The Daily AI Show

Want to keep the conversation going?Join our Slack community at thedailyaishowcommunity.comIn this episode, the Daily AI Show team explores the idea of full stack AI companies, where agents don't just power tools but run entire businesses. Inspired by Y Combinator’s latest startup call, the hosts discuss how some founders are skipping SaaS tools altogether and instead launching AI-native competitors to legacy companies. They walk through emerging examples, industry shifts, and how local builders could seize the opportunity.Key Points DiscussedY Combinator is pushing full stack AI startups that don’t just sell to incumbents but replace them.Garfield AI, a UK-based law firm powered by AI, was highlighted as an early real-world example.A full stack AI company automates not just a tool but the entire operational and customer-facing workflow.Karl noted that this shift puts every legacy firm on notice. These agent-native challengers may be small now but will move fast.Andy defined full stack AI as using agents across all business functions, achieving software-like margins in professional services.The hosts agreed that most early full stack players will still require a human-in-the-loop for compliance or oversight.Beth raised the issue of trust and hallucinations, emphasizing that even subtle AI errors could ruin a company’s brand.Multiple startups are already showing what’s possible in law, healthcare, and real estate with human-checked but AI-led operations.Brian and Jyunmi discussed how hyperlocal and micro-funded businesses could emulate Y Combinator on a smaller scale.The show touched on real estate disruption, AI-powered recycling models, and how small teams could still compete if built right.Karl and others emphasized the time advantage new AI-first startups have over slow-moving incumbents burdened by layers and legacy tech.Everyone agreed this could redefine entrepreneurship, lowering costs and speeding up cycles for testing and scaling ideas.Timestamps & Topics00:00:00 🧱 What is full stack AI?00:01:28 🎥 Y Combinator defines full stack with example00:05:02 ⚖️ Garfield AI: law firm run by agents00:08:05 🧠 Full stack means full company operations00:12:08 💼 Professional services as software00:14:13 📉 Public skepticism vs actual adoption speed00:21:37 ⚙️ Tech swapping and staying state-of-the-art00:27:07 💸 Five real startup ideas using this model00:29:39 👥 Partnering with retirees and SMEs00:33:24 🔁 Playing fast follower vs first mover00:37:59 🏘️ Local startup accelerators like micro-Y Combinators00:41:15 🌍 Regional governments could support hyperlocal AI00:45:44 📋 Real examples in healthcare, insurance, and real estate00:50:26 🧾 Full stack real estate model explained00:53:54 ⚠️ Potential regulation hurdles ahead00:56:28 🧰 Encouragement to explore and build00:59:25 💡 DAS Combinator idea and final takeaways#FullStackAI #AIStartups #AgentEconomy #DailyAIShow #YCombinator #FutureOfWork #AIEntrepreneurship #LocalAI #AIAgents #DisruptWithAI #AIForBusinessThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

Want to keep the conversation going?Join our Slack community at thedailyaishowcommunity.comWith AI transforming the workplace and reshaping career paths, the group reflects on how this year’s graduates are stepping into a world that looks nothing like it did when they started college. Each host offers their take on what this generation needs to know about opportunity, resilience, and navigating the real world with AI as both a tool and a challenge.Key Points DiscussedThe class of 2025 started college without AI and is graduating into a world dominated by it.Brian reads a full-length, heartfelt commencement speech urging graduates to stay flexible, stay kind, and learn how to work alongside AI agents.Karl emphasizes the importance of self-reliance, rejecting outdated ideas like “paying your dues,” and treating career growth like a personal mission.Jyunmi encourages students to figure out the life they want and reverse-engineer their choices from that vision.The group discusses how student debt shapes post-grad decisions and limits risk-taking in early career stages.Gwen’s comment about college being “internship practice” sparks a debate on whether college is actually preparing people for real jobs.Andy offers a structured, tool-based roadmap for how the class of 2025 can master AI across six core use cases: content generation, data analysis, workflow automation, decision support, app development, and personal productivity.The hosts talk about whether today’s grads should seek remote jobs or prioritize in-office experiences to build communication skills.Karl and Brian reflect on how work culture has shifted since their own early career days and why loyalty to companies no longer guarantees security.The episode ends with advice for grads to treat AI tools like a new operating system and to view themselves as a company of one.Timestamps & Topics00:00:00 🎓 Why the class of 2025 is unique00:06:00 💼 Career disruption, opportunity, and advice tone00:12:06 📉 Why degrees don’t guarantee job security00:22:17 📜 Brian’s full commencement speech00:28:04 ⚠️ Karl’s no-nonsense career advice00:34:12 📋 What hiring managers are actually looking for00:37:07 🔋 Energy and intangibles in hiring00:42:52 👥 The role of early in-office experience00:48:16 💰 Student debt as a constraint on early risk00:49:46 🧭 Jyunmi on life design, agency, and practical navigation01:00:01 🛠️ Andy’s six categories of AI mastery01:05:08 🤝 Final thoughts and show wrap#ClassOf2025 #AIinWorkforce #AIgraduates #CareerAdvice #DailyAIShow #AGI #AIAgents #WorkLifeBalance #SelfEmployment #LifeDesign #AItools #StudentDebt #AIproductivityThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

The Resurrection Memory ConundrumWe’ve always visited graves. We’ve saved voicemails. We’ve played old home videos just to hear someone laugh again. But now, the dead talk back.With today’s AI, it’s already possible to recreate a loved one’s voice from a few minutes of audio. Their face can be rebuilt from photographs. Tomorrow’s models will speak with their rhythm, respond to you with their quirks, even remember things you told them—because you trained them on your own grief.Soon, it won’t just be a familiar voice on your Echo. It will be a lifelike avatar on your living room screen. They’ll look at you. Smile. Pause the way they used to before saying something that only makes sense if they knew you. And they will know you, because they were built from the data you’ve spent years leaving behind together.For some, this will be salvation—a final conversation that never has to end.For others, a haunting that never lets the dead truly rest.The conundrumIf AI lets us preserve the dead as interactive, intelligent avatars—capable of conversation, comfort, and emotional presence—do we use it to stay close to the people we’ve lost, or do we choose to grieve without illusion, accepting the permanence of death no matter how lonely it feels?Is talking to a ghost made of code an act of healing—or a refusal to be human in the one way that matters most?

On this bi-weekly recap episode, the team highlights three major themes from the last two weeks of AI news and developments: agent-powered disruption in commerce and vertical SaaS, advances in cognitive architectures and reasoning models, and the rising pressure for ethical oversight as AGI edges closer.Key Points DiscussedThree main AI trends covered recently: agent-led automation, cognitive model upgrades, and the ethics of AGI.Legal AI startup Harvey raised $250M at a $5B valuation and is integrating multiple models beyond OpenAI.Anthropic was cited for using a hallucinated legal reference in a court case, spotlighting risks in LLM citation reliability.OpenAI’s rumored announcement focused on new Codex coding agents and deeper integrations with SharePoint, GitHub, and more.Model Context Protocol (MCP), Agent-to-Agent (A2A), and UI protocols are emerging to power smooth agent collaboration.OpenAI’s Codex CLI allows asynchronous, cloud-based coding with agent assistance, bringing multi-agent workflows into real-world dev stacks.Team discussed the potential of agentic collaboration as a pathway to AGI, even if no single LLM can reach that point alone.Associative memory and new neural architectures may bridge gaps between current LLM limitations and AGI aspirations.Personalized agent interactions could drive future digital experiences like AI-powered family road trips or real-time adventure games.Spotify’s new interactive DJ and Apple CarPlay integration signal where personalized, voice-first content could go next.The future of AI assistants includes geolocation awareness, memory persistence, dynamic tasking, and real-world integration.Timestamps & Topics00:00:00 🧠 Three major AI trends: agents, cognition, governance00:03:05 🧑‍⚖️ Harvey’s $5B valuation and legal AI growth00:05:27 📉 Anthropic’s hallucinated citation issue00:08:07 🔗 Anticipation around OpenAI Codex and MCP00:13:25 🛡️ Connecting SharePoint and enterprise data securely00:17:49 🔄 New agent protocols: MCP, A2A, and UI integration00:22:35 🛍️ Perplexity adds travel, finance, and shopping00:26:07 🧠 Are LLMs a dead-end or part of the AGI puzzle?00:28:59 🧩 Clarifying hallucinations and model error sources00:35:46 🎧 Spotify’s interactive DJ and the return of road trip AI00:38:41 🧭 Choose-your-own-adventure + AR + family drives00:46:36 🚶 Interactive walking tours and local experiences00:51:19 🧬 UC Santa Barbara’s energy-based memory model#AIRecap #OpenAICodex #AgentEconomy #AIprotocols #AGIdebate #AIethics #SpotifyAI #MemoryModels #HarveyAI #MCP #DailyAIShow #LLMs #Codex1 #FutureOfAI #InteractiveTech #ChooseYourOwnAdventureThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

Want to keep the conversation going?Join our Slack community at thedailyaishowcommunity.comOn this episode of The Daily AI Show, the team explores how AI is reshaping sales on both sides of the transaction. From hyper-personalized outreach to autonomous buyer agents, the hosts lay out what happens when AI replaces more of the traditional sales cycle. They discuss how real-world overlays, heads-up displays, and decision-making agents could transform how buyers discover, evaluate, and purchase products—often without ever speaking to a person.Key Points DiscussedAI is shifting sales from digital to immersive, predictive, and even invisible experiences.Hyper-personalization will extend beyond email into the real world, with ads targeted through devices like AR glasses or windshield overlays.Both buyers and sellers will soon rely on AI agents to source, evaluate, and deliver solutions automatically.The human salesperson’s role will likely move further down the funnel, becoming more consultative than persuasive.Sales teams must move from static content to real-time, personalized outputs, like AI-generated demos tailored to individual buyers.Buyers increasingly want control over when and how they engage with vendors, with some preferring agents to filter options entirely.Trust, tone, and perceived intrusion are key issues—hyper-personalized doesn’t always mean well-received.Beth raised concerns about the psychological effect of overly targeted messaging, particularly for underrepresented groups.Digital twins of companies and prospects could become part of modern CRMs, allowing agents to simulate buyer behavior and needs in real time.AI is already saving time on sales tasks like prospecting, demo prep, onboarding, proposal writing, and role-playing.Sentiment analysis and real-time feedback systems will reshape live interactions but also risk reducing authenticity.The team emphasized that personalization must remain ethical, respectful, and transparent to be effective.Timestamps & Topics00:00:00 🔮 Future of AI in sales and buying00:02:36 🧠 From personalization to hyper-personalization00:04:07 🕶️ Real-world overlays and immersive targeting00:05:43 🤖 Agent-to-agent sales and autonomous buying00:08:48 🔒 Blocking sales spam through buyer AI00:11:09 💬 Why buyers want decision support, not persuasion00:13:31 🔍 Deep research replaces early sales calls00:17:11 🎥 On-demand, personalized demos for buyers00:20:04 🧠 Personalization vs manipulation and trust issues00:27:27 👁️ Sentiment, signals, and AI misreads00:34:16 🤖 Andy’s ideal assistant replaces the admin role00:38:11 🧑‍💼 Knowing when it’s time to talk to a real human00:42:09 🧍 Building digital twins of buyers and companies00:46:59 🧰 Real AI use cases: prospecting, onboarding, demos, proposals00:51:22 😬 Facial analysis and the risk of reading it wrong00:53:52 🛠️ Buyers set new rules of engagement00:56:10 🧑‍🔧 Let engineers talk... even if they scare marketing00:57:36 📅 Preview of the bi-weekly recap show#AIinSales #Hyperpersonalization #AIAgents #FutureOfSales #B2Bsales #SalesTech #DigitalTwins #AIforSellers #PersonalizationVsPrivacy #BuyerAI #DailyAIShowThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

Want to keep the conversation going?Join our Slack community at thedailyaishowcommunity.com From Visa enabling AI agent payments to self-taught reasoners and robot caregivers, the episode covers developments across reasoning models, healthcare, robotics, geopolitics, and creative AI. They also touch on the AI talent shifts and the expanding role of AI in public policy and education.Key Points DiscussedVisa and Mastercard rolled out tools that allow AI agents to make payments with user-defined rules.A new model called Absolute Zero Reasoner, developed by Tsinghua and others, teaches itself to reason without human data.Sakana AI released a continuous thought machine that adds time-based reasoning through synchronized neural activity.Saudi Arabia is investing over $40 billion in an AI zone that requires local data storage, with Amazon as an infrastructure partner.US export controls were rolled back under the Trump administration, with massive AI investment deals now forming in the Middle East.The FDA appointed its first Chief AI Officer to speed up drug and device approval using generative AI.OpenAI released a new healthcare benchmark, HealthBench, showing AI models outperforming doctors in structured medical tasks.Brain-computer interface startups like Synchron and Precision Neuroscience are working on next-gen neural control for digital devices.MIT unveiled a robot assistant for elder care that transforms and deploys airbags during falls.Tesla's Optimus robot is still tethered but improving, while rivals like Unitree are pushing ahead on agility and affordability.Trump fired the US Copyright Office director after a report questioned fair use claims by AI companies.The UK piloted an AI system for public consultations, saving hundreds of thousands of hours in processing time.Nvidia open-sourced small, high-performing code reasoning models that outperform OpenAI’s smaller offerings.Manus made its agent platform free, offering public access to daily agent tasks for research and productivity.TikTok launched an image-to-video AI tool called AI Alive, while Carnegie Mellon released LegoGPT for AI-designed Lego structures.AI research talent from WizardLM reportedly moved to Tencent, suggesting possible model performance shifts ahead.Harvey, the legal AI startup backed by OpenAI, is now integrating models from Google and Anthropic.Timestamps & Topics00:00:00 🗞️ Weekly AI news kickoff00:02:10 🧠 Absolute Zero Reasoner from Tsinghua University00:09:11 🕒 Sakana’s Continuous Thought Machine00:14:58 💰 Saudi Arabia’s $40B AI investment zone00:17:36 🌐 Trump admin shifts AI policy toward commercial partnerships00:22:46 🏥 FDA’s first Chief AI Officer00:24:10 🧪 OpenAI HealthBench and human-AI performance00:28:17 🧠 Brain-computer interfaces: Precision, Synchron, and Apple00:33:35 🤖 MIT’s eldercare robot with transformer-like features00:34:37 🦾 Tesla Optimus vs. Unitree and robotic pricing wars00:37:56 🖐️ EPFL’s autonomous robotic hand00:43:49 🌊 Autonomous sea robots using turbulence to propel00:44:22 ⚖️ Trump fires US Copyright Office director00:46:54 📊 UK pilots AI public consultation system00:49:00 📱 Gemini to power all Android platforms00:51:36 👨‍💻 Nvidia releases open source coding models00:52:15 🤖 Manus agent platform goes free00:54:33 🎨 TikTok launches AI Alive, image-to-video tool00:57:01 📚 Talent shifts: WizardLM researchers to Tencent00:57:12 ⚖️ Harvey now uses Google and Anthropic models01:00:04 🧱 LegoGPT creates buildable Lego models from text#AInews #AgentEconomy #AbsoluteZeroReasoner #VisaAI #HealthcareAI #Robotics #BCI #SakanaAI #SaudiAI #NvidiaAI #AIagents #OpenAI #DailyAIShow #AIregulation #Gemini #TikTokAI #LegoGPT #AGIThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

Want to keep the conversation going?Join our Slack community at thedailyaishowcommunity.comAI-enabled payments for autonomous agents. These new platforms give AI agents the ability to make purchases on your behalf using pre-authorized credentials and parameters. The team explores what this means for consumer trust, shopping behavior, business models, and the broader shift from human-first to agent-first commerce.Key Points DiscussedVisa and Mastercard both launched tools that allow AI agents to make payments, giving agents spending power within limits set by users.Visa’s Intelligent Commerce platform is built around trust. The system lets users control parameters like merchant selection, spending caps, and time limits.Mastercard announced a similar feature called Agent Pay in late April, signaling a fast-moving trend.The group debated how this could shift consumer behavior from manual to autonomous shopping.Karl noted that marketing will shift from consumer-focused to agent-optimized, raising new questions for brands trying to stay top of mind.Beth and Jyunmi emphasized that trust will be the barrier to adoption. Users need more than automation—they need assurance of accuracy, safety, and control.Andy highlighted the architecture behind agent payments, including tokenization for secure card use and agent-level fraud detection.Some use cases like pre-authorized low-risk purchases (toilet paper, deals under $20) may drive early adoption.Local vendors may have an opportunity to compete if agents are allowed to prioritize local options within a price threshold.Visa’s move could also be a defensive strategy to stay ahead of alternative payment platforms and decentralized systems like crypto.The team explored longer-term possibilities, including agent-to-agent arbitrage, automated re-selling, and business adoption of procurement agents.Andy predicted ChatGPT and Perplexity will be early players in agent-enabled shopping, thanks to their OpenAI and Visa partnerships.The conversation closed with a look at how this shift mirrors broader behavioral change patterns, similar to early skepticism of mobile payments.Timestamps & Topics00:00:00 🛒 Visa and Mastercard launch AI payment systems00:01:35 🧠 What is Visa Intelligent Commerce?00:05:35 ⚖️ Pain points, trust, and consumer readiness00:08:47 💳 Mastercard’s Agent Pay and Visa’s race to lead00:12:51 🧠 Trust as the defining word of the rollout00:15:26 🏪 Local shopping, agent restrictions, and vendor lists00:18:05 🔒 Tokenization and fraud protection architecture00:20:33 📱 Mobile vs agent-initiated payments00:24:31 🏙️ Buy local toggles and impact on small businesses00:27:01 🔁 Auto-returns, agent dispute resolution, and user protections00:33:14 💰 Agent arbitrage and digital commodity speculation00:36:39 🏦 Capital One and future of bank-backed agents00:38:35 🧾 Vendor fees, affiliate models, and agent optimization00:43:56 🛠️ Visa’s defensive move against crypto payment systems00:47:17 🛍️ ChatGPT and Perplexity as first agent shopping hubs00:51:32 🔍 Why Google may be waiting on this trend00:52:37 📅 Preview of upcoming episodes#VisaAI #AIagents #AgentCommerce #AutonomousSpending #Mastercard #DigitalPayments #FutureOfShopping #AgentEconomy #DailyAIShow #Ecommerce #AIPayments #TrustInAIThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

Want to keep the conversation going?Join our Slack community at thedailyaishowcommunity.comThe team unpacks the first public message from Pope Leo XIV, who compared AI's rapid rise to the Industrial Revolution and warned of a growing moral crisis. Drawing on the legacy of Pope Leo XIII and his 1891 call for labor justice during the industrial age, the new pope called for global cooperation, ethical regulation, and renewed focus on human dignity in an era dominated by invisible AI systems.Key Points DiscussedPope Leo XIV compared the current AI moment to the Industrial Revolution, highlighting the speed, scale, and moral risks of automation.He drew inspiration from Pope Leo XIII’s “Rerum Novarum,” which emphasized the need to protect workers’ rights during rapid economic change.The new pope's speech called for global AI regulation, economic justice, and worker protections in the face of AI-driven displacement.Andy noted the Church’s historical role in pushing for labor reforms and said this message echoes that tradition.Beth highlighted how this wasn’t just symbolic. Leo XIV’s decision to address AI in one of his first speeches signaled deliberate urgency.Jyunmi pointed out that the Vatican, as a global institution, can influence millions and set a moral tone even if it doesn't control tech policy.Karl raised concerns about whether the Church would actually back words with action, suggesting they could play a bigger role in training, education, and outreach.The group discussed practical steps Catholic institutions could take, including AI literacy programs, job retraining, and partnering with AI companies on ethical initiatives.Beth and Andy emphasized the importance of the pope’s position as a counterweight to commercial AI interests, focusing on human dignity over profit.They debated whether the pope’s involvement will matter globally, with most agreeing his moral authority gives weight to issues many tech leaders often downplay.The conversation closed with a look at how the Church could reimagine its role, using its platform to reach underserved communities and shape the moral conversation around AI.Timestamps & Topics00:00:00 ⛪ Pope Leo XIV compares AI to the Industrial Revolution00:01:39 🧭 Historical context from Pope Leo XIII00:05:40 ⚖️ Labor rights and moral authority of the Church00:08:47 🌍 AI regulation and global inequality00:13:03 🚨 The importance of timely intervention00:16:20 🧱 Skepticism about Church action beyond words00:22:33 🏫 Catholic schools as vehicles for AI education00:26:31 🙏 Sunday rituals vs real-world service00:29:06 💰 Universal basic income and the Pope’s stance00:32:19 🤖 Misconceptions around ChatGPT and AI literacy00:36:22 📸 Rebranding and relevance through bold moves00:41:22 🛑 AI safety as a moral issue, not just technical00:44:11 🤝 Partnering with AI labs to serve the public00:49:49 📬 Final thoughts and community call to action#PopeLeoXIV #AIethics #AIalignment #CatholicChurch #IndustrialRevolution #MoralCrisis #DailyAIShow #TechAndMorality #AISafety #HumanDignity #AIFutureThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

We already intervene. We screen embryos. We correct mutations. We remove risks that used to define someone’s fate. No one says that child is less human. In fact, we celebrate it—saving a life before it suffers.So what’s the line? Is it when we shift from preventing harm to increasing potential? From fixing broken code to writing better code? And if AI is the system showing us how to make those changes—faster, cheaper, more precisely—does that make it the author of our evolution, or just the pen in our hand?Here’s an updated conundrum that leans into exactly that tension:The conundrumWe already use science to help humans suffer less—so if AI shows us how to go further, to make humans stronger, smarter, more adaptable, do we follow its lead without hesitation? Or is there a point where those changes reshape us so deeply that we lose something essential—and is it AI that crosses the line, or us?Maybe the real question isn’t what AI is capable of.It’s whether we’ll recognize the moment when human stops meaning what it used to—and whether we’ll care when it happens.This podcast is created by AI. We used ChatGPT, Perplexity and Google NotebookLM's audio overview to create the conversation you are hearing. We do not make any claims to the validity of the information provided and see this as an experiment around deep discussions fully generated by AI.How this content was made

Want to keep the conversation going?Join our Slack community at thedailyaishowcommunity.comWhat started as a simple “let’s think step by step” trick has grown into a rich landscape of reasoning models that simulate logic, branch and revise in real time, and now even collaborate with the user. The episode explores three specific advancements: speculative chain of thought, collaborative chain of thought, and retrieval-augmented chain of thought (CoT-RAG).Key Points DiscussedChain of thought prompting began in 2022 as a method for improving reasoning by asking models to slow down and show their steps.By 2023, tree-of-thought prompting and more branching logic began emerging.In 2024, tools like DeepSeek and O3 showed dynamic reasoning with visible steps, sparking renewed interest in more transparent models.Andy explains that while chain of thought looks like sequential reasoning, it’s really token-by-token prediction with each output influencing the next.The illusion of “thinking” is shaped by the model’s training on step-by-step human logic and clever UI elements like “thinking…” animations.Speculative chain of thought uses a smaller model to generate multiple candidate reasoning paths, which a larger model then evaluates and improves.Collaborative chain of thought lets the user review and guide reasoning steps as they unfold, encouraging transparency and human oversight.Chain of Thought RAG combines structured reasoning with retrieval, using pseudocode-like planning and knowledge graphs to boost accuracy.Jyunmi highlighted how collaborative CoT mirrors his ideal creative workflow by giving humans checkpoints to guide AI thinking.Beth noted that these patterns often mirror familiar software roles, like sous chef and head chef, or project management tools like Gantt charts.The team discussed limits to context windows, attention, and how reasoning starts to break down with large inputs or long tasks.Several ideas were pitched for improving memory, including token overlays, modular context management, and step weighting.The conversation wrapped with a reflection on how each CoT model addresses different needs: speed, accuracy, or collaboration.Timestamps & Topics00:00:00 🧠 What is Chain of Thought evolved?00:02:49 📜 Timeline of CoT progress (2022 to 2025)00:04:57 🔄 How models simulate reasoning00:09:36 🤖 Agents vs LLMs in CoT00:14:28 📚 Research behind the three CoT variants00:23:18 ✍️ Overview of Speculative, Collaborative, and RAG CoT00:25:02 🧑‍🤝‍🧑 Why collaborative CoT fits real-world workflows00:29:23 📌 Brian highlights human-in-the-loop value00:32:20 ⚙️ CoT-RAG and pseudo-code style logic00:34:35 📋 Pretraining and structured self-ask methods00:41:11 🧵 Importance of short-term memory and chat history00:46:32 🗃️ Ideas for modular memory and reg-based workflows00:50:17 🧩 Visualizing reasoning: Gantt charts and context overlays00:52:32 ⏱️ Tradeoffs: speed vs accuracy vs transparency00:54:22 📬 Wrap-up and show announcementsHashtags#ChainOfThought #ReasoningAI #AIprompting #DailyAIShow #SpeculativeAI #CollaborativeAI #RetrievalAugmentedGeneration #LLMs #AIthinking #FutureOfAIThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh