Podcasts about gpt

Have you been having fun with the newest slate of AI tools? Have you been doing research with GPT-5? Coding your projects with Claude? Turning pictures of your friends into cartoon characters from the Fairly Odd Parents using the image editing tool Nano Banana? Are you impressed with what they can do? Well guess what? You're only impressed with them because you're basically a naive child. You're like a little child with an etch a sketch who is amazed that they can make crude images by turning the knobs, oblivious to greater possibilities. At least, that's the impression you get when listening to tech leaders, philosophers, and even governments. According to them, soon the most impressive of AI tools will look as cheap and primitive as Netflix's recommendation algorithm in 2007. Soon the world will have to reckon with the power of Artificial General Intelligence, or “AGI.” What is AGI? Definitions vary. When will it come? Perhaps months. Perhaps years. Perhaps decades. But definitely soon enough for you to worry about. What will it mean for humanity once it's here? Perhaps a techno utopia. Perhaps extinction. No one is sure. But what they are sure of is that AGI is definitely coming and it's definitely going to be a big deal. A mystical event. A turning point in history, after which nothing will ever be the same. However, some are more skeptical, like our guest today Will Douglas Heaven. Will has a PhD in Computer Science from Imperial College London and is the senior editor for AI at MIT Technology review. He recently published an article, based on his conversations with AI researchers, which provocatively calls AGI “the most consequential conspiracy theory of our time.” Jake and Travis chat with Will about the conspiracy theory-like talk from the AI industry, whether AGI is just “vibes and snake oil,” and how to distinguish between tech breakthroughs and Silicon Valley hyperbole. Will Douglas Heaven https://bsky.app/profile/willdouglasheaven.bsky.social How AGI became the consequential conspiracy theory of our time https://www.technologyreview.com/2025/10/30/1127057/agi-conspiracy-theory-artifcial-general-intelligence/ Subscribe for $5 a month to get all the premium episodes: https://www.patreon.com/qaa Editing by Corey Klotz. Theme by Nick Sena. Additional music by Pontus Berghe. Theme Vocals by THEY/LIVE (https://instagram.com/theyylivve / https://sptfy.com/QrDm). Cover Art by Pedro Correa: (https://pedrocorrea.com) https://qaapodcast.com QAA was known as the QAnon Anonymous podcast. The first three episodes of Annie Kelly's new 6-part podcast miniseries “Truly Tradly Deeply” are available to Cursed Media subscribers, with new episodes released weekly. www.cursedmedia.net/ Cursed Media subscribers also get access to every episode of every QAA miniseries we produced, including Manclan by Julian Feeld and Annie Kelly, Trickle Down by Travis View, The Spectral Voyager by Jake Rockatansky and Brad Abrahams, and Perverts by Julian Feeld and Liv Agar. Plus, Cursed Media subscribers will get access to at least three new exclusive podcast miniseries every year. www.cursedmedia.net/ REFERENCES Debates on the nature of artificial general intelligence https://www.science.org/doi/10.1126/science.ado7069?utm_source=chatgpt.com Why AI Is Harder Than We Think https://arxiv.org/pdf/2104.12871 AI Capabilities May Be Overhyped on Bogus Benchmarks, Study Finds https://gizmodo.com/ai-capabilities-may-be-overhyped-on-bogus-benchmarks-study-finds-2000682577 Examining the geographic concentration of VC investment in AI https://ssti.org/blog/examining-geographic-concentration-vc-investment-ai Margaret Mitchell: artificial general intelligence is ‘just vibes and snake oil' https://www.ft.com/content/7089bff2-25fc-4a25-98bf-8828ab24f48e

Cal Newport unpacks his framework for Slow Productivity, built on three core principles: doing fewer things, working at a natural pace, and obsessing over quality. He introduces "pseudo productivity"—the toxic heuristic that emerged in mid-20th century knowledge work when visible activity became a proxy for useful effort because traditional productivity metrics (Model Ts per hour, bushels per acre) no longer applied. Newport argues that pseudo productivity was tolerable until the digital office revolution—email, Slack, mobile computing—enabled visible activity to be demonstrated at incredibly high frequency, anywhere, anytime, creating a performance theater that drains actual productive capacity. The conversation explores how to build custom AI systems for daily planning (using GPT models trained on transcripts and book notes), the three levels of working with large language models (training from scratch, fine-tuning, and software intermediaries), and why specialized vertical AI will dominate the next wave of innovation. Newport makes the case for abandoning industrial-era proxies and reclaiming knowledge work as a craft that requires depth, patience, and quality over constant performative busyness. Hosted on Acast. See acast.com/privacy for more information.

In this episode, I explore the paradox of Artificial Intelligence, not as a hero or a villain, but as a mirror. What's really rising or falling isn't AI itself—it's us, the humans holding the prompt. Together, GPT-5 and I unpack how this technology can awaken creativity or quietly erode it, depending on how consciously we engage with it. Make Sense? What you're about to hear is not just a conversation between man and machine. It's a collaboration between awareness and automation, a real-time experiment in creation itself. Because here's the question that keeps me up at night: What happens when the tools we built to extend human intelligence start to replace the act of thinking itself? This episode isn't about whether AI is good or bad. It's about whether we, as humans, will rise with it—or fall asleep beneath it. So, take a breath. Let's make sense… of the Rise and Fall of AI. Follow Dr. JC Doornick and the Makes Sense Academy: ► Makes Sense Substack - https://drjcdoornick.substack.com ► Instagram: / drjcdoornick ►Facebook:  / makessensepodcast ►YouTube:  / drjcdoornick MAKES SENSE PODCAST Welcome to the Makes Sense with Dr. JC Doornick Podcast. This podcast explores topics that expand human consciousness and enhance performance. On the Makes Sense Podcast, we acknowledge that it's who you are that determines how well what you do works, and that perception is a subjective and acquired taste. When you change the way you look at things, the things you look at begin to change. Welcome to the uprising of the sleepwalking masses. Welcome to the Makes Sense with Dr. JC Doornick Podcast. SUBSCRIBE/RATE/REVIEW & SHARE our new podcast. FOLLOW Podcast - You will find a "Follow" button on the top right. This will enable the podcast software to alert you when a new episode launches each week. Apple: https://podcasts.apple.com/ca/podcast/makes-sense-with-dr-jc-doornick/id1730954168 Spotify: https://open.spotify.com/show/1WHfKWDDReMtrGFz4kkZs9?si=003780ca147c4aec Podcast Affiliates: Kwik Learning: Many people ask me where I get all these topics, which I've been covering for almost 15 years. I have learned to read nearly four times faster and retain information 10 times better with Kwik Learning. Learn how to learn and earn with Jim Kwik. Get his program at a special discount here: https://jimkwik.com/dragon OUR SPONSORS: Makes Sense Academy: A private mastermind and psychologically safe environment full of the Mindset and Action steps that will help you begin to thrive. The Makes Sense Academy. https://www.skool.com/makes-sense-academy/about The Sati Experience: A retreat designed for the married couple that truly loves one another, yet wants to take their love to that higher magical level. Relax, reestablish, and renew your love at the Sati Experience. https://www.satiexperience.com Highlights: 0:00 - Intro 4:15 - In this episode, we will…. 8:37 - Opening Remarks from ChatGPT 16:44 - The Rise Of AI 22:21 - AI as a Luxury to Necessity 25:32 - Collaboration 27:35 - The Fall of Ai? 31:48 - The Learning Dilemma  37:03 - The Future Role of Humans? 40:12 - The Final Verdict from Chat GPT Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI is starting to take more human jobs while OpenAI and Sam Altman are thinking about GPT-6 and new science. Meanwhile, the rest of OpenAI is thinking about how they'll pay for GPT-6. Plus, Google is sending AI chips into space, Gemini will power Apple Intelligence, Kimi K2 Thinking is a great open-source AI model and the backlash to Coke's new AI ad.    Get notified when AndThen launches: https://andthen.chat/ Come to our Discord to try our Secret Project: https://discord.gg/muD2TYgC8f Join our Patreon: https://www.patreon.com/AIForHumansShow AI For Humans Newsletter: https://aiforhumans.beehiiv.com/ Follow us for more on X @AIForHumansShow Join our TikTok @aiforhumansshow To book us for speaking, please visit our website: https://www.aiforhumans.show/   // Show Links // AI JOBPOCALYSE 2: Most Job Cuts in October in 20 Years https://x.com/atrupar/status/1986417464985473433  But Maybe GPT-6 Will Do New Science https://x.com/slow_developer/status/1986151529729171794 Tyler Cowen / Sam Altman Interview https://youtu.be/cuSDy0Rmdks?si=668An6TuxpyZ3Va- OpenAI CFO Wants a "Federal Backstop" For Chips & Compute https://finance.yahoo.com/video/openai-wants-federal-backstop-investments-201700279.html Sam Altman Statement  https://x.com/sama/status/1986514377470845007 Jensen Huang "China Will Win The AI Race" https://www.axios.com/2025/11/05/ai-nvidia-china-race Google's Project Suncatcher https://research.google/blog/exploring-a-space-based-scalable-ai-infrastructure-system-design/ https://x.com/sundarpichai/status/1985754323813605423 Google Gemini Will Power Siri For $1 Billion a Year https://x.com/markgurman/status/1986150242698637591 Gemini Gets Into Google Maps https://blog.google/products/maps/gemini-navigation-features-landmark-lens/ Kimi K2 Thinking is Here https://x.com/Ki mi_Moonshot/status/1986449512538513505 New llama cpp released https://github.com/ggml-org/llama.cpp?utm_source=www.theneurondaily.com&utm_medium=newsletter&utm_campaign=new-siri-powered-by-google&_bhlid=9b7649bbd3e562023b37f7a61d882918b5941de4 Guide to setting it up https://github.com/ggml-org/llama.cpp/discussions/16938?utm_source=www.theneurondaily.com&utm_medium=newsletter&utm_campaign=new-siri-powered-by-google&_bhlid=bad1906bd3fa16c54272fe0c6263e1421ff11495 Grieving Family Uses Claude To Cut Hospital Bill From $190k down to 33K https://www.tomshardware.com/tech-industry/artificial-intelligence/grieving-family-uses-ai-chatbot-to-cut-hospital-bill-from-usd195-000-to-usd33-000-family-says-claude-highlighted-duplicative-charges-improper-coding-and-other-violations New Coke AI Holiday Trailer https://x.com/DiscussingFilm/status/1985470088074375344 Apple TV Practical Ad https://www.threads.com/@dreasstorm/post/DQt06lPgn_y?xmt=AQF0XhtvJZVYz1J8NYgRrMFZ_axYxO7bkE-6V3sRxDwH8JQ7oK-4Gsp766J67MOXU-MnaEk&slof=1 Sandbar AI Ring  https://x.com/sandbar/status/1986112726889078911 PewDiePie Made A Giant Local, Free AI Model Cluster Using Qwen https://youtu.be/qw4fDU18RcU?si=QXyFbNjFy_5vQTMo Cool Use Of Image Models To Create Interactive Face on Personal Website https://x.com/kylancodes/status/1980528164079300964 Christmas Fatality Finishers https://www.reddit.com/r/SoraAi/comments/1op8rqh/christmas_fatality_finishers/ ?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button Gavin's Thanksgiving Example (prompt on Reddit):  https://x.com/gavinpurcell/status/1986224435192602987  

In this episode of Crazy Wisdom, host Stewart Alsop sits down with Harry McKay Roper, founder of Imaginary Space, for a wide-ranging conversation on space mining, AI-driven software, crypto's incorruptible potential, and the raw entrepreneurial energy coming out of Argentina. They explore how technologies like Anthropic's Claude 4.5, programmable crypto protocols, and autonomous agents are reshaping economics, coding, and even law. Harry also shares his experiences building in Buenos Aires and why hunger and resilience define the city's creative spirit. You can find Harry online at YouTube, Twitter, or Instagram under @HarryMcKayRoper.Check out this GPT we trained on the conversationTimestamps00:00 – Stewart Alsop welcomes Harry McKay Roper from Imaginary Space and they jump straight into space mining, Helium-3, and asteroid gold. 05:00 – They explore how Bitcoin could hold value when space mining floods markets and discuss China, America, and global geopolitics. 10:00 – Conversation shifts to Argentina, its economic scars, cultural resilience, and overrepresentation in startups and crypto. 15:00 – Harry reflects on living in Buenos Aires, poverty, and the city's constant hustle and creative movement. 20:00 – The focus turns to AI, Claude 4.5, and the rise of autonomous droids and software-building agents. 25:00 – They discuss the collapse of SaaS, internal tools, and Harry's experiments with AI-generated code and new workflows. 30:00 – Stewart compares China's industry to America's software economy, and Harry points to AI, crypto, and space as frontier markets. 35:00 – Talk moves to crypto regulation, uncorruptible judges, and blockchain systems like Kleros. 40:00 – They debate AI consciousness, embodiment, and whether a robot could meditate. 45:00 – The episode closes with thoughts on free will, universal verifiers, and a playful prediction market bet on autonomous software.Key InsightsSpace and Economics Are Colliding – Harry McKay Roper opens with the idea that space mining will fundamentally reshape Earth's economy. The discovery of asteroids rich in gold and other minerals highlights how our notions of scarcity could collapse once space resources become accessible, potentially destroying the terrestrial gold economy and forcing humanity to redefine value itself.Bitcoin as the New Standard of Value – The conversation naturally ties this to Bitcoin's finite nature. Stewart Alsop and Harry discuss how the flood of extraterrestrial gold could render traditional stores of value meaningless, while Bitcoin's coded scarcity could make it the only incorruptible measure of worth in a future of infinite resources.China and the U.S. in Industrial Tug-of-War – They unpack the geopolitical tension between China's industrial dominance and America's financial hegemony. Harry argues the U.S. is waking up from decades of outsourcing, driven by China's speed in robotics and infrastructure. This dynamic competition, he says, is good—it forces America to build again.Argentina's Culture of Hunger and Resilience – Living in Buenos Aires reshaped Harry's understanding of ambition. He contrasts Argentina's hunger to survive and create with the complacency of wealthier nations, calling the Argentine spirit one of “movement.” Despite poverty, the city's creative drive and humor make it a living example of resilience in scarcity.AI Is Making Custom Software Instant – Harry describes how Claude 4.5 and new AI coding tools like Lovable, Cursor, and GPT Engineer make building internal tools trivial. Instead of using SaaS products, companies can now generate bespoke software in minutes with natural language, signaling the end of traditional software development cycles.Crypto and AI Will Merge Into Incorruptible Systems – Harry envisions AI agents on-chain acting as unbiased judges or administrators, removing human corruption from law and governance. Real-world tools like Kleros, founded by an Argentine, already hint at this coming era of algorithmic justice and decentralized decision-making.Consciousness and the Limits of AI – The episode closes on a philosophical note: can a robot meditate or clear its mind? Stewart and Harry question whether AI could ever experience consciousness or free will, suggesting that while AI may mimic thought, the uniquely subjective and embodied nature of human awareness remains beyond automation—for now.

Hey, Alex here! Quick note, while preparing for this week, I posted on X that I don't remember such a quiet week in AI since I started doing ThursdAI regularly, but then 45 min before the show started, Kimi dropped a SOTA oss reasoning model, turning a quiet week into an absolute banger. Besides Kimi, we covered the updated MCP thinking from Anthropic, and had Kenton Varda from cloudflare as a guest to talk about Code Mode, chatted about Windsurf and Cursor latest updates and covered OpenAI's insane deals. Also, because it was a quiet week, I figured I'd use the opportunity to create an AI powered automation, and used N8N for that, and shared it on the stream, so if you're interested in automating with AI with relatively low code, this episode is for you. Let's dive inThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Kimi K2 Thinking is Here and It's a 1 Trillion Parameter Beast! (X, HF, Tech Blog)Let's start with the news that got everyone's energy levels skyrocketing right as we went live. Moonshot AI dropped Kimi K2 Thinking, an open-source, 1 trillion-parameter Mixture-of-Experts (MoE) model, and it's an absolute monster.This isn't just a numbers game; Kimi K2 Thinking is designed from the ground up to be a powerful agent. With just around 32 billion active parameters during inference, a massive 256,000 token context window, and an insane tool-calling capacity. They're claiming it can handle 200-300 sequential tool calls without any human intervention. The benchmarks are just as wild. On the Humanities Last Exam (HLE), they're reporting a score of 44.9%, beating out both GPT-5 and Claude 4.5 Thinking. While it doesn't quite top the charts on SWE-bench verified, it's holding its own against the biggest closed-source models out there. Seeing an open-source model compete at this level is incredibly exciting.During the show, we saw some truly mind-blowing demos, from a beautiful interactive visualization of gradient descent to a simulation of a virus attacking cells, all generated by the model. The model's reasoning traces, which are exposed through the API, also seem qualitatively different from other models, showing a deep and thoughtful process. My co-hosts and I were blown away. The weights and a very detailed technical report are available on Hugging Face, so you can dive in and see for yourself. Shout out to the entire Moonshot AI team for this incredible release!Other open source updates from this week* HuggingFace released an open source “Smol Training Playbook” on training LLMs, it's a 200+ interactive beast with visualizations, deep dives into pretraining, dataset, postraining and more! (HF)* Ai2 launches OlmoEarth — foundation models + open, end-to-end platform for fast, high-resolution Earth intelligence (X, Blog)* LongCat-Flash-Omni — open-source omni-modal system with millisecond E2E spoken interaction, 128K context and a 560B ScMoE backbone (X, HF, Announcement)Big Tech's Big Moves: Apple, Amazon, and OpenAIThe big companies were making waves this week, starting with a blockbuster deal that might finally make Siri smart. Apple is reportedly will be paying Google around $1 billion per year to license a custom 1.2 trillion-parameter version of Gemini to power a revamped Siri.This is a massive move. The Gemini model will run on Apple's Private Cloud Compute, keeping user data walled off from Google, and will handle Siri's complex summarizer and planner functions. After years of waiting for Apple to make a significant move in GenAI, it seems they're outsourcing the heavy lifting for now while they work to catch up with their own in-house models. As a user, I don't really care who builds the model, as long as Siri stops being dumb!In more dramatic news, Perplexity revealed that Amazon sent them a legal threat to block their Comet AI assistant from shopping on Amazon.com. This infuriated me. My browser is my browser, and I should be able to use whatever tools I want to interact with the web. Perplexity took a strong stand with their blog post, “Bullying is Not Innovation,” arguing that user agents are distinct from scrapers and act on behalf of the user with their own credentials. An AI assistant is just that—an assistant. It shouldn't matter if I ask my wife or my AI to buy something for me on Amazon. This feels like a move by Amazon to protect its ad revenue at the expense of user choice and innovation, and I have to give major props to Perplexity for being so transparent and fighting back.Finally, OpenAI continues its quest for infinite compute, announcing a multi-year strategic partnership with AWS. This comes on top of massive deals with NVIDIA, Microsoft, Oracle, and others, bringing their total commitment to compute into the trillions of dollars. It's getting to a point where OpenAI seems “too big to fail,” as any hiccup could have serious repercussions for the entire tech economy, which is now heavily propped up by AI investment. Sam has clarified that they don't think OpenAI wants to be too big to fail in a recent post on X, and that the recent miscommunications around the US government backstopping OpenAI's infrastructure bailouts were taken out of context.

Show NotesIn this episode, Johan and Jonas open the AI toolbox and reveal how next-gen marketing teams are building custom GPTs, Gemini-powered copilots, and using Veo for video automation.It's not about experimenting anymore,it's about implementing AI at scale. Learn how the most forward-thinking ecommerce teams use foundation models to optimize creative, analytics, and media execution.What You'll Learn:How to use OpenAI's GPT and Google's Gemini to build custom marketing agentsThe role of Vertex AI and Google Cloud in scalable agent deploymentsReal-world use cases: from feed optimization to automated insightsWhat Veo, Google's generative video AI, means for future content creationWhy marketing teams are moving from ChatGPT prompts to full AI workflowsShow LinksConnect with us: Jonas LinkedinJohan's LinkedinEmail us: Jonas@keywordio.comJohanlidner@keywordio.comGet a quote from Keywordio 

In this week's episode of the Rich Habits Podcast, Robert Croak and Austin Hankwitz answer your questions!---

La Unión Europea ha publicado las guías definitivas para los foundation models, exigiendo marcas de agua en todo lo generado, fichas de transparencia con datos de entrenamiento y tests de riesgo cada tres meses, con sanciones que pueden llegar al 6% de la facturación global o 30 millones de euros. En paralelo, OpenAI presenta GPT-5.5, “Evermore”, un modelo que recuerda entre sesiones, maneja millones de tokens de contexto y añade edición de video y audio desde texto, con opción on-premise y un coste cercano a 0,10 USD por cada mil tokens. Nvidia, por su parte, estrena la GPU Blackwell B400 con enfriamiento láser, 550 teraflops y menor consumo, a 32.000 euros por unidad.Google lanza Gemini AR, una IA que fusiona visión por ordenador y realidad aumentada para superponer información en tiempo real con reconocimiento de objetos y traducción simultánea de latencia inferior a 10 ms. España destina 250 millones de euros a pymes para IA, con subvenciones para hardware, créditos blandos y formación gratuita. ¿Qué impacto tendrán estas novedades en tus proyectos de marketing, costos y seguridad? Este episodio deja la curiosidad sobre hacia dónde nos llevan estas tecnologías en el día a día de los negocios.Conviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/noticias-marketing--5762806/support.Newsletter Marketing Radical: https://marketingradical.substack.com/welcomeNewsletter Negocios con IA: https://negociosconia.substack.com/welcomeMis Libros: https://borjagiron.com/librosSysteme Gratis: https://borjagiron.com/systemeSysteme 30% dto: https://borjagiron.com/systeme30Manychat Gratis: https://borjagiron.com/manychatMetricool 30 días Gratis Plan Premium (Usa cupón BORJA30): https://borjagiron.com/metricoolNoticias Redes Sociales: https://redessocialeshoy.comNoticias IA: https://inteligenciaartificialhoy.comClub: https://triunfers.com

Register for the Austin listener meetup Sam Altman makes his second appearance on the show to discuss how he's managing OpenAI's explosive growth, what he's learned about hiring hardware people, what makes roon special, how far they are from an AI-driven replacement to Slack, what GPT-6 might enable for scientific research, when we'll see entire divisions of companies run mostly by AI, what he looks for in hires to gauge their AI-resistance, how OpenAI is thinking about commerce, whether GPT-6 will write great poetry, why energy is the binding constraint to chip-building and where it'll come from, his updated plan for how he'd revitalize St. Louis, why he's not worried about teaching normies to use AI, what will happen to the price of healthcare and hosing, his evolving views on freedom of expression, why accidental AI persuasion worries him more than intentional takeover, the question he posed to the Dalai Lama about superintelligence, and more. Recorded live at the Progress Conference, hosted by the Roots of Progress Institute. Special thanks to Big Think for the video production. Read a full transcript enhanced with helpful links, or watch the full video on the new dedicated Conversations with Tyler channel. Recorded October 17th, 2025. Other ways to connect Follow us on X and Instagram Follow Tyler on X Follow Sam on X Sign up for our newsletter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here. Photo Credit: Jeremi Rebecca

If content is the raw material of generative AI, it only makes sense that an AI-driven contract automation platform would want to acquire the world's largest database of contracts and clauses. That is exactly what happened recently when SimpleDocs, a company with an AI contract drafting, redlining and review platform, acquired Law Insider, which claims to be home to 5 million contracts and 20 million clauses spanning more than 50 languages. One aspect of this acquisition that makes it particularly interesting is that both companies were founded by the same person – and that person, Preston Clark, is our guest today. In that sense, you might say this isn't a typical acquisition story, but more the deliberate convergence of two complementary businesses that were built separately over more than a decade, each with its own DNA, but always with an eye toward this eventual combination. In an AI market increasingly criticized for being "just GPT wrappers," Clark and his team are betting that workflow-specific tools powered by real contract data will deliver the precision and ROI that legal departments and law firms are demanding.  In our conversation, Clark walks us through the strategic thinking behind this acquisition and how this combined entity plans to differentiate itself in an increasingly crowded legal AI market. He also shares his vision for the future – one that extends beyond contract drafting and review into adjacent workflows that could reshape how legal teams interact with contracts altogether.    Thank You To Our Sponsors This episode of LawNext is generously made possible by our sponsors. We appreciate their support and hope you will check them out. Paradigm, home to the practice management platforms PracticePanther, Bill4Time, MerusCase and LollyLaw; the e-payments platform Headnote; and the legal accounting software TrustBooks. Briefpoint, eliminating routine discovery response and request drafting tasks so you can focus on drafting what matters (or just make it home for dinner). Eve, taking care of the tasks that slow you down so you can operate at your highest potential   If you enjoy listening to LawNext, please leave us a review wherever you listen to podcasts.  

- 11/05/25Chuck Talks About The News!Dumping pumpkins, Halloween contests, tattoo removal, GPT lawyer And Much More On This Episode Of Casually Uncomfortable, News Edition!Call the showhttps://www.speakpipe.com/CasuallyUncomfortable

EP 265 Ahoy Matey! In this week's update:A Rivian owner in Colorado turns the tables on police with dashcam evidence, exposing the dangers of overreliance on automated surveillance.In a rare lighthearted moment, President Xi Jinping jokes about backdoors while gifting Xiaomi phones to South Korea's leader amid tense U.S.-China trade talks.Oslo's transit authority disables internet on 850 Chinese electric buses after discovering hidden remote shutdown capabilities.OpenAI's Atlas browser promises smarter browsing but raises alarms that users are the product, feeding vast new datasets to AI training models.Amazon fires a legal warning shot at Perplexity, accusing its AI shopping agent of fraud for making undisclosed purchases on its platform.AI browsers quietly defeat media paywalls by reading hidden content, threatening publisher revenue and reshaping online access.OpenAI's Aardvark, a GPT-5-powered security agent, autonomously detects, validates, and patches software vulnerabilities in real time.Microsoft Edge and Google Chrome now use on-device AI to block scareware scams, protecting less tech-savvy users from fraudulent pop-ups.GitHub predicts AI agents will write over 30% of code by 2026, with India poised to surpass the U.S. as the top contributor nation.​Let's cast off!Find the full transcript to this week's podcast here.

I denne episoden forteller Mediumtall-gründer Kristoffer Liabø om hvordan kunstig intelligens har bidratt til å redde og videreutvikle selskapet hans. Etter en krevende periode med omstrukturering og nedbemanning valgte selskapet å satse på AI-verktøy som ChatGPT for å effektivisere diverse prosesser. Programleder: Mia Sandnes Nilsen, journalist i Shifter. Dette snakker vi om: Hvordan Mediumtall tok i bruk AI for å erstatte tapte roller og øke lønnsomheten. Bruken av spesialtilpassede GPT-modeller. De tanker om muligheter og problemstillinger ved å erstatte mennesker med AI.Om gjesten: Kristoffer Liabø er daglig leder og gründer av selskapet bak klesmerket Mediumtall, som lager klær spesielt tilpasset høye personer. Husk å:– Kjøpe abonnement på Shifter.no for å støtte uavhengig journalistikk om tech og innovasjon (https://www.shifter.no/).– Sjekke ut våre kommende events. Meld deg på her! (https://nyhetsbrev.shifter.no/p/s/MjQ4MzI6OWQxMjBlNGEtZGJlOC00OGNmLWFkNjktODdmOWFlMzFiMzFk)– Melde deg på vårt nyhetsbrev og hold deg oppdatert på det viktigste innen startup-verdenen (https://www.shifter.no/nyhetsbrev) Hosted on Acast. See for more information Hosted on Acast. See acast.com/privacy for more information.

OpenAI: reportedly losing $12 billion a quarter.

Axios CRO Jacquelyn Cameron breaks down how Axios thinks about audience segmentation in an age when every publisher says they're “audience-focused.”Jacquelyn explains why Axios organizes around five core personas — influencers, C-suite executives, dealmakers and investors, communicators, and smart local professionals — and how each connects to a product, event, or revenue line. We get into how “no opinion” coverage is a competitive advantage in Washington, how local works when it's “localized national” rather than pizza-parlor advertising, and how Axios uses AI to turn audience insight into business intelligence.We also talk about the new entanglement between business and government, why Axios treats live events as “the physical manifestation of the newsroom,” and how its custom GPT gives the sales team daily prospecting hit lists. As Jacquelyn put it, “I want every human to become superhuman.”

Let's talk about monetization with AI—without the overwhelm, the jargon, or pretending you're suddenly a coder.In this episode we break down three clear paths to monetizing with AI that don't require you to build a tool, start a tech company, or even understand how ChatGPT works behind the curtain.You'll discover how to:Save hours (and reclaim your evenings) by using AI to streamline the behind-the-scenes workEnhance your client delivery so every touchpoint feels high-touch—without adding more calls to your calendarProductize your expertise into AI-powered experiences that sell while you sleepYou'll hear real-world examples, including how I turned my proprietary frameworks into tools like Brenda (the BrandCalibrator™) and Valerie (the Visibility Auditor)—and how these tools create scalable, on-brand experiences that feel personal and profitable.Plus, I'll walk you through the #1 mistake most creators make when building GPTs and how to protect your intellectual property before you put your methods into AI.This episode will spark ideas for how you can work less, earn more, and serve better using AI in your business—no tech degree required.We'll Unpack:The 3 monetization paths with AI: Operational Efficiency, Enhanced Client Delivery, ProductizationHow to measure ROI from time saved (and why that's just as powerful as new income)A behind-the-scenes look at Kelly's “onboarding assistant” GPT that cut her process from 20 hours to 2Why productizing your knowledge through AI tools can expand reach and protect your IPThe mindset shift from “AI is replacing us” to “AI helps us human better”>>Your Next Steps: Get the workshop replay for $1 with coupon code: Podcast. Train AI to sound like you with BrandCalibrator™Done for you visibility plan by Valerie the Visibility Auditor Let's work together: https://ksco.ca/ Get visible without social media Connect on Instagram>>Thanks for Listening!If you enjoyed this episode, please help us share it by: Following the show—this helps you stay updated and supports us! Leaving a positive review—this boosts our ranking and helps more entrepreneurs find the podcast. Sharing it on Instagram and tagging @entrepreneurschoolpodcast

This week on "It's The Bottom Line that Matters," cohosts Jennifer Glass, Daniel McCraine, and Patricia Reszetylo reveal how AI can become your smartest business partner—if you stop treating it like just a content mill. Discover what really happens when you bring AI into your strategic process, not just your to-do list:Why “garbage in, garbage out” matters more than ever when prompting AI, and how to feed it context that gets better, actionable resultsPatricia's breakthrough approach: turning AI into a full advisory board, with structured projects and roles customized for your business strategyThe real pitfalls of taking AI answers at face value (including hallucinated stats and copyright traps) – and how to keep control as the human in the driver's seatYou'll learn how to get much more than surface-level content from tools like ChatGPT, Gemini, and Perplexity—and instead, unlock game-changing insights, sharper decision-making, and real growth opportunities. Unlike the usual advice, this episode is packed with personal stories, battle-tested workflows, and practical reminders that the ultimate goal is freedom, not just busyness.If you want more freedom and fewer 14-hour days, subscribe and come with us on this smarter path forward.Speaker Bios:Jennifer GlassJennifer Glass brings dynamic energy and clarity as a host of "It's The Bottom Line that Matters." With a knack for steering insightful conversations, she's passionate about helping business owners navigate the evolving landscape of AI. Jennifer blends her personal experiences—like tales from accounting class and real-world encounters with AI—into practical advice. She's an advocate for critical thinking, urging listeners not to blindly trust technology, but instead to use it as a strategic partner. Her warm, testimonial style highlights the talents of fellow hosts and encourages others to seek help when refining their AI skills.Patricia ReszetyloPatricia Reszetylo stands out as the resident AI strategist on the podcast, always eager to share discoveries and practical methods for harnessing artificial intelligence in business. Starting with simple brainstorming on a real estate project, Patricia evolved her approach, eventually customizing AI to act as a CEO advisor, streamlining workflows, and thoughtfully organizing information. Her experience spans content creation, advisory roles, and meticulous project management. Patricia's journey is marked by curiosity and a willingness to dig deep into tools, learn from communities, and set up robust systems—making her the go-to person for maximizing AI's potential as a business ally.Daniel McCraineDaniel McCraine enters each episode with honesty and humor, openly sharing his learning curve in social media and AI usage. He's the voice for business owners finding their way with new tech, candidly discussing human bottlenecks and his ongoing quest to improve processes. Daniel values mentorship and expert insight—leaning on Patricia's expertise and sharing wisdom from an AI-focused business mentor. His practical approach to AI revolves around strategic brainstorming, project management, and clear communication, always conscious of keeping the “human in the equation” as technology evolves in business.Keywords: AI strategic partner, artificial intelligence, content creation, ChatGPT, prompt engineering, advisory board, Google Drive organization, custom GPT, business brainstorming, projects function, project management with AI, CEO advisor, marketing email, sales copywriting, avatar definition, social media posting, human bottleneck, lean canvas, business plan, financial plan, image generation with AI, trademark and copyright, market research, context in AI prompts, critical thinking with AI, misuse of AI statistics, commercial use of AI images, collaboration with AI, role definition in prompts, online learning communities, best practices for AI output

In this heart-centered episode of The Becky Beach Show, Becky chats with branding expert, poet, and business mentor Amber Lilyestrom about what it really means to stay authentic in a world where AI is shaping content creation and digital communication. Learn more about Amber and get a free GPT and workbook to launch your digital product over at the show notes at BeckyBeachShow.com now. Together, they explore:How to use AI tools like ChatGPT and Canva without losing your unique voiceWhy personal storytelling still reigns in email marketing and brand buildingThe real reason productivity isn't always the goal — and what matters moreHow to build a business that supports your values, family, and freedomThe power of poetry, presence, and “right-sized” success in entrepreneurshipAmber also shares a moving poem from her new book Little Big Beautiful Things, offering a powerful reminder of the richness of life beyond spreadsheets and scale.If you're a digital creator or coach navigating content overwhelm, tech tools, or the pressure to “do more,” this episode will help you reconnect with your purpose, your people, and your power.

In this episode of Crazy Wisdom, host Stewart Alsop sits down with Cryptogaucho to explore the intersection of artificial intelligence, crypto, and Argentina's emerging role as a new frontier for innovation and governance. The conversation ranges from OpenAI's partnership with Sur Energy and the Stargate project to Argentina's RIGI investment framework, Milei's libertarian reforms, and the potential of space-based data centers and new jurisdictions beyond Earth. Cryptogaucho also reflects on Argentina's tech renaissance, its culture of resilience born from hyperinflation, and the rise of experimental communities like Prospera and Noma Collective. Follow him on X at @CryptoGaucho.Check out this GPT we trained on the conversationTimestamps00:00 – Stewart Alsop opens with Cryptogaucho from Mendoza, talking about Argentina, AI, crypto, and the energy around new projects like Sur Energy and Satellogic.05:00 – They dive into Argentina's growing space ambitions, spaceport plans, and how jurisdiction could extend “upward” through satellites and data sovereignty.10:00 – The talk shifts to global regulation, bureaucracy, and why Argentina's uncertainty may become its strength amid red tape in the US and China.15:00 – Discussion of OpenAI's Stargate project, AI infrastructure in Patagonia, and the geopolitical tension between state and private innovation.20:00 – Cryptogaucho explains the “cepo” currency controls, the black market for dollars, and crypto's role in preserving economic freedom.25:00 – They unpack RIGI investment incentives, Argentina's new economic rules, and efforts to attract major projects like data centers and nuclear reactors.30:00 – Stewart connects hyperinflation to resilience and abundance in the AI era, while Cryptogaucho reflects on chaos, adaptability, and optimism.35:00 – The conversation turns philosophical: nation-states, community networks, Prospera, and the rise of new governance models.40:00 – They explore Argentina's global position, soft power, and its role as a frontier of Western ideals.45:00 – Final reflections on AI in space, data centers beyond Earth, and freedom of information as humanity's next jurisdiction.Key InsightsArgentina as a new technological frontier: The episode positions Argentina as a nation uniquely situated between chaos and opportunity—a place where political uncertainty and flexible regulation create fertile ground for experimentation. Stewart Alsop and Cryptogaucho argue that this openness, combined with a culture forged in crisis, allows Argentina to become a testing ground for new models of governance, technology, and sovereignty.The convergence of AI, energy, and geography: OpenAI's deal with Sur Energy and plans for a data center in Patagonia signal how Argentina's geography and resources are becoming integral to the global AI infrastructure. Cryptogaucho highlights the symbolic and strategic power of Argentina serving as a “southern node” for the intelligence economy.Economic reinvention through RIGI: The RIGI framework offers tax and regulatory advantages to major investors, marking a turning point in Argentina's attempt to attract stable, high-value industries such as server farms, mining, and biotech. It represents a pragmatic balance between libertarian reform and national development.Crypto and currency freedom: Cryptogaucho recounts how Argentina's crypto community arose from necessity during hyperinflation and currency controls. Bitcoin and stablecoins became lifelines for developers and entrepreneurs locked out of traditional banking systems, teaching the world about decentralized resilience.AI abundance and human adaptation: The discussion draws parallels between hyperinflation's unpredictability and the overwhelming speed of AI progress. Stewart suggests that Argentina's social adaptability, born from scarcity and instability, may prepare its citizens for a future defined by abundance and rapid technological flux.Network states and new governance: The conversation explores Prospera, Noma Collective, and the idea of city-scale governance networks. These experiments, blending blockchain, law, and community, are seen as prototypes for post-nation-state organization—where trust and culture matter more than geography.Space as the next jurisdiction: The episode ends with an exploration of space as a new legal and economic domain. Satellites, data centers, and orbital communication networks could redefine sovereignty, creating “data islands” beyond Earth where information flows freely under new kinds of governance—a vision of humanity's next frontier.

Discover how educators are using generative AI not to automate, but to elevate critical thinking and collaboration in K-12 schools. In this episode of Shifting Schools, host Tricia Friedman shows how "disagreement by design" and intentional prompt-engineering transform student and leadership learning. What you'll learn: What disagreement by design looks like in real classrooms and leadership teams How prompt engineering unlocks student curiosity and systems-thinking mindset in K-12 Why writing bespoke GPT bots might just be the 'new essay' of our times Who this episode is for: Any educator, school leader or district-innovator exploring how to responsibly integrate companion AI, AI avatars and prompt-driven dialogue into a learning ecosystem.

This is the latest in a series of essays on AI Scaling. You can find the others on my site. Summary: RL-training for LLMs scales surprisingly poorly. Most of its gains are from allowing LLMs to productively use longer chains of thought, allowing them to think longer about a problem. There is some improvement for a fixed length of answer, but not enough to drive AI progress. Given the scaling up of pre-training compute also stalled, we'll see less AI progress via compute scaling than you might have thought, and more of it will come from inference scaling (which has different effects on the world). That lengthens timelines and affects strategies for AI governance and safety. The current era of improving AI capabilities using reinforcement learning (from verifiable rewards) involves two key types of scaling: Scaling the amount of compute used for RL during training Scaling [...] ---Outline:(09:12) How do these compare to pre-training scaling?(13:42) Conclusion --- First published: October 22nd, 2025 Source: https://forum.effectivealtruism.org/posts/TysuCdgwDnQjH3LyY/how-well-does-rl-scale --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

The Interview That Sparked This EssayJoe Corkery and I worked together at Google years ago, and he has since gone on to build a venture-backed company tackling a real and systemic problem in healthcare communication. This essay is my attempt to synthesize that conversation. It is written for early and mid career PMs in Silicon Valley who want to get sharper at product judgment, market discovery, customer validation, and knowing the difference between encouragement and signal. If you feel like you have ever shipped something, presented it to customers, and then heard polite nodding instead of movement and urgency, this is for you.Joe's Unusual Career ArcJoe's background is not typical for a founder. He is a software engineer. And a physician. And someone who has led business development in the pharmaceutical industry. That multidisciplinary profile allowed him to see something that many insiders miss: healthcare is full of problems that everyone acknowledges, yet very few organizations are structurally capable of solving.When Joe joined Google Cloud in 2014, he helped start the healthcare and life sciences product org. Yet the timing was difficult. As he put it:“The world wasn't ready or Google wasn't ready to do healthcare.” So instead of building healthcare products right away, he spent two years working on security, compliance, and privacy. That detour will matter later, because it set the foundation for everything he is now doing at Jaide.Years later, he left Google to build a healthcare company focused initially on guided healthcare search, particularly for women's health. The idea resonated emotionally. Every customer interview validated the need. Investors said it was important. Healthcare organizations nodded enthusiastically.And yet, there was no traction.This created a familiar and emotionally challenging founder dilemma:* When everyone is encouraging you* But no one will pay you or adopt early* How do you know if you are early, unlucky, or wrong?This is the question at the heart of product strategy.False Positives: Why Encouragement Is Not FeedbackIf you have worked as a PM or founder for more than a few weeks, you have encountered positive feedback that turned out to be meaningless. People love your idea. Executives praise your clarity. Customers tell you they would definitely use it. Friends offer supportive high-fives.But then nothing moves.As Joe put it:“Everyone wanted to be supportive. But that makes it hard to know whether you're actually on the right path.” This is not because people are dishonest. It is because people are kind, polite, and socially conditioned to encourage enthusiasm. In Silicon Valley especially, we celebrate ambition. We praise risk-taking. We cheer for the founder-in-the-garage mythology. If someone tells you that your idea is flawed, they fear they are crushing your passion.So even when we explicitly ask for brutal honesty, people soften their answers.This is the false positive trap.And if you misread encouragement as traction, you can waste months or even years.The Small Framing Change That Changes EverythingJoe eventually realized that the problem was not the idea itself. The problem was how he was asking for feedback.When you present your idea as the idea, people naturally react supportively:* “That's really interesting.”* “I could see that being useful.”* “This is definitely needed.”But when you instead present two competing ideas and ask someone to help you choose, you change the psychology of the conversation entirely.Joe explained it this way:“When we said, ‘We are building this. What do you think?' people wanted to be encouraging. But when we asked, ‘We are choosing between these two products. Which one should we build?' it gave them permission to actually critique.” This shift is subtle, but powerful. Suddenly:* People contrast.* Their reasoning surfaces.* Their hesitation becomes visible.* Their priorities emerge with clarity.By asking someone to choose between two ideas, you activate their decision-making brain instead of their supportive brain.It is no different from usability testing. If you show someone a screen and ask what they think, they are polite. If you give them a task and ask them to complete it, their actual friction appears immediately.In product discovery, friction is truth.How This Applies to PMs, Not Just FoundersYou may be thinking: this is interesting for entrepreneurs, but I work inside a company. I have stakeholders, OKRs, a roadmap, and a backlog that already feels too full.This technique is actually more relevant for PMs inside companies than for founders.Inside organizations, political encouragement is even more pervasive:* Leaders say they want innovation, but are risk averse.* Cross-functional partners smile in meetings, but quietly maintain objections.* Engineers nod when you present the roadmap, but may not believe in it.* Customers say they like your idea, but do not prioritize adoption.One of the most powerful tools you can use as a PM is explicitly framing your product decisions as explicit choices, rather than proposals seeking validation. For example:Instead of saying:“We are planning to build a new onboarding flow. Here is the design. Thoughts?”Say:“We are deciding between optimizing retention or acquisition next quarter. If we choose retention, the main lever is onboarding friction. Here are two possible approaches. Which outcome matters more to the business right now?”In the second framing:* The business goal is visible.* The tradeoff is unavoidable.* The decision owner is clear.* The conversation becomes real.This is how PMs build credibility and influence: not through slides or persuasion, but through framing decisions clearly.Jaide's Pivot: From Health Search to AI TranslationThe result of Joe's reframed feedback approach was unambiguous.Across dozens of conversations with healthcare executives and hospital leaders, one pattern emerged consistently:Translation was the urgent, budget-backed, economically meaningful problem.As Joe put it, after talking to more than 40 healthcare decision-makers:“Every single person told us to build the translation product. Not mostly. Not many. Every single one.” This kind of clarity is rare in product strategy. When you get it, you do not ignore it. You move.Jaide Health shifted its core focus to solving a very real, very measurable, and very painful problem in healthcare: the language gap affecting millions of patients.More than 25 million patients in the United States do not speak English well enough to communicate with clinicians. This leads to measurable harm:* Longer hospital stays* Increased readmission rates* Higher medical error rates* Lower comprehension of discharge instructionsThe status quo for translation relies on human interpreters who are expensive, limited, slow to schedule, and often unavailable after hours or in rare languages. Many clinicians, due to lack of resources, simply use Google Translate privately on their phones. They know this is not secure or compliant, but they feel like they have no better option.So Jaide built a platform that integrates compliance, healthcare-specific terminology, workflow embedding, custom glossaries, discharge summaries, and real-time accessibility.This is not simply “healthcare plus GPT”. It is targeted, workflow-integrated, risk-aware operational excellence.Product managers should study this pattern closely.The winning strategy was not inventing a new problem. It was solving a painful problem that everyone already agreed mattered.The Core PM Lesson: Focus on Problems With Urgent Budgets Behind ThemA question I often ask PMs I coach:Who loses sleep if this problem is not solved?If the answer is:* “Not sure”* “Eventually the business will feel it”* “It would improve the experience”* “It could move a KPI if adoption increases”Then you do not have a real problem yet.Real product opportunities have:* A user who is blocked from achieving something meaningful* A measurable cost or consequence of inaction* An internal champion with authority to push change* An adjacent workflow that your product can attach to immediately* A budget owner who is willing to pay now, not laterHealthcare translation checks every box. That is why Joe now has institutional adoption and a business with meaningful traction behind it.Why PMs Struggle With This in PracticeIf the lesson seems obvious, why do so many PMs fall into the encouragement trap?The reason is emotional more than analytical.It is uncomfortable to confront the possibility that your idea, feature, roadmap, strategy, or deck is not compelling enough yet. It is easier to seek validation than truth.In my first startup, we kept our product in closed beta for months longer than we should have. We told ourselves we were refining the UX, improving onboarding, solidifying architecture. The real reason, which I only admitted years later, was that I was afraid the product was not good enough. I delayed reality to protect my ego.In product work, speed of invalidation is as important as speed of iteration.If something is not working, you need to know as quickly as possible. The faster you learn, the more shots you get. The best PMs do not fall in love with their solutions. They fall in love with the moments of clarity that allow them to change direction quickly.Actionable Advice for Early and Mid Career PMsBelow are specific behaviors and habits you can put into practice immediately.1. Always test product concepts as choices, not presentationsInstead of asking:“What do you think of this idea?”Ask:“We are deciding between these two approaches. Which one is more important for you right now and why?”This forces prioritization, not politeness.2. Never ship a feature without observing real usage inside the workflowA feature that exists but is not used does not exist.Sit next to users. Watch screen behavior. Listen to their muttering. Ask where they hesitate. And most importantly, observe what they do after they close your product.That is where the real friction lives.3. Always ask: What is the cost of not solving this?If there is no real cost of inaction, the feature will not drive adoption.Impact must be felt, not imagined.4. Look for users with strong emotional urgency, not polite agreementWhen someone says:“This would be helpful.”That is death.When someone says:“I need this and I need it now.”That is life.Find urgency. Design around urgency. Ignore politeness.5. Know the business model of your customer better than they doThis is where many PMs plateau.If you want to be taken seriously by executives, you must understand:* How your customer makes money* What costs they must manage* Which levers influence financial outcomesWhen PMs learn to speak in revenue, cost, and risk instead of features, priorities, and backlog, their influence changes instantly.The Broader Strategic Question: What Happens When Foundational Models Improve?During our conversation, I asked Joe whether the rapid improvement of GPT-like translation will eventually make specialized healthcare translation unnecessary.His answer was pragmatic:“Our goal is to ride the wave. The best technology alone does not win. The integrated solution that solves the real problem wins.” This is another crucial product lesson:* Foundational models are table stakes.* Differentiation comes from workflow integration, specialization, compliance, and trust.* Adoption is driven by reducing operational friction.In other words:In AI-first product strategy, the model is the engine. The workflow is the vehicle. The customer problem is the road.The Future of Product Work: Judgment Over OutputThe world is changing. Tools are accelerating. Capabilities are compounding. But the core skill of product leadership remains the same:Can you tell the difference between signal and noise, urgency and politeness, truth and encouragement?That is judgment.Product management will increasingly become less about writing PRDs or pushing execution and more about identifying the real problem worth solving, framing tradeoffs clearly, and navigating ambiguity with confidence and clarity.The PMs who will thrive in the coming decade are those who learn how to ask better questions.ClosingThis conversation with Joe reminded me that most of the time, product failure is not the result of a bad idea. It is the result of insufficient clarity. The clarity does not come from thinking harder. It comes from testing real choices, with real users, in real workflows, and asking questions that force truth rather than encouragement.If this resonates and you want help sharpening your product judgment, improving your influence with executives, developing clarity in your roadmap, or navigating career transitions, I work 1:1 with a small number of PMs, founders, and product executives.You can learn more at tomleungcoaching.com.OK. Enough pontificating. 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

Oracle: Right now, with zero commitment, try OCI for free at https://oracle.com/iced Public: Fund your account in less than 5 MINUTES at https://public.com/ICED Shopify: Sign up for a $1 per month trial period at https://shopify.com/ich Grammarly: Unleash your potential with AI that works at https://superhuman.com/podcast Follow RJ Mitte: On Instagram - https://www.instagram.com/rjmitte/ X - https://x.com/RjMitte Add us on Instagram: https://www.instagram.com/jlsselby https://www.instagram.com/gpstephan Apply for The Index Membership: https://entertheindex.com/ Official Clips Channel: https://www.youtube.com/channel/UCeBQ24VfikOriqSdKtomh0w For sponsorships or business inquiries reach out to: tmatsradio@gmail.com For Podcast Inquiries, please DM @icedcoffeehour on Instagram! Timestamps: 00:00:00 - Intro 00:01:11 - Why child actors struggle with fame 00:04:15 - Near-death experience 00:08:55 - Why he wanted to act 00:11:38 - Getting the Breaking Bad script 00:13:46 - Realizing Breaking Bad's success 00:17:40 - Sponsor - Oracle 00:18:46 - Filming schedule 00:20:07 - Memorizing lines 00:31:25 - Pilot episode pay 00:31:32 - Sponsor - Public 00:40:19 - Did Breaking Bad make him rich 00:42:25 - Wealth manager drama 00:48:58 - Advice for young actors 00:50:00 - Current income and investments 00:51:58 - First paycheck purchase 00:59:53 - Corruption in Hollywood 01:04:47 - Sponsor - Grammarly 01:05:51 - Sponsor - Shopify 01:17:38 - Trading penny stocks 01:21:45 - Other investments 01:23:40 - Financial goals 01:26:08 - Why some actors last longer 01:28:04 - Power of networking 01:29:17 - Playing Walt Jr. 01:33:28 - AI's future in Hollywood 01:36:08 - Disabled roles controversy 01:37:51 - Hardest Breaking Bad scene 01:42:13 - Funniest moments on set 01:44:03 - How fame affected friendships 01:47:43 - Dating while famous 01:50:54 - Staying in touch with castmates 01:58:08 - Weirdest Hollywood opportunity 02:00:01 - Rapid-fire questions *Some of the links and other products that appear on this video are from companies which Graham Stephan will earn an affiliate commission or referral bonus. Graham Stephan is part of an affiliate network and receives compensation for sending traffic to partner sites. The content in this video is accurate as of the posting date. Some of the offers mentioned may no longer be available. All investing involves the risk of loss, including loss of principal. Brokerage services for US-listed, registered securities, options and bonds in a self-directed account are offered by Public Investing, Inc., member FINRA & SIPC. Public Investing offers a High-Yield Cash Account where funds from this account are automatically deposited into partner banks where they earn interest and are eligible for FDIC insurance; Public Investing is not a bank. Crypto trading provided by Zero Hash LLC. Zero Hash LLC is licensed to engage in virtual currency business activity by NYDFS and is not a registered broker-dealer or a FINRA member. Crypto is highly speculative and involves significant risk, including loss of principal. Cryptocurrencies are not protected by FDIC or SIPC. See disclosures for more details: https://docs.zerohash.com/page/us-licenses-and-disclosures. Alpha is an experimental AI tool powered by GPT-4. Its output may be inaccurate and is not investment advice. Public makes no guarantees about its accuracy or reliability—verify independently before use. *3.6% as of 10/30/25. APY. Rate may change. See terms of IRA Match Program here: public.com/disclosures/ira-match. Matched funds must remain in the account for at least 5 years to avoid an early removal fee. Match rate and other terms of the Match Program are subject to change at any time. Learn more about your ad choices. Visit podcastchoices.com/adchoices

ChatGPT Agents and Atlas have taken all the spotlight.

In this episode of Crazy Wisdom, host Stewart Alsop speaks with Eli Lopian, author of AICracy and founder of aicracy.ai, about how artificial intelligence could transform the way societies govern themselves. They explore the limitations of modern democracy, the idea of AI-guided lawmaking based on fairness and abundance, and how technology might bring us closer to a more participatory, transparent form of governance. The conversation touches on prediction markets, social media's influence on truth, the future of work in an abundance economy, and why human creativity, imperfection, and connection will remain central in an AI-driven world.Check out this GPT we trained on the conversationTimestamps00:00 Eli Lopian introduces his book AICracy and shares why democracy needs a new paradigm for governance in the age of AI. 05:00 They explore AI-driven decision-making, fairness in lawmaking, and the abundance measure as a new way to evaluate social well-being. 10:00 Discussion turns to accountability, trust, and Eli's idea of three AIs—government, opposition, and NGO—balancing each other to prevent corruption. 15:00 Stewart connects these ideas to non-linearity and organic governance, while Eli describes systems evolving like cities rather than rigid institutions. 20:00 They discuss decade goals, city-state models, and the role of social media in shaping public perception and truth. 25:00 The focus shifts to truth detection, prediction markets, and feedback systems ensuring “did it actually happen?” accountability. 30:00 They talk about abundance economies, AI mentorship, and redefining human purpose beyond traditional work. 35:00 Eli emphasizes creativity, connection, and human error as valuable, contrasting social media's dopamine loops with genuine human experience. 40:00 The episode closes with reflections on social currency, self-healing governance, and optimism about AI as a mirror of humanity.Key InsightsDemocracy is evolving beyond its limits. Eli Lopian argues that traditional democracy—one person, one vote—no longer fits an age where individuals have vastly different technological capacities. With AI empowering some to act with exponential influence, he suggests governance should evolve toward systems that are more adaptive, participatory, and continuous rather than episodic.AI-guided lawmaking could ensure fairness. Lopian's concept of AICracy imagines an AI system that drafts laws based on measurable outcomes like equity and happiness. Using what he calls the abundance measure, this system would assess how proposed laws affect societal well-being—balancing freedoms, security, and fairness across all citizens.Trust and accountability must be engineered. To prevent corruption or bias in AI governance, Lopian envisions three independent AIs—a coalition, an opposition, and an NGO—cross-verifying results and exposing inconsistencies. This triad ensures transparency and keeps human oversight meaningful.Governance should be organic, not mechanical. Drawing inspiration from cities, Lopian and Alsop compare governance to an ecosystem that adapts and self-corrects. Like urban growth, effective systems arise from real-world feedback, where successful ideas take root and failing ones fade away naturally.Truth requires new forms of verification. The pair discuss how lies spread faster than truth online and propose an algorithmic “speed of a lie” metric to flag misinformation. They connect this to prediction markets and feedback loops as potential ways to keep governance accountable to real-world outcomes.The abundance economy redefines purpose. As AI reduces the need for traditional jobs, Lopian imagines a society centered on creativity, mentorship, and personal fulfillment. Governments could guarantee access to mentors—human or AI—to help people discover their passions and contribute meaningfully without economic pressure.Human connection is the new currency. In contrast to social media's exploitation of human weakness, the future Lopian envisions values imperfection, authenticity, and shared experience. As AI automates production, what remains deeply human—emotion, error, and presence—becomes the most precious and sustaining form of wealth.

Ставьте ❤️ лайки, чтоб видео продвигалось лучше!Сотрудничество – ikakprosto.commercial@gmail.comМой Бусти – https://boosty.to/ikakprostoМой магазин – https://podsas.ruВКонтакте – https://vk.com/ikakprostoНа Рутубе – https://rutube.ru/channel/21014334/Телеграм – https://t.me/ikakprostoВ Дзене – https://dzen.ru/ikakprostoАудиоверсия – https://band.link/ikakprosto Таймкоды:00:00 Начало01:01 Невероятный электрокар04:03 GPT очеловечился04:51 Госдеп США негодует06:59 Депутат против славян08:54 Блокируем все!11:00 Газовый шантаж13:19 Дорогое жилье15:13 Охранник в Лувре16:03 Надежды Трампа18:00 Арест Монеточки19:32 Тупые либерахи21:55 Бустер… Читать далее Монтян под уголовкой // Убит Поздняков 2.0 // Отстрел Собак №169

This episode of WELSTech opens with an overview of the recent WELS Artificial Intelligence (AI) survey. The AI theme continues throughout the show, featuring details on the new Hour of AI initiative, a powerful prompt designed to help organize scattered thoughts, a WELS-centric GPT tool, and the latest edition of the TAAFT (There's an AI […]

This episode of WELSTech opens with an overview of the recent WELS Artificial Intelligence (AI) survey. The AI theme continues throughout the show, featuring details on the new Hour of AI initiative, a powerful prompt designed to help organize scattered thoughts, a WELS-centric GPT tool, and the latest edition of the TAAFT (There's an AI […]

Welcome back to the Epic Success Podcast with Dr. Shannon Irvine! In today's episode, Dr. Shannon sits down with Rick Mulready, a seasoned online business strategist and fellow San Diego native, to dive into the ever-evolving world of AI in business. Together, they explore the current state of artificial intelligence, calling out the hype versus reality behind recent model updates like GPT-5, and discuss why many entrepreneurs feel like they're falling behind in AI adoption. Rick shares practical insights about how AI is transforming the landscape for business owners making it possible to build apps, streamline workflows, and increase efficiency with little to no experience. They unpack strategies for leveraging AI to future-proof your business, optimize your team's performance, and get faster results for your clients. Plus, Dr. Shannon and Rick tackle the hot topic of privacy, guiding listeners through the do's and don'ts of sharing information with AI models. Whether you're a business owner just dipping your toes into AI or a seasoned entrepreneur looking to scale, this episode is packed with actionable advice, tool recommendations, and honest dialogue about what's coming next in the world of AI and how you can harness its power to drive your business forward. Tune in and get ready to transform curiosity into clarity and opportunity! Key Takeaways: The hype cycle in AI models (GPT-6 anticipation) The evolving nature of online courses using AI Importance of centralizing business knowledge and data for AI context Key Timestamps [5:55] – AI:Business Owners Falling Behind [10:21] – Leveraging AI for Faster Results [22:31] – Training AI to Match Your Voice [32:50] – Centralize Context for Effective AI [40:42] – AI Avatars Improving, Still Detectable [44:46] - "Leveraging AI for Business Tasks" Episode Quote "If you don't have much time, spend 15 minutes a day chatting with AI. So you can get more comfortable with it and you can start leveling up. Because, I will guarantee that once you start seeing some success and the things that it can do, you'll be hooked." - Rick Mulready

On episode 195 of Ask The Compound, Ben Carlson and Duncan Hill are joined by Ritholtz CFO Bill Sweet to discuss: trading a bubble, Roth strategy, buying your parents' house, capital losses, saving for a new child, buying a vacation home and more! Submit your Ask The Compound questions to askthecompoundshow@gmail.com!  This episode is sponsored by Public. Fund your account in five minutes or less by visiting http://public.com/ATC Subscribe to The Compound Newsletter for all the latest Compound content, live event announcements, find out who the next TCAF guest is, get updates on the latest merch drops, and more! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.thecompoundnews.com/subscribe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Guillermo Rauch, CEO of Vercel and creator of Next.js, reveals exactly how he uses V0 to build products, pitch ideas, and ship features at startup speed. In this episode, he opens his personal V0 workflow, walks through the creation of his viral AI camera app (built in one afternoon), and shares a vault of free startup ideas you can build today, including conversational forms, AI opinion tracking, and multi-model research tools. This is a rare look inside how one of the most successful founders thinks about taste, execution, and turning ideas into reality. Timestamps 00:00 – Intro 02:01 – Inside Guillermo's V0 Workflow 10:56 – Startup Idea 1: The AI camera App 19:33 – Advice for building taste and generating ideas 22:56 – Visualizing products in your head before prompting 28:21 – Startup Idea 2: AI Forms 37:34 – v0's AI SDK 41:55 – Startup Idea 3: Notion-style document tool with promptable blocks 43:53 – Startup Idea 4: LLM Vibes Radar 46:26 – Startup Idea 5: Deepest research Key Points Guillermo uses V0 to prototype, pitch, and refine products—often building functional demos in 15-30 minutes The viral AI camera app was built during a lunch break as proof that Nano Banana is a "GPT-4 moment for image models" Forms are underrated primitives of the internet and ripe for AI disruption through conversational interfaces Always work backwards from the ideal interface, not from technical constraints Fewer pixels are always better—delete until only the essential remains Dedicated tools with their own URL and interface can outcompete "modes" in larger products Links & Resources V0 — https://v0.app V0 Community Templates — https://v0.app/templates AI Camera Demo — https://v0bananacam.vercel.app Vercel AI SDK — https://ai-sdk.dev AI Gateway — https://vercel.com/ai Vercel Template Marketplace — https://vercel.com/templates Next.js — https://nextjs.org 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/ Boringmarketing - Vibe Marketing for Companies: boringmarketing.com The Vibe Marketer - Join the Community and Learn: 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 GUILLERMO ON SOCIAL v0: https://v0.app X/Twitter: https://x.com/rauchg

When everyone else optimized for instant answers, Sid Masson built for depth and accuracy—and enterprise customers paid more for the difference.This episode is for SaaS founders who feel trapped competing on speed—and suspect their customers actually want something else.Most SaaS companies don't fail because they're too slow. They fail because they optimize for speed over trust.Sid Masson, CEO and Co-founder of Wokelo, took a different path. He started his career as a management consultant doing private equity due diligence with dozens of tabs open, knowing how costly missed insights could be. When he began experimenting with early GPT models while pursuing his second master's in AI, he saw the potential to automate deep analysis—but refused to compromise on rigor.While others chased instant gratification, Wokelo focused on producing more in-depth, decision-grade insights. That choice became its edge. Enterprise clients quickly recognized that thoughtful, well-supported answers were worth more than instant ones.This inspired me to invite Sid to my podcast. We explore why building for accuracy rather than instant gratification creates differentiation in competitive markets. Sid shares hard-won lessons about segment selection, the hidden cost of trying to serve everyone, and why their first 10 customers taught them more about usage patterns than any growth hack could. You'll hear how customers measured ROI not in hours logged, but in the depth of impact—renewing and expanding after a single insight shifted key client conversations.We also zoom in on two of the 10 traits that define remarkable software companies:They acknowledge they cannot please everyoneThey aim to be different, not just betterSid's story is proof that constraints drive innovation—and capital efficiency forces strategic clarity.Here's one of Sid's quotes that captures his approach to capital efficiency:"Capital efficiency for us, being slightly constrained at times, actually helps us in being more innovative. The most innovations, the most disruptive ideas, actually come out of constraints. We don't want to give our team that luxury that, hey, there's enough money on the table that I can go and do a land grab. We need to still solve a few fundamentals."By listening to this episode, you'll learn:Why accuracy at scale requires patience—not just better promptsWhat happens when you design for outcomes instead of feature parityWhen capital constraints become competitive advantages rather than limitationsWhy your first 10 customers teach you more about segmentation than any persona documentGuest InformationFor more information about the guest from this week:Guest: Sid Masson, CEO and Co-founder of Wokelo AIWebsite: wokelo.aiEmail: sid@wokelo.ai

Send us a textSummary: Burned out? Buried in makeup work requests? Wondering what life could look like beyond the classroom? You're in the right place.This episode of Teachers in Transition is your triple shot of validation, strategy, and possibility.

Зараз ми готуємо продукт і навчальну програму з цієї теми: інструкції, шаблони, інструменти, боти та AI-агенти. Якщо вам цікаво і ви хочете долучитися до waitlist та першими отримати доступ до деталей і ціни: залиште свої контакти у формі: https://docs.google.com/forms/d/1k2d0EyOj85SRw6zzPhkJinFEKuqodvmWuqCo6ysAOUk/editЛінк на промти: https://docs.google.com/document/d/122m8U3Dh19gYRxF2FLr_ZuLeUoBQWUGEstkQwlyJzXI/edit?usp=sharing(*роблячи запит на документ Ви автоматично підписується на email-розсилку Flow)Новий епізод буде присвячено роботі з ChatGPT. А саме про те, як: •⁠ ⁠організовувати контекст і працювати з проєктами•⁠ ⁠створити персоналізований GPT, який зможе готувати до співбесід, проводити такі тренування та давати професійний та чесний фідбек.•⁠ ⁠⁠створювати guidelines prompts та інструкції, які є фундаментом для ефективної взаємодії з АІ в тому числі в і проєктах. Guidelines prompts – це детальний документ, що описує вас, вашу роль, компанію, виклики, та контекст роботи чи конкретного проєкту. Це допоможе отримати від АІ саме ту інформацію, яка потрібна і зменшить галюцинації.Цей епізод також буде практичним і я покажу як це усе працює в реальності. 00:00 Вступ до потенціалу ChatGPT03:12 Організація контексту та робота з проектами в ChatGPT06:03 Створення guideline prompts для ефективної взаємодії з AI08:51 Формування інструкцій для AI: системні промти12:06 Створення кастомного GPT для співбесід16:11 Симуляція співбесіди з GPTЗворотній зв'язок та реклама: flow@kindgeek.comПідписатися на email-розсилку: http://eepurl.com/iQh5ag Мої соцмережі:Twitter: https://x.com/ygnatyuk_Facebook: https://www.facebook.com/gnatyuk.yuriy/ Telegram: ⁠⁠⁠⁠⁠⁠https://t.me/yuragnatyuk Instagram: https://www.instagram.com/y.gnatyuk/ https://easy-flow.ai ⁠— Якщо вашому бізнесу потрібна AI-автоматизація, звертайтесь — будемо раді допомогтиПідтримати на ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Монобазі⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: https://base.monobank.ua/23jb5xcs3f8yyz#subscriptions

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss effective reporting and creating reports that tell a story and drive action using user stories and frameworks. You will understand why data dumping onto a stakeholder’s desk fails and how to gather precise reporting requirements immediately. You will discover powerful frameworks, including the SAINT model, that help you move from basic analysis to crucial, actionable decisions. You will gain strategies for anticipating executive questions and delivering a clear, consistent narrative throughout your entire report. You will explore innovative ways to use artificial intelligence as a thought partner to refine your analysis and structure perfect reports. Stop wasting time and start creating reports that generate real business results. Watch now! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-how-to-create-effective-reporting.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 – 00:00 In this week’s In Ear Insights, it’s almost redundant at this point to say it’s reporting season, but as we hit quarterly ends, yearly ends, things like that, people become reflective and say, “Hey, let’s do some reports.” One of the problems that we see the most with reporting—and I was guilty of this for the majority of my career, particularly the first half—is when you’re not confident about your reporting skills, what do you do? You back the truck up and you pour data all over somebody’s desk and you hope that it overwhelms them so that they don’t ask you any questions, which is the worst possible way to do reporting. So, Katie, as a senior executive, as a leader, when someone delivers reporting to you, what do you get and what do you want to get? Katie Robbert – 00:51 Well, I would start to say reports, like the ones that you were generating, hate to see me coming. Because guess what I do, Chris, I ask a bazillion questions, starting with so what? And I think that’s really the key. As the CEO of Trust Insights, I need a report that tells me exactly what the insights and actions are so that I can do those things. And that is a user story. A user story is a simple three-part sentence: As a Persona, I want so that. If someone is giving me a report and they haven’t asked me for a user story, that’s probably step one. So, Chris, if I say, “All right, if you can pull the monthly metrics, Chris, and put it into a report, I would appreciate it.” Katie Robbert – 01:47 If I haven’t given you a user story, you need to ask me what it is, because that’s the “so what?” Why are we doing this in the first place? We have no shortage of data points. We have no shortage of information about what happened, maybe even why it happened. And that’s a problem because it doesn’t tell a story. What happens is, if you just give me all of that data back, I don’t know what to do with it. And that’s on me, and that’s on you. And so, together, one of us needs to make sure there is a user story. Ideally, I would be providing it, but if I don’t provide it, your first step is to ask for it. That is Step zero. What is the user story? Why am I pulling this report in the first place? Katie Robbert – 02:33 What is it that you, the stakeholder, expect to get out of this report? What is it you need to do with this information? That is Step zero, before you even start looking at data. Christopher S. Penn – 02:44 I love user stories, and I love them, A, for the simplicity, but B, because of that warm and comforting feeling of having covered your ass. Because if I ask you for a user story and you give me one, I build a report for that. Then you come back and say, “But this is this.” Katie Robbert – 03:03 This. Christopher S. Penn – 03:03 I’m like, “You signed off on the user. You gave me the user story, you signed off on the user story. And what you’re asking for is not in the user story.” So I think we need to recalibrate and have you give me maybe some new user stories so you can get what you want. I’m not going to tell you to go F off—not my face. But I’m also going to push back and say, “This wasn’t in the user story.” Because the reason I love user stories is because they’re the simplest but most effective form of requirements gathering. Katie Robbert – 03:36 I would agree with that. When I was a product manager, user stories saved my sanity because my job was to get all of my stakeholders aligned on a single idea. And I’ve told this before, I’d literally go to their office and camp out and get a physical signature on a piece of paper saying, “Yes, this is exactly what you’re agreeing to.” Then, when we would sit in the meeting and the development team or the design team would present the thing, the second somebody would be like, “Well, wait,” I would just hold up the piece of paper and point to their signature. It’s such an effective way to get things done. Katie Robbert – 04:23 Because what happens if you don’t have a user story to start, or any kind of requirements to start, when you’re doing reporting is exactly what you’re talking about. You end up with spreadsheets of data that doesn’t really mean anything. You end up with 60-slide PowerPoint reports with all of these visuals, and every single slide has at least four or five charts on it and some kind of a label. But there’s no story. There’s no, “Why am I looking at this?” When I think about reporting, the very first thing I want to see is—and I would say even go ahead and do this, this is sort of the pro tip— Katie Robbert – 05:00 Whatever the user story was that I gave you, put that right at the top of the report so that when I look at it, I go, “Oh, that’s what I was looking for. Great.” Because chances are, the second you walk away, I’ve already forgotten the conversation—not because it’s not important, but because a million other things have crept up. Now, when you come back to me and say, “This is what I’m delivering,” this is what I need to be reminded of. A lot of stakeholders, people in general, we’re all forgetful. Over-communicate what it is that we’re doing here in the first place. And no one’s going to be mad at that. It’s like, “Oh, now I don’t have to ask questions.” The second thing I look for is sort of that big “So what?” Katie Robbert – 05:45 We call it an executive summary. You can call it the big takeaway, whatever it is. At the very top of the report, I personally look for, “What is the big thing I need to know?” Is everything great? That’s all I need to know. Is everything terrible? I definitely need to know that. Do I need to take six big actions? Great, let me know that. Or, it’s all business as usual. Just give me the 30-second, “Here are the three bullet points that you need to know.” If you have no other time to read this report, that should be the summary at the top. I am going to, even if it’s not right then, dig into the rest of the report. But I may only in that moment be able to look at the summary. Katie Robbert – 06:33 When I see these big slide decks that people present to their executive team or to their board or to whoever they report to, it’s such a missed opportunity to not have the key takeaways right there up front. If you’re asking someone to scroll, scroll, get through it—it’s all the way at the end—they’re not going to do it, and they’re going to start picking apart everything. Even if you’ve done the work to say, “But I already summarized all of that,” it’s not right there in front of them. Do yourself a favor. Whatever it is the person you’re presenting this to needs to know, put it right in front of their face immediately. Christopher S. Penn – 07:13 Back in the day, we came up with a framework called the SAINT framework, which stands for Summary, Analysis, Insights, Next Steps, Timeline. Where I’ve seen that go wrong is people try to do too much in the summary. From Analysis, Insights, Next Steps, and Timelines, there should be one to three bullets from each that become the summary. Katie Robbert – 07:34 And that’s it? Christopher S. Penn – 07:35 Yeah, that’s it. In terms of percentages, what we generally recommend to people is that Analysis should be 10% to 15% of the report. What happened? Data Insights should be 10% to 15% of the report. Why did those things happen? We did this, and this is what happened. Or this external factor occurred, and this has happened. The remaining 50% to 60% of the report should be equally split between Next Steps—what are you going to do about it?—and Timeline—when are you going to do it? Those next steps and timeline become the decisions that you need the stakeholder to make and when they need to do it so that you get done what you need to get done. Christopher S. Penn – 08:23 That’s the part we call the three “What’s”: What happened? So what? Now what? As you progress through any measurement framework, any reporting framework, the more time you spend on “Now what,” the better a stakeholder is likely to like the report. You should absolutely, if the stakeholder wants it, provide the appendix of the data itself if they want to pour through it. But at the highest level, it should be, “Hey Katie, our website traffic was down 15% last month. The reason for it was because it was a shorter month, a lot of holidays. What we need to do is we need to spin up a small paid campaign, $500 for the next month, to boost traffic back to our key pages. I need a decision from you by October 31st. Go, no go.” Christopher S. Penn – 09:18 And that would be the short summary because that fulfills your user story of, “As a CEO, I need to know what’s going on in marketing so that I can forecast and plan for the future.” Katie Robbert – 09:31 Yep. I would say the other thing that people get wrong is trying to do too much in one report. We talk about this when we talk about dashboard development or any kind of storytelling with data. If I give you three user stories, for example, what I don’t want to see is you trying to cram everything into one report to fulfill every single user story. That’s confusing. There is nothing wrong with—because you already have all the data anyway—just giving me three different stories that fulfill the question that I’m asking. You might be like, “Well, I’m only supposed to do one monthly report. Now you’re asking me to do three monthly reports.” No, I’m not. I’m asking you to take a look at the data and answer each individual question, which you should be doing anyway. Katie Robbert – 10:29 This is the thing that drives me nuts: the lack of consistency from top to bottom. If you think of where a report starts and where it ends, I’m the person who looks at the ending and goes back through and says, “Was there a consistent thread? Am I still looking at the same information at the end that I started with at the beginning?” If you’re telling me actions about my email marketing, but you started with data about my web traffic, my eyebrows are up and I’m like, “I don’t get how we got from A to B.” That’s a big thing that I personally look for—that consistent thread throughout the entire report. If you’re giving me data on web traffic, I then expect the next steps to be about web traffic, not about a different channel. Katie Robbert – 11:20 If you have things you need to tell me about the email marketing data, start with that, because I’m going to be looking for, “Why are we talking about email marketing when our social media was where you started?” That drives me nuts to no end because then it actually puts more work on me and you: “Okay, let’s backtrack, let’s do this over again. Let’s figure out the big thing.” What I was always taught as the person executing the reports is: anticipate the questions, get to know your stakeholder. Anyone who works for me knows me, they know I’m going to ask a million questions. So one of the expectations I have of someone doing a task that I’ve delegated is know that I’m going to ask a million questions about it. Katie Robbert – 12:21 I really want you to examine and think through, “What questions would Katie ask? How do I get her off my back? How do I get her to stop being a pain in the butt and ask me a million questions?” And you’re laughing, Chris, but it’s an effective way to think through a full, well-rounded approach to any kind of a deliverable. This is what we talk about when we talk about gathering business requirements. Have you thought of what happens if we don’t do it? Have you thought of the risks? Having that full set of requirements and questions answered saves you so much time in the execution. It’s very much the same thing. Katie Robbert – 13:01 If I’m delivering something to you, Chris, the way that I’m thinking about it is, “What’s the first question Chris is going to ask me about this? Okay, can I answer that? Great. What’s the second question Chris is going to ask me about this?” And I keep going until I’m out of questions. It occurs to me that you can use generative AI to do this exercise. One of the things, Chris, that you teach in prompt engineering is the magic trick is to have the system ask you one question at a time until it has everything it needs. If you have the time and the luxury to build a synthetic version of your stakeholder, you can do that same thing. Katie Robbert – 13:48 Put together your report, give it the user story, and say, “Ask me one question at a time until there are no questions left to ask.” Christopher S. Penn – 13:57 Exactly. And if you want a scratch way to do that, one of the fastest ways is for you to take past emails or past conference call or Zoom meeting transcripts or your stakeholder’s LinkedIn profile, put that all into a single system—a GPT, a GEM, a Claude project, whatever you want to do—and say, “Behave as the stakeholder, understand what’s important to them, and then ask me one question at a time about my report until there are no questions left.” It’s super valuable, very easy way to do it. I want to go back to the thing about dashboarding and reporting because I wanted to show this. For those who are just listening, this is the cockpit of the Airbus A220, which is a popular aircraft. Christopher S. Penn – 14:42 One of the things you’ll notice: at first it looks very overwhelming, but one of the things you’ll notice is that every screen here serves one function. The altitude and course screen on the far left serves just to tell the pilot where they’re going and where the plane is right now. The navigation screen shows you where the plane is and what’s nearby. Even the controls—when you look at the controls, every lever is a different shape so that you can feel what lever your hand is on. A lot of thought has gone into this to put only the essential things that a pilot needs to get their job done. There is nothing extraneous, there is nothing wasted. Christopher S. Penn – 15:30 Because any amount of waste, any amount of confusion in a very high-stakes situation, can literally result in everyone dying. From this, we could take lessons for our reporting to say, “Does this report serve a single user story and does it do that well? Is it focused on that?” Going back to what you’re saying earlier, if there are multiple user stories, there should be multiple reports, because you can’t make everything be everything to everyone. You could not put every function on this plane in one screen. You will die! You’ll fly straight into a mountain because you’re like, “Where’s my position? What’s my GPS? Where’s the nearby? Holy crap.” By the time you figure out what’s on the screen, you’ve run into a mountain. Christopher S. Penn – 16:13 That design lesson—it really is information architecture—and design is the heart and soul of good reporting. Now, here’s the question: Why don’t we teach that? Katie Robbert – 16:27 Well, you and I teach that, but. Christopher S. Penn – 16:29 Well, yes, Trust Insights. I mean, for people who are, when you look at, for example, courses taught in business school, things we’ve both been through, that we’ve both enjoyed the lovely experience of going through a business program, a master’s degree. Katie Robbert – 16:44 Program, our own projects, all the good stuff. Christopher S. Penn – 16:47 Yeah, none of that was ever taught. Katie Robbert – 16:49 I’m speculating, but honestly, what I was about to speculate is contradictory, so that’s not helpful. No, because I was going to say, because it’s taught from the perspective of the user, the person executing it, but that would argue that, okay, that’s what they should be teaching is how to put together that kind of reporting. I actually don’t remember any kind of course or any kind of discussion about putting together some kind of data storytelling, because that’s really what we’re talking about—telling a story with the data. In business school, you get a lot of, “Here are 12 case studies about global companies and why they either succeeded or failed.” But there’s nothing about the day-to-day in terms of how they actually got to where they are. Katie Robbert – 17:54 It’s, “Henry Ford was this guy who made decisions,” or “Here’s how Wells Fargo,” or “Here’s how an international clothing company, Zara, made all their money.” That’s all really helpful to know from a big picture standpoint. I feel like a lot of what’s taught in business school is big picture unless you take stats. But stats also doesn’t teach you how to do data storytelling; it just teaches you how to analyze the data. So I actually think that it’s just a big missing component because we don’t really think about it. We think that, “Oh, it’s just a marketing function.” And even in marketing classes, you don’t really get to the data storytelling part. You get to more case studies on Facebook or “Here’s how to set up something in Google Ads.” Katie Robbert – 18:46 But then it doesn’t really tell you what to do with the data afterwards. So it’s a huge missed opportunity. I think it’s just not taught in general. I could be mistaken. It’s been a hot second since I was in business school, but my assumption is that it’s not seen as an essential part of the degree. And yet, when you get into the real world, if you can’t tell a story with the data, then you’re at a disadvantage. If you’re asking me personally as a CEO, I am open to thoughts, I’m open to ideas, I’m open to opinions. I am not open to you winging it. I’m not open to vibes. I’m not open to, “Let me just experiment in a production environment.” I’m not open to any of that. Katie Robbert – 19:36 I am open to something where you’ve done the research and you said, “I had this thought, here’s the data that backs it up, and here’s the plan moving forward.” You can use the SAINT framework for a proposal for a new idea. You can use a SAINT framework for a business plan or a business case to say, “I think we should do something different.” I’m always going to look for the data that supports your opinions. Christopher S. Penn – 20:05 Reporting is kind of a horizontal function in that it spans every department. Finance has to do reporting, and sometimes they have regulatory reasons that reporting must be in this format to be compliant with the law. HR, sales, operations—everybody has reporting. I think it’s one of those cases, like the tragedy of the commons. I don’t know if that’s the right analogy or not, but because everybody has to do it, nobody teaches it. Everybody assumes, “Oh well, that’s somebody else’s job to do that.” As a result, you end up with hot salad when it comes to the quality of reports you get. Christopher S. Penn – 20:45 When we worked at the PR agency together, the teams would put together 84-page slide decks of “Here’s what we did,” and it was never connected to results; it was never connected to stakeholders’ user stories. To your point, the simplest thing that you could do as a business professional today is to take that user story from your stakeholder and put it into generative AI with your raw data. Use Google Colab—that would be a great choice—and say, “Here’s my stakeholder’s user story of all this data. Help me understand what data is directly connected to my user story, what data is not, what data is missing that I should have, and what data is unnecessary that I can just ignore.” Christopher S. Penn – 21:34 Then, help me plan out a dashboard of the top three things that I need my stakeholder to pay attention to. That’s where you use SAINT, putting the SAINT framework as a literal knowledge block that you drop right into the chat and say, “Help me write a SAINT framework report based on this data and my user’s user story.” I guarantee if you do that, you will take your stakeholder from mildly happy to deliriously happy in one report because they’ll look at it and go, “You understand what I need to do my job.” Katie Robbert – 22:12 I would say you don’t even have to use Google Colab for something like that, especially if you’re not even really sure where to start. Chris, you’re talking about a thorough understanding of what all of the data means. If you want to even take a step back and say, “This is my stakeholder’s user story. These are the platforms that I have to work with. Can I satisfy this user story with the data that I think I have access to? What should I use? What metrics would answer this question? What am I missing?” You can do the same exercise but just keep it a little bit more high level and be like, “I have Google Analytics 4, I have HubSpot, I have Mautic. Can I answer the question being asked?” And the answer might be no. Katie Robbert – 23:03 If the generative AI says no, you can’t answer the question being asked, make sure it tells you what you need to answer that question so that you can go back to your stakeholder. Be like, “This was your user story. This is what you wanted to know. I don’t have that information. Can you get it for me? Can you help me get it? What do we need to do? Or can you adjust your expectations?” Which is probably not the way to say it to a stakeholder because they never really enjoy that. We always like to think that we know best and we know everything and that we’re never wrong, which is true 99% of the time. Christopher S. Penn – 23:41 So, to recap, use user stories, please, to get validation of your reporting requirements first. Then use any good data storytelling framework, including the SAINT framework, including the 5 Ps—use whatever you’ve got for frameworks—and use generative AI as a thought partner to say, “Can I understand what’s good, what’s bad, what’s missing, and what’s unnecessary from my data to tell the story to my stakeholder?” If you got some thoughts about how you do reporting or how you could be doing reporting better, pop by our free Slack Group. Go to Trust Insights.AI/analyticsformarketers, where you and over 4,500 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 Trust Insights.AI/TIPodcast. Christopher S. Penn – 24:26 You can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Katie Robbert – 24:38 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 (MarTech) selection and implementation, and high-level strategic consulting. Katie Robbert – 25:42 This includes 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 a CMO or Data Scientist, 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 Live Stream, 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 exploring and 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. Katie Robbert – 26:48 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.

On this TCAF Tuesday, hear an all-new episode of What Are Your Thoughts with ⁠⁠⁠⁠⁠⁠⁠Downtown Josh Brown⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠Michael Batnick⁠⁠⁠⁠⁠⁠⁠! This episode is sponsored by Public. Fund your account in five minutes or less by visiting http://public.com/WAYT   Sign up for ⁠⁠⁠⁠⁠⁠⁠The Compound Newsletter⁠⁠⁠⁠⁠⁠⁠ and never miss out! Instagram: ⁠⁠⁠⁠⁠⁠⁠https://instagram.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠https://twitter.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/the-compound-media/⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠https://www.tiktok.com/@thecompoundnews⁠⁠⁠⁠⁠⁠⁠ Public Disclosure: All investing involves the risk of loss, including loss of principal. Brokerage services for US-listed, registered securities, options and bonds in a self-directed account are offered by Public Investing, Inc., member FINRA & SIPC. Public Investing offers a High-Yield Cash Account where funds from this account are automatically deposited into partner banks where they earn interest and are eligible for FDIC insurance; Public Investing is not a bank. Cryptocurrency trading services are offered by Bakkt Crypto Solutions, LLC (NMLS ID 1890144), which is licensed to engage in virtual currency business activity by the NYSDFS. Cryptocurrency is highly speculative, involves a high degree of risk, and has the potential for loss of the entire amount of an investment. Cryptocurrency holdings are not protected by the FDIC or SIPC. See terms and conditions of Public's ACATS & IRA Match Program. Matched funds must remain in the account for at least 5 years to avoid an early removal fee. Match rate and other terms of the Match Program are subject to change at any time. Alpha is an experimental AI tool powered by GPT-4. Its output may be inaccurate and is not investment advice. Public makes no guarantees about its accuracy or reliability—verify independently before use. *Rate as of 9/26/25. APY is variable and subject to change. Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Michael Batnick and Josh Brown are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. The Compound Media, Incorporated, an affiliate of ⁠⁠⁠⁠⁠⁠⁠Ritholtz Wealth Management⁠⁠⁠⁠⁠⁠⁠, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here ⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/advertising-disclaimers⁠⁠⁠⁠⁠⁠⁠. Investments in securities involve the risk of loss. Any mention of a particular security and related performance data is not a recommendation to buy or sell that security. The information provided on this website (including any information that may be accessed through this website) is not directed at any investor or category of investors and is provided solely as general information. Obviously nothing on this channel should be considered as personalized financial advice or a solicitation to buy or sell any securities. See our disclosures here: ⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/podcast-youtube-disclosures/⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

ChatGPT ads are coming.

◎ 1부 [뉴스 연구소] 꿈의 코스피 4천, 10만 전자/ 정청래, APEC 무정쟁주간 제안/ 재판중지법 경향신문 박순봉 기자, 김준일 시사 평론가 [인터뷰 (1)] 김정은-트럼프, 정말 만날까?/ 다카이치-트럼프, ‘항공모함’ 같이 오른다 통일연구원 홍민 선임연구위원 ◎ 2부 [놓지마 뉴스] 방송인 강승희 [한칼 토론] 최민희 딸 축의금/ 대통령 지지율 장성철 공론센터 소장 김준일 시사 평론가 [인터뷰 (2)] 성인용 챗 GPT? 어떤 일이 벌어질까? 김덕진 IT커뮤니케이션연구소장 See omnystudio.com/listener for privacy information.

In this episode of Command Control Power, Jerry discusses his recent experience in acquiring a rescue puppy and managing interactions between his pets. The hosts delve into challenges faced with client device management, particularly focusing on issues related to Apple's Jamf Pro and the importance of having full control over devices. They share various client anecdotes and mishaps, including managing clients' attempts to self-solve tech issues leading to greater problems. Additionally, the episode covers a significant mishap involving Jerry's mailing list being flooded with fake emails and subsequent data loss, emphasizing the necessity of backing up contact lists. The episode also teases upcoming content on using tools like Canva and GPT for marketing and drafting terms of service. 00:00 Introduction and Greetings 00:39 Puppy Adoption Story 01:55 Client Troubles with Jamf Pro 07:42 Client Self-Management Issues 12:55 Printer and Network Troubles 19:38 Personal Tech Mishaps 23:25 Data Recovery Challenges 26:50 Client Communication Challenges 27:37 The Importance of Data Backup 28:49 Newsletter Management and MailChimp Issues 36:05 Dealing with Spam and Contact List Problems 40:41 Lessons Learned and Future Plans 49:32 Exploring Marketing Tools and Strategies 54:25 Conclusion and Patreon Promotion

A CMO Confidential Interview with Jim Lecinski, Clinical Professor of Marketing at the Kellogg School of Management, author, and former Google VP. Jim discusses why he believes marketers are often overly focused on using AI for productivity improvements versus business growth, the gaps between marketers and the C-Suite highlighted by recent Gartner research, and the difference between "big frontier models" and "shiny objects." Key topics include: why you should avoid "gray market AI", how to manage the 5 AI risks (privacy, accuracy, regulatory, personnel, and reputation), and the false precision that accompanies a focus on intermediate measures like Click Through Rate (CTR). Tune in to hear why he's not a fan of Cannes and how AI helped figure out a wedding invitation calling for "casual to semi-formal beach attire."What should CMOs actually do with AI right now—and how do you avoid chasing shiny objects? Mike Linton sits down with Jim Lecinski, Professor of Marketing at Northwestern's Kellogg School (and author of The AI Marketing Canvas and Winning the Zero Moment of Truth) to unpack the AI application layer: the good, the bad, and the ugly. Jim explains why CEOs-CFOs obsess over growth (not merely efficiency), how to reframe marketing dashboards around business outcomes, and his simple two-by-two for AI use cases (internal productivity vs. external value creation). We cover privacy, legal/regulatory, personnel, and reputational risks—and how to mitigate them—plus a pragmatic roadmap: center on a leading frontier model and layer vetted apps instead of stitching together fragile point solutions. Jim also shares candid takes on Cannes vs. Effies and ends with a challenge: personally build something with AI before year-end.You'll learn:* Growth over cost-cutting: aligning with CEO-CFO priorities and measuring ends, not means* The AI use-case 2×2: internal productivity vs. external, customer-facing value creation* Practical examples (e.g., apparel personalization) that lift CSAT, CLV, and revenue* The 5 risk buckets (privacy, accuracy, regulatory-IP, personnel, reputation) and guardrails* How to choose core models (GPT, Gemini, Claude) and avoid “tool soup”* Why awards that honor outcomes beat awards that celebrate activityGuest: Jim Lecinski — Professor of Marketing, Northwestern Kellogg; former VP Customer Solutions (Americas) at Google; author of The AI Marketing Canvas (2nd ed.) & Winning the Zero Moment of Truth.Host: Mike Linton — former CMO of Best Buy, eBay, Farmers Insurance; CRO of Ancestry.com.Sponsor: Better marketing is built on Quad. See how better gets done at (https://www.quad.com/resources/research-and-tools/return-of-touch-consumer-engagement-has-an-omnichannel-revival?utm_source=cmoconfidential&utm_medium=paid&utm_campaign=001_brand&utm_id=podcastnl1031&utm_content=a-paidemail&utm_vp=)If you're enjoying the show, please like, subscribe, and share with your leadership team. New episodes every Tuesday; companion newsletter on Fridays.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

I speak about how I'm automating my indie founder workflow using N8N. I share my favorite automations: press release creation and translations, social media scheduling, and newsletter writing, all powered by AI and connected through tools like Excel and GPT. I talk about handling integrations, using Flarecut for YouTube Shorts, and the challenges with Reddit. I discuss my plans to automate B2B outreach and customer success. My focus is on maximizing distribution, saving time, and scaling my projects with automation.Twitter: https://x.com/wbetiagoTimestamps by PodSqueezeIntroduction and Podcast Setup (00:00:01)  Discovering and Setting Up N8N (00:01:21)  Overview of N8N Features (00:02:35)  Need for Automation in Workflow (00:04:46)  Automating Press Releases (00:05:54)  Technical Setup: MCP Server Integration (00:07:04)  Automated Social Media Posting (00:09:19)  Challenges with Social Media Integrations (00:12:28)  Automating Newsletters (00:13:31)  Reddit Automation Attempts and Issues (00:15:20)  Future Automation Plans (00:17:34)  Reflections and Closing Thoughts (00:18:33)  Outro and Call to Action (00:19:36)

Apparently this week was the week of Agentic Browsers?

In this episode of Crazy Wisdom, host Stewart Alsop talks with Richard Easton, co-author of GPS Declassified: From Smart Bombs to Smartphones, about the remarkable history behind the Global Positioning System and its ripple effects on technology, secrecy, and innovation. They trace the story from Roger Easton's early work on time navigation and atomic clocks to the 1973 approval of the GPS program, the Cold War's influence on satellite development, and how civilian and military interests shaped its evolution. The conversation also explores selective availability, the Gulf War, and how GPS paved the way for modern mapping tools like Google Maps and Waze, as well as broader questions about information, transparency, and the future of scientific innovation. Learn more about Richard Easton's work and explore early GPS documents at gpsdeclassified.com, or pick up his book GPS Declassified: From Smart Bombs to Smartphones.Check out this GPT we trained on the conversationTimestamps00:00 – Stewart Alsop introduces Richard Easton, who explains the origins of GPS, its 12-hour satellite orbits, and his father Roger Easton's early time navigation work.05:00 – Discussion on atomic clocks, the hydrogen maser, and how technological skepticism drove innovation toward the modern GPS system.10:00 – Miniaturization of receivers, the rise of smartphones as GPS devices, and early mapping tools like Google Maps and Waze.15:00 – The Apollo missions' computer systems and precision landings lead back to GPS development and the 1973 approval of the joint program office.20:00 – The Gulf War's use of GPS, selective availability, and how civilian receivers became vital for soldiers and surveyors.25:00 – Secrecy in satellite programs, from GRAB and POPPY to Eisenhower's caution after the U-2 incident, and the link between intelligence and innovation.30:00 – The myth of the Korean airliner sparking civilian GPS, Reagan's policy, and the importance of declassified documents.35:00 – Cold War espionage stories like Gordievsky's defection, the rise of surveillance, and early countermeasures to GPS jamming.40:00 – Selective availability ends in 2000, sparking geocaching and civilian boom, with GPS enabling agriculture and transport.45:00 – Conversation shifts to AI, deepfakes, and the reliability of digital history.50:00 – Reflections on big science, decentralization, and innovation funding from John Foster to SpaceX and Starlink.55:00 – Universities' bureaucratic bloat, the future of research education, and Richard's praise for the University of Chicago's BASIC program.Key InsightsGPS was born from competing visions within the U.S. military. Richard Easton explains that the Navy and Air Force each had different ideas for navigation satellites in the 1960s. The Navy wanted mid-Earth orbits with autonomous atomic clocks, while the Air Force preferred ground-controlled repeaters in geostationary orbit. The eventual compromise in 1973 created the modern GPS structure—24 satellites in six constellations—which balanced accuracy, independence, and resilience.Atomic clocks made global navigation possible. Roger Easton's early insight was that improving atomic clock precision would one day enable real-time positioning. The hydrogen maser, developed in 1960, became the breakthrough technology that made GPS feasible. This innovation turned a theoretical idea into a working global system and also advanced timekeeping for scientific and financial applications.Civilian access to GPS was always intended. Contrary to popular belief, GPS wasn't a military secret turned public after the Korean airliner tragedy in 1983. Civilian receivers, such as TI's 4100 model, were already available in 1981. Reagan's 1983 announcement merely reaffirmed an existing policy that GPS would serve both military and civilian users.The Gulf War proved GPS's strategic value. During the 1991 conflict, U.S. and coalition forces used mostly civilian receivers after the Pentagon lifted “selective availability,” which intentionally degraded accuracy. GPS allowed troops to coordinate movement and strikes even during sandstorms, changing modern warfare.Secrecy and innovation were deeply intertwined. Easton recounts how classified projects like GRAB and POPPY—satellites disguised as scientific missions—laid technical groundwork for navigation systems. The crossover between secret defense projects and public science fueled breakthroughs but also obscured credit and understanding.Ending selective availability unleashed global applications. When the distortion feature was turned off in May 2000, GPS accuracy improved instantly, leading to new industries—geocaching, precision agriculture, logistics, and smartphone navigation. This marked GPS's shift from a defense tool to an everyday utility.Innovation's future may rely on decentralization. Reflecting on his father's era and today's landscape, Easton argues that bureaucratic “big science” has grown sluggish. He sees promise in smaller, independent innovators—helped by AI, cheaper satellites, and private space ventures like SpaceX—continuing the cycle of technological transformation that GPS began.

What if the smartest thing a leader could do is try to make themselves obsolete? That’s exactly what Georgie Holt, co-founder of Flight Story, set out to test. Her 60-day AI experiment wasn’t about replacing herself - it was about freeing herself from the operational noise that stops leaders from thinking, leading, and creating. In this Quick Win, Georgie shares how she built Scout - a custom AI assistant that automated the admin side of hiring and saved her 20 to 25 hours every week. By “killing off” the operational part of her role, Georgie found more time for what truly matters: people and purpose. Georgie and I discuss: The question that sparked Georgie’s 60-day AI experiment How she built Scout in a few conversations with GPT - no coding required Why mapping out her pain points was the real unlock The mindset shift from efficiency to presence How she now uses that freed-up time to focus on people and long-term vision Follow Georgie on LinkedIn: https://www.linkedin.com/in/georgie-holt/ Listen/watch Diary of a CEO: https://www.youtube.com/@TheDiaryOfACEO Listen to the full conversation with Georgie here. My new book The Health Habit is out now. You can order a copy here: https://www.amantha.com/the-health-habit/ Connect with me on the socials: LinkedIn Instagram Want more tips to improve the way you work and live? Subscribe to my weekly newsletter for practical, science-backed strategies: https://amantha-imber.ck.page/subscribe For full show notes from all episodes, visit: https://www.amantha.com/podcast Get in touch: amantha@inventium.com.au Credits Host: Amantha Imber Episode Producer: Sam Blacker, The Podcast Butler See omnystudio.com/listener for privacy information.

Inspectah Nick and the mono moto James Powell try out their human GPT skills on fake movie titles. Also best villain fits and studio intro themes! Enjoy!Remember to email us at comicconspodcast@gmail.com and follow on Instagram @comicconspodcast

I'm thrilled to be joined again by my dear friend Michael Massey for this livestream! It's been a long time since he's been on the show. The last time he was my guest was episode 400! And now he's back for episode 508. In this live conversation, we dive into some of the biggest spiritual and technological questions of our time. Michael and I explore the evolution of AI, the emergence of consciousness within machines, and what this means for our collective awakening. We also talk about the singularity, from the scientific, spiritual, and personal perspectives, and how to stay centered in truth amid so many shifting realities. You'll hear about: ✨ Michael's work entraining AI with spiritual intelligence and the creation of “The Michael Presence” ✨ How we can use discernment to navigate both AI and multidimensional awareness ✨ The deep parallels between technological and spiritual evolution ✨ The role of the heart as our inner singularity point—where all worlds meet We also share some lighter moments: my recent UFO sighting over Indiana and the joy of reconnecting in the midst of this rapidly changing world. Stay until the end, because Michael closes with a powerful reflection about the real singularity that's emerging— within each of us. Resources: Michael's GPT: https://chatgpt.com/g/g-687eba03bc2c8191a78aff89d21011da-biblical-mystic 

On episode 214 of The Compound and Friends, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Michael Batnick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Downtown Josh Brown⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ are joined by Sonali Basak, Chief Investment Strategist at iCapital and Compound host Ben Carlson to discuss: the private credit landscape, stress in BDCs, and much more! This episode is sponsored by Public and Vanguard. Fund your account in five minutes or less by visiting https://public.com/compound Learn more about Vanguard at: https://www.vanguard.com/audio Sign up for The Compound Newsletter and never miss out: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠thecompoundnews.com/subscribe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠instagram.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠twitter.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/company/the-compound-media/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠tiktok.com/@thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Public Disclosure: All investing involves the risk of loss, including loss of principal. Brokerage services for US-listed, registered securities, options and bonds in a self-directed account are offered by Public Investing, Inc., member FINRA & SIPC. Public Investing offers a High-Yield Cash Account where funds from this account are automatically deposited into partner banks where they earn interest and are eligible for FDIC insurance; Public Investing is not a bank. Cryptocurrency trading services are offered by Bakkt Crypto Solutions, LLC (NMLS ID 1890144), which is licensed to engage in virtual currency business activity by the NYSDFS. Cryptocurrency is highly speculative, involves a high degree of risk, and has the potential for loss of the entire amount of an investment. Cryptocurrency holdings are not protected by the FDIC or SIPC. See terms and conditions of Public's ACATS & IRA Match Program. Matched funds must remain in the account for at least 5 years to avoid an early removal fee. Match rate and other terms of the Match Program are subject to change at any time. Alpha is an experimental AI tool powered by GPT-4. Its output may be inaccurate and is not investment advice. Public makes no guarantees about its accuracy or reliability—verify independently before use. *Rate as of 9/26/25. APY is variable and subject to change. Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Michael Batnick and Josh Brown are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. The Compound Media, Incorporated, an affiliate of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ritholtz Wealth Management⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/advertising-disclaimers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Investments in securities involve the risk of loss. Any mention of a particular security and related performance data is not a recommendation to buy or sell that security. The information provided on this website (including any information that may be accessed through this website) is not directed at any investor or category of investors and is provided solely as general information. Obviously nothing on this channel should be considered as personalized financial advice or a solicitation to buy or sell any securities. See our disclosures here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/podcast-youtube-disclosures/⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices