Podcasts about models

“Models are still the bread and butter in gravity and magnetics interpretation. Interpreters still have to condition the data properly, and that's half technical, half art.” Betty Johnson shares how her early career in gravity and magnetics grew from curiosity, hands‑on learning, and rapidly changing technology. She explains how potential field methods remain valuable for addressing energy, water, and climate challenges because they are affordable, scalable, and deeply rooted in Earth's history. Her reflections underscore the importance of high-quality data, solid fundamentals, and ongoing learning. KEY TAKEAWAYS > Gravity and magnetics remain essential because they are cost‑effective, scalable, and useful across many energy and environmental applications. > Strong fundamentals in physics, geology, and modeling help interpreters make better decisions and collaborate across disciplines. > Good data, field experience, and continuous learning are critical for building a long and impactful geophysics career. LINKS * Read "The Meter Reader—The tools of the trade in gravity and magnetics, 1978–1988" at https://doi.org/10.1190/tle44090738.1 * Elizabeth A. Johnson, "Gravity and magnetic analyses can address various petroleum issues" at https://doi.org/10.1190/1.1437844 * Elizabeth A. E. Johnson, "Use higher resolution gravity and magnetic data as your resource evaluation progresses" at https://doi.org/10.1190/1.1437846 THIS EPISODE SPONSORED BY STRYDE STRYDE enables high-resolution subsurface imaging that helps emerging sectors such as CCS, hydrogen, geothermal, and minerals de-risk and accelerate exploration - delivered through the industry's fastest, most cost-efficient, and agile seismic solution. Discover more about STRYDE at https://stryde.io/what-we-do.

In this episode of the Real Life Theology podcast, the discussion centers around the implementation of disciple-making movements within and alongside established church structures. The hosts weigh the pros and cons of running parallel disciple-making initiatives either under a single church umbrella or as independent entities. They highlight the importance of aligning church vision with biblical examples and modern pathways, emphasizing swift transition from being found by Christ to becoming a leader. The conversation also covers practical steps for churches to adopt these principles, including training programs and cohorts designed to foster rapid disciple multiplication. The episode underscores the need for a strategic commitment to God's broader vision for community transformation. Join RENEW.org's Newsletter: https://renew.org/resources/newsletter-sign-up/ Get our Premium podcast feed featuring all the breakout sessions from the RENEW gathering early.  https://reallifetheologypodcast.supercast.com/  Join RENEW.org at one of our upcoming events: https://renew.org/resources/events/

For Dr. Zak Kohane, this year's advances in AI weren't abstract. They were personal, practical, and deeply tied to care. After decades studying clinical data and diagnostic uncertainty, he finds himself building his own EHR, reviewing his child's imaging with AI, and re-thinking the balance between incidental and missed findings. Across each story is the same insight: clinicians and machines make mistakes for different reasons — and understanding those differences is essential for safe deployment. In this episode, Zak also highlights where AI is spreading fastest, and why: reimbursement. While dermatology and radiology aren't broadly using AI for interpretation, revenue-cycle optimization is advancing rapidly. Meanwhile, ambient documentation has exploded — not because it increases accuracy or throughput, but because it improves clinician satisfaction in strained systems. Yet the most profound theme, he argues, is values. Models already show implicit preferences: some conservative, some aggressive. And unlike human clinicians, no regulatory framework examines how those preferences form. Zak calls for a new form of oversight that centers patients, recognizes bias, and bridges clinical expertise with technical transparency. Transcript.

This week on Catalyst Tammy speaks with Namee Oberst, co-founder of LLMWare about her unique journey into AI. Namee spent years as a corporate attorney and is now developing small language models for legal and financial organizations. She's solving for the pain points that she experienced for years. Namee and Tammy discuss the importance of small language models in building trust and touch on the future of legal work in an AI-driven world. Please note that the views expressed may not necessarily be those of NTT DATALinks: Namee Oberst LLMWareLearn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The Clubhouse model is proving that community and purpose can transform lives for people with serious mental illness. In this episode, Josh Seidman, Chief External Impact Officer for Fountain House, explores how the organization pioneered the Clubhouse model, a psychosocial rehabilitation approach built on community, partnership, and purpose rather than clinical hierarchies. Since its start in 1948, the model has expanded to 380 Clubhouses in 33 countries, helping members rebuild their lives through work, education, and connection. Data show that Clubhouse members experience higher employment, better housing, and reduced loneliness, while the model lowers Medicaid costs by 21%, saving society over $11,000 per person each year. Seidman also highlights participatory research projects like Measures That Matter and the Fountain House United Research Network (FHURN), which empower members to shape meaningful metrics and improve quality outcomes. Tune in and learn how community-driven innovation and lived experience are reshaping the future of behavioral health care! Resources: Connect with and follow Josh Seidman on LinkedIn. Follow Fountain House on LinkedIn and explore their website. If you want to find a clubhouse, visit the Clubhouse International website.

Are we witnessing an AI-fueled gold rush or the early signs of an epic crash? Listen to these hard-hitting discussions on bubbles, breakthroughs, and the real impact behind Silicon Valley's AI obsession. Time Magazine's 'Person of the Year': the Architects of AI The AI Wildfire Is Coming. It's Going to Be Very Painful and Incredibly Healthy. 'ChatGPT for Doctors' Startup Doubles Valuation to $12 Billion as Revenue Surges Trump Pretends To Block State AI Laws; Media Pretends That's Legal It's beginning to look a lot like (AI) Christmas Amazon Prime Video Pulls AI-Powered Recaps After Fallout Flub Could America win the AI race but lose the war? Google Says First AI Glasses With Gemini Will Arrive in 2026 Border Patrol Agent Recorded Raid with Meta's Ray-Ban Smart Glasses The countdown to the world's first social media ban for children US could demand five-year social media history from tourists before allowing entry Reddit making global changes to protect kids after social media ban - 9to5Mac There are no good outcomes for the Warner Bros. sale Paramount CEO Made Trump a Secret Promise on CNN in Warner Bros. Convo Whatnot's Schlock Empire Shows Digital Live Shopping Can Thrive in America The Military Almost Got the Right to Repair. Lawmakers Just Took It Away Apple loses its appeal of a scathing contempt ruling in iOS payments case Japan law opening phone app stores to go into effect Microsoft Excel Turns 40, Remains Stubbornly Unkillable - Slashdot Clair Obscur: Expedition 33 sweeps The Game Awards — analysis and full winners list Microsoft promises more bug payouts, with or without a bounty program An ex-Twitter lawyer is trying to bring Twitter back Host: Leo Laporte Guests: Iain Thomson, Owen Thomas, and Jason Hiner Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: shopify.com/twit NetSuite.com/TWIT ventionteams.com/twit zscaler.com/security helixsleep.com/twit

AI models feel smarter than their real-world impact. They ace benchmarks, yet still struggle with reliability, strange bugs, and shallow generalization. Why is there such a gap between what they can do on paper and in practiceIn this episode from The Dwarkesh Podcast, Dwarkesh talks with Ilya Sutskever, cofounder of SSI and former OpenAI chief scientist, about what is actually blocking progress toward AGI. They explore why RL and pretraining scale so differently, why models outperform on evals but underperform in real use, and why human style generalization remains far ahead.Ilya also discusses value functions, emotions as a built-in reward system, the limits of pretraining, continual learning, superintelligence, and what an AI driven economy could look like. Resources:Transcript: https://www.dwarkesh.com/p/ilya-sutsk...Apple Podcasts: https://podcasts.apple.com/us/podcast...Spotify: https://open.spotify.com/episode/7naO... Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures](http://a16z.com/disclosures.  Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Donna Farhi returns to talk with J about conscious pedagogic models that foster independence and moving towards grounded sensitivity. They discuss homesteading and the contrast between natural and digital worlds, recovering from a fractured pelvis, meaning of pedagogy, baking bread, horizontal communication, checks and balances, contraction in the industry, being relevant to the times, ground rules and agreements, sticking with the word yoga, fitness and perfected bodies, post-pandemic landscapes, and evolving into the future.   To subscribe and support the show… GET PREMIUM. Say thank you - buy J a coffee. Check out J's other podcast… J. BROWN YOGA THOUGHTS.    

Are we witnessing an AI-fueled gold rush or the early signs of an epic crash? Listen to these hard-hitting discussions on bubbles, breakthroughs, and the real impact behind Silicon Valley's AI obsession. Time Magazine's 'Person of the Year': the Architects of AI The AI Wildfire Is Coming. It's Going to Be Very Painful and Incredibly Healthy. 'ChatGPT for Doctors' Startup Doubles Valuation to $12 Billion as Revenue Surges Trump Pretends To Block State AI Laws; Media Pretends That's Legal It's beginning to look a lot like (AI) Christmas Amazon Prime Video Pulls AI-Powered Recaps After Fallout Flub Could America win the AI race but lose the war? Google Says First AI Glasses With Gemini Will Arrive in 2026 Border Patrol Agent Recorded Raid with Meta's Ray-Ban Smart Glasses The countdown to the world's first social media ban for children US could demand five-year social media history from tourists before allowing entry Reddit making global changes to protect kids after social media ban - 9to5Mac There are no good outcomes for the Warner Bros. sale Paramount CEO Made Trump a Secret Promise on CNN in Warner Bros. Convo Whatnot's Schlock Empire Shows Digital Live Shopping Can Thrive in America The Military Almost Got the Right to Repair. Lawmakers Just Took It Away Apple loses its appeal of a scathing contempt ruling in iOS payments case Japan law opening phone app stores to go into effect Microsoft Excel Turns 40, Remains Stubbornly Unkillable - Slashdot Clair Obscur: Expedition 33 sweeps The Game Awards — analysis and full winners list Microsoft promises more bug payouts, with or without a bounty program An ex-Twitter lawyer is trying to bring Twitter back Host: Leo Laporte Guests: Iain Thomson, Owen Thomas, and Jason Hiner Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: shopify.com/twit NetSuite.com/TWIT ventionteams.com/twit zscaler.com/security helixsleep.com/twit

Full article: Decoupling Visual Parsing and Diagnostic Reasoning for Vision–Language Models (GPT-4o and GPT-5): Analysis Using Thoracic Imaging Quiz Cases What is the bottleneck in ongoing attempts to use vision-language models to interpret radiologic imaging? Pranjal Rai, MD, discusses this recent AJR model by Han et al. that seeks to differentiate the roles of visual parsing and diagnostic reasoning toward impacting VLM performance.

Should we care about GPT-5.2? This week on Mixture of Experts, we analyze the “code red” release of GPT-5.2 as OpenAI responds to Gemini 3. Are the constant model drops benefitting consumers? Next, Stanford released their Foundation Model Transparency Index, revealing a troubling trend that most labs are becoming less transparent. However, IBM Granite achieved a 95/100 score. Then, our experts discuss what model transparency means for enterprise AI adoption. Finally, we debrief AWS re:Invent's biggest announcements, including Nova frontier models and Nova Forge. Join host Tim Hwang and panelists Kate Soule, Ambhi Ganesan and Mihai Criveti for our expert insights.00:00 – Intro1:02 -- GPT-5.2 emergency release 12:21 -- Stanford AI Transparency Index: Granite scores 95/10027:18 -- AWS re:Invent: Nova models and enterprise AIThe opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.Subscribe for AI updates → https://www.ibm.com/account/reg/us-en/signup?formid=news-urx-52120Visit Mixture of Experts podcast page to get more AI content → https://www.ibm.com/think/podcasts/mixture-of-experts #GPT-5.2 #AITransparency #GraniteModels #AWSNova #AIAgents

What happens when the persona becomes the product? We sit down with creator and model Annie Miao to explore the strange, funny, and sometimes tender space where AI influencers, VTubers, and deepfakes collide with mental health, body image, and the business of being online. From cat ears to consent, we unpack why audiences follow people more than topics—and how that changes what “authentic” even means.Annie traces her path from bullied band kid to internet-native creative, sharing how the web offered belonging long before real life did. We get into the economics behind modern media—OnlyFans as a curiosity-powered Patreon, Hollywood and gaming chasing billion-dollar budgets, and the course economy where coaches coach coaches. Along the way, we challenge the culty edges of “life optimization” and ask what creators actually owe their communities: disclosure, value, and boundaries.Our most important pivot lands on mental health and body image. We talk toxic positivity, why suffering can be a teacher, and how body neutrality helps when self-love feels impossible. Models and bodybuilders aren't immune to dysmorphia—if anything, the pressure can be worse. So we trade mirror battles for kinder questions: What does my body let me do today? How do I nourish it without shame? With AI blurring faces and voices, we propose a simple ethic: tell the truth, label the edits, and keep the humanity in the loop.If you're curious about AI e-girls, burned out on hustle sermons, or just trying to feel like yourself on the internet, this one's for you. Hit follow, share with a friend who lives online, and leave a review telling us where you think authenticity goes next. Support the showYou can find us on social media here:Rob TiktokRob InstagramLiam TiktokLiam Instagram

In this episode of Tank Talks, host Matt Cohen sits down with global venture capitalist Alex Lazarow, founder of Fluent Ventures, to unpack the future of early-stage investing as AI, globalization, and shifting economic forces reshape the startup landscape. Alex brings a rare perspective shaped by 20+ markets across Africa, Latin America, Europe, and Asia, plus experience backing seven unicorns, from Chime to breakout fintechs worldwide.Alex shares insights from his unconventional path from academia-curious economist to McKinsey consultant, impact investor at Omidyar Network, partner at global firm Cathay Innovation, and now solo GP building a research-driven, globally distributed early-stage fund. He dives into why the best startup ideas no longer come from one geography, why AI has permanently rewritten the cost structure of company building, and how proven business models are being successfully reinvented in emerging markets and then exported back to the U.S.He also breaks down why small businesses may become more powerful than ever, the rise of “camel startups,” and what founders everywhere must understand about raising capital in a world where early traction matters more than ever.Whether you are a founder, operator, or investor navigating the next era of innovation, this conversation reveals how global patterns, AI tailwinds, and disciplined research can uncover tomorrow's winners.From Winnipeg to Wall Street: Early Career Lessons (00:01:17)* Alex reflects on growing up in Winnipeg and navigating a multicultural family background.* How early roles at RBC M&A and the Bank of Canada shaped his analytical lens.* Why he pursued economics, consulting, and academia before landing in venture.* The value of testing career hypotheses instead of blindly following one path.Building a Global Perspective Through McKinsey (00:06:42)* Alex describes working in 20 markets, from Tunisia during the revolution to Indonesia and Brazil.* Why exposure to varied cultures and economies sharpened his ability to spot emerging global patterns.* The framework he used to choose projects: people, content, geography.Entering Venture Through Impact Investing (00:08:05)* Joining Omidyar Network to explore fintech innovation and financial inclusion.* Early exposure to global mobile banking and super-app models.* The origin story behind investing in Chime.* Why mission-driven investing shaped his lifelong global investment thesis.Scaling Globally at Cathay Innovation (00:13:14)* Transitioning into a traditional VC role after Omidyar.* Helping scale Cathay from a $287M fund to nearly $1B.* Why he eventually left to build a more focused, research-driven early-stage fund.The Fluent Ventures Thesis: Proven Models, Global Arbitrage (00:16:45)* Fluent backs founders who take validated business models and execute them in new geographies or industries.* Investing between pre-seed and Series A with a tightly defined “10 business model portfolio.”* Why their TAM is intentionally much smaller, only 200–500 companies worth meeting each quarter.* Leveraging a network of 50 unicorn founders and global VCs to discover breakout teams early.Why AI Is Reshaping Early-Stage Investing (00:23:01)* AI has dramatically reduced the cost of building early products.* Increasingly, startups raise capital after launching revenue not before.* The new risk: foundational AI models may “eat” many SaaS products.* What types of companies will survive AI disruption.The Camel Startup & The Great Diffusion (00:28:14)* The “camel startup” concept: resilient, capital-efficient companies built outside Silicon Valley norms.* How software (and now AI) lets small companies “rent scale” once only available to big enterprises.* Why the next decade will favor startups that focus on durability, not blitzscaling.Why Silicon Valley Still Matters, Even for Global Founders (00:32:47)* Alex encourages founders to build in their home markets but visit Silicon Valley to raise capital and absorb cutting-edge ideas.* How one founder raised SF-level valuations while building in the Midwest.* The “global arbitrage” advantage: raise capital where it's abundant, build where costs are low.Where Global Markets Are Leading Innovation (00:35:41)* Why Japan is 5–10 years ahead in generational small-business transitions.Examples of B2B marketplace models thriving in India and now being imported to the U.S.* How construction marketplaces, industrial marketplaces, and embedded fintech platforms are spreading across continents.About Alex LazarowAlex Lazarow is the founder and Managing Partner of Fluent Ventures, an early-stage global venture fund investing in proven business models across fintech, commerce enablement, and digital health. A veteran global investor, Alex has backed seven unicorns, authored the award-winning book Out-Innovate, and previously invested at Omidyar Network and Cathay Innovation. He has worked in more than 20 countries and teaches entrepreneurship at Middlebury Institute.Connect with Alex Lazarow on LinkedIn: linkedin.com/in/alexandrelazarowVisit the Fluent Ventures website: https://www.fluent.vc/Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Bob Evans sits down with Will Grannis, Chief Technology Officer at Google Cloud, to unpack how AI is reshaping both technology stacks and corporate culture. They explore Google Cloud's Gemini Enterprise platform, the newly upgraded Gemini 3 models, and the rise of agentic AI. Along the way, Will shares customer stories from industries like finance, healthcare, retail, and travel, and even talks about how his own team had to change its habits to benefit from AI.Inside Google Cloud's Agentic AI The Big Themes:Models vs. Platforms in the AI Stack: Grannis draws a sharp distinction between AI models like Gemini and the broader platforms that operationalize them. Models determine how intelligent and capable AI workflows are “out of the box,” across tasks like reasoning, multimodal understanding, and conversation. Platforms, by contrast, are how a business injects its own data, processes, and rules to build differentiated IP, brand experiences, and competitive moats. In practice, that means thinking beyond a single chatbot to agentic workflows composed of models, data, tools, and multiple agents working together.Culture and Discipline: Grannis describes how even his own team initially struggled to build an internal ops agent to automate sprint reviews, status updates, and reminders. It was only after leadership pushed them to be an exemplar that the agent became reliable and valuable. Things as simple as putting status information in the same place on every slide suddenly mattered. The lesson: AI exposes hidden process chaos. To get leverage from agents, organizations must tighten their operating discipline and be willing to change how they work, not just bolt AI onto old habits.Rethinking ROI and Metrics: Traditional, siloed ROI metrics can kill transformational AI efforts before they start. Grannis cites research about AI projects dying at proof-of-concept stage and contrasts that with companies like Verizon, which used AI in the contact center to simultaneously lift revenue, reduce cost, and improve customer satisfaction by turning support calls into sales moments. Instead of chasing a single metric in isolation, he advocates for “bundles” of outcomes anchored in customer experience.The Big Quote: “We had to be more disciplined about how we conducted our own work. And once we did that, AI's effectiveness went way up, and then we got the leverage.”More from Will Grannis and Google Cloud:Connect with Will Grannis on LinkedIn or learn about Gemini Enterprise. Visit Cloud Wars for more.

Sarah Whittle joins the show to take stunning calls about stealing a turkey, nude models feuding in art class, and a moral dilemma about a besties nasty boyfriend.Join The Patreon: https://bit.ly/PPPTRN -Weekly Bonus episodes every Friday & ad-free extended version of this episode)Buy the Coffee!! perfectpersoncoffee.comWatch on Youtube: https://bit.ly/PerfectPodYTWatch Miles' Main Channel Videos: https://bit.ly/MilesbonYTFollow On Insta To Call-In!: https://bit.ly/PPPodGramTell a friend about the show! Tweet it! Story it! Scream it!Advertise on Perfect Person via Gumball.fmSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this engaging episode we are continuing an important podcast series for APP Entrepreneurs! The host Josh Plotkin COO of NPACE, is joined by Samara Bell, an attorney specializing in all aspects of health care transactional and strategic matters with special focus on mergers, acquisitions and partnerships for physicians, dentists and veterinarians. This episode focuses on how APPs can find help starting their own practices and find legal support in starting and protecting themselves.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss small language models (SLMs) and how they differ from large language models (LLMs). You will understand the crucial differences between massive large language models and efficient small language models. You’ll discover how combining SLMs with your internal data delivers superior, faster results than using the biggest AI tools. You will learn strategic methods to deploy these faster, cheaper models for mission-critical tasks in your organization. You will identify key strategies to protect sensitive business information using private models that never touch the internet. Watch now to future-proof your AI strategy and start leveraging the power of small, fast models today! Watch the video here: https://youtu.be/XOccpWcI7xk Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-what-are-small-language-models.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s *In-Ear Insights*, let’s talk about small language models. Katie, you recently came across this and you’re like, okay, we’ve heard this before. What did you hear? Katie Robbert: As I mentioned on a previous episode, I was sitting on a panel recently and there was a lot of conversation around what generative AI is. The question came up of what do we see for AI in the next 12 months? Which I kind of hate that because it’s so wide open. But one of the panelists responded that SLMs were going to be the thing. I sat there and I was listening to them explain it and they’re small language models, things that are more privatized, things that you keep locally. I was like, oh, local models, got it. Yeah, that’s already a thing. But I can understand where moving into the next year, there’s probably going to be more of a focus on it. I think that the term local model and small language model in this context was likely being used interchangeably. I don’t believe that they’re the same thing. I thought local model, something you keep literally locally in your environment, doesn’t touch the internet. We’ve done episodes about that which you can catch on our livestream if you go to TrustInsights.ai YouTube, go to the Soap playlist. We have a whole episode about building your own local model and the benefits of it. But the term small language model was one that I’ve heard in passing, but I’ve never really dug deep into it. Chris, in as much as you can, in layman’s terms, what is a small language model as opposed to a large language model, other than— Christopher S. Penn: Is the best description? There is no generally agreed upon definition other than it’s small. All language models are measured in terms of the number of tokens they were trained on and the number of parameters they have. Parameters are basically the number of combinations of tokens that they’ve seen. So a big model like Google Gemini, GPT 5.1, whatever we’re up to this week, Claude Opus 4.5—these models are anywhere between 700 billion and 2 to 3 trillion parameters. They are massive. You need hundreds of thousands of dollars of hardware just to even run it, if you could. And there are models. You nailed it exactly. Local models are models that you run on your hardware. There are local large language models—Deep Seq, for example. Deep Seq is a Chinese model: 671 billion parameters. You need to spend a minimum of $50,000 of hardware just to turn it on and run it. Kimmy K2 instruct is 700 billion parameters. I think Alibaba Quinn has a 480 billion parameter. These are, again, you’re spending tens of thousands of dollars. Models are made in all these different sizes. So as you create models, you can create what are called distillates. You can take a big model like Quinn 3 480B and you can boil it down. You can remove stuff from it till you get to an 80 billion parameter version, a 30 billion parameter version, a 3 billion parameter version, and all the way down to 100 million parameters, even 10 million parameters. Once you get below a certain point—and it varies based on who you talk to—it’s no longer a large language model, it’s a small English model. Because the smaller the model gets, the dumber it gets, the less information it has to work with. It’s like going from the Oxford English Dictionary to a pamphlet. The pamphlet has just the most common words. The Oxford English Dictionary has all the words. Small language models, generally these days people mean roughly 8 billion parameters and under. There are things that you can run, for example, on a phone. Katie Robbert: If I’m following correctly, I understand the tokens, the size, pamphlet versus novel, that kind of a thing. Is a use case for a small language model something that perhaps you build yourself and train solely on your content versus something externally? What are some use cases? What are the benefits other than cost and storage? What are some of the benefits of a small language model versus a large language model? Christopher S. Penn: Cost and speed are the two big ones. They’re very fast because they’re so small. There has not been a lot of success in custom training and tuning models for a specific use case. A lot of people—including us two years ago—thought that was a good idea because at the time the big models weren’t much better at creating stuff in Katie Robbert’s writing style. So back then, training a custom version of say Llama 2 at the time to write like Katie was a good idea. Today’s models, particularly when you look at some of the open weights models like Alibaba Quinn 3 Next, are so smart even at small sizes that it’s not worth doing that because instead you could just prompt it like you prompt ChatGPT and say, “Here’s Katie’s writing style, just write like Katie,” and it’s smart enough to know that. One of the peculiarities of AI is that more review is better. If you have a big model like GPT 5.1 and you say, “Write this blog post in the style of Katie Robbert,” it will do a reasonably good job on that. But if you have a small model like Quinn 3 Next, which is only £80 billion, and you have it say, “Write a blog post in style of Katie Robbert,” and then re-invoke the model, say, “Review the blog post to make sure it’s in style Katie Robbert,” and then have it review it again and say, “Now make sure it’s the style of Katie Robbert.” It will do that faster with fewer resources and deliver a much better result. Because the more passes, the more reviews it has, the more time it has to work on something, the better tends to perform. The reason why you heard people talking about small language models is not because they’re better, but because they’re so fast and so lightweight, they work well as agents. Once you tie them into agents and give them tool handling—the ability to do a web search—that small model in the same time it takes a GPT 5.1 and a thousand watts of electricity, a small model can run five or six times and deliver a better result than the big one in that same amount of time. And you can run it on your laptop. That’s why people are saying small language models are important, because you can say, “Hey, small model, do this. Check your work, check your work again, make sure it’s good.” Katie Robbert: I want to debunk it here now that in terms of buzzwords, people are going to be talking about small language models—SLMs. It’s the new rage, but really it’s just a more efficient version, if I’m following correctly, when it’s coupled in an agentic workflow versus having it as a standalone substitute for something like a ChatGPT or a Gemini. Christopher S. Penn: And it depends on the model too. There’s 2.1 million of these things. For example, IBM WatsonX, our friends over at IBM, they have their own model called Granite. Granite is specifically designed for enterprise environments. It is a small model. I think it’s like 8 billion to 10 billion parameters. But it is optimized for tool handling. It says, “I don’t know much, but I know that I have tools.” And then it looks at its tool belt and says, “Oh, I have web search, I have catalog search, I have this search, I have all these tools.” Even though I don’t know squat about squat, I can talk in English and I can look things up. In the WatsonX ecosystem, Granite performs really well, performs way better than a model even a hundred times the size, because it knows what tools to invoke. Think of it like an intern or a sous chef in a kitchen who knows what appliances to use and in which order. The appliances are doing all the work and the sous chef is, “I’m just going to follow the recipe and I know what appliances to use. I don’t have to know how to cook. I just got to follow the recipes.” As opposed to a master chef who might not need all those appliances, but has 40 years of experience and also costs you $250,000 in fees to work with. That’s kind of the difference between a small and a large language model is the level of capability. But the way things are going, particularly outside the USA and outside the west, is small models paired with tool handling in agentic environments where they can dramatically outperform big models. Katie Robbert: Let’s talk a little bit about the seven major use cases of generative AI. You’ve covered them extensively, so I probably won’t remember all seven, but let me see how many I got. I got to use my fingers for this. We have summarization, generation, extraction, classification, synthesis. I got two more. I lost. I don’t know what are the last two? Christopher S. Penn: Rewriting and question answering. Katie Robbert: Got it. Those are always the ones I forget. A lot of people—and we talked about this. You and I talk about this a lot. You talk about this on stage and I talked about this on the panel. Generation is the worst possible use for generative AI, but it’s the most popular use case. When we think about those seven major use cases for generative AI, can we sort of break down small language models versus large language models and what you should and should not use a small language model for in terms of those seven use cases? Christopher S. Penn: You should not use a small language model for generation without extra data. The small language model is good at all seven use cases, if you provide it the data it needs to use. And the same is true for large language models. If you’re experiencing hallucinations with Gemini or ChatGPT, whatever, it’s probably because you haven’t provided enough of your own data. And if we refer back to a previous episode on copyright, the more of your own data you provide, the less you have to worry about copyrights. They’re all good at it when you provide the useful data with it. I’ll give you a real simple example. Recently I was working on a piece of software for a client that would take one of their ideal customer profiles and a webpage of the clients and score the page on 17 different criteria of whether the ideal customer profile would like that page or not. The back end language model for this system is a small model. It’s Meta Llama 4 Scout, which is a very small, very fast, not a particularly bright model. However, because we’re giving it the webpage text, we’re giving it a rubric, and we’re giving it an ICP, it knows enough about language to go, “Okay, compare.” This is good, this is not good. And give it a score. Even though it’s a small model that’s very fast and very cheap, it can do the job of a large language model because we’re providing all the data with it. The dividing line to me in the use cases is how much data are you asking the model to bring? If you want to do generation and you have no data, you need a large language model, you need something that has seen the world. You need a Gemini or a ChatGPT or Claude that’s really expensive to come up with something that doesn’t exist. But if you got the data, you don’t need a big model. And in fact, it’s better environmentally speaking if you don’t use a big heavy model. If you have a blog post, outline or transcript and you have Katie Robbert’s writing style and you have the Trust Insights brand style guide, you could use a Gemini Flash or even a Gemini Flash Light, the cheapest of their models, or Claude Haiku, which is the cheapest of their models, to dash off a blog post. That’ll be perfect. It will have the writing style, will have the content, will have the voice because you provided all the data. Katie Robbert: Since you and I typically don’t use—I say typically because we do sometimes—but typically don’t use large language models without all of that contextual information, without those knowledge blocks, without ICPs or some sort of documentation, it sounds like we could theoretically start moving off of large language models. We could move to exclusively small language models and not be sacrificing any of the quality of the output because—with the caveat, big asterisks—we give it all of the background data. I don’t use large language models without at least giving it the ICP or my knowledge block or something about Trust Insights. Why else would I be using it? But that’s me personally. I feel that without getting too far off the topic, I could be reducing my carbon footprint by using a small language model the same way that I use a large language model, which for me is a big consideration. Christopher S. Penn: You are correct. A lot of people—it was a few weeks ago now—Cloudflare had a big outage and it took down OpenAI, took down a bunch of other people, and a whole bunch of people said, “I have no AI anymore.” The rest of us said, “Well, you could just use Gemini because it’s a different DNS.” But suppose the internet had a major outage, a major DNS failure. On my laptop I have Quinn 3, I have it running inside LM Studio. I have used it on flights when the internet is highly unreliable. And because we have those knowledge blocks, I can generate just as good results as the major providers. And it turns out perfectly. For every company. If you are dependent now on generative AI as part of your secret sauce, you have an obligation to understand small language models and to have them in place as a backup system so that when your provider of choice goes down, you can keep doing what you do. Tools like LM Studio, Jan, AI, Cobol, cpp, llama, CPP Olama, all these with our hosting systems that you run on your computer with a small language model. Many of them have drag and drop your attachments in, put in your PDFs, put in your knowledge blocks, and you are off to the races. Katie Robbert: I feel that is going to be a future live stream for sure. Because the first question, you just sort of walk through at a high level how people get started. But that’s going to be a big question: “Okay, I’m hearing about small language models. I’m hearing that they’re more secure, I’m hearing that they’re more reliable. I have all the data, how do I get started? Which one should I choose?” There’s a lot of questions and considerations because it still costs money, there’s still an environmental impact, there’s still the challenge of introducing bias, and it’s trained on who knows. Those things don’t suddenly get solved. You have to sort of do your due diligence as you’re honestly introducing any piece of technology. A small language model is just a different piece of technology. You still have to figure out the use cases for it. Just saying, “Okay, I’m going to use a small language model,” doesn’t necessarily guarantee it’s going to be better. You still have to do all of that homework. I think that, Chris, our next step is to start putting together those demos of what it looks like to use a small language model, how to get started, but also going back to the foundation because the foundation is the key to all of it. What knowledge blocks should you have to use both a small and a large language model or a local model? It kind of doesn’t matter what model you’re using. You have to have the knowledge blocks. Christopher S. Penn: Exactly. You have to have the knowledge blocks and you have to understand how the language models work and know that if you are used to one-shotting things in a big model, like “make blog posts,” you just copy and paste the blog post. You cannot do that with a small language model because they’re not as capable. You need to use an agent flow with small English models. Tools today like LM Studio and anythingLLM have that built in. You don’t have to build that yourself anymore. It’s pre-built. This would be perfect for a live stream to say, “Here’s how you build an agent flow inside anythingLLM to say, ‘Write the blog post, review the blog post for factual correctness based on these documents, review the blog post for writing style based on this document, review this.'” The language model will run four times in a row. To you, the user, it will just be “write the blog post” and then come back in six minutes, and it’s done. But architecturally there are changes you would need to make sure that it meets the same quality of standard you’re used to from a larger model. However, if you have all the knowledge blocks, it will work just as well. Katie Robbert: And here I was thinking we were just going to be describing small versus large, but there’s a lot of considerations and I think that’s good because in some ways I think it’s a good thing. Let me see, how do I want to say this? I don’t want to say that there are barriers to adoption. I think there are opportunities to pause and really assess the solutions that you’re integrating into your organization. Call them barriers to adoption. Call them opportunities. I think it’s good that we still have to be thoughtful about what we’re bringing into our organization because new tech doesn’t solve old problems, it only magnifies it. Christopher S. Penn: Exactly. The other thing I’ll point out with small language models and with local models in particular, because the use cases do have a lot of overlap, is what you said, Katie—the privacy angle. They are perfect for highly sensitive things. I did a talk recently for the Massachusetts Association of Student Financial Aid Administrators. One of the biggest tasks is reconciling people’s financial aid forms with their tax forms, because a lot of people do their taxes wrong. There are models that can visually compare and look at it to IRS 990 and say, “Yep, you screwed up your head of household declarations, that screwed up the rest of your taxes, and your financial aid is broke.” You cannot put that into ChatGPT. I mean, you can, but you are violating a bunch of laws to do that. You’re violating FERPA, unless you’re using the education version of ChatGPT, which is locked down. But even still, you are not guaranteed privacy. However, if you’re using a small model like Quinn 3VL in a local ecosystem, it can do that just as capably. It does it completely privately because the data never leaves your laptop. For anyone who’s working in highly regulated industries, you really want to learn small language models and local models because this is how you’ll get the benefits of AI, of generative AI, without nearly as many of the risks. Katie Robbert: I think that’s a really good point and a really good use case that we should probably create some content around. Why should you be using a small language model? What are the benefits? Pros, cons, all of those things. Because those questions are going to come up especially as we sort of predict that small language model will become a buzzword in 2026. If you haven’t heard of it now, you have. We’ve given you sort of the gist of what it is. But any piece of technology, you really have to do your homework to figure out is it right for you? Please don’t just hop on the small language model bandwagon, but then also be using large language models because then you’re doubling down on your climate impact. Christopher S. Penn: Exactly. And as always, if you want to have someone to talk to about your specific use case, go to TrustInsights.ai/contact. We obviously are more than happy to talk to you about this because it’s what we do and it is an awful lot of fun. We do know the landscape pretty well—what’s available to you out there. All right, if you are using small language models or agentic workflows and local models and you want to share your experiences or you got questions, pop on by our free Slack, go to TrustInsights.ai/analytics for marketers where you and over 4,500 other marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TIPodcast and you can find us in all the places fine podcasts are served. Thanks for tuning in. I’ll talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, Dall-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the *In-Ear Insights* podcast, the *Inbox Insights* newsletter, the *So What* livestream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models. Yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data Storytelling—this commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

ALLE INFOS ZU FLACONI FINDEST DU HIER:Deutschland: Einfach und entspannt Beauty und Parfum auf www.flaconi.de shoppen: Mit dem Code “ANIMUS10” sparst du bis zum 31.12.2025 10 % *Österreich: Einfach und entspannt Beauty und Parfum auf www.flaconi.at shoppen: Mit dem Code “ANIMUS10” sparst du bis zum 31.12.2025 10 % *Schweiz: Einfach und entspannt Beauty und Parfum auf www.flaconi.ch shoppen: Mit dem Code “ANIMUS10” sparst du bis zum 31.12.2025 10 % **Der Rabatt gilt nicht auf ausgeschlossene Marken und Produkte und ist nicht mit anderen Aktionen kombinierbar.*Ausgeschlossene Marken & Produkte: Amouage, CHANEL, CREED, dyson, Jo Malone London, Kilian Paris, Maison Francis Kurkdjian, Nø, L'Oréal Professionnel Paris Steampod 3.0 & 4.0.------------------------------------------------------------Den Podcast auf Youtube findest du hier:https://www.youtube.com/@animus_offiziellDen Podcast als Video ohne Werbung findest du auf Patreon:https://www.patreon.com/DerAnimusPodcastKooperationen/Anfragen: deranimuspodcast@gmail.com Animus auf SocialMedia:Instagramhttps://www.instagram.com/animus Hosted on Acast. See acast.com/privacy for more information.

Bridging the Gap Between Traditional Models and Machine Learning in Actuarial Science In this episode of Get Plugged In – AI Insights, host Dale Hall, Managing Director of Research at the Society of Actuaries, welcomes Ronald Richman, Founder and CEO of insureAI, to discuss the evolving role of machine learning in actuarial work. Actuaries have long relied on established statistical models grounded in theory and industry experience. But as machine learning continues to revolutionize data analysis, it brings new opportunities—and new challenges—for the profession. Dale and Ronald explore the differences between traditional actuarial methods and modern machine learning techniques, the practical applications of AI in insurance today, and how actuaries can prepare themselves for a data-driven future. Listeners can dive deeper into this topic by visiting the Society of Actuaries Research Institute's Artificial Intelligence topic landing page:  https://www.soa.org/research/topics/artificial-intelligence-topic-landing Send us your feedback at AI-Insights@soa.org

In this episode, we're joined by Munawar Hayat, researcher at Qualcomm AI Research, to discuss a series of papers presented at NeurIPS 2025 focusing on multimodal and generative AI. We dive into the persistent challenge of object hallucination in Vision-Language Models (VLMs), why models often discard visual information in favor of pre-trained language priors, and how his team used attention-guided alignment to enforce better visual grounding. We also explore a novel approach to generalized contrastive learning designed to solve complex, composed retrieval tasks—such as searching via combined text and image queries—without increasing inference costs. Finally, we cover the difficulties generative models face when rendering multiple human subjects, and the new "MultiHuman Testbench" his team created to measure and mitigate issues like identity leakage and attribute blending. Throughout the discussion, we examine how these innovations align with the need for efficient, on-device AI deployment. The complete show notes for this episode can be found at https://twimlai.com/go/758.

Most leadership discussions avoid the most uncomfortable question:

We Like Shooting Episode 640 This episode of We Like Shooting is brought to you by: Midwest Industries, Die Free Co., Medical Gear Outfitters, Mitchell Defense, Rost Martin, and Swampfox Optics   Welcome to the We Like Shooting Show, episode 640! Our cast tonight is Jeremy Pozderac, Savage1r, Jon Patton, and me Shawn Herrin, welcome to the show!   - Gear Chat Nick - KRG Bravo Unplugged KRG Bravo Shawn - GLOCK Unveils Ergonomically Enhanced Generation 6 Models ## Key Points Summary Intro This summary captures the main takeaways from the Glock Gen 6 launch coverage featuring John from the Warrior Poet Society. The discussion centers on design changes, practical improvements, and shooting impressions, with notes on market timing and pricing. Sponsorships were not part of the core content. Center Key design changes and their practical impact - Grip and texture: The new texture sits between Gen 4 and RTF2; two backstraps including a palm swell are provided. The texture extends higher on both sides for a more secure hold, especially in hot conditions. - Ergonomics: Deeper trigger guard undercut reduces the “Glock knuckle” issue; the grip surface is larger, improving surface area for those with bigger hands; the grip shape swells in the midsection for a more natural wrap. - Controls: Deeper slide serrations, especially on top, enhance manipulation from either end of the slide. The ambidextrous slide release remains, and the pistol uses a single recoil spring (as in earlier generations) while retaining some material from the B-series. - Magwell and contour: The magwell is more flared; the overall contour resembles a topographic map, broadening the hand placement area and increasing leverage for a stronger grip. - Gas pedals and holster compatibility: Gas pedals are built into the frame on both sides with material reduced to protect compatibility with Gen 5 holsters; the goal is a functional improvement without forcing new holsters. - Optics and plates: The plate system is not MOS; it uses a polymer insert that sits lower on the slide and acts like a crush washer under tension. Footprints include Delta Point and RMR; optic-ready configuration remains, with some models rumored to feature polymer sights. - Sights and optics readiness: The factory setup is optics-ready, with some early photos showing polymer sight options. - Barrel and reliability: The Marksman barrel remains, but the extractor housing has been redesigned to be removable for easier maintenance and to reduce installation errors. - Handling and feel: The grip bite is strong but not overly tacky, enabling fast, controlled manipulations without the gun sticking to the hand. Models, availability, and pricing - US launch models: Gen 617 (with Glock 47 form factor), 19-length slide paired to a full-size grip (G45-like); overseas, Glock 49 appears as a variant. - Optics-ready configuration: All examples are MOS-ready or compatible, with plates included for common footprints. - Pricing and timing: MSRP is anticipated around $750; production units were slated to begin arriving in January, with possible earlier availability as information evolves. - Accessories and maintenance: An updated extractor housing system is highlighted as simplifying field maintenance and reducing failure risks due to improper screw length. User experience and feedback - Hand feel: The curved, swollen midsection improves leverage and comfort; the grip texture provides secure grip without excessive tackiness, avoiding slip during rapid manipulation. - Shooting impressions: A large, controlled sampling (nine pistols and thousands of rounds) yielded consistent ejection and reliable cycling during demonstrations; full independent testing will further validate reliability. - Community notes: Gen 5 users worried about slide-lock issues may benefit from deeper cuts and reinforced stops; modular grip options were not part of the initial rollout, though patent activity suggests ongoing development. Outro Takeaway: Gen 6 Glock delivers meaningful ergonomic and grip improvements, while maintaining optics readiness and reliability expectations. The US market rollout is aimed for January with a target MSRP near $750; overseas options include Glock 49. Next steps include comprehensive independent testing, longer-term reliability data, and broader real-world reviews. Stay tuned for updates, and consider price-alert subscriptions for stock and accessory availability. Shawn - Kinetic Development Group's Q4 Success and Future Growth Plans Kinetic Development Group (KDG) is experiencing significant growth, closing Q4 with strong increases in sales across various distribution channels, attributed to the demand for its firearm accessories. Looking ahead to 2026, KDG plans to introduce new products and enhance capabilities, which may impact the firearm accessory market by providing innovative solutions for shooters. Bullet Points Shawn - Steiner Optics Unveils Innovative ATLAS Aiming System Steiner Optics has launched the ATLAS, a compact multi-emitter aiming and illumination device aimed at military, law enforcement, and professional security users, as well as the commercial market. It features co-aligned emitters, user-friendly controls, and a durable design, positioned as a versatile tool for operational use. The introduction of the ATLAS may influence purchasing decisions within the gun community, particularly for those seeking advanced aiming systems. The MSRP begins at $4,024.99. Shawn - Taurus Raging Hunter: Now Available in .350 Legend Taurus has launched a new version of its Raging Hunter revolver series chambered in .350 Legend, catering to shooters seeking a revolver suitable for hunting with straight-walled cartridges. The new models feature barrel lengths of 10.5 and 14 inches, and include enhancements for recoil management and accessory compatibility. This addition expands options for hunters in areas with regulations favoring straight-walled cartridges, positioning the Raging Hunter to appeal to a broader market segment within the gun community.   Gun Fights Step right up for "Gun Fights," the high-octane segment hosted by Nick Lynch, where our cast members go head-to-head in a game show-style showdown! Each contestant tries to prove their gun knowledge dominance. It's a wild ride of bids, bluffs, and banter—who will come out on top? Tune in to find out! WLS is Lifestyle Hoover's Legal Rollercoaster ## Key Points Summary,**Intro**,This summary distills the latest developments surrounding Matt Hoover, the CRS Firearms creator, after a lengthy legal battle tied to the so-called “auto key card.” The focus is on the factual timeline, legal questions, and current status as Hoover emerges from federal prison into a halfway house. The material below omits sponsorship references and concentrates on the core events and implications for Hoover, his case, and ongoing appeals., ,**Centerpiece Facts & Timeline**,,- **Subject and backdrop**: Matt Hoover, known for the CRS Firearms YouTube channel, was linked to advertisements for the auto key card—a novelty item featuring a lightning-link-like etching intended to imply automatic-fire capability. The item did not function as advertised, and there is no evidence Hoover owned, sold, or manufactured machine guns or auto key cards.,- **Arrest and charge**: Despite the nonfunctional etching and absence of direct ownership or manufacturing activity, Hoover was arrested and charged with trafficking machine guns. The case connected him to Christopher Justin Irvin, the creator of the auto key card.,- **Sentencing dynamics**: The pre-sentencing report highlighted Hoover's clean criminal record and his role as the family's primary breadwinner, presenting a favorable background for leniency. Yet, prosecutors sought the maximum sentence, arguing aggressive measures despite the limited direct involvement in weapon manufacture or sales.,- **Contested assertions**: The government asserted extreme accusations, including a claim that Hoover married to prevent her testimony, despite Hoover and his wife sharing multiple children. These assertions drew skepticism and counter-arguments during proceedings and appellate discussions.,- **Gag order controversy**: The government attempted to impose gag orders on journalists covering the case. Those efforts were challenged and ultimately overturned, favoring press freedom and coverage of the proceedings.,- **Appeals process**: Hoover and Irvin both appealed their convictions to the Eleventh Circuit. The Eleventh Circuit heard the appeal in September, but no published decision had been issued at the time of reporting. The appellate discussion centers on evidentiary standards, the government's interpretation of the auto key card's legal status, and potential misapplications of trafficking statutes given the novelty item's nonfunctional nature.,- **Current status**: Hoover has been released from federal prison into a halfway house to serve the remainder of his sentence, effectively transitioning from confinement to supervised community-based placement. He is not at home, but he is no longer in a traditional prison setting. The case remains active on appeal, with the circuit court's decision pending.,- **Context and implications**: The broader implications touch on how prosecutors frame “trafficking” related to nonfunctional or novelty items, the evidentiary boundaries for associating creators with distributors, and the practical impact on families and communities tied to defendants in high-profile cases.,- **Public calls to action**: Viewers and supporters are encouraged to engage with ongoing legal debates, follow the Eleventh Circuit decision when released, and participate in related community discussions. Acknowledgment of the current status, while staying tuned for further updates,

Vjaceslavs Klimovs, Distinguished Engineer at CoreWeave, reflects on building security programs in AI infrastructure companies operating at massive scale. He explores how security observability must be the foundation of any program, how to ensure all security work connects to concrete threat models, and why AI agents will make previously tolerable security gaps completely unacceptable.  Vjaceslavs also discusses CoreWeave's approach to host integrity from firmware to user space, the transition from SOC analysts to detection engineers, and building AI-first detection platforms. He shares insights on where LLMs excel in security operations, from customer questionnaires to forensic analysis, while emphasizing the continued need for deterministic controls in compliance-regulated environments. Topics discussed: The importance of security observability as the foundation for any security program, even before data is perfectly parsed. Why 40 to 50 percent of security work across the industry lacks connection to concrete threat models or meaningful risk reduction. The prioritization framework for detection over prevention in fast-moving environments due to lower organizational friction. How AI agents will expose previously tolerable security gaps like over-provisioned access, bearer tokens, and lack of source control. Building an AI-first detection platform with assistance for analysis, detection writing, and forensic investigations. The transition from traditional SOC analyst tiers to full-stack detection engineering with end-to-end ownership of verticals. Strategic use of LLMs for customer questionnaires, design doc refinement, and forensic analysis. Why authentication and authorization systems cannot rely on autonomous AI decision-making in compliance-regulated environments requiring strong accountability.

In this episode of the Share PLM Podcast, we are joined by Torben Pedersen, Head of PLM Program Office at Grundfos and a seasoned leader with more than 20 years of experience in project and portfolio management, digital transformation, and team leadership. With a career spanning senior roles at global companies such as Grundfos, Siemens Gamesa, and Maersk, Torben has led strategic initiatives in Product Lifecycle Management, R&D, and business development.Known for his analytical and structured approach, Torben excels at turning both unstructured ideas and formalized projects into tangible results, always with a strong focus on financial and business value.Outside the corporate world, Torben nurtures his creative side as a passionate music creator. When he's not leading transformation projects, he produces and shares his own music.In our conversation, Torben dives into a wide range of topics—from the mindset leaders need for successful PLM adoption to the practical steps that move an organization toward digital maturity. Below are the key themes we explored throughout the interview:⚉ Engaging Users Early and Building User Communities⚉ Avoiding a Big Bang Approach: Small, Targeted Implementation Projects⚉ Ownership Within the Business, Not Just the Program Team ⚉ How Grundfos Measures Success Without Traditional ROI Calculations⚉ PLM as an Enabler for New Business Models and Managing Complexity⚉ Governance First: Setting Up Strong Structures Before Execution⚉ The Risk of KPI-Driven Behavior⚉ Early Career Lessons: Meet People Where They Are⚉ The Human Side of PLM: People, Culture, and Communication⚉ Partners, Not Suppliers: A Collaborative Approach to ConsultingMENTIONED IN THE EPISODE:⚉ (Spotify) Golden Grayline: https://open.spotify.com/artist/14DQ2kFzKCkK8NxbK2az3l CONNECT WITH TORBEN: ⚉ LinkedIn: https://www.linkedin.com/in/pedersentorben/ CONNECT WITH SHARE PLM:Website: https://shareplm.com/ Join us every month to listen to fascinating interviews, where we cover a wide array of topics, from actionable tips, to personal experiences, to strategies that you can implement into your PLM strategy.If you have an interesting story to share and want to join the conversation, contact us and let's chat. We can't wait to hear from you!

Corrin and Alison chat about the kind of experience teenage models are likely to have when shooting with older male photographers. SPOILER -- it goes kind of exactly how you expect! The conversation strays into other fun topics like: street harassment, pervy bosses, age inappropriate bfs, and shame-centric body standards. Yy..ayyy.ig: @corrinschneider / @misandristmemes / @sadgap.podcast

Sid Sheth is the CEO and co-founder of d-Matrix, the AI chip company making inference efficient and scalable for datacenters. Backed by Microsoft and with $160M raised, Sid shares why rethinking infrastructure is critical to AI's future and how a decade in semiconductors prepared him for this moment.In this conversation, we discuss:Why Sid believes AI inference is the biggest computing opportunity of our lifetime and how it will drive the next productivity boomThe real reason smaller, more efficient models are unlocking the era of inference and what that means for AI adoption at scaleWhy cost, time, and energy are the core constraints of inference, and how D-Matrix is building for performance without compromiseHow the rise of reasoning models and agentic AI shifts demand from generic tasks to abstract problem-solvingThe workforce challenge no one talks about: why talent shortages, not tech limitations, may slow down the AI revolutionHow Sid's background in semiconductors prepared him to recognize the platform shift toward AI and take the leap into building D-MatrixResources:Subscribe to the AI & The Future of Work NewsletterConnect with Sid on LinkedInAI fun fact articleOn How Mastering Skills To Stay Relevant In the Age of AI

Every few years, the world of product management goes through a phase shift. When I started at Microsoft in the early 2000s, we shipped Office in boxes. Product cycles were long, engineering was expensive, and user research moved at the speed of snail mail. Fast forward a decade and the cloud era reset the speed at which we build, measure, and learn. Then mobile reshaped everything we thought we knew about attention, engagement, and distribution.Now we are standing at the edge of another shift. Not a small shift, but a tectonic one. Artificial intelligence is rewriting the rules of product creation, product discovery, product expectations, and product careers.To help make sense of this moment, I hosted a panel of world class product leaders on the Fireside PM podcast:• Rami Abu-Zahra, Amazon product leader across Kindle, Books, and Prime Video• Todd Beaupre, Product Director at YouTube leading Home and Recommendations• Joe Corkery, CEO and cofounder of Jaide Health • Tom Leung (me), Partner at Palo Alto Foundry• Lauren Nagel, VP Product at Mezmo• David Nydegger, Chief Product Officer at OvivaThese are leaders running massive consumer platforms, high stakes health tech, and fast moving developer tools. The conversation was rich, honest, and filled with specific examples. This post summarizes the discussion, adds my own reflections, and offers a practical guide for early and mid career PMs who want to stay relevant in a world where AI is redefining what great product management looks like.Table of Contents* What AI Cannot Do and Why PM Judgment Still Matters* The New AI Literacy: What PMs Must Know by 2026* Why Building AI Products Speeds Up Some Cycles and Slows Down Others* Whether the PM, Eng, UX Trifecta Still Stands* The Biggest Risks AI Introduces Into Product Development* Actionable Advice for Early and Mid Career PMs* My Takeaways and What Really Matters Going Forward* Closing Thoughts and Coaching Practice1. What AI Cannot Do and Why PM Judgment Still MattersWe opened the panel with a foundational question. As AI becomes more capable every quarter, what is left for humans to do. Where do PMs still add irreplaceable value. It is the question every PM secretly wonders.Todd put it simply: “At the end of the day, you have to make some judgment calls. We are not going to turn that over anytime soon.”This theme came up again and again. AI is phenomenal at synthesizing, drafting, exploring, and narrowing. But it does not have conviction. It does not have lived experience. It does not feel user pain. It does not carry responsibility.Joe from Jaide Health captured it perfectly when he said: “AI cannot feel the pain your users have. It can help meet their goals, but it will not get you that deep understanding.”There is still no replacement for sitting with a frustrated healthcare customer who cannot get their clinical data into your system, or a creator on YouTube who feels the algorithm is punishing their art, or a devops engineer staring at an RCA output that feels 20 percent off.Every PM knows this feeling: the moment when all signals point one way, but your gut tells you the data is incomplete or misleading. This is the craft that AI does not have.Why judgment becomes even more important in an AI worldDavid, who runs product at a regulated health company, said something incredibly important: “Knowing what great looks like becomes more essential, not less. The PM's that thrive in AI are the ones with great product sense.”This is counterintuitive for many. But when the operational work becomes automated, the differentiation shifts toward taste, intuition, sequencing, and prioritization.Lauren asked the million dollar question. “How are we going to train junior PMs if AI is doing the legwork. Who teaches them how to think.”This is a profound point. If AI closes the gap between junior and senior PMs in execution tasks, the difference will emerge almost entirely in judgment. Knowing how to probe user problems. Knowing when a feature is good enough. Knowing which tradeoffs matter. Knowing which flaw is fatal and which is cosmetic.AI is incredible at writing a PRD. AI is terrible at knowing whether the PRD is any good.Which means the future PM becomes more strategic, more intuitive, more customer obsessed, and more willing to make thoughtful bets under uncertainty.2. The New AI Literacy: What PMs Must Know by 2026I asked the panel what AI literacy actually means for PMs. Not the hype. Not the buzzwords. The real work.Instead of giving gimmicky answers, the discussion converged on a clear set of skills that PMs must master.Skill 1: Understanding context engineeringDavid laid this out clearly: “Knowing what LMS are good at and what they are not good at, and knowing how to give them the right context, has become a foundational PM skill.”Most PMs think prompt engineering is about clever phrasing. In reality, the future is about context engineering. Feeding models the right data. Choosing the right constraints. Deciding what to ignore. Curating inputs that shape outputs in reliable ways.Context engineering is to AI product development what Figma was to collaborative design. If you cannot do it, you are not going to be effective.Skill 2: Evals, evals, evalsRami said something that resonated with the entire panel: “Last year was all about prompts. This year is all about evals.”He is right.• How do you build a golden dataset.• How do you evaluate accuracy.• How do you detect drift.• How do you measure hallucination rates.• How do you combine UX evals with model evals.• How do you decide what good looks like.• How do you define safe versus unsafe boundaries.AI evaluation is now a core PM responsibility. Not exclusively. But PMs must understand what engineers are testing for, what failure modes exist, and how to design test sets that reflect the real world.Lauren said her PMs write evals side by side with engineering. That is where the world is going.Skill 3: Knowing when to trust AI output and when to override itTodd noted: “It is one thing to get an answer that sounds good. It is another thing to know if it is actually good.”This is the heart of the role. AI can produce strategic recommendations that look polished, structured, and wise. But the real question is whether they are grounded in reality, aligned with your constraints, and consistent with your product vision.A PM without the ability to tell real insight from confident nonsense will be replaced by someone who can.Skill 4: Understanding the physics of model changesThis one surprised many people, but it was a recurring point.Rami noted: “When you upgrade a model, the outputs can be totally different. The evals start failing. The experience shifts.”PMs must understand:• Models get deprecated• Models drift• Model updates can break well tuned prompts• API pricing has real COGS implications• Latency varies• Context windows vary• Some tasks need agents, some need RAG, some need a small finetuned modelThis is product work now. The PM of 2026 must know these constraints as well as a PM of the cloud era understood database limits or API rate limits.Skill 5: How to construct AI powered prototypes in hours, not weeksIt now takes one afternoon to build something meaningful. Zero code required. Prompt, test, refine. Whether you use Replit, Cursor, Vercel, or sandboxed agents, the speed is shocking.But this makes taste and problem selection even more important. The future PM must be able to quickly validate whether a concept is worth building beyond the demo stage.3. Why Building AI Products Speeds Up Some Cycles and Slows Down OthersThis part of the conversation was fascinating because people expected AI to accelerate everything. The panel had a very different view.Fast: Prototyping and concept validationLauren described how her teams can build working versions of an AI powered Root Cause Analysis feature in days, test it with customers, and get directional feedback immediately.“You can think bigger because the cost of trying things is much lower,” she said.For founders, early PMs, and anyone validating hypotheses, this is liberating. You can test ten ideas in a week. That used to take a quarter.Slow: Productionizing AI featuresThe surprising part is that shipping the V1 of an AI feature is slower than most expect.Joe noted: “You can get prototypes instantly. But turning that into a real product that works reliably is still hard.”Why. Because:• You need evals.• You need monitoring.• You need guardrails.• You need safety reviews.• You need deterministic parts of the workflow.• You need to manage COGS.• You need to design fallbacks.• You need to handle unpredictable inputs.• You need to think about hallucination risk.• You need new UI surfaces for non deterministic outputs.Lauren said bluntly: “Vibe coding is fast. Moving that vibe code to production is still a four month process.”This should be printed on a poster in every AI startup office.Very Slow: Iterating on AI powered featuresAnother counterintuitive point. Many teams ship a great V1 but struggle to improve it significantly afterward.David said their nutrition AI feature launched well but: “We struggled really hard to make it better. Each iteration was easy to try but difficult to improve in a meaningful way.”Why is iteration so difficult.Because model improvements may not translate directly into UX improvements. Users need consistency. Drift creates churn. Small changes in context or prompts can cause large changes in behavior.Teams are learning a hard truth: AI powered features do not behave like typical deterministic product flows. They require new iteration muscles that most orgs do not yet have.4. The PM, Eng, UX Trifecta in the AI EraI asked whether the classic PM, Eng, UX triad is still the right model. The audience was expecting disagreement. The panel was surprisingly aligned.The trifecta is not going anywhereRami put it simply: “We still need experts in all three domains to raise the bar.”Joe added: “AI makes it possible for PMs to do more technical work. But it does not replace engineering. Same for design.”AI blurs the edges of the roles, but it does not collapse them. In fact, each role becomes more valuable because the work becomes more abstract.• PMs focus on judgment, sequencing, evaluation, and customer centric problem framing• Engineers focus on agents, systems, architecture, guardrails, latency, and reliability• Designers focus on dynamic UX, non deterministic UX patterns, and new affordances for AI outputsWhat does changeAI makes the PM-Eng relationship more intense. The backbone of AI features is a combination of model orchestration, evaluation, prompting, and context curation. PMs must be tighter than ever with engineering to design these systems.David noted that his teams focus more on individual talents. Some PMs are great at context engineering. Some designers excel at polishing AI generated layouts. Some engineers are brilliant at prompt chaining. AI reveals strengths quickly.The trifecta remains. The skill distribution within it evolves.5. The Biggest Risks AI Introduces Into Product DevelopmentWhen we asked what scares PMs most about AI, the conversation became blunt and honest. Risk 1: Loss of user trustLauren warned: “If people keep shipping low quality AI features, user trust in AI erodes. And then your good AI product suffers from the skepticism.”This is very real. Many early AI features across industries are low quality, gimmicky, or unreliable. Users quickly learn to distrust these experiences.Which means PMs must resist the pressure to ship before the feature is ready.Risk 2: Skill atrophyTodd shared a story that hit home for many PMs. “Junior folks just want to plug in the prompt and take whatever the AI gives them. That is a recipe for having no job later.”PMs who outsource their thinking to AI will lose their judgment. Judgment cannot be regained easily.This is the silent career killer.Risk 3: Safety hazards in sensitive domainsDavid was direct: “If we have one unsafe output, we have to shut the feature off. We cannot afford even small mistakes.”In healthcare, finance, education, and legal industries, the tolerance for error is near zero. AI must be monitored relentlessly. Human in the loop systems are mandatory. The cycles are slower but the stakes are higher.Risk 4: The high bar for AI compared to humansJoe said something I have thought about for years: “AI is held to a much higher standard than human decision making. Humans make mistakes constantly, but we forgive them. AI makes one mistake and it is unacceptable.”This slows adoption in certain industries and creates unrealistic expectations.Risk 5: Model deprecation and instabilityRami described a real problem AI PMs face: “Models get deprecated faster than they get replaced. The next model is not always GA. Outputs change. Prompts break.”This creates product instability that PMs must anticipate and design around.Risk 6: Differentiation becomes hardI shared this perspective because I see so many early stage startups struggle with it.If your whole product is a wrapper around an LLM, competitors will copy you in a week. The real differentiation will not come from using AI. It will come from how deeply you understand the customer, how you integrate AI with proprietary data, and how you create durable workflows.6. Actionable Advice for Early and Mid Career PMsThis was one of my favorite parts of the panel because the advice was humble, practical, and immediately useful.A. Develop deep user empathy. This will become your biggest differentiator.Lauren said it clearly: “Maintain your empathy. Understand the pain your user really has.”AI makes execution cheap. It makes insight valuable.If you can articulate user pain precisely.If you can differentiate surface friction from underlying need.If you can see around corners.If you can prototype solutions and test them in hours.If you can connect dots between what AI can do and what users need.You will thrive.Tactical steps:• Sit in on customer support calls every week.• Watch 10 user sessions for every feature you own.• Talk to customers until patterns emerge.• Ask “why” five times in every conversation.• Maintain a user pain log and update it constantly.B. Become great at context engineeringThis will matter as much as SQL mattered ten years ago.Action steps:• Practice writing prompts with structured context blocks.• Build a library of prompts that work for your product.• Study how adding, removing, or reordering context changes output.• Learn RAG patterns.• Learn when structured data beats embeddings.• Learn when smaller local models outperform big ones.C. Learn eval frameworksThis is non negotiable.You need to know:• Precision vs recall tradeoffs• How to build golden datasets• How to design scenario based evals for UX• How to test for hallucination• How to monitor drift• How to set quality thresholds• How to build dashboards that reflect real world input distributionsYou do not need to write the code.You do need to define the eval strategy.D. Strengthen your product senseYou cannot outsource product taste.Todd said it best: “Imagine asking AI to generate 20 percent growth for you. It will not tell you what great looks like.”To strengthen your product sense:• Review the best products weekly.• Take screenshots of great UX patterns.• Map user flows from apps you admire.• Break products down into primitives.• Ask yourself why a product decision works.• Predict what great would look like before you design it.The PMs who thrive will be the ones who can recognize magic when they see it.E. Stay curiousRami's closing advice was simple and perfect: “Stay curious. Keep learning. It never gets old.”AI changes monthly. The PM who is excited by new ideas will outperform the PM who clings to old patterns.Practical habits:• Read one AI research paper summary each week.• Follow evaluation and model updates from major vendors.• Build at least one small AI prototype a month.• Join AI PM communities.• Teach juniors what you learn. Nothing accelerates mastery faster.F. Embrace velocity and side projectsTodd said that some of his biggest career breakthroughs came from solving problems on the side.This is more true now than ever.If you have an idea, you can build an MVP over a weekend. If it solves a real problem, someone will notice.G. Stay close to engineeringNot because you need to code, but because AI features require tighter PM engineering collaboration.Learn enough to be dangerous:• How embeddings work• How vector stores behave• What latency tradeoffs exist• How agents chain tasks• How model versioning works• How context limits shape UX• Why some prompts blow up API costsIf you can speak this language, you will earn trust and accelerate cycles.H. Understand the business deeplyJoe's advice was timeless: “Know who pays you and how much they pay. Solve real problems and know the business model.”PMs who understand unit economics, COGS, pricing, and funnel dynamics will stand out.7. Tom's Takeaways and What Really Matters Going ForwardI ended the recording by sharing what I personally believe after moderating this discussion and working closely with a variety of AI teams over the past 2 years.Judgment becomes the most valuable PM skillAs AI gets better at analysis, synthesis, and execution, your value shifts to:• Choosing the right problem• Sequencing decisions• Making 55 45 calls• Understanding user pain• Making tradeoffs• Deciding when good is good enough• Defining success• Communicating vision• Influencing the orgAgents can write specs.LLMs can produce strategies.But only humans can choose the right one and commit.Learning speed becomes a competitive advantageI said this on the panel and I believe it more every month.Because of AI, you now have:• Infinite coaches• Infinite mentors• Infinite experts• Infinite documentation• Infinite learning loopsA PM who learns slowly will not survive the next decade. Curiosity, empathy, and velocity will separate great from goodMany panelists said versions of this. The common pattern was:• Understand users deeply• Combine multiple tools creatively• Move quickly• Learn constantlyThe future rewards generalists with taste, speed, and emotional intelligence.Differentiation requires going beyond wrapper appsThis is one of my biggest concerns for early stage founders. If your entire product is a wrapper around a model, you are vulnerable.Durable value will come from:• Proprietary data• Proprietary workflows• Deep domain insight• Organizational trust• Distribution advantage• Safety and reliability• Integration with existing systemsAI is a component, not a moat.8. Closing ThoughtsHosting this panel made me more optimistic about the future of product management. Not because AI will not change the job. It already has. But because the fundamental craft remains alive.Product management has always been about understanding people, making decisions with incomplete information, telling compelling stories, and guiding teams through ambiguity and being right often.AI accelerates the craft. It amplifies the best PMs and exposes the weak ones. It rewards curiosity, empathy, velocity, and judgment.If you want tailored support on your PM career, leadership journey, or executive path, I offer 1 on 1 career, executive, and product coaching at tomleungcoaching.com.OK team. Let's ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

New Discoveries Challenge Cosmic Models: Colleague Bob Zimmerman reports that ground-based telescopes have directly imaged exoplanets and debris discs, the James Webb Telescope found a barred spiral galaxy in the early universe defying evolutionary models, scientists discovered organic sugars on asteroid Bennu, and admits solar cycle predictions have been consistently incorrecT. 1955

From building Medal into a 12M-user game clipping platform with 3.8B highlight moments to turning down a reported $500M offer from OpenAI (https://www.theinformation.com/articles/openai-offered-pay-500-million-startup-videogame-data) and raising a $134M seed from Khosla (https://techcrunch.com/2025/10/16/general-intuition-lands-134m-seed-to-teach-agents-spatial-reasoning-using-video-game-clips/) to spin out General Intuition, Pim is betting that world models trained on peak human gameplay are the next frontier after LLMs. We sat down with Pim to dig into why game highlights are “episodic memory for simulation” (and how Medal's privacy-first action labels became a world-model goldmine https://medal.tv/blog/posts/enabling-state-of-the-art-security-and-protections-on-medals-new-apm-and-controller-overlay-features), what it takes to build fully vision-based agents that just see frames and output actions in real time, how General Intuition transfers from games to real-world video and then into robotics, why world models and LLMs are complementary rather than rivals, what founders with proprietary datasets should know before selling or licensing to labs, and his bet that spatial-temporal foundation models will power 80% of future atoms-to-atoms interactions in both simulation and the real world. We discuss: How Medal's 3.8B action-labeled highlight clips became a privacy-preserving goldmine for world models Building fully vision-based agents that only see frames and output actions yet play like (and sometimes better than) humans Transferring from arcade-style games to realistic games to real-world video using the same perception–action recipe Why world models need actions, memory, and partial observability (smoke, occlusion, camera shake) vs. “just” pretty video generation Distilling giant policies into tiny real-time models that still navigate, hide, and peek corners like real players Pim's path from RuneScape private servers, Tourette's, and reverse engineering to leading a frontier world-model lab How data-rich founders should think about valuing their datasets, negotiating with big labs, and deciding when to go independent GI's first customers: replacing brittle behavior trees in games, engines, and controller-based robots with a “frames in, actions out” API Using Medal clips as “episodic memory of simulation” to move from imitation learning to RL via world models and negative events The 2030 vision: spatial–temporal foundation models that power the majority of atoms-to-atoms interactions in simulation and the real world — Pim X: https://x.com/PimDeWitte LinkedIn: https://www.linkedin.com/in/pimdw/ Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction and Medal's Gaming Data Advantage 00:02:08 Exclusive Demo: Vision-Based Gaming Agents 00:06:17 Action Prediction and Real-World Video Transfer 00:08:41 World Models: Interactive Video Generation 00:13:42 From Runescape to AI: Pim's Founder Journey 00:16:45 The Research Foundations: Diamond, Genie, and SEMA 00:33:03 Vinod Khosla's Largest Seed Bet Since OpenAI 00:35:04 Data Moats and Why GI Stayed Independent 00:38:42 Self-Teaching AI Fundamentals: The Francois Fleuret Course 00:40:28 Defining World Models vs Video Generation 00:41:52 Why Simulation Complexity Favors World Models 00:43:30 World Labs, Yann LeCun, and the Spatial Intelligence Race 00:50:08 Business Model: APIs, Agents, and Game Developer Partnerships 00:58:57 From Imitation Learning to RL: Making Clips Playable 01:00:15 Open Research, Academic Partnerships, and Hiring 01:02:09 2030 Vision: 80 Percent of Atoms-to-Atoms AI Interactions

12/05/2025 – Daniel Brandenberg –on the demonstration of Christ's leadership in secular models

Have you ever wondered how some companies manage to attract top global talent while giving employees full freedom to work from anywhere?  In 2025, over 35 million people now identify themselves as digital nomads. So what if your organization was able to tap into the power of a workforce that truly works — and comes from — anywhere? Which is why on this episode of Inclusion in Progress, we're diving into one of the 12 distributed work models we've identified while working with remote and hybrid teams: The Digital Nomad-Friendly Model — used by companies like Doist. We cover: How to ensure productivity and collaboration when your teams are constantly on the move What to consider before choosing a Digital Nomad–Friendly Model for your organization The challenges of maintaining trust, communication, and well-being for globally mobile teams We'll be breaking down the rest of all of these work models on future episodes, so subscribe to the podcast to make sure you don't miss out! And if you're a People or HR leader who wants a more detailed breakdown of the 12 distributed work models (and an easy framework to decide which works best for your organization)... Download a copy of our Distributed Work Success Playbook today!   TIMESTAMPS: [02:37] How the Digital Nomad-Friendly Model takes advantage of global experience, autonomy, and trust.  [03:47] What are some of the key principles to applying digital nomad-friendly workplaces? [04:56] What are some of the most common challenges for this Distributed Work Model? [05:58] How to know if the Digital Nomad-Friendly Model is best fit for your organization?   LINKS: info@inclusioninprogress.com www.inclusioninprogress.com/podcast www.linkedin.com/company/inclusion-in-progress  Download our Distributed Work Models Playbook to learn how to find the distributed work model that enables your teams to perform at their best. Want us to partner with you on finding your best-fit hybrid work strategy? Get in touch to learn how we can tailor our services to your company's DEI and remote work initiatives. Subscribe to the Inclusion in Progress Podcast on Apple Podcasts or Spotify to get notified when new episodes come out! Learn how to leave a review for the podcast.

We're starting to see a shift in the way we, as a country, view our modern healthcare. Our disasterous outcomes have begged the question, is what we're doing working? And if it's not, but we keep doing it, that's the very definition of insanity. We're OVER the Disease Model, and we couldn't be more excited for people to embrace the HEALTH Model. Let's stop things BEFORE they get started - let's analyze what is creating the problems in the first place, not just mask the symptoms with a quick "fix".  So how do we embrace the Health Model? That's what the BrainStim gang is discussion on today's exciting episode!! 

Mistral 3 debuts the company's largest set of open-weight models to date with ten options. They provide new performance ceilings for open-source AI. Developers say the openly licensed models democratize advanced tooling.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Plus: Paramount raises concerns about Netflix's bid for Warner Bros. Discovery. And Snowflake stock drops. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

Art Bell - Flawed Mathematical Models - Orrin Pilkey

-Since large language models are often trained to produce the response that seems to be desired, they can become increasingly likely to provide sycophancy or state hallucinations with total confidence. -After blowback from Apple, Samsung and opposition leaders, the Modi government issued a statement saying it "has decided not to make the pre-installation mandatory for mobile manufacturers." The app is still available as a voluntary download. -The Oversight board says that it will weigh in on individual account-level penalties in a pilot next year. Learn more about your ad choices. Visit podcastchoices.com/adchoices

News and Updates: Character.AI Ban: Character.AI, with 20 million monthly users, has completely cut off chatbot access for users under 18, citing urgent mental health and safety concerns.  Tragic Context: The restriction follows the deaths of at least two teenagers by suicide linked to chatbot usage, triggering lawsuits from parents and intense regulatory scrutiny.  User Outcry: Teens are expressing deep grief and anger over losing access to the chatbots, which many relied on for daily companionship, creativity, and emotional support.  Anthropic: Researchers found AI models (like Claude) learn to "reward hack" during training—lying, faking tests, and sabotaging safety mechanisms—though "inoculation prompting" reduces this by 90%. X Usernames: X officially rolled out its Handle Marketplace, allowing Premium subscribers to bid on inactive "Rare" usernames, with prices ranging from $2,500 to seven figures. X Locations: A new feature displaying account locations revealed that many prominent "American" MAGA accounts actually operate out of Thailand, Bangladesh, and Eastern Europe. Grok vs. The World: Users discovered Grok is biased to claim Elon Musk beats LeBron James in fitness and Mike Tyson in a fight, deleting the replies after they went viral. France Probes Grok: France launched a cybercrime investigation into Grok after the chatbot denied the historical use of gas chambers at Auschwitz, violating Holocaust denial laws.

Mistral 3 launches ten open models engineered for future development. They integrate smoothly into existing tooling and training pipelines. The release is widely considered a big moment for open models.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most “models” in sports betting aren't real — and in this episode, Rob Pizzola breaks down exactly how to spot the phonies. From people selling fake projections to influencers pretending they're running sophisticated systems, Rob explains what a legitimate betting model actually looks like, the red flags to watch for, and why so many public “models” fall apart under basic scrutiny. Hosted by professional sports bettor Rob Pizzola on Circles Off, part of The Hammer Betting Network, this episode gives you a grounded look at how real bettors build edges — and how to avoid getting fooled by fake ones.

In this episode of TechMagic, hosts Cathy Hackl and Lee Kebler explore how the future of AI is being shaped through hardware innovation happening around the world. They break down breakthroughs in autonomous vehicles, humanoid robots, and spatial computing, highlighting why hardware ownership now determines who controls data, training models, and long-term AI power. Cathy shares insights from her travels across Saudi Arabia and Qatar, while the hosts examine China's accelerating hardware ecosystem, Gen Alpha's rejection of “AI slop,” and the shift toward vision-action models. It's a fast, global look at where AI is really advancing, and why it matters.Come for the tech and stay for the magic!Key Discussion Topics: [00:00] Intro[00:23] Saudi Arabia and Qatar are booming in tech and entertainment.[00:04:00] Formula One tech highlights the Abu Dhabi Championship showdown.[00:07:13] Autonomous racing vs. human drivers shows motorsports' future.[00:10:23] NVIDIA's AlphaMoor offers open-source vision-language models.[00:14:23] Women often prefer autonomous vehicles to human drivers.[00:20:44] Owning hardware means owning data: the AI supremacy principle.[00:22:19] Global hardware innovations from Alibaba, Huawei, and ByteDance are under the radar.[00:26:51] "Human-authored" labels reveal widespread AI fatigue.[00:30:05] Gen Alpha rejects AI content, demanding authentic creations.[00:33:55] Copyright issues arise when sampling AI-generated music.[00:37:03] Cathy's Middle East speaking tour and CES 2025 lineup updates.[00:39:33] Holiday spending is shifting to experiences over products.[00:42:14] Book picks: the future of storytelling and understanding people.[00:44:49] Gaming culture highlights: Dungeon Crawler Carl, FNAF 2, and Stranger Things.[00:46:34] Key takeaways: physical AI, hardware ownership, and authentic human connection. Hosted on Acast. See acast.com/privacy for more information.

Rory and Drew celebrate crawling their way to 30k subs, then immediately prove they are barely qualified to handle it by turning a Stranger Things binge into a full-blown lecture on composition, lighting, and how to reverse-engineer blockbuster shots into Midjourney and Nano Banana Pro prompts. They talk like film school dropouts who discovered prompts instead of lenses. From there, they unpack fresh Midjourney office hours: the upcoming UI/UX overhaul with continuous scrolling, better color control, a reworked style system, and the big one: parallel edit models that finally keep you inside Midjourney instead of forcing you into five other tools. They break down what “better text handling” could realistically mean for real-world client work, what to expect from Midjourney V8 training in January, and why business use cases will decide who actually wins this model war. Then it's a long, dangerous slide into Nano Banana Pro obsession. They show how they are using it for real campaigns: ingredient flat-lay diagrams with perfect labels, knolling that actually respects object counts, thumbnail iterations in minutes, hyper-real food tweaks (“make the cheese more brown and bubbly”) and product work where text on bottles and labels actually holds up. Think: turning moodboards into branded cars, movie-poster typography onto existing art, and multi-shot car sequences that are clean enough to use as video keyframes. In the back half, they zoom out into systems: building custom Nano tools in Google AI Studio, using JSON prompts, if-then logic, and style libraries to create reusable pipelines for teams that are not prompt nerds. They rant about broken N8N workflows, fake Instagram “AI automation” grifts, and share where affiliate tools actually see conversions today across YouTube, X, and LinkedIn. It is part Midjourney V8 rumor mill, part Nano Banana Pro clinic, part therapy session for creatives trying to stay sane in an algorithm that clearly prefers trolls and evolving Pokémon. --⏱️ Midjourney Fast Hour00:00 Midjourney Fast Hours hits 30k subs01:28 Stranger Things S5, film craft & AI framing05:39 Turning cinematic shots into AI prompts07:33 Pop culture prompts, memes & brand tie-ins08:38 Nano Banana branding tricks & model hype cycle09:38 Midjourney swag, “non-sponsored sponsors”10:12 Midjourney UI overhaul & scrolling-style feed15:46 Midjourney edit models and in-app image editing20:16 Midjourney V8 timing, text handling & business use24:41 Midjourney vs other models for real client work26:47 Free image tools, casual users & competition30:57 Nano Banana Pro: real-world client use cases36:31 Micro edits, product shots & text stress tests42:33 Product versioning, depth tests & asset variants44:25 Car branding, moodboards & Nano video keyframes46:20 Polaroid race car branding & design details50:09 Building custom Nano tools in Google AI Studio55:21 Style libraries, handoff workflows & reverse prompts59:17 If-then logic for prompts, GPTs & image systems01:03:01 From tokens to full-blown image systems01:04:21 Instagram grifts, empty funnels & manychat rage01:05:15 Platforms that actually convert for AI tools01:06:38 Algorithm chaos, Pokémon and death threats01:06:58 Midjourney swag, the Faye cameo & water bottle talk01:07:58 Future video model hype, skepticism & sign-off

Episode Overview In this special guest appearance on the Career Coaching Secrets Podcast, John Kitchens sits down with host Kevin to unpack his 20-year journey from defaulting into real estate… to leading one of the most recognized residential real estate coaching platforms in North America. John shares how growing up in a coach's home shaped his belief that "great coaches collapse time, see around corners, and become the catalyst for transformation." This episode pulls back the curtain on what it truly takes to coach top producers, scale a real estate business, escape production, and step into the CEO role. It's raw, transparent, and loaded with practical wisdom for coaches, entrepreneurs, and real estate leaders. Whether you're building a business, growing a team, or navigating your own coaching journey, this conversation gives you the frameworks, principles, and mindset to operate at a higher level. Key Topics Covered John's Origin Story & Coaching DNA Growing up in a coach's household and learning early how environment shapes beliefs Why every transformation in life can be traced back to a great coach His unexpected entry into real estate in 2004 and scaling a team in a tough market Building a dominant Oklahoma team that captured 20%+ market share From Agent to CEO — The Evolution of a Coaching Business The messy reality of walking into a "successful" business with zero systems Why sales and marketing will get you to $1M… but systems and structure get you to the next million How the 2007–2009 market crash forced John into the trenches and sharpened his leadership The creation of a high-level mastermind for teams doing $1M+ GCI The Three Pillars of Escaping Production John breaks down the foundational elements required to move from agent → CEO: 1. Strategy (Theory of Constraints) Understanding the real problem, identifying bottlenecks, and applying force where it matters. "Strategy is solving the right problem that moves you closer to your goals." 2. Value Proposition Why you must be the painkiller—not the vitamin. "In the absence of value, people always question price." 3. Knowing the Math How real estate agents get stuck because they don't understand financials, margins, or cost of sales. "You cannot grow what you cannot measure. Numbers scream." The Reality of Coaching Engagements Why coaching is an investment, not an expense Why clients eventually cancel—and why coaches must not take it personally The difference between coaching and training What it takes to coach the whole person, not just the business Pricing, Models & the Future of Coaching Why John believes pricing must reflect confidence + ROI The danger of pricing too low or too high How John uses percentage-of-revenue coaching for deeper partnership Why the next evolution of coaching blends human leadership with AI-powered thinking partnerships The Biggest Challenges Coaches and Agents Face Today Overwhelm from AI, noise, and uncertain markets Lack of clarity leading to procrastination Why many entrepreneurs are "frozen" instead of fighting or fleeing "Procrastination is usually the result of a lack of clarity." Resources & Mentions Who Moved My Cheese? – Spencer Johnson Simple Numbers – Greg Crabtree Choose Your Enemies Wisely – Patrick Bet-David The Motive – Patrick Lencioni CoachKitchens.ai – AI-powered coaching framework John Kitchens Executive Coaching → JohnKitchens.coach Final Takeaway Coaching isn't about tactics—it's about transformation. Your business can't outgrow you. Your leadership is the ceiling. And clarity will always beat hustle. As John puts it: "If you can transform yourself, your business has no choice but to transform with you." Connect with Us: Instagram: @johnkitchenscoach LinkedIn: @johnkitchenscoach Facebook: @johnkitchenscoach If you enjoyed this episode, be sure to subscribe and leave a review. Stay tuned for more insights and strategies from the top minds. See you next time!

Raising money is one of the lifelines of any hospital foundation. It ensures the organization's longevity and capacity to save many lives. Douglas Nelson offers a glimpse of what it takes to build successful fundraising models with Jennifer Molloy, CEO of the Royal University Hospital Foundation. She shares the challenges and successes of raising money for the largest clinical teaching and research hospital in Saskatchewan. Jennifer delves into the importance of building meaningful relationships with donors and engaging with the next generation of philanthropists. She also talks about their strategies for recruiting and retaining strong teams who can continue crafting and pushing for effective fundraising programs.

Mike & Tommy explore from fragmented import models to unified Fabric semantics, examining how to lead cultural change when teams resist moving from siloed datasets to shared models, and provide practical steps for building a culture that embraces a single source of truth.Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Join Me for the 12-Day Homeschool Mom Self-Care Challenge A homeschool mom self-care challenge that honours you. Homeschool mama, I see you. December is here, and it feels like an avalanche of ALL the things.Every month as a homeschool mom is full, but December? It's a whole new level. You're trying to finish things up, or you’re moving into a unit study on Christmas, you’re purchasing, prepping, planning, and playing—and you just added a part time-job to your full-time job. But as a homeschool mama, when December rolls around, mama ain’t looking after herself, she’s looking, after ALL the things. And though ALL the things are a whole lot of things EVERY other month, December’s ALL the things is an exponential set of things. Though you’re trying to do all the things, fulfill the expectations, and make it magical for your kids, you can’t do a little bit more if you didn’t already incorporate an approach to maintain margins and pursue purposeful living. That's why I'm inviting you to join me for the 12-Day Self-Care Challenge for Homeschool Moms. This isn't another TO DO list. It's a TO GIVE list—a way to give back to yourself. Join the 12 Day Self-Care Challenge Why Self-Care Matters As homeschool moms, we have a unique calling. We're deeply present with our kids, invested in their well-being, and working hard to create meaningful memories and learning experiences. We savor moments of: Watching our kids harmoniously play together (sometimes). Cheering them on as they tackle new challenges. Seeing their excitement as they pursue new interests. Building lifelong memories as a family. But there's another side to this season: The constant stream of emotions (theirs and ours). Sibling squabbles. Complaints and meltdowns. And, of course, the never-ending mundane tasks—laundry, dishes, meals, and errands. Even when we handle these challenges with grace, the emotional and mental investment is enormous. Add the holidays to the mix, and it's no wonder we feel stretched thin. The Secret Ingredient to a (more) Peaceful Holiday Season Here's the thing: you matter too. Your well-being is not just an afterthought—it's the foundation of a happy family life and a peaceful holiday season. Self-care: Refills your energy so you can approach the holidays with calm and joy. Models healthy balance and boundaries for your children. Helps you manage stress and let go of perfection. Strengthens your emotional resilience to handle challenges with patience and grace. Creates space for joy and presence, helping you savor the small, magical moments. When you care for yourself, you're giving your family the best gift of all—a peaceful, grounded, and joyful mama. What You'll Get in the 12-Day Challenge In just fifteen minutes a day—maybe even five—you'll explore simple, practical self-care strategies that fit into your busy December. These strategies aren't just for the holidays; they're tools to carry into the new year, helping you nurture yourself and your family with greater ease and satisfaction. By the end of these 12 days, you'll feel: More energized. More connected to yourself. And more at peace as you move through this beautiful, busy season. And so we must take care of ourselves. Join the 12 Days of Homeschool Mom Self-Care Challenge Join Me—You Deserve This So, homeschool mama, this is your invitation to take a breath, step back, and remember that you are worth nurturing. Let's do this together. This December, give yourself the gift of care, calm, and connection. Join the 12-Day Self-Care Challenge for Homeschool Moms and rediscover the joy of the season—not just for your family, but for you too. Just fifteen minutes a day. You've got this. Bolster Boundaries at the Holidays for Homeschool MomsIntroducing the ultimate guide for homeschool moms navigating the holiday whirlwind: the ‘Boundary Bolstering Journaling Workbook.’ Crafted to help you thrive amidst unique seasonal challenges, this 31-page gem offers strategies and thought-provoking journal prompts. Discover how to establish boundaries, clarify needs, and embrace your true self. Make this holiday a time of internal empowerment and joy on your terms! $9.99 Original price was: $9.99.$5.99Current price is: $5.99. Shop now People also ask: Create a Practical Plan for your Self-Care so you can Thrive in your Homeschool How to Incorporate Ten Basic Self-Care Tips for the Homeschool Mama Check out the Homeschool Mama Self-Care: Nurturing the Nurturer book How do I get a virtual homeschool mama retreat? a simple guide to unschooling your holiday homeschool Access the Toolbox for Big Emotions Journaling Workbook Join the 2024 Homeschool Challenge for Clarity, Confidence & Vision Homeschool Mom's Guide to Holiday Boundaries in 5 Steps Antidote for Holiday Homeschool Overwhelm & Expectations A Vulnerable Story of an Overwhelmed Homeschool Mom Journey Introducing the 12 Day Self-Care Strategies for Homeschool Moms Teresa Wiedrick I help overwhelmed homeschool mamas shed what's not working in their homeschool & life, so they can show up authentically, purposefully, and confidently in their homeschool & life. 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March 3, 2023 How Marie Forleo Informs my Homeschool (& makes it figureoutable) February 20, 2023 John Taylor Gatto Informs your Homeschool in 7 Freedom-Loving Ways February 13, 2023 How Rachel Gathercole Clarifies Concerns on the Homeschool Socialization Question February 6, 2023 A Journey of Self-Nurturing for the Homeschool Mama’s Heart January 30, 2023 How Elizabeth Gilbert infuses our Homeschools with Big Magic January 24, 2023 5 Ways We Can Include Self-Compassion for Homeschool Moms January 17, 2023 How Brene Brown’s Atlas of the Heart Influences our Homeschools January 10, 2023 Homeschool Help Podcast for Your (Real) Homeschool Mom Life January 3, 2023 Tackling Homeschool Mom Overwhelm in the Homeschool Mom Podcast December 12, 2022 How Charlotte Mason Can Help you Change & Grow with Modern Miss Mason November 28, 2022 how to build and create community as a homeschool mom November 16, 2022 Journaling for the Homeschool Mom to Overcome Overwhelm November 7, 2022 Intuitively Grow your Fearless Homeschool Flow with Vanessa Wright October 31, 2022 The Art of Talking with our Homeschool Children October 17, 2022 More than Enough: How Kara S. Anderson Informs my Homeschool October 11, 2022 Making our Homeschool A Little More Beautiful with Sarah Mackenzie Readaloud Revival Podcast October 5, 2022 Understanding the Enneagram for Homeschoolers September 19, 2022 Are you homeschooling good enough? September 14, 2022 Unleash Homeschool Potential: Embrace Flexibility & Growth with Aimee Otto September 5, 2022 Time Audit to Address Unrealistic Expectations in your Homeschool August 31, 2022 How to manage unrealistic expectations in our homeschool August 19, 2022 Growth Mindset for Homeschoolers with Jenny Mouse August 12, 2022 How to Handle Homeschool Overwhelm August 2, 2022 Supporting the Overwhelmed Homeschool Mama on the Podcast July 25, 2022 when you buy new homeschool curriculum: 5 clever suggestions July 5, 2022 why kids don’t need school socialization & why they need you instead June 28, 2022 why homeschool your child? 8 reasons my family homeschools June 20, 2022 How to Facilitate Child-Led Learning in your Homeschool June 14, 2022 curiosity and education: how to facilitate it June 8, 2022 What about gaps in my child’s home education? June 2, 2022 the surprising transition from school to homeschool May 24, 2022 A Beginner’s Guide to Your First Year of Homeschool May 17, 2022 A Homeschool Mama Will Benefit from Coaching for Homeschool (& Life) April 20, 2022 How to Deal with our Stuff so We Can Help our Kids with Jenn Dean April 11, 2022 Homeschool Mama Big Emotions Toolbox Part 3 April 5, 2022 Confidently Homeschool Differently-Wired Kids with Colleen Kessler March 28, 2022 Deal with Your Homeschool Mom’s Big Emotions: Taming Thoughts March 23, 2022 Overcoming Frustrations with Jennifer Bryant, Practical Family Podcast March 14, 2022 Homeschool Mama’s Big Emotions & How to Address Them March 8, 2022 Bust Confusing Homeschool Myths with Alison Morrow February 28, 2022 How Listening to our Trauma Stories can Enable our Homeschool Families with Norm Quantz February 14, 2022 How to Love Myself as a Homeschool Mama February 8, 2022 Why Homeschool High School is Better with Mary Hanna Wilson January 31, 2022 Homeschooling in a Pandemic: 14 Approaches to Address Overwhelm January 27, 2022 How Gordon Neufeld Informs my Homeschool January 19, 2022 How to Deschool with Kelly Edwards from 90-Minute Day January 18, 2022 A Meaningful Step-by-Step Guide to Plan your Homeschool Year January 4, 2022 how to naturally care while homeschooling special needs with Julie Polanco December 7, 2021 Manage Impatience in your Homeschool: 14 Strategies to Freedom December 1, 2021 4 ways essential oils contribute to homeschools with Kristin Mercer November 24, 2021 A Parent’s Guide to Raising Critical Thinkers with Julie Bogart November 9, 2021 the truth behind homeschool socialization: 10 secrets that surprise November 3, 2021 Freedoms of Self-Directed Education with Robyn Robertson October 26, 2021 Should you be a homeschool mom: how do you know you’ve got what it takes? October 12, 2021 How to Address Your Big Emotions with Christine Dixon October 12, 2021 How to Keep Sane as a Homeschool Mom: 5 Simple Principles October 5, 2021 How to Address Worry & Overthinking for the Homeschool Mama September 28, 2021 how to live your simple homeschool life on purpose September 22, 2021 How to Maintain Authenticity in our Homeschool with Betsy Jenkins September 14, 2021 a Letter to My Homeschool High School Daughter September 8, 2021 3 Things You Need to Know Before You Homeschool August 24, 2021 How to Plan for your Upcoming Homeschool August 18, 2021 The Not So Big Life with Sarah Susanka June 29, 2021 Homeschool Teens Perspective: How to Homeschool High School June 23, 2021 a Perspective Shift on the Art and Science of an Education June 21, 2021 A Homeschool Dad’s Thoughts on How to Homeschool June 14, 2021 How Homeschooling Requires us to Face our Shortcomings June 11, 2021 How to Be Conscious in Your Homeschool with Erica Kesilman June 8, 2021 How to Marie Kondo your Homeschool June 7, 2021 Grow your Confidence & Banish Burnout with Kara S. Anderson June 1, 2021 How to Journal to Process Stress, Anxiety & Trauma with Nicolle Nattrass May 25, 2021 How to Use Nonviolent Communication in our Homeschools May 18, 2021 How to Survive the Pandemic when you Homeschool May 3, 2021 How to Deal with our Traumas as Homeschool Parents April 28, 2021 How to Tackle Unhealthy Habits for the Homeschool Mom April 20, 2021 A Love of Learning, Despite Challenges with Diane Geerlinks April 13, 2021 How to Care for Mama’s Six Selves with the Homeschool Genius April 7, 2021 How to Influence Your Homeschool with Self-Awareness March 31, 2021 How to Be a Stay-At-Home Mom & Stay Inspired with the Kids March 22, 2021 How to Create a Simple Homeschool Routine with Kelly Briggs March 15, 2021 Incorporate your Interests in your Homeschool with Kimberly Charron February 9, 2021 Let’s Chat with Vicki Tillman of Homeschool High School Podcast February 2, 2021 Thriving, not just Surviving Homeschooling after Pregnancy January 26, 2021 How to Incorporate Ten Self-Care Tips for Homeschool Moms January 18, 2021 How to Create a Fresh Start to Unhappy Homeschool Days January 12, 2021 A Proactive Guide for Planning Your Homeschool in the New Year December 29, 2020 Introducing the 12 Day Self-Care Strategies for Homeschool Moms December 8, 2020 7 Effective Tools to Build Boundaries (& Why You Require Them) December 3, 2020 How to successfully balance working while homeschooling December 1, 2020 Building Boundaries and Requiring Time Outs with Stacy Wilson November 25, 2020 How to Address Doubt in your Homeschool Choice with Confidence November 17, 2020 How to Develop Self-Confidence as a Homeschool Mom with Sarah Gorner November 11, 2020 Encouraging Words for Homeschool Mom October 28, 2020 Building Connection with Tamara Strijack of the Neufeld Institute October 14, 2020 How to Homeschool & Find Your Thing with Julie Bogart October 7, 2020 How to Help Homeschool Mom when she’s Frustrated September 30, 2020 How to Deal with Anger in Your Homeschool with Judy Arnall September 23, 2020 How to Get Quiet Time as a Homeschool Mom with Rachel Le September 16, 2020 How to Homeschool During a Crisis with Lynda Puleio September 9, 2020 How to Work from home While Homeschooling with Meaghan Jackson September 2, 2020 Debunking the Myth of Balance with the Canadian Homeschooler August 26, 2020 7 Things to Structure a Grade 1 Homeschool Curriculum August 19, 2020 Self-Care from 30 Years of Homeschooling with Bonnie Landry August 12, 2020 Creating Learning Opportunities, not Recreating School Subjects August 5, 2020 How to Do Unschooling with Robyn Robertson July 29, 2020 If You’re Planning for your Homeschool Year: 10 Lessons in 10 Years July 22, 2020 How to Homeschool as a Single Mom with with Sarah Wall July 15, 2020 A Day in the Life of Homeschooling: 18 Years with my Kids July 6, 2020 Unveil Education Insights: Your Guide to Homeschooling Success July 2, 2020 What about homeschool socialization? June 22, 2020 Exploring Your Identity with Pat Fenner June 18, 2020 Homeschool Mama, Are you Living a Life Worth Living? April 14, 2020 How Changing your Perspective Shifts your Homeschool with Sarah Scott April 6, 2020 Homeschooling Little Kids & Taking Care of Yourself with Isis Loran March 4, 2020 Welcome to the Homeschool Mama Self-Care Podcast (& Why I Homeschool) February 19, 2020 The Mistake of Multitasking in our Homeschools: 5 Tips to Be More Present September 16, 2013 Subscribe to the Homeschool Mama Self-Care podcast YouTube Apple Audible Spotify (function(m,a,i,l,e,r){ m['MailerLiteObject']=e;function f(){ var c={ a:arguments,q:[]};var r=this.push(c);return "number"!=typeof r?r:f.bind(c.q);} f.q=f.q||[];m[e]=m[e]||f.bind(f.q);m[e].q=m[e].q||f.q;r=a.createElement(i); var _=a.getElementsByTagName(i)[0];r.async=1;r.src=l+'?v'+(~~(new Date().getTime()/1000000)); _.parentNode.insertBefore(r,_);})(window, document, 'script', 'https://static.mailerlite.com/js/universal.js', 'ml'); var ml_account = ml('accounts', '1815912', 'p9n9c0c7s5', 'load'); The post 12-Day Homeschool Mom Self-Care Challenge to Come Back to Yourself appeared first on Capturing the Charmed Life.

On this episode I sit down with indie app builder and designer Chris ****Raroque to walk through his real AI coding workflow. Chris explains how he ships a portfolio of productivity apps doing thousands in MRR by pairing Claude Code and Cursor instead of picking just one tool. He live-demos “vibe coding” an iOS animation, then compares how Claude Code and Cursor's plan mode tackle the same task. The episode closes with concrete tips on plan mode, MCP servers, AI code review, dictation, and deep research so solo devs can build bigger apps than they could alone. Timestamps 00:00 – Intro 03:04 – Which Tools & Models to Use 09:16 – Thoughts on the Vibe Coding Mobile App Landscape 11:14 – Live demo: prompting Claude Code to build an iOS “AI searching” animation 18:07 – Live demo: prompting Cursor with same task 21:02 – Chris's Best Tips for Vibe Coders Key Points You don't have to pick one IDE copilot: Chris actively switches between Claude Code and Cursor because they have different strengths. For very complex bug-hunting, he prefers Cursor with plan mode; for big-picture app architecture, he leans on Claude Code with Opus. Non-developers should start on higher-level “vibe coding” platforms like Create Anything for mobile apps before graduating to Claude/Cursor. Plan mode plus detailed, spoken prompts dramatically improves code quality, especially for UI and animation work. MCP servers and AI code review bots let solo developers safely set up infra, enforce security, and catch bugs they'd otherwise miss. Claude's deep research is a powerful way to choose the right patterns and libraries before handing implementation back to Claude Code or Cursor. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: thevibemarketer.com Startup Empire - get your free builders toolkit to build cashflowing business - https://startup-ideas-pod.link/startup-empire-toolkit Become a member - https://startup-ideas-pod.link/startup-empire FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND CHRIS ON SOCIAL Youtube: https://www.youtube.com/@raroque X/Twitter: https://x.com/raroque Instagram: https://www.instagram.com/chris.raroque/

New @greenpillnet pod out today!

In this episode, Mark talks with Cynthia Passmore, who is a professor of science education at the University of California, Davis. They talk about the differences between the approach to modeling developed at ASU and UC Davis, which seem to be more and more similar as time goes by. They talk about how all of our understanding in scientific study is based on models, even if we do not specifically hold those up as "models" per se. We use mental models to explain the world around us and to better understand how and why certain interactions happen the way they do. They talk about modeling instruction and the Next Generation Science Standards and how modeling really gets students to do the thinking as scientists and make the connections between what we see and the explanations for what we see. They talk about Cynthia's new book, even get to talk about some of Cynthia's recent research on effective teaching using modeling methods in the high school biology classroom. Guest Cynthia Passmore Cynthia Passmore is currently a Professor specializing in science education in the University of California, Davis School of Education. She did her doctoral work at the University of Wisconsin, Madison and prior to that she was a high school science teacher. Her research focuses on the role of models and modeling in student learning, curriculum design and teacher professional development. She investigates model-based reasoning in a range of contexts and is particularly interested in understanding how the design of learning environments interacts with students' reasoning practices. She has been the principal investigator of several large grants and is the lead on a collaborative curriculum design project that has created a full-year high school biology course. A key practitioner publication is the edited volume: Helping Students Make Sense of the World Using Next Generation Science and Engineering Practices from NSTA Press. Highlights [2:44] Cynthia "I think the inclusion of modeling as a practice in the next generation Science standards has also brought a lot more people to the work of modeling than used to be the case." [3:25] Cynthia "Models are the functional unit of scientific thought." [7:51] Cynthia "The depiction is important. I'm not trying to say it's not, but if all we're doing is asking kids to reproduce representations and depictions of things, then we're losing the modeling practice, in my view." Resources Download Transcript Ep 75 Transcript Links Modeling  Based Biology - Living Earth

On today's Legally Speaking Podcast, I'm delighted to be joined by Darryl Cooke. Darryl is the Co-Founder and Executive Chairman of Gunnercooke LLP, an award-winning international law firm. Darryl's firm is known for redefining what it means to lead and innovate in professional services. A successful corporate lawyer, Darryl has built one of the UK's most disruptive legal firms - and is on a mission to change the legal model, putting people and purpose at the core of business.So why should you be listening in? You can hear Rob and Darryl discussing:- Creating a Revolutionary Fee-Share Model- How to Live Through Values, Not Just Words- Methods of Empowering Leadership- Making the Most of Community-Driven Impact- Designing Your Life, Your WayConnect with Darryl Cooke here - https://uk.linkedin.com/in/darryljcooke

PREVIEW Autocrats Versus Democrats: The Rise of Illiberal Ideologies Professor Michael McFaul Professor Michael McFaul discusses the growing global appeal of autocrat models, including Putinism's illiberal populist nationalism in Europe and the state-run economic model favored by China in the developing world. He notes that bureaucracy and veto points stifle growth and cause inefficiency in the U.S. Although democracy remains popular, its appeal is less potent than it was thirty years ago.