – OneStream's Acquisition Impact[05:00] – Transition from Public to Private[06:00] – Scott's Career Path[10:00] – What OneStream Does[14:00] – The Power of Unified Platforms[18:00] – Automation in Financial Planning[19:00] – The Rise of Automation[26:00] – Building Trust in Automation[29:00] – Improving Forecast Accuracy[35:00] – The Future of Finance[50:00] – Fun Questions with Scott[55:00] – Closing Thoughts

Struggling to find your purpose or feel fulfilled in your career? In this episode, Krista sits down with renowned career coach and TED Talk expert Ashley Stahl for a surprising take on why “following your passion” might be sabotaging your professional happiness—and what to do instead. Morning Microdose is a podcast curated by Krista Williams and Lindsey Simcik, the hosts and founders of Almost 30, a global community, brand, and top rated podcast. With curated clips from the Almost 30 podcast, Morning Mircodose will set the tone for your day, so you can feel inspired through thought provoking conversations…all in digestible episodes that are less than 10 minutes. Wake up with Krista and Lindsey, both literally and spiritually, Monday-Friday. If you enjoyed this conversation, listen to the full episode on Spotify here and on Apple here.

Are you an ambitious EA or administrative professional who wants more out of your career? Tune into this episode for insights and inspiration. Recorded at EA Ignite Fall 2025 and produced by the American Society of Administrative Professionals - ASAP. Learn more and submit a listener question at asaporg.com/podcast.

Kate Shattuck is a powerhouse leader and Managing Partner at Korn Ferry, the world's top talent and organizational consulting firm. She specializes in shaping dynamic leadership teams at the C-Suite and board levels. Known for her expert communication and energetic ability to inspire, Kate is a master at blending profit with purpose. A graduate of West Point, Harvard Business School, and Harvard Kennedy School, she is deeply committed to service and to championing emerging leaders, underdogs, and caregivers.   LinkedIn: https://www.linkedin.com/in/kateshattuck/ Website: https://www.morethanalivingblueprint.com/ ​If you're ready to take your emotional growth to the next level, join the EQ Mafia at https://www.eqgangster.com/.

(0:00) Intro (1:34) About the podcast sponsor: The American College of Governance Counsel (2:21) Start of interview  (3:20) Marie's origin story (5:19) Career Path in Law and Governance. Her time at HP and Agilent Technologies. (7:50) Transition to eBay  (9:57) Shareholder Activism and eBay's Story *CNBC clip with Ryan Cohen (14:42) Governance Roles and Board Memberships (16:50) Her teaching positions on the role of the General Counsel  (18:57) Chair and Director Succession (23:37) On separating Chair and CEO roles (25:44) Governance in Private Companies (30:40) The Impact of AI on Governance. She thinks of it in three buckets: 1) Customer/revenue opportunity; 2) from an enterprise wide standpoint; and 3) AI risks (34:36) Questions board members should ask management regarding AI opportunities and challenges (38:09) Energy Sector and AI *Marie serves on the board of Portland General Electric (43:10) Geopolitical Challenges in Business *reference to Meta-Manus China breakup (45:24) Building Trust in the Boardroom (48:30) Books that have greatly influenced her life: The Book of Alchemy, by Suleika Jaouad (2025) Phoenix in a Jade Bowl, by Bonnie Bongwan Cho Oh (her mother) (2013) Atomic Habits, by James Clear (2018) (50:32) Her mentors (52:38) Quotes that she thinks of often or lives her life by. (54:00) An unusual habit or an absurd thing that she loves. (56:00) The living person she most admires: Lisa Su. Marie Oh Huber has over 30 years of experience of strategic business, legal, regulatory and public policy experience in large global public technology companies, including eBay, Agilent Technologies, and HP. She currently serves on the board of Portland General Electric You can follow Evan on social media at:X: @evanepsteinLinkedIn: https://www.linkedin.com/in/epsteinevan/ Substack: https://evanepstein.substack.com/__To support this podcast you can join as a subscriber of the Boardroom Governance Newsletter at https://evanepstein.substack.com/__Music/Soundtrack (found via Free Music Archive): Seeing The Future by Dexter Britain is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License

After the last discussion on studying overseas and the realities many students face after graduation, this video continues the conversation by looking at some of the most common pathways students of knowledge move into after their studies.In this first part, we discuss:Tawakkul and taking the meansPreparation and responsibilityThe role of the ImamYouth work and mentorshipThe reality of the Resident Scholar positionThis discussion is primarily from the perspective of American Muslim communities and the realities many Western students may face upon returning home after studying overseas.BarakAllahu feekum for watching. Feel free to share your thoughts and experiences in the comments below.

Pool Pros text questions hereNatalie Hood (the Grit Game) explores the diverse roles and responsibilities of Coast Guard aviation professionals with Nicholas L. Gavin. This includes, but is not limited to myths about AMTs, AETs, and ASTs, and gain insights into their training, missions, and career paths. keywordsCoast Guard, aviation, AMT, AET, AST, rescue missions, military careers, aviation maintenance, rescue swimmer, career in Coast Guard key  topicsRoles of AMT, AET, and AST in Coast GuardTraining and skills required for Coast Guard aviation rolesMyths and realities of rescue missions and responsibilitiesCareer paths and opportunities in Coast Guard aviationThe importance of physical and technical skills in rescue operations guest  nameNicholas L. Gavin sound bites"Most of my career has been anything but routine.""Coordination with pilots is vital during rescues.""My favorite job was doing search and rescue."Chapters00:00 Behind the Scenes of Coast Guard Aviation04:38 Understanding the Roles of AMTs and ASTs22:11 The Training and Skills of Aviation Survival Technicians32:33 Realistic Expectations for Joining the Coast Guard resourcesGo Coast Guard - https://gocoastguard.comUS Coast Guard - https://www.uscg.milRescue Swimmer School - https://www.uscg.mil/Our-Organization/Assistant-Commandant-for-Prevention-Policy-CG-5P/Rescue-Swimmer/ guest linksEmail - Nicholas.L.Gavin@USCG.mil The Grit GameThe Grit Game, is not just playing the game, we're changing it. 500+ years industry experience, Revdup Apparel a custom apparel company built for the pool industry. Founded by pool professionalsDisclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showThank you so much for listening! You can find us on social media:FacebookInstagramTik TokEmail us: talkingpools@gmail.com

The path to becoming a CFO is rarely linear. In today's environment of constant change, finance leaders are expected to move between strategy, operations, and capital markets while developing the judgment needed to navigate uncertainty and drive growth.In this episode of The CFO Show, Melissa Howatson speaks with Greg Mrva, Chief Financial Officer of GoFundMe, about how varied career experiences shape stronger finance leaders. Drawing on decades of experience across investment banking, public company leadership, and high-growth technology businesses, Greg shares how moving between roles builds pattern recognition, resilience, and better decision-making. His journey from investment banking to CFO offers a practical lens on how finance leaders can expand their capabilities over time.Together, they explore:Why non-linear career paths create stronger CFOsHow experience in banking and operating roles builds strategic insightLessons from integrating acquisitions and building unified teamsHow GoFundMe is scaling a platform at the intersection of money, trust and technologyThe role of AI in accelerating finance workflows and decision-makingHow to develop “range of motion” across strategy, operations and analyticsThe critical skills CFOs need to lead in environments defined by ambiguity and changeWhether building a finance career, leading transformation, or preparing for the next generation of CFO leadership, this conversation offers practical insights on adaptability, growth, and long-term success.

Data from Minneapolis College says that there is an uptick. Why? Find out from Vince Thomas, Dean of the School of Trade Technologies at Minneapolis College

In this episode of People-First Builders, Fletcher Wimbush sits down with Carrie Stokes, CEO and President of Barge Design Solutions, to explore what it truly means to build a people-first engineering firm. Carrie's journey—from student intern to becoming the first woman to lead a 70-year-old, employee-owned company—is a powerful example of long-term growth, intentional leadership, and investing in people. With nearly three decades in the architecture and engineering industry, she shares how Barge is redefining career paths, leadership development, and talent retention in a highly competitive market. They dive into how creating flexible career tracks—where engineers can grow without being forced into management—helps retain top talent and build stronger teams. Carrie also breaks down how employee ownership, internal mobility, and leadership transparency are shaping a culture where people stay, grow, and thrive. If you're a leader navigating the "war for talent" or looking to build a company where people actually want to stay, this conversation is packed with actionable insights. In this episode, you'll learn: Why forcing engineers into management is a mistake How to create multiple career paths that retain top talent The role of employee ownership in engagement and performance Why internal mobility ("climbing the rock wall") drives long-term growth How leadership transparency builds future executives

I'm delighted to welcome historical novelist Caroline Montague to the castle, where we talk about how writing offers a refuge from everyday life and how she protects her creative time in her office with her dogs. Caroline shares the remarkable history of Burnt Norton, its links to T.S. Eliot's “Four Quartets,” and the dramatic tale of Sir William Kite, whose scandal, bankruptcy, and death by fire helped give the house its name, alongside stories of the “white lady” said to haunt the top floor. We discuss her path from law and interior design to writing, her planning process shaped by a firm agent, shifting titles and covers, and her current rewrite of a book about a famous royal swap. We also chat about spaniels, horses and the comfort animals bring.00:49 Writing Routine and Space01:35 Career Path to Author02:41 Burnt Norton and TS Eliot04:42 William Kite House Tragedy07:02 Ghost Stories White Lady09:51 House History and Hauntings11:25 Plotting Process and Ideas13:16 Deadlines Output and Titles15:18 Rewriting The Hook16:08 Jigsaw Writing Method17:17 Past Healing Present18:17 Woods And Creativity18:35 Spaniel Life And Social Media19:59 Dressage Highs And Loss21:09 New Horse Gio22:33 Italy And Spanish Stallions25:08 Books Animals And ImaginationYou can hear more episodes of Lady Carnarvon's Official Podcasts at https://www.ladycarnarvon.com/podcast/New episodes are published on the first day of every month. 

Some episodes just hit different the second time around.I'm re-releasing one of my favorite conversations with someone I've known since middle school in Severna Park, MD. You may know him now as the anchor of the CBS Evening News. But when we sat down to record this, I thought I knew his story.I didn't.Tony Dokoupil opens up about growing up with a father who was a federal drug trafficker, the nickname he carried in our hometown that I never knew about, how baseball became his lifeline, and how he ended up in one of the most iconic chairs in journalism...not because he planned it, but because he kept saying yes.This one is raw, real, and genuinely surprising.And if you've never heard this episode, then you're in for a treat!—--In this special re-release episode, Kara sits down with Tony Dokoupil, now the anchor of the CBS Evening News as of January 2026, to revisit a story that hits even deeper today.What looks like a polished, successful journalism career on the surface is actually built on a foundation of resilience, identity shifts, and unexpected turns. From a childhood shaped by his father's involvement in a major drug trafficking operation to rebuilding life in Maryland, Tony shares the moments that quietly shaped his path.This conversation isn't about chasing a dream job. It's about saying yes to what's in front of you, discovering your strengths along the way, and stepping into opportunities you never planned for.Listening now, with the weight of his recent promotion, this story lands differently. It's a reminder that the path rarely looks how we expect, but it often makes perfect sense looking back.Episode Topics:Tony Dokoupil's journey from childhood adversity to national news anchorThe impact of his father's arrest and starting over in a new lifeIdentity, humility, and navigating different socioeconomic realitiesHow baseball created direction and opportunityTransition from writing to broadcast journalismOvercoming shyness and imposter syndromeThe role of storytelling in human connectionWhy success often comes from saying yes, not having a perfect planBalancing career, parenthood, and purposeThe power of curiosity and lifelong learningInsights:Your starting point does not define your ceilingSkills built in one season often prepare you for something unexpected laterShyness and self-doubt don't disqualify you from visible rolesSaying yes consistently can shape a career more than having a clear visionAdversity can create empathy, which becomes a powerful professional advantageCritical thinking about your work is different from negative self-talkImposter syndrome is common, even at the highest levelsPhysical movement can unlock mental clarity and creativityWe are far less “figured out” as humans than we think, and that curiosity mattersStorytelling is the original human connection toolHighlights:00:00 Guest Introduction & Context02:15 Early Life and Family History12:14 Education and Baseball Scholarship13:22 Career Path into Journalism23:32 Imposter Syndrome and Self-Critique28:46 Uplift Segment and Current Work32:59 Interest in Writing and Future Book33:29 Curiosity About Scientific Unknowns36:40 Inspiration and Creative Process38:09 Closing and Audience Call-to-Action40:58 Podcast episode endedIf this episode shifted your perspective even a little, share it with someone who needs to hear it right now. Take a moment to rate and review the podcast to help more people discover conversations like this. And ask yourself this: what's the next opportunity in front of you that you've been hesitating to say yes to?Resources:Follow Tony Dokoupil on Instagram and Twitter (active in DMs)Watch CBS Morning and CBS Evening NewsCBS News Streaming: “Uplift” segment and extended featuresNational Archives (public records access mentioned in episode)Connect with Tony Dokoupil:CBS News Website - https://www.cbsnews.com/cbs-mornings/Instagram - https://www.instagram.com/tonydokoupil/Twitter - https://twitter.com/tonydokoupilLinkedIn - https://www.linkedin.com/in/tony-dokoupil-605898a/Connect with Kara to share your thoughts on the series:Website - http://www.kcdrealestate.com/Email - kara@kdcrealestate.comInstagram - https://www.instagram.com/karachaffindonofrio/Facebook - https://www.facebook.com/karachaffin1?_rdc=1&_rdrYouTube - https://www.youtube.com/user/KaraChaffinLinkedIn - https://www.linkedin.com/in/karachaffin/Don't forget to visit freegiftfromkara.com for our special giveaway, the Dynamic Life Journal to help you maintain your authentic voice and intuitive wisdom while navigating the balance between technology and human connection in your business and personal life.Special Listener Offer: Unlock Your Soul-Aligned Brand with Jen CudmoreAs a gift to our Soul Inspiring Business community, I've convinced my incredible mentor and business coach, Jen Cudmore, to create an exclusive package just for you—our loyal listeners. This special offer includes a powerful private session to dive into your branding archetypes and a 3-month coaching package at a deeply discounted rate.Ready to clarify your message, magnetize your dream clients, and grow your business from the inside out?Click here to claim your exclusive Soul Inspiring Business listener package

They Never Expected the Career Paths that Brought Them TogetherNeither Adrienne nor Frank Signorino expected to spend the bulk of their employment years serving as guards in California jails. Join us for a fascinating discussion of the challenges and rewards that came with doing their best to provide a safe environment for incarcerated individuals...even as they put their own safety and well-being on the line.(Podcast sound effects source: ⁠⁠http://www.freesfx.co.uk⁠⁠)

What happens when you wake up one day and realize the career you built no longer fits your life? In this episode of Wickedly Smart Women, host Anjel B. Hartwell welcomes Megan Applegate, Career Strategist, Certified Personal Consultant, and the founder of Career Blueprint Solutions.  Megan shares her powerful journey from a 20-year corporate career in hospitality to building her own business in recruiting and career strategy. What started as a feeling of disconnection and burnout turned into a bold leap into entrepreneurship, without a clear roadmap. This conversation is a must-listen for anyone feeling stuck, questioning their career direction, or ready to make a change but unsure where to start.   What You Will Learn: How to recognize the signs that a career is no longer aligned and what to do next. What it means to shift from being a job seeker to someone who attracts opportunities. How to identify and articulate transferable skills when changing industries. Why proficiency in a role does not always equal passion or fulfillment. How to build confidence by understanding your true market value. What it takes to become "market ready" before making a career move. How to navigate career transitions without waiting for a breaking point. Connect with Megan Applegate Career Blueprint Solutions https://www.careerblueprintsolutions.com/   Connect with Wickedly Smart Women® Wickedly Smart Women Wickedly Smart Women on X Wickedly Smart Women on Instagram Wickedly Smart Women Facebook Community Wickedly Smart Women Store on TeePublic [5X Award-Winning Book] Wickedly Smart Women: Trusting Intuition, Taking Action, Transforming Worlds Email: listeners@wickedlysmartwomen.com

Guest: Dr. Tracy FanaraIf you've ever fallen down a science rabbit hole on social media and thought, “Wow… I just learned something and had fun doing it,” there's a good chance you've crossed paths with Inspector Planet. Today, we're joined by Dr. Tracy Fanara — scientist, investigator, and one of the most recognizable voices in modern science communication. She's built a career on asking bold questions, testing claims, and making complex science feel approachable, playful, and empowering. We'll talk about how she spreads weather geekiness online, what it takes to cut through misinformation with creativity and humor, and why making science joyful might be one of the most powerful tools we have.Chapters00:00 Introduction to Dr. Tracy Fanara and Inspector Planet02:35 The Journey to Science and Engineering05:42 The Birth of Inspector Planet08:31 Career Path and NOAA Experience11:35 Understanding Florida Red Tide and the 2018 Water Crisis17:10 Break 117:42 Project INKI: Transforming Flood Data into Actionable Intelligence23:25 The Importance of Communication in Science27:47 Break 229:33 Leveraging Social Media for Science Communication35:25 Future of Science and Technology in Environmental ResearchSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Description: Keith sits down with Ryan Booth — former network automation specialist, Apstra alum, and now principal consultant at Blue Ridge Consulting — to trace the real career arc from infrastructure engineer to applied AI practitioner. They dig into the transferable skills that actually matter (hint: it's not the coding), why curiosity-driven entry points like [...]

Send us Fan MailGrowing up, were you told the only "respectable" careers were doctor, lawyer, nurse, or engineer? What happens to the kids who don't fit that mold — and who decided those were the only paths to success?This week, Zahra joins the MotivateMe313 podcast to break down one of the most damaging mindsets holding the next generation back. We're talking real numbers, real careers, and real talk about what success actually looks like in 2025 and beyond. THIS EPISODE WE'RE GETTING INTO: Where does this "big 3" career pressure really come from — parents, culture, or the school system? A 4-year medical degree can leave you $200K+ in debt. A licensed electrician can be earning $80K+ with ZERO debt in 2–3 years. Why aren't we having this conversation? Are we confusing prestige with success — and what's the real cost of chasing a title over a fulfilling life? Careers that people sleep on: trades, entrepreneurship, certifications, apprenticeships, and blue-collar paths that build real wealth Trade school vs. college — are non-4-year paths finally getting the respect they deserve? Should entrepreneurship be in the same conversation as a career path? What would you tell the 17-year-old who feels like a failure because they don't want to be a doctor? What does "success" actually look like — and who gets to define that for the next generation? Final Word Drop your thoughts in the comments: What career path do YOU think is most slept on? What were YOU told growing up? Follow OZ Media:  Website: Ozmedia313.com  MotivateMe313 Podcast — new episodes every weekFollow us on social media:- Instagram: @motivateme313 or @ozmedia313- Website: ozmedia313.com- Facebook: ozmedia313-TikTok: @ozmedia313-Apple Podcast: ozmedia-Spotify Podcast: ozmediaThis show was sponsored by:-The Family Doc https://thefamilydocmi.com/-Juice Box Juiceboxblend.com-Holy Bowly http://www.myholybowly.com-Wingfellas thewingfellas.com-Hanley International Academy https://www.hanleyacademy.com-Malek Al-Kabob malekalkabob.com-Bayt Al Mocha https://baytalmocha.com/-Chill Box https://www.chillboxstore.com/-Royal Kabob https://www.royalkabob.com/-GEE Preparatory Academy https://www.gee-edu.com/schools/geepreparatory/index#MotivateMe313 #OZMedia #CareerAdvice #TradeSchool #BlueCollarCareers #TradeJobs #CareerPaths #Motivation #Detroit313 #Podcast #BreakingBarriers #SuccessMindset #Entrepreneurship #TradeSchoolVsCollege

Presales teams are being asked more and more to do post-sales work. This isn't a fad or something we're doing in the short term. This is a significant change for the broader profession of presales and solutions consulting. What was once occasional support for implementations or customer renewals is now becoming formal job responsibilities for solutions consultants across the industry.   In this episode, Jack Cochran sits down with Shamil Turner, Global Technical Solutions Leader at Figma and Presales Collective advisory board member, to explore why this shift is happening and what it means for the future of presales.   Shamil brings a unique perspective to this conversation: he was the first SE hired at Figma, built their entire solutions engineering organization from the ground up, and has now transitioned into a post-sales technical leadership role. Together they discuss the strengths that SEs bring to the table that has a profound impact on a company-customer relationships AFTER the sale has happened, and how this plays out in a new role that has been emerging over the past year, the Forward Deployed Engineer. Whether you're a leader making organizational decisions or an IC just getting started, this conversation will help you understand how the world of solutions is evolving. Thank you to Saleo for sponsoring this episode! Follow Us Connect with Jack Cochran: https://www.linkedin.com/in/jackcochran/  Connect with Shamil Turner: https://www.linkedin.com/in/shamil-turner/  Links and Resources Mentioned Join Presales Collective Slack: https://www.presalescollective.com/slack  Sol/Con 2026 (Chicago, August 2026): https://www.presalescollective.com/solcon-2026  Presales Collective Podcast: https://www.presalescollective.com/podcast Saleo: https://saleo.io  Key Topics Covered The Growing Trend of Presales Teams Doing Post-Sales Work Historical Context: How SEs Have Always Supported Post-Sales Forward Deployed Engineer Roles in Consumption-Based Models Why Presales Skills Are Valuable for Customer Onboarding Shamil's Journey from First SE at Figma to Global Technical Solutions Leader The Convergence of Pre and Post-Sales Functions Career Implications for Presales Professionals at All Levels What This Means for the Future of Solutions Organizations  

"I'm going to leave the ladder, I'm going to leave the window open, I'm going to leave the lights on."This week on the podcast, I am joined by Rob Gelb, CEO of Vālenz. Rob has led Vālenz through an era of explosive growth, transforming the company from a 60-person shop in 2018 into a 550-employee powerhouse driving innovation in the self-funded healthcare space.But this episode isn't just about healthcare - it's about leadership, vulnerability, and knowing when to pass the torch.In a surprising and highly emotional moment, Rob uses this episode to make a massive public announcement regarding his future at Valenz and the next chapter of his career. He opens up about the deep self-reflection required to make this major life decision, why true leaders must surround themselves with people smarter than they are, and his ultimate mission to "leave a ladder" for the next generation of executives.We also discuss the "Kobe Bryant mentality" of embracing failure, why playing "not to lose" is a recipe for disaster, and why the future of self-funded healthcare relies entirely on "coopetition" and sharing the blueprint for success.If you are a business leader, an entrepreneur, or anyone trying to make a meaningful impact in your industry, this is an absolute must-listen.Thank you to our 2026 sponsors!ParetoHealth: ParetoHealth empowers midsize employers with a long-term solution to reduce volatility and lower overall health benefits costs. Visit https://www.paretohealth.com/fully-insured-vs-self-funding-with-paretohealth-spencer-podcast/?utm_source=youtube&utm_medium=referral&utm_campaign=SelfFundedwSpencer to learn more.Samaritan Fund: A program that connects those who need help to the support they need. We are proud to offer the Samaritan Fund Program. Visit SamaritanFundProgram.com to learn more.Vālenz Health: We're Vālenz Health, your partner in improving health literacy, reducing plan spend, and delivering high-value healthcare. Visit ValenzHealth.com to learn more.Imagine360: Imagine360 helps self-funded employers save on healthcare with smarter health plans. Cut expenses by 20-30% with custom solutions. Contact us today at Imagine360.com.Chapters:(00:00:00) Intro: Why the Smartest Leaders Are the "Dumbest" in the Room (00:02:44) The "Broken Glass" Leader & Leaving Ego at the Door (00:04:18) The Kobe Bryant Mentality: Loving to Win vs. Hating to Lose (00:11:41) Playing "Not to Lose" & The Danger of Playing it Safe (00:18:09) Becoming a First-Time CEO at 52 (The "Lattice" Career Path) (00:26:07) "The Obvious is Only Obvious to Those Who Can See It" (00:28:28) Time as the Great Contextualizer (The 3 Dots Framework) (00:32:51) Operating in the "Land Before Time" (Why Healthcare is Broken) (00:39:51) The "Coaching Tree" of Healthcare(00:43:02) Leaving a Ladder: Mentorship & Creating the Future (00:51:27) Overcoming the Inability to Scale in Self-Funded Healthcare (00:56:01) Why We Need "Coopetition" to Fix the $5 Trillion Problem (00:58:42) Rob's Huge Announcement Regarding His Future at Valenz (01:05:15) Closing Thoughts: The Legacy of Leadership & What's NextKey Links for Social:@SelfFunded on YouTube for video versions of the podcast and much more - https://www.youtube.com/@SelfFundedListen/watch on Spotify - https://open.spotify.com/show/1TjmrMrkIj0qSmlwAIevKA?si=068a389925474f02Listen on Apple Podcasts - https://podcasts.apple.com/us/podcast/self-funded-with-spencer/id1566182286Follow Spencer on LinkedIn - https://www.linkedin.com/in/spencer-smith-self-funded/Follow Spencer on Instagram - https://www.instagram.com/selffundedwithspencer/

Alise Fox, an Australian mathematician, fisheries scientist, and science communicator, shares how a winding path through teaching, biomechanics, and pavement engineering led her to become a “mathmafishian” — using data to understand fish populations and support sustainable fishing policies. In conversation with host Sam East, Alise reflects on the power of “side quests” in shaping a career, from studying mushrooms with local mycology groups to painting the fish she researches for scientific reports. She also discusses her experience as a woman in STEM with late-diagnosed neurodivergence, how creativity and curiosity fuel better science communication, and why asking questions is a powerful tool for building confidence. — The Society of Women Engineers is a powerful, global force uniting nearly 45,000 members of all genders spanning 90+ countries. We are the world's largest advocate and catalyst for change for women in engineering and technology. To join and access all the exclusive benefits to elevate your professional journey, visit membership.swe.org.

In this live episode of The Modern People Leader recorded in Toronto, the crew reflects on the evolving role of HR leaders amid rising AI pressure, shifting expectations, and increasing emotional load. They unpack real-time challenges around strategy clarity, leadership trust, and how people teams must adapt to stay effective in a rapidly changing world. Our guests for this episode:- Kate Railton, Chief People Officer @ Mejuri- Katya Laviolette, Chief People Officer @ 1Password- Jenny Do Forno, Chief People Officer @ TouchBistro----  Sponsor Links:

While the podcast team is taking a Radical Sabbatical, Kim is interviewing authors of the books that have had a big impact on her in the past two years. In this episode she's speaking with Tom Rath about his new book What's The Point. Graduation speeches are often filled with lofty advice for how to approach the upcoming transition from school to the real world–a topic that feels especially fraught at this moment of AI Anxiety.  Speakers often urge newly minted graduates to “follow your passion.” But is that the best way to decide what type of work to focus on as a career?  Perhaps a better approach is to figure out what the world needs and how you can best contribute. Tom Rath stresses the importance of surveying the landscape and identifying the big problems the world is facing. Then, identify your skills and develop them so that you can help address the issues that concern you.  One fascinating point Tom makes is that 90% of people in the workforce fall into roughly 50 different occupations. However, most of us are only exposed to a handful of these 50, often only what their parents or parents' friends do for a living. Wouldn't it be better to give young adults exposure to a much wider range of careers before they pursue career goals?  In fact, we could all benefit from this exposure. It's never too late to change careers. Tom Rath's CareerSight team brings together industry experts committed to helping people discover career possibilities and find purpose. Background on Tom Rath: Tom is an author and researcher who studies how careers impact health and well-being. He has written 12 books that have sold more than 10 million copies and made hundreds of appearances on global bestseller lists. Tom's first book, How Full Is Your Bucket?, was an instant #1 New York Times bestseller. His book StrengthFinder 2.0 was listed as Amazon's top-selling non-fiction book of all time. Tom's other bestsellers include Strengths Based Leadership, Wellbeing, Eat Move Sleep, and Are You Fully Charged? Tom is currently co-founder and CEO of CareerSight. He previously led Gallup's workplaces business and served as a Senior Scientist. Tom was also a Vice-Chair of the VHL cancer research organization. He is a graduate of the University of Michigan and the University of Pennsylvania, where he has also been a guest lecturer. CHAPTERS: (00:00) Introduction to Radical Sabbatical and Tom Rath's Book (01:52) The Problem with Passion (06:56) Purpose vs. Passion: Finding Meaning in Work (11:22) Job, Career, and Calling: Understanding the Differences (13:10) Shifting Focus: From What You Do to Who You Help (21:28) Skepticism About Childhood Dreams and Career Paths (24:29) Reevaluating Life Choices (28:01) Exploring Career Options (30:40) The Importance of Exploration (33:02) Navigating Career Pressures (34:40) The Evolution of Work (39:57) Understanding Comparison Detox (43:10) Finding Meaning in Daily Life Connect with the Radical Candor team: ⁠⁠⁠⁠⁠⁠Website⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Kris Mitchener is a professor of economics at Santa Clara University and is an economic and monetary historian. In Kris's first appearance on the show, he discusses how he fell in love with building data sets out of old dusty archives, the origins and fall of bimetallism, the pros and cons of the gold standard, the problem of operating losses on the Fed's balance sheet, what truly anchors the price level, and much more.  Watch the full length video on our new YouTube Channel! Check out the transcript for this week's episode, now with links. Recorded on March 4th, 2026 Subscribe to David's Substack: Macroeconomic Policy Nexus Follow David Beckworth on X: @DavidBeckworth Follow the show on X: @Macro_Musings Check out our Macro Musings merch! Timestamps 00:00:00 - Intro 00:01:33 - Kris' Career Path 00:06:32 - What Is Bimetallism? 00:14:41 - The Gold Standard 00:28:55 - Disinflation Policies and Central Bank Finances 00:49:25 - What Anchors the Price Level 00:55:22 - Outro

This Week Grace and Mamrie discuss flying internationally, sassy flight attendants, selling burping videos, mimosas, and shoebills. Wake up with clearer skin, smoother hair, and cooler sleep. Use code TMGW for an extra 30% off at blissy.com/TMGW. For a limited time get 40% off your first box PLUS get a free item in every box for life. Go to Hungryroot.com/tmgw and use code tmgw. Whatever challenges you're facing, Grow Therapy is here to help. Visit GrowTherapy.com/TMGW today to get started. Make the switch to Sundays. Go right now to sundaysfordogs.com/TMGW50 and get 50% off your first order. Or, you can use code TMGW50 at checkout. Learn more about your ad choices. Visit megaphone.fm/adchoices