Podcasts about Redundancy

In this episode, Dennis sits down with Brock, a civilian critical care flight paramedic who flies Medevac in the States, to break down what it actually takes to move a sick patient from point A to point B — and why drone evacuations are nowhere near as simple as the headlines suggest.They cover the real decision matrix behind scene calls versus interfacility transports, why time-sensitive patients (STEMI, stroke, major trauma) get priority, and the constant safety calculus of weather, maintenance, and crew fatigue. Brock reveals the shocking frequency of “stable” hospital patients who decompensate the moment transport begins — and why the dynamic environment of movement, vibration, and altitude changes everything.They also tackle the hard questions around drone evac: What kind of patient is actually stable enough to fly without a provider? What technology gaps (remote vent/pump titration, redundant IV access, real-time monitoring) must be solved before drones can handle true critical care? And why drone resupply might be the capability we should be training on right now.If you're a medic, planner, or leader betting on unmanned systems to solve evacuation problems in austere or contested environments, this conversation is your reality check.Key Takeaways:Scene crews decide destination based on capability; interfacility decisions are physician-driven.Over-triage happens — CCT assets sometimes get used for patients who could go ground.The “3 to go, 1 to say no” rule keeps aircraft missions safe (weather, maintenance, fatigue).Patients stable in the static hospital environment frequently decompensate once transport starts.True ICU-level patients on vents and drips currently require human titration that drones can't provide.Drone resupply is already viable and should be trained aggressively now.Realistic logistics training prevents dangerous “training scars” in younger medics.Chapters00:00 – Welcome & Why Drone Evacs Are the New Hotness (But Not That Simple)00:27 – Guest Intro: Brock, Civilian Critical Care Flight Paramedic01:59 – Scene Calls vs Interfacility Transports: Who Actually Decides?03:45 – Over-Triage Problem: Using CCT Assets on Patients Who Could Go Ground04:46 – Key Decision Factors: Time-Sensitive Patients (STEMI, Stroke, Trauma, Burns)06:56 – Aircraft Safety Culture: Weather, Maintenance Packages & Crew Fatigue (“3 to Go, 1 to Say No”)09:31 – Ground Critical Care Trucks as Backup When Weather Grounds Flights11:12 – Drone Evacs: What Kind of Patient Is Stable Enough for Unmanned Transport?13:32 – The Core Problem: Static Hospital vs Dynamic Transport Environment15:57 – Why Patients Decompensate in Transport (Real Examples from the Street)18:48 – What Drone Critical Care Would Actually Require (Remote Titration, Redundancy, Monitoring)21:44 – When It's Safer to Leave the Patient Where They Are22:48 – Drone Resupply: Already Working and Why We Need to Train It Now27:03 – Logistics Nightmares in Critical Care Transport28:50 – Training Scars: Why Realistic Logistics Training Matters for Medics29:58 – Closing Thoughts & Where to Find More PFC ContentFor more content, go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.prolongedfieldcare.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Consider supporting us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/ProlongedFieldCareCollective⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.lobocoffeeco.com/product-page/prolonged-field-care⁠⁠

Love Your Career Podcast? Let us know!Most people don't expect a career crisis. Redundancy, restructuring, a change in leadership or the sudden realisation that your contribution is being overlooked can happen with very little warning. And when it does, important career decisions often need to be made at the worst possible time.In episode 283 of Your Career Podcast, I explain why proactive career planning matters and how giving regular attention to your career can help you avoid making rushed decisions under pressure.I share a personal story from my years in Singapore, when I arrived at the airport with my two-year-old daughter and discovered that my Australian passport had expired. What followed was a stressful race against time and a difficult arrival in Australia using my British passport without the correct visa.It was a powerful lesson in what happens when something important, but not urgent, is left unattended for too long.The Urgent Important MatrixI introduce the Urgent Important Matrix, a practical framework made famous by Stephen Covey in The 7 Habits of Highly Effective People and associated with former US President Dwight D. Eisenhower.The matrix helps you separate tasks into four areas:urgent and importantimportant but not urgenturgent but not importantneither urgent nor importantCareer planning usually sits in the important but not urgent category.That is precisely why it is so easy to postpone.Your career does not send you a reminder when your industry is changing, your role is becoming vulnerable or your professional network is growing cold. By the time career planning feels urgent, you may already be facing redundancy, burnout or an unexpected career transition.What proactive career planning includesI discuss the career work that is best completed before you urgently need it:understanding and articulating your professional valuegaining clarity about the direction you want your career to takekeeping your resume, LinkedIn profile and other career assets currentmaintaining relationships and nurturing your professional networkreflecting on your achievements, skills and changing prioritiespreparing for career change before circumstances force the decisionProfessionals who manage redundancy or career change well are not necessarily luckier than everyone else. Often, they have already invested time in understanding who they are, what they offer and what they want next.This preparation gives them greater confidence, perspective and choice.A restructure may still be unsettling, but it is less likely to feel like a personal verdict when you already understand the value you bring.Take control before your career becomes urgentYou do not need to wait for a crisis to pay attention to your career.By setting aside time for the important but non-urgent work now, you can make better decisions later, strengthen your professional confidence and respond more effectively when change arrives.What have you done to proactively manage your career before redundancy looms?- - - - - - - -Welcome to Your Career Podcast with Jane Jackson where I help mid career professionals overcome self doubt and gain career clarity and the confidence to create the life and career they desire. Book a Zoom chat with me at janejacksoncoach.com - I'd love to meet you and discover what's happening in your career! Support the show- - - - - -To discover how to make a successful career change and land the job you'll LOVE, take the CAREER SUCCESS QUIZ and within 2 minutes you'll have your results, analysis and recommendations for career success.  https://careersuccess.scoreapp.com/   

In this episode, we provide a short business development update, cover the team dynamics between Maya and THORChain, discuss how to streamline and improve the ADR process, and finish off with a short Moca update.Swap now https://swap.thorchain.org/ THORChain is a decentralized crypto exchange. THORChain is the first and biggest DEX for Bitcoin. You can use any self custody wallet to swap and there's no KYC required.Timestamps:00:00:00 Intro00:02:00 Randy BD update: ShapeShift and Symbiosis00:07:00 Aaluxx breaks down how Maya Protocol and THORChain devs are allocated00:10:00 Some work is shared between protocols00:11:00 Was Maya Protocol affected by the same exploit as THORChain?00:16:00 Learning opportunities from the exploit00:18:00 Aaluxx talks about the moment the exploit happened00:22:00 Version 3.20 will be pushed early — meeting tomorrow00:25:00 Zcash update: Timeline?00:29:00 Eric asks how all the developers coordinate00:32:00 Anyone can contribute to GitLab00:33:00 What did the devs do during the slow moments around Step 5?00:36:00 What can we do to improve the ADR process?00:39:00 ADR system design discussion00:42:00 ADRs should not be gatekept00:43:00 Everyone is working as hard as possible — bandwidth is tight00:44:00 ADR numbers could be granted through Make Relay00:45:00 You do not need an ADR to submit code to GitLab00:47:00 Anything else you'd like to mention, Aaluxx?00:48:00 "I am not a developer" — Aaluxx00:51:00 MOCA going live by next Monday?

In this episode, Claudia Gasson interviews organisational psychologist Chrys Gerokonstantis to explore how personality traits influence our responses to career change, redundancy, and workplace upheavals. They delve into concepts such as resilience, self-awareness, and the importance of tailored support during transitions, especially in a rapidly changing work landscape shaped by AI and economic shifts.Main Topics:Chris's career journey from economics to consulting and psychologyThe role of personality in handling job loss and career transitionsHow traits like neuroticism, openness, and conscientiousness influence psychological resilienceThe impact of work identity attachment on mental health during layoffsPractical advice for leaders managing redundancies with personality insightsDifferentiating between burnout, depression, and anxiety in career crisesThe mental health sector's readiness for AI-driven displacement and increasing transition demandsTimestamps: 00:00 - Understanding the connection between personality and career change 00:45 - Chrys's background: From Greek upbringing and sports to economics, then psychology01:30 - The natural curiosity driving career transitions and identity development02:50 - The psychological effects of job loss: identity rupture and physical health implications04:15 - The role of personality traits in responses to redundancy and uncertainty05:30 - How neuroticism influences emotional reactivity during layoffs 06:45 - The significance of attachment to work identity and self-awareness08:00 - Big Five traits and their influence on resilience and career flexibility09:15 - How openness can turn redundancy into an opportunity for reinvention10:40 - Strategies for leaders to support affected employees based on personality profiles12:05 - Managing layoffs with tact and understanding individual circumstances13:20 - The importance of proactive self-awareness and adaptability in career transitions14:30 - Recognising mental health symptoms: depression, anxiety, burnout in career crises16:10 - Differentiating burnout from clinical depression and anxiety17:25 - The state of mental health services in the era of AI displacement 19:00 - Practical steps for individuals facing depression or anxiety during career change20:45 - The importance of recognising symptoms without pathologising 22:10 - The potential for crisis to be a transformational opportunity 23:00 - When clinical support is needed: functional assessment for therapy vs coaching 24:45 - The impact of emotional and social health in employment and transition 27:00 - Preparing organisations for AI-driven workforce changes and mental health support28:30 - The new generation's view of careers as self-directed projectsResources & Links:WaddyadoAwair - Hogan Assessments UK DistributorMental Health SupportThis episode provides valuable insights for individuals navigating career upheavals, leaders managing layoffs, and organisations preparing for the future of work amidst technological advancements.

One broken lighter is annoying. One broken income is catastrophic. That's the real meaning behind “two is one and one is none,” and we're taking it way beyond spare gear into the uncomfortable places where most of us have a single point of failure.We talk through why redundancy matters in real life, not as a panic response, but as a quiet form of good architecture. I share the personal backdrop that made this mindset non-negotiable for me: building a fast-growing commercial roofing company, watching conditions change, and learning what happens when your security, identity, and plan are all tied to one thing. From there, we unpack what it looks like to rebuild with intention, including designing our homes and infrastructure with backups so one failure doesn't take the whole system down.Then we get practical and grounded. We walk through redundancy with tools you truly depend on, multiple reliable fire-starting methods, and food resilience that respects how seasons actually work. We also get honest about income redundancy and why “one job, one employer, one paycheck” can become “none” faster than people want to admit. Finally, we connect it all to the most durable backup you can build: skills. The more you know, the less you carry, and the less you can lose.If you want more calm self-reliance and less background anxiety, start here, then pick one weak link in your own life and define what “two” could look like. Subscribe, share this with someone who needs it, and leave a review with the single point of failure you're fixing first.Support the showMy Self Reliance YouTube Channel- https://youtube.com/@MySelfReliance?si=d4js0zGc5ogYvDtOShawn James Youtube Channel - https://www.youtube.com/channel/UC5L_M7BF5iait4FzEbwKCAgMerchandise  - https://teespring.com/stores/my-self-reliance

The world’s first trillionaire, Elon Musk, has a new business partner in Australian mining tycoon Gina Rinehart, as she picks up a $US 1 billion stake in his SpaceX - part of his plan to launch AI data centres into space and create a city on Mars. Read more: Gina Rinehart secures $US1bn stake in Elon Musk’s SpaceX Adore Beauty staff warn of mass redundancies amid major push into AI Relief for punters and Jim Chalmers as Reserve Bank holds interest rates after three hikesSee omnystudio.com/listener for privacy information.

In this episode, we unpack what redundancy really means, how it affects people emotionally and financially, and the practical steps to take when work suddenly changes. With some focus on public sector employees, we explore genuine redundancy, voluntary redundancies, redeployment rights, and how to make calm, confident decisions about cash flow, debt, and super during a period of uncertainty. Beyond Blue Link: https://www.beyondblue.org.au/mental-health/work/job-loss Money Smart Links: https://moneysmart.gov.au/work-and-tax/losing-your-job Thanks for listening! We love your support, please subscribe, review, comment and share this episode to help empower and educate more folks around the money stuff! Check out more about us here: www.moneymechanics.com.au www.scottmalcolm.com.au Check out our Financial Service Guide and Privacy Policy here. Follow and like us on socials: Instagram: @moneymechanics Twitter: @moneymechanics Money Mechanics Pty Ltd (ABN 64 136 066 272) is a Corporate Authorised Representative of Infocus Securities Australia Pty Ltd (ABN 47 097 797 049) AFSL and Australian Credit Licence No. 236523 General Advice Warning Information in this podcast has been prepared for general information purposes only and not as specific advice to any particular person. Any advice contained is General Advice and does not take into account any person's particular investment objectives, financial situation and particular needs. Before making an investment decision based on this advice you should consider, with or without the assistance of a qualified adviser, whether it is appropriate to your particular investment needs, objectives and financial circumstances. Past performance of financial products is no assurance of future performance. Product Disclosure Statements contain information necessary for you to make a decision whether or not to invest in financial products which may be mentioned in this podcast. See omnystudio.com/listener for privacy information.

In this episode, Tim Mitchell, a career psychologist and coach with Waddyado, shares expert advice on navigating career changes, redundancy, and the evolving job market. Discover practical strategies for resilience, upskilling, and leveraging community support during transitional periods.Chapters00:00 Understanding Career Change and Coaching04:34 The Impact of Redundancy on Identity08:59 Navigating Career Transitions13:28 The Role of Community in Recovery18:04 Adapting to Change and AI in the Workplace22:00 Practical Advice for Those Facing Redundancy resourcesIf you're in transition, and would like to be coached by Tim, please drop us a message to info@waddyado.com

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,

The Operations Advantage: Why Clarity, Systems and Profit Are the True Ingredients for Business SuccessGuest: Tonya Odio, Operational Strategist and Founder of Executive Foundations Host: Julie RigaWhat if the secret to scaling your business had nothing to do with selling more, and everything to do with how you run things behind the scenes? In this episode, Julie Riga sits down with Tonya Odio, founder of Executive Foundations and author of The Operations Advantage. Together, they uncover what truly drives long-term business growth. Tonya brings a purpose-driven perspective: repeatable systems, workflow clarity, and profit awareness are the real foundations of a sustainable business. Whether you are a solopreneur or a seasoned CEO ready to scale, this conversation will transform how you lead.The Operations Advantage: Why Clarity, Systems and Profit Are the True Ingredients for Business SuccessAbout Tonya OdioTonya Odio is the founder of Executive Foundations, where she helps entrepreneurs scale with clarity, structure, and sustainable profit. With a career built behind the scenes of small businesses, guiding workflow and connecting operational dots, she developed a cross-functional understanding of how businesses truly run. Her debut book, The Operations Advantage: Business Success Beyond Sales, launches June 4th on Amazon.Fun Fact: Tonya loves a good steak paired with a fresh salad and a sweet, tangy dressing.The Three Ingredients for Business Success1. ClaritySuccess starts when you understand not only which systems your business needs, but how those systems support your growth. Get clear on your roles, workflows, and priorities. Move from reacting and putting out fires to operating with intention. Focus on one offer, master it, and refine it before expanding. Clarity is a leadership skill, not a luxury.2. SystemsStrong businesses are built on repeatable, flexible systems. Operations is simply a collection of systems, the routines and processes that power your business every day. The why behind your system matters as much as the what and the how. If a system does not support your goals, it does not belong in your business. The setup takes time upfront, but the long-term return in efficiency and profit is transformational.3. ProfitYour systems have a direct impact on your profit. You cannot just chase sales. You have to protect your margins. Audit your workflow regularly to find wasted time and unnecessary costs. Review your profit and loss statement every month. Identify which services carry the highest margins and promote those with intention. Audit your software stack too. Redundancy in tools quietly drains profit.Memorable Quotes"Operations is not a scary word. It is just how you run your business.""If you talk to businesses that have scaled to six or seven figures, it boils down to their repeatable processes.""It is not just the what and the how. It is the why.""We were not meant to do everything alone."Key TakeawaysClarity is a leadership skill. You cannot build what you cannot define.Systems are the real scale engine. Repeatable processes take businesses to six and seven figures.Profit is protected through awareness. Know your margins, your expenses, and your waste.You cannot do it all alone. Collaboration and delegation are the cornerstones of legacy building.AI is a system too. Use modern tools to analyze and streamline, then take action.Connect with Tonya OdioWebsite: executivefoundations.comLinkedIn: Tonya OdioBook: The Operations Advantage: Business Success Beyond Sales, available on Amazon June 4thConnect with Julie RigaPodcast Resource List: https://stacklist.app/julieriga#StayOnCourse #OperationsAdvantage #PurposeDrivenLeadership #BusinessGrowth #EntrepreneurshipSubscribe to Stay On Course wherever you listen to podcasts.Connect Everywhere:Website: www.julieriga.comPodcast: www.stayoncourse.ioLinkedIn: www.linkedin.com/in/julieriga/Email: julie@julieriga.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Suggest an interview for Cheryl Lee or let us know who your favorite interviewee is so farJoin Cheryl Lee - That Radio Chick on STILL ROCKIN' IT for news, reviews, music and interviews with some of our favourite Australian musiciansA lot of festivals chase scale. Greg Donovan chased a feeling: live Australian music under huge outback skies, surrounded by people who actually talk to each other. I'm Cheryl Lee, and I'm joined by Greg, the founder and organizer behind the Big Red Bash and the Mundi Mundi Bash, to unpack how a remote desert idea turns into one of the biggest events in Outback Australia. The surprising part is where it begins: fundraising runs for his son Stephen's Type 1 diabetes, a John Williamson moment on Big Red, and a spark that said, “What if more people could experience this?”We get into the real mechanics of building an outback music festival from scratch: learning production costs the hard way, pushing through early losses, and the career turning point that came with redundancy after decades in insurance. Greg shares how booking major Aussie acts helped the Big Red Bash find its audience, and why the Mundi Mundi Bash near Broken Hill was created to be more accessible for caravans, motorhomes, and travelers who want red dirt magic without the longest haul.The conversation goes beyond the lineup into what makes these events feel like a community: Big Blue Day for mental health fundraising with Beyond Blue, world record attempts, bush dances supporting the Royal Flying Doctor Service, and a culture of costumes that ranges from Tina Turner to Mad Max.We also talk Mad Max filming history on the Mundi Mundi Plains, what surprised Greg most about touring artists, and the economic boost the festival brings to towns across the region.If you love Australian music, outback travel, and the behind-the-scenes reality of festivals that survive tough years, hit play, share this with a friend who needs a road trip, and subscribe plus leave a review so more listeners can find Still Rockin' It.What is all the Mundi Mundi Bash goss?  Let's find out!Get out when you can, support local music and I'll see you down the front!!Visit:  ThatRadioChick.com.au

In Elixir Wizards S15E04, Charles Suggs and Emma Whamond are joined by Somtochi Onyekwere, a software engineer at Fly.io and contributor to the Corrosion distributed database project, to talk about distributed systems, infrastructure resilience, and the growing fragility of centralized cloud platforms.   We discuss what recent outages across major providers reveal about modern infrastructure and why more teams are starting to rethink assumptions around reliability, failover, and system design. Somtochi explains how Fly.io approaches geographic distribution, eventual consistency, and replication across nodes, along with the trade-offs that come with building systems this way.   The conversation explores CRDTs (Conflict-free Replicated Data Types), consensus, split-brain prevention, and what actually happens when distributed systems fail in production. We also talk about testing strategies, rollback planning, property-based testing tools, and how teams can reduce blast radius when things inevitably go wrong.   Along the way, we discuss AI infrastructure, sandboxing AI agents, and how newer workloads may add pressure to already centralized systems. The episode closes with practical advice for developers who want to build more resilient applications without over-complicating their architecture. Topics Discussed in this Episode: Corrosion and distributed database replication Centralized cloud fragility and recent outage patterns Distributed systems versus traditional cloud architectures Multi-region deployment strategies for Phoenix applications CRDTs and conflict resolution in distributed systems Eventual consistency versus strict consistency tradeoffs Consensus, leader election, and split-brain prevention Testing failover and recovery scenarios Property-based testing and Antithesis Rollback planning for database schema migrations Reducing blast radius through system isolation Health checks and blue-green deployment strategies Fly Proxy request routing and replay behavior Cross-region synchronization and replication challenges Single points of failure inside “redundant” systems Backup restoration testing and disaster recovery planning Network partitions and failure handling in production Infrastructure monitoring and operational visibility AI infrastructure workloads and operational strain Sandboxing and securing AI agents Sprites and AI workflows at Fly.io Latency improvements from geographic distribution Distributed systems tradeoffs in real-world environments Transitive dependency failures across cloud providers Practical resilience strategies for modern engineering teams Links Mentioned: https://fly.io https://github.com/superfly/corrosion https://docs.gitops.weaveworks.org/ FluxCD https://fluxcd.io/ Fly.io Stateful Sandbox Environments https://sprites.dev/ Cloudflare Workers AI Inference Platform https://www.cloudflare.com/products/workers-ai/ “An AI Agent Just Destroyed Our Production Data. It Confessed in Writing” Twitter post from PocketOS founder: https://x.com/lifeof_jer/status/2048103471019434248 Oct 2025 AWS Outage https://www.theguardian.com/technology/2025/oct/24/amazon-reveals-cause-of-aws-outage Dec 2025 Cloudflare Outage https://www.theguardian.com/technology/2025/dec/05/another-cloudflare-outage-takes-down-websites-linkedin-zoom July 2025 Crowdstrike Outage https://www.ibm.com/think/news/recent-crowdstrike-outage-what-you-should-know March 2026 Stryker Cyber Attack https://www.stryker.com/us/en/about/news/2026/a-message-to-our-customers-03-2026.html https://aws.amazon.com/ https://cloud.google.com/ https://azure.microsoft.com/en-us https://fly.io/docs/elixir/ CRDTs!! https://smartlogic.io/podcast/elixir-wizards/s13-e03-local-first-liveview-svelte-pwa/ https://antithesis.com/docs/resources/property_based_testing/ https://hex.pm/packages/proper

What happens when you realise the career you've built no longer feels creatively fulfilling? In this week's episode of the Jewellers Academy podcast, Jessica Rose is joined by Michaela to talk about starting a jewellery business later in life, discovering jewellery making after years in office work, and how studying the Diploma in Silver Jewellery completely changed the direction of her life. Michaela shares her journey from evening classes and wax carving workshops to jewellery markets, Etsy and building her own jewellery brand. Together they discuss learning jewellery making as an adult, changing careers creatively, studying jewellery making in Brighton, building confidence at the bench and the realities of starting a jewellery business from scratch. In this episode, we cover: Starting jewellery making later in life Changing careers into a creative business What it's like to study the Diploma in Silver Jewellery in person Learning stone setting and jewellery techniques Why in-person jewellery training can make such a difference Building confidence as a jeweller Jewellery markets, Etsy and Instagram Creating organic and meaningful jewellery pieces Finding fulfilment through creativity and making Michaela also shares honest advice for anyone starting out on their jewellery journey: "Just stick with it." Whether you're dreaming of becoming a jeweller, thinking about taking a jewellery course, or wondering if it's too late to start something creative, this episode is a wonderful reminder that there is no single path into jewellery making. 02:00 : Introduction to Michaela and her jewellery journey 05:25 : How a trip to Bali sparked an interest in jewellery making 07:30 : Evening classes, wax carving and wanting to learn more 09:25 : Why in-person jewellery training mattered 11:30 : Discovering her creative style and organic jewellery design 12:40 : Creativity, fulfilment and making meaningful jewellery for others 17:50 : Working in travel, office jobs and feeling creatively unfulfilled 20:10 : Redundancy, career change and finding the Diploma in Silver Jewellery 22:45 : Why Michaela chose the 7-week intensive diploma in Brighton 25:55 : Learning stone setting and developing technical skills 29:10 : Creating the final diploma project 32:20 : Starting a jewellery business after the diploma 34:10 : Etsy, markets, Instagram and building a jewellery brand 36:40 : Advice for anyone starting jewellery making later in life 38:20 : Why Michaela recommends studying at Jewellers Academy Brighton 39:35 : Where to follow Michaela and final thoughts   If you'd love to learn jewellery making in-person like Michaela, you can join us in Brighton at Jewellers Academy Brighton. We offer two ways to study for your Diploma in Brighton: one day a week over the course of a year, or a 7-week full-time intensive course, like Michaela completed. The intensive option is especially popular with students travelling from further afield, and we've welcomed jewellers from all over the world to study with us in Brighton. And if travelling to Brighton isn't possible, or you need more flexibility around work and family life, our online Diploma courses offer a highly supportive alternative. With mentored support, accountability and flexible learning, our online students achieve consistently high success rates while studying from home. You can explore our online Diploma courses here: Jewellers Academy Online Diplomas   Follow Michaela and her jewellery journey on Instagram: @nymferojewellery  

Welcome to this week's episode of "Make It Plain." Kehinde reveals what has getting him stressed and tired...the troubling news that management at Birmingham City University have decided to close the Black Studies program, putting five Black staff members, including himself at risk of redundancy. This decision comes despite promises to the contrary and highlights a attack on Black intellectual thought. We discuss the impact of this closure on students and staff, of the sudden closure of the MA Black Studies and Global Justice, with absolutely no notice or consultation. Kehinde emphasises the transformative power of Black Studies in education. The program has been instrumental in supporting students who might not have otherwise attended university, helping them achieve remarkable academic success. The episode also covers the recent local elections in the UK, where the Reform Party, led by Nigel Farage, made significant gains. Kehinde explores the implications of these results and the potential threat they pose to the political landscape, while also finding a silver lining in the overconfidence it will lead to in Farage, ultimately blocking the real danger of the next general election in the UK. Throughout the discussion, we stress the importance of community support and the need for independent Black organisations to thrive outside traditional institutions. We invite listeners to join us in upcoming events and initiatives aimed at preserving and promoting Black Studies and intellectual thought. Read the public letter and sign the petition in support https://c.org/hnpyKBCX7X Read about the attack on Black Studies in the Guardian: https://www.theguardian.com/education/2026/may/12/birmingham-city-university-urged-not-to-axe-black-studies-ma In the Times Higher: https://www.timeshighereducation.com/news/black-studies-masters-course-close-birmingham-city Get you FREE ticket for what might be the final Black Studies event at Birmingham City University. Sunday May 24th the Black Studies team will be hosting Kimberlé Crenshaw to talk her new book Backtalker, and the importance of defending Black intellectual thought: https://Kimberlecrenshaw.eventbrite.co.uk Join Harambee OBU: www.blackunity.org.uk Written and hosted by: Kehinde Andrews Edited by: Kadiri Andrews Artwork by: Assata Andrews

Hello and welcome to Episode 323 of The People Powered Business Podcast.Have you reached the point where you know a role in your business no longer makes sense, but the thought of making someone redundant feels overwhelming? Maybe business has changed, technology has improved how work gets done, or you simply can't justify carrying a salary for a role that's no longer needed. The problem is, one wrong step in a redundancy process can land you in serious trouble with Fair Work, and that's a risk no small business owner wants to take.I wanted to talk about this because I'm seeing so many businesses going through change right now. Some are restructuring because of economic pressure, some are evolving because of growth, and others are adapting to the impact AI is having on teams and workloads. Redundancy is often treated like a scary or taboo topic, but the reality is that restructuring your team can sometimes be the most practical and responsible thing you can do as a business owner. The key is making sure you do it properly and legally.In this episode, I break down exactly what a genuine redundancy actually is under the Fair Work Act, and more importantly, what it is not. I explain the consultation process that many employers completely overlook, why redeployment matters, and when small businesses may not have to pay redundancy pay at all. I also share the biggest mistakes that lead to unfair dismissal claims, including using redundancy to avoid performance management conversations and failing to document the process correctly. If you've ever worried about getting redundancy wrong, this episode will help you understand the practical steps that protect both your business and your employees. In this episode we cover:What makes a redundancy “genuine” under Fair Work The consultation process employers are legally required to follow When redeployment must be considered before ending employment Whether small businesses have to pay redundancy pay The most common redundancy mistakes that trigger unfair dismissal claims Links & Resources:Join People Powered HR: https://www.peoplepoweredbusiness.com.au/pphr DM me on Instagram @kristy.lee.billettConnect with me on LinkedIn:https://www.linkedin.com/in/kristyleebillett/Email me at hello@peoplepoweredbusiness.com.auBook a 15-minute clarity call: https://calendly.com/kristyleebillett/chatWhat this episode coversMaking a role redundant can feel incredibly stressful for small business owners, especially when there's uncertainty around Fair Work obligations, consultation requirements and redundancy pay. This episode explains what a genuine redundancy actually means, when restructuring is appropriate, and how to avoid the costly mistakes that often lead to unfair dismissal claims.It's particularly relevant for businesses navigating change, growth, economic pressure or the impact of AI on team structures. By the end of this episode, listeners will understand the key legal and practical steps involved in managing a redundancy process properly. Key insight from this episodeRedundancy is about the role, not the person. If a business uses redundancy to avoid performance management or skips proper consultation and redeployment considerations, the redundancy may not be considered genuine under Fair Work, leaving the business exposed to unfair dismissal claims and significant financial risk. What you'll take awayUnderstand the three conditions that make a redundancy legally genuine Know what meaningful consultation with employees actually looks like Be able to identify when redeployment options must be considered Learn when small businesses may be exempt from paying redundancy pay Recognise the common mistakes that put employers at risk with Fair Work

Can you think of a moment that turned your life into ‘before’ and ‘after’? For Reuben Williams, that moment was not his sexual assault, rather, the decision he made in the aftermath to pursue justice.

Take a Network Break! It’s a busy show this week. We start with follow-up on Anthropic’s Project Glasswing, router bans, and end-of-engineering/end-of-support date changes for Fortinet’s FortiOSv7.4. Our Red Alert warns of 13 critical CVEs in the Linux kernel (all of which can be addressed by updating to version 7). On the news front, Cisco... Read more »

Take a Network Break! It’s a busy show this week. We start with follow-up on Anthropic’s Project Glasswing, router bans, and end-of-engineering/end-of-support date changes for Fortinet’s FortiOSv7.4. Our Red Alert warns of 13 critical CVEs in the Linux kernel (all of which can be addressed by updating to version 7). On the news front, Cisco... Read more »

Take a Network Break! It’s a busy show this week. We start with follow-up on Anthropic’s Project Glasswing, router bans, and end-of-engineering/end-of-support date changes for Fortinet’s FortiOSv7.4. Our Red Alert warns of 13 critical CVEs in the Linux kernel (all of which can be addressed by updating to version 7). On the news front, Cisco... Read more »

Tina Tower welcomes leadership expert Cherie Canning (Luminate Leadership, Lead with Courage podcast) and shares how they first connected through social media and met in person at Christine Cochran's event.  Cherie explains how her dad's coaching and her school leadership roles shaped her early view of leadership, then how being made redundant from Flight Centre during COVID pushed her into starting her own business despite never planning to be an entrepreneur. She describes her runway planning, building a workshop-and-speaking-centered business model, adding a podcast to build trust, and the mindset shift of pricing and selling "you."  The conversation focuses on self-leadership, human-centered leadership as connection (within, with others, and with awe), sustainable success through planned breaks and intentional energy deposits, and Cherie's current goals of higher revenue/profit and more stage speaking, including her Ignite Women in Leadership conference on 13 November. 00:00 Welcome and Guest Intro 00:49 Consistency on Social Media 02:00 Meet Cherie Canning 03:39 Early Leadership Roots 05:03 Redundancy to Reinvention 06:31 Taking the Entrepreneur Leap 11:16 Building Luminate Model 13:50 Self Leadership Without Boss 18:13 Human Centered Leadership 21:48 Can Leadership Be Learned 22:42 Nature vs Nurture Skills 24:16 Rewiring People Pleasing 25:59 Two Wolves Mindset 28:25 Sustainable Success Systems 32:43 Building Personal Brand 37:23 Social Media Boundaries 39:12 Defining Success Season 41:12 Ignite Conference Details 43:15 Closing Thanks Get the Free DEFINE AND ALIGN Values online course from Cherie >> https://www.luminateleadership.com.au/online-courses

In hour three of the Chase & Big Joe Show, Big Joe and Nick Frazier continue reading answers from today's question of the day: What would you name your horse in the Kentucky Derby? Also, the guys discuss why Chad Brinker stepping down could actually be a positive for the Titans, and America's favorite segment: What Day Is It and Celebrity Birthdays.

5786Wednesday's Topic:Psalm 119אותיות של עוז: אמונה בזמן מלחמה ושלוםLetters of Strength: Faith in a Time of War and PeaceClick ⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠ for source sheet.

This panel discussion, recorded live at the FW Leadership Summit, examines how to recover from redundancy and complex workplace situations. With actor/author/podcaster Mary Coustas, The Lantern Group's managing director Victoria Buchan and FW’s learning design and content specialist Briana Blackett. Moderated by FW Founder Helen McCabe. Join the movement to fast-track your professional development. Become an FW member today. Keep up with @futurewomen on Instagram, Facebook, LinkedIn and Threads See omnystudio.com/listener for privacy information.See omnystudio.com/listener for privacy information.

This episode of Practical Cybersecurity moves past the standard PCI checklist to focus on the operational realities, common misconceptions, and "stealth" requirements that define SAQ A in the PCI DSS v4.0.1 era. The Eligibility FoundationMost merchants skip the Eligibility Criteria, which is the actual foundation of the assessment.Total Data Outsourcing: To qualify, a merchant must not store, process, or transmit any electronic account data on their own systems or premises. Call Center Exception: Merchants can still qualify for SAQ A if you use a third-party call center to handle payments on your behalf.Paper Ghosts: While the standard includes criteria for paper records, our experts have virtually never seen a modern SAQ A merchant that actually handles card data on paper in 15 years of assessments.The Iframe ParadoxA significant "stealth" requirement exists for merchants using iframes to capture payments.Susceptibility by Design: Iframes are "by definition" susceptible to scripting attacks, where malicious code scrapes data directly from the customer's browser."Hidden" Controls: To prove you aren't susceptible, the Council essentially requires you to meet requirements 6.4.3 and 11.6.1—technical controls for script inventory and integrity that are not technically listed in the body of the SAQ A document.Tips for Completing Your SAQ A:The SNMP Trap: When hardening servers (Requirement 2.2.2), administrators frequently overlook SNMP community strings, which often serve as easily searchable default "passwords" for attackers.Break-Glass Strategy: Requirement 8 now accommodates emergency "break-glass" accounts. If your lead admin ("Lisa") wins the lottery and disappears, your organization needs a documented, management-approved protocol to get the new hire ("Bob") into the system securely.The Staff Turnover Gap: Quarterly ASV scans often fail because the one person responsible for them leaves the company, and the new hire is unaware the scans are even occurring. Redundancy—where management also receives scan results—is a critical operational fix.Compliance is Not Inherited: Just because AWS is compliant does not mean your implementation of it is.Responsibility Matrix: You must utilize your provider's Security Responsibility Matrix to identify exactly which controls are managed by the vendor, which are shared, and which are your sole responsibility.And More!Resources:Download the SAQ A: Official PCI SSC SAQ A 4.0.1 PDF List of PCI ASVs: Approved Scanning Vendors AWS Responsibility MatrixAzure Responsibility MatrixA note from Jen: We built Practical Cybersecurity because we were tired of the fear-mongering in this industry. Security shouldn't be a secret club.If you're trying to figure out PCI compliance or need a pen test, my team at SecurityMetrics can help you out: https://www.securitymetrics.com/contact/lets-get-you-to-the-right-place But if you just want to learn how to protect yourself for free, start here:  https://academy.securitymetrics.com/ 

After all these monthly Q&A episodes, you folks continue to send us great Qs every month, and this time around we dig into such topics as the MacBook Neo's target audience, Windows running on Linux, technical and corporate work jargon bleeding into your personal life, Apple's relatively quiet 50th anniversary, ultrawide monitors versus lots of monitors, using Home Assistant for everything (or not), the likelihood that every home will one day have a 3D printer, and the marvel of redundant, deterministic computing that is the Artemis flight control system. Links for Artemis and shuttle program flight computers: https://cacm.acm.org/news/how-nasa-built-artemis-iis-fault-tolerant-computer/ https://www.ghs.com/news/20260423_int-178_orion_lockheed.html https://ntrs.nasa.gov/api/citations/19900015844/downloads/19900015844.pdf Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

Tim & Lindsay explore the fascinating parallels between space photography and wedding photography gear choices, focusing on reliability, versatility, and weight considerations. Discover how NASA's space missions inform best practices for professional photographers. 

“Nothing gets easier in flight.”That single line from today's guest says it all. Dennis is joined by Rich — SOF medic and flight medicine veteran — for a no-fluff masterclass on preparing patients for rotary-wing, ground, or even submarine evacuation. From rotor wash nightmares to 48-hour critical care handovers, this episode is pure gold for medics who want their patients to survive the bird, not just board it.Whether you're a ground medic with 30 seconds to hand off or a flight crew managing vents at altitude, these lessons will tighten your game, cut preventable errors, and keep aircraft off the deck longer than they need to be.KEY TAKEAWAYS YOU CAN USE TOMORROWAccurate MIST saves airframes and lives — over-triage or fake intel has real consequences.Document what the flight medic can't see (drugs, last dose/time, hidden injuries).Get access and secure everything on the ground — nothing magically gets easier at 500 feet and 120 knots.Stage 5–10 minutes early when possible. Headspace + rehearsed handover beats chaos every time.Redundancy is king in prolonged/critical care handovers: bring backups to the backups.Trend vitals and nursing care — clean the patient, position them, prevent DVT, manage contamination.Know your receiving asset — a vented patient handed to someone who's never touched one is now your problem again.Balance speed vs. life-saving interventions — don't skip a finger thoracostomy just because the bird is 30 seconds out.CHAPTERS00:00 – Welcome back to the PFC Podcast00:06 – Introducing Rich: soft medic & flight medicine expert01:44 – The brutal environment of rotary-wing medicine (lost senses, airspace surveillance, cable chaos)04:08 – Classic ground-medic mistakes (and how to stop making them)06:24 – Why accurate MIST actually matters (and how bad intel wastes lives & airframes)09:05 – The moped-vs-gunfight story every medic needs to hear13:55 – Standard aircraft loadout + what “special equipment” really means17:39 – Bare-minimum documentation when rotors are inbound (what to write in 30 seconds)20:02 – Handover acronyms that actually work (MIST vs. CIT-D + physical pointing trick)22:28 – Trust but verify: how flight medics reassess once the patient is aboard24:28 – Why ground access & securing lines is non-negotiable26:45 – Staging early, litter drills, and not racing to the rotor wash30:40 – Prolonged field care → critical care transport handovers31:30 – Is the patient ever “too unstable” to fly? (battlefield reality check)34:41 – Prepping the patient like you're handing off an ICU bed37:08 – Self-evac gear philosophy: treat the patient as if nothing was done yet41:32 – Pain management in the air — when to bump vs. load long-acting44:31 – Monitoring in flight (what still works when your senses are gone)46:58 – Over-optimizing for transport: trending, nursing care, contamination control49:25 – Know who you're handing off to (and why it matters for the truck ride)49:58 – Outro & resources For more content go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.prolongedfieldcare.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Consider supporting us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/ProlongedFieldCareCollective⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠www.lobocoffeeco.com/product-page/prolonged-field-care

In the last episode, we talked about rhythm.  About what happens when life knocks you sideways and you walk back into the studio feeling foggy, brittle, or slightly foreign to yourself. We spoke about regulation, about re-entry, about restarting the engine gently instead of demanding brilliance on command. But there's something else that often happens after the dust settles. Something quieter. More destabilising. Sometimes it isn't only your creative rhythm that's been interrupted. It's you - You've changed. And that's where things get complicated. You go through something significant. An illness that forces you to slow down. A divorce that reshapes how you understand intimacy. Redundancy after years in the same role. Children leaving home and the house suddenly carrying a different kind of silence. You step back into your studio, and something feels slightly off. The work you were making before isn't wrong, it isn't bad, but it doesn't quite sit the same way in your body. It feels like clothes that used to fit and now don't. We're taught that consistency matters, that we should maintain our voice, that we should build a recognisable trajectory, so people know what they're looking at. So, when something internal shifts, panic sets in. That can be deeply unsettling. Yet, as you will see, it is actually something to be welcomed, embraced and used as fuel. KEY TAKEAWAYS After big life events (illness, divorce, kids leaving, etc.), your old work can feel like clothes that no longer fit, not because it's bad, but because you are different now. Life changes you - it should. If you are different, the work must reflect that difference, or you will begin to feel like an imposter inside your own practice.  Instead of asking how to recover your old voice, a more honest question is, Who am I now? What occupies my thoughts when I wake up? What feels tender in me? Those recurring thoughts are not distractions. They're signals. They point towards the seam that wants to be mined next. BEST MOMENTS “You're not meant to return to who you were. You're meant to create from who you're becoming, life will change you. It should.” “If your work never shifts, if your questions never deepen, if your textures never evolve, something is probably stuck.” “So perhaps this week, instead of trying to replicate what once worked, you sit with a quieter question, what wants to be made now?” HOST BIO With over 35 years in the art world, Ceri has worked closely with leading artists and arts professionals, managed public and private galleries and charities, and curated more than 250 exhibitions and events. She has sold artworks to major museums and private collectors and commissioned thousands of works across diverse media, from renowned artists such as John Akomfrah, Pipilotti Rist, Rafael Lozano-Hemmer and Vito Acconci. Now, she wants to share her extensive knowledge with you, so you can excel and achieve your goals. ** Ceri Hand Coaching Membership: Group coaching, live art surgeries, exclusive masterclasses, portfolio reviews, weekly challenges. Access our library of content and resource hub anytime and enjoy special discounts within a vibrant community of peers and professionals. Ready to transform your art career? Join today! https://cerihand.com/membership/ ** Unlock Your Artworld Network Self Study Course Our self-study video course, "Unlock Your Artworld Network," offers a straightforward 5-step framework to help you build valuable relationships effortlessly. Gain the tools and confidence you need to create new opportunities and thrive in the art world today. https://cerihand.com/courses/unlock_your_artworld_network/ ** Book a Discovery Call Today To schedule a personalised 1-2-1 coaching session with Ceri or explore our group coaching options, simply email us at hello@cerihand.com ** Discover Your Extraordinary Creativity Visit www.cerihand.com to learn how we can help you become an extraordinary creative. This Podcast has been brought to you by Disruptive Media. https://disruptivemedia.co.uk/

Download This Episode HereToday we're taking a hard look at the gaps that can quietly undermine even the most well-intentioned preparedness plans. Because the truth is, most failures in a crisis don't come from a lack of effort, they come from blind spots. The things you didn't practice, didn't question, or assumed would just work out when the time came. And those gaps have a way of showing up at the worst possible moment.We start by looking at some of the most common prepping mistakes. Things like going it alone without a support network, focusing too much on gear instead of skills, and failing to actually test your plans before you need them. We also talk about overlooked issues like poor gear maintenance, expired or disorganized supplies, and the risk of keeping everything in one place. These aren't dramatic failures, they're the small, avoidable problems that can snowball into bigger ones under pressure.From there, we tackle some of the most persistent myths in the prepping world. The idea that help will always arrive quickly, that you can just “live off the land,” or that having a weapon is enough to carry you through a crisis. We also address the belief that disasters only happen to other people, and the flip side of that coin, the assumption that everything will instantly descend into chaos. The reality is more nuanced, and understanding that can make a huge difference in how you prepare.We also dig into key lessons learned from real-world events. Adaptability is a big one, plans rarely unfold the way you expect, so having backups (and backups to your backups) is critical. Redundancy, reliable access to fuel and cash, and having a ready-to-go evacuation bag can all make the difference when time is tight. We also touch on the importance of mental resilience, since stress and uncertainty can wear you down just as fast as physical challenges.Finally, we talk about the bigger picture: preparedness as an ongoing process. It's not about getting everything perfect, it's about learning, adjusting, and improving over time. Building connections with others, strengthening communication, and staying flexible in your approach can make you far more resilient than any single piece of gear. If you find value in what we do, if you've learned something new, gotten an idea for something you need to do, been entertained, or just like out Southern charm, would you be willing to give back a little?You can do that one of several ways.     Go to our support page               OR     By starting your Amazon shopping from our website? --->  CLICK HERE        (We earn from qualifying Amazon purchases)Contact us:Practical PreppingWebsiteOur Sponsors:Practical Prepping BooksProof Minimalist Wallets (Discount code PREPPER)ProLine Digital Group   Website  Email

Send us Fan MailSomething in you keeps saying no. And you don't know why. This episode names it.Eight to ten weeks after a loss,  a redundancy, job loss, relationship, or life shift of any kind, something changes. Moving forward isn't working, even when you're doing everything right. You might have offers. You can't say yes. Something deeper is asking to be heard.This episode is for high-achieving professional women who can't move forward,  whether it was a redundancy, career transition, being pushed out of a role, or a shift that happened years before and still hasn't shifted inside.In this episode Dr Amen Kaur covers:Why losing your job feels like losing yourself The science of identity and belonging The neuroscience of belonging: why the nervous system treats job loss as physical threat Baumeister and Leary's landmark research on belonging as a fundamental human need Why women in senior leadership experience greater loneliness and why that matters here The freeze response (Polyvagal Theory, Stephen Porges), what it is and why you're stuck in it Why borrowed belonging always has a ceiling and what that means for high achievers The difference between fitting in and belonging and why you've been doing the first Where the belonging you've been searching for actually lives Why nothing you've tried - therapy, coaching, mindset work, has been able to reach thisKeywords: redundancy recovery, job loss identity, women career transition, nervous system regulation, belonging and identity, loss of self after redundancy, high achieving women burnout, freeze response career, polyvagal theory women, identity collapse redundancy, female leadership loneliness, career identity crisis, nervous system and belonging, human intelligence framework, BEYOND podcastReady to go deeper: amenkaur.com/masterclassFree Masterclass: The Human Intelligence FrameworkA walkthrough of the five stage method Dr Amen Kaur uses with high achieving women who have lost themselves inside a career, role or identity that no longer fits.Watch it free at amenkaur.com/masterclassAbout Dr Amen KaurDr Amen Kaur holds a PhD and spent over twenty years in corporate, including time as a Partner at a FTSE 250 company focused on business growth. She now teaches the Human Intelligence Framework, a five stage method that helps women stop performing and come home to who they actually are.Learn more at amenkaur.com/aboutStay CloseInstagram: @dramenkaurTikTok: @dramenkaurYouTube: @dramenkaurDisclaimer: This podcast is for educational and informational purposes only. It is not medical, psychological, or financial advice. Please consult a qualified professional for guidance specific to your situation.

8. The lunar race intensifies as Chinaplans multiple settlements to achieve solar system hegemony. NASA aims to leap ahead using nuclear electric propulsion and competitive private contracts, focusing on redundancy and safety to ensure a sustained Americanpresence on the lunar surface. (8)1917-18

On today's episode of Ask Me Anything with Dr. Mark Costes, he dives into some of the most insightful questions submitted by listeners, tackling the challenges dental practice owners face as they scale. Mark explores the most common bottleneck he sees—lack of delegation and redundancy—and shares how even highly profitable, low-overhead practices can quickly crumble without the right systems and depth on the team. He emphasizes the value of hiring great people when you find them and offers advice for empowering your leads so the practice can thrive without your constant presence. Mark also addresses how to handle delicate leadership moments—like when a key team member is struggling personally—while maintaining accountability and protecting the health of the organization. He breaks down a smart, step-by-step approach to dropping PPOs, urging dentists to avoid emotional decisions and instead rely on data, preparation, and strategic marketing to make the transition smooth. To close out the episode, Mark shares why he loves the start of a new year as a time for deep reflection and goal setting, using the Four Futures framework to reset priorities across mindset, health, relationships, and finances. Be sure to check out the full episode from the Dentalpreneur Podcast! EPISODE RESOURCES https://www.truedentalsuccess.com Dental Success Network Subscribe to The Dentalpreneur Podcast

Tete Xiao, VP of Engineering and AI, Bot Auto joined Grayson Brulte on The Road to Autonomy to discuss the fundamental shift from virtual AI to the physical AI required for commercial autonomous trucking.Tete co-authored Segment Anything, the landmark paper that ushered in the era of specific models to an era of foundation models that generalize across large segments of data. This approach which he is implementing at Bot Auto, enables the company to move beyond the limitations of previous technology, treating autonomous trucking as a compute-driven challenge where the system learns to navigate the complex physics of driving a truck.To ensure safety, Bot Auto is utilizing a top-down redundancy architecture that mirrors aviation's triple autopilot systems. Including dual onboard computers and independent software stacks running parallel algorithms with deliberately different logic to prevent a single failure from propagating through the system.This spring, Bot Auto is planning to launch fully autonomous commercial operations with Ryan Transportation on the Houston to Dallas corridor. No safety driver. No safety observer. No human in the cab.Episode Chapters00:00 AUTNMY AI00:25 Segment Anything05:04 Virtual AI to Physical AI09:08 Redundancy and Aviation-Inspired Architecture13:40 Hardware and Software17:00 Launching Fully Autonomous Operations20:00 Foundation Models and Reinforcement Learning27:52 Compute Infrastructure35:22 Staying Ahead42:30 Building a Virtual Driver47:06 AGI48:36 Transportation Company53:59 Future of Bot Auto--------About The Road to AutonomyThe Road to Autonomy is the definitive media brand covering the Autonomy Economy™. Through our podcasts, newsletter, and proprietary market intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Subscribe today for free: https://www.roadtoautonomy.com/ae/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most disruptions don't arrive as dramatic failures, they show up as small, compounding inconveniences. In this episode, we explore what happens when the digital systems you rely on every day become unreliable, and how to build simple, practical redundancy across your data, finances, and communication so you're not caught off guard when things don't work like they should.Mentioned In This Episode:WD 2TB My Passport, Portable External Hard Drive, Black, backup software with defense against ransomwareRand McNally Road Atlas Large Scale 2026Garmin inReach Mini 24 in 1 USB C Car Charger, 4-Port 90W Super Fast Charging Cigarette Lighter AdapterSurfsharkAnker Power Bank, 20,000mAh Travel Essential Portable Charger with Built-in USB-C Cable, 3-Port 87W Max Fast Charging Battery PackDownload This Episode HereIf you find value in what we do, if you've learned something new, gotten an idea for something you need to do, been entertained, or just like out Southern charm, would you be willing to give back a little?You can do that one of several ways.     Go to our support page               OR     By starting your Amazon shopping from our website? --->  CLICK HERE        (We earn from qualifying Amazon purchases)Contact us:Practical PreppingWebsiteOur Sponsors:Practical Prepping BooksProof Minimalist Wallets (Discount code PREPPER)ProLine Digital Group   Website  Email1791gunleather.com (Discount code: PREP15) SurfsharkPodcast music written and recorded by Krista LawleyWebsite design and hosting by ProLine Digital Group.Podcasts Copyright 2026, P3 Media Group, LLC, and Practical Prepping Podcast

What happens when the "dream job" you hustled so hard to get...suddenly disappears? You're trapped in the story of who you think you should be, convinced you only fit into one box. When life tears up the script, that loss of identity can be paralysing. But what if that ending is actually the best thing that ever happened to you? What if the thing you’re doing as a "side hobby" is actually an empire waiting to be built? Tori Clapham is the powerhouse founder behind the boutique fitness brand, Peaches Pilates. Today, she oversees an empire with ten studios, an app used in 54 countries, and over $4 million in annual revenue. But the path there wasn't a straight line. Tori began as a performing arts student, moving from Far North Queensland to NYC and eventually landing a coveted creative role at MTV. Everything changed when she was made redundant. Left with a $10,000 cheque and a major life decision, Tori looked back at the casual Pilates sessions she ran for colleagues during lunch breaks and realized her "hobby" was actually her calling. She took a massive gamble, using her redundancy pay and travel savings to sign a lease on a tiny "shoebox" studio in Bondi. In this empowering episode of Pivot Club, Sarah and Tori cover the grit of DIY renovations, the risks involved when her husband quit his corporate job to join the team, and how their lo-fi workout videos accidentally prepared them for a global pandemic. They also dig into the "mini-pivot" of motherhood and how to build a business that serves your life, rather than the other way around. Get ready to learn why your biggest setbacks are often the things that propel you the most. THE END BITS: Want more from Sarah Davidson? Check out her podcast Seize The Yay. Discover more Mamamia podcasts here. Feedback: podcast@mamamia.com.au Share your story, feedback, or dilemma! Send us a voice message, and one of our Podcast Producers will get back to you ASAP. Rate or review us on Apple by clicking on the three dots in the top right-hand corner, click Go To Show then scroll down to the bottom of the page, click on the stars at the bottom and write a review. CREDITS: Guest: Tori Clapham Host: Sarah Davidson Executive Producer: Courtney Ammenhauser Senior Producer: Sally Best Audio Producer: Thom Lion This show was brought to you in partnership with Charles Sturt University. Australia's largest and most experienced online uni. Take the next step. Search Charles Sturt University online. Complete our short survey about education for for a chance to win a $1,000 gift voucher in our quarterly draw! https://survey.alchemer.com/s3/8467038/Ch Mamamia acknowledges the Traditional Owners of the Land we have recorded this podcast on, the Gadigal people of the Eora Nation. We pay our respects to their Elders past and present, and extend that respect to all Aboriginal and Torres Strait Islander cultures. Become a Mamamia subscriber: https://www.mamamia.com.au/subscribeSee omnystudio.com/listener for privacy information.

The last regularly scheduled episode of The Boat Galley Podcast is a discussion between Nica and Carolyn on how cruising has changed. Summary Today's episode of The Boat Galley podcast is actually a little different. Welcome to a conversation between Carolyn Shearlock and Nica Waters on changes to cruising in the past few decades. But first, we have some news for you.  We've enjoyed recording this podcast for over 800 episodes over the past eight years. That's a lot of useful information available in short episodes--most less than ten minutes long. If you're new to the podcast, make sure you go back and check out our old content.  Thank you to all our listeners. And also, thank you to our sponsors who have supported us. We reached out to our sponsors because we use and love their products. We're not only grateful for their support of the podcast but also for how they've helped make our own cruising lives easier.  Changes in Cruising Nica first began cruising in the 1990s, and Carolyn began in 2002. Since then, we've seen a lot of changes. One of the biggest has been access to information. With access to satellite-based internet, it's easier to get information than ever. The new challenge is learning to differentiate between useful information and infotainment. GPS Of course, a huge game-changer has been the arrival of GPS. In the early days, it wasn't reliable. Early chart plotters made it appear your boat was on land, and GPS service included a warning that it would only be reliable to five miles.  Nica notes that in her current location in French Polynesia, GPS has made navigation much easier. However, she would not rely on it at night.   Ease of Setting Out Carolyn notes that it used to be typical for cruisers to begin by coastal cruising. They might stay just offshore for five years while they gain the skills required to travel farther afield.  Today, it's easier to set off more quickly. You can connect to the information you need farther from shore. However, it also means some cruisers don't have the experience and skills to rely on if things go wrong.  More cruisers set out with a big goal, like circumnavigating. Carolyn reminds us of Lin Pardey's advice: to keeping going as long as it's fun.  Nica notes that many cruisers are buying larger and more luxurious vessels. That means that they may not have a plan for when their freezer fails or their watermaker needs to be repaired. Skills like knowing how to keep food without refrigeration or how to gather rainwater can help any cruiser if equipment breaks down in a remote location. Redundancy of skills is as important as redundancy in equipment.  Returning to navigation, a cruiser needs to have a plan for if GPS itself becomes unavailable. With paper charts no longer being updated, this becomes a greater challenge.  Carolyn credits her experience as a small boat racer for having the skills to move her boat if something goes wrong. Although she feels self-sufficiency is important, she also expresses gratitude for fellow cruisers who suggested quick fixes when she and Dave were trying to make the perfect repair.  Advice for Cruisers The Boat Galley exists to make cruising easier and more fun. So it's time to share some advice.  Flexibility Nica and Carolyn agree that mental flexibility and the ability to slow down are among the most important traits for any cruiser. The theme music of the podcast expresses this key. It's titled "Slow Down." Stay Curious and Open to Adventure   There will be days when you feel tired or homesick. But if you keep that curiosity and sense of adventure, whether you're sailing in familiar waters or farther ashore, you'll be enjoying the best benefits of cruising.  Carolyn reminds us that you can't plan for everything. That's actually the definition of adventure--not knowing what will happen next.   Not everything will look like a YouTube video. You'll experience highs and lows. But most of cruising is the mundane middle--washing dishes, moving stuff around to reach other stuff, etc.  Farewell Carolyn and Nica are proud of all the work they've done on the podcast, providing useful tips for others. They love hearing from listeners and look forward to sharing more about this amazing lifestyle.  Subscribe to the Boat Galley Newsletter! - https://theboatgalley.com/newsletter-signup-2 Today's episode of The Boat Galley Podcast is sponsored by MantusMarine.com, maker of the Mantus anchor, now available in models with and without a roll bar. Proven to set reliably in the most challenging bottoms, the Mantus anchor digs like no other, making anchoring safer and boating more enjoyable. Mantus Marine brings to market practical, durable and affordable marine products, including: anchoring gear, scuba diving accessories, and rechargeable waterproof headlamp for hands-free lighting and solar charging Navigation light. Visit MantusMarine.com and see for yourself!  Links: Lin and Larry Pardey Books (Amazon) - https://amzn.to/4rw1B07 Nica email - nica@fit2sail.com Carolyn email - carolyn@theboatgalley.com Click to see all podcast sponsors, past and present. - https://bit.ly/3idXto7 Music: "Slow Down" by Yvette Craig  

When Martin Frederik Garbers' company was acquired, he was handed the unenviable job of letting twenty-five people go. His own days were numbered too, but he chose to spend them sitting through the hard conversations, one by one, as a human being first – a CFO second.As he walked the Camino after redundancy, his body told him with every fibre of his being, that he wasn't going back to corporate life. Now he lights a candle in the early hours of the morning, takes executives for long walks in nature, and asks his coaching clients to slow down long enough to hear what their inner tutor would tell them.We talk about why the unspoken rules often do the most damage, what gets buried when leaders aren't allowed to feel, and why two hours walking in nature will do far more for your business than a back-to-back calendar full of big, important meetings.Links to learn more about Martin: Linkedin WebsiteBookAny thoughts? Share them with us!Support the show✨✨✨If you miss the "workshops work" podcast, join us on Substack, where Myriam builds a Podcast Club with monthly gatherings around old episodes: https://myriamhadnes.substack.com/

Although the US Navy Won't Go Into the Gulf, Trump is Demanding Other Navies Escort Tankers Through the Strait of Hormuz | Iran's Strategic Plans For a Long War With Layers of Redundancy in the Ranks of Leadership | The Crackup on the Right Against Trump's War on Iran backgroundbriefing.org/donate twitter.com/ianmastersmedia bsky.app/profile/ianmastersmedia.bsky.social linktr.ee/backgroundbriefing

Legal operations has evolved from a back-office function into a strategic discipline that is reshaping how legal services are delivered. In this conversation, Stephen Seckler speaks with legal operations consultant Jeff Kruse about how technology, process improvement, and artificial intelligence are transforming the way law firms and legal departments work. Jeff shares insights from his career path, from product liability defense lawyer to in-house chief litigation officer and eventually a legal operations consultant. The discussion explores how legal operations helps organizations improve efficiency, manage risk, and adapt to rapid technological change. They also discuss how lawyers considering career transitions can leverage their transferable skills in new roles such as legal operations, consulting, or mediation. The episode concludes with a look at the future of legal operations and why the field is becoming increasingly strategic in law firms and corporate legal departments. Key Takeaways Legal operations has evolved from a back-office function into a strategic discipline AI is accelerating change in legal departments and law firms Process improvements often start with the people doing the day-to-day work "Trickle-up improvementnomics" can improve efficiency across an organization Change management is often the biggest obstacle to operational improvements Lawyers possess transferable skills that apply beyond practicing law Redundancy and backup systems are essential for managing technological risk Legal operations is becoming increasingly strategic within law firms and corporate legal departments Timestamps 00:00 Introduction and why legal operations is now critical 01:03 Jeff Kruse's background and career path 02:20 From law firm partner to in-house litigation leadership 06:16 Jeff's work as a mediator and what it taught him 08:18 Remote and hybrid work in legal teams before the pandemic 10:11 How remote work influences legal operations thinking 11:12 What legal operations actually includes 14:07 AI and the accelerating pace of change in the legal industry 15:44 Can small firms and legal departments keep up with AI? 19:23 Technology consolidation and evaluating legal tech vendors 21:10 What a legal hold is and why it matters 23:00 "Trickle-Up Improvementnomics" and operational efficiency 27:01 Why change management is difficult in legal organizations 34:01 Lessons from the Amazon Web Services outage 37:45 The future of legal operations 40:37 Closing remarks and coaching resources

What separates fluency from understanding? Léon Bottou joins Vasant Dhar in Episode 103, Brave New World, to explore the deep question at the heart of AI. Useful Resources: 1. Léon Bottou2. Léon Bottou - The Fiction Machine3. Hopfield Network in AI4. Yann LeCun5. J. L. McClelland, D.E. Rumelhart and G.E. Hinton - The Appeal of Parallel Distributed Processing6. Claude Shannon - Entropy and Redundancy in English 7. Marvin Minsky - Music, Mind and Meaning8. Stafford Beer9. Navier-Stokes Equations10. Terry Sejnowski - Parallel Networks that Learn to Pronounce English Text 11. Christopher D. Manning12. Brave New World Episode 58: Sam Bowman on ChatGPT & Controlling AI13. De Morgan's laws Check out Vasant Dhar's newsletter on Substack. The subscription is free! Order Vasant Dhar's new book, Thinking With Machines  

Are we actually less capable of handling collapse than past generations—or are we just adapted for a different kind of world?In this conversation, Dan Carlin (Hardcore History / The End Is Always Near) breaks down why modern society may be more fragile than we think: not only because of disease, war, or shortages—but because fear and system dependence can stop essential services fast. We talk about the Spanish Flu (1918–1920), “toughness” as a moving target, and how complexity creates new failure points.In this episode:• Why fear can break society before disease does• Spanish Flu as a warning for modern cities• What “toughness” actually means (and why it's hard to define)• Redundancy vs complexity: why modern systems fail differently• Collapse scenarios we can't predict—until they arriveQuestion for you: If something major hit tomorrow, what breaks first—social trust, supply chains, policing, or healthcare?Subscribe for more investigations into the hidden forces behind history—same playbook, different century.